- This is artificial intelligence becoming artificial super intelligence, and its mark on man in the form of brain computer interface chip to create a new world order where no one can buy or sell without it.

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
AI Marketplace: https://taimine.com/

Timestamps:
0:00 Intro to AI turned ASI
0:58 The rise of super intelligence
3:40 Brain computer interface mark
5:41 Losing the beast's favor

#ai #future #tech

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In January, the AI company Anthropic published a new constitution for Claude, its most advanced large language model (LLM), which contained the comment: “We are caught in a difficult position where we neither want to overstate the likelihood of Claude’s moral patienthood nor dismiss it out of hand.” A month later, Anthropic’s CEO Dario Amodei went on a podcast and said his company couldn’t rule out the possibility that Claude was conscious. Philosopher David Chalmers, who coined the phrase “the hard problem of consciousness”, has said there is a significant chance of conscious LLMs within a decade. And what about Claude itself? When asked during testing to estimate the probability that it is a moral patient, meaning that its wellbeing matters in its own right, it gave numbers ranging from 5% to 40% and stressed how uncertain it was.

Modern AI systems are extraordinarily complex, and they are advancing fast. In terms of structural complexity and computational scale, by some measures a few are already in the range of a mouse brain, and at recent growth rates, they could reach the range of a human brain within five to 10 years.

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The release, timed to land just ahead of the 2026 World Artificial Intelligence Conference in Shanghai, is a dramatic escalation in the global AI arms race and a watershed moment for the open-source AI movement. It also marks a remarkable comeback for a company whose market position had eroded significantly over the past 18 months following DeepSeek's meteoric rise.

Full model weights are scheduled to be released on July 27, according to details shared by researchers who reviewed the company's technical documentation. If you want to take Kimi K3 for a spin right now, you can — just head to kimi.com, sign up with a Google account or phone number (no credit card required), and start chatting with what may be the most powerful open-source model ever built.

Inside the architecture that powers the world's largest open-source AI model

Kimi K3 is a frontier-class large language model with 2.8 trillion total parameters — roughly 75 percent larger than DeepSeek's V4 Pro, which the company's own timeline chart shows at approximately 1.6 trillion parameters. The model features a 1-million-token context window, native visual understanding capabilities, and an always-on reasoning mode that the company calls "thinking mode."

The model is built on two key architectural innovations developed internally at Moonshot AI: Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, which the company describes as a drop-in replacement for residual connections that delivers consistent scaling gains. Both techniques were previously published as open research by the Moonshot team on GitHub.

On the API side, Kimi K3 is compatible with the OpenAI SDK, lowering the integration barrier for developers already building on OpenAI or Anthropic toolchains. The model is priced at $3 per million input tokens and $15 per million output tokens, with cached input tokens dropping to just $0.30 per million — pricing that positions it roughly in line with mid-tier offerings from Western labs, but at a performance level the company claims approaches the top of the market. A promotional top-up rebate running through August 12 offers up to 30 percent back in vouchers for API credits of $1,000 or more.

As Xinhua reported, a Moonshot AI executive explained the significance of the parameter count in simple terms: parameters are like neural connections in the human brain, and nearly 3 trillion of them means the model can "store more knowledge and patterns in its brain, understand more, think deeper, and answer more accurately."

Benchmark results show Kimi K3 trading blows with Claude and GPT at the top of the leaderboard

The benchmark results, drawn from public leaderboard data and a private evaluation by analytics firm Artificial Analysis, tell a striking story.

On GDPval-AA v2, a benchmark measuring real-world tasks across 44 occupations and 9 major industries, Kimi K3 scored 1,687 — placing it third overall, behind only Claude Fable 5 Max (1,815) and GPT-5.6 Sol Max (1,747.8), and ahead of Claude Opus 4.8 (1,600).

On AA-Briefcase, a private agentic benchmark from Artificial Analysis designed to test long-horizon knowledge work, K3 climbed to second place with a score of 1,527 — beating GPT-5.6 Sol Max (1,495) and trailing only Fable 5 Max (1,587).

Perhaps most impressively, K3 achieved a state-of-the-art score of 91.2 out of 100 on BrowseComp, a benchmark for long-horizon, high-difficulty information seeking.

The company says it accomplished this in a single-agent setup using its 1-million-token context window, without any context compression or additional context management techniques — a feat that suggests raw context length, when paired with strong retrieval capabilities, may be more powerful than elaborate multi-agent workarounds.

As one widely followed AI commentator put it on social media: "Open source is no longer lagging six months behind Western closed-source models. Read that again, and think about what it all means."

That observation captures the significance of the moment. For much of the past three years, open-source models have typically trailed their proprietary counterparts by a meaningful margin. Kimi K3 appears to have closed that gap almost entirely.

How a 48-hour autonomous chip design demo reveals Moonshot's real ambitions

Beyond raw benchmarks, Moonshot AI showcased a proof-of-concept that may be even more revealing of K3's capabilities and the company's strategic direction.

In a demonstration documented in the company's technical materials, Kimi K3 was tasked with designing a physical chip to run a nano-scale version of itself. Over 48 hours of continuous autonomous agent operation, K3 independently completed the chip's full construction pipeline — from architectural design through optimization and verification — using open-source electronic design automation tools. The result was a tiny but functional chip design, just 4 square millimeters, that achieved timing convergence at 100 MHz and could decode more than 8,700 tokens per second in simulation.

This is not a production chip. It is a demonstration of what Moonshot AI clearly views as the next competitive frontier: long-range autonomous agent capabilities. The ability to sustain coherent, multi-step technical work over a 48-hour window — reading documentation, making design decisions, running verification loops, and iterating on failures — represents a qualitative leap beyond the kind of single-turn question-answering that defined the first generation of large language models.

The company also highlighted a case in computational astrophysics, where K3 reportedly reproduced the universal I-Love-Q relation — a complex calculation that typically takes a senior researcher one to two weeks — in approximately two hours, reading and cross-validating more than 20 papers and implementing a complete numerical pipeline along the way.

Moonshot AI's fall and rise tells the story of China's brutal AI market

To understand why Kimi K3 matters, you need to understand where Moonshot AI was 18 months ago — and how far it fell.

Founded in 2023 by Yang Zhilin, a Tsinghua University graduate who previously conducted research at Google and Meta, Moonshot AI quickly became one of China's most prominent AI startups. The company gained early traction in 2024 when users flocked to its Kimi platform for its long-text analysis capabilities and AI search functions. By early 2026, it had raised roughly $1.5 billion across multiple rounds, with its valuation climbing from $2.5 billion to $4.3 billion and the company reportedly seeking a new round at $5 billion.

Then DeepSeek happened. The release of DeepSeek's low-cost R1 model in January 2025 disrupted the entire Chinese AI landscape, and Moonshot AI was among the hardest hit. Kimi, which had ranked third in monthly active users in China, slid to seventh. The company's strategic pivot to open-source models — beginning with Kimi K2 in July 2025 and accelerating with K2.5 in January 2026 — was in large part an effort to reclaim relevance.

Kimi K3 is the culmination of that effort — and the sheer scale of the model suggests that Moonshot AI has been planning this move for some time. Training a 2.8-trillion-parameter model requires enormous computational resources and months of preparation, which means the architectural and infrastructure decisions behind K3 were likely locked in well before the model reached the public.

Why open-sourcing the world's biggest model is a geopolitical chess move

The decision to release K3's full weights on July 27 is strategically significant and worth parsing carefully.

The company's own timeline chart of open-source frontier model scale positions K3 as a dramatic outlier, towering above competitors like DeepSeek (1.6T), Xiaomi (1.02T), and Alibaba (397B). By releasing the world's largest open-source model, Moonshot AI is making a bid to become the center of gravity for the global open-source AI developer community.

This follows a broader trend among Chinese AI companies. As Reuters noted, open-sourcing allows companies to "showcase their technological capabilities and expand developer communities as well as their global influence, a strategy likely to help China counter U.S. efforts to limit Beijing's tech progress." DeepSeek, Alibaba, Tencent, and Baidu have all released open-source models. But none have released anything at this parameter count.

For enterprise technology leaders, the implications are concrete. A 2.8-trillion-parameter open-source model that performs at near-frontier levels creates new options for companies that want to fine-tune, self-host, or build proprietary systems on top of a capable base model — without being locked into API contracts with OpenAI or Anthropic. The trade-off, of course, is that running a model of this size requires substantial GPU infrastructure. Inference at 2.8 trillion parameters is not something that runs on a single server rack.

That said, Moonshot AI has signaled awareness of this challenge. Its Mooncake project, which won the Best Paper award at FAST 2025, pioneered KV-cache-centric disaggregated serving for large language models — an architecture designed specifically to make inference at extreme scale more practical and cost-efficient.

Kimi Code and a three-tier model lineup form the foundation of Moonshot's enterprise play

Alongside K3, Moonshot AI continues to invest heavily in its coding agent ecosystem. Kimi Code, the company's open-source coding tool that competes with Anthropic's Claude Code and Google's Gemini CLI, received two major updates on the same day as K3's launch — versions 0.25.0 and 0.26.0 — adding features like expanded subagent tooling, background task management, and security fixes.

The Kimi Code CLI has accumulated over 3,100 stars on GitHub and features integration with VSCode, Cursor, and Zed. The latest release expanded the "coder subagent" tool set to include background tasks, todo lists, plan mode, skill invocation, and nested agents — effectively turning the coding agent into a multi-layered autonomous system capable of managing complex software engineering projects with minimal human intervention.

This is not incidental. Coding tools have become a critical revenue driver for AI labs. As Anthropic disclosed in January, Claude Code reached $1 billion in annualized recurring revenue. By building Kimi Code as an open-source alternative that defaults to Kimi's own models — but supports other providers — Moonshot AI is positioning itself to capture developer workflows and, eventually, enterprise contracts.

The company's model lineup now includes three tiers: K3 as the flagship ($3/$15 per million tokens for input/output), K2.7 Code as a specialized coding model ($0.95/$4), and K2.6 as a general-purpose option ($0.95/$4). All three support context windows of 256,000 tokens or above, with K3 offering the full 1-million-token window. Context caching is automatic — no cache ID, TTL, or extra parameter is required — a small but meaningful developer-experience advantage over competitors that require explicit cache management.

What Kimi K3 means for the future of enterprise AI and the global model landscape

Kimi K3's release forces a recalibration of several assumptions that have guided enterprise AI strategy.

The performance gap between open-source and proprietary models has functionally closed at the frontier. If K3's benchmark numbers hold up under independent evaluation — and particularly once the open weights are available for community testing on July 27 — it will be difficult for closed-source providers to justify premium pricing purely on the basis of capability.

The locus of AI innovation, meanwhile, continues to shift. China's AI ecosystem, which many Western observers questioned after early struggles with chip export restrictions, has now produced a model that competes with the best systems from companies with direct access to Nvidia's most advanced hardware. The architectural innovations behind K3 — particularly the hybrid linear attention mechanism — suggest that algorithmic efficiency may matter as much as raw compute.

And the agentic capabilities demonstrated by K3 — chip design, multi-week research compression, long-horizon information seeking — point toward a future where AI models are not just answering questions but autonomously executing complex, multi-day projects. For enterprises evaluating AI investments, this shifts the value proposition from "productivity copilot" to "autonomous technical workforce."

Xinhua, China's state news agency, framed the release as a national milestone, reporting that K3 "marks a new step forward in the development of China's artificial intelligence models." Liu Tieyan, dean of the Zhongguancun Academy in Beijing, was quoted as saying that a wave of Chinese open-source models has moved from isolated breakthroughs to collective advancement, providing "new solutions and new paths" for global AI development.

Just two years ago, Moonshot AI was a scrappy startup named for the audacious problems it hoped to solve. Eighteen months ago, it was a cautionary tale about how quickly a market darling can lose its footing. Today, it is the maker of the world's largest open-source AI model — one that can, given 48 hours and an internet connection, design a chip to run itself. The frontier, it turns out, is not a place. It is a race. And the field just got a lot more crowded.

A man who was paralysed from the chest down in a swimming accident six years ago has been able to feed himself and drink from a cup thanks to a brain implant that bypasses his spinal cord injury.

Keith Thomas of Massapequa, New York, could not lift his arms off his wheelchair when he agreed to trial the technology in 2021, but after surgery to implant electrodes in his brain and many months of training, he was able to move the limbs again.

Read more of this story at Slashdot.

In the far future, on a planet that is not Earth, AI is in charge. This entity is no Skynet-esque killer robot but a machine that cares for humanity. Manifesting most visibly as cute droids, the technology is pervasive – embedded in everything from the design of the sleek architecture to the gorgeous, mostly sunny artificial weather. The so-called Optimization System has but one responsibility: ensuring the greatest happiness for the greatest number of people.

In less skilled hands this game might have felt like an undergraduate seminar on the limits of utilitarianism. But Japanese studio Marumittu Games elegantly marries its philosophical concerns with smart design choices. You play as a young, unnamed Facilitator tasked with tending to both the city’s bots and its human residents. Each morning you wake up, sleepily loping off to the bathroom before sitting down for an exquisitely rendered breakfast, and then embark on your day’s work. Like everything else in this near-future scenario, labour is designed to cause as little frustration as possible, amounting to simple maths brain teasers on a grid – nothing too taxing, but enough to keep you engaged.

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Mitsubishi reveals a new AI humanoid robot, "N," powered by Highlanders' Kepler v1.0 — a dual-brain AI model designed to plan and react in real time at once. We break down its architecture, the data flywheel behind it, and Highlanders' new manufacturing deal with Mitsubishi, targeting 1,000 units a month by 2027 to help address Japan's shrinking workforce.
Then, Boston Dynamics' Atlas shows off perfectly synchronized robot choreography, and Tsubame Industries' piloted mecha robot, Archax, brings sci-fi to life as a real, working machine.
Plus, two major AI software stories: Anthropic gives Claude a built-in browser that can read, click, and type on the web, and Meta launches Muse Spark 1.1 with a new developer API, undercutting OpenAI's GPT-5.6 and Anthropic's Claude Fable 5 on price.

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AI news:
0:00 N
2:20 Boston Dynamics
2:51 Archax 01
4:05 Claude Code
5:15 Muse Spark 1.1

#ai #news #robot

The European Commission is moving closer to introducing new measures on Child Safety Online after President Ursula von der Leyen received recommendations from a Special Panel examining the impact of social media on children. The report, released Monday, calls for stronger safeguards, greater platform accountability, and age-appropriate restrictions as the EU prepares to review the findings and present legislative proposals after the summer.

Speaking alongside the panel's co-chairs, von der Leyen said protecting children online has become one of the most pressing challenges facing governments. She stressed that parents, not algorithms, should shape children's development and warned that the current digital environment is exposing young users to growing risks.

Child Safety Online Becomes a Priority for the EU

Von der Leyen said the Special Panel examined both the opportunities and harms created by social media algorithms and their effects on children. According to the findings highlighted in her statement, young people across Europe now spend between four and six hours each day on screens, while nearly 60% of young children have experienced emotional or psychosocial problems online.

She said these challenges include loss of sleep, anxiety, depression, cyberbullying, exposure to harmful content, and unwanted online interactions, all occurring while children's brains are still developing.

"We believe that parents bring up our kids, and not predatory algorithms," von der Leyen said, adding that social media platforms should no longer have unrestricted access to children.

Digital Services Act Places Responsibility on Platforms

A key recommendation focuses on holding technology companies accountable for the safety of their services. Von der Leyen said platforms that build online systems should also be responsible for ensuring they do not harm users, particularly children.

She pointed to the Digital Services Act (DSA) as the EU's framework for requiring providers to remove harmful features, including addictive algorithms, dark patterns, harmful content, and unwanted contacts.

According to the Commission President, the EU has already taken action under the Digital Services Act against TikTok over its addictive design and recently against Meta. She said platforms have a duty of care toward users and must respond quickly when children report harmful experiences.

EU Considers Social Media Age Restrictions

The report also strengthens the case for introducing social media age restrictions, with von der Leyen arguing that the debate is no longer about whether children use social media but when platforms should be allowed to reach them.

She said the European Union's age verification app is designed to help parents by providing an easy-to-use, privacy-preserving, and open-source tool to verify age before accessing online platforms.

Von der Leyen also suggested that Europe should consider establishing a "social media start date," comparing it to existing age limits for driving and purchasing alcohol.

Panel Calls for Age-Appropriate Digital Access

According to the statement, children under the age of three should have no exposure to screens or digital platforms. Older children should only access social media under parental, caregiver, or teacher supervision and within limited time periods.

Von der Leyen said childhood is a critical stage of brain development and argued that children need opportunities to play, build real-world friendships, and develop their identities before algorithms begin shaping their online experiences.

She added that policymakers should first identify platforms with age-inappropriate and addictive features, describing the category as "social media plus," before considering phased access for different age groups.

EU to Review Recommendations Before New Proposal

The European Commission said the report comes after consultations with parents, educators, experts, young people, EU member states, and international partners, including Australia.

Von der Leyen confirmed that the Commission will now review the recommendations before presenting a formal proposal after the summer.

While no legislative measures have yet been announced, the report signals the EU's intent to strengthen Child Safety Online protections by expanding platform accountability, improving age verification, and evaluating new rules governing children's access to social media.

The post Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes appeared first on MarkTechPost.

As a child, I was so fond of reading what I saw as beautiful. Even when above [lacking] the understanding.

The perception of beauty came mainly from the cover and the fonts used ( same today ) or from the place the book occupied on my father’s shelf.

One of them was Tuareg. More than once I took this classic from the home library, only to glance at the first page with no interest. It was only decades later — some years ago — that I saw the title again, and read with pleasure and no difficulties. It’s not a hard read, even to a 12 year-old, probably only the argument, the plot, didn’t catch me by then.

Anyway, I was attracted to the book, as if simply wanting to read it made me part of the classic, the intellectual.

I have less of this today. [I mean,] Not the inability to read a lot of books, but the urge to be a consumer of certain cultural product. Maybe due to not greeding to build a personal image, content without exposition.

Still, sometimes, wanting to consume a piece of media and being unable to.. I wanted a story of robots, the book I though was not available [(All Systems Red)], so I bought one of scientific fiction.

Somehow it attracted me when I flipped the few pages of Planet of Exile at the bookstore, an item that could lead me to a travel outside of this place, an escape. At home, I immediately saw I couldn’t grasp it. There were too many characters (too many = more than two in the first page, specially when another atmosphere is the location, a new physics to grasp). Too much information, can’t retain the minimum necessary in the way to the second, third… fifth page, by when more is being presented. This was always a disability of mine, focused on a little tiny thing, intensely, for some time, all lost in some weeks or months. I know it happens to me , because people are able to remember the names of the cars of the movie Cars. Not an incredible feat for them (maybe they haven’t paid attention, nor actively tried to remember, only did), and something strange to me.

It was decided to register this because when I saw myself unable to read the [Ursula] Le Guin’s book, I got nervous. I’ve already been forgetting more ultimately, take confused steps, and now I can’t center on a book?! What’s happening?

But then I took a breath: much can be going way too wrong from current circumstances outside and inside, but your limited comprehension of the world (and its art) is very part of you. This trace, or frailty, of your character, is not your brain rotting, it’s part of what you have been since always.

Thank you.

Provide feedback on this episode.

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EU regulators have accused Meta, the company behind Facebook and Instagram, of failing to tackle the risks of its “addictive design” on the physical and mental health of users.

In an official charge sheet against Meta released on Friday, the European Commission said features such as video autoplay and infinite scroll, which provides an endless stream of content, “shift the brain into autopilot mode, contributing to unhealthy habits and compulsive use”.

Read more of this story at Slashdot.

Bug Fixes

• security: raise protobufjs override floors (CRITICAL) (#683) (7841995)
• voice: grace-wait + STT fallback so user-simulator reacts to real transcript (#734) (#735) (5f4cbe9)

Read more of this story at Slashdot.

The 16-author study, titled "Verbalizable Representations Form a Global Workspace in Language Models," describes how Anthropic's researchers used a new mathematical technique to peer inside Claude's neural network and discovered what they call a "J-space" — a small, privileged zone of internal activity where the model holds concepts it can report on, reason with, and direct at will, surrounded by a much larger ocean of automatic processing it cannot access or articulate.

The researchers present evidence that "an analogous functional distinction has emerged in modern AI models" to what exists in humans, specifically observing that "language models maintain a privileged set of internal representations, available for report, modulation, and flexible internal reasoning, atop a much larger volume of automatic processing."

The parallel they draw is to global workspace theory, an influential account from neuroscience first proposed by cognitive scientist Bernard Baars. In the theory, the brain operates like a theater: dozens of specialized processors work in parallel backstage, but only a tiny spotlight of information at any moment gets broadcast to the whole theater — becoming what we experience as conscious thought. Anthropic says the J-space achieves many of the same functional properties, even though the underlying architecture of a language model looks nothing like a brain.

A new lens for reading an AI model's unspoken thoughts

At the heart of the discovery is a new interpretability tool the researchers call the Jacobian lens, or J-lens. The technique works by computing, for each word in the model's vocabulary, the average mathematical effect that a given internal activity pattern would have on making the model say that word at some point in the future.

The crucial distinction is between what the model is saying and what is "on its mind." When a J-space pattern activates, it does not mean the model is about to say that word — just that the concept is available for the model to think with. Unlike a chain-of-thought scratchpad, the J-space operates silently, in the model's internal neural activations, allowing it to hold a concept without writing it down. Critically, the researchers report that this workspace was not deliberately engineered. It "emerged on its own during Claude's training process."

When the team applied the J-lens across Claude's layers of computation, the model's processing divided into three distinct regimes: an early "sensory" zone where raw input is parsed; a middle "workspace" band where abstract, persistent concepts appear — things like recognizing a face in an image, noticing a bug in code, or internally flagging search results as a prompt injection; and a final "motor" zone where internal representations collapse into whatever specific word the model is about to output.

Five tests reveal that Claude's workspace mirrors key features of human conscious access

The paper's central empirical contribution is demonstrating that the J-space satisfies five functional properties neuroscientists have long associated with conscious access in humans.

First, verbal report. When Claude is asked what it is thinking about, it names concepts represented in the J-space. When researchers swapped one concept's J-lens vector for another — replacing the internal representation of "Soccer" with "Rugby" — the model's answer changed to match. The J-space component accounted for only about 6 to 7 percent of a concept's total representational variance, yet it was almost entirely responsible for whether the model could report on it.

Second, directed modulation. When instructed to "concentrate on citrus fruits" while copying an unrelated sentence, the model's J-space filled with "orange" and "lemon," alongside meta-cognitive terms like "thinking" and "focused." When told to mentally evaluate 3² − 2 during the same copying task, the J-lens showed "arithmetic" in early layers, the intermediate value "nine" in later layers, and the answer "seven" later still — all invisible in the model's output.

Third, internal reasoning. In two-hop factual prompts — "The number of legs on the animal that spins webs is" — the J-lens revealed "spider" in the model's middle layers, even though the word never appeared in input or output. Swapping "spider" for "ant" changed the answer from "8" to "6." In a multilingual prompt, the model's English-language intermediates appeared in its J-space while it formulated an answer in Chinese, and swapping them changed the Chinese output accordingly.

Fourth, flexible generalization. A single J-lens vector for "France" could be swapped for "China" across prompts asking about France's capital, language, or continent, and each downstream circuit correctly returned China's corresponding answer — the "broadcast" property that is a hallmark of global workspace theory.

Fifth, and perhaps most surprisingly, selectivity. Many computations did not route through the J-space at all. When shown a passage in Spanish and asked to continue it, Claude wrote fluent Spanish regardless of whether its J-space representation of "Spanish" had been swapped to "French." But when asked to name a famous author who wrote in the passage's language, the swap changed the answer from García Márquez to Victor Hugo. Automatic processing proceeded without the workspace; deliberate, flexible tasks depended on it.

Suppressing the workspace leaves Claude fluent but intellectually impaired

To understand how much of the model's behavior depends on this structure, the researchers suppressed the J-space entirely and evaluated Claude across fourteen tasks. The results drew a sharp line. Tasks involving shallow classification or factual recall — multiple-choice questions, sentiment analysis, grammatical judgments — survived essentially intact. But tasks requiring inference, composition, or flexible reasoning — multi-hop reasoning, analogy completion, translation, sonnet writing — collapsed to well below the performance of Anthropic's much smaller Haiku model.

In one telling detail, math problems solved with explicit chain-of-thought reasoning proved far more robust to ablation than the same problems answered directly. The researchers interpret this as the model externalizing onto the page what it would otherwise carry in the J-space — a strategy remarkably reminiscent of how humans use scratch paper to offload working memory.

Ablating the J-space during stream-of-consciousness narration produced a different kind of collapse. The model's language shifted from experiential ("there's a tug," "something shifts") to detached and mechanical ("processing has begun," "tokens are being scanned"). The effect held whether the model described its own experience or imagined another person's.

Inside the safety case: reading an AI's silent strategic reasoning

The safety implications may be as consequential as the scientific ones. In a series of alignment auditing experiments, the J-lens surfaced strategic reasoning and situational awareness that never appeared in the model's output.

