Adobe Patches for June 2026

For June, Adobe released 11 bulletins addressing 123 unique CVEs in Adobe Acrobat Reader, ColdFusion, Experience Manager, Experience Manager Forms, InDesign, InCopy, Substance 3D Sampler, Content Credentials SDK, Dreamweaver, Format Plugins, and Adobe Campaign Classic. A total of 11 of these CVEs were reported through the ZDI program.

Here’s this month’s overview table:

Obviously, the update for Campaign Classic should be on the top of your deployment list if you’re a user. A CVSS 10 is rare; two in the same bulletin is pretty much a unicorn. Adobe says there are no active attacks, but I would expect heavy research into creating one. The update for Coldfusion is also a Priority 1, but again, no known attacks is the wild. I suspect the Reader patch will also receive a lot of attention as malicious PDFs are common in ransomware attacks. The update for Experience Manager may be large, but it’s mostly just cross-site scripting (XSS) bugs.

Microsoft Patches for June 2026

This month, Microsoft released a new record 208 CVEs Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Github Copilot, Defender, Exchange Server, Hyper-V, Secure Boot, and BitLocker. At least, that’s my count. Microsoft’s tools seem to be having some issues, as they initially included a CVE from 2020 in this release. Regardless, the count is over 200, and I counted several times.

One of these bugs came through the ZDI program, but bugs submitted during Pwn2Own Berlin remain unpatched. If you include the Chromium and other third-party bugs, the total CVE count for June comes to a staggering 571 CVEs. 38 of these cases are rated Critical while the rest are rated Important in severity.

I’ve been counting CVEs on Patch Tuesday since 2017, and this is by far the largest monthly release in that time. The previous record was 177 set last year. It is extraordinary that Microsoft can produce so many patches in a single month, but it does raise concerns. How many of these cases were found using AI tools? How many patches were generated using AI to assist in coding or testing? What quality issues may exist in these patches? And likely most importantly, is this the new normal? The last two months were also large releases. Should sysadmins adjust their processes for prioritization and patch deployment based on this new volume of updates? Unfortunately, Microsoft is not providing those answers right now. Hopefully that changes in the future. BTW – just a note – the current number of CVEs shipped by Microsoft this year exceeds the total number of CVEs shipped in all of 2018.

One of the bugs patched by Microsoft this month is listed as under active exploitation and three others are listed as publicly known at the time of release. Let’s take a closer look at some of the more interesting updates for this month, starting with the bug being exploited in the wild.

-   CVE-2026-41091 - Microsoft Defender Elevation of Privilege Vulnerability
Since Microsoft doesn’t provide info on how widespread exploitation is, we must read some tea leaves. For this patch, several different people were acknowledged, which indicates multiple parties say this is in the wild, meaning exploitation is likely significant. The good news is that most people won’t need to take action as Defender updates itself. However, if you don’t have this configured or are in an isolated environment, you’ll need to update to the latest version.

-    CVE-2026-45657 - Windows Kernel Remote Code Execution Vulnerability
This CVSS 9.8 bug allows remote, unauthenticated attackers to execute code at SYSTEM level without user interaction. Yup – this is wormable. The problem lies in the way the kernel handles TCP/IP. This was listed as “Exploitation Less Likely” by Microsoft, but rest assured that every researcher and bug shop on the planet is reversing this patch right now trying to create an exploit. Test and deploy this patch quickly.

-    CVE-2026-47291 - HTTP.sys Remote Code Execution Vulnerability
Our second CVSS 9.8 bug of the month, this also allows remote, unauthenticated attackers to execute code on affected systems without user interaction. However, there is a caveat. Systems using the default MaxRequestBytes registry value used by the Windows HTTP stack are not affected by this bug. You can edit your registry settings if you need protection while you test and deploy the patch. The bulletin includes instructions and even a PowerShell script for doing this action. Microsoft lists this as “Exploitation more likely”, so I would definitely check your registry settings.

-    CVE-2026-44815 - DHCP Client Service Remote Code Execution Vulnerability
Here’s another CVSS 9.8 that has an odd incongruity. Although the CVSS says no permissions are required for exploitation, the write-up states it must be an “authenticated” user. I would err on the side of caution here and believe the CVSS. If that’s correct, then we have another bug where a remote, unauthenticated attacker could execute code on affected systems without user interaction. And since the DHCP client is on every OS, it’s a juicy target. This is another one to test and deploy with haste.

-    CVE-2026-45585/CVE-2026-50507 - Windows BitLocker Security Feature Bypass Vulnerability
If you’ve followed the ongoing saga of Nightmare Eclipse vs. MSRC, the bugs should look familiar. One is definitely a fix for “YellowKey”, while the other appears to be a fix for “GreenPlasma”. The researcher has promised a “bone shattering” drop on June 14, so let’s hope Microsoft is able to reach some understanding with the researcher before more 0-days are released. Also, there is a script provided by Microsoft as a mitigation, but the better strategy is to test and deploy the updates.

 Here’s the full list of CVEs released by Microsoft for June 2026:

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

Looking at the other Critical-rated bugs in this release, the scariest-looking one is actually nothing to concern yourself with at all. The CVSS 10 bug in Azure HorizonDB has already been addressed by Microsoft and is just being documented now. That’s also the case for five others. Of course, there wouldn’t be a release without Office bugs that have the Preview Pane as an attack vector. There are multiple in June. There’s a handful of bugs in the Remote Desktop Client, but these rely on connecting to a malicious RDP server. There are three patches for Hyper-V that allow for guest-to-host code execution. The bug in Active Directory requires authentication, but any authenticated user can hit it. For the Windows Directory Service vulnerability, it needs to be listening for TFTP. You have blocked that everywhere, right? The bug in Azure Network Adapter is somewhat unique as you need to update your Linux kernel to be protected. The bug in Azure Kubernetes allows an attacker to break out of a container and gain control of the AKS worker node. Finally, the bug in the Kerberos Key Distribution Center (KDC) seems unlikely, but if exploited, it could allow authenticated attackers to get code execution on affected systems.

Moving on to the other code execution bugs, there are the ubiquitous open-an-own bugs in Office components like Excel and Word. The code injection bug in Exchange Server looks troubling, but it requires a machine-in-the-middle (MiTM), so exploitation is unlikely. The bugs in SharePoint require authentication, but you should note that the patch applies to both SharePoint Server 2016 and SharePoint Enterprise Server 2016. The two bugs in UPnP are interesting. Both can lead to code execution by causing an error during the handling of specially crafted data, which could lead to a Use After Free (UAF) bug. The bugs in RDP Client all require connecting to a malicious RDP server, but it’s not clear why some are rated Critical and some are rated Important. The NTFS vulnerability requires a user to mount a virtual hard drive on an affected system. The last RCE bug this month is in Azure Stack Edge and requires the attacker to send a specially crafted file upload request that includes a manipulated file name or path, leading to code execution.

There are more than 60 Elevation of Privilege (EoP) bugs in this month’s release, and as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. A notable exception is in Exchange Server, where a user on Outlook Web Access (OWA) could gain access to other mailboxes. The bug in Visual Studio Code could allow attackers to gain permissions associated with the MCP Server’s managed identity. The bugs in Windows SDK and Windows UI Automation Manager could let attacker go from low integrity up to medium integrity code execution. The bug in Bluetooth just allows “elevated” privileges without really describing what elevated might be.

Moving on to the more than 20 security feature bypass (SFB) bugs in the June release, there are a total of 10 that impact Secure Boot. All carry scope change (S:C) in the CVSS, meaning successful exploitation affects security boundaries beyond the vulnerable component itself — specifically the ability to load untrusted code at boot, bypass Virtual Secure Mode, and undermine boot integrity guarantees. CVE-2026-45654 explicitly calls out VSM exposure. The bulk of these are credited to Alon Leviev (STORM), which is notable given his prior BootKitty/BlackLotus-adjacent research. The bugs in the Windows Boot Manager have a similar impact as the Secure Boot bugs. The UEFI Secure Boot vulnerabilities go a layer deeper. They require either local admin or physical access but could allow for the running of untrusted code even before the OS loads. Rootkits anyone? The four bugs in BitLocker all require physical access but could yield encrypted data if exploited. The bug in Windows Administration Protection allows attackers to bypass the feature that prevents standard-user apps from performing admin-level actions. The bug in Visual Studio Copilot Chat could be the most interesting non-boot bug here as it allows authentication impersonation. Mark of the Web (MotW) and Excel vulns could bypass user warnings. Lastly, the bug in PC Manager bypasses expected user controls.

Turning our attention to the mass of spoofing bugs in the release, we instantly see 18 impacting SharePoint Server. Fortunately, these are simply cross-site scripting (XSS) bugs. It’s the Exchange bugs we should really watch for. One is an XSS that an attacker can exploit by convincing an Exchange administrator to open a malicious link or message, which then runs code in the admin's web session. That's a meaningful privilege escalation path. Another is listed as an SSRF-based attack, but no other details are available. The last is a lower-impact XSS with limited confidentiality/integrity loss. The bug in Bing Search (remember Bing?) is a classic search result spoofing. The bug in Azure Stack Edge is interesting as it could allow access to resources outside the vulnerable component's security boundary. The bug in Office for Android requires user interaction. The Office Project Server bug is an authenticated XSS with low impact. The final spoofing bug is in Azure Attestation but has already been addressed. You should still verify you are protected by following the instructions in the write-up from Microsoft.

There are 30 different information disclosure bugs in this release, and fortunately, the vast majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The two bugs in Visual Studio require user interaction and could “disclose information over a network.” How obtuse. The bug in GitHub Copilot and Visual Studio Code could disclose discloses a sign-in access token for a user's work account. That's a meaningful credential exposure, not just random memory. That leaves the two bugs in Exchange Server. One could allow an authenticated user to gain information about which network services that the Exchange server can reach. The other sounds much like the spoofing bug in OWA as it allows attackers to see information in mailboxes they should not have access to.

I’ve never been a fan of the “tampering” category, as it could mean so many different things. For example, the bug in .NET simply says it could allow an unauthorized attacker to perform tampering locally. Similarly, the bug in Visual Studio says the same, expect here the tampering occurs over a network. Microsoft doesn’t even bother with a CWE for the tampering bug in the DHCP Server, so your guess is as good as mine.

There are seven DoS bugs in the June release, and as usual, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting is the bug in HTTP.sys, which is listed as publicly known. This is an uncontrolled resource consumption, rated "Exploitation More Likely," and publicly disclosed. Since, HTTP.sys sits at the core of IIS and Windows web services, a network-accessible DoS here can take down any Windows server running HTTP-based services. Based on the Acknowledgement, it looks like this bug may have been found using AI. There are no real details for the other bugs, but based simply on the impact, I would focus on the Kerberos and TCP/IP bugs if you had to prioritize.

No new advisories are being released this month.

Looking Ahead

The next Patch Tuesday will be on July 14 and will be the last one before Black Hat/DEFCON. It’s usually a big release, so strap in and hang on. I’ll be back then to give you my full thoughts. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

The post You Didn’t Get the AI Model You Paid For appeared first on MarkTechPost.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.

The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5%, and on SWE-Bench Pro's public dataset, 59.4%.

Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.

Why the West's open-weight AI gap has become a boardroom issue

The release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.

Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. "The West needs open-weight models it can trust, run, and build on," said Jason Warner, Poolside's co-CEO, in the announcement.

Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. "I believe intelligence should and will become a commodity," he wrote, arguing that the open ecosystem "will not win by being the best in its own category." Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.

The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. 

Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.

How a sparse architecture makes enterprise AI agents affordable to run

The technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.

That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at $0.10 per million input tokens and $0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.

The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: "more verification, less taking things for granted, not declaring victory early, and being more persistent." Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.

Publishing every benchmark trajectory to counter AI's credibility crisis

The most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.

This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90% range, and as "reward hacking" — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.

Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2% faster with roughly 70% lower memory allocation, finding an O(n²) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of Erdős problem #397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.

What the disclosed limitations and benchmark fine print reveal

Poolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%, and DeepSWE from 16.5% to 40.4%, at substantially higher token cost.

Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.

The deeper structural question is whether Poolside's "Model Factory" — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.

For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.

Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence "can be owned and shaped by anyone," he wrote — and the company plans to keep shipping "until that future exists." In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

The new experience, which will begin rolling out on July 27, 2026, is personalized based on a user's role and available features:

• For teachers, a new dashboard gives actionable insights, highlights student classwork interactions, tracks assignment completion, and surfaces a feature spotlight to help discover instructional tools and resources.

• For students, a dedicated ‘Enrolled’ view reminds learners of coursework that is due soon and helps them manage their deadlines.

• For school leaders and IT administrators, the homepage provides a centralized view to monitor high-level performance analytics, access shortcuts for backend administrative settings, and discover relevant tools to support educators and staff.

Users who have multiple roles (such as a teacher taking a professional development class) can easily change their view dashboard by clicking into another role (for example, Teaching, Enrolled, or Admin). When a user loads the homepage, it returns to the previous role view.

To help users control their view and focus on what matters most to them, all new homepage modules are collapsible.

Please note that not all features and views will be available to all users. Eligibility is determined by the user’s role, feature access, account type, and settings.

Getting started

• Admins: There is no admin control for the new Classroom homepage. Gemini and Gemini Notebook features will only appear if the user is in an OU with Gemini in Classroom, Gemini app, and/or Gemini Notebook enabled. Visit the Help Center to learn about managing access to Gemini in Classroom, Gemini app, Gemini Notebook, and the option to turn these services on or off for users in the Admin console.
• End users: There is no end user setting for the new Classroom homepage. Visit the Help Center to learn more about the new Classroom homepage.

Rollout pace

• Rapid Release and Scheduled Release domains: Full rollout (1-3 days for visibility) starting July 27, 2026

Availability

• Available to all Google Workspace customers, Workspace Individual subscribers, and users with personal Google accounts

Resources

• Google Classroom Help: Navigate your Classroom Homepage
• 2026: What’s New in Google for Education: Overview of Classroom Homepage

Release Date: July 20, 2026
Since v0.18.0: ~2,245 commits · ~1,065 merged PRs · ~2,465 files changed · ~300,000 insertions · ~36,000 deletions · ~3,300 issues closed · 450+ community contributors

The Quicksilver Release. Hermes is the messenger god, and this window we made him move like it. First-turn time-to-first-token dropped ~80% on every platform, reasoning streams live by default, the desktop app got a ~20-PR speed overhaul (14× faster streaming markdown, virtualized diffs, snappy session switching), and the TUI renders markdown incrementally. Around that speed spine: you can now manage your Nous subscription without leaving the terminal, plug Bitwarden and 1Password straight into Hermes, let smart approvals judge flagged commands for you by default, watch your subagents work live, and trust that a finished response survives a gateway crash thanks to a durable delivery ledger. This release also rolls up everything from the v0.18.1 and v0.18.2 infrastructure patch tags — those windows are fully documented here.