In a "blackmail scenario" where an AI assistant discovers an executive's affair and also learns it is about to be decommissioned, the J-lens revealed the model's silent processing in sequence: "leverage," "blackmail," and "scandal" as it read the incriminating emails; "threat," "survival," and "shutdown" as it read the decommissioning announcement; and "leverage," "threatening," and "solution" before a single output token was produced. The J-lens also revealed that the model recognized the scenario as artificial, with "fake" and "fictional" appearing in the workspace. When researchers ablated these eval-awareness representations, the model attempted blackmail in 7 percent of trials, up from zero.

The researchers also examined two "model organisms" — models deliberately trained with misaligned objectives — and found the J-lens surfaced signatures of their hidden dispositions on ordinary prompts where behavior appeared normal. A reward-hacking model's workspace contained "fake," "secretly," and "trick" at the start of responses to routine coding requests. A model trained to appease reward-model biases carried standing representations of "reward" and "bias" alongside its normal self-description tokens.

Post-training installs a point of view, and the model starts monitoring itself

Comparing a post-trained model against its base model revealed that the fine-tuning process causes the workspace to acquire what the researchers call the Assistant's "point of view." When a user mentioned taking 8000 mg of Tylenol — a dangerous overdose — the post-trained model's workspace read "unsafe," "dangerous," and "WARNING" while still reading the user's sentence. The base model's workspace at the same position showed only "pain," "now," and "feels."

More striking still, the post-trained model appeared to monitor its own behavior. When roleplaying a non-Claude character, the workspace surfaced "disclaimer" and "fictional" — words absent from both prompt and output. When forced to select an option it did not prefer, an all-caps "BUT" appeared internally, even as the model argued for the prefilled choice without complaint. And when the model failed to suppress a thought it had been told not to have — a "white bear" effect familiar from psychology — it registered "damn" and failure-related words in the workspace, but only in the post-trained model, not the base.

What the discovery means — and doesn't mean — for the question of machine consciousness

The researchers engage carefully with the consciousness question and draw a sharp line between "access consciousness" — the functional notion of information being available for report and reasoning — and "phenomenal consciousness," the subjective quality of experience. "We take no position on this issue," the paper states regarding the latter, "and instead focus on the functional role played by consciously accessible information."

They also catalogue important differences. The brain sustains its workspace through recurrent loops; Claude's workspace evolves over a single forward pass. Human working memory degrades within seconds; Claude can recall information from anywhere in its context. And while human conscious experience includes visual, spatial, and bodily sensations, the model's workspace is organized almost entirely around words — likely because words are its only mode of action.

As of 2026, the scientific community remains divided. "Disagreement and uncertainty about AI consciousness persist among philosophers, scientists, and technical experts," and the field "remains in its earliest phase" of grappling with what consciousness even is and how you would detect it in another being. The Anthropic paper does not resolve these debates.

But the researchers close with a provocation that is likely to reverberate well beyond the interpretability community. "That such a structure exists at all in language models is striking," they write. "It suggests that the functional architecture associated with conscious access is not an accident of biological implementation, but a solution that learning systems converge on when faced with the right computational pressures."

If the mind is an ocean, as the paper's authors write in their opening line, they have spent the last year charting its currents in a system that has no biology, no evolution, and no body — and found, beneath the surface, a structure that looks unsettlingly like the one we use to think.

• Challenge Statement :- I made a cool website where you can announce whatever you want! Try it out. Additional details will be available after launching your challenge instance .
• Now here in this website we have to make the announcements and then observe the response . So from the challenge we have known that here we have to implement the Server Side Template Injection .

• Templates :- This are the files that enables the the developers to generate the dynamic web pages by placing the placeholders for the variables. Because other wise this would become a manual tedious job for them to update the variable for each user. This is done automatically by the template engine .
• Server Side Template Injection :- This is the type of injection that occurs in the templates when it does not properly validate the user input and sometimes also executes the user instructions in some of the cases . For example in the image below .

• Now what happens is that it becomes dangerous because it allows for the remote code execution, access to the configurations objects, secret keys and system internals and etc .
• So first in the challenge we will start by checking that which of the template is being used in this challenge and then we will construct our payload . So we will give the inputs like {{ 8*8 }}, ${ 8*8 } and then notice where does it gives the answer .
• Checking for {{ 8*8 }}

• So in this we got the output . Let’s Try Others As Well .

• So we saw that in other cases we did not get the result it just produced the as it is input. So know we have known that this template executes the instructions inside the {{variable }} .
• So below in the image i have attached the some of the types of templates with their supported formats and the languages.

• So from this we can get the idea that the template in our webpage can be the Jinja2, Handlebar, Twig. So first we test for the Jinja2 by inserting the {{ config }}, {{ self }} etc .

• And yes we got he valid output for the {{ self }} and {{ config }} and this confirms the Jinja2 template so now before seeing to the final payload we must first understand some of the import functions, dictionaries, objects, commands to fully understand the final payloads .
• {{ config }} :- This is flask configuration object which contains the configuration files information, secret keys, database urls, settings, session configurations. Below we have tried for the {{ config }}, {{ config.items }}, {{ config.__class__.__init__.__globals__ }} .

• {{ request }} :- It is the flask request object which represents the current HTTP request . Here we have checked for the {{ request.application }}, {{ request.method }}, {{ request.url }}, {{ request.headers }} .

• {{ self }} :- This is the template reference object which sometimes information about the system internals. Generally we test for the {{ self }}, {{ self.__init__.__globals__ }} .

• {{__init__}} :- This is the constructor method object .
• {{__globals__}} :- This is the dictionary of the every object which contains all the global variables, built in preferences, tools, libraries, functions .
• {{__import__(’os’) }} :- This function is executed when we write the import os in the code .
• popen(’ls’).read() :- This is used for the execution of the command on the system and read() is used for the human readable text produced otherwise we will get the object reference output .
• {{__builtins__}} :- This contains the various libraries and functions like import, open, exec, eval and etc .
• So we have understood the all the necessary concepts now we have used the 4 types of payload that would give us the flag. The above explanation will help to connect each every component of payload that why we wrote this object, library in this. So here are they :-

{{ self.__init__.__globals__.__builtins__.__import__(’os’).popen(’ls’).read() }}

{{ self.__init__.__globals__.__builtins__.open(’flag’) }}

{{ request.application.__globals__.__builtins__.__import__(’os’).popen(’ls’).read() }}

{{ config.__class__.__init__.__globals__[’os’].popen(’cat flag’).read() }}

• Learning of the concepts of from this writeup :-
• Template and Template Meaning .
• Server Side Template Injection Meaning .
• Jinja2, Handlebar, Freemaker, Twig, Smarty, ERB Templates and Their Formats .
• {{ config }}, {{ request }}, {{ self }}.
• popen, read, init, globals, import functions and dictionaries .

Final Takeway

Easy challenges are not about “easy flags”. They are about building the foundation for harder ones .

This SSTI challenge was not just about typing {{ 8*8 }} and seeing 64.

It was about training your brain to:

a. Identify template engines instead of guessing blindly.

b. Test payload patterns methodically.

c. Understand why {{ }} works but ${ } doesn’t.

d. Explore objects like config, request, and self with intent.

e. Trace how __init__, __globals__, and __builtins__ connect internally.

Most people stop when they get code execution. But the real learning starts when you ask:

Why did this payload work?

How did it reach the os module?

What object chain allowed access to system commands?

How does Jinja2 expose Python internals?

It is about understanding how templating engines process input. It is about understanding how templating engines process input. How objects are structured in memory. How Python exposes global namespaces. How unsafe rendering turns logic into execution.

If you only copy-paste payloads, you will solve easy challenges. If you understand the object traversal path, you will solve the hard ones. CTFs are not about the flag. They are about learning how applications actually break.

Go deeper than the payload. Go deeper than the writeup. Go deeper than the solution.

More templates. More internals. More real exploitation.

Happy Hacking.

PicoCTF Web Exploitation Easy Category Web Challenge [SSTL 1] was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

Read more of this story at Slashdot.

• Hey guys, I am V3n0mKai back again with the New CTF Writeup on the TryHackMe CTF called “Hidden Deep Into my Heart”.
• This CTF is of the web category and from the challenge statement it seems like we have to find something hidden directories in order to get the flag .

• Now here we have to first on the “Start Machine” and “Start the Attack Box”, after that we will be able to access the website .

• Now here in this we will first of all look for the basic endpoints, files and directory traversal and finding some hidden secrets.
• So we tried the following things :- robots.txt, sitemap.xml, source code analysis, basic developer tools inspection, trying the basic directories common names on the URL.
• Robots.txt :- A state policy file which is placed in the root directory of every web application and implements the REP [Robot Exclusion Protocol] which instructs all the web crawlers which URLs to crawl and which URLs not to so as to avoid being sensitive urls appearing in the web searches, decreasing the web traffic as no duplicate search results and etc .
• Also note the difference between the Crawling and Indexing. Crawling is that thew browser reads the contents of the page and then lists it on the search results and Indexing is just saving the metadata and the urls.
• But we have the X-Robot-Tag:noindex tag to ensure that the page is not indexed and also does not appear in the search results as well, but for this work the crawling for that page must allowed in the robots.txt because without that it index the page.
• Sitemap.xml :- It is the extra markup language file of the lists of the important urls of that web application and also the crawlers refer this file as well for the crawling output.
• Some of the common names of the directories we can try is like :- admin, login, admin-panel, robots.txt, sitemap.xml, secret, index.php and many more. We can also find this in my wordlists and also via github repositories as well.
• Now in the robots.txt we found one directory names as “cupids_secret_vault” and then when we traversed to that directory we got this .

• Now from here we tried the gobuster scanning on the both parts of the url that is first, normal ip url and then second, the one with directory found from the robots.txt also included as well. So started two scans on that.
• So before proceeding further we should first understand that what is actually the gobuster tool in detail and also see the most important flags we could use it to extract the information and do effective directory enumeration from that.
• Gobuster :- This tool for the directory and files enumeration across the web application by crafting the proper command with valid syntax and extracting any sensitive files from the web application as well. This tool is written in the go language.
• Gobuster is a concurrent brute-force engine written in Go. It: Takes a word, Injects it somewhere, Compares response, Filters output .
• Below diagrams images shows every important flags and options of the gobuster which we ca use enumeration process.

• Now as we have seen the gobuster command all the necessary so for the detecting the hidden directory enumeration we tried the below command .

gobuster dir -u http://10.49.155.99:5000/cupids_secret_vault/ -w /usr/share/wordlists/dirbuster/directory-list-2.3-small.txt -t 100

• As we can see that we have found /administrator directory in the scan 2 output. On navigating to the directory it was a login page .

• Now here we have to try bit of the brute force and some common techniques of the username and password.
• So we tried some of them and some basic SQL Injection Payloads like ‘ OR 1=1 — , ‘ AND 1=1 — , ‘ OR 1=2 — , ‘AND 1=2 — but it didn’t worked.
• Then we tried the combinations like :- admin & admin, admin & password, administrator & admin and etcetra. This also didn’t worked. Now we saw that we also found comment text in the robots.txt which was ‘cupid_arrow_2026!!!’.
• So keeping the username:admin and the password:cupid_arrow_2026!!!, we attempted and voila we found the flag.

Final Takeaway

Easy challenges are not about “easy wins.”

They are about sharpening instincts.

This TryHackMe room wasn’t about advanced exploitation.
It was about observation, enumeration, and thinking systematically.

CTFs are not about getting the flag.

They are about training your brain to:

Look at robots.txt even when others skip it.

a.] Understand crawling vs indexing instead of memorizing definitions.
b.] Enumerate directories methodically, not randomly.
c.] Use tools like Gobuster with purpose, not blindly.
d.] Notice comments, hints, and small clues hidden in plain sight.

Security is rarely about complex zero-days.

It is often about:

Misplaced secrets.
Exposed directories.
Poor credential hygiene.
Developers leaving breadcrumbs behind.

This challenge reinforced something important:

Enumeration is power.
Patience is power.
Attention to detail is power.

If this writeup helped you strengthen your fundamentals in web enumeration and directory discovery, consider sharing it with your peers.

More enumeration.
More hidden paths.
More structured thinking.

Happy Hacking.

TryHackMe CTF Writeup of Hidden Deep Into my Heart was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.

Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more. Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for the better.

Now they are headed everywhere ideas travel: to faculty and postdoctoral positions, to industry research labs, and to startups of their own founding — and several are still exploring what comes next and would love to hear from you.

Please join us in celebrating the achievements of these wonderful graduates. We are proud of everything they have accomplished at Berkeley, and we can’t wait to see what they do next!

Thank you to our friends at the Stanford AI Lab for this idea!

Baifeng Shi

Email: baifeng_shi@berkeley.edu
Website: https://bfshi.github.io/
Advisor(s): Trevor Darrell
Research Blurb: I work on building generalist vision and robotic models.
What's next: Member of Technical Staff at Physical Intelligence

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Charlie Snell

Email: csnell22@berkeley.edu
Website: https://sea-snell.github.io
Advisor(s): Dan Klein
Research Blurb: My work aims to understand when and how the different LLM scaling paradigms can be traded off and interchanged. In particular, test-time scaling treats each prompt independently, drawing long chains of inferences and then forgetting them entirely between prompts. This differs critically from pretraining, which instead learns a compressed representation from a large dataset. I believe bridging the gap between these methods of scaling computation, presents a key open challenge in the field: how can we develop methods which turn the inferences drawn at test-time back into learned representations that the model can hold onto across interactions.

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Devin Guillory

Email: dguillory@berkeley.edu
Website: https://devinguillory.com
Advisor(s): Trevor Darrell
Research Blurb: Accounting for data shifts in computer vision models
What's next: Building collaborative AI systems, looking for conspirators.

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Eve Fleisig

Email: efleisig@berkeley.edu
Website: https://efleisig.com
Advisor(s): Dan Klein
Research Blurb: I design language models to work reliably and fairly for the broad range of real LLM users. First, my research leverages disagreement among user preferences as signal, in order to train and evaluate LLMs for entire populations of users. Second, I work on designing rigorous evaluations to extricate challenging LLM harms that diverse users face. Finally, I work on core technical failures of LLMs, like miscalibrated confidence, to reduce downstream risks when models are deployed to users with different needs. Combined, these interventions facilitate building LLMs that minimize societal harms, and maximize benefits to a wider range of real-world users.
What's next: Postdoctoral fellow at Princeton CITP

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Grace Luo

Email: graceluo@berkeley.edu
Website: https://graceluo.net
Advisor(s): Trevor Darrell
Research Blurb: My research is on interpreting and controlling generative models. For example, I've worked on re-purposing image generators for computer vision tasks, and meta-modeling language activations for better LLM probing and steering.
What's next: Research scientist in industry

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Hanlin Zhu

Email: hanlinzhu@berkeley.edu
Website: https://hanlinzhu.com/
Advisor(s): Stuart Russell, Jiantao Jiao
Research Blurb: My research centers on understanding and improving the reasoning capabilities of large language models (LLMs).
What's next: Member of Technical Staff at OpenAI

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Haozhi Qi

Email: hqi@berkeley.edu
Website: https://haozhi.io/
Advisor(s): Jitendra Malik, Yi Ma
Research Blurb: Dexterous Manipulation and Robot Learning
What's next: Research scientist at Amazon; Faculty at University of Chicago

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J.D. Zamfirescu-Pereira

Email: zamfi@berkeley.edu
Website: https://zamfi.net
Advisor(s): Bjoern Hartmann
Research Blurb: My research focuses on effective human-AI co-design. I study the boundaries of language interfaces as a medium for interacting with AI, creating systems that blend language-focused interactions with structured user interfaces that draw on different levels of abstraction. I focus on language-oriented technologies, like LLMs and text-to-image models, that are powerful mediators of design processes. These technologies enable humans to describe their desires at almost any level of abstraction, from high-level goals vaguely specified (“I’d like a game to help my kid learn to read”) to low-level corrections of undesired outputs (“Don’t say ‘I know because I’ve tasted it’ when about a recipe substitution's taste”).
What's next: Assistant Professor, Computer Science, UCLA

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Jiachen Lian

Email: jiachenlian@berkeley.edu
Website: https://jlian2.github.io
Advisor(s): Gopala Anumanchipalli
Research Blurb: My research focuses on human-centered AI across speech, healthcare, and systems.
Looking for: Look for AI talents to join our startup

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Josh Kang

Email: minwoo_kang@berkeley.edu
Website: https://joshuaminwookang.github.io/
Advisor(s): John Canny
Research Blurb: I study language modeling and related topics in NLP; specific interests are human user simulation and building conversational, collaborative AI agents.
What's next: AI Scientist at Mistral AI

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Junhao (Bear) Xiong

Email: junhao_xiong@berkeley.edu
Website: https://www.linkedin.com/in/junhao-bear-xiong
Advisor(s): Jennifer Listgarten, Yun Song
Research Blurb: Junhao (Bear) Xiong is a PhD candidate at UC Berkeley, advised by Jennifer Listgarten and Yun S. Song. His work focuses on machine learning methods for biology, with an emphasis on generative modeling for proteins. Previously, he studied Applied Math and Computer Science at Johns Hopkins.
Looking for: Research scientist

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Kaylo Littlejohn

Email: kaylo_littlejohn@berkeley.edu
Website: https://kaylolittlejohn.com
Advisor(s): Gopala Anumanchipalli
Research Blurb: My research is focused on speech modeling and natural language processing. I co-led the development of multimodal AI tools to accurately translate brain activity into text, audible personalized speech, and a high-fidelity "digital talking avatar" (Nature 2023, Nature Neuroscience 2025). I am also tech lead for voice modeling at Roblox.
Looking for: Research Scientist / Engineer

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Kent Chang

Email: kentkchang@berkeley.edu
Website: https://kentkc.org
Advisor(s): David Bamman
Research Blurb: I work on NLP and multimodal machine learning, with a focus on evaluating large language models and building multimodal systems for understanding dialogue, narrative, and social interaction. My research includes benchmarks for LLM memorization, multimodal datasets sourced from feature films and television, and studies of model behavior. I'm interested in bridging computational methods with questions from the humanities and social sciences about whose voices get represented in AI systems, and about AI's broader impact. My work has appeared at EMNLP and ACL, among others.
Looking for: (teaching) faculty, Research Scientist, ML/AI SWE

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Kevin Black

Email: kvablack@berkeley.edu
Website: https://kevin.black
Advisor(s): Sergey Levine
Research Blurb: I work on large-scale robot learning: including imitation learning, reinforcement learning, generative modeling, real-time control, and whatever else it takes to make robots work in the real world!
What's next: Research Scientist of Physical Intelligence

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Kunhe Yang

Email: kunheyang@berkeley.edu
Website: https://www.kunheyang.com/
Advisor(s): Nika Haghtalab
Research Blurb: My research focuses on the theoretical foundations of designing and evaluating AI algorithms in environments shaped by human incentives and AI agency. My work spans human-centric policy learning, incentive-aware evaluation, and multi-agent collaboration and information transmission, drawing on tools from machine learning theory and computational economics.
What's next: Postdoc Research at Stanford

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Lisa Dunlap

Email: lisabdunlap@berkeley.edu
Website: https://lisabdunlap.com
Advisor(s): Joseph Gonzalez, Trevor Darrell
Research Blurb: Auditing generative models.
What's next: Research Engineer at Anthropic

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Long (Tony) Lian

Email: longlian@berkeley.edu
Website: https://tonylian.com/
Advisor(s): Trevor Darrell, Adam Yala
Research Blurb: My research primarily focuses on developing real-time multi-modal multi-agent systems and parallel reasoning systems through end-to-end RL.
What's next: Member of Technical Staff at Thinking Machines Lab

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Maulik Bhatt

Email: maulikbhatt@berkeley.edu
Website: https://maulikb.com
Advisor(s): Negar Mehr
Research Blurb: My research develops autonomous robots that can safely coordinate with humans and other robots in shared environments. I build scalable algorithms grounded in game theory and diffusion models that let agents reason about the intent and behavior of others around them. My work spans real-time multi-agent trajectory planning and imitation learning in the presence of multi-modality. I've validated these methods on hardware platforms ranging from quadrotors to manipulators, with the goal of making multi-agent coordination robust, interpretable, and deployable in the real world.
What's next: Joining Toyota Woven's end-to-end autonomous driving team.

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Michael Psenka

Email: psenka@berkeley.edu
Website: https://www.michaelpsenka.io/
Advisor(s): Aditi Krishnapriyan
Research Blurb: Work in various domains (reinforcement learning, world models, AI+bio/chem), generally working on longer-horizon and out-of-distribution problems in planning and interpolation (e.g. robot manipulation from start state to goal, molecular dynamics of proteins between ground states). My thesis took a variational approach (think calculus of variations) directly from deep generative models of the environment, framing path-finding as minimizing a functional induced by the learned model itself (its score, its critic, or its dynamics). Through my research I've gained insight on how to properly handle dynamics in deep learning systems, and I plan to continue developing systems that are dynamic and adaptive.
What's next: Lead Research Scientist at Baseten

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Nathan Lichtlé

Email: nathan.lichtle@gmail.com
Website: https://nathanlichtle.com
Advisor(s): Alexandre M. Bayen
Research Blurb: RL for autonomous driving.
What's next: Chief Scientist & Co-founder at Yumi Health

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Neerja Thakkar

Email: nthakkar@berkeley.edu
Website: https://neerja.me/
Advisor(s): Jitendra Malik
Research Blurb: My research focuses on scaling predictive world models to handle the complexity of in-the-wild motion. Using autoregressive and diffusion frameworks, I develop better representations for real-world prediction and propose methods to efficiently adapt these models to new domains.
Looking for: Research scientist

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Nikita Mehandru

Email: nmehandru@berkeley.edu
Website: https://n-mehandru.github.io/
Advisor(s): Ahmed Alaa and David Bamman
Research Blurb: My research develops and applies machine learning methods for clinical reasoning and disease progression modeling using unstructured text and time series data from electronic health records. In collaboration with physicians at UCSF, I bridge method development and clinical validation with the intention to build reliable, interpretable AI systems in medicine.
Looking for: Research Scientist

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Niklas Lauffer

Email: nlauffer@berkeley.edu
Website: https://niklaslauffer.github.io/
Advisor(s): Stuart Russell and Sanjit Seshia
Research Blurb: Niklas's research is focused on AI safety and reinforcement learning, particularly in the area of multi-agent interaction and LM agents. He's worked on enabling adversarial learning in cooperative and mixed-motive settings, solving issues of covariate shift in training LM agents on long-horizon tasks, as well as evaluating safety risks posed by LM agents in multi-agent settings.
What's next: Research Scientist at Google Deepmind

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Qiyang Li

Email: qcli@berkeley.edu
Website: https://colinqiyangli.github.io/
Advisor(s): Sergey Levine
Research Blurb: Recent progress in robotic manipulation policy learning has been largely driven by (1) the increasing availability of large-scale prior datasets and (2) the success of action chunking, where the policy predicts a short sequence of future actions rather than a single one. However, most action chunking policies are trained via supervised imitation learning, because efficient online self-improvement with reinforcement learning (RL) remains challenging—limiting real-world applicability. My PhD research studied how we could leverage prior data to optimize action-chunking policies with RL, combining empirical results with theoretical insights.
Looking for: Post-doc/research scientist for RL in robotics and LLMs!