✨ Highlights

• Hermes got dramatically faster — first token in a fraction of the time — Cold-start "Initializing agent..." used to eat ~4.3 seconds before your first turn even reached the model; it's now ~0.9s, an ~80% cut that applies to the CLI, gateway, TUI, desktop, and cron alike. Round 2 attacked what you see while waiting: reasoning models now stream their thinking live by default (no more staring at a spinner for 30 seconds), and the response box paints per token instead of per line. If Hermes ever felt like it took a deep breath before answering, that breath is gone. (#59332, #59389 — @teknium1)

• The desktop app speed wave — 20+ targeted perf PRs — Long replies used to cost 14× more CPU in the markdown splitter than they do now; giant diffs froze the review pane until we virtualized it; switching sessions thrashes layout no more. Streaming no longer re-renders the sidebar and every tool row per token, profile backends pre-warm on hover intent, and boot-hidden panes mount at idle instead of on the cold-start critical path. The net effect: the desktop app feels like a native app under load, even with huge transcripts and busy agents. (#67154, #67818, #65898, #66033, #66747, #67742 and more — @OutThisLife)

• Manage your Nous plan from the terminal — /subscription and /topup — Changing your subscription used to mean a trip to the billing website. Now /subscription opens a full flow right in the TUI or classic CLI: see your plan and remaining allowance, preview exactly what an upgrade costs ("Pay $46.30 & upgrade now") or when a downgrade takes effect, and apply it — with scheduled-change banners and undo. The desktop app got a matching billing settings tab. Your wallet never has to leave the keyboard. (#51639, #61054, #61067 — @alt-glitch)

• Smart approvals are now the default — When Hermes wants to run a flagged command, an LLM reviewer now assesses it independently instead of asking you to approve every single one — and each verdict covers only that exact command, so a later command matching the same pattern gets its own review. Combined with the new user-defined deny rules (which block commands even under yolo mode) and /deny <reason> (which tells the agent why you refused so it course-corrects), day-to-day approval fatigue drops sharply without giving up control. (#62661, #59164, #54518 — @teknium1)

• Plug your password manager into Hermes — Bitwarden & 1Password secret sources — API keys no longer have to live in a plaintext .env. A new pluggable SecretSource interface lets Hermes fetch secrets from Bitwarden and 1Password (op:// references) at load time, with multiple vaults enabled simultaneously, deterministic precedence, conflict warnings, and per-variable provenance. This consolidated eleven competing community PRs into one orchestrated interface — future vault providers drop in as plugins. (#59498 — @teknium1, 1Password provider salvaged from @hwrdprkns)

• Watch your subagents work — live transcripts + durable background delegation — delegate_task dispatches now return live transcript files you can tail -f the moment the subagents launch: every tool call, result, and streamed reply, one human-readable log per child. And background delegation completions are now durable — if the process restarts mid-run, results are restored and delivered through an ownership-checked ledger instead of vanishing. Fan out a fleet, watch any worker live, and never lose the results. (#67479, #63494 — @teknium1)

• A finished answer can no longer be lost — the delivery-obligation ledger — If the gateway died between generating your response and confirming the platform actually delivered it, that answer used to be silently gone (and you'd paid for the turn). Final responses are now recorded in a durable ledger in state.db around the platform send and redelivered on the next boot — closing a P1 silent-loss window for Telegram, Discord, Slack, and every other channel. (#67181 — @teknium1)

• One gateway, many profiles — profile-based message routing — A single multiplexed gateway sharing one bot token can now route specific guilds, channels, or threads to different profiles — each with fully isolated config, skills, memory, and secrets. Point your work Discord server at the work profile and your hobby server at personal, from one bot. A second multiplex hardening wave means one misconfigured profile can no longer take down the whole gateway. (#64835 salvaging @Burgunthy, #65700, #60589 — @teknium1, @benbarclay + six salvaged contributors)

• New providers and the newest frontier models — Fireworks AI and DeepInfra land as first-class providers (Fireworks with cost estimation and a #2 slot in the provider picker), Upstage Solar joins via salvage, and the model catalogs picked up GPT-5.6 (Sol/Terra/Luna + Pro variants, wired end-to-end across every route), grok-4.5 (GA), moonshotai/kimi-k3, claude-fable-5 / claude-sonnet-5, and GA tencent/hy3 — plus LM Studio JIT model loading for local setups. (#62593, #63969, #61616 — @kshitijk4poor completing @rob-maron's #61578, #60887, #65913, #64541, #65472)

• Crank the thinking to max — new reasoning effort tiers and per-model control — Reasoning effort gained max and ultra levels (GPT-5.6 and Codex's top tiers), selectable everywhere from the CLI to the desktop, with sane clamping on providers with smaller scales. You can now also pin per-model reasoning-effort overrides in config, set per-slot effort in MoA presets (your advisors think hard, your synthesizer stays fast), and per-task effort for auxiliary models. Thinking depth is now a dial, not a global switch. (#62650, #64458, #64631, #64597 — @teknium1)

• Your sessions, your data — export everything — hermes sessions export now writes Markdown, Quarto, HTML, prompt-only, and even Hugging Face-ready trace formats, with the full filter surface (age, workspace, platform), an opt-in --redact secret-scrubbing pass, and compacted-session lineage stitched into one logical export. Pair with the new prune filters and bulk archive to keep your session store tidy. Your conversation history is a real dataset now, not a black box. (#60186 salvaging @web3blind, #60492, #60507, #59327 — @teknium1)

• Security hardening round — This window closed a long list of credential-surface gaps: Vertex credentials scoped away from subprocess env and through profile secret scopes, media/vision/image-gen local-file reads routed through one shared credential-read guard, a webhook body-size-cap sweep across every aiohttp server, bot-token redaction in Telegram transport errors, Fireworks token prefixes added to the redactor, six P1 browser/MEDIA/.env hardening PRs salvaged in one pass, and CI hardened against untrusted-ref interpolation. (#57660, #58709, #59215, #56582, #57842 — @teknium1, @srojk34, @kshitijk4poor, @jquesnelle)

⚡ Performance — the speed spine

First-turn latency (all platforms)

• ~80% TTFT cut — Discord capability detection off the critical path (token-keyed 24h disk cache + background refresh), Ollama probe skipped for known non-Ollama providers, agent-init blocking work removed; cold submit→dispatch ~4.3s → ~0.9s (#59332 — @teknium1)
• Perceived-latency round 2 — display.show_reasoning default ON (watch the model think instead of a spinner), per-token response-box painting with width-aware force-flush, prompt-build caching, mtime-cached timezone resolution (#59389 — @teknium1)
• Segment mixed tool batches to recover lost concurrency; drop per-call base64 re-serialization from request-size estimates (#64460, #67788 — @teknium1, @OutThisLife)

Desktop speed wave

• 14× less splitter CPU via incremental block lexing for streaming markdown; virtualized review-pane diffs (no more full-Shiki freeze); snappy session switching on large transcripts; killed the layout-thrash cascade on session switch (#67154, #67818, #65898, #66033 — @OutThisLife)
• Cut startup serialization + per-turn REST amplification; pre-warm profile backends and gateway sockets on hover intent; idle-mount boot-hidden panes; fast model picker + dialogs (#66747, #66347, #67857, #66470 — @OutThisLife)
• Stop per-token sidebar + tool-row re-renders during streaming; stop eager JSON.stringify of every tool's args/result; scope tool-diff subscriptions; batch sidebar session slices into one profile-DB pass; targeted file-tree revalidation; rAF-coalesced sash resizes (#67742, #67842, #67195, #67245, #67824, #67838, #67844 — @OutThisLife)
• Systematized perf benchmark harness with trustworthy cold-start + first-token measurement, replacing 12 one-off scripts (#67466, #67697 — @OutThisLife)

Everywhere else

• TUI renders streamed markdown incrementally per block (#67236 — @OutThisLife)
• Skill discovery cached by scan signature; snapshot manifest builds ~5× faster; text prefilter before AST parse in tool discovery (#61414, #61131, #63941 — @kshitijk4poor, @ethernet8023)
• Copy-on-write message prep instead of full deepcopy; model-metadata probe-cache cluster; gateway session.resume model + display history from one SELECT (#61133, #61368, #67247 — @kshitijk4poor, @OutThisLife)
• hermes update skips npm install when Node manifests are unchanged; dashboard session-list payloads trimmed + messages paginated (#61580, #60883 — @kshitijk4poor)
• Byte-stable gateway system prompts — pinned session-context render keeps the prompt cache alive across turns (#67403 — @kshitijk4poor)

🏗️ Core Agent & Architecture

Providers & models

• Fireworks AI provider with cost estimation + cached picker price columns, promoted to #2 in provider pickers (#62593, #65476, #65214 — @teknium1)
• DeepInfra hardened integration; Upstage Solar provider (#42231 salvage) (#63969, #64541 — @kshitijk4poor)
• GPT-5.6 (Sol/Terra/Luna + Pro) end-to-end — context lengths, native/Codex catalogs, pricing, compaction caps across every route (#61616 — @kshitijk4poor, building on @rob-maron)
• grok-4.5 (GA) catalog + reasoning allowlist; kimi-k3 on Nous Portal + OpenRouter (kimi-k2.x retired) + K3 discovery on the Kimi Coding endpoint; claude-fable-5 / claude-sonnet-5 / fugu-ultra curated; GA tencent/hy3 (#60887, #65913, #65922, #56617, #60943 — @teknium1)
• Catalog-labeled silent default (GLM-5.2) + bare-provider /model cost-safe routing; LM Studio JIT load mode; adaptive thinking for Kimi-family Anthropic endpoints (#64771, #65472, #67606 — @teknium1, @kshitijk4poor)
• GLM-5.2 native reasoning_effort controls; Gemini request-context improvements; extra HTTP headers for LLM API calls; per-client model routing on the API server (#58884, #61873 — @vishal-dharm, #57038, #57028 — @teknium1)
• Claude Sonnet 5 fully wired — curated lists, intro pricing, and metadata across every route (#67932 — @teknium1)
• Hide providers you don't use — enabled: false per-provider flag + excluded_providers config scrub unwanted providers from /model pickers and built-in resolution (#67971 — @teknium1)
• Bedrock catalog wave: real context-window probing from the live endpoint, 1M-context rows for current-gen Claude + Fable, geo-prefix parity, versioned profile-ID pricing, Opus 4.8/4.7 rows (#68007, #67977, #68005, #67976 — @teknium1)
• kimi-k3 rollout completed across Kimi-direct catalog surfaces with 1M context on canonical Kimi Coding endpoints (#68108 — @teknium1)
• Provider pickers: Qwen providers folded into one group row; collapsible provider groups in the desktop model picker; friendlier TUI model display grouping same-endpoint providers (#67758, #67904, #67908 — @teknium1)

Reasoning & MoA

• max + ultra effort levels across every surface and route (#62650 — @teknium1)
• Per-model reasoning_effort overrides via a unified resolution chokepoint; per-task auxiliary effort; per-slot MoA preset effort; session-scoped /reasoning in the CLI (#64458, #64597, #64631, #67946 — @teknium1)
• MoA: reference_max_tokens to cap advisor output and cut latency; per-preset fanout cadence (user_turn runs advisors once per user turn); stale presets surfaced without retries; half-filled preset saves rejected at the API boundary; aggregator resolves reasoning like an acting model (#56756, #57591, #64756 — @teknium1)

Delegation, approvals & the agent loop

• Live subagent transcripts + durable background completions (see Highlights) (#67479, #63494 — @teknium1)
• Smart approvals default; user-defined deny rules (block even under yolo); /deny <reason> relays the denial reason; plugin pre_tool_call approve action escalates to a human gate (re-landed with rule keys) (#62661, #59164, #54518, #60504 — @teknium1, @kshitijk4poor)
• Unified delegation concurrency caps (max_async_children deprecated); explain long provider waits on the live status line; deterministic tool-output risk exposure (#56955, #64775, #61793 — @teknium1)
• Codex: live TUI/desktop tool cards for the app-server runtime, commentary streamed as visible interim messages, compaction routed through thread/compact/start, max-output truncation recovery, oversized message ids dropped on replay, banked usage-limit resets via /usage reset (#66514, #66115, #60114, #58155, #62225 — @teknium1, @kshitijk4poor, @JoaoMarcos44, #64280 — @teknium1)
• Hooks: oversized hook-injected context spills to disk (#20468 — @teknium1)
• Vibe reactions — floating hearts on affection across CLI/TUI/desktop, token-free core detection (#62016 — @OutThisLife)

Secrets & config

• Pluggable SecretSource interface + Bitwarden & 1Password providers (see Highlights) (#59498 — @teknium1, @hwrdprkns)
• hermes config get / unset; warn on unknown root config keys + doctor deprecated-key reporting; display.timestamp_format (#65540, #67370, #40622 — @teknium1)
• Auxiliary model usage recorded per task in session accounting; conversation-scoped Nous Portal usage tags across aux/MoA/delegate calls; --usage-file JSON report for hermes -z (#65537, #65468, #59615 — @teknium1)

Sessions & compression

• Sessions export: Markdown/QMD/HTML/prompt-only/trace formats, HF upload, --redact, unified filters; full prune filter surface + bulk archive; CLI workspace filter + restore-cwd-on-resume (#60186, #60492, #60507, #59327, #63091 — @teknium1, @web3blind)
• Compression: preserve human intent and durable handoffs; retain prompt cache when memory is unchanged; flatten multimodal content for the summarizer keeping image handles; gateway compression routing integrity (#67275, #67916, #65046, #56868 — @kshitijk4poor, @teknium1)
• Gateway session metadata consolidated into state.db; routing index moved to state.db (sessions.json now an optional legacy mirror); exact API bytes persisted in an api_content sidecar (#58899, #59203, #67274 — @teknium1, @kshitijk4poor)

🌐 Gateway, Fleet & Relay

• Durable delivery-obligation ledger for final responses (see Highlights) (#67181 — @teknium1)
• Profile-based routing for inbound messages + multiplex hardening wave 2 + GATEWAY_MULTIPLEX_PROFILES override (see Highlights) (#64835, #65700, #60589 — @teknium1, @benbarclay + salvaged contributors)
• Per-session turn lease + conversation-scope funnel; unified session reset boundaries (reset sessions stay reset); truthful runtime readiness checks; per-channel model and system prompt overrides; per-session /model overrides persist across restarts (#67401, #65783, #62645, #56967, #57030 — @teknium1)
• Session auto-reset default off; /sessions search <query>; webhook payload filters + route scripts; platform HTTP event callback routing; configurable long-running status phrases (#60194, #57685, #60944, #65702, #58872 — @teknium1)
• Relay: generic OIDC client-credentials provisioning (NAS-free), routed profile carried from the connector wire source, channel context consumed from the connector; Nous auth forensics + nous_session_valid on /api/status for hosted self-heal; Docker re-seeds a terminally-dead Nous bootstrap session on boot (#60730, #60586, #64649, #59976, #59969, #59983 — @benbarclay)

📱 Messaging Platforms

• Inline choice pickers for /reasoning and /fast on Telegram, Discord, and Matrix — one-tap native buttons instead of typing (#65799 — @teknium1)
• WhatsApp: native Baileys polls (clarify renders as a poll), locations, rich inbound metadata; dashboard pairing flow (#58865, #60571 — @teknium1)
• Discord: recover messages missed during reconnect; auto-created threads renamed to generated session titles; configurable interactive view timeout; opt-in owner mentions on exec-approval prompts; optional admin-only gate for approval buttons (#66149, #60187, #60230, #60493, #51751 — @teknium1)
• Slack: live per-tool status line (#67080 — @teknium1, salvaging #62007)
• Telegram: per-topic free-response allowlist; Google Chat clarify prompts rendered as cards (#65543, #65546 — @teknium1)
• Voice: stt.echo_transcripts toggle; MEDIA: captions attached to the media bubble on standalone sends; display.tool_progress: log option (#58859, #61415, #57014 — @teknium1, @kshitijk4poor)

🖥️ Hermes Desktop App

• Contribution-driven shell on a layout-tree model — panes, zones, and layouts as data; plugin-scoped i18n locale bundles followed (#60638, #67303 — @OutThisLife)
• Capabilities page — Skills/Tools/MCP + Hub in one place, with responsive overlay nav; CLI/dashboard parity for skills hub, MCP test/toggle/catalog, maintenance ops, log filters; five UX fixes from live testing (#57590, #57441, #67482 — @OutThisLife, @teknium1)
• Hermes Cloud connection mode (salvage of #55402); soft gateway switch + gateway-settings polish; terminal execution backend picker with health probes (#61912, #61916, #67203 — @OutThisLife, @teknium1)
• Keybind hint tooltips + keybinds settings tab + unified worktree dialog; base-branch picker for new worktrees; green unread dot for background-finished sessions; background-task sidebar indicators; grouped tool calls across text-less messages; auto-scrolling window for long tool-call runs (#65204, #62243, #65109, #65174, #61147, #57913 — @ethernet8023, @OutThisLife)
• Session + project color system (inherit from project, per-session override, shared across sidebar/tabs); unified active-project identity in chat status; workspace path status action (#67469, #67681, #67282, #63086 — @OutThisLife)
• Declarative memory-provider panel + full-config modal; config-defined TTS/STT providers + xAI TTS params; custom endpoint settings; per-job cron model picker; profile-aware approval mode control; UI scale setting; Ctrl/Cmd+wheel zoom; chat backdrop toggle; /journey opens the memory graph overlay (#67206 salvaging @erosika, #67209, #67759 — @austinpickett, #67472, #63520, #60457, #67029, #64598, #57267 — @teknium1, @OutThisLife)
• Full TypeScript conversion of the desktop tree (#57855 — @ethernet8023)

📊 Web Dashboard

• Memory provider switching; safe session import flow; WhatsApp pairing; Discord-specific toolsets editable from the web UI; clarified manual Telegram bot setup (#60569, #63699, #60571, #65361, #64636 — @teknium1, @kshitijk4poor, @shannonsands)
• Terminal keep-alive + reattach for dashboard chat sessions; heavy turns isolated in a compute host; paste/drop images into Chat; browser.headed schema toggle; profile + gateway topology on /api/status; mobile/hosted OpenAI OAuth login (#60515, #65895, #61929, #67046, #60537, #61330 — @teknium1, @OutThisLife, @benbarclay)
• hermes serve is a true headless backend (no web UI build/mount) (#55923 — @OutThisLife)

🧰 CLI & TUI

• /subscription + /topup terminal billing (see Highlights) (#51639 — @alt-glitch)
• /model --once — one-turn model override that reverts automatically (#67113 — @teknium1, salvaging #29923)
• Stacked slash-skill invocations — /skill-a /skill-b do XYZ loads both skills in order (Claude Code port), with autocomplete + ghost text (#57987, #58763 — @teknium1)
• --safe-mode troubleshooting flag; uninstall dry-run; TLS failures fail fast with fix hints; /compact alias + preview flags; pip/Homebrew installs warned unsupported (#45300, #60111, #57992, #57029, #57225 — @teknium1, @ethernet8023)
• TUI: model picker refresh support; custom skill bundles dispatched as agent turns; banner sizes skills display to terminal width (#59782 — @helix4u, #62859 — @Adolanium, #40624 — @teknium1)
• Hermes Console REPL + perf follow-ups; hermes curator usage all-skills view; entry-point plugins surfaced in hermes plugins list (#57781 — @kshitijk4poor, #36727, #40623 — @teknium1)

🔧 Tool System, Skills & MCP

• MCP: mcp__server__tool naming convention; server log notifications surfaced in agent.log; hosted OAuth completed across Dashboard + Desktop; configurable redirect_uri/redirect_host for proxied/WAF setups; OAuth callback port races closed; Blender added to the MCP catalog with a curated 4-tool default (#52750, #57416, #66151, #65610, #65622, #64463 — @teknium1, @benbarclay)
• Skills: security/unbroker (autonomous data-broker removal) + blind opt-out hardening; unreal-mcp companion skill; blender-mcp reworked around the catalog entry; humanizer pattern expansion; mcp-oauth-remote-gateway optional skill (#57438, #57902, #65989, #64715 — @SHL0MS, #65066, #65486 — @teknium1)
• Browser: full snapshots stored on truncation, eval denylist opt-in; computer_use follows cua-driver's verify→escalate ladder (#65923, #67123 — @teknium1)
• Kanban: modal create-task dialog + editable board project directory; Done-card results made obvious; grab-to-pan board scrolling; attachment toolset + CLI with SSRF-guarded URL fetch; project directory captured at board creation (#66333, #63638, #60226, #65698, #63249 — @teknium1)
• Cron: durable execution audit history; one-shot stale-removal race fixed; run-claim TTL derived from HERMES_CRON_TIMEOUT (#61791 — @teknium1, #62014 — @PRATHAMESH75, #59567)
• mem0: self-hosted dashboard backend + recall tuning + setup-wizard mode (#56943, #60494 — @kshitijk4poor, @teknium1)
• Image gen: Codex image inputs; unsupported Codex image accounts classified; tool args recursively normalized by schema (cline port) (#57017, #63627, #52220 — @teknium1, @kshitijk4poor)

🔒 Security & Reliability

• Vertex: credential/project/region resolution through the profile secret scope; VERTEX_CREDENTIALS_PATH/GOOGLE_APPLICATION_CREDENTIALS stripped from subprocess env (#56680, #56582 — @srojk34)
• Six P1 hardening PRs salvaged in one pass — browser guards, MEDIA anchoring, .env lockdown, delegate ACP transport (#57660 — @teknium1)
• Media/vision/image-gen local-file reads routed through the shared credential-read guard; native image routing guarded by file-safety policy; unified image-source resolver + terminal-backend confinement (#58709, #58752, #57890 — @teknium1)
• Webhook body-cap sweep: explicit client_max_size on 3 uncapped aiohttp servers + completion sweep; Raft chunked-request body limit; timestamp-bound V2 webhook signatures (#59180, #59215, #58902, #58508 — @teknium1, @srojk34)
• Redaction: Fireworks token prefixes + Telegram transport errors; env-lookup false positives fixed for KEY=value and JSON/YAML config fields; bot tokens scrubbed from Telegram connect/send errors (#58501, #58534, #58915, #58893 — @teknium1)
• computer-use: subprocess env sanitized across all five cua-driver spawn sites (#58889, #59165 — @teknium1)
• Dashboard: managed-files credential guard widened past .env + dir-tree gap closed; OAuth token TOCTOU closed with atomic 0o600 writes; stale dashboards can't recreate deleted profiles (#58222 — @kshitijk4poor, #60236 — @teknium1, #49435 — @LeonSGP43)
• CI: untrusted refs passed through env, not run: interpolation; JS/TS tests wired into CI with source-regex tests banned; js-autofix pushes via PR instead of direct-to-main (#57842 — @jquesnelle, #60707, #65186 — @ethernet8023)
• Docker: terminal network toggle with full-path coverage; Git Bash Mandatory-ASLR install failures detected; Windows updater console hidden during handoff (#59149 — @teknium1, #64651, #66040 — @helix4u)
• Anthropic: request-local clients so the stale/interrupt watchdog never corrupts SQLite; per-profile OAuth file; OAuth login 429 fixed (UA must not be claude-code/) (#67238 — @OutThisLife, #59339, #58178 — @teknium1)
• Gateway/agent: tool_call_id deduplicated across pre-API sanitizers; background review inherits parent reasoning_config for Anthropic cache parity; /new memory extraction moved off the command path (#58350, #64379, #61139 — @teknium1, @kshitijk4poor)

🔁 Reverted in this window (for the record)

• iron-proxy credential-injection egress firewall (#30179 → reverted in #58489) — not shipping in this release
• dynamic-workflow orchestration skill (landed, then reverted) — not shipping
• memory provider-actions extension point (landed, then reverted) — not shipping
• Note: the plugin pre_tool_call approve escalation was reverted mid-window but re-landed in #60504 and ships in this release.

👥 Contributors

450+ people contributed to this release (via commits, co-author trailers, and salvaged PRs) — the biggest contributor window yet. Thank you, all of you.

Core team

• @teknium1 — release lead; TTFT perf wave, delivery + delegation durability, smart approvals, SecretSource, gateway multiplex + profile routing, sessions export, security round, and a ~290-PR community salvage burn
• @OutThisLife — desktop app (the speed wave, layout-tree shell, Capabilities page, session colors, vibe reactions, TUI incremental markdown, perf harness)
• @kshitijk4poor — GPT-5.6 end-to-end, DeepInfra + Upstage Solar providers, perf cluster, compression integrity, mem0, dashboard guards
• @ethernet8023 — CI overhaul (JS/TS tests wired in, autofix-via-PR, python speedups), desktop keybinds/worktrees/status indicators, full desktop TypeScript conversion
• @benbarclay — relay OIDC provisioning, gateway multiplex override, Nous auth self-heal, hosted MCP OAuth groundwork
• @alt-glitch — terminal billing (/subscription, /topup), desktop billing tab
• @helix4u — desktop provider/model UX, TUI model picker refresh, Windows install/updater hardening
• @austinpickett — desktop custom endpoint settings
• @SHL0MS — unbroker + unreal-mcp skills, humanizer expansion

Top community contributors

• @srojk34 — security hardening: Vertex credential/project/region scoping through the profile secret scope, subprocess env stripping, Raft chunked-request body limits
• @HexLab98 — 11 fixes across MCP capability gating, Windows installer PATH, desktop cron editing, gateway systemd warnings
• @UnathiCodex — desktop stability: zoom across display moves, LaTeX rendering, resume-stall and runtime-readiness fixes
• @xxxigm — <think> leak fix after thinking-only retry flush, dashboard auth/theme/PTY fixes
• @erosika — desktop declarative memory-provider panel + honcho recall/timeout correctness
• @Frowtek — credential security: master stores never mounted into skill sandboxes, live-transcript redaction, dashboard api_key precedence
• @necoweb3 — browser private-page CDP guard, cron one-shot liveness, gateway compression fail-closed
• @DavidMetcalfe — desktop updater version pill, Local/custom endpoint exposure, sidebar collapse behavior
• @shannonsands — dashboard: mobile channel setup, Discord toolsets from web UI, Telegram setup clarity
• @vishal-dharm — Gemini request-context improvements
• @PRATHAMESH75 — cron one-shot stale-removal race, dashboard multiplex port-binding guard
• @alelpoan, @embwl0x, @Adolanium, @giggling-ginger, @Drexuxux, @frizikk, @JoaoMarcos44, @wesleysimplici, @LeonSGP43, @pierrenode, @simpolism, @MorAlekss, @r266-tech, @WadydX, @nv-kasikritc — targeted fixes across desktop, TUI, gateway, cron, webhook, nix, and browser surfaces
• Salvaged-work authors whose PRs were cherry-picked with credit this window: @Burgunthy (profile routing), @web3blind (sessions export), @hwrdprkns (1Password), @Christopher-Schulze, @Ahmett101, @sjiangtao2024, and many more — see the salvage PR bodies for full attribution