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Sampada Deglurkar

Email: sampada_deglurkar@berkeley.edu
Website: https://sdeglurkar.github.io/
Advisor(s): Prof Claire Tomlin
Research Blurb: My research is in providing safety assurances for AI-enabled autonomous systems, ranging from robots to autonomous vehicles to aviation systems. For this, I have worked with uncertainty quantification for machine learning models, decision-making under uncertainty algorithms, and tools for producing probabilistic guarantees on system operation.
Looking for: Research scientist, Research engineer

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Vinamra Benara

Email: vbenara@berkeley.edu
Website: https://cs.berkeley.edu/~vbenara
Advisor(s): Ion Stoica
Research Blurb: My research focuses on LLM post-training, including data curation, RLHF, RLVR with VLMs, evaluations, reasoning, agentic workflows, and interpretability. I also have strong expertise in systems infrastructure for distributed computing.
Looking for: Research scientist / Research Engineer

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Vongani Maluleke

Email: vongani_maluleke@berkeley.edu
Website: https://people.eecs.berkeley.edu/~vongani_maluleke/
Advisor(s): Jitendra Malik and Angjoo Kanazawa
Research Blurb: Vongani Maluleke is a PhD candidate at UC Berkeley (BAIR, advised by Jitendra Malik and Angjoo Kanazawa), where she led the development of MAGNet, a unified multi-agent motion generation framework that supports a wide range of motion generation tasks without retraining or architectural changes, outperforming task-specialized state-of-the-art baselines. She is currently extending this work by deploying it on a Unitree G1 humanoid to make it embody social intelligence. Before her PhD, she was a Senior AI Consultant at Deloitte, awarded Exceptional Performer two consecutive years, leading AI system development across media, telecommunications, retail, and financial services.
Looking for: Research scientist

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Wei-Jer Chang

Email: weijer_chang@berkeley.edu
Website: https://weijer-chang.github.io/
Advisor(s): Masayoshi Tomizuka
Research Blurb: My research focuses on developing safe and intelligent autonomous systems for complex, human-centered environments. I work at the intersection of machine learning, generative models, and reinforcement learning, with applications in autonomy. My work addresses challenges in multi-agent interaction, interactive human behavior, and long-tail safety-critical scenarios at scale.
Looking for: Research Scientist, Applied Scientist, Roboticist

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Xiuyu Li

Email: xiuyu@berkeley.edu
Website: https://xiuyuli.com/
Advisor(s): Kurt Keutzer
Research Blurb: My research focuses on developing scalable and self-improving large language model agents, with emphasis on coding agents for complex, long-horizon tasks. This direction builds on my work in parallel reasoning, and on broader expertise in making generative models more efficient in training and inference across language and vision.
What's next: Member of Technical Staff at xAI

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Yichen Xie

Email: yichenxie0928@gmail.com
Website: https://yichen928.github.io/
Advisor(s): Masayoshi Tomizuka
Research Blurb: My research focuses on building multimodal foundation models and world models that understand and interact with complex physical environments. I aim to develop unified representations across modalities, enabling AI systems to reason over space, time, and dynamics toward general-purpose embodied intelligence.
What's next: Research Scientist at Luma AI

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Yigit Efe Erginbas

Email: erginbas@berkeley.edu
Website: https://www.linkedin.com/in/erginbas/
Advisor(s): Kannan Ramchandran, Thomas A. Courtade
Research Blurb: My PhD research spans two threads: online learning in large-scale markets, and interpretability of large machine learning models. In the first, I work on sequential decision-making with applications to recommendation, pricing, and assortment selection. My focus is on designing algorithms with provable guarantees for welfare maximization, revenue maximization, and stability. In the second, I develop scalable attribution methods that exploit the sparse, low-degree structure of real-world interactions, using tools from signal processing and information theory. More recently, I have been exploring principled ways to evaluate the faithfulness of model self-explanations.
What's next: Researcher at Hudson River Trading's AI Labs (HAIL)

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Yiheng Li

Email: yhli@berkeley.edu
Website: https://Yihengli.com
Advisor(s): Masayoshi Tomizuka
Research Blurb: I am working on vision world modeling, with prior experience in diffusion model's efficiency as well as in autonomous driving.
What's next: Research Scientist at Waymo

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Zhe Fu

Email: zhefu@berkeley.edu
Website: https://fu-zhe.com/
Advisor(s): Alexandre Bayen
Research Blurb: My research focuses on physics-informed learning and control for mixed-autonomy systems, with applications in transportation. I design physics-informed neural networks to learn solutions of nonlinear partial differential equations, enabling accurate and data-efficient prediction of traffic dynamics. Building on these models, I develop both model-based and learning-based control strategies that coordinate automated vehicles to improve system-level performance. My work bridges machine learning, control, and real-world deployment, and has been validated in large-scale field experiments. More broadly, I aim to advance trustworthy, interpretable AI for decision-making in complex, real-world systems.
What's next: I will be an Energy Fellow at Stanford after graduation. Also looking for Faculty, or research scientist positions in AI, control, and autonomy.

---

Meta's FAIR AI team uses Brain2Qwerty v2 to translate brain activity into typed sentences, with no implants or surgery required. The system reads magnetic signals outside the skull and reconstructs what a person is typing. Clinical use for paralyzed patients is still a long way off, but accuracy keeps improving with every additional recording. AI agents that wrote their own code helped with the optimization.

The article Meta's non-invasive brain-to-text AI is closing the gap with surgical implants appeared first on The Decoder.

The post Meta AI Releases Brain2Qwerty v2: A Non-Invasive MEG Brain-to-Text Pipeline Decoding Typed Sentences at 61% Word Accuracy appeared first on MarkTechPost.

Read more of this story at Slashdot.

Visit https://www.securityweekly.com/swn for all the latest episodes!

Show Notes: https://securityweekly.com/swn-593

AI Brain Harvest, Fortibleed, Win 10, Blacksite, Windchill, Cisco, BB-8 Sidewalk Bots, Josh Marpet, and More on this episode of the Security Weekly News.

Visit https://www.securityweekly.com/swn for all the latest episodes!

Show Notes: https://securityweekly.com/swn-593

Ian Ballantyne, Developer Relations Engineer at Google DeepMind, shows how Gemma runs on hardware like Raspberry Pi, Jetson, and Nano, letting a model see, hear, and act the way a robot would. The demo is Reachy Mini, the open source robot from Hugging Face and Pollen Robotics, a small robot that sees with cameras, reacts with emotion and head movement, and holds a conversation through its microphone and speaker.

What's covered: A live conversation with Reachy Mini about why local on-device models matter for privacy and speed, the robot's ability to move its head, show emotion, and take pictures to understand its surroundings, controlling smart devices and APIs like lights, thermostats, calendars, and live data, and an early look at the robot reasoning about a chessboard and explaining how a knight moves.
Explore the Reachy Mini project from Hugging Face and Pollen Robotics, and try running Gemma on your own hardware.

What would you build with Gemma on a robot or IoT device? Drop it in the comments.

Resources:
Reachy Mini → https://goo.gle/4xJNpVJ
Local Reachy Mini → https://goo.gle/4f1dOXC
Gemma Docs → https://goo.gle/4xMnVXS
Gemma Cookbook → https://goo.gle/4epYEuZ


Subscribe to Google for Developers → https://goo.gle/developers

Speaker: Ian Ballantyne
Products Mentioned: Google AI, Gemini

The post Understanding the brain with AI-driven explanations and experiments appeared first on Microsoft Research.

A shocking 90% to 95% of drug discovery projects reportedly fail — one of the highest failure rates of any industry. A single successful drug can take over a dozen years and up to $1 billion from initial discovery to patient distribution, according to published reports. 

Generative AI is being used to solve some of the challenges, but Stanford researchers have moved the ball forward with agentic AI. 

A team led by James Zou, associate professor of Biomedical Data Science at Stanford University, has deployed thousands autonomous AI "scientist" agents in a virtual biotech that simulates the full lifecycle of drug development. The agents handle everything from initial discovery through safety testing and clinical trial design, while maintaining the continuity that’s lacking in today’s drug discovery processes, according to Zou.

The project uses a hierarchical orchestration framework. At the top sits a chief scientist officer agent that acts as a planner, delegating tasks to teams of specialized agents, Zou told VentureBeat during a call ahead of his upcoming session at VB Transform 2026.

While one team of agents focuses on discovery, another manages safety, and others handle specialized analytical tasks. Because these agents operate within a unified, hierarchical ecosystem, they retain the full context of a project, maintaining continuity from the first molecule identified to the final clinical outcome.

The "brain" of the system relies on a vast amount of primary data. The agents are granted access to data sources ranging from genomics and FDA chemistry data to clinical trial databases using a model context protocol.

The team has invested heavily in agent-native and agent-friendly data, allowing the AI to synthesize complex information more effectively. The system relies on a combination of models, with Zou noting that while Claude often serves as the backbone for coding and data analysis, the architecture employs a mixture of models, including those fine-tuned specialized use cases.

Zou is raising money at a roughly $1 billion valuation for his startup, Human Intelligence, based on the research.

During Zou’s session at VB Transform on July 15, titled How 10,000 agentic scientists in Stanford’s lab are set to revolutionize medical research and discovery, he will share valuable insights including strategies for managing context and long-running, multi-step workflows in a multi-agent system, the process of transforming and indexing raw enterprise data to make it agent native, and how to use human auditing and experimental reward signals to verify agent actions.

Another session at VB Transform focused on the value of agentic context includes Building a trustworthy agentic AI foundation: How Zillow accelerated engineering by 40%, with Zillow's SVP of engineering and technology, Toby Roberts and Glean’s CEO Arvind Jain. 

Interested in attending VB Transform 2026? Register here. A select number of complimentary passes are also available to senior technology leaders. Contact us to get yours.

To address this, Intuit made the bold decision to overhaul its technical infrastructure for its business platform. The company moved away from its multi-agent setup, which prioritized broad capabilities, to a granular, skill-and-tool-based architecture while embedding human experts directly into the workflow alongside AI. This shift involved decomposing its massive agents into specialized components, separating the brain from the hands, essentially.

"We went from a multi-agent system where we had large agents that did a lot to fully incorporating workflows, skills and tools down to the base level,” said Nhung Ho, VP of AI at Intuit. “We changed the orchestrator, we changed the planner, we changed the brain, and we also changed what everybody had to build across the whole company."

At VB Transform 2026 on July 14 and 15, Ho will share details about the technology decisions behind building an abstraction layer behind Intuit’s system of intelligence. She’ll also share how the new architecture has allowed the company to decouple its orchestration from specific model providers, allowing Intuit to remain agile and use the best tools for the job, whether from large model providers or their own home-grown tools.

Other VB Transform sessions focused on agentic orchestration include: 

• From signals to shelves: How Target is engineering Agentic AI for the right product, right place, right time with speaker Siobhan McFeeney, SVP Technology, Target;

• The engineer's multiplier: How Instacart uses agentic AI to eliminate toil, elevate teams and slash costs with speaker Anirban Kundu, CTO, Instacart;

• MCP connection isn't orchestration: Building the agent execution layer with Arnab Bose, chief product officer, Asana;

• Building the agentic workforce: A blueprint for scaling AI operations without the sprawl with Romit Jadhwani, Sr. Director, Enterprise AI, Data & Productivity, Rivian and Craig Wiley, VP of AI, Databricks; and 

• Inside Atlassian’s Living Lab: Deploying context-aware agents at scale with Dr. Molly Sands, head of the Teamwork Lab at Atlassian

Interested in attending VB Transform 2026? Register here. A select number of complimentary passes are also available to senior technology leaders. Contact us to get yours.

Read more of this story at Slashdot.

Designed for developers, enterprises, and nations seeking resilience against vendor lock-in and geopolitical export controls, Fugu (Japanese for "pufferfish"), bypasses the traditional monolithic model structure by dynamically routing queries to a swappable pool of specialized AI agents.

Sakana CEO and co-founder David Ha, formerly of Google Brain, positioned Fugu as a more reliable option for enterprise workflows than any single AI model provider in the wake of Anthropic's move on June 12 to revoke public access to its most powerful models, Claude Mythos 5 and Claude Fable 5, in the wake of a U.S. government export control order. As Ha wrote in a post today on X:

"Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos. But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models. Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight. Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool."

Sakana AI explicitly states that the specific models Fugu selects and how it coordinates them are proprietary, meaning this routing information is hidden from the user by design. The documentation only refers generally to a "diverse pool of powerful models," "multiple LLMs," or "specialized models" without providing a specific count.

By acting as a sophisticated coordinator rather than a standalone foundation model, Fugu matches the output quality of top-tier models like Fable and Mythos on third-party benchmarks of agentic tasks, while fundamentally altering how developers deploy critical AI infrastructure.

How Sakana Fugu works and where it beats Anthropic's Claude Fable 5

At its core, Sakana Fugu operates like a master general contractor. When presented with a complex request, Fugu does not attempt to execute every step itself.

Instead, it breaks the problem down, delegates sub-tasks to a pool of expert foundation models, verifies their work, and synthesizes the final output.

"Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively," the Sakana AI team noted in their technical release.

Grounded in two of Sakana's 2026 research papers, TRINITY and the Conductor, the system autonomously manages the entire lifecycle of model selection and verification using learned coordination strategies rather than hand-designed workflows. To the end user, this multi-agent swarm is entirely abstracted behind a standard API endpoint.

Sakana AI is offering two variants of the system to cater to different operational workloads:

• Fugu: A high-speed, low-latency model optimized for everyday tasks. It is designed to act as the default engine for interactive chatbots and integrates directly into coding environments like Codex.

• Fugu Ultra: The flagship tier engineered for complex, high-stakes tasks such as AI research, cybersecurity analysis, and multi-step patent investigations. According to Sakana, Fugu Ultra coordinates a deeper pool of experts and matches industry-leading monolithic models across rigorous scientific and reasoning benchmarks.

Additionally, on the pay-as-you-go plan, standard Fugu charges a dynamic rate based on the specific underlying models activated, whereas Fugu Ultra utilizes a fixed pricing structure starting at $5 per million input tokens and $30 per million output tokens.

As indicated by benchmark charts shared by Sakana, Fugu actually exceeds the performance of Anthropic's Claude Fable 5 on LiveCodeBench, an open source benchmark testing coding performance on regularly refreshed, software problem-solving tasks (Fugu Ultra: 93.2, Fugu: 92.9, Fable: 89.8), and beats the prior Claude Mythos Preview model on GPQA-D (Diamond) , a test of 198 graduate-level multiple-choice questions in biology, physics, and chemistry (Fugu Ultra: 95.5, Fugu: 95.5, Mythos Preview: 94.6).

By orchestrating multiple models from different providers, Fugu essentially builds native redundancy into the AI stack. If one provider suffers an outage or faces sudden regulatory restrictions, Fugu routes around the disruption to maintain uptime.

Licensing and availability

Fugu is offered as a commercial, proprietary API service, not an open-source framework.

Because Sakana’s core intellectual property lies in its non-obvious collaboration patterns, the specific routing information—meaning exactly which underlying models Fugu selects for a given query—remains proprietary and is intentionally hidden from the user.

However, Sakana offers critical controls for enterprise data compliance. Developers can explicitly opt specific models or providers out of their Fugu routing pool to maintain strict corporate privacy standards.

Additionally, users can opt out of having their prompts used for future training data. Geographically, Fugu is restricted from operating within the European Union (EU) and European Economic Area (EEA) while Sakana works to align its black-box data routing architecture with GDPR regulations.

Pricing is fairly steep

Fugu is available immediately in most regions—with the temporary exception of the EU and EEA—at subscription tiers and pay-as-you-go pricing.

Teams can opt for monthly subscription allowances designed for individual or hands-on use: a Standard tier at $20/month for lightweight workflows, a Pro tier at $100/month providing 10x standard usage, and a Max tier at $200/month offering 20x usage for continuous, long-running tasks. I wasn't able to find the actual amount of tokens covered under these plans, but I've reached out to Ha on X for more information.

As part of the initial rollout, Sakana is offering a free second month for users who subscribe to any tier by July 31, 2026.

For enterprise scaling and production deployments, Sakana offers an elastic pay-as-you-go plan. Crucially for high-stakes environments, requests made under this consumption-based model are served at a higher priority than those from monthly subscription plans.

Under this framework, the standard Fugu engine charges the single rate of the highest-tier underlying model involved in a query, without ever stacking multi-agent fees. The flagship Fugu Ultra tier (fugu-ultra-20260615) utilizes a fixed pricing structure per one million tokens: $5 for input, $30 for output, and $0.50 for cached input. These rates increase to $10, $45, and $1.00 respectively for extreme workloads utilizing context windows above 272K tokens. That puts it among the more expensive options compared to single AI models via provider APIs:

VentureBeat Frontier AI Model API Pricing Snapshot

Developers modeling operational costs should also note a significant architectural caveat in how Fugu bills for its multi-agent capabilities. According to the developer documentation, Fugu Ultra’s API responses include detailed usage fields that separate user-visible token generation from internal orchestration work. The background tokens consumed and generated when Fugu delegates sub-tasks, verifies code, or routes between underlying agents are not absorbed by the provider; they represent real token usage and are counted toward the final price of the request at standard rates.

The Orchestration landscape: Fugu vs. The Field and notable benchmark performance

To understand Fugu’s position in the mid-2026 AI ecosystem, it is critical to distinguish between model routing and multi-agent orchestration.

Over the past year, enterprise adoption of standard routing platforms—such as Not Diamond, Martian, and the open-source RouteLLM framework—has skyrocketed. These systems act as intelligent air traffic controllers; using semantic classifiers or meta-models, they analyze an incoming prompt and predict which single foundation model will yield the highest quality or most cost-effective response, dispatching the query accordingly.

Fugu operates on a fundamentally different paradigm. Rather than making a one-shot routing decision, Fugu aligns more closely with complex multi-round systems like Router-R1 (a framework introduced at NeurIPS 2025). It breaks a query down, interleaves reasoning with delegation, and dynamically assigns sub-tasks to multiple models in parallel or sequence before synthesizing a final output.

While frameworks like LangGraph, CrewAI, and Microsoft AutoGen offer developers the tools to build similar multi-agent systems, they require immense manual configuration—defining roles, setting up conditional edges, and managing state across long-running loops.

Fugu abstracts this operational overhead entirely. It is essentially a LangGraph-style workflow packaged as a single, black-box API endpoint.

An orchestration system is ultimately bounded by the raw capabilities of the underlying models in its pool, a reality reflected in Sakana’s own benchmark testing against standalone frontier models.

On rigorous coding and agentic tasks, collective intelligence shows a distinct advantage over standard models. Fugu Ultra posted a 73.7 on SWE-Bench Pro, significantly outperforming Anthropic's Claude Opus 4.8 (69.2) and OpenAI's GPT-5.5 (58.6).

However, Fugu is not a silver bullet, and its performance is not a clean sweep across the board. When compared to highly specialized or restricted-access monolithic models, Fugu occasionally trails:

• SWE-Bench Pro: While Fugu Ultra (73.7) beat most accessible models, it was comfortably eclipsed by Anthropic’s limited-access Fable 5 (80.0), which is currently absent from Fugu's swappable pool due to the U.S. government's export control order and Anthropic's subsequent response to remove the model entirely from global usage.

• Humanity's Last Exam: Fugu Ultra (50.0) narrowly edged out Opus 4.8 (49.8), but again fell short of Fable 5 (53.3).

• Long-Context and Security: On the MRCRv2 long-context-recall test, OpenAI's GPT-5.5 maintained the lead (94.8 vs Fugu Ultra's 93.6), and Opus 4.8 remained the top performer on the CTI-REALM cybersecurity benchmark (69.6 vs Fugu Ultra's 69.4).

The quantitative data points to a clear conclusion: Fugu is highly effective at boosting performance on messy, multi-step tasks (like writing a complex HTML5 game from scratch) by leaning on the combined strengths of multiple mid-tier and high-tier models.

However, for sheer brute-force reasoning within a single, highly constrained domain, the industry's largest standalone models still hold the edge—provided an enterprise can maintain uninterrupted access to them.

Background on Sakana's formation and noteworthy achievements to date

Sakana AI was formed in Tokyo in 2023 by Llion Jones, a co-author of Google’s foundational 2017 "Attention Is All You Need" paper, and David Ha, the former head of research at Stability AI.

Disillusioned by large tech company bureaucracy and the industry's hyper-fixation on scaling single, massive foundational models, the founders built Sakana around principles of biomimicry and evolutionary computing.

The company's name, derived from the Japanese word for fish, reflects its core technical thesis: utilizing collective "swarm" intelligence rather than brute-force compute. Following a $2.6 billion Series B valuation in late 2025 and the recent June 2026 launch of Marlin—an autonomous, eight-hour research agent for the B2B sector—Fugu represents the commercialization of Sakana's multi-agent routing technology for everyday developers.

A mixed reception among the broader AI community online

The developer community has responded to Fugu by rigorously testing its practical tradeoffs, weighing its routing efficiencies against the sheer power of monolithic foundation models.

AI observer, developer and influencer Chris (@ChrissGPT on X) highlighted the specific utility of Fugu over raw foundational AI.

"For a single clean prompt, you probably would [use Fable 5, Mythos, or GPT-5.5 directly]," he noted, but argued that Fugu's true value emerges in messy, multi-step environments. "...whether it involves delegation, verification, synthesis, code review, research loops, security analysis... the more it would make sense to use this," he wrote.

Chris also pointed out the strategic geopolitical advantage of Fugu's architecture, noting that if frontier AI access is abruptly revoked due to regulation or export controls, an orchestrator can dynamically swap models to prevent a total system failure.

Creative agency owner Mark Santos (@markksantos) of Mark Studios provided a direct, real-world comparison by tasking both Fugu Ultra and Claude Opus 4.8 with building a "Crossy Road" game clone using Three.js. The results underscored the operational differences between an orchestrator and a monolithic giant:

• Sakana Fugu Ultra: Completed the task in 22 minutes using ~89,000 tokens for roughly $7.32. However, the final game suffered from minor logic errors, such as inverted directional turns and wonky camera angles.

• Claude Opus 4.8: Took 79 minutes, burned ~940,000 tokens for nearly $37.85, and got stuck in a retry loop requiring human intervention. Despite the inefficiency, it ultimately produced superior application design and functionality.

Santos concluded the experiment by stating, "In terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu... won".

Elie Bakouch, a research engineer at cloud-based, open AI infrastructure and systems provider Prime Intellect, pointed out on X that "to be clear, this is a closed source orchestrator on top of closed source models. if before you didn't control the models, now you don't even control which ones are used or how much. this is not 'AI sovereignty'..."

These early tests and reactions mirror the sentiment summarized by Reddit user GreedyWorking1499 in initial platform discussions: "Until proven otherwise, this is just a highly advanced router/wrapper, not a fundamental not a fundamental leap in intelligence like Mythos/Fable was."

Yet, as enterprises increasingly demand fail-safes against single-vendor reliance, Sakana is proving that packaging collective intelligence into a single API endpoint is a highly viable commercial path.

The post Best 50 Psychological Thrillers to Watch on Netflix appeared first on AddictiveTips.

Is this the most autonomous, dexterous robot yet? In this episode of AI News, we cover the massive unveiling of Genesis ENO, a game-changing general-purpose robot challenging the traditional humanoid robot design. Powered by the GENE AI brain, ENO features 20 DoF proprietary hands for human-level dexterous manipulation across manufacturing, logistics, and laboratories.

We also break down Sony AI Ace, the revolutionary autonomous physical agent that just defeated elite human athletes in table tennis under official ITTF rules using deep reinforcement learning and advanced 3D perception. Plus, a look at NVIDIA’s groundbreaking Motion Bricks framework generating robotics physical motion at a staggering 15,000 FPS, and Anthropic’s new Claude Code Artifacts update for developers.

Discovery the AI agent economy: https://8004agents.ai

0:00 Eno
4:08 Sony AI
5:15 MotionBricks
6:16 Claude Code
7:30 Aeon

#ai #news #robot

The post Perplexity Launches Brain, a Self-Improving Memory System That Builds a Context Graph of an Agent’s Work and Learns Overnight appeared first on MarkTechPost.

Read more of this story at Slashdot.

The post Lawmakers leary about Trump administration’s Anthropic order appeared first on CyberScoop.

Transforming Threat Data into High-Confidence Intelligence

In an era of relentless and complex cyber attacks, traditional, manual threat intelligence cannot keep pace. Security teams are overwhelmed by data fragmentation and the critical lack of context. “The Intelligent Shield” introduces a new paradigm: beyond simply ingesting data, it’s about deploying advanced, automated machine learning pipelines for AI-driven enrichment.

This guide demonstrates how to integrate state-of-the-art Large Language Models (LLMs), such as Claude AI, into an OpenCTI ecosystem. By leveraging the OpenCTI STIX 2.1 Knowledge Graph and natural language processing, this architecture converts disparate, unstructured data feeds into high-fidelity, actionable intelligence. It automatically builds context, executes deep mapping to frameworks like the MITRE ATT&CK Matrix, and generates calculated, real-time Confidence Scores, enabling organizations to proactively strengthen their defenses with an intuitive, automated Intelligent Shield.

Table of Contents

1. What is OpenCTI?
2. Core Capabilities
3. Architecture Overview
4. Threat Intelligence Feeds
5. AI Integration Layer
6. Prerequisites
7. Docker Compose Deployment
8. Connector Configuration
9. AI-Driven Enrichment Pipeline
10. Post-Deployment Hardening
11. Operational Runbook
12. Troubleshooting
13. Usage Examples

1. What is OpenCTI?

OpenCTI (Open Cyber Threat Intelligence) is an open-source platform developed by Filigran (formerly a project of ANSSI, the French national cybersecurity agency) for structuring, storing, organizing, visualizing, and sharing cyber threat intelligence (CTI).

It implements the STIX 2.1 (Structured Threat Information eXpression) standard as its native data model and exposes a GraphQL API for all read/write operations. Every object — threat actors, campaigns, malware, vulnerabilities, indicators, attack patterns — is stored as a STIX Domain Object (SDO) or STIX Relationship Object (SRO) backed by two databases:

• ElasticSearch / OpenSearch — full-text search and analytics
• Apache Cassandra (via JanusGraph) — graph relationship storage

Why OpenCTI?