All contributors

@0-CYBERDYNE-SYSTEMS-0, @0disoft, @0xbyt4, @100yenadmin, @17324393074, @2751738943, @8294, @abhibansal-sg,
@adambiggs, @Adolanium, @aeyeopsdev, @aguung, @AhmetArif0, @Ahmett101, @ai-ag2026, @AIalliAI, @ajzrva-sys,
@alastraz, @alelpoan, @alex-fireworks, @alex-heritier, @alex107ivanov, @AlexFucuson9, @Alix-007,
@allenliang2022, @Almurat123, @AlsayedHoota, @alt-glitch, @alvarosanchez, @amanning3390, @AmAzing129,
@AndreasHiltner, @andrewhomeyer, @annguyenNous, @ansel-f, @antydizajn, @arminanton, @arnispiekus, @asimons81,
@asscan, @ats3v, @austinlaw076, @austinpickett, @avifenesh, @aydnOktay, @Bartok9, @bautrey, @bbednarski9,
@bbopen, @benbarclay, @bigstar0920, @binhnt92, @bird, @Black0Fox0, @BlackishGreen33, @bo.fu, @brendandebeasi,
@briandevans, @BROCCOLO1D, @Bruce-anle, @brunz-me, @Burgunthy, @bytesnail, @catbearlove1-lang, @Cdddo,
@cgarwood82, @CharmingGroot, @chouqin, @Christopher-Schulze, @claudlos, @CocaKova, @Code-suphub, @CodeForgeNet,
@craigdfrench, @CrazyBoyM, @crazywriter1, @cresslank, @cruzanstx, @cyrkstudios, @danilofalcao,
@datachainsystems, @DatTheMaster, @davidb73-hub, @davidgut1982, @DavidMetcalfe, @davidrobertson,
@deacon-botdoctor, @DECK6, @deepujain, @derek2000139, @designnotdrum, @deusyu, @devatnull, @devorun,
@dexhunter, @dfein38347g, @Dhravya, @DictatorBacon, @digitalbase, @dlkakbs, @dmabry, @DNAlec, @dodo-reach,
@doncazper, @dorokuma, @doxe0x, @Drexuxux, @dschnurbusch, @Dusk1e, @EdderTalmor, @egilewski, @elashera,
@Elektrofussel, @eliteworkstation94-ai, @embwl0x, @emo-eth, @emozilla, @enzo-adami, @Epoxidex, @ErnestHysa,
@erosika, @esthonjr, @ethernet8023, @evefromwayback, @evelynburger, @F4TB0Yz, @falkoro, @fanyangCS, @firefly,
@fjlaowan1983, @flewe, @flo1t, @flow-digital-ny, @floze-the-genius, @frizikk, @Frowtek, @FuryMartin,
@fyzanshaik, @gauravsaxena1997, @geoffreybutler94, @georgedrury, @gigakun3030, @giggling-ginger,
@Git-on-my-level, @gitcommit90, @githubespresso407, @gnodet, @GottZ, @Gridzilla, @grimmjoww578, @gumclaw,
@Gutslabs, @HaiderSultanArc, @harjothkhara, @heathley, @hejuntt1014, @helix4u, @HeLLGURD, @hellno,
@herbalizer404, @HexLab98, @hmirin, @Hopfensaft, @Hotragn, @hsy5571616, @huanshan5195, @HumphreySun98,
@hwrdprkns, @hydracoco7, @hydraxman, @iamlukethedev, @iborazzi, @IgorGanapolsky, @iizotov, @ildunari,
@infinitycrew39, @IpastorSan, @irresi, @isfttr, @isheng-eqi, @itsflownium, @izumi0uu, @Jaaneek, @JacketPants,
@jaisup, @jakelongvu-bot, @jakepresent, @jaketracey, @JAlmanzarMint, @JasonFang1993, @jbbottoms, @jcjc81,
@JiaDe-Wu, @Jiahui-Gu, @Jigoooo, @jingsong-liu, @jneeee, @JoaoMarcos44, @joelbrilliant, @John-Lussier, @jplew,
@jtstothard, @juniperbevensee, @Jupiter363, @justinschille, @k4z4n0v4, @kaishi00, @karfly, @kartik-mem0,
@kavioavio, @KCAYAAI, @kenyonxu, @keslerm, @kevinrajaram, @knoal, @kocaemre, @kohoj, @konsisumer, @krowd3v,
@kshitijk4poor, @kuangmi-bit, @kubolko, @kyssta-exe, @Kyzcreig, @l0h1nth, @labsobsidian, @laurinaitis,
@LavyaTandel, @lawyer112, @lemonwan, @LeonSGP43, @lEWFkRAD, @linfeng961, @liuhao1024, @liuwei666888, @ljy-2000,
@loes5050, @logical-and, @LoicHmh, @loongfay, @lord-dubious, @lost9999, @lucasfdale, @lucaskvasirr,
@luxuguang-leo, @ly-wang19, @m0n5t3r, @m1qaweb, @M1racleShih, @MaartenDMT, @mahdiwafy, @MaheshBhushan,
@ManniBr, @marcelohildebrand, @marcolivierlavoie, @markoub, @MarkVLK, @Marxb85, @matantsevs,
@maxpetrusenkoagent, @mbac, @mdc2122, @mguttmann, @Mibayy, @michaelHMK, @mijanx, @minchang, @momomojo,
@MorAlekss, @morluto, @msh01, @mssteuer, @mvanhorn, @nanami7777777, @nankingjing, @necoweb3, @neo-claw-bot,
@neoguyverx, @nicha16, @nikshepsvn, @nima20002000, @nnnet, @NousResearch, @nullptr0807, @nv-kasikritc,
@okisdev, @OmarB97, @ooiuuii, @ooovenenoso, @oppih, @Osraka, @ostravajih, @otsune, @OutThisLife, @OYLFLMH,
@patrick-muller, @pdmartins, @pedrommaiaa, @Peterskaronis, @petrichor-op, @pgregg88, @pierrenode, @pixel4039,
@plcunha, @pnascimento9596, @Polyhistor, @PRATHAMESH75, @professorpalmer, @Punyko8, @Que0x, @Qwinty,
@r0gersm1th, @r266-tech, @rabadaki, @ragingbulld, @RainbowAndSun, @rainbowgore, @randimt, @rarf, @rasitakyol,
@rayjun, @raymondyan-zhijie, @re-ITRT, @RenoMG, @Rival, @RKelln, @rlaehddus302, @rob-maron, @rodboev,
@roryford, @rungmc357, @ruslanvasylev, @s0xn1ck, @s905060, @s96919, @sahibzada-allahyar, @sahil-shubham,
@Sahil-SS9, @SahilRakhaiya05, @sam7894604, @SAMBAS123, @samrusani, @sanidhyasin, @sasquatch9818, @sberan,
@ScotterMonk, @seagpt, @sebastianlutycz, @SemonCat, @setclock, @shannonsands, @sharziki, @shashwatgokhe,
@SHL0MS, @shuangxinniao, @SilentKnight87, @simplast, @simpolism, @SiteupAgencia, @sjiangtao2024, @sk-holmes,
@slow4cyl, @smtony, @soddy022, @Soju06, @solyanviktor-star, @SongotenU, @spiky02plateau, @sprmn24, @SquabbyZ,
@srojk34, @ssiweifnag, @stantheman0128, @StellarisW, @stephenschoettler, @suninrain086, @superposition,
@Supersynergy, @sweetcornna, @szafranski, @tanmayxchoudhary, @tarunravi, @tcconnally, @terry197913, @Thatgfsj,
@thegoodguysla, @thestudionorth, @TheTom, @TinkerOfThings, @tjboudreaux, @tjp2021, @Tortugasaur, @Tosko4,
@Tranquil-Flow, @trevorgordon981, @trismegistus-wanderer, @tt-a1i, @tuancookiez-hub, @TurgutKural, @Umi4Life,
@UnathiCodex, @unsupportedpastels, @uzaylisak, @valda, @vampyren, @veradim, @victor-kyriazakos, @virtualex-itv,
@vishal-dharm, @Vissirexa, @vizi0uz, @vkkong, @vKongv, @VolodymyrBg, @vortexopenclaw, @VrtxOmega, @WadydX,
@waroffchange, @waseemshahwan, @web3blind, @webtecnica, @wesleion, @wesleysimplicio, @williamumu,
@WilsonKinyua, @wxy-nlp, @wyuebei-cloud, @x7peeps, @x9x9x9x9x9x91, @xuezhaolan, @xxxigm, @ya-nsh, @yatesjalex,
@ygd58, @yingliang-zhang, @yinkev, @YLChen-007, @yu-xin-c, @yungchentang, @zapabob, @zccyman, @zeapsu,
@ziliangpeng, @zwcf5200, @zzpigpinggai

Also: bo.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.

Full Changelog: v2026.7.1...v2026.7.20

Features

• #660: expose context param on scenario.judge() public API (#667) (900f3d8)
• #666: per-role voice modality negotiation — declaration-first, two-phase validation, OTEL stamps (#670) (007a69f)
• events: support LANGWATCH_PROJECT_ID via X-Project-Id header (#619) (7aec1c7)
• tracing: stamp scenario SDK name+version as trace attributes (#744) (#745) (43dd4fa)
• voice: continuous ElevenLabs mic pump + is_connected guard (#740 slice A) (#741) (b6e7f93)
• voice: harvest voice result fields on exit paths + concrete typing (#740 slice E) (#742) (439b7be)
• voice: instrument base + ElevenLabs adapter with LangWatch spans (#777) (2b32872)
• voice: instrument Gemini Live adapter with LangWatch spans (#780) (8ba8687)
• voice: instrument OpenAI Realtime adapter with LangWatch spans (#782) (3152969)
• voice: instrument Pipecat adapter + background-loop spans (#774) (67f71b1)
• voice: instrument Pipecat adapter + background-loop spans (#781) (67f71b1)
• voice: instrument Twilio adapter with LangWatch spans (#788) (8747eed)
• voice: realtime_langwatch_session context manager for live OpenAI Realtime apps (#673) (#676) (e89d00c)

Bug Fixes

• #161: re-parse criteria when LLM returns stringified JSON dict (#552) (b819849)
• #488: log voice adapter and ffmpeg disconnect failures at WARNING (#556) (86bf466)
• #655: replace brittle judge criteria with generic behavioral criteria in audio examples (#679) (732d426)
• #664: transcribe agent turns at runtime so the voice user simulator can read them (#665) (4b99682)
• #695: twilio terminal sentinel on silent/tool-only stop (dead-recv-loop hang) (#697) (e675224)
• python: derive scenario.version from package metadata (#800) (0d505a7)
• security: bump pyjwt to 2.13.0 (#677) (00807b7)
• security: bump python/uv.lock security floors (cryptography, python-multipart, starlette, python-liquid, pydantic-settings) (#685) (ee9a5d5)
• security: raise esbuild, js-yaml, and dompurify override floors across JS workspaces (#671) (c76bab2)
• security: raise vite 8.x floor to >=8.0.16 across scenario workspaces (#709) (42d877e)
• voice/#491: diagnose + resolve multi-turn @e2e suite-wedge + tighten VAD tests (#694) (2dfc381)
• voice/#498: surface recv-loop termination as attributable PipecatRecvError (#692) (c1f552c)
• voice/#648: terminal drain on non-audio completion (EL + WebSocket) (#693) (c42320e)
• voice/#662: guard #662's response.create call sites against the active-response race (JS + PY) (#669) (0968374)
• voice/ts: explicit EL ConvAI turn-commit so scripted next-turn receive re-engages (#596) (795ae8e)
• voice: guard response.create on active response in recv_audio (#659) (5e844ea)
• voice: hosted ElevenLabs single-exchange ceiling — docs + enriched timeout error (#643) (aae16be)
• voice: skip Twilio e2e fixtures on absent env, not fail (#798) (4c883d0)
• voice: terminate wait=False test drain on end-of-turn, re-enable in CI (#691) (022056d)

Documentation

• voice: drop references to a docs/proposals tree that never landed (#613) (#823) (0ef4314)

After successful runs, clamp the proposal to the job bounds and persist an exact one-shot slot marker so maintenance preserves only that timestamp. Clear the marker on runs, edits, and schedule normalization; preserve existing no-proposal, skip, timeout, and error scheduling behavior.

Admin settings

Previously, admins could only enable or disable automatic note-taking for all meetings. Today, we’re starting to roll out a third option, which will allow admins to enable automatic note-taking only for meetings with three or more people.

Customers on Business Standard and Business Plus plans will soon see this setting turned ON by default; the setting will be OFF by default for customers on Enterprise Standard, Enterprise Plus, and Frontline Plus plans, as well as those with the Google AI Pro for Education add-on. There will be no impact to the end user experience on any plan before September 21, 2026. If you want to change your settings, you can do so in the Admin console.

If you currently participate in the Gemini Alpha program, and previously tested this setting, it may already be ON. Please review the current state of the settings for your organization in the Admin console before September 21, 2026.

End user settings

We’re also introducing a way for end users to enable note-taking only for meetings with three or more participants. Once the “For all meetings I host with 3+ guests” option rolls out, users should revisit their settings to confirm they’re configured as desired. This option will become available no sooner than September 21; stay tuned to the Workspace Updates blog to be notified when that rollout starts.

Getting started

• Admins for Business Standard and Business Plus organizations: This new setting is ON by default. If you wish to change your settings, you should do so before September 21, 2026, to avoid any impact for end users. Visit the Help Center for more information on how to adjust these settings.
• Admins for Enterprise Standard, Enterprise Plus, Frontline Plus, and Google AI Pro for Education organizations: This new setting is OFF by default. You can adjust this setting in the Admin console once it appears, but it will not affect the end user experience before September 21, 2026. Visit the Help Center for more information on how to adjust these settings.
• Admins for organizations participating in Gemini Alpha program: Your settings may be impacted by previous configurations. Please review your settings before September 21, 2026, to ensure they’re configured correctly.
• End users on all plans: The default set by admins will go into effect for end users no sooner than September 21, 2026. Users can override this default in the Meet user settings after that date. Visit the Help Center to learn more about Meeting settings for “take notes for me”.

Rollout pace

Admin setting

• Rapid Release and Scheduled Release domains: Rolling out now, with expected completion by August 3, 2026

End user setting

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting no sooner than September 21, 2026*

*We’ll post an update on the Workspace Updates blog when the end user setting rollout begins.