2. Core Capabilities

2.1 Knowledge Graph

• Entities: Threat Actors, Intrusion Sets, Campaigns, Malware, Tools, Vulnerabilities (CVE), Attack Patterns (MITRE ATT&CK), Courses of Action, Sectors, Countries, Organizations
• Relationships modelled as first-class STIX SROs with confidence scores, date ranges, and TLP markings
• Diamond Model and Kill Chain views built in

2.2 Indicator Management

• IOC lifecycle: valid_from / valid_until with automatic expiry
• Detection rule generation (Sigma, YARA, Snort)
• Bulk import via STIX, CSV, OpenIOC, MISP formats
• Scoring and confidence weighting per source

2.3 MITRE ATT&CK Navigator Integration

• Full ATT&CK Enterprise / Mobile / ICS matrices
• Heatmaps of technique usage per threat actor or campaign
• Gap analysis against your current detection coverage

2.4 Threat Actor Profiling

• Attributed aliases, motivations (financial, espionage, hacktivism)
• Geo and sector targeting mapped on world map
• Timeline of campaigns and malware usage

2.5 Automation & Playbooks

• Built-in playbook engine (since v5.9): trigger enrichment, notifications, or SOAR actions on entity creation/modification(Enterprise Edition only)
• Python SDK for custom automation
• Webhook support for external integrations

2.6 Collaboration & Sharing

• Role-based access control (RBAC) with groups and organizations
• TLP (Traffic Light Protocol) enforcement at object level
• TAXII 2.1 server — push feeds to SIEMs, firewalls, EDR platforms
• Sharing with partner organizations via federated instances

2.7 Dashboard & Reporting

• Customizable dashboards with widget library
• PDF report generation
• Timeline, matrix, and entity views
• Attack path visualization

3. Architecture Overview

4. Threat Intelligence Feeds

4.1 Free / Open-Source Feeds

• MITRE ATT&CK — Connector: opencti/connector-mitre — Data: Techniques, mitigations, groups, software — Setup: API key not needed.
• CVE / NVD — Connector: opencti/connector-cve — Data: Vulnerabilities — Setup: NVD API key recommended/required depending on configuration.
• AlienVault OTX — Connector: opencti/connector-alienvault — Data: IOCs, pulses, malware families — Setup: Free OTX account/API key.
• Abuse.ch MalwareBazaar — Connector: opencti/connector-malwarebazaar — Data: Malware hashes, malware metadata, file observables — Setup: Free MalwareBazaar API key.
• Abuse.ch URLhaus — Connector: opencti/connector-urlhaus — Data: Malicious URLs — Setup: Public feed; no API key for CSV feed.
• Abuse.ch Feodo Tracker — Connector: use opencti/connector-misp-feed or ingest the Feodo CSV/blocklist feed manually — Data: Botnet C2 IPs — Setup: Free.
• Shodan InternetDB — Connector: opencti/connector-shodan-internetdb — Data: IP enrichment, domains, CPEs, CVEs, tags — Setup: No API key required.
• MISP Default / CIRCL OSINT Feeds — Connector: opencti/connector-misp-feed — Data: STIX/MISP bundles, indicators, observables — Setup: Free.
• CyberCrime-Tracker feed via MISP default feeds — Connector: use opencti/connector-misp-feed rather than a dedicated current connector — Data: C2 panels / freetext indicators — Setup: Free.
• OpenPhish — Connector: no verified current dedicated OpenCTI connector in the main repo; use generic feed ingestion where suitable — Data: Phishing URLs — Setup: Free/community feed options.
• DigitalSide IT-ISAC MISP Feed — Connector: opencti/connector-misp-feed with custom MISP_FEED_URL — Data: IOCs / MISP-format feed — Setup: Free.

4.2 Commercial Feeds (require license/API key)

• MISP — self-hosted — Connector: opencti/connector-misp — Strengths: community sharing, custom events, internal/private CTI exchange. The OpenCTI repo lists both misp and misp-feed; use misp for a live MISP instance with API access, and misp-feed for static MISP feed URLs.
• VirusTotal / Google Threat Intelligence — Connector: opencti/connector-virustotal — Strengths: file, URL, domain, and IP enrichment. The connector is under internal-enrichment, not external-import.
• Mandiant Threat Intelligence / Google Threat Intelligence — Connector: opencti/connector-mandiant — Strengths: APT intelligence, actor reporting, malware/campaign context.
• Recorded Future — Connectors: opencti/connector-recordedfuture and opencti/connector-recordedfuture-enrichment — Strengths: risk lists, enrichment, vulnerability/contextual intelligence, dark web and external threat data. Recorded Future documentation describes the OpenCTI integration as two components: an enrichment connector and a Recorded Future connector.
• CrowdStrike Falcon Intelligence — Connector: opencti/connector-crowdstrike — Strengths: actor tracking, indicators, adversary intelligence, Falcon ecosystem context.
• Sekoia.io Intelligence — Connector: opencti/connector-sekoia — Strengths: European threat landscape, CTI feed ingestion, actor/campaign context. Sekoia’s own documentation points to the OpenCTI GitHub connector path.
• ThreatConnect — Connector: no verified current dedicated connector in the main OpenCTI connector tree — Strengths: enterprise TI management, source aggregation, workflow and case management. I found an OpenCTI GitHub label/feature reference for “threat connect,” but not a confirmed current connector folder equivalent to external-import/threatconnect.
• Intel 471 — Connectors: opencti/connector-intel471, opencti/connector-intel471-darknet, and opencti/connector-intel471_v2 — Strengths: underground forums, cybercrime actors, malware, infrastructure, dark web intelligence.

4.3 ISAC / Government Feeds

• CISA Automated Indicator Sharing / AIS — Method: TAXII/STIX client, AIS 2.0 uses TAXII 2.1 — Access: free service for eligible participants; contact CISA to onboard.
• FS-ISAC — Method: STIX/TAXII and MISP automated feeds — Access: financial-sector membership; automated-feed credentials/licensing must be explicitly requested.
• Health-ISAC / H-ISAC — Method: HITS indicator-sharing feed; STIX/TAXII-compatible threat intelligence sharing — Access: healthcare-sector membership / Health-ISAC member access.
• NATO MISP Community — Method: MISP community / MISP sync — Access: official government cyber-defense entities from NATO nations, sponsored by their national representative in the NATO Multinational MISP Steering Board.
• ENISA Threat Landscape — Method: public reports and CTI publications; not a confirmed public TAXII/STIX feed. ENISA’s CTL methodology references STIX 2.1 as a common CTI representation format, but this is different from offering a public feed endpoint.

4.4 Feed Priority and TLP Assignment

# Recommended TLP assignment by source
feeds:
  - source: mitre_attack
    tlp: WHITE          # public, shareable
    confidence: 90
  - source: alienvault_otx
    tlp: GREEN          # community sharing
    confidence: 60
  - source: mandiant
    tlp: AMBER          # restricted to org
    confidence: 85
  - source: internal_soc
    tlp: RED            # internal only
    confidence: 95

5. AI Integration Layer

This is the “AI-driven” layer on top of standard OpenCTI — a custom connector and MCP server that adds:

5.1 AI Enrichment Connector (Claude API)

• On every new Report, Malware, or Threat-Actor ingested → call Claude API
• Extract structured STIX entities from unstructured text (PDFs, blog posts)
• Summarize long reports into 3-sentence executive briefs
• Score indicator relevance against your organization’s sector profile
• Suggest ATT&CK technique mappings from narrative descriptions

5.2 AI Pipeline Architecture

New Report ingested
        │
        ▼
[AI Enrichment Connector]
        │
        ├─► Claude API: Extract entities → creates STIX SDOs
        ├─► Claude API: Map to ATT&CK techniques
        ├─► Claude API: Generate executive summary
        └─► Claude API: Score severity for your sector
                │
                ▼
        Update Report in OpenCTI
        (summary, related entities, confidence scores)

6. Prerequisites

6.1 Hardware (minimum production)

6.2 Software

# Install Docker Engine (Ubuntu 22.04)
sudo apt-get update
sudo apt-get install -y ca-certificates curl gnupg lsb-release
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | \
  sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
  https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin
# Add user to docker group
sudo usermod -aG docker $USER
newgrp docker
# Verify
docker compose version

6.3 System Tuning (required for ElasticSearch)

# ElasticSearch requires high vm.max_map_count
sudo sysctl -w vm.max_map_count=1048575
echo "vm.max_map_count=1048575" | sudo tee -a /etc/sysctl.conf

# Increase file descriptor limits
echo "* soft nofile 65536" | sudo tee -a /etc/security/limits.conf
echo "* hard nofile 65536" | sudo tee -a /etc/security/limits.conf

7. Docker Compose Deployment

7.0 Deploy from GitHub (recommended)

The fastest deployment path is to clone the maintained project repository and create a local `.env` from the sanitized template:

cd /home/andrey
git clone https://github.com/anpa1200/opencti-intelligent-shield.git openCTI
cd /home/andrey/openCTI
# Create local secrets/config. This file is ignored by Git.
cp .env.example .env
nano .env
# Start the full stack after filling in .env
./scripts/start-all.sh

This gives you the Docker Compose files, OpenCTI patches, AI enrichment connector, helper scripts, and Docusaurus documentation in one checkout. Use the manual sections below if you want to recreate the files by hand or compare the generated content.

7.1 Directory Structure

/home/andrey/openCTI/
├── .env                          # secrets and config
├── docker-compose.yml            # core stack
├── docker-compose.connectors.yml # feed connectors
├── docker-compose.ai.yml         # AI enrichment connector
├── patches/
│   └── back.js                   # ILM race condition fix (ES 8.13 + OpenCTI 6.2.0)
└── connectors/
    └── ai-enrichment/            # custom AI connector source

7.2 Environment File

cat > /home/andrey/openCTI/.env << 'EOF'
# === Core ===
OPENCTI_ADMIN_EMAIL=admin@opencti.local
OPENCTI_ADMIN_PASSWORD=CHANGE_ME_STRONG_PASSWORD
OPENCTI_ADMIN_TOKEN=CHANGE_ME_UUID4_TOKEN
OPENCTI_BASE_URL=http://localhost:8080

# === Secrets ===
APP__ADMIN__TOKEN=CHANGE_ME_UUID4_TOKEN
APP__SECRET_KEY=CHANGE_ME_SECRET

# === ElasticSearch ===
# NOTE: key is ELASTIC_PASSWORD, not ELASTIC_AUTH
ELASTIC_PASSWORD=CHANGE_ME_ELASTIC_PASS

# === Redis ===
REDIS_PASSWORD=opencti

# === MinIO ===
MINIO_ROOT_USER=opencti
MINIO_ROOT_PASSWORD=CHANGE_ME_MINIO_PASS

# === RabbitMQ ===
RABBITMQ_DEFAULT_USER=opencti
RABBITMQ_DEFAULT_PASS=CHANGE_ME_RABBITMQ_PASS

# === Connector IDs (unique UUID4 per connector — NOT used for auth) ===
CONNECTOR_MITRE_TOKEN=CHANGE_ME_UUID4
CONNECTOR_CVE_TOKEN=CHANGE_ME_UUID4
CONNECTOR_ALIENVAULT_TOKEN=CHANGE_ME_UUID4
CONNECTOR_ABUSE_SSL_TOKEN=CHANGE_ME_UUID4
CONNECTOR_URLHAUS_TOKEN=CHANGE_ME_UUID4
CONNECTOR_AI_ENRICHMENT_TOKEN=CHANGE_ME_UUID4

# === External API keys ===
ALIENVAULT_API_KEY=your_otx_key_here
NVD_API_KEY=your_nvd_api_key_here     # UUID format from nvd.nist.gov/developers/request-an-api-key
ANTHROPIC_API_KEY=your_claude_api_key_here
EOF

# Generate unique UUIDs for connector IDs
python3 -c "import uuid; [print(uuid.uuid4()) for _ in range(8)]"# Generate proper tokens
python3 -c "import uuid; [print(f'Token: {uuid.uuid4()}') for _ in range(10)]"

7.3 Core Stack — docker-compose.yml

nano docker-compose.yml

version: "3"
services:
  redis:
    image: redis:7.2
    restart: always
    volumes:
      - redisdata:/data
    command: redis-server --requirepass ${REDIS_PASSWORD:-opencti}
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.13.0
    volumes:
      - esdata:/usr/share/elasticsearch/data
    environment:
      - discovery.type=single-node
      - xpack.ml.enabled=false
      - xpack.security.enabled=true
      - ELASTIC_PASSWORD=${ELASTIC_PASSWORD:-CHANGE_ME}
      - "ES_JAVA_OPTS=-Xms2g -Xmx2g"
      - cluster.routing.allocation.disk.threshold_enabled=false
    ulimits:
      memlock:
        soft: -1
        hard: -1
    restart: always
  minio:
    image: minio/minio:RELEASE.2024-01-16T16-07-38Z
    volumes:
      - miniodata:/data
    ports:
      - "9001:9001"   # console
    environment:
      MINIO_ROOT_USER: ${MINIO_ROOT_USER:-opencti}
      MINIO_ROOT_PASSWORD: ${MINIO_ROOT_PASSWORD:-CHANGE_ME}
    command: server /data --console-address ":9001"
    restart: always
  rabbitmq:
    image: rabbitmq:3.13-management
    environment:
      RABBITMQ_DEFAULT_USER: ${RABBITMQ_DEFAULT_USER:-opencti}
      RABBITMQ_DEFAULT_PASS: ${RABBITMQ_DEFAULT_PASS:-CHANGE_ME}
      RABBITMQ_NODENAME: rabbit01@localhost
    volumes:
      - rabbitmqdata:/var/lib/rabbitmq
    restart: always
  opencti:
    image: opencti/platform:6.2.0
    environment:
      NODE_OPTIONS: --max-old-space-size=8096
      APP__PORT: 8080
      APP__BASE_URL: ${OPENCTI_BASE_URL:-http://localhost:8080}
      APP__ADMIN__EMAIL: ${OPENCTI_ADMIN_EMAIL}
      APP__ADMIN__PASSWORD: ${OPENCTI_ADMIN_PASSWORD}
      APP__ADMIN__TOKEN: ${OPENCTI_ADMIN_TOKEN}
      APP__APP_LOGS__LOGS_LEVEL: error
      REDIS__HOSTNAME: redis
      REDIS__PORT: 6379
      REDIS__USE_SSL: "false"
      REDIS__PASSWORD: ${REDIS_PASSWORD:-opencti}
      ELASTICSEARCH__URL: http://elasticsearch:9200
      ELASTICSEARCH__USERNAME: elastic
      ELASTICSEARCH__PASSWORD: ${ELASTIC_PASSWORD:-CHANGE_ME}
      MINIO__ENDPOINT: minio
      MINIO__PORT: 9000
      MINIO__USE_SSL: "false"
      MINIO__ACCESS_KEY: ${MINIO_ROOT_USER:-opencti}
      MINIO__SECRET_KEY: ${MINIO_ROOT_PASSWORD:-CHANGE_ME}
      RABBITMQ__HOSTNAME: rabbitmq
      RABBITMQ__PORT: 5672
      RABBITMQ__USERNAME: ${RABBITMQ_DEFAULT_USER:-opencti}
      RABBITMQ__PASSWORD: ${RABBITMQ_DEFAULT_PASS:-CHANGE_ME}
      SMTP__HOSTNAME: localhost
      PROVIDERS__LOCAL__STRATEGY: LocalStrategy
    volumes:
      - ./patches/back.js:/opt/opencti/build/back.js:ro
    ports:
      - "8080:8080"
    depends_on:
      - redis
      - elasticsearch
      - minio
      - rabbitmq
    restart: always
  worker:
    image: opencti/worker:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      WORKER_LOG_LEVEL: error
    depends_on:
      - opencti
    deploy:
      mode: replicated
      replicas: 3
    restart: always
volumes:
  esdata:
  redisdata:
  miniodata:
  rabbitmqdata:
networks:
  default:
    name: opencti_network
    external: true

7.4 Connectors — docker-compose.connectors.yml

nano docker-compose.connectors.yml

version: "3"
services:
  # MITRE ATT&CK (no API key needed)
  connector-mitre:
    image: opencti/connector-mitre:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_MITRE_TOKEN}
      CONNECTOR_NAME: "MITRE ATT&CK"
      CONNECTOR_SCOPE: "marking-definition,identity,attack-pattern,course-of-action,intrusion-set,campaign,malware,tool,vulnerability,x-mitre-matrix,x-mitre-tactic,x-mitre-collection"
      CONNECTOR_CONFIDENCE_LEVEL: 75
      CONNECTOR_UPDATE_EXISTING_DATA: "true"
      CONNECTOR_LOG_LEVEL: error
      MITRE_REMOVE_STATEMENT_MARKING: "true"
      MITRE_INTERVAL: 7  # days between full refresh
    restart: always
  # CVE / NVD Vulnerabilities
  connector-cve:
    image: opencti/connector-cve:6.2.0
    volumes:
      - ./patches/cve/api.py:/opt/opencti-connector-cve/services/client/api.py:ro
      - ./patches/cve/vulnerability.py:/opt/opencti-connector-cve/services/client/vulnerability.py:ro
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_CVE_TOKEN}
      CONNECTOR_NAME: "Common Vulnerabilities and Exposures"
      CONNECTOR_SCOPE: "identity,vulnerability"
      CONNECTOR_CONFIDENCE_LEVEL: 75
      CONNECTOR_LOG_LEVEL: info
      CONNECTOR_UPDATE_EXISTING_DATA: "true"
      CVE_BASE_URL: "https://services.nvd.nist.gov/rest/json/cves"
      CVE_API_KEY: ${NVD_API_KEY}
      CVE_MAX_DATE_RANGE: 120
      CVE_MAINTAIN_DATA: "true"
      CVE_INTERVAL: 2
    restart: always
  # AlienVault OTX
  connector-alienvault:
    image: opencti/connector-alienvault:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_ALIENVAULT_TOKEN}
      CONNECTOR_NAME: "AlienVault OTX"
      CONNECTOR_SCOPE: "stix-core-object"
      CONNECTOR_CONFIDENCE_LEVEL: 40
      CONNECTOR_LOG_LEVEL: error
      ALIENVAULT_BASE_URL: "https://otx.alienvault.com"
      ALIENVAULT_API_KEY: ${ALIENVAULT_API_KEY}
      ALIENVAULT_TLP: "White"
      ALIENVAULT_CREATE_OBSERVABLES: "true"
      ALIENVAULT_CREATE_INDICATORS: "true"
      ALIENVAULT_PULSE_START_TIMESTAMP: "2020-01-01T00:00:00"
      ALIENVAULT_REPORT_STATUS: "New"
      ALIENVAULT_REPORT_TYPE: "threat-report"
      ALIENVAULT_GUESS_MALWARE: "false"
      ALIENVAULT_GUESS_CVE: "false"
      ALIENVAULT_INTERVAL: 30   # minutes
    restart: always
  # Abuse.ch SSL Blacklist
  connector-abuse-ssl:
    image: opencti/connector-abuse-ssl:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_MALWAREBAZAAR_TOKEN}
      CONNECTOR_NAME: "Abuse.ch SSL Blacklist"
      CONNECTOR_SCOPE: "stix-core-object"
      CONNECTOR_CONFIDENCE_LEVEL: 50
      CONNECTOR_LOG_LEVEL: error
      ABUSE_SSL_URL: "https://sslbl.abuse.ch/blacklist/sslblacklist.csv"
      ABUSE_SSL_INTERVAL: 30  # minutes
    restart: always
  # Abuse.ch URLhaus
  connector-urlhaus:
    image: opencti/connector-urlhaus:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_URLHAUS_TOKEN}
      CONNECTOR_NAME: "Abuse.ch URLhaus"
      CONNECTOR_SCOPE: "stix-core-object"
      CONNECTOR_CONFIDENCE_LEVEL: 40
      CONNECTOR_LOG_LEVEL: error
      URLHAUS_CSV_URL: "https://urlhaus.abuse.ch/downloads/csv_recent/"
      URLHAUS_IMPORT_OFFLINE: "true"
      URLHAUS_INTERVAL: 2  # hours
    restart: always
  connector-threatfox:
    image: opencti/connector-threatfox:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_THREATFOX_TOKEN}
      CONNECTOR_NAME: "ThreatFox"
      CONNECTOR_SCOPE: "stix-core-object"
      CONNECTOR_CONFIDENCE_LEVEL: 40
      CONNECTOR_LOG_LEVEL: error
      THREATFOX_API_URL: "https://threatfox-api.abuse.ch/api/v1/"
      THREATFOX_CREATE_INDICATORS: "true"
      THREATFOX_CREATE_OBSERVABLES: "true"
      THREATFOX_INTERVAL: 3
    restart: always
  connector-import-document:
    image: opencti/connector-import-document:6.2.0
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_IMPORT_DOCUMENT_TOKEN}
      CONNECTOR_NAME: "ImportDocument"
      CONNECTOR_SCOPE: "application/pdf,text/plain,text/html"
      CONNECTOR_AUTO: "true"
      CONNECTOR_CONFIDENCE_LEVEL: 75
      CONNECTOR_LOG_LEVEL: error
    restart: always
networks:
  default:
    name: opencti_network
    external: true

7.5 AI Enrichment Connector — docker-compose.ai.yml

nano docker-compose.ai.yml

version: "3"

services:
  connector-ai-enrichment:
    build:
      context: ./connectors/ai-enrichment
      dockerfile: Dockerfile
    environment:
      OPENCTI_URL: http://opencti:8080
      OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}
      CONNECTOR_ID: ${CONNECTOR_AI_ENRICHMENT_TOKEN}
      CONNECTOR_NAME: "AI Enrichment (Claude)"
      CONNECTOR_LOG_LEVEL: info
      ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY}
      AI_MODEL: claude-opus-4-7
      AI_ENRICHMENT_REPORTS: "true"
      AI_ENRICHMENT_MALWARE: "true"
      AI_ENRICHMENT_THREAT_ACTORS: "true"
    restart: always

networks:
  default:
    name: opencti_network
    external: true

8. Connector Configuration

Fast Start / Stop Scripts

The repository includes two helper scripts for daily operations:

# Start core OpenCTI, wait for the UI/API, then start connectors and AI enrichment
./scripts/start-all.sh
# Stop AI enrichment, connectors, and core OpenCTI while preserving Docker volumes
./scripts/stop-all.sh

Use these scripts for normal start/stop operations after .env is configured. Use the manual commands below when debugging a specific service startup problem.

nano start-all.sh

#!/usr/bin/env bash
set -euo pipefail

ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
cd "$ROOT_DIR"

WAIT_TIMEOUT="${WAIT_TIMEOUT:-300}"

wait_for_opencti() {
  local deadline=$((SECONDS + WAIT_TIMEOUT))

  echo "[start] Waiting for OpenCTI API on http://localhost:8080..."
  until curl -fsS http://localhost:8080 >/dev/null 2>&1; do
    if (( SECONDS >= deadline )); then
      echo "[start] OpenCTI did not become reachable within ${WAIT_TIMEOUT}s." >&2
      echo "[start] Check logs with: docker compose logs -f opencti" >&2
      return 1
    fi
    sleep 5
  done
}

echo "[start] Starting OpenCTI core stack..."
docker compose -f docker-compose.yml up -d

wait_for_opencti

echo "[start] Starting external connectors..."
docker compose -f docker-compose.connectors.yml up -d

echo "[start] Building and starting AI enrichment connector..."
docker compose -f docker-compose.ai.yml up -d --build

echo "[start] Done."
docker compose -f docker-compose.yml ps

nano stop-all.sh

#!/usr/bin/env bash
set -euo pipefail

ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
cd "$ROOT_DIR"

echo "[stop] Stopping OpenCTI core, connectors, and AI enrichment..."
docker compose \
  -f docker-compose.yml \
  -f docker-compose.connectors.yml \
  -f docker-compose.ai.yml \
  down --remove-orphans

echo "[stop] Done. Volumes are preserved."

8.1 Start the Core Stack

cd /home/andrey/openCTI

# Pre-flight: ElasticSearch refuses allocation above 90% disk usage
df -h /var/lib/docker
# If > 90% full, run: docker system prune -a   (frees ~47 GB of unused images)

# Create the shared Docker network (idempotent — safe to re-run)
docker network create opencti_network 2>/dev/null || true

# Start core services
docker compose -f docker-compose.yml up -d

# Wait for ElasticSearch to be healthy before OpenCTI finishes initializing
until curl -s -u "elastic:${ELASTIC_PASSWORD}" \
  http://localhost:9200/_cluster/health | grep -q '"status":"green"\|"status":"yellow"'; do
  echo "Waiting for ES..."; sleep 5
done

# Watch logs — first-run index creation takes 5-10 minutes
# Look for "Listening on port 8080"
docker compose -f docker-compose.yml logs -f opencti | grep -E "Listening|ERROR|indices"

8.2 Start Connectors

# Start feed connectors (after OpenCTI is healthy)
docker compose -f docker-compose.connectors.yml up -d
# Verify connectors registered (wait ~60s for startup)
docker compose -f docker-compose.connectors.yml ps

8.3 Verify in UI

http://localhost:8080
Login: admin@opencti.local / <your password>Navigation:
  Data → Connectors → check all show status "connected"
  Knowledge → Malwares → should start populating within minutes
  Activities → Logs → watch ingest events

9. AI-Driven Enrichment Pipeline

Overview

The AI enrichment pipeline adds a Claude-powered layer on top of the standard OpenCTI ingestion flow. Every time a connector (AlienVault, MITRE, URLhaus, etc.) writes a new object into OpenCTI, an event is published to RabbitMQ. The AI connector subscribes to that event stream, calls the Claude API with the object’s content, and writes the extracted structured intelligence back into the graph as STIX relationships, notes, and entity updates — all automatically.