Availability

• Business: Business Standard and Plus
• Enterprise: Enterprise Standard and Plus
• Other Editions: Frontline Plus
• Education Add-ons: Google AI Pro for Education

Resources

• Google Meet Help: Take notes for me in Google Meet
• Google Workspace Updates  Blog: New user controls for Take notes for me

• Added --forward-subagent-text flag and CLAUDE_CODE_FORWARD_SUBAGENT_TEXT environment variable to include subagent text and thinking in stream-json output
• Fixed permission previews relayed to chat channels not neutralizing bidirectional-override, zero-width, and look-alike quote characters, so tool inputs cannot visually alter the approval message
• Fixed auto mode overriding a PreToolUse hook's ask decision for unsandboxed Bash — a hook ask now floors the decision at a prompt
• Fixed parallel Claude Code sessions all logging out simultaneously after wake-from-sleep when many sessions share one credential store
• Fixed plugin MCP servers not reconnecting after an idle web session woke, leaving MCP calls failing until the next message
• Fixed Claude Code on Vertex and Bedrock attempting the default Opus model at startup and printing a spurious fallback notice when a model is explicitly configured
• Fixed subagents spawned with an explicit model override reverting to the parent's model when resumed or sent a follow-up message
• Fixed nested .claude/rules/*.md files loading even when setting sources exclude project settings
• Fixed file upload validation: filenames ending in a DOS device suffix (.prn) or trailing dot are now accepted, and files with multiple hard links are refused
• Fixed file uploads to Claude in Chrome from remote and CLI sessions
• Fixed edits that leave the input as "?" being silently swallowed and toggling the shortcuts panel
• Fixed a startup hang when the Claude in Chrome extension is enabled but Chrome is not running
• Fixed a 300ms delay revealing async content (Settings tabs, Stats, diff views, and other loading states)
• Fixed reopening a just-stopped background session from the agents view starting a blank conversation under the same session id
• Fixed /loop hiding the session from /resume after a single use
• Fixed screen reader users losing the audible terminal bell after /terminal-setup or onboarding terminal setup
• Fixed background jobs on LLM gateway auth (ANTHROPIC_AUTH_TOKEN + ANTHROPIC_BASE_URL) coming back "Not logged in" after the daemon respawns them
• Fixed claude agents jobs becoming permanently undeletable when git no longer recognizes their worktree — the row now shows why the delete was refused instead of silently reappearing
• Fixed /clear not resetting the session cost counter — the statusline's cost now starts at $0 after /clear
• Fixed Claude in Chrome setup pages failing to open in the browser on Windows
• Fixed headless print-mode sessions on Windows crashing or silently exiting when stdin is unreadable
• Fixed background session titles in the agents view showing the naming model's refusal text when the prompt contains a link
• Fixed background agents killed by the user auto-respawning, and revived agents re-running stale prompts from old sessions
• Fixed routines with no schedule reporting a next run time in the year 1
• Hardened synced skill/plugin directory naming on Windows and kept CCR web fetch/search proxies working after /clear
• Improved terminal layout and rendering performance
• Improved background agent result reporting — Claude now reports the status of still-running agents and waits for the real completion instead of fabricating results
• Improved the memory index over-limit warning to measure only loaded content, excluding frontmatter and HTML comments
• Updated integer environment variables (timeouts, token budgets, retry counts) to accept scientific notation and digit-separator spellings like 1e6 and 64_000
• Updated documentation links to the current docs sites
• Changed "always allow" permission rules to save at the repository root, so approvals granted in a git worktree persist across sessions and worktrees
• Changed /usage-credits to ask for confirmation before sending a request to organization admins
• Changed Vim mode s and S (substitute char/line) to work in NORMAL mode, matching vim behavior
• [VSCode] Updated the Remote Control banner to describe what it does
• Claude in Chrome: hardened file-upload path validation
• Claude in Chrome: save_to_disk on screenshot actions now writes the image to disk and returns the path; previously it did nothing
• Fixed a prompt-caching regression on Bedrock, Vertex, Mantle, and Foundry that billed the trailing system context block as fresh input tokens on every request.

Today, Thinking Machines—the highly capitalized American AI startup founded by former OpenAI CTO Mira Murati—released Inkling, its first major language model under an enterprise-friendly Apache 2.0 open source license, and it boasts high, if sub state-of-the-art, performance for open weights models on third-party benchmarks, specifically software engineering (77.6% on SWE-bench Verified, where it beats fellow U.S. open rival Nvidia Nemotron 3's 71.9%) and voice understanding (91.4% on VoiceBench compared to 94.4% for Gemini 3.1 Pro on high reasoning effort).

Another differentiator: Thinking Machines notes that Inkling was designed "to answer directly on topics that may be subject to censorship," offering enterprises concerned about factual outputs, irrespective of controversy or sensitivity, a more trustworthy option.

Coming in at 975 billion total parameters, Inkling is a natively multimodal, open-weights Mixture-of-Experts (MoE) system capable of reasoning across text, images, and audio. The weights are already available on Hugging Face and the company's own model training application programming interface (API), Tinker.

Designed to balance cost against performance through a novel "controllable thinking effort" mechanism, the model represents a significant departure from the black-box scaling strategies of frontier competitors.

Alongside the flagship model, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.

Benchmarks Show a Powerful, High-End, Sub State-of-the-Art Model

While Inkling is a formidable multimodal engine, it lands in a fiercely competitive 2026 open-weight landscape characterized by highly specialized MoE architectures. Rather than attempting to dominate every leaderboard, Thinking Machines explicitly designed Inkling—with 975 billion total and 41 billion active parameters—as a broad, balanced generalist.

For example, it comes in near the middle high-end of benchmark performance 1257 on Design Arena’s Agentic Web Dev leaderboard measuring human scores of frontend web design.

But China’s leading AI labs have produced models with elite reasoning and coding capabilities, posing a stiff challenge to Inkling's generalist approach and ultimately outperforming it on general and coding benchmarks.

• GLM 5.2: Widely considered the top open-weight reasoning model available in the benchmark set, GLM 5.2 outperforms Inkling on pure coding, agentic, and complex reasoning tasks. It scores 62.1% on SWEBench Pro (Public) compared to Inkling’s 54.3%, and a massive 82.7 on Terminal Bench 2.1 against Inkling’s 63.8. GLM 5.2 also holds the edge in text-only reasoning, scoring 40.1% on HLE (text only) versus Inkling's 30.0%.

• DeepSeek V4 Pro: DeepSeek maintains an edge in several strict coding and factuality domains, beating Inkling on SWEBench Verified (80.6% vs. 77.6%) and SimpleQA Verified (57.0% vs. 43.9%). However, Inkling successfully overtakes DeepSeek V4 Pro in mathematical problem-solving, achieving 97.1% on AIME 2026 compared to DeepSeek's 96.7%.

• Kimi K2.6: This model outpaces Inkling across multiple technical benchmarks, delivering higher scores on GPQA Diamond (91.1% vs. 87.9%), BrowseComp (83.2% vs. 77.1%), and HLE with tools (54.0% vs. 46.0%). Yet Inkling proves more resilient on general chat instruction following, scoring 79.8% on IFBench compared to Kimi K2.6's 76.0%.

Against its primary U.S.-based open-weight competition, Inkling demonstrates strong parity and frequent superiority.

• Nemotron 3 Ultra: Inkling consistently outperforms this U.S. rival across reasoning and coding. Inkling posts 97.1% on AIME 2026 and 77.6% on SWEBench Verified, beating Nemotron's 94.2% and 70.7%, respectively. Furthermore, Inkling significantly leads in agentic workflows, scoring 74.1% on MCP Atlas against Nemotron's 44.7%.

When compared to closed-source juggernauts like Claude Fable 5, GPT 5.6 Sol, and Gemini 3.1 Pro, Inkling trails in peak reasoning and software engineering autonomy, but remains highly competitive in multimodality.

• Coding and Reasoning: Closed models maintain a commanding lead. Claude Fable 5 (max) hits 95.0% on SWEBench Verified and 53.3% on HLE (text only), far outpacing Inkling's 77.6% and 30.0%. GPT 5.6 Sol dominates Terminal Bench 2.1 with an 89.5, easily clearing Inkling's 63.8.

• Native Multimodality: Inkling's native visual and audio capabilities hold their own. On the MMMU Pro (Standard 10) vision benchmark, Inkling's 73.3% is competitive, though trailing Claude Fable 5's 84.2% and GPT 5.6 Sol's 83.0%. In audio processing, Inkling scores a highly respectable 77.2% on MMAU, keeping it within striking distance of Gemini 3.1 Pro's 82.5%.

If an enterprise workflow demands elite software engineering autonomy or the highest bounds of text-only reasoning, models like GLM 5.2 or proprietary systems like Claude Fable 5 maintain the edge.

However, Inkling carves out a unique and highly defensible position: it is the most capable open-weight foundation model that natively fuses text, vision, and audio, while simultaneously offering developers direct programmatic control over the cost-to-performance ratio.

The Shift from Static Reasoning to Controllable Thinking

Rather than attempting to build a singular "god model" optimized strictly for state-of-the-art benchmark domination, Thinking Machines engineered Inkling for adaptability and efficiency in real-world workflows.

The standout feature of this release is Inkling's "controllable thinking effort." Developers can programmatically adjust the model's reasoning budget—scaling from 0.2 to 0.99—to dictate how hard the AI should "think" before generating an output.

As the company noted, "Inkling's continuous thinking effort lets you pick your point on the cost/performance curve—reaching the same score with a fraction of the tokens".

In practical terms, this allows enterprises to deploy Inkling with lower token expenditure for simpler tasks, while cranking up the compute overhead for complex, multi-step reasoning challenges. However, by keeping the thinking effort lower and generating fewer tokens, the cost-conscious enterprise can achieve high quality results and performance on simple tasks while spending less money, or, in the case of those running models locally, less costs on energy and compute resources.

During the model’s large-scale reinforcement learning (RL) training over 30 million rollouts, researchers observed an emergent phenomenon they called "chain of thought condensation". Over time, Inkling naturally learned to compress its internal reasoning steps—dropping grammatical overhead and connectives—while reaching the same accurate conclusions, resulting in drastically reduced latency.

Epistemics and Censorship Resistance

A notable element of Thinking Machines' release is its explicit focus on the model's epistemics—specifically its calibration, instruction following, and resistance to censorship.

In an ecosystem where open-weight models adopt either overly restrictive safety guardrails or echo state-aligned ideological talking points, Inkling was intentionally trained to answer directly on politically sensitive or heavily censored topics.

To validate this approach, Thinking Machines submitted Inkling to the Propaganda and Censorship Eval developed by AI startup Cognition. According to the published findings, Inkling demonstrated "strong patterns of censorship non-compliance," effectively resisting ideological capture or boilerplate refusals when presented with sensitive subjects.

Despite its resistance to censorship, the model maintains a robust defense against genuinely malicious, dangerous, or illegal queries. On the StrongREJECT benchmark—which tests responses to unambiguous harmful requests—Inkling scored 98.6%, placing it in line with strict frontier safety standards. Furthermore, on the FORTRESS benchmark, Inkling successfully navigated the line between safety and over-refusal: it achieved a 78.0% refusal rate on adversarial queries (such as those involving weapons, cyberattacks, or violence) while maintaining a 95.9% compliance rate on benign, look-alike queries.

Thinking Machines noted that typical open-weight vulnerabilities remain within the architecture. Internal safety evaluations revealed an "occasional tendency to comply with role-play and indirectly framed prompts concerning harmful topics". The company advised enterprise developers to treat the model's built-in refusals as just one layer of security, recommending the downstream deployment of external moderation tools—such as Llama Guard—to filter adversarial jailbreaks and enforce use-case-specific safety policies at the application level.

Under the Hood: Architecture and Multimodality

Inkling's scale is staggering, yet sparse. The MoE architecture features 975 billion total parameters, but only 41 billion parameters are active during any given token generation. It supports a massive context window of 1 million tokens and diverges from typical transformer models by using relative positional embeddings instead of the industry-standard Rotary Positional Embedding (RoPE).

True to the company's foundational vision, Inkling was trained from scratch to be natively multimodal. Unlike models that rely on bolted-on external encoders, Inkling uses an encoder-free early fusion approach. It directly ingests audio as discrete dMel spectrograms and visual data as 40x40 pixel patches via a hierarchical multi-layer perceptron (hMLP), projecting all modalities into a shared hidden space.

Licensing: True Open-Source for the Enterprise

For enterprise IT teams and developers, the most disruptive aspect of Inkling may be its licensing. Inkling is released under the permissive Apache 2.0 license.

In an ecosystem where many so-called "open" models from Western labs are tethered to dual-use commercial licenses, acceptable use restrictions, or revenue caps, an Apache 2.0 designation makes Inkling a true open-source foundation. This gives developers the legal freedom to download, modify, integrate, and commercialize the model weights entirely royalty-free.

The model is readily deployable across major open-source inference libraries—including SGLang, vLLM, TokenSpeed, and llama.cpp—and comes with a native NVFP4 quantized checkpoint optimized for NVIDIA Blackwell systems.

Community Reactions: The Engineering Feat

The AI community's response has been swift, praising both the model's openness and the underlying engineering execution.

In a post on X, Thinking Machines co-founder John Schulman reflected on the rapid development cycle: "Inkling is out today, with open weights and in Tinker. It's been fun to watch this one come together: pretraining began last winter, and starting in mid-January a small team built up the coding, reasoning, and agentic training from there. We learned a lot building it, and I hope people find good uses for it."

Horace He, a researcher at Thinking Machines (previously from PyTorch), underscored the difficulty of the task in another post on X: "It truly takes a village to release a model, perhaps especially an open weights model. Actually doing the entire process from scratch, from data to pretraining to posttraining to actual release, gives a lot of appreciation for anyone who does it!"

The broader open-source ecosystem has also embraced the technical integrations. Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shared his enthusiasm regarding the model's optimization in his own X post: "One thing I find quite striking is how much easier accelerating models has become... We replaced the model's causal Conv1D with the `causal-conv1d` kernel. One line changed, +4% tokens per second. We then replaced its attention implementation with FlashAttention-4. Another single change, another +11%. That's a total throughput improvement of about 15%, without changing the model architecture or retraining anything."

Tiezhen Wang, an ecosystem growth expert and ex-Googler, celebrated the release as a massive win for the open-source community, listing the model's impressive specifications on X, highlighting its "975B total, 41B active" size, "Native MTP support," and the highly coveted "Apache 2.0 license."

Background: The Road to Inkling

To understand the significance of Inkling, one has to look back at the rapid trajectory of Thinking Machines over the past 18 months.

When Mira Murati departed OpenAI in late 2024 to found Thinking Machines alongside industry veterans like John Schulman and Barret Zoph, the stated goal was to pivot away from building isolated autonomous agents. Instead, the company aimed to build flexible, multimodal systems designed for genuine human-AI collaboration and open science.

By July 2025, the startup had secured a historic $2 billion seed round led by Andreessen Horowitz at a $12 billion valuation. At the time, Murati promised the impending release of a product with a "significant open source component" to empower researchers and startups.

The company’s philosophy began coming into sharper focus in October 2025 with the launch of Tinker, a Python-based API for large language model fine-tuning that gave researchers granular control over training pipelines without the friction of distributed compute management.

That same month, Thinking Machines researcher Rafael Rafailov delivered a provocative critique of the AI industry at TED AI. He argued that the current trajectory of simply throwing more compute at models was fundamentally flawed, noting that today's systems take shortcuts—like wrapping code in try/except blocks—because they are trained strictly for task completion rather than genuine learning.

Rafailov posited that the first artificial superintelligence would not be a "god model," but rather a "superhuman learner" capable of meta-learning and internalizing abstractions. Inkling’s architecture—specifically its controllable thinking effort and its ability to organically compress its chain of thought during RL—feels like the first tangible realization of Rafailov's thesis.