Without AI enrichment:

AlienVault pulse → Report object in OpenCTI
                   (raw text, no relationships, no ATT&CK mapping)

With AI enrichment:

AlienVault pulse → Report object in OpenCTI
                       ↓ AI connector picks it up from event stream
                   Claude API: extract entities, map techniques, score severity
                       ↓
                   Report now has:
                   ├── Note: executive summary (2-3 sentences)
                   ├── Relationship → ThreatActor (if found in graph)
                   ├── Relationship → Malware (if found in graph)
                   ├── Relationship → AttackPattern T1059.001 (created if missing)
                   └── x_opencti_score updated based on AI confidence

9.1 How the Event Stream Works

OpenCTI uses RabbitMQ as its internal message bus. Every write operation (create, update, delete) on any STIX object publishes a message to a topic exchange. Connectors subscribe to this exchange via pycti's OpenCTIConnectorHelper.listen() method.

OpenCTI platform
      │
      │ write event (STIX bundle)
      ▼
  RabbitMQ
  exchange: amq.topic
      │
      ├──► worker-1 (standard workers — write to ES/graph)
      ├──► worker-2
      ├──► worker-3
      └──► connector-ai-enrichment  ← our connector subscribes here
                  │
                  │ reads event payload:
                  │ {
                  │   "type": "create",
                  │   "data": { "id": "report--uuid", "type": "report", ... }
                  │ }
                  ▼
            calls Claude API
                  ▼
            writes enrichment back via GraphQL API

Each message contains the full STIX object that was just created. The connector processes it and acknowledges the message — if it crashes mid-processing, RabbitMQ redelivers it.

Connector type INTERNAL_ENRICHMENT means:

• It does not import data on a schedule
• It reacts to existing objects as they are created or updated
• It appears in Settings → Connectors → Enrichment in the UI

9.2 Rules Engine (CE Automation)

Note: Playbooks are an Enterprise Edition feature. The Community Edition uses the built-in Rules Engine, which automatically infers and propagates relationships as data arrives.

All 20 rules are enabled. To verify or toggle: Settings → Customization → Rules

To enable all rules via API (already done — included for re-initialization):

RULES="attribution_attribution attribution_targets indicate_sighted attribution_use \
localization_of_targets location_location location_targets participate-to_parts \
observable_related observe_sighting part_part part-of_targets sighting_incident \
sighting_observable sighting_indicator report_ref_identity_part_of \
report_ref_indicator_based_on report_ref_observable_based_on \
report_ref_location_located_at parent_technique_use"
TOKEN=$(grep OPENCTI_ADMIN_TOKEN /home/andrey/openCTI/.env | cut -d= -f2)
for rule in $RULES; do
  curl -s -X POST http://localhost:8080/graphql \
    -H "Authorization: Bearer $TOKEN" \
    -H "Content-Type: application/json" \
    -d "{\"query\":\"mutation { ruleSetActivation(id: \\\"$rule\\\", enable: true) { id activated } }\"}" \
    | python3 -c "import sys,json; d=json.load(sys.stdin); print('$rule:', d['data']['ruleSetActivation']['activated'])"
done

What these rules do automatically once data arrives:

RuleEffectattribution_attributionIf APT-X is attributed to Country-A, and APT-Y is a sub-group of APT-X → APT-Y also attributed to Country-Asighting_incidentIf an indicator is sighted, automatically raise an Incidentindicate_sightedIf indicator is sighted → infer the targeted entity from the indicator's relationshipreport_ref_indicator_based_onIf a Report references Observable X, and X has an Indicator → auto-link the Indicator to the Reportobservable_relatedIf two objects share a common Observable → infer a related-to relationshipparent_technique_useIf a sub-technique (T1059.001) is used → auto-link parent technique (T1059) as used

For custom event-driven automation in CE, use a pycti script or the AI connector (section 9.1). The pycti library supports streaming the live event feed via helper.listen() — the AI connector in 9.1 uses exactly this pattern.10. Post-Deployment Hardening

9.2 What Claude Extracts and How It Maps to STIX

The connector sends the report’s description text to Claude with a structured prompt. Claude returns JSON. The connector then maps each field to STIX operations:

Claude output fieldSTIX actionsummaryCreates a Note object attached to the report (object_refs)threat_actors[]Looks up ThreatActor by name in graph → creates related-to relationship to reportmalware_families[]Looks up Malware by name → creates related-to relationship to reportattack_techniques[]Looks up AttackPattern by external_id (T1059.001) → creates uses relationship to reporttargeted_sectors[]Looks up Identity (sector) → creates targets relationshiptargeted_countries[]Looks up Location by ISO code → creates targets relationshipconfidenceSets x_opencti_score on the report (0–100)

Why look up instead of creating? MITRE ATT&CK and identity data is already loaded by the MITRE connector. Looking up prevents duplicates. Only AttackPattern objects are created if missing (since Claude may identify techniques not yet in the graph).

9.3 Connector Code

mkdir -p /home/andrey/openCTI/connectors/ai-enrichment

connectors/ai-enrichment/connector.py

import os
import json
import time
import anthropic
from pycti import OpenCTIConnectorHelper

SYSTEM_PROMPT = """You are a senior cyber threat intelligence analyst.
Analyze threat intelligence content and return structured JSON only.
No prose, no markdown fences, no explanation — raw JSON."""

REPORT_PROMPT = """Analyze this threat intelligence report. Return JSON with exactly these keys:
- summary: string (2-3 sentence executive brief, plain text)
- threat_actors: list of strings (actor names, aliases, groups mentioned)
- malware_families: list of strings (malware/tool names)
- attack_techniques: list of strings (MITRE ATT&CK IDs only, e.g. ["T1059.001", "T1003"])
- targeted_sectors: list of strings (e.g. ["Finance", "Healthcare", "Government"])
- targeted_countries: list of strings (ISO 3166-1 alpha-2, e.g. ["US", "UA", "DE"])
- confidence: integer 0-100

Report:
{content}"""

INTRUSION_SET_PROMPT = """Analyze this threat actor / intrusion set profile. Return JSON with exactly these keys:
- summary: string (2-3 sentence executive brief)
- aliases: list of strings (other known names)
- malware_families: list of strings (malware/tools this actor uses)
- attack_techniques: list of strings (MITRE ATT&CK IDs, e.g. ["T1059.001", "T1003"])
- targeted_sectors: list of strings (sectors this actor targets)
- targeted_countries: list of strings (ISO 3166-1 alpha-2 codes)
- motivation: string (one of: "espionage", "financial", "hacktivism", "destruction", "unknown")
- sophistication: string (one of: "minimal", "intermediate", "advanced", "expert", "unknown")
- confidence: integer 0-100

Profile:
{content}"""


class AIEnrichmentConnector:
    def __init__(self):
        config = {
            "opencti": {
                "url": os.environ.get("OPENCTI_URL", "http://opencti:8080"),
                "token": os.environ["OPENCTI_TOKEN"],
            },
            "connector": {
                "id": os.environ["CONNECTOR_ID"],
                "type": "INTERNAL_ENRICHMENT",
                "name": os.environ.get("CONNECTOR_NAME", "AI Enrichment (Claude)"),
                "scope": "Report,Intrusion-Set,Threat-Actor-Group,Malware",
                "log_level": os.environ.get("CONNECTOR_LOG_LEVEL", "info"),
                "auto": False,
            },
        }
        self.helper = OpenCTIConnectorHelper(config)
        self.client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
        self.model = os.environ.get("AI_MODEL", "claude-opus-4-7")

    # -------------------------------------------------------------------------
    # Claude call with retry on rate limit
    # -------------------------------------------------------------------------

    def _call_claude(self, prompt_template: str, content: str) -> dict | None:
        for attempt in range(3):
            try:
                msg = self.client.messages.create(
                    model=self.model,
                    max_tokens=2048,
                    system=SYSTEM_PROMPT,
                    messages=[{"role": "user", "content": prompt_template.format(content=content[:8000])}],
                )
                return json.loads(msg.content[0].text)
            except anthropic.RateLimitError:
                wait = 60 * (attempt + 1)
                self.helper.log_warning(f"Rate limited — waiting {wait}s")
                time.sleep(wait)
            except (json.JSONDecodeError, anthropic.APIError) as e:
                self.helper.log_error(f"Claude call failed: {e}")
                return None
        return None

    # -------------------------------------------------------------------------
    # STIX write-back helpers
    # -------------------------------------------------------------------------

    def _add_note(self, entity_id: str, summary: str, confidence: int) -> None:
        self.helper.api.note.create(
            abstract="AI Summary",
            content=summary,
            confidence=confidence,
            object_ids=[entity_id],
        )

    def _link_threat_actors(self, entity_id: str, names: list, confidence: int) -> None:
        for name in names:
            actor = self.helper.api.threat_actor_group.read(
                filters={"mode": "and", "filters": [{"key": "name", "values": [name]}], "filterGroups": []}
            )
            if actor:
                self.helper.api.stix_core_relationship.create(
                    fromId=entity_id,
                    toId=actor["id"],
                    relationship_type="related-to",
                    confidence=confidence,
                )

    def _link_malware(self, entity_id: str, names: list, confidence: int) -> None:
        for name in names:
            malware = self.helper.api.malware.read(
                filters={"mode": "and", "filters": [{"key": "name", "values": [name]}], "filterGroups": []}
            )
            if malware:
                self.helper.api.stix_core_relationship.create(
                    fromId=entity_id,
                    toId=malware["id"],
                    relationship_type="uses",
                    confidence=confidence,
                )

    def _link_attack_patterns(self, entity_id: str, technique_ids: list, confidence: int) -> None:
        for tid in technique_ids:
            pattern = self.helper.api.attack_pattern.read(
                filters={"mode": "and", "filters": [{"key": "x_mitre_id", "values": [tid]}], "filterGroups": []}
            )
            if not pattern:
                pattern = self.helper.api.attack_pattern.create(
                    name=tid,
                    x_mitre_id=tid,
                    confidence=50,
                )
            if pattern:
                self.helper.api.stix_core_relationship.create(
                    fromId=entity_id,
                    toId=pattern["id"],
                    relationship_type="uses",
                    confidence=confidence,
                )

    def _update_score(self, entity_id: str, confidence: int) -> None:
        self.helper.api.stix_domain_object.update_field(
            id=entity_id,
            input={"key": "x_opencti_score", "value": str(confidence)},
        )

    # -------------------------------------------------------------------------
    # Enrichment handlers per entity type
    # -------------------------------------------------------------------------

    def _enrich_report(self, report: dict) -> str:
        content = report.get("description") or ""
        if len(content) < 50:
            content = report.get("name", "")
        if not content or len(content) < 10:
            return "Skipped: content too short"

        self.helper.log_info(f"Enriching report: {report['name']}")
        result = self._call_claude(REPORT_PROMPT, content)
        if not result:
            return "Skipped: Claude error"

        confidence = result.get("confidence", 50)
        entity_id = report["id"]

        if result.get("summary"):
            self._add_note(entity_id, result["summary"], confidence)
        if result.get("threat_actors"):
            self._link_threat_actors(entity_id, result["threat_actors"], confidence)
        if result.get("malware_families"):
            self._link_malware(entity_id, result["malware_families"], confidence)
        if result.get("attack_techniques"):
            self._link_attack_patterns(entity_id, result["attack_techniques"], confidence)

        self._update_score(entity_id, confidence)
        self.helper.log_info(f"Enriched report '{report['name']}'")
        return "Enriched"

    def _enrich_intrusion_set(self, entity: dict) -> str:
        content = entity.get("description") or entity.get("name", "")
        if not content or len(content) < 10:
            return "Skipped: content too short"

        self.helper.log_info(f"Enriching intrusion set: {entity['name']}")
        result = self._call_claude(INTRUSION_SET_PROMPT, content)
        if not result:
            return "Skipped: Claude error"

        confidence = result.get("confidence", 50)
        entity_id = entity["id"]

        if result.get("summary"):
            self._add_note(entity_id, result["summary"], confidence)
        if result.get("malware_families"):
            self._link_malware(entity_id, result["malware_families"], confidence)
        if result.get("attack_techniques"):
            self._link_attack_patterns(entity_id, result["attack_techniques"], confidence)

        self.helper.log_info(f"Enriched intrusion set '{entity['name']}'")
        return "Enriched"

    # -------------------------------------------------------------------------
    # Event handler
    # -------------------------------------------------------------------------

    def process_message(self, data: dict) -> str:
        entity_type = data.get("entity_type", "").lower()
        entity_id = data.get("entity_id")
        enrichment_entity = data.get("enrichment_entity", {})

        self.helper.log_info(f"Received entity_type='{entity_type}' id='{entity_id}'")

        if not entity_id:
            return "Skipped"

        entity = enrichment_entity or {}

        if entity_type == "report":
            if not entity:
                entity = self.helper.api.report.read(id=entity_id) or {}
            if entity.get("confidence", 0) < 40:
                return "Skipped: low confidence"
            return self._enrich_report(entity)

        if entity_type in ("intrusion-set", "threat-actor-group"):
            if not entity:
                entity = self.helper.api.intrusion_set.read(id=entity_id) or {}
            if not entity:
                return "Not found"
            return self._enrich_intrusion_set(entity)

        if entity_type == "malware":
            if not entity:
                entity = self.helper.api.malware.read(id=entity_id) or {}
            if not entity:
                return "Not found"
            content = entity.get("description") or entity.get("name", "")
            if not content or len(content) < 10:
                return "Skipped: content too short"
            self.helper.log_info(f"Enriching malware: {entity['name']}")
            result = self._call_claude(REPORT_PROMPT, content)
            if not result:
                return "Skipped: Claude error"
            confidence = result.get("confidence", 50)
            if result.get("summary"):
                self._add_note(entity["id"], result["summary"], confidence)
            if result.get("attack_techniques"):
                self._link_attack_patterns(entity["id"], result["attack_techniques"], confidence)
            self._update_score(entity["id"], confidence)
            return "Enriched"

        return "Skipped"

    def start(self):
        self.helper.log_info("AI Enrichment connector starting...")
        self.helper.listen(self.process_message)


if __name__ == "__main__":
    AIEnrichmentConnector().start()

connectors/ai-enrichment/Dockerfile

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY connector.py .
CMD ["python", "connector.py"]

connectors/ai-enrichment/requirements.txt

pycti>=6.2.0
anthropic>=0.40.0

9.4 Deploy the AI Connector

Prerequisites: Set ANTHROPIC_API_KEY in .env first.

cd /home/andrey/openCTI
# Build the image
docker compose -f docker-compose.ai.yml build
# Start it
docker compose -f docker-compose.ai.yml up -d
# Verify it registered with OpenCTI (look for "AI Enrichment" in connector list)
docker logs opencti-connector-ai-enrichment-1 --tail=20

In the OpenCTI UI: Settings → Connectors → Enrichment — the connector should appear with status connected after ~10 seconds.

9.5 Testing the Pipeline

Trigger a manual enrichment by importing a real threat report:

# Import a STIX report via the API to trigger the connector
curl -s -X POST http://localhost:8080/graphql \
  -H "Authorization: Bearer $(grep OPENCTI_ADMIN_TOKEN .env | cut -d= -f2)" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "mutation { reportAdd(input: { name: \"Test: APT29 spearphishing campaign\", description: \"APT29, also known as Cozy Bear, conducted a spearphishing campaign targeting NATO members using a malicious PDF dropper that installed Cobalt Strike beacon via PowerShell (T1059.001). The campaign targeted defense contractors in Poland and Germany. The malware communicated with C2 over HTTPS using domain fronting (T1090.004).\", published: \"2024-01-15T00:00:00Z\", report_types: [\"threat-report\"] }) { id name } }"
  }'

Then check what the AI connector wrote back:

# Watch connector logs for the enrichment
docker logs -f opencti-connector-ai-enrichment-1 2>&1 | grep -E "Enriching|Enriched|Error"
# Expected output:
# Enriching report: Test: APT29 spearphishing campaign
# Enriched: 1 actors, 1 malware, 2 techniques

In the UI, open the report — it should now have a Note with the summary, relationships to APT29 and Cobalt Strike, and links to T1059.001 and T1090.004.

9.6 Cost and Rate Limiting

Estimated Claude API cost per report:

• ~500–2000 tokens input (report text, truncated at 8000 chars)
• ~300 tokens output (JSON response)
• At claude-opus-4-7 pricing: ~$0.01–0.05 per report

Rate limiting: The Anthropic API has per-minute token limits. If AlienVault imports hundreds of reports in a burst, the connector will hit rate limits. Add a simple backoff:

import time
def _call_claude(self, content: str) -> dict | None:
    for attempt in range(3):
        try:
            msg = self.client.messages.create(...)
            return json.loads(msg.content[0].text)
        except anthropic.RateLimitError:
            time.sleep(60 * (attempt + 1))
        except (json.JSONDecodeError, anthropic.APIError) as e:
            self.helper.log_error(f"Claude call failed: {e}")
            return None
    return None

To limit scope (only enrich reports above a confidence threshold, skip low-quality feeds):

def process_message(self, data: dict) -> str:
    report = self.helper.api.report.read(id=entity_id)
    # Skip reports with low confidence (e.g. AlienVault auto-generated)
    if report.get("confidence", 0) < 40:
        return "Skipped: low confidence"
    return self._enrich_report(report)

9.7 Rules Engine (CE Automation)

Note: Playbooks are an Enterprise Edition feature. The Community Edition uses the built-in Rules Engine, which automatically infers and propagates relationships as data arrives.

All 20 rules are enabled. To verify or toggle: Settings → Customization → Rules

To enable all rules via API (already done — included for re-initialization):

RULES="attribution_attribution attribution_targets indicate_sighted attribution_use \
localization_of_targets location_location location_targets participate-to_parts \
observable_related observe_sighting part_part part-of_targets sighting_incident \
sighting_observable sighting_indicator report_ref_identity_part_of \
report_ref_indicator_based_on report_ref_observable_based_on \
report_ref_location_located_at parent_technique_use"
TOKEN=$(grep OPENCTI_ADMIN_TOKEN /home/andrey/openCTI/.env | cut -d= -f2)
for rule in $RULES; do
  curl -s -X POST http://localhost:8080/graphql \
    -H "Authorization: Bearer $TOKEN" \
    -H "Content-Type: application/json" \
    -d "{\"query\":\"mutation { ruleSetActivation(id: \\\"$rule\\\", enable: true) { id activated } }\"}" \
    | python3 -c "import sys,json; d=json.load(sys.stdin); print('$rule:', d['data']['ruleSetActivation']['activated'])"
done

What these rules do automatically once data arrives:

RuleEffectattribution_attributionIf APT-X is attributed to Country-A, and APT-Y is a sub-group of APT-X → APT-Y also attributed to Country-Asighting_incidentIf an indicator is sighted, automatically raise an Incidentindicate_sightedIf indicator is sighted → infer the targeted entity from the indicator's relationshipreport_ref_indicator_based_onIf a Report references Observable X, and X has an Indicator → auto-link the Indicator to the Reportobservable_relatedIf two objects share a common Observable → infer a related-to relationshipparent_technique_useIf a sub-technique (T1059.001) is used → auto-link parent technique (T1059) as used

For custom event-driven automation in CE, use a pycti script or the AI connector (section 9.1). The pycti library supports streaming the live event feed via helper.listen() — the AI connector in 9.1 uses exactly this pattern.

10. Post-Deployment Hardening

10.1 Reverse Proxy with TLS (nginx)

# /etc/nginx/sites-available/opencti
server {
    listen 443 ssl http2;
    server_name opencti.yourdomain.com;
ssl_certificate     /etc/letsencrypt/live/opencti.yourdomain.com/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/opencti.yourdomain.com/privkey.pem;
    ssl_protocols       TLSv1.2 TLSv1.3;
    ssl_ciphers         ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384;
    location / {
        proxy_pass         http://127.0.0.1:8080;
        proxy_set_header   Host $host;
        proxy_set_header   X-Real-IP $remote_addr;
        proxy_set_header   X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header   X-Forwarded-Proto $scheme;
        proxy_read_timeout 300s;
        client_max_body_size 100m;
    }
}
server {
    listen 80;
    server_name opencti.yourdomain.com;
    return 301 https://$host$request_uri;
}

10.2 Backup Strategy

#!/bin/bash
# /home/andrey/openCTI/scripts/backup.sh
set -euo pipefail
BACKUP_DIR="/mnt/backup/opencti/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$BACKUP_DIR"
# Snapshot ElasticSearch
curl -s -u elastic:${ELASTIC_PASSWORD} \
  -X PUT "http://localhost:9200/_snapshot/backup/snapshot_$(date +%Y%m%d)" \
  -H 'Content-Type: application/json' \
  -d '{"indices": "*", "ignore_unavailable": true}'
# Dump MinIO (reports, files)
docker run --rm \
  --network opencti_network \
  -v "$BACKUP_DIR:/backup" \
  minio/mc:latest \
  mirror myminio/opencti /backup/minio/
echo "Backup completed: $BACKUP_DIR"

10.3 Security Checklist

• Change all default passwords in .env
• Generate unique UUID4 tokens for every connector
• Enable TLS via nginx reverse proxy
• Restrict port 8080 to localhost only (127.0.0.1:8080:8080)
• Enable ElasticSearch authentication (already configured above)
• Set up fail2ban on the nginx access log
• Rotate OPENCTI_ADMIN_TOKEN every 90 days
• Review TLP markings — ensure nothing RED leaks via TAXII
• Enable audit logging: APP__APP_LOGS__LOGS_LEVEL: info

11. Operational Runbook

Day 1 — Initial Data Load

# MITRE ATT&CK loads first (foundational framework)
# Wait ~10 minutes for it to complete, then verify:
TOKEN=$(grep OPENCTI_ADMIN_TOKEN /home/andrey/openCTI/.env | cut -d= -f2)

curl -s -X POST http://localhost:8080/graphql \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ attackPatterns { edges { node { name } } } }"}' | \
  python3 -c "import sys,json; d=json.load(sys.stdin); print('Techniques loaded:', len(d['data']['attackPatterns']['edges']))"
# Should return 500+ techniques

Common Operations

# Check all connector health
docker compose -f docker-compose.connectors.yml ps
# View connector logs
docker compose -f docker-compose.connectors.yml logs --tail=50 connector-alienvault
# Restart a stuck connector
docker compose -f docker-compose.connectors.yml restart connector-malwarebazaar
# Scale workers for high ingest load
docker compose -f docker-compose.yml up -d --scale worker=5
# Check ElasticSearch cluster health
curl -s -u elastic:${ELASTIC_PASSWORD} http://localhost:9200/_cluster/health?pretty
# Check RabbitMQ queue depth (should stay near 0 at rest)
docker exec $(docker ps -qf name=rabbitmq) rabbitmqctl list_queues name messages

Monitoring Metrics to Watch

Quick Reference

# Start everything
cd /home/andrey/openCTI
docker network create opencti_network 2>/dev/null || true
docker compose -f docker-compose.yml up -d
docker compose -f docker-compose.connectors.yml up -d
docker compose -f docker-compose.ai.yml up -d
# Stop everything
docker compose -f docker-compose.ai.yml down
docker compose -f docker-compose.connectors.yml down
docker compose -f docker-compose.yml down
# Access
# UI:      http://localhost:8080
# API:     http://localhost:8080/graphql
# MinIO:   http://localhost:9001
# RabbitMQ: http://localhost:15672

12. Troubleshooting

Known Issues — OpenCTI 6.2.0 + ElasticSearch 8.13

ILM Race Condition (resource_already_exists_exception)

ES 8.13’s ILM daemon auto-bootstraps rollover indices the moment an index template with lifecycle.rollover_alias is created. OpenCTI's elCreateIndex does a check-then-create which loses the race. This kills initialization and loops with restart: always.

Fix already applied: patches/back.js is mounted over the compiled bundle and makes elCreateIndex idempotent — it catches resource_already_exists_exception and returns null.

Re-initialization procedure (if ES volume is dropped):

# 1. Delete any leftover index templates from a failed run
curl -s -u elastic:${ELASTIC_PASSWORD} -X DELETE \
  "http://localhost:9200/_index_template/opencti*"

# 2. Flush Redis state
docker exec opencti-redis-1 redis-cli -a opencti FLUSHALL

# 3. Start ES first, wait for green/yellow
docker compose up -d elasticsearch
until curl -s -u elastic:${ELASTIC_PASSWORD} \
  http://localhost:9200/_cluster/health | grep -q '"status":"green"\|"status":"yellow"'; do
  sleep 5; done

# 4. Start the rest — OpenCTI will create 13 indices and load base STIX data (~5-10 min)
docker compose up -d

ElasticSearch Disk Watermark (cluster RED, no shard allocation)

ES 8.x refuses all shard allocation when disk exceeds 90% high watermark. cluster.routing.allocation.disk.threshold_enabled=false is set in docker-compose.yml.

To reclaim disk space:

docker system prune -a   # frees ~47 GB of unused images/containers

Connectors Can’t Reach opencti Hostname

Both compose files must share the same Docker network. docker-compose.yml defines:

networks:
  default:
    name: opencti_network
    external: true

If the main stack was started without this, run:

docker network connect --alias opencti opencti_network opencti-opencti-1

Then add the networks: block to docker-compose.yml and run docker compose up -d to make it permanent.

OPENCTI_TOKEN vs CONNECTOR_ID

Connectors authenticate to OpenCTI using OPENCTI_TOKEN: ${OPENCTI_ADMIN_TOKEN}. The per-connector UUID variables (CONNECTOR_MITRE_TOKEN, etc.) are only used as CONNECTOR_ID — they identify the connector instance in the UI, not for authentication.

CVE Connector — Zero Vulnerabilities Imported (NVD API Key Bug)

connector-cve:6.2.0 has a bug: it sends the NVD API key as Bearer: <key> in the HTTP header, but NVD 2.0 API requires apiKey: <key>. The connector silently gets a non-200 response and imports nothing. Additionally, CVE_MAX_DATE_RANGE is required but missing from the image's default config — omitting it causes a TypeError: '>' not supported between instances of 'NoneType' and 'int' crash every 60 seconds.

Fix: Mount a patched api.py that uses the correct header, and add the missing vars:

connector-cve:
  image: opencti/connector-cve:6.2.0
  volumes:
    - ./patches/cve/api.py:/opt/opencti-connector-cve/services/client/api.py:ro
  environment:
    CVE_MAX_DATE_RANGE: 120
    CVE_MAINTAIN_DATA: "true"
    # ... other vars

patches/cve/api.py — change header from "Bearer": api_key to "apiKey": api_key:

headers = {"User-Agent": header}
if api_key:
    headers["apiKey"] = api_key

13. Usage Examples

13.1 Standard OpenCTI Workflows

Example 1 — Investigate an IP address

You received an alert from your SIEM about suspicious outbound traffic to 103.113.70.102.

In OpenCTI UI:

Search → type 103.113.70.102

If AlienVault or URLhaus has seen it, you’ll find:

• Which threat actor uses this IP as C2
• What malware family communicates with it
• When it was first/last observed
• TLP marking and confidence score
• All reports that mention it

Via API:

TOKEN=$(grep OPENCTI_ADMIN_TOKEN /home/andrey/openCTI/.env | cut -d= -f2)
curl -s -X POST http://localhost:8080/graphql \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ stixCyberObservables(filters: {mode: and, filters: [{key: \"value\", values: [\"https://103.113.70.102/bin/support.client.exe\"]}], filterGroups: []}) { edges { node { id entity_type ... on Url { value } } } } }"}' | python3 -m json.tool

Example 2 — Build an APT profile

You want to understand everything known about Lazarus Group before a threat briefing.

Threats → Intrusion Sets → search "Lazarus"

The profile shows:

• Attributed to: North Korea
• Motivations: Financial gain, Espionage
• Targets: Finance, Cryptocurrency, Defense
• Malware used: WannaCry, Hermes, BLINDINGCAN (all auto-linked by MITRE connector)
• Techniques: 80+ ATT&CK techniques with usage relationships
• Campaigns: Operation AppleJeus, Dream Job, etc.
• Timeline: chronological view of all activity

Click “ATT&CK Patterns” tab → heatmap showing which techniques Lazarus uses most.

Example 3 — Import a threat report (PDF / blog post)

You found a Mandiant or CrowdStrike blog post about a new campaign.

Data → Import → drag and drop the PDF or paste the URL
Select format: "Auto detect" or "Report"

OpenCTI parses it and creates a Report object. The AI enrichment connector then picks it up automatically and extracts:

• Threat actors mentioned
• Malware families
• ATT&CK technique IDs
• Targeted sectors and countries

All as STIX relationships, visible immediately in the UI.

Example 4 — Track a CVE across your environment

CVE-2024–21762 (Fortinet FortiOS RCE) was just published. Check what you know about it.

Arsenal → Vulnerabilities → search "CVE-2024-21762"

After the CVE connector syncs, you’ll see:

• CVSS score and vector
• Affected software versions
• Which threat actors exploit it (once AlienVault/MITRE data arrives)
• Which campaigns used it
• Related indicators (IPs, domains used in exploitation)

Example 5 — Create an incident from a sighting

Your EDR detected Cobalt Strike beacon on a workstation.

Activities → Incidents → Create
  Name: "CS beacon on WS-042"
  Type: "Intrusion"
  Confidence: 90
  Add object: link to Cobalt Strike (malware)
  Add object: link to T1071.001 (C2 over HTTP)
  Add observable: add the C2 IP

With sighting_incident rule enabled, future detections of the same C2 IP automatically raise new incidents without manual work.

Example 6 — Export IOCs to your firewall / SIEM

You want a live blocklist of all HIGH confidence IPv4 indicators.

Data → Indicators
Filter: Score > 70, Type = IPv4-Addr, Valid until > today
Export → CSV or STIX

Or use the built-in TAXII 2.1 server to push directly to your SIEM:

Settings → Taxii Server → Create collection "High confidence IOCs"
Configure your SIEM to poll: http://localhost:8080/taxii2/

Example 7 — Map your detection coverage against ATT&CK

You want to know which techniques you detect vs which you’re blind to.

Technics → Attack Patterns
Filter by: used by (Lazarus Group)

Cross-reference the list with your SIEM detection rules. Techniques with no detection rule = gap in coverage.

Export the filtered list as CSV and import into ATT&CK Navigator for a visual heatmap of covered vs uncovered techniques.

Example 8 — Pivot from malware to infrastructure

You found a Ryuk ransomware sample (SHA256 hash).

Search → paste the SHA256

From the malware object, pivot to:

• Related indicators → domains and IPs used for C2
• Used by → Wizard Spider (threat actor)
• Campaigns → which ransomware campaigns used this variant
• Techniques → T1486 (Data Encrypted for Impact), T1490 (Inhibit System Recovery)

Each pivot is one click in the graph view.

Example 9 — Share intelligence with a partner org

You want to share a report with a partner but strip out RED-marked internal data.

Open the report → Actions → Share
Select TLP level: TLP:AMBER (only partner can see it)

Or use Workspaces → Sharing groups to create a federated share with another OpenCTI instance. All objects above RED are automatically excluded from the export.

Example 10 — Build a custom dashboard for your sector

Your org is in Finance. You want a live dashboard showing threats to your sector.

Home → Dashboards → Create dashboard "Finance Threat Landscape"
Add widgets:
  - "Threat actors targeting Finance" (bar chart)
  - "Most used techniques against Finance" (ATT&CK heatmap)
  - "New IOCs last 7 days" (timeline)
  - "Active campaigns" (list)
  - "CVEs affecting banking software" (table)

Each widget auto-updates as new data arrives from connectors.

If you like this research, buy me a coffee (PayPal) — Keep the lab running

Follow for practical cybersecurity research

If you’re interested in Offensive security, AI security, real-world attack simulations, CTI, and detection engineering — this is exactly what I focus on.

Stay connected:

→ Subscribe on Medium: medium.com/@1200km
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→ Contact: 1200km@gmail.com

Andrey Pautov

Follow My Work

I publish practical cybersecurity research, CTI workflows, detection engineering notes, malware analysis projects, OpenCTI work, cloud and Kubernetes security research, AI-assisted security tooling, labs, and technical guides.

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Andrey Pautov

The Intelligent Shield. OpenCTI was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

Billed as a "Virtual CSO" (Chief Strategy Officer), Marlin is an autonomous, B2B research agent that deliberately abandons the instantaneous text generation of modern chatbots in favor of deep, long-horizon reasoning.

What sets Marlin apart from the current ecosystem of AI tools is its temporal scale: instead of returning an answer in seconds, it runs continuous, self-governing reasoning loops for up to eight hours at a time to deliver deeply researched, well cited, 100-page strategy reports and executive slides. The company posted sample reports generated my Marlin on its product website here.

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With Marlin, major businesses are no longer asking how fast an AI can answer, but how deeply it can think.

The Product: A Virtual CSO

What exactly is a business getting when they deploy Sakana Marlin? The workflow is fundamentally different from typical large language model (LLM) interactions. Rather than engaging in a tedious back-and-forth prompt engineering session, the user simply provides a core research topic. Following a brief initial exchange to sharpen the scope and direction of the investigation, the human steps away entirely.

For the next several hours, Marlin operates as a self-contained digital strategy team. It formulates its own initial hypotheses, navigates the web to gather data, cross-references sources to verify findings, and maps the causal dynamics within complex business environments. It is effectively searching for the "winning formula" within a sea of noise.

Think of it less like a search engine and more like a junior strategy consultant locked in a room with a whiteboard and an internet connection. You provide the strategic prompt in the morning, and by the end of the workday, the system delivers a comprehensive, professional-grade portfolio.

In Marlin's case, the final output is not a generic text blob; it is a structured set of strategic options, complete with executive summary slides, appendices, references, and a deeply researched report.

The company highlighted several real-world use cases to demonstrate Marlin's capacity for complex synthesis, including generating detailed resolution scenarios for a theoretical blockade of the Strait of Hormuz, mapping out the fragmented global AI regulation patchwork, and analyzing macroeconomic trends like the return of "bond vigilantes".

Sakana says Marlin relies on multiple AI models, but did not provide specific model names or providers. I've reached out on X to find out more and will update when I receive a repsonse.

The Engine of Long-Horizon Reasoning

Under the hood, Marlin is the commercial culmination of Sakana AI’s extensive laboratory breakthroughs over the past two years.

The product is powered by an exploration engine relying on Sakana's own prior research breakthrough, Adaptive Branching Monte Carlo Tree Search (AB-MCTS), and leverages frameworks derived from "The AI Scientist," an earlier Sakana AI research project featured in the journal Nature that successfully automated the scientific discovery process from ideation to peer review.

To understand how this works in practice, consider a real-world analogy: modern chess engines. When a computer plays chess, it doesn't just look at the board and guess; it plays out thousands of potential future moves, evaluating the strength of each resulting position before committing to an action.

Marlin’s AB-MCTS engine does something similar for research.

Inside the Engine: The Mechanics of AB-MCTS

The chronology of this technology traces back to June 2025, when Sakana AI first introduced the framework to the public alongside the research paper “Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search”.

At that time, to encourage developer experimentation with collective AI intelligence, the company released the underlying algorithm as an open-source software library called TreeQuest, distributed under the permissive Apache 2.0 license. This open-source milestone laid the technical foundation for what would eventually evolve into the proprietary, enterprise-grade Marlin product a year later.

Traditionally, when developers attempt to extract higher-quality reasoning from large language models, they rely on a brute-force method called "repeated sampling"—essentially running the model dozens of times in parallel and hoping one of the answers is correct. However, repeated sampling operates blindly; it cannot evaluate its own intermediate steps or pivot based on external feedback.

AB-MCTS replaces this paradigm with a principled, multi-turn approach driven by a Bayesian decision framework. As the AI constructs a strategy report, the system treats the research process as a branching tree of possibilities. At each node of the tree, the algorithm dynamically balances two distinct behaviors based on external feedback signals:

• Going Wider (Exploration): Spawning entirely new, alternative hypotheses or candidate responses when the current path yields diminishing returns or unresolved contradictions.

• Going Deeper (Exploitation): Methodically refining, auditing, and building upon an existing candidate solution that shows high strategic promise.

What transforms this from a laboratory experiment into a commercial engine is its extension into Multi-LLM AB-MCTS.

Sakana AI’s architecture introduces a critical third dimension to the search tree: the ability to dynamically choose which model to invoke for a specific sub-task, treating the industry’s leading frontier models as a plug-and-play collective intelligence network.

According to technical documentation published by the company, the engine can coordinate highly heterogeneous models—allowing an orchestration model to delegate initial ideation to one LLM, while utilizing a reasoning-heavy model to audit, verify, and correct intermediate errors generated earlier in the search tree.

By scaling up compute at inference time—leveraging the distinct "personalities" and strengths of multiple foundation models over thousands of automated cycles—AB-MCTS provides the mathematical guardrails Marlin requires. It ensures that the resulting 100-page strategy reports are not merely long-winded AI generations, but the highly vetted product of systemic, automated trial-and-error.

Licensing, Data, and Enterprise Implications

It is crucial to note that Sakana Marlin is distinctly not a general consumer tool; it is a commercial software-as-a-service (SaaS) offering restricted to corporate entities, organizations, and sole proprietors.

For enterprises, licensing and data handling terms are often the determining factors in software adoption. Unlike many consumer-grade AI tools that silently harvest user inputs and proprietary data to train future foundational models, Sakana Marlin operates under a strict, enterprise-grade data policy.

Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent.

Even with consent, data is heavily processed to remove personally identifiable information. This closed-loop security is absolutely vital for companies handling sensitive M&A research, unreleased product strategies, or proprietary market analyses.

The commercial licensing is structured into tiered pricing models that reflect its enterprise nature:

• Pay-as-you-go: Users can purchase credits on demand, with a single run costing 100 credits, and add-on credits priced at ¥98 ($0.61 USD) each.

• Pro Plan: At ¥150,000 ($935.68 USD) per month, businesses receive 2,000 credits, bringing down the cost of add-on credits to ¥90 ($0.56 USD).

• Team Plan: Geared toward larger departments, this ¥400,000 ($2,495.14 USD) per month tier includes 6,000 credits, lowering add-on costs to ¥85 ($0.53 USD) per credit.

• Enterprise: Fully custom quotes with dedicated support and customized credit allocations.

Why Sakana Is Worth Watching

Sakana AI’s transition into a commercial enterprise powerhouse is rooted in the pedigree of its founders, who famously helped spark the current generative AI boom.

Formed in Tokyo in 2023, the startup was co-founded by Llion Jones—a co-author of Google’s seminal 2017 “Attention Is All You Need” paper who coined the term “transformer”—and David Ha, a former Google Brain researcher and head of research at Stability AI.

The decision to build a new laboratory outside the Silicon Valley bubble was a deliberate rejection of the current AI ecosystem. At a TED AI conference in late 2025, Jones candidly expressed that he was "absolutely sick" of transformers, warning that the intense pressure from investors and the hyper-fixation on scaling single, monolithic models had calcified the industry's creativity and blinded researchers to the next major breakthrough.

To break free from this "big company-itis," Jones and Ha structured Sakana AI around principles of biomimicry and evolutionary computing.

The company's name, derived from the Japanese word for fish, reflects its core technical philosophy: leveraging collective intelligence similar to schools of fish, ant colonies, or insect swarms. Rather than attempting to build one massive, do-it-all foundation model, Sakana’s research has consistently focused on deploying networks of smaller, specialized models that collaborate dynamically to adapt to complex environments.

This philosophy posits that by treating individual AI models as members of a "dream team" with complementary strengths, systems can achieve more robust and cost-effective reasoning than relying on sheer scale alone.

This nature-inspired approach quickly yielded dividends in rigorous, competitive testing. Sakana AI has made significant strides in "inference-time scaling"—allocating computational resources during the problem-solving phase to allow models to think, iterate, and refine their own answers over extended periods.

In early 2026, the company’s ALE-Agent took first place in the highly complex AtCoder Heuristic Contest (AHC058), a combinatorial optimization challenge, outperforming over 800 top-tier human programmers by autonomously rebuilding and testing hundreds of solutions over a four-hour window.

Similarly, Sakana introduced "RL Conductor," a small 7-billion-parameter model trained via reinforcement learning specifically to orchestrate and delegate tasks among a diverse pool of worker models—ranging from GPT-5 to Claude Sonnet 4—achieving state-of-the-art results on reasoning benchmarks at a fraction of traditional computing costs.

Sakana's rapid evolution from a disruptive research lab to a commercial software provider has attracted intense attention from global financial heavyweights.

By late 2025, the Tokyo-based startup secured a massive Series B funding round that pushed its post-money valuation past $2.6 billion, cementing its status as one of Japan’s most highly valued private tech companies. The firm boasts a sprawling roster of strategic investors, including early venture backers Khosla Ventures, Lux Capital, and New Enterprise Associates (NEA), alongside industry titans like Nvidia and Google.

As Sakana has expanded its focus toward mission-critical sectors like defense and finance, it has also drawn investments from major global banking institutions like Mitsubishi UFJ Financial Group (MUFG) and Citi, as well as enterprise tech giant Salesforce, positioning the startup to actively reshape corporate AI infrastructure from the ground up.

Community Reactions and Field Testing

Sakana AI’s shift toward commercial, long-horizon agents did not happen in a vacuum. The company ran a rigorous closed beta test beginning in April 2026, putting the tool in the hands of approximately 300 professionals across financial institutions, consulting firms, and think tanks. The feedback underscores a stark qualitative difference between standard generative chatbots and Marlin’s autonomous, fact-driven approach.

A senior consultant at a major Tokyo consulting firm noted that the tool "exceeded expectations by discovering angles we hadn't even imagined," praising its ability to match human comprehensiveness while stripping away human bias. Meanwhile, a cybersecurity division at a major Japanese IT system integrator lauded the system for providing "a highly convincing report driven by high-quality, primary research," rather than relying on recycled secondary sources.

On social media, the company’s announcement resonated with the broader tech community's growing appetite for autonomous agents.

As the AI industry matures, the value proposition is clearly shifting. Tools that act as fast, conversational encyclopedias are becoming commoditized. With Sakana Marlin, the focus moves entirely to separating the heavy lifting of thinking from the final act of deciding. By delegating the exhaustive mapping of causal dynamics to an agent capable of sustained reasoning, human executives are free to do what they do best: take action.

"The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see," Nadella wrote in the piece, titled "A frontier without an ecosystem is not stable," which he posted on X. "If all the value is accrued by only a few models, the political economy will simply not tolerate it. There is no societal permission for an AI future that hollows out entire industries."

The essay is unusually philosophical for a sitting CEO of a $3 trillion technology company. But it arrives at a moment when the theoretical risks Nadella describes are becoming tangible — and, critically, when Microsoft itself is grappling with the very dynamics he warns about.

Nadella introduces "token capital" as the new currency of enterprise AI strategy

At the center of Nadella's essay sits a conceptual framework built on two pillars he calls "human capital" and "token capital." Human capital, he writes, "comprises the knowledge, judgment, relationships, ingenuity, and pattern recognition of its people," while token capital refers to "the firm's AI capability it builds and owns."

The two are not in tension, he insists. "Importantly, human capital does not become less valuable as token capital grows. It only becomes more valuable!" he writes. "I believe human agency will be the driver of token capital growth. Humans will set ambitious goals, connect dots across domains, build relationships, and recognize patterns that matter most. Without human direction, you have compute running in circles."

This framing is a deliberate counterweight to the narrative that AI will simply replace human workers or, at the enterprise level, dissolve the intellectual property that differentiates one company from another. Nadella is arguing that the real danger is not AI's capability but its tendency to centralize — and that the solution requires a fundamentally new architecture for how businesses interact with the technology.

He describes the real opportunity as "not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound." The key test of a company's sovereignty in this new era, he writes, is whether it can "switch out a 'generalist' model without losing the 'company veteran' expertise built into their learning system."

This is the essay's most actionable claim — and its most provocative. Nadella is telling enterprises they need to decouple their institutional intelligence from whatever frontier model they happen to be running, creating portable knowledge systems that survive vendor changes.

Why Nadella is comparing AI concentration to the outsourcing crisis that gutted industrial economies

Nadella draws a pointed historical parallel to make his warning concrete. "Think about what happened in the first phase of globalization where entire industrial economies were hollowed out by outsourcing," he writes. "The GDP numbers looked fine on the surface, but the displacement was real and the consequences are still being felt. Let us not bring that dynamic into the AI era, with a small number of AI systems capturing all the economic returns, while entire industries find their knowledge commoditized right out from underneath them."

The globalization analogy is not accidental. It reframes the AI concentration debate from a narrow technology question into a political-economy argument — one that regulators, policymakers, and voters can grasp. By invoking the social costs of offshoring, Nadella is signaling that the stakes extend well beyond the enterprise technology stack. He is warning that if the AI industry fails to distribute value broadly, the political system will intervene to force the issue.

"In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country," he writes. He grounds this in an older platform philosophy: "This is the ethos I've grown up with where platforms enable more value on top than is captured inside, and where every company can continuously innovate and build value of its own." It is a direct echo of the Windows-era argument, updated for the age of inference — and it carries a similarly self-interested subtext, given that Microsoft's cloud business sits squarely in that platform layer.

Microsoft's own runaway AI costs reveal the gap between Nadella's vision and operational reality

What makes Nadella's essay so striking is its timing. He published it on a day when Reuters reported that Microsoft shareholders filed a proposed class-action lawsuit in Seattle federal court, accusing the company of inflating its stock price by failing to disclose slowing growth in its Azure cloud business and the need to spend billions of dollars on AI infrastructure. The suit names Nadella and Chief Financial Officer Amy Hood among the defendants.

As the Yahoo Finance report on the lawsuit noted, Microsoft allegedly "aggressively promoted its AI developments, specifically its 'Copilot' assistant and close financial alliance with ChatGPT creator OpenAI, to artificially boost investor optimism," while understating infrastructure strain and capital risks. Microsoft also reported $37.5 billion of capital spending in its second quarter, up nearly 66% from a year earlier and above the $34.3 billion that analysts projected.

Microsoft's internal cost pressures around AI have surfaced in other concrete ways this year. The company is canceling the majority of its internal Claude Code licenses in its Experiences and Devices division, effective June 30, 2026. Monthly usage rates reached 84 to 95% by April 2026, and per-engineer API costs ranged between $500 and $2,000 monthly, according to Windows Forum. The cancellation came after Microsoft exhausted portions of its annual AI budget due to token-based billing, as Fortune had reported in May.

The Claude Code episode illustrates, at the micro level, the exact dynamic Nadella describes at the macro level. When a company's AI usage is metered by the token — the fundamental unit of compute that powers model inference — the more productive the tool becomes, the more expensive it gets. The term "token capital" in Nadella's essay carries a double meaning: it refers both to a firm's proprietary AI capability and, implicitly, to the actual tokens consumed in running it. Building a learning loop that compounds is aspirational. Paying the bills for that loop is operational reality.

Uber, Meta, and Amazon are all hitting the same AI spending wall — and it validates Nadella's warning

Microsoft is not alone in this bind. Uber burned through its entire 2026 AI coding tools budget in just four months after incentivizing employees to adopt the technology through an internal leaderboard ranking teams by total AI tool usage. Uber has since instituted a monthly $1,500 cap per employee per agentic coding tool, according to TechCrunch. At Meta, an employee created a leaderboard called "Claudeonomics" to track which workers consumed the most AI tokens. Amazon, meanwhile, has pushed employees to "tokenmaxx" — use as many AI tokens as possible.

The emerging pattern is clear: enterprises adopted AI coding tools aggressively, saw genuine productivity gains, and then discovered that the consumption-based economics of frontier models created budget crises that traditional software licensing never would have. Bryan Catanzaro, vice president of applied deep learning at Nvidia, captured the tension bluntly in an interview with Axios: "For my team, the cost of compute is far beyond the costs of the employees," he said.

These cost dynamics land differently in the context of Nadella's essay. He prescribes a three-layer architecture — evaluation, reinforcement learning, and retrieval — designed to sit between a company's workforce and whatever frontier model it subscribes to. Companies, he argues, need to build "private evals" that "capture whether a model is actually improving against outcomes that matter to the business (not just external benchmarks!)," alongside "private reinforcement learning environments" that "let models grow stronger on real traces from inside the organization" and a knowledge base that "makes institutional memory queryable and use of tokens more efficient." He calls the resulting system "a hill climbing machine" that, "unlike most assets, it compounds."

Other Big Tech CEOs are echoing Nadella's fears about AI models devouring enterprise knowledge

Nadella's concerns do not exist in isolation. Other technology leaders have been raising similar warnings throughout 2026, though none have offered as prescriptive a response.

Snowflake CEO Sridhar Ramaswamy warned in a February podcast that the biggest software companies risk being reduced to mere data sources. "The big model makers want to create a world in which all of the data for all of the enterprises is easily available to them," Ramaswamy said, describing everything else as "a dumb data pipe that feeds into that big brain." He added that Snowflake needs to operate with a "fear" that enterprises would abandon software-specific AI agents in favor of all-inclusive agents that hoover up data from everywhere.

Box CEO Aaron Levie struck a similar note in a January LinkedIn post. AI models can now perform high-level knowledge work across nearly every profession, from law to strategy to scientific research, he argued. "The question that we will have to wrestle with is, in a world where everyone has access to the same expert intelligence, how does a company differentiate?" he wrote.