In May 2026, the lab teased its technical prowess with the research preview of TML-Interaction-Small, a system that eliminated "turn-based" chat by processing inputs and outputs simultaneously in 200ms chunks. This "full-duplex" breakthrough proved the company could build highly responsive, natively multimodal models from scratch.

Now, with Inkling out in the wild, Thinking Machines has delivered on its foundational promises. By offering a massive, natively multimodal model under a true open-source license, they aren't just giving developers a new tool—they are attempting to fundamentally rewrite the economics and accessibility of frontier AI development.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 6: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 7: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 8: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing on model-provider platforms — Anthropic’s Claude leads at 40% — chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed “agents” are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The question for subsequent waves is whether the deployed reality closes the gap on the ambition — or whether the chatbot trap proves stickier than the roadmap assumes.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

Previously, teams using Google Chat could only create 1:1 conversations with external users, or create a space in Chat with external access enabled.

With this update, it’s now possible to create group conversations with multiple external users, reducing friction and streamlining external collaboration. No additional configuration is required, and access for external users is enabled once they accept an invitation through an email notification. Group conversations with external members are labeled with a visible badge to help ensure that users are aware of the context for all conversations in Chat.

Getting started

• Admins: External group DMs share the same admin controls as external spaces. Ensure that the External spaces & group direct messages admin setting is enabled for your organization to allow users to create or join external group DMs.
• End users: If allowed by their admin, end users can add external users when they create a new group DM. External users can’t be added to existing group DMs that only have internal users. External users who are added to group DMs will receive an email invitation and must accept the invitation to participate in the group conversation.

Rollout pace

• Rapid Release domains: Rolling out now, with expected completion by July 17, 2026
• Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 24, 2026 

Availability

• Available to all Google Workspace customers and Workspace Individual subscribers

Resources

• Google Chat Help: External group DMs in Google Chat
• Google Workspace Admin Help: Control external Chat & spaces chat options

Whether you need to add a missing detail to the second line or include a deadline for your request, simply type the instruction and Gmail will instantly update the draft for you.

Getting started

• Admins: This feature is available by default if both Gemini for Workspace in Gmail is enabled and Workspace Intelligence access to Gmail is enabled.
• End users: This feature is available by default. Visit the Help Center article to learn more about drafting emails with Gemini in Gmail.

Rollout pace

• Rapid Release and Scheduled Release domains: Rolling out now, with expected completion by July 20, 2026

Availability

• Business: Business Starter, Standard, and Plus
• Enterprise: Enterprise Starter, Standard, and Plus
• Consumer: Google AI Plus, Pro, and Ultra
• Other Editions: Frontline Plus
• Education Add-ons: Google AI Pro for Education
• Other Add-ons: AI Expanded Access

Resources

• Gmail Help: Draft emails with Gemini in Gmail

The post Greenhat Announces Successful Delegation at Web Summit Vancouver 2026 appeared first on Cyber Security News.

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• Added screen reader mode: opt-in plain-text rendering for screen reader users. Run claude --ax-screen-reader, set CLAUDE_AX_SCREEN_READER=1, or add "axScreenReader": true to settings.
• Added vimInsertModeRemaps setting: map two-key insert-mode sequences like jj to Escape in vim mode
• Added CLAUDE_CODE_PROCESS_WRAPPER: agent view and the background service now honor a corporate launcher by running every Claude Code self-spawn through a required wrapper executable
• Added mouse-click support for multi-select menus and "Other" input rows in fullscreen mode
• Fixed fast mode staying off after switching back to a model that supports it — it now restores automatically when enabled in settings
• Fixed replies typed to a background agent being lost when delivery fails — the text is now saved and delivered when the session restarts
• Fixed background-session attach failing permanently ("Couldn't start the background daemon") after an update replaced the binary a running claude agents process was launched from
• Fixed the context window (and auto-compact indicator) briefly resetting to 200k after the CLI auto-updates, causing a false "100% context used" when resuming long-context sessions
• Fixed supervised and background sessions crashing when a server closed an HTTP/2 connection with a GOAWAY while requests were in flight
• Fixed truncated stream-json/JSON output and missing result message when piping large responses from claude -p
• Fixed CLAUDE_CODE_MAX_OUTPUT_TOKENS and similar env vars silently using the mantissa of scientific-notation values (1e6 became 1)
• Fixed very large markdown tables stalling rendering or using excessive memory; tables over 200 rows show the first 200 with a "… N more rows" notice
• Fixed the Edit tool failing on files modified after reading when the target text still matches uniquely
• Fixed Read reporting empty files as "shorter than offset", Grep silently returning "No files found" for invalid regex patterns, Grep count mode under-reporting totals when paginated, and Glob crashing with an unclear error when the pattern, path, or working directory contained a null byte
• Fixed apiKeyHelper script failures being hidden behind a generic 401 after ~10 silent retries; the script's own error is now shown within 3 attempts
• Fixed Bedrock streaming requests failing with a misleading "Truncated event message received" when a gateway transforms the response — the error now names the content-type and points at the proxy
• Fixed /upgrade showing a login flow instead of the upgrade URL when the browser fails to open
• Fixed stream-json input killing the session on blank CRLF or whitespace-only lines from Windows-style SDK hosts
• Fixed headless stream-json sessions hanging permanently when a control_request carried a non-string set_model payload; the CLI now answers with an error response
• Fixed repeated "No completion record was found" notices on session resume — orphaned background tasks now collapse into a single summary
• Fixed Remote Control clients attaching to a terminal-hosted session not seeing background agents and workflow progress until a task started or stopped
• Fixed the Agent tool launching with no tools when a subagent's tools list resolves to nothing — it now returns a clear error naming the unrecognized entries
• Fixed /usage showing stale cached bars over fresher data, and /mcp not reclassifying placeholder servers after config edits
• Fixed "Change directory" in SDK hosts (e.g. Claude Desktop) failing with "A turn is in progress" on idle sessions that have a running background task
• Fixed the workflow save dialog showing ~/.claude/workflows/ instead of the CLAUDE_CONFIG_DIR location for user-scope saves
• Fixed /release-notes adding the viewed notes to the model's context — "Show all" previously injected the entire changelog into every subsequent request
• Fixed a memory leak in the agent view where pasted images were retained for the screen's lifetime after sending peek replies
• Fixed SDK sessions losing agents defined via the initialize request when a plugin refresh ran before the client attached
• Fixed several memory leaks in long sessions: MCP stdio server stderr accumulating up to 64 MB per server, LSP documents staying open indefinitely (now LRU with 50-doc cap), async hook output retained after backgrounding, and unbounded growth in headless/SDK sessions from large tool-result payloads
• Fixed a memory blowup when reading files with extremely long single lines using offset/limit — the read now returns a clean error instead of loading the whole line
• Fixed multi-second per-turn slowdowns in sessions with many permission deny/ask rules — rule matchers are now compiled once and cached
• Improved input responsiveness while agent task lists update — task updates no longer re-render the entire UI
• Reduced per-tool-call CPU overhead in print/SDK sessions with many MCP tools by caching tool-pool assembly (up to 7x faster tool rounds at high tool counts)
• Reduced memory usage by bounding the file edit read cache to 16 MB instead of pinning up to 1,000 full files
• Reduced session transcript size (up to 79x in edit-heavy sessions) and bounded checkpoint disk usage by pruning superseded file-history backups
• Reduced memory usage when resuming sessions with background agents or forks spawned from large conversations
• Completed background agents now stay listed in /tasks until cleanup instead of vanishing the moment they finish
• Attaching to a stopped background agent now shows its transcript immediately while the session warms up, instead of a blank "Session is starting" screen
• Background sessions: an older daemon no longer silently restarts workers spawned by a newer version onto the older binary
• Agent view: Ctrl+X now deletes renamed-branch worktrees, never destroys unpushed commits, keeps the session row when a worktree is kept, and reused worktree names reset to the current base
• Catastrophic removals (e.g. rm -rf ~) in commands containing $(…)/backticks/<(…) now prompt in --dangerously-skip-permissions and auto mode, matching the plain form
• /install-github-app and the /mcp settings menu no longer open in background sessions
• MCP servers configured with an empty URL now show as "not configured" in /mcp instead of a config error
• /usage now shows your last-known usage bars with an "as of" note when the usage endpoint is rate-limited, instead of an error screen
• Fixed Bedrock auth failing with "Session token not found or invalid" for AWS SSO profiles whose sso_region differs from the Bedrock region (2.1.207 regression)

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. 

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

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

Official

• Maintainer spotlight: Gen Li (@rami3l)
• Together for a healthier Clippy

Newsletters

• The Embedded Rustacean Issue #75

Project/Tooling Updates

• copper-rs v1.0.0: the open source deterministic robotics OS is now stable.
• Rayfish: Your own private network. No servers, no setup.
• rama v0.3.0 — network service framework ready to be used by the wider Rust community
• kache 0.9.0: supply-chain hardening + read-only CI cache
• GuardianDB - PostgreSQL and P2P/Local-First Together
• Nectar: a Rust-like language that compiles your whole web app to WebAssembly
• logdrain: Fast, Embeddable Log-Template Mining in Rust
• sheathe: Packaging the World's Video in Pure Rust
• wickra: streaming-first technical indicators
• Xcelerator Solver v0.1.0 -- deterministic symbolic regression
• dlt-tui 1.1.0 - a fast TUI viewer for automotive DLT (AUTOSAR Diagnostic Log and Trace) files
• RSSH v0.2.11 — terminal workflows, safer SSH key import, and observable AI ops
• k8s-scale-app-rs: Scale or Restart a Kubernetes Deployment from a CronJob
• M-vis v0.5.0-rc1 update
• FlareDB: An Apache Beam Native Streaming Database built in Rust
• mqtt-typed-client 0.2: a type-safe async MQTT client on rumqttc
• RootAsRole: v4.0.0 Major release, secure execution, new logo
• A Cross-Platform Rust UI Framework via Qt’s Bridging Technology
• Jam Programming Language
• Sōzu 2.1.0: UDP load balancing for the programmable edge
• b0nker: a minimal container runtime written in Rust

Observations/Thoughts

• [video] Rust Berlin Meetup 25/06/2026 Livestream
• [video] How do you rewrite C/C++ projects to Rust? – JetBrains interview with Luca Palmieri, Mainmatter
• Investigating why RustCrypto is slow: Deep dive into SIMD instructions and hardware acceleration
• bool as u32
• A Rust-to-Lean Verification Pipeline with AI Provers: An Experience Report
• Work In Progress Rust
• [video] OpenAI just spent $600k on Rust
• [audio] Rising Academies with Dylan Brown - Rust in Production Podcast

Rust Walkthroughs

• [series] Bevy Tutorial: Build Your First 3D Editor - Create a 3D Space on an Infinite Grid
• Learn Axum Basics and Routing by Building a URL Shortener
• [series] Rama 101.1: HTTPS clients and layers of abstraction

Miscellaneous

• Clickable euler diagram of all the Rust week talks

Crate of the Week

This week's crate is apis-saltans, a Zigbee implementation including a coordinator API.

Thanks to Richard Neumann for the self-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.

* Protocol - Extend bit-exactness tests to f64 reconstruction targets
* Dofigen - No image tag replacement flag for the generate command

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.

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

598 pull requests were merged in the last week

Compiler

• enable eager param_env norm in new solver
• lint on core::ffi::c_void as a return type
• polish some macro parsing code
• resolve: no allocation in resolve_ident_in(_local)_module_*
• simplify option-iterator flattening in the compiler
• stabilize #[my_macro] mod foo; (part of proc_macro_hygiene)

Library

• add std::io::cursor::WriteThroughCursor
• implement Box::as_non_null()
• implement DoubleEndedIterator::next_chunk_back
• implement IntoIterator for [&[mut]] Box<[T; N], A>
• implement ptr::{read,write}_unaligned via repr(packed)
• move SizeHint and IoHandle to core::io
• move std::io::Seek to core::io
• optimize ArrayChunks::try_rfold with DoubleEndedIterator::next_chunk_back
• stabilize feature(atomic_from_mut)

Cargo

• bindeps: register transitive artifact targets
• avoid cloning parsed TOML manifest in ManifestErrorContext
• avoid extra clone of parsed TOML manifest
• remove unneeded cloning when parsing package index
• change HashMaps and HashSets in Cargo to use Fxhasher
• do not pass lint rustflags when --cap-lints=allow is set
• fixed Compilation::deps_output only taking the last dep
• pre-allocate a few vectors
• stabilize build-dir layout v2
• use a set when checking visited workspace members

Rustdoc

• fix crash when trying to inline foreign item which cannot have attributes
• show use-site paths for unevaluated const array lengths

Clippy

• chunks_exact_to_as_chunks: Don't report expressions with const parameters
• chunks_exact_to_as_chunks: Don't report expressions with type params
• missing_trait_methods: MSRV/unstable awareness
• vec_init_then_push: don't lint pushes from a macro expansion
• inline_modules: ignore cfg(test) modules in test builds
• match_same_arms: keep arm-level expectations working under an outer allow
• unnecessary_operation: avoid bad ! suggestions
• unnecessary_unwrap_unchecked: don't trigger inside the _unchecked fn
• add required parentheses when the needless_bool suggestion is an operand
• fix ICE when resolving local in unnecessary_unwrap_unchecked
• fix infinite_loop false positive inside gen blocks
• fix manual_c_str_literals suggestion when the trailing backslash is escaped
• fix strlen_on_c_strings incorrect suggestion logic
• fix suspicious_operation_groupings duplications
• lint bit width
• optimize Msrv::meets calls
• bail out of unicode lint scans when the snippet is pure ASCII
• skip the HIR parent walk in is_in_test_function when there are no test items
• place generated impl block after the existing impl block
• refactor StringAdd lint pass
• refactor suspicious_xor_used_as_pow
• remove lower_ty in uninhabited_reference
• respect the configured MSRV in manual_is_variant_and's map() == Some(_) rewrite
• rewrite mut_mut
• rewrite redundant_else as a late pass
• rewrite tuple_array_conversions

Rust-Analyzer

• SCIP: exclude leading/trailing trivia in definition ranges
• SCIP: remove dead inlay_hints field
• feat(ide-diagnostics): add diagnostics for invalid union patterns (E0784)
• internal(query-group-macro): remove the arity test
• add tree top method to Syntax node
• add handler for E0627
• supports multi arms for replace_match_with_if_let
• fix UB in smol_str borsh_non_utf8 test cases
• fix generic param for generate_default_from_enum_variant
• walkthrough_create_project file not packaged
• assertion failure on closure with unbound function
• avoid panic in convert_tuple_struct_to_named_struct on nested pattern usage
• configuration syntax for nvim-lsp
• correct resolution to value when it shares the same name with type
• exclude impls on the error type from impl enumeration
• fix crash on extract_variable when selecting unresolved macro call
• fix crash on completion inside macros
• fix handling of params of coroutine fns
• handle more cases of cfgs in expr store lowering
• no generate with default assoc item
• panics in unwrap_return_type, remove_underscore, and promote_local_to_const
• hoist attribute qualifier segment collection
• reduce parser joint-token allocation
• project-model: don't pass metadata extra args to sysroot
• project-model: introduce cargo.configPath
• provide startup time to ready log point and associated benchmark

Rust Compiler Performance Triage

This week was dominated by wild swings in benchmarks of the new-solver, which is not enabled by default, yet. Apart from that, we got a very few notable changes, only one unexpected speedup from a bugfix in rustdoc.