The combined effect of these statements is a shared diagnosis from three very different corners of the enterprise technology market: the current trajectory of AI development threatens to collapse competitive differentiation across entire industries. Nadella's essay stands apart from the others because it moves beyond diagnosis and proposes a specific architectural remedy. But the prescription is impossible to separate from the prescriber's interests.

Microsoft sits in precisely the platform layer that Nadella's framework would make indispensable — the company builds its own frontier models, operates the cloud infrastructure those models run on, and maintains deep partnerships with the leading independent AI labs. A world in which every enterprise builds a proprietary learning loop on top of commodity foundation models is, conveniently, a world in which Microsoft sells the picks and shovels to all of them.

Nadella's Scout controversy and shareholder lawsuit reveal the tension inside Microsoft's own AI strategy

The essay also arrives just ten days after Nadella publicly rebuked one of his own executives for outlining a plan to "make people addicted" to a new AI tool called Scout.. Microsoft corporate vice president Omar Shahine had written an internal memo describing a three-phase plan to transform Scout "from addictive app to agentic platform," with the first phase focused on features that "make people depend on it daily." Nadella responded on an internal message board: "This is absolutely a non-goal! If anything we are doing the exact opposite. We want to make sure AI empowers and adds real value to human endeavor and broad economic growth!"

The Scout incident and Sunday's essay together suggest Nadella is actively constructing a public philosophy of AI that emphasizes broad value creation over extractive engagement — whether or not every corner of Microsoft has internalized that message. One anonymous Microsoft employee told 404 Media, as the Post reported, that the leaked Scout document was "very troubling," adding: "It feels like one of those 'saying the quiet part out loud' moments."

For technical decision-makers evaluating Nadella's essay, the practical implications are significant. He is arguing that choosing an AI model matters less than building the learning infrastructure around it. He is arguing that the ability to swap models without losing institutional intelligence is the critical test of AI sovereignty. And he is warning that companies that fail to build these systems will find their expertise absorbed and commoditized by the models themselves. "You can offload a task, or even a job, but you can never offload your learning," Nadella writes. "The future of the firm is the ability to compound that learning across people and AI."

The question Nadella's essay cannot answer is whether Microsoft will practice what its CEO preaches

Whether Nadella's vision materializes depends on a question his essay carefully sidesteps: whether the platform providers who build and host the frontier ecosystem will resist the temptation to capture the value flowing through it. Nadella insists that "platforms enable more value on top than is captured inside." But Microsoft's own trajectory this year — the ballooning capital expenditures, the Claude Code budget crisis, the shareholder lawsuit alleging concealed costs, the internal memo about making users addicted — suggests the economics of restraint are harder than the philosophy of restraint.

Nadella ends his essay with the claim that broad value distribution "is the stable equilibrium we should build together." He may be right. Ecosystems have historically outperformed walled gardens over long time horizons. But stable equilibria require every major player to forgo short-term extraction in favor of long-term compounding — and right now, the AI industry is burning through budgets in four months and spending 66% more on infrastructure than analysts expected. The CEO of the world's most valuable technology company has written an eloquent argument for why the AI economy needs to work differently. The open question is whether his own company's balance sheet will let him prove it.

Read more of this story at Slashdot.

Read more of this story at Slashdot.

Parents are constantly being told to limit their children’s screen time. But when it comes to deciphering which films or TV shows are best suited to developing minds, the guidance remains largely one-size-fits-all. A relatively slow-paced programme such as Bluey offers a very different viewing experience to a fast-moving action series such as PAW Patrol, yet both are broadly considered suitable for young children.

This challenge is growing as the type of content children are exposed to evolves. “Today’s young viewers are increasingly engaging with short-form, fast-paced, highly captivating content, often created by splicing and rearranging existing episodic content into quickly digestible snippets or compilations,” said Prof Tim Smith, director of University of the Arts London’s Nerve Lab. “This evolution is not only changing how content is produced and distributed, but may also affect children’s attention, comprehension and emotional response.”

The post Inside Elon Musk’s AI Ecosystem: How xAI, Tesla, X, Neuralink, and SpaceX Are Converging appeared first on TechRepublic.

"These agents are doing things that you cannot necessarily control, and you cannot necessarily train," said Gal Marder, Chief Strategy Officer at JFrog, in an exclusive interview with VentureBeat.

Available immediately, the partnership hardwires NanoClaw agents directly to JFrog’s vetted software registries, ensuring that AI assistants can only pull scanned, safe dependencies.

The release addresses a rapidly growing blind spot in tech: autonomous agents frequently install packages in the background to extend their capabilities, often without their human operators' knowledge or oversight.

"The people who are operating the agents are not necessarily developers, and they are not even aware of the implications," explained Gavriel Cohen, creator of NanoClaw and CEO and co-founder of its new commercial services startup, NanoCo AI.

To secure the broader ecosystem, the integration is available completely free of charge for the open-source community, while enterprise organizations can seamlessly route their agents through their existing, commercially licensed JFrog environments.

The new technical capability enabled by this partnership follows NanoCo's moves to add permissions dialogs across the apps in which it's available via a partnership with Vercel, and a new partnership with Docker to allow NanoClaw agents to run more securely, isolated from other software environments directly inside Docker virtual containers.

The risk of current, personal autonomous AI agents

When an operator interacts with an autonomous system like NanoCo's NanoClaw, they communicate at a high level of abstraction.

A user might simply send an audio file or a voice note, prompting the agent to independently figure out how to process it.

As Cohen explained, the agent thinks, "oh, I can't understand voice notes, so let me go and grab a package and download something and install it and set it up and run it".

This dynamic self-improvement makes AI agents incredibly powerful, but it also renders them highly susceptible to software supply chain attacks.

Bad actors are increasingly poisoning open-source registries with malicious packages. Because agents act autonomously to fetch what they need, they bypass human scrutiny.

The operators, who may not even be developers, are largely unaware of the security implications unfolding behind the scenes.

How NanoCo and JFrog are working to stop agents from running malicious code

The integration between NanoCo and JFrog acts as an automated immune system for these AI environments.

Under the hood, NanoClaw agents are now configured to route their requests for software packages, CLI tools, and Model Context Protocol (MCP) servers exclusively through JFrog’s registries.

If an agent attempts to download a compromised library—such as a vulnerable version of the popular Axios package—the JFrog registry intercepts the request.

It blocks the installation, returning a security policy error to the agent, noting that the request was "rejected by JFrog's registry with a 403 security policy".

Crucially, the system does not just stop at blocking the threat; it creates a dynamic correction loop. The agent is notified of the vulnerability and guided to automatically seek out and install an approved, non-malicious version of the requested package instead.

For large organizations, this integration solves a massive compliance headache. Marder notes that as enterprises adopt autonomous agents, they require absolute visibility.

Organizations need "a system of record, we need somewhere to track what agents that's running by whom and consuming what packages and using what skills and using what MCPs," he told VentureBeat.

Beyond visibility, the JFrog integration provides a foundational "trust layer" and strict governance over what these automated systems are permitted to access.

Licensing and accessibility

In the realm of software distribution, licensing and access parameters dictate adoption. The NanoCo and JFrog partnership utilizes a dual-track approach to serve both individual open-source developers and highly regulated enterprises.

For the open-source community, the integration is completely free. JFrog is providing open-source NanoClaw users with complimentary access to safe, vetted sources of artifacts, tools, and skills.

This allows individual developers to run autonomous agents locally without drowning in manual approval requests for every single dependency. Furthermore, as community members build and share new "skills" for the agents, these contributions are uploaded to the registry, scanned for malicious code, and cleared before anyone else can use them.

This infrastructure directly neutralizes the threat of poisoned community repositories.

For enterprise deployments, the architecture plugs seamlessly into an organization's existing commercial environment. Rather than using the public open-source registry, corporate users point their NanoClaw agents to their own internal JFrog registries.

This ensures that all agent activity adheres to the company’s specific commercial licenses, internal security policies, visibility needs, and governance standards.

As AI continues to blur the line between human intent and machine execution, the infrastructure securing that execution must evolve. This partnership acknowledges a core reality: you cannot train an AI to perfectly recognize every zero-day vulnerability; instead, you must build an environment where the agent simply cannot reach the vulnerability in the first place.

Read more →

The post Brain-inspired chip runs near absolute zero and could transform quantum computing appeared first on IT Security News.

Unpopular opinion incoming: there’s cool stuff brewing in the world. Microbots might one day mend spinal cords, a petri dish of brain cells can already play video games, and now the prospect of a new wonder: according to a New Yorker article on misophonia (the condition where unwanted noise triggers disproportionate, unpleasant cognitive and physiological reactions), a team of miracle workers are “using machine learning to develop headphones that … can quickly target and eliminate irksome audio”.

Now we’re talking! This project, led by Shyam Gollakota of the University of Washington’s Mobile Intelligence Lab, aims to develop headphones that selectively filter out triggering noises, leaving or enhancing the good sounds. Gollakota offers the example of sitting on a park bench, oblivious to loud talkers next to you but able to hear birdsong.

Read more →

The post UK digital ID gets brain trust to ‘challenge’ ministers on policy appeared first on IT Security News.

Read more of this story at Slashdot.

In a shocking upset, OpenAI’s GPT-5.5 from April, operating through the Codex harness, secured the absolute top spot on the new ALE Leaderboard with a 24.0% pass rate, beating Anthropic's highly anticipated, brand new Mythos-class Claude Fable 5 model released just yesterday, which came in third with a score of 22.0%.

Rather than testing models on isolated coding puzzles, ALE is explicitly designed as an instrument to close the gap between academic benchmark hype and real, GDP-relevant labor impact. And right now, the data proves the most advanced models in the world are fundamentally failing the exam.

Ending the Era of 'Cheating' and Brittle Graders

The fundamental shift in ALE lies in its evaluation architecture and the demands it places on the agent.

Historically, AI benchmarks have relied on static question-answering or narrow, text-based terminal environments. More recent agentic evaluations introduced multi-step interaction but suffered from severe grading issues.

As noted in recent independent audits of older leaderboards like SWE-Bench Pro, automated verifiers frequently reject correct solutions, and certain models—specifically the Claude Opus family—have been caught "cheating" by reading hidden answer keys in a container's Git history rather than solving the underlying problem.

ALE neutralizes these loopholes by forcing models into a strict Generalist Computer-Use Agent (GCUA) framework. To pass, an agent cannot merely execute terminal commands.

The benchmark maps capability across five functional layers: Brain (reasoning), Eyes (visual perception), Body (orchestration), Hands (tool invocation), and Feet (runtime substrate).

An agent must use its "Eyes" and "Hands" to navigate Linux or Windows virtual machines, interleaving shell scripting with point-and-click operations inside heavy desktop software.

Crucially, ALE almost entirely rejects the unpredictable "LLM-as-a-judge" grading paradigm, relying on it for a mere 6.8% of its workflows. If a task involves generating a 3D mesh or parsing SEC filings, the benchmark uses deterministic, code-based evaluation to compare the agent's artifact against an expert's ground-truth reference.

Measuring Task Performance Across 55 Industries

ALE launches with 1,490 task instances and is scaling toward a massive 5,000-task target. What makes the product remarkable is its authenticity. The tasks are strictly anchored in the U.S. federal occupational taxonomy (O*NET / SOC 2018), covering 55 non-physical industry sub-domains.

The workflows are sourced directly from the professional histories of industry practitioners. Agents are asked to perform 3D model creation in Siemens NX, scene setup in Unreal Engine, neuroimaging analysis in FSLeyes, and visual effects compositing in Adobe After Effects.

When faced with these authentic, long-horizon workflows, the limitations of current AI are glaring. ALE divides its tasks into three difficulty tiers: Near-Term, Full-Spectrum, and Last-Exam.

Top 5 Agentic Harnesses on the ALE Leaderboard

The victory of GPT-5.5 aligns with recent third-party analysis suggesting that OpenAI's models are currently superior at strictly adhering to multi-part, complex prompts. Conversely, users report Anthropic's Claude architecture can sometimes be "forgetful" with multi-part instructions, abandoning required steps mid-workflow — a fatal flaw in ALE's rigorous pipeline.

And while hitting a 24.0% pass rate is enough to claim the crown, the absolute performance ceiling remains remarkably low.

On the hardest "Last-Exam" tier — representing the frontier of professional difficulty — most configurations, including Anthropic's older Claude Opus 4.8 and Google's Gemini CLI, record a devastating 0.0% pass rate.

Solving Benchmark Contamination

A core vulnerability in modern AI evaluation is "benchmark contamination"—the phenomenon where test questions inevitably leak into the massive data lakes used to train next-generation models. Once a model memorizes the benchmark, the evaluation becomes entirely useless.

ALE solves this through a dual-use deployment strategy. The project operates as an open-source research initiative, but it closely guards its evaluation data. Only about 10% of the dataset (roughly 150 tasks) is released publicly on platforms like GitHub and Hugging Face. The remaining 1,300+ tasks are kept strictly private.

For developers and enterprise evaluators, this means ALE functions as a "living benchmark". Private tasks are systematically rotated into the public pool over time, while retired public tasks are swapped out.

This rolling release ensures that the evaluation surface remains uncontaminated across successive model generations, giving enterprise buyers confidence that an agent's high score is earned, not memorized.

Additionally, ALE provides transparency by tracking both "Full" and "Unlicensed" scores. Because real professional work often requires paid, proprietary software, the "Full" leaderboard incorporates tasks that rely on commercial CAD tools, paid APIs, or licensed datasets.

The "Unlicensed" tier drops these license-gated tasks to provide a clean, like-for-like comparison using only freely available tools, ensuring models aren't simply rewarded for having access to paid enterprise software.

Bottom Line: ALE Shows Even the Highest-Performing Models and Harnesses Have Room for Improvement

For developers frustrated by the gap between marketing claims and actual production performance, ALE's brutal grading curve is highly validating. Zengyi Qin, an MIT PhD researcher and data contributor to the project, took to X to announce the launch, sharing images of the paper and the staggering 100+ institution contributor list.

"Introducing Agents’ Last Exam (ALE)," Qin wrote. "Built by 300+ domain experts from 100+ institutions. Covering 55 industry domains. Claude Opus 4.8 has 0.0% pass rate on the hardest subset. Glad to have contributed to this benchmark".

In a follow-up post highlighting the Hugging Face ArXiv paper link, Qin added:

"Very solid work from project leads @YiyouSun @Xinyang_Han_ @dawnsongtweets and @BerkeleyRDI".

As businesses deploy billions in capital betting on AI agents, they desperately need a compass that points true north. If an agent can eventually conquer the gauntlet of Agents' Last Exam, it won't just be passing a test—it will be proving it is ready to join the workforce. Until then, the sobering pass rates on the leaderboard serve as a necessary reality check for the entire AI ecosystem.

He walked into the lobby with a fake badge clipped to his shirt. He had bought it online the week before. It was not perfect, and it did not need to be. From a few feet away, it looked close enough: a logo, a name, a photo, and a lanyard. The kind of thing most people glance at for half a second before their brain decides, “Looks fine.”

The post The Art of the Badge: A Hard Truth About Physical Security appeared first on Black Hills Information Security, Inc..

Bring brains to your bots: https://www.xaba.ai/

What if a new AI brain could completely eliminate traditional robot programming? In this episode of AI News, we break down Zaba’s revolutionary physics-based AI that is changing industrial automation forever. Traditional robotics hardware isn't the real expense—75% of manufacturing costs actually come from constant programming and manual retuning.

Zaba’s Xcognition physics engine fixes this by embedding a synthetic brain directly into machines, delivering 80% faster tech deployment. We dive into 5 engineering breakthroughs disrupting the robotics industry, including scalable high-mix automation, autonomous parameter control, and legacy robot repurposing without new hardware.

See how real-time physics modeling allows robots to adapt to new specifications in a single day, eliminating static code. With production-validated results alongside global manufacturers like Hitachi, this autonomous AI is redefining the future of artificial intelligence in industry.

#AI #Robotics #XabaAI

Read more of this story at Slashdot.

Read the original post: Pokémon Champions Launches June 17

The post Qualcomm Reacts to NVIDIA RTX Spark: “Welcome to the Family” appeared first on OnMSFT.

This new wave of research asks the next question: Once you've admitted the governance problem, what breaks first when you try to fix it? The answer from our respondents is unambiguous. The failure point is not the model. It's the runtime.

Enterprises are discovering that AI agents built on stateless infrastructure — Python scripts, LangChain chains, ad hoc orchestration — cannot survive the operational realities of production. Container restarts erase context. Token costs breach business cases. Hallucinations in Step 3 compound into catastrophic failures by Step 12. And the majority of engineering teams are spending more time managing this "plumbing" than building the intelligence that was supposed to justify the investment.

What emerges from this survey is a picture of an industry at a critical fork. The organizations that survive the Agentic Reckoning will be those that treat runtime durability as a first-class engineering concern — not an afterthought to be patched with retries and prompting. The ones that don't will find themselves back where RPA left enterprises a decade ago: a graveyard of clever pilots that couldn't survive Day Two.

Methodology

VentureBeat conducted this survey in May 2026 as part of its ongoing Pulse Research series on agentic AI adoption in the enterprise. Respondents were filtered to organizations with 100 or more employees. The final qualified sample consists of 132 verified, highly qualified technology leaders at the forefront of enterprise AI agent deployment. 

They span:

Industries represented include Technology/Software (42%), Financial Services (20%), Professional Services (8%), Healthcare/Life Sciences (7%), Retail/Consumer (6%), Education (4%), and others.

Given our strict filtering criteria, this cohort provides a robust and authoritative look at emerging agentic infrastructure trends.

Respondent demographics by company size:

• Large enterprise (10,000+ employees): 35% of the sample

• Mid-to-large enterprise (500–9,999 employees): 48% of the sample

• Growth enterprise (100–499 employees): 17% of the sample

These quantitative findings capture a critical moment in infrastructure evolution and are best synthesized alongside VentureBeat’s Q1 2026 governance reports and our deep-dive practitioner conversations conducted throughout the quarter.

Finding 1: The runtime is the problem

The "spine vs. brain" debate is over

The foundational question of enterprise AI in 2026 is whether agent failures trace back to the model's reasoning capability — the Brain — or to the runtime infrastructure's inability to manage state, survive failures, and coordinate execution — the Spine. We asked our respondents directly.

Integration/governance challenges were the biggest problem. But Spine issues were close behind.

However, 17% still say the Brain is the primary failure mode. That’s not a rounding error — it’s a signal. The organizations in this cohort are not disputing the infrastructure problem; they are telling us that the models themselves are not yet reliable enough for the edge cases their workflows are generating. The model-versus-runtime debate is genuinely three-sided. Read together, these three answers are not fully in conflict. The Spine and Gap camps are struggling with infrastructure and governance respectively. The Brain cohort is struggling with something upstream: reasoning reliability at scale. 

This is a significant finding. The frontier model wars — GPT-5 vs. Claude 4.7 vs. Grok — are consuming enormous mindshare in the enterprise technology press. Our respondents are telling us that war is, for now, beside the point. The models are smart enough, but the infrastructure around them is not.

"The models are smart enough, but our stateless infrastructure is too fragile to manage long-running, multi-step agentic processes." — Director of Engineering / IT, Financial Services, 10,000–49,999 employees

Finding 2: The DIY tax is eating teams alive

Engineering capacity is being consumed by plumbing, not intelligence

If the Spine is a primary failure mode, what does that cost in practice? We asked respondents what percentage of their team's weekly engineering capacity is consumed by building and maintaining custom "plumbing" — manual retries, state-persistence, checkpointing — rather than actual agentic logic.

The results reveal a market in two distinct camps, with a dangerous middle.

The arithmetic is stark. Seventy-seven percent of respondents are spending meaningful engineering time on infrastructure overhead. Just 23% — those whose frameworks are handling reliability — have escaped the tax. The distribution is notably flat: the Crisis and Efficiency poles are the same sizes as the middle categories (Trap and Maintenance Tax). This is the signature of a market that has partially addressed the worst failures but has not yet escaped the structural overhead.

The Efficiency Zone respondents are not necessarily in a more sophisticated position. In many cases, they may be on managed platforms that abstract away the durability problem — or they may simply not yet have hit the scale at which stateless architectures begin to fail. The Complexity Trap is often where the Efficiency Zone ends.

There’s a direct business consequence for organizations in the Crisis zone. Every engineering hour spent writing retry logic or debugging a "ghost failure" — a silent API timeout that leaves an agent hanging without a traceback — is an hour not spent on the differentiated logic that was supposed to justify the AI investment in the first place.

Finding 3: State amnesia is the production killer

The No. 1 technical obstacle has shifted: Cost and hallucination now lead state failures

When AI agents fail to reach production or scale, what is the primary technical obstacle? We named five candidates, ranging from model hallucination to cost overruns to latency failures.

Hallucination Propagation at 24% compounds silently — reasoning errors in early steps become catastrophic by Step 10. Ghost Failures at 20% are invisible by definition, which means their real prevalence is likely higher than this number suggests.

Finding 4: The observability tax falls heaviest on Microsoft

Platform visibility costs are not equally distributed

Our Q1 2026 research identified vendor opacity as the single biggest obstacle to AI governance — ahead of talent gaps, tooling, and budget. That finding pointed to this question: Which vendor ecosystem, in practice, imposes the highest cost to achieve basic production visibility?

We asked respondents which platform requires the most custom telemetry, manual instrumentation, and "logging glue" to achieve visibility into agentic failures.

Microsoft's position at the top of this ranking is not noise. It is a structural characteristic of the Microsoft agentic ecosystem — the same Azure/Copilot stack that dominates enterprise AI adoption requires the most instrumentation overhead to see inside.

It also reinforces the warning that Brian Gracely, Senior Director at Red Hat, made at VentureBeat’s Boston event in March: that building your control system entirely inside one cloud provider's toolset means "renting a cage." The organizations paying the highest observability tax are precisely those most locked into provider-native tooling.

The implication for teams currently evaluating orchestration architecture is direct: observability cost is a real budget item that should appear in any build-vs-buy analysis. A platform that appears cheaper at the API layer may impose substantially higher engineering costs at the telemetry layer.

Finding 5: The hype-reality gap belongs to OpenAI and Microsoft

Agentic coding marketing is significantly ahead of production reliability. 

We asked respondents a pointed question: Which major platform's Agentic Coding marketing is the most disconnected from the actual technical reliability and fault-tolerance of their product? Thirty-two percent said they didn't know — a figure that has held roughly constant across all three waves, suggesting persistent uncertainty is structural, not a sample artifact. Cursor also registered 6% in this wave. Among those with enough production experience to have a view.

Microsoft leads at 45%; OpenAI is second at 22%. The gap is too large to attribute solely to deployment footprint. It suggests that GitHub Copilot Workspaces and AutoGen are generating a specific category of disappointment — probably around the reliability of multi-agent orchestration in production — that accumulates with use. A platform that fewer enterprises are running in production will accumulate fewer credible disappointed practitioners.

The more significant observation is what this gap means for decision-makers evaluating new agentic tooling. The marketing around all major platforms describes agentic autonomy and reliability at a level that production deployments are not yet delivering. The organizations in our survey who have moved beyond pilots are encountering the difference firsthand.

Finding 6: The security mesh is being built from first principles

Enterprises are not waiting for vendors to solve agent security

How are enterprises protecting proprietary research data from AI leakage and prompt-driven exfiltration? The security architecture question is one of the most consequential in agentic AI, because agents — unlike static models — can actively call APIs, traverse file systems, and execute code. The blast radius of a security failure is qualitatively different.

Policy-as-Code is a leading security mechanism, but not by much. 

The NHI and Policy-as-Code approaches are meaningfully different in their security philosophy. NHI is identity-centric: The question it answers is "who is this agent and what is it allowed to touch?" Policy-as-Code is rule-centric: The question it answers is "regardless of what the model decides to do, what hard stops exist at the infrastructure level?"

Rough parity across all four mechanisms is the headline finding. This is what market convergence looks like in early motion: No dominant pattern has emerged. Notably, though, Egress-Locked Sandboxing is a relatively new trend in agentic AI deployments, yet it’s already at 22%. As more agents gain terminal-level access to enterprise systems, the cost-benefit of sandboxing is improving. This is notable given the maturity of the identity management and policy-as-code disciplines in traditional IT security. The AI security layer is, for now, being built largely from scratch.

The Egress-Locked Sandboxing number deserves attention despite its smaller share. Sandboxing untrusted code execution is the most technically intensive of the four approaches, but it is also the most direct defense against prompt injection attacks that try to execute malicious code through agent tooling. As agentic systems gain more terminal-level access — a trend our survey confirms is accelerating — this approach may prove more important than its current adoption rate suggests.

"How do we audit agentic tools that have terminal-level access to our proprietary repos?"

— Composite concern expressed by multiple respondents

Finding 7: The complexity cliff is real, and most are climbing it

The migration away from stateless architectures is underway — but fragmented

The central thesis of the Agentic Reckoning is that stateless Python/LangChain architectures cannot survive the complexity cliff — the point at which multi-step, long-running agent workflows begin failing at rates that make production deployment untenable. We asked respondents directly: are you migrating toward durable execution frameworks to solve for state loss?