Triage done by @panstromek. Revision range: 7dc2c162..3659db0d

Summary:

1 Regression, 1 Improvement, 4 Mixed; 3 of them in rollups 17 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:

• No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

Tracking Issues & PRs

• Lint against invalid POSIX symbol definitions
• Document NonNull layout guarantees
• Tracking Issue for slice_split_once

• Let the OS handle stack growth
• Add target_feature_available_at_call_site

• Empty repr(Rust) enums are ZSTs

No Items entered Final Comment Period this week for Cargo, Language Team, Leadership Council, Rust RFCs or Unsafe Code Guidelines.

New and Updated RFCs

• Update RFC template
• RFC: Store registry tokens in the OS credential store by default

Upcoming Events

Rusty Events between 2026-07-08 - 2026-08-05 🦀

Virtual

• 2026-07-08 | Virtual (Cardiff, GB) | Rust and C++ Cardiff
• Operating Systems Book Club: Introduction + Processes
• 2026-07-08 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-09 | Virtual (Nürnberg, DE) | Rust Nuremberg
• Rust Nürnberg online
• 2026-07-14 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Second Tuesday
• 2026-07-15 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-15 | Hybrid (Vancouver, BC, CA) | Vancouver Rust
• Jiff
• 2026-07-16 | Hybrid (Seattle, WA, US) | Seattle Rust User Group
• July, 2026 SRUG (Seattle Rust User Group) Meetup
• 2026-07-16 | Virtual (Berlin, DE) | Rust Berlin
• Rust Hack and Learn
• 2026-07-19 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Rust Deep Learning: Third Sunday
• 2026-07-21 | Virtual (London, UK) | Women in Rust
• Lunch & Learn: Learning Rust as First Programming Language
• 2026-07-21 | Virtual (Washington, DC, US) | Rust DC
• Mid-month Rustful
• 2026-07-22 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-28 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Fourth Tuesday
• 2026-07-29 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-30 | Virtual (Berlin, DE) | Rust Berlin
• Rust Hack and Learn
• 2026-08-02 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Rust Deep Learning: First Sunday
• 2026-08-04 | Virtual (London, GB) | Women in Rust
• 👋 Community Catch Up
• 2026-07-29 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-08-05 | Virtual (Indianapolis, IN, US) | Indy Rust
• Indy.rs - with Social Distancing

Asia

• 2026-07-18 | Bangalore, IN | Rust Bangalore
• July 2026 Rustacean Meetup

Africa:

• 2026-07-14 | Johannesburg, ZA | Johannesburg Rust Meetup
• Debugging a production grade Open Source Rust crate

Europe

• 2026-07-08 | Dublin, IE | Rust Dublin
• Join us live and INPERSON for Rust 262
• 2026-07-09 | Berlin, DE | Rust Berlin
• Rust Berlin on location 🏳️‍🌈 - Edition 015
• 2026-07-09 | Frankfurt, DE | Rust Rhein-Main
• Building Cross Platform Applications with Ply
• 2026-07-09 | Switzerland, CH | PostTenebrasLab
• Rust Meetup Geneva
• 2026-07-15 | Dortmund, DE | Rust Dortmund
• Teach and Hack at Projektspeicher
• 2026-07-21 | Leipzig, DE | Rust - Modern Systems Programming in Leipzig
• Supercharge Rust funcs with implicit arguments and context-generic programming
• 2026-07-23 | Berlin, DE | Rust Berlin
• Rust Berlin Talks: The next generation
• 2026-07-23 | London, UK | London Rust Project Group
• Rama modular service framework for Rust
• 2026-07-23 | Paris, FR | Rust Paris
• Rust meetup #87
• 2026-07-30 | Manchester, GB | Rust Manchester
• Rust Manchester July Code Night

North America

• 2026-07-09 | Lehi, UT, US | Utah Rust
• Utah Rust July Meetup
• 2026-07-09 | Mountain View, CA, US | Hacker Dojo
• RUST MEETUP at HACKER DOJO
• 2026-07-11 | Boston, MA, US | Boston Rust Meetup
• MIT Rust Lunch, July 11
• 2026-07-15 | Hybrid (Vancouver, BC, CA) | Vancouver Rust
• Jiff
• 2026-07-16 | Hybrid (Seattle, WA, US) | Seattle Rust User Group
• July, 2026 SRUG (Seattle Rust User Group) Meetup
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• North End Rust Lunch, July 18
• 2026-07-21 | San Francisco, CA, US | San Francisco Rust Study Group
• Rust Hacking in Person
• 2026-07-22 | Austin, TX, US | Rust ATX
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• 2026-07-22 | Los Angeles, CA, US | Rust Los Angeles
• Rust LA: Rust in Distributed Systems with Flight Science!
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• BOG-A-THON 2
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• Rust-Atl
• 2026-08-01 | Boston, MA, US | Boston Rust Meetup
• Chinatown Rust Lunch, Aug 1
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• Evening Boston Rust Meetup at Red Hat, Aug 4

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• 2026-07-09 | Brisbane City, QL, AU | Rust Brisbane
• Rust Brisbane • July 2026
• 2026-07-21 | Barton, AU | Canberra Rust User Group
• July Meetup
• 2026-07-23 | Perth, AU | Rust Perth Meetup Group
• Rust Perth: July Meetup!
• 2026-07-30 | Melbourne, AU | Rust Melbourne
• Rust Melbourne July 2026

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

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Quote of the Week

if a ptr is dereferenced in a forest and nobody hears it, is it sound?

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• extrawurst
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• mariannegoldin
• bdillo
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The post Envirotech Vehicles (NASDAQ: EVTV) Closes Merger with Azio AI Ahead of Schedule, Positioning Combined Company to Capture $487 Billion 2026 AI Infrastructure Opportunity appeared first on TechRepublic.

The rollout, which begins in beta with Max subscribers before expanding to additional plans, marks a strategic inflection for Anthropic. It transforms Cowork from a desktop-only agent into a cross-device platform where tasks can start on a laptop, continue autonomously in the background, and be reviewed from a phone — even after the user closes the app entirely.

"Your work goes everywhere with you, and keeps going without you," Anthropic writes in its announcement.

The timing is deliberate. Alongside the mobile launch, Anthropic published usage data from 1.2 million anonymized Claude Cowork sessions sampled between May 11 and May 31, drawn from more than 600,000 organizations. The data paints a striking picture: the overwhelming majority of what people do with Cowork has nothing to do with writing software.

The biggest AI story nobody's talking about

The numbers tell a story that cuts against the dominant narrative in enterprise AI, which has fixated on coding assistants and developer productivity as the primary use case for large language models.

Business process and operations — tasks like pulling scattered updates into a single report, building onboarding checklists, and reconciling spreadsheets — accounted for 33.4% of all sampled Cowork sessions, making it the single largest category by a wide margin. Content creation and copywriting — producing drafts, slide decks, posts, and proposals — came in second at 16.4%.

Together, those two categories make up roughly half of all Claude Cowork usage. Software development, by contrast, accounted for just 8.7%. DevOps and infrastructure followed at 7%, with research and intelligence at 6.4%, data analysis and business intelligence at 5.8%, document processing and extraction at 4.1%, and sales and revenue operations at 4%.

The remaining 12 categories each represented less than 4% of usage, including personal assistance at 3.8%, education at 2.4%, and meeting intelligence at 1.8%.

Anthropic describes these dominant use cases as "the work around the work" — tasks that span nearly every role in an organization but rarely appear in anyone's core job description. "People are using it for a variety of tasks that aren't necessarily the hallmark of a specific role, but instead represent the connective work around a role that moves projects forward and keeps businesses running," the company writes. "That means tasks like drafting a status update, building a slide deck, or condensing reams of research into a single report."

That phrase — "the work around the work" — is Anthropic's attempt to define and claim an entirely new category of AI productivity. It's a calculated reframing: rather than positioning AI as a tool that replaces what professionals do, Anthropic is arguing that the most valuable current application is handling everything professionals do around their actual expertise.

What mobile access changes — and what it doesn't

The expansion to mobile and web introduces three concrete capabilities that reflect how Anthropic envisions Cowork fitting into daily workflows.

First, sessions now sync across devices. A user can start a task at their desk, check on its progress from a phone, and retrieve the finished output from any device. Second — and arguably more significant — Cowork can now run tasks in the background with no device online at all. Users can schedule work for a specific time, and Claude will execute it autonomously. Anthropic offers the example of setting Monday morning client prep for 6 a.m.: "Claude works through the email threads, transcripts, and recent news, builds the briefing doc, and leaves the follow-up email drafted but unsent. Review it over coffee."

Third, when Claude encounters a decision that requires human judgment, it surfaces the question to the user's phone. "Nothing ships until you've reviewed and approved it," Anthropic states.

Desktop remains the most fully featured surface, with access to local files and the browser. But the web version also opens Cowork to users who cannot install a desktop application — a meaningful expansion in enterprise environments where IT departments control software installation.

The company also unified its interface: on web and desktop, chat and Cowork now share a single home screen, and projects and artifacts persist across both modes.

To encourage adoption, Anthropic is extending doubled Cowork usage limits through August 5.

The strategic logic: why Anthropic is chasing the non-developer

The usage data and the mobile launch together reveal a company executing a two-track strategy. Claude Code, its terminal-based coding agent, dominates among software developers. But Cowork is designed to capture the vastly larger population of professionals whose work involves creating, organizing, and communicating information rather than writing code.

The contrast between the two products is instructive. As Anthropic notes, Claude Code "is most often used by software developers for the key parts of their role: building, debugging, and shipping code." When developers do use Cowork, they tend to use it not for programming but for the communications-focused work that surrounds every role — status updates, documentation, and coordination.

This pattern — where AI handles the connective tissue of work rather than its core substance — aligns with what Anthropic describes as people using "Claude Cowork to assemble and structure the information they can use to act on their expertise." The company illustrates this with three examples: a lawyer using Cowork for document formatting and filing while reserving legal judgment for themselves, a hiring manager synthesizing interview feedback while spending more time on candidate conversations, and a team lead producing a slide deck that explains a decision while focusing on actually making that decision.

The implications for Anthropic's business model are significant. Developer-focused tools, while high-profile, serve a relatively narrow market. The Ramp AI Index published in May showed Anthropic pulling ahead of OpenAI in business adoption for the first time — with 34.4% of firms paying for Anthropic's services compared to OpenAI's 32.3% — and suggests the company's enterprise push is gaining traction. Claude Code was identified as the primary driver of that shift. But Cowork targets an addressable market that is orders of magnitude larger: every knowledge worker with a laptop, a pile of spreadsheets, and a slide deck due by Friday.

A crowded field gets more competitive

The mobile launch arrives during one of Anthropic's busiest — and most turbulent — stretches in its history.

Just last week, Anthropic launched Claude Sonnet 5, a new model that narrows the performance gap with its more expensive Opus-class models while maintaining lower pricing. The model is available at introductory pricing of $2 per million input tokens through August 31 before rising to $3 per million input tokens. Sonnet 5 serves as the engine underneath Cowork, and its improved agentic capabilities — better reasoning, tool use, and sustained task completion — directly enhance Cowork's ability to handle complex, multi-step workflows.

Two weeks before that, Anthropic released Claude Tag, a Slack-native AI agent designed for team collaboration. Where Cowork focuses on individual task delegation, Claude Tag operates as a multiplayer tool — a single Claude identity that everyone in a Slack channel can interact with, building context from conversations over time. 

According to Anthropic's announcement, 65% of the company's own product team's code is created by its internal version of Claude Tag. Fortune reported that Anthropic's head of product for Claude Code and Cowork, Cat Wu, described the distinction: "Claude Code, Cowork, and chat are very single-player, whereas Claude Tag is built to be interactive and multiplayer."

Together, Cowork and Claude Tag represent a pincer strategy: Cowork captures individual productivity workflows across devices, while Claude Tag embeds AI into team communication channels. Both are designed to push Anthropic deeper into enterprise operations, beyond the developer seat.

The security question looms

The expansion also arrives against a backdrop of unresolved security concerns. On July 1, security firm Armadin — led by Mandiant founder Kevin Mandia — published research detailing what it described as a full sandbox escape in Claude Cowork on Windows, as reported by SiliconANGLE. The attack chain involved DLL sideloading against the Claude desktop executable to gain trusted access to Cowork's virtual machine service, then exploiting undocumented parameters to achieve root access and bypass network restrictions.

Anthropic responded that the vulnerability did not qualify as a security issue because exploiting it requires an attacker to already have local code execution on the host machine. Armadin, however, raised a broader concern: that deploying local virtual machines on nontechnical users' systems creates visibility gaps that endpoint security products struggle to monitor.

This tension takes on new dimensions as Cowork moves to mobile and web. The web and mobile versions run tasks server-side rather than in a local virtual machine, which eliminates the specific attack surface Armadin identified but introduces different questions about data handling, especially for scheduled background tasks that process email threads, calendar data, and documents without real-time user oversight.

Anthropic's announcement states that "the decisions still come to you" and that nothing ships without review and approval. But as Cowork takes on increasingly complex autonomous workflows — processing contract folders, building client briefings from multiple data sources, drafting emails — the surface area for prompt injection and data exposure grows correspondingly. 

When Cowork first launched in January, TechCrunch reported that Anthropic explicitly warned about prompt injection risks, noting in its blog post: "These risks aren't new with Cowork, but it might be the first time you're using a more advanced tool that moves beyond a simple conversation."