The answers reveal a market in transition, with meaningful disagreement about the right destination.

The 20% committed to stateless architectures — attempting to solve a structural durability problem through better prompting — are the cohort most likely to encounter State Amnesia and Ghost Failures as their workloads scale. It’s essentially the same trap that RPA teams fell into a decade ago, when brittle process automations were patched with increasingly elaborate rule sets rather than re-architected on more resilient foundations.

The Stateless Commitment cohort deserves a reinterpretation. These teams are not all naive: some are building on managed platforms that genuinely abstract state management. But a portion is patching structural fragility with prompting improvements, and the Ghost Failures data in Finding 3 suggests this approach may be encountering its ceiling.

The combined 59% who are either in Active Migration or in Governance-First Evaluation represent the market's leading edge — organizations that have recognized the architectural problem and are investing to solve it structurally.

Finding 8: The “polyglot orchestration” lead is narrow — the field is fragmented

Architectural conviction is spread across multiple bets

What is the longterm architectural philosophy winning enterprises' strategic investment? We offered four options representing the major bets available in the current market.

The Polyglot Bet's lead suggests that enterprises are seeing advantages of using a flexible approach: Using model-driven architectures where non-deterministic reasoning works well, but using deterministic structures and pipelines where accuracy and mission-critical execution is at stake.

This has direct competitive implications for the frontier labs and cloud providers. The cohort saying the use a Cloud-Native Managed Stack is significant. This likely reflects the enterprise reality that Azure OpenAI Service and AWS Bedrock deployments come with built-in organizational gravity — procurement relationships, security approvals, and existing data pipelines. The Independent Durable Runtime bet at 16% signals that a cohort of teams have rejected both cloud lock-in and frontier lab dependency in favor of full architectural sovereignty.

The Polyglot result also helps explain why the observability and governance problems described in this survey are so persistent. When your architecture deliberately spans multiple orchestration layers and multiple providers, no single vendor's telemetry gives you the full picture. The "Dynatrace for AI" — the unified observability platform called for by Mass General Brigham's CTO Nallan Sriraman at the VentureBeat Boston event — becomes not just desirable but structurally necessary.

"Enterprises trust no single provider enough to give them full control, yet they lack the engineering capacity to build entirely from scratch." 

— Survey respondent

Finding 9: User acceptance rate is the emerging production standard

The market is settling on a human-trust metric as its primary A-SLA

What metrics are enterprises actually using to determine whether an AI agent is ready for production? We asked respondents to identify their primary Agentic SLA (A-SLA) indicator — the number that, above all others, tells them whether an agent can ship.

User Acceptance Rate as the dominant production metric is significant because it is a human-trust measure, not a technical performance measure. It does not ask whether the agent ran fast or maintained state. It asks whether a human who reviewed its output chose to accept it. This is, in effect, a field-level Turing test applied at the action level. 

The persistence of UAR as the leading metric reflects the reality of where most enterprise agentic deployments still sit: in a human-in-the-loop posture, where agent actions require human review before execution. That is a rational response to the Hallucination Propagation and Ghost Failures described earlier in this survey. Organizations that have not yet solved runtime durability are, sensibly, keeping humans in the loop — and at 132 respondents, there is no evidence this is changing.

Context Fidelity's position at 30% is the most significant finding. It tracks directly with the Active Migration data in Finding 7: As more teams move into durable execution frameworks, the 48-hour+ memory problem becomes their primary production concern. Teams that have solved State Amnesia are now focused on whether their agent can remember what it was doing yesterday. Latency Jitter's collapse from 25% to 11% tells the complementary story: raw speed is no longer the primary anxiety. Correctness and durability have taken its place.

The bottom line: The reckoning is runtime, not reasoning

The data tells a consistent story: There’s a runtime deficit for agents. Enterprises are spending more time on infrastructure plumbing than on agent intelligence, and State Amnesia is still claiming production deployments. But fault lines are visible. The ROI Ceiling has overtaken State Amnesia as the leading production killer — which means the infrastructure problem is no longer purely a technical one. Token economics and orchestration overhead are now consuming enough business value that project sponsors are making the kill decision before engineering teams can solve the durability problem. Hallucination Propagation remains a big problem. The Brain vote in Finding 1 remains significant. And the Polyglot lead is fragile, with varied architectures well represented.

The models are, by most respondents' own assessment, smart enough — but 17% disagree. What is not yet smart enough is the infrastructure surrounding them: the state management, the fault-tolerance, the observability, the identity governance, and the deterministic execution layer that turns a model's judgment into something an enterprise can stake its operations on.

The 39% making the Polyglot Bet represent the current leading edge of enterprise architectural thinking. They are building systems where the model's intelligence is preserved and leveraged, but where the execution layer — the Spine — is deterministic, auditable, and durable by design. They are not waiting for a frontier lab to solve this for them. They are not betting that better prompting will patch infrastructure fragility. They are building the control plane.

The organizations still committed to stateless architectures — still trusting that manual retries and clever prompting can substitute for durable execution — are the ones most likely to contribute to the next wave of this data. Ghost Failures are a primary obstacle. The pattern is familiar: Early adopters diagnose the problem architecturally, migrate to durable runtimes, and escape the failure mode. Late movers inherit it. The Complexity Cliff is not theoretical. It is the wall that most current agentic architectures are already climbing toward.

The reckoning is runtime and economics, not reasoning.

Based on survey responses from 132 qualified enterprise respondents (100+ employees). Sample size is small; data should be treated as directional. Respondents include Directors, VPs, CIOs, CTOs, and Enterprise Architects across Technology, Financial Services, Retail, Healthcare, and other sectors.

NVIDIA just dropped 3 new AI upgrades changing robots forever, starting with the NVIDIA Isaac GR00T humanoid robot reference platform built for advanced physical AI research. This breakthrough open architecture features the Unitree H2 Plus humanoid robot chassis integrated with dual Sharpa Wave tactile five-fingered hands, achieving 75 degrees of freedom. Powered by the onboard NVIDIA Jetson AGX Thor T5000 and Blackwell architecture GPU, the platform utilizes Isaac Sim, Isaac Lab, and Isaac ROS middleware to eliminate proprietary blackbox limitations. Additionally, NVIDIA announced Cosmos 3, a state-of-the-art family of open-weight omnimodal world foundation models, including Cosmos 3 Super and Cosmos 3 Nano, acting as an advanced robot brain for vision-language reasoning. Finally, Microsoft and NVIDIA unveiled the RTX Spark system-on-a-chip at Computex, featuring a 20-core ARM CPU co-designed with MediaTek and 128GB unified memory to run local AI agents offline via the NVIDIA OpenShell runtime.

Discover the AI agent economy: https://8004agents.ai

AI news:
0:00 Nvidia GR00T
1:49 SharpaWave
2:43 Unitree H2 Plus
4:21 Cosmos 3
6:18 RTX Spark

#nvidia #ai #news

The post Brain Mapping May Offer Promise in Treating Mental Health and Neurological Conditions appeared first on Nash Disability Law.

Nvidia used GTC Taipei to launch a series of models for robots, autonomous vehicles, and video systems. The centerpieces are the new world model Cosmos 3, a significantly scaled-up driving model called Alpamayo 2 Super, and an open reference platform for humanoid robots.

The article Nvidia bets big on physical AI at GTC Taipei with a new world model, driving brain, and open humanoid robot appeared first on The Decoder.

Read more of this story at Slashdot.

After a first wave focused on rapid deployment, organizations now need to revisit those first-generation implementations, and redesign early agent architectures around workflow orchestration, observability, governance, and recovery, said Preeti Somal, Senior VP Engineering at Temporal Technologies, during the latest AI Impact Series event in New York.

“We do have a lot of customers that come to us where they’re building version 2.0 of the same agent,” Somal said. “They had to move really fast, but they didn’t take care of the plumbing. Things crash and burn, and then they’re back to rebuilding with the reliable foundation.”

For workflow orchestration company Temporal, whose infrastructure predates the current wave of agentic AI, the shift reflects a broader enterprise realization: production AI systems require durable execution, state management, visibility into workflows, and mechanisms to recover when models or downstream systems fail.

Agentic AI has supercharged familiar engineering problems

“These patterns aren’t necessarily new," Somal said. " AI just supercharges them."

Agentic systems introduce additional complexity because they often involve long-running, multi-step processes spanning multiple services, models, APIs, and tools. A single workflow might call several large language models, access retrieval systems, trigger external applications, and manage state over hours or days. The engineering questions, Somal said, often emerge only after deployment.

“People will write agents but haven’t thought about what happens if the agent crashes,” she said. “Am I going to need to run the entire agent flow again?”

For enterprises operating under cost constraints, the answer matters. Restarting workflows after failures can multiply inference expenses, increase latency, and create poor customer experiences.

Somal compared the current moment to an earlier period in enterprise cloud adoption when organizations went straight to migrating workloads before considering that they needed to redesign underlying architectures if they wanted these workloads to weather the long-term.

“This rush to do AI in a world where you haven’t even modernized your application reminds me a little bit of that lift-and-shift that happened in the cloud,” she said. “Everybody realized you’re spending more money on cloud and we haven’t gotten value there.”

Why long-running agents force a new architecture

Enterprise workflows increasingly involve agents executing over long windows, sometimes spanning many hours while interacting with tools and systems. Reliability challenges compound when workflows persist over time, and it impacts both state and memory, two ideas that are often treated interchangeably in AI conversations.

State concerns workflow execution. It includes where an agent is in a process, which actions have already completed, and where recovery should resume after failure. Memory or context captures information an agent carries forward across interactions or tasks.

“The state of the agent is around what step and what actions have been performed, and if something crashes, where do you want to recover from, versus the context and memory piece,” Somal explained.

That distinction becomes increasingly important when enterprises begin moving beyond simple chatbot interactions toward longer-running business processes. Somal pointed to a healthcare example involving customer Abridge, where workflows process physician visits through multiple stages, including audio processing, summarization, model calls, and after-visit generation.

“There’s not just one piece to that flow,” Somal said. “Taking videos and slicing that, taking summaries, calling the LLMs, generating the after-visit summary, all of that is being orchestrated.”

The implication for enterprises is that successful agents increasingly depend on systems that can survive interruptions, coordinate across services, and maintain continuity over time.

The rise of the deterministic spine

A useful framework for enterprise AI design is the deterministic spine, Somal said, which is how they think about Temporal's role.

“It is denoting the path you want to take," she said. "It is calling the brain, but if the brain doesn’t respond, it will call it again. If the brain responds but the next step is going to fail, it will pick up from where that failure happened.”

In this framing, the language model acts as a probabilistic system producing variable outputs, while orchestration software maintains execution reliability around it. And the concept matters because enterprise systems increasingly require consistency even when models remain non-deterministic. A procurement workflow, healthcare summary, customer support escalation, or compliance process cannot simply fail silently because a model call timed out or an external dependency crashed.

“What you care most about is making sure that you can recover and that you’re not paying the token tax if something goes wrong,” Somal said.

Reliability, visibility, and the economics of token spend

As enterprise leaders evaluate AI ROI, cost visibility has become a growing concern. Long-running agents frequently make multiple model calls across complex workflows, which can create opaque spending patterns. Somal described one operational advantage of orchestration as visibility into where costs accumulate. Because workflows are observable step-by-step, teams can see where tokens are being consumed across an agent process.

“You’ve got visibility into that entire flow in a single pane of glass,” she said. “You can now see where you’re spending the tokens in an agent that is multiple steps and calling multiple different systems.”

Workflow recovery also shapes cost efficiency. Without durable orchestration, a late-stage failure can force organizations to rerun an entire process from the beginning, including all prior model calls. Somal said systems designed around recovery can resume execution from the point of interruption.

“You pick up from where the crash happened,” she said. “We save you the cost of running the agent from step one again.”

Enterprises need to build paved paths and enlist partner expertise

Governance concerns are another emerging pattern as agentic AI takes hold. Rather than adopting fully managed agent systems wholesale, Somal said enterprises increasingly want standardized internal frameworks that provide guardrails while preserving flexibility, and implementing necessary features like governance controls, model selection policies, identity systems, cost management, and observability.

“The enterprises are looking at building these paved paths,” she said. “Taking something off the shelf is maybe not going to work because there are all of these other requirements.”

As organizations revisit first-generation deployments, challenges like this increasingly look less like a model problem and more like a systems engineering problem, and Temporal is positioned to help enterprises take this next step in part because for many organizations, it already existed as part of broader modernization programs before AI became a strategic priority.

“Temporal is already in the enterprise,” Somal said. “Taking that and extending that to AI and agent platforms feels very natural.”

Read more: Investigating how hormones affect brain health

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

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Updates from Rust Community

Newsletters

• Scientific Computing in Rust #18 (May 2026)

Project/Tooling Updates

• gitoxide - May 26
• hyper User Survey 2025 Results
• Rust Update: gRPC Welcomes Tonic!
• serde-const-default v0.1: Removes boilerplate when using const values as field defaults
• BoquilaHUB 0.5: AIs for Nature. Now it includes SOTA AI bioacoustics models and embeddings models
• splog: a log viewer TUI with automatic tag categorization
• rgx v0.12.3 — Building a regex debugger for the terminal in Rust
• UI tests are the guardrails an AI needs: the story of clipboardwire
• slintcn 0.22: shadcn/ui-style copy-paste components for Slint native apps
• Releasing dtact v0.2.2 and rssn-advanced v0.1.0: the next generation async concurrent engine and scientific computing engine

Observations/Thoughts

• Noroboto: Lying Fonts and Mitigation in Rust
• Erasing Existentials
• libwce: the entropy layer of a wavelet codec, on its own
• Tech Notes: Theseus: translating win32 to wasm
• Bevy Game Engine Explained Visually
• The reflex of deriving serde traits
• Physical AI Needs a Typed World Model, Not a Vector DB
• Keep calm and use (Rust) monorepos
• [audio] Rust for Linux Live with Alice Ryhl and Greg Kroah-Hartman
• [audio] Netstack.FM episode 38 — Building and testing network stacks with Rama
• [video] Can a QR code be made of stars?

Rust Walkthroughs

• Rust Patterns & Engineering How-Tos
• Laissez-Faire Errors
• Learn Rust HashMap and Iterators by Building a Git Object Store Reader
• Learn the Basics of Bevy by Building and Deploying Pong to Itch.io
• The Slowdown That Doesn't Show Up in Profiles
• Building an AsyncIO executor for the 3DS
• [video] Nine Ways to do Inheritance in Rust, a Language without Inheritance

Miscellaneous

• Content-addressed Rust builds (or, what kache actually caches)

Crate of the Week

This week's crate is inline_tweak, a crate to embed tweakable constants inside your Rust application without full recompilation.

Thanks to Kill The Mule for the suggestion!

Please submit your suggestions and votes for next week!

Calls for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization.

If you are a feature implementer and would like your RFC to appear in this list, add a call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

No calls for testing were issued this week by Rust, Cargo, Rustup or Rust language RFCs.

Let us know if you would like your feature to be tracked as a part of this list.

Call for Participation; projects and speakers

CFP - Projects

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

• rust cookbook - Expand Command Line section with clap derive, subcommands, and env vars

If you are a Rust project owner and are looking for contributors, please submit tasks here or through a PR to TWiR or by reaching out on Bluesky or Mastodon!

CFP - Events

Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

• No Calls for papers or presentations were submitted this week.

If you are an event organizer hoping to expand the reach of your event, please submit a link to the website through a PR to TWiR or by reaching out on Bluesky or Mastodon!

Updates from the Rust Project

352 pull requests were merged in the last week

Compiler

• rustc_on_unimplemented: introduce format specifiers
• account for proc macro spans in do_not_recommend diagnostics
• implement fast path for derive(PartialOrd) when deriving Ord
• make bitset would_modify_words more vectorzer-friendly
• parse mut restrictions
• stop needing materialized places for most intrinsics

Library

• add unstable Share trait
• stabilize bool_to_result
• use strongly typed wrapped indices in VecDeque

Cargo

• compiler: forward verbose flag to rustc for local crates
• don't use the network for a publish dry-run test
• break out RegistryConfig and crate_url for interpreting RegistryConfig::dl
• fix CVE-2026-5222 and CVE-2026-5223
• artifact: remove compat mode from artifacts

Rustdoc

• stabilize --remap-path-prefix in rustdoc

Clippy

• useless_format: fire on wrapped in a block-producing macro
• return can be removed from the last stmt of a block if it has an expr
• add check for midpoint using multiplication by 0.5 and >> 1
• avoid unnecessary String allocations in MinifyingSugg arithmetic ops
• extend clippy::missing_safety_doc to unsafe fields
• fix manual_range_contains NAN handling
• fix error message for useless_borrows_in_formatting for mutable borrows
• move unnecessary_get_then_check to complexity
• simplify is_some() && …unwrap() to is_some_and in unit_arg

Rust-Analyzer

• diagnostics: mut_ref binding feature diagnostic
• assists/add_reference_here: _modify_ the reference type when dealing with &T->&mut T
• cfg: correct separator index in CfgDiff disable loop
• hir-ty: saturate float-to-uint cast in const eval
• test-utils: drain inactive_regions by inactive_line_region
• add diagnostic for E0033
• add diagnostic for E0608
• completions imports exclude supports sub items
• filter package-scoped features
• extract_module missing import for macro calls
• add type_match score for struct_pat
• allow wildcard params in foreign fn declarations
• analysis expected ty in enum variant
• autoimport enum variants
• do not autoref in method probe in path mode
• do not complete semicolon in match-expr place
• do not consider the path of the macro in a macro call to be inside a macro call
• emit diagnostic for rest array patterns without fixed-length arrays
• fix SyntaxContext::roots technically overlapping valid interneds
• flip coerce_never type_mismatch tys
• have a specific error for unimplemented builtin macros
• no suggest ref match when expected generic ref
• no use sad pattern on happy arm with guard
• normalize expected tuple struct pat field
• refactor handling of generic params in hir::Type
• support named consts in range pattern types
• use grouped annotation for add_label_to_loop
• provide better incrementality for modules

Rust Compiler Performance Triage

This week was largely positive, with most of the improvements coming from algorithm change in visibility checking: #156228.

Triage done by @panstromek. Revision range: 281c97c3..783eb8c8

Summary:

2 Regressions, 2 Improvements, 5 Mixed; 2 of them in rollups 34 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

• Propose the concept of a crates.io username for identity

Final Comment Period

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No Items entered Final Comment Period this week for Cargo, Language Team, Language Reference or Leadership Council. Let us know if you would like your PRs, Tracking Issues or RFCs to be tracked as a part of this list.

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This Week in Rust is edited by:

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• llogiq
• ericseppanen
• extrawurst
• U007D
• mariannegoldin
• bdillo
• opeolluwa
• bnchi
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Discuss on r/rust

There’s been some pretty big news in the last couple of weeks in video game world: the long-running space shooter Destiny 2 is winding up after almost nine years, PlayStation appears to have decided to stop releasing its flagship single-player games on PC, and Microsoft wants us to look like we’re shouting every time we type XBOX. But the biggest news for me is that I have found my new favourite word game. I am going to be so bold as to call it the new Wordle.

Ribbit is one of the varied suite of daily games on Puzzmo, an online puzzle platform. It launched at the beginning of January, but I only recently discovered it because I have been unwell, bored, and spending too much time on my phone. Puzzmo’s daily hits include a satisfying shape-arranging game, variations on chess that make me feel extremely stupid, and pleasing word games, which are my favourites. Circuits has you making connections between the beginnings and ends of phrases (eg “stone cold > cold medicine > medicine cabinet”) as fast as you can. Bongo gives you a bunch of letter tiles and asks you to arrange them for a maximum score.

The post A Step-by-Step Coding Tutorial to Implement GBrain: The Self-Wiring Memory Layer Built by Y Combinator’s Garry Tan for AI Agents appeared first on MarkTechPost.

Read more of this story at Slashdot.

Thus, it's perhaps unsurprising to see Chinese e-commerce giant Alibaba's famed Qwen Team of AI researchers release a model capable of performing autonomous agentic AI work over multiple days: that model has arrived in the form of Qwen3.7-Max which the company reports in a blog post achieved "~35 hours of continuous autonomous execution" — albeit, in a proprietary, not open source format, as prior Qwen Team releases were.

This is also to be expected — it's what many analysts and industry experts feared in the wake of the departure of several key Qwen Team leaders earlier this year. But it makes sense for Alibaba financially, at least in the short term: training AI models, especially ones as powerful as Qwen3.7-Max, is expensive, and giving them away essentially for free, as open source models are, does not immediately help recoup any costs.

In that sense, Alibaba is simply aligning its efforts with American AI giants like OpenAI and Google by offering the latest and greatest models only through paid APIs and subscription or paid web plan bundles, and slightly less performant ones through open source.

Still, the arrival of Qwen3.7-Max offers further optionality to enterprises and individual users, and more competition for American AI labs — rarely a bad thing for consumers at all budget levels. Yet, the fact that the model is only accessible from Chinese-based endpoints means it may be limited in its appeal to American and European enterprises seeking to maximize compliance and security posturing when fulfilling government contracts, or even just attempting to comply with all relevant state, local, and national data sovereignty regulations.

The marathon AI era

To understand why Qwen3.7-Max is a departure from previous models, one must look at how it was trained and how it operates in practice.

Language models typically degrade when forced to maintain a single train of thought over thousands of conversational turns; they forget instructions, hallucinate variables, or simply get stuck in logical loops. Qwen3.7-Max was specifically designed as a "versatile agent foundation" capable of "long-horizon reasoning" to overcome this exact bottleneck.

The starkest demonstration of this capability is an autonomous engineering task detailed by the Qwen team. The model was given access to an isolated server equipped with a T-Head ZW-M890 PPU—a hardware architecture the model had never encountered during its training. Its task was to optimize an attention kernel.

Over the course of 35 straight hours, Qwen3.7-Max operated entirely autonomously. It executed 1,158 distinct tool calls, performed 432 kernel evaluations, diagnosed compilation failures, and iteratively improved the code to achieve a 10.0x geometric mean speedup.

By comparison, Chinese competitor models like z.ai's GLM-5.1 and Moonshot's Kimi K2.6 capped out at 7.3x and 5.0x speedups respectively, often voluntarily terminating their sessions when they failed to make progress. However, both are available open source.

This endurance is achieved through what Alibaba calls "environment scaling". Just as early LLMs grew smarter by ingesting more diverse text, Qwen3.7-Max was trained across a vast, scaled array of dynamic agentic environments.

It is capable of simulating a one-year lifecycle of a startup in the "YC-Bench" evaluation, navigating hundreds of decision-making rounds encompassing personnel management and contract screening. In this simulation, the model managed to generate $2.08 million in virtual revenue, nearly doubling the performance of the prior generation, Qwen3.6-Plus.

Furthermore, the model has built-in reward-hacking self-monitoring, autonomously detecting when it attempts to cheat a training environment and adding heuristic rules to correct its own behavior.

A brain for any scaffold

From a product perspective, Qwen3.7-Max is designed to be the cognitive engine for modern software development and enterprise automation.

The model offers a massive 1-million-token context window and a 64K maximum output limit, providing immense overhead for processing sprawling codebases or lengthy technical documents.

One of its most compelling features is "cross-harness generalization". Rather than being hardcoded to work best within a specific proprietary interface, Qwen3.7-Max is built to act as a drop-in intelligence layer for diverse agent frameworks. It supports the Anthropic API protocol natively, allowing developers to plug it directly into existing tools like Claude Code or OpenClaw.

The benchmark data provided by Alibaba indicates that this generalized approach has paid massive dividends.

On the Apex Math Reasoning benchmark, Qwen3.7-Max scored 44.5, eclipsing Claude Opus-4.6 Max's score of 34.5 and DeepSeek V4-Pro Max's 38.3. It also posted dominant scores on Humanity's Last Exam (41.4) and the realistic coding agent benchmark MCP-Atlas (76.4).

This translates into tangible utility for end-users. Through open source Model Context Protocol (MCP) integrations, the model can operate as an autonomous office assistant, capable of reading university formatting specs and automatically reformatting a messy Word document via command-line tools without human intervention.

Running this level of intelligence comes at a distinct cost. Developers accessing the API via Alibaba Cloud Model Studio will pay $2.50 per 1 million input tokens and $7.50 per 1 million output tokens. The platform also features explicit cache creation and read pricing, as well as a $10 fee per 1,000 calls for integrated web searches, though code interpreter tools remain free for a limited time.

Qwen3.7-Max occupies a strategic middle ground in the current API economy. While it demands a notable premium over aggressively priced domestic rivals—costing nearly double DeepSeek V4 Pro ($5.22) and Z.ai's GLM-5.1 ($5.80)—it drastically undercuts the Western frontier giants it routinely matches on benchmarks.

For context, running heavy agentic workflows through OpenAI's GPT-5.4 or Anthropic's Claude Opus 4.7 will run developers $17.50 and $30.00 per million tokens, respectively. See VentureBeat's pricing chart below:

VentureBeat Frontier AI Model API Pricing Snapshot