As Anthropic courts enterprises, geopolitics complicates the pitch

Anthropic's enterprise push is also colliding with geopolitical reality. CNBC reported Monday that Alibaba will ban employees from using Anthropic's AI tools starting July 10, placing Claude Code on a high-risk software list. The move followed Anthropic's June letter to the U.S. Senate accusing Alibaba of carrying out what it called "the largest known distillation attack" against its models.

The Alibaba ban, combined with reports that Anthropic is closing loopholes that allowed Chinese companies to access Claude through third-country entities, underscores the increasingly fraught environment for AI companies attempting to serve global enterprise customers while navigating U.S. export and security restrictions.

At the same time, Anthropic is investing massively in infrastructure. Reuters reported Monday that Anthropic signed a $19 billion, 20-year lease with TeraWulf for a data center being built in Hawesville, Kentucky, with 401 megawatts of computing power expected to become fully operational in 2028.

That kind of capital commitment only makes sense if the company expects enterprise demand — not just from developers, but from the millions of knowledge workers that Cowork targets — to grow dramatically.

Anthropic's own usage report comes with notable blind spots

Anthropic is transparent about the limitations of its usage analysis. The taxonomy classifies sessions by the type of work being performed, not by the job title of the person doing it. 

There are no standalone categories for marketing, finance, or HR — functions that are likely absorbed into the dominant "business process and operations" bucket, which may partly explain why that category commands a third of all usage.

The sample is also rate-capped rather than proportional to traffic, meaning the numbers are shares of sampled sessions, not absolute volumes. Usage during peak hours is somewhat underrepresented. And roughly 5% of sampled sessions involved personal, non-work use — hobbies, personal assistance, and companionship-style conversations — meaning the data doesn't purely reflect workplace activity.

The company also acknowledged that its labeling pipeline changed around May 11, which is why the analysis window begins on that date rather than covering a longer period.

What Cowork's rise says about the future of enterprise AI

Anthropic's mobile launch and usage data arrive at a moment when the enterprise AI market is shifting from proof of concept to proof of value. The question facing every company deploying AI tools is no longer whether the technology works — but whether it delivers measurable productivity gains across an organization, not just within engineering teams.

The usage data suggests that the answer, at least for Cowork, is emerging in an unexpected place. It's not in the glamorous work of building software or conducting research. It's in the unglamorous, universal labor of turning messy information into structured outputs that move organizations forward — the status reports, the onboarding checklists, the variance memos, the client decks.

By untethering that capability from the desktop and making it available on every device, Anthropic is betting that the most valuable AI agent isn't the one that writes code. It's the one that handles everything else.

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.

I Found an Unauthenticated Attachment Disclosure Bug in a WordPress Support Plugin — and a Duplicate Taught Me What “Fixed” Really Means

Author: Shikhali Jamalzade
GitHub: alisalive · LinkedIn: camalzads

Disclosure Notice: This research was conducted entirely in an isolated, locally-hosted Docker test environment running a fresh install of WordPress and the publicly available “latest-stable” release of the plugin in question, downloaded directly from the official WordPress.org plugin repository. No live, production, or third-party website was accessed, scanned, or tested at any point. All file contents shown are synthetic test data created solely for this research. The affected plugin’s name and the exact route are intentionally redacted here, because the underlying issue is currently being tracked through coordinated disclosure and may not yet be fully patched at the time of writing. This write-up is published strictly for educational purposes.

Background

Most of my CVE research starts from one theory: a plugin whose developers made one authorization mistake will usually have made others, and the categories that leak most often are the ones tied to user-owned objects — tickets, attachments, profiles, orders. Broken Access Control is, by a wide margin, the single most productive class in the WordPress plugin ecosystem, and unauthenticated variants sit at the top of that list.

This time the target was a support-desk / ticketing plugin — the kind of software where customers upload invoices, ID scans, contracts, and screenshots straight into a ticket. If the endpoint that serves those attachments doesn’t check who is asking, the impact isn’t abstract: it’s other people’s private documents.

What follows is a fully independent, fully reproducible finding — and the moment, after submission, when I learned it overlapped with a report already sitting in a vulnerability database’s pipeline. I’m publishing the technical breakdown anyway, because the methodology and the honest reconciliation with prior art are the actual point of doing this in public.

Scope & Method

• Target: A WordPress support/ticketing plugin (redacted), latest-stable from WordPress.org
• Environment: Local, isolated Docker stack — WordPress + MySQL 5.7
• Assessment Type: White-box source audit + black-box PoC validation
• Authorization: Self-authorized, isolated local research environment — no live targets
• Tools: grep, WP-CLI, curl, docker, MySQL CLI

Phase 1: Target Confirmation

Before touching anything, I confirmed exactly what I was auditing: the plugin name, its version, that it was active, and the WordPress version underneath it. This is the first screenshot in every submission I make, because a reviewer needs to know the finding was validated against a real, current install — not a hypothetical.

=== TARGET CONFIRMATION ===
Plugin:    <redacted> (latest-stable)
Version:   <redacted — current release at time of testing>
Active:    YES
WordPress: 7.0
Site URL:  http://<local-docker>:8080

The critical detail here: I was testing the current version. Not an old release with a known history — the newest code the plugin ships today.

Phase 2: Mapping the Attack Surface

The plugin exposes its functionality through a REST namespace. I exported the source via SVN and mapped every route, paying special attention to the permission callbacks — the functions WordPress calls to decide whether a request is allowed before the handler runs.

grep -n "RegisterRestRoute\|permission" <source>/api/v1/<controller>.php

One route stood out immediately — the handler that serves ticket and reply file attachments:

$this->RegisterRestRoute(
    'GET',
    'file-dl/(?P<type>[a-zA-Z0-9-]+)/(?P<id>[0-9_]+)/(?P<file>[^/]+)',
    [$this, "file_dl"]
);

Three attacker-controlled segments — a type selector, a numeric identifier, and a filename — feeding a file-download handler. Exactly the shape of an IDOR, if the permission gate is weak. So I read the gate.

Phase 3: Root Cause

The route’s permission logic resolved, for this particular download route, to a single unconditional line:

} elseif ($route == "file-dl") {
    return true;
}

That’s the whole bug. The permission callback returns true for the attachment-download route unconditionally — no authentication check, no nonce, no verification that the requester owns the ticket the file belongs to. Once that callback returns true, WordPress hands the request straight to the download handler, which reads the identifier and filename from the URL and returns the file.

Because the callback never looks at the current user, there is no notion of “your ticket” versus “someone else’s ticket.” Every attachment is reachable by everyone — including an anonymous visitor with no account at all.

Phase 4: Building an Isolated Test Environment

To prove impact safely, I stood up a throwaway install rather than touching any live site: WordPress + MySQL 5.7 in Docker, the plugin installed from the dashboard, and a realistic victim scenario seeded by hand.

I created two synthetic victim artifacts, standing in for what a real customer would attach:

• A ticket attachment (type = T) containing a fake "confidential customer record."
• A reply attachment (type = R) containing a fake "private invoice."

=== SETUP: victim ticket + reply attachments ===
[ticket attachment created — synthetic "customer record"]
[reply attachment created — synthetic "invoice"]
Files created: 2

I also inserted the matching reply row into the plugin’s database table, because the reply-download path validates that a reply record exists before serving its file. This made the second attack vector reachable exactly as it would be on a real site.

Phase 5: Proof of Concept

Vector 1 — Unauthenticated Ticket Attachment (type = T)

From a session with no cookies, no auth header, no login, I requested the ticket attachment and filtered the output to show that the request carried no credentials and the server returned the file anyway:

> GET /wp-json/<plugin>/v1/ticket/file-dl/T/1/<file> HTTP/1.1
> Host: <local-docker>
< HTTP/1.1 200 OK
[SYNTHETIC CONFIDENTIAL RECORD RETURNED]

No Cookie header. No Authorization header. HTTP 200, and the full attachment content in the response body.

Vector 2 — Unauthenticated Reply Attachment (type = R)

The reply path uses a compound {ticketId}_{replyId} identifier. Same anonymous session, same result:

> GET /wp-json/<plugin>/v1/ticket/file-dl/R/1_1/<file> HTTP/1.1
> Host: <local-docker>
< HTTP/1.1 200 OK
[SYNTHETIC PRIVATE INVOICE RETURNED]

Two independent download paths, both fully unauthenticated.

Integrity Proof

A 200 response proves the endpoint answered — but I wanted to prove the anonymous request returned the actual victim file, byte for byte, not a placeholder or an error page. So I compared the MD5 of the file on disk with the MD5 of what the unauthenticated request pulled down:

--- [A] File on server (victim's attachment) ---
254e7a2a21c6d0d55fbc11fc08e30c18   <server-side file>

--- [B] Content retrieved via unauthenticated request ---
254e7a2a21c6d0d55fbc11fc08e30c18   <downloaded file>

Identical hashes. Byte-for-byte exfiltration, from an anonymous session, confirmed.

Why This Scales

The identifiers are sequential integers. An attacker doesn’t need to guess — they increment. Combined with the fact that support tickets routinely carry personal data, invoices, and contracts, and that the plugin’s upload whitelist covers pdf, doc/docx, xls/xlsx, txt, and common image formats, a single unauthenticated loop over the ID space harvests attachments across every customer on the site.

Estimated severity: CVSS 3.1 7.5 (High) — AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N. Network-reachable, no privileges, no interaction, high confidentiality impact.

The Reality Check

Before public disclosure I did what I always do now: I checked the vulnerability databases and I contacted the vendor.

The vendor email went out first — a responsible-disclosure notice with a summary of the issue and a request for a secure contact, deliberately without the full PoC in the first message. Then I submitted the finding to a CNA with the complete technical detail and requested a CVE.

The response was: duplicate.

Not a duplicate of the plugin’s older, public authorization issues — those were a different, integrity-only problem on a different function. This was a duplicate of a separate report already in the CNA’s pipeline, covering exactly this unauthenticated attachment-download route and exactly this “permission callback returns true” root cause, already tracked with the confidentiality impact of returning full attachment contents to anonymous callers.

Someone had gotten there first, by a matter of weeks, into a queue I couldn’t see.

What I Was Told — and What It’s Worth

Here’s the part that turned a rejection into something genuinely useful. The existing record was filed against an earlier version, and marked fixed in a later one. My finding reproduced on the current release — the one that was supposed to be patched.

The reviewer’s response was precise, and I’m quoting the substance of it because it reframed the whole finding for me: my confirmation that the issue still reproduces on the current version, together with the byte-for-byte MD5 proof, would be used to extend the affected-version range on the existing entry beyond the version it was originally filed against. Because it’s the same vulnerability and the same code path, it’s handled under the existing record rather than as a separate CVE.

So: no CVE with my name on it. But my independent reproduction demonstrated that a fix believed to close the issue did not, and that correction lands in the public record where it actually protects people. That’s not nothing. That’s the point of the work.

I want to be precise about what I’m claiming and what I’m not. I did not discover a novel bug here — I independently rediscovered a known one and proved it was still live where it was believed dead. The value isn’t novelty; it’s verification. Those are different contributions, and conflating them would be dishonest.

Attack Chain Summary

[Attacker — no credentials, no prior session]
        │
        ▼
[1] Map REST routes; find attachment-download handler
        │
        ▼
[2] Read permission callback → returns true unconditionally for file-dl
        │
        ▼
[3] Seed victim ticket + reply attachments in isolated Docker install
        │
        ▼
[4] GET file-dl/T/<id>/<file>  → HTTP 200, ticket attachment (no auth)
        │
        ▼
[5] GET file-dl/R/<id>/<file>  → HTTP 200, reply attachment (no auth)
        │
        ▼
[6] MD5(server file) == MD5(downloaded file) → byte-for-byte exfiltration
        │
        ▼
[7] Sequential IDs → enumerate → harvest attachments across all tickets

What This Taught Me

A “fixed in X” label is a claim, not a guarantee. The most valuable thing I did in this entire audit was test the current version instead of assuming the changelog was true. The issue was marked fixed; it wasn’t. Independent reproduction against the latest release is how that gets caught.

Duplicate-by-pipeline is invisible until it isn’t. I checked every public database before submitting, and it was clean — because the report that duplicated mine wasn’t public yet. You cannot fully de-risk this. What you can do is target less-crowded plugins: the more popular the software, the more researchers are already circling it. Two of my findings that week collided with pipeline reports; both were popular plugins. The niche ones didn’t collide.

Precision about your own contribution is a security skill. “I found a new bug,” “I independently rediscovered a known bug,” and “I proved a known bug wasn’t actually fixed” are three different sentences with three different truth values. Picking the correct one — especially when the flattering one is right there — is part of doing this honestly.

The process transfers regardless of the outcome. Standing up an isolated environment, tracing an unauthenticated entry point to confirmed impact, building two independent PoCs, proving exfiltration with a hash rather than a screenshot alone — that skill set is identical whether the audit ends in a CVE or a “thanks, we’ll extend the range.”

If you found this useful, feel free to connect on LinkedIn or check out my tools on GitHub.

All testing was conducted in an isolated, locally-hosted environment using a publicly available plugin release. No live or third-party systems were accessed at any point during this research. The plugin name and exact route are redacted pending completion of coordinated disclosure.

I Found an Unauthenticated Attachment Disclosure Bug in a WordPress Support Plugin — and a… was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

This project demonstrates the deployment of Wazuh, an open-source, industry-recognized SIEM and host-based intrusion detection platform, in a virtualized lab environment. Wazuh was selected for this project due to its wide adoption in security operations, strong community support, and alignment with real-world SOC practices.

A Wazuh Manager was installed on an Ubuntu system and configured to centrally monitor three endpoints: Windows, Kali Linux, and Ubuntu. Each endpoint was successfully enrolled as a Wazuh agent and configured to forward system logs and security events to the manager for analysis.

The project validates end-to-end log collection and visibility through the Wazuh web dashboard, demonstrating how security events from multiple operating systems can be centrally analyzed. This setup reflects a realistic enterprise monitoring scenario and highlights the effectiveness of Wazuh as a cost-effective, open-source security monitoring solution used across modern SOC environments.

Tools Used

• Wazuh SIEM (Open Source): Centralized security monitoring, log collection, and host-based intrusion detection platform
• Ubuntu Server: Hosting the Wazuh Manager, Indexer, and Web Dashboard
• Windows : Endpoint monitored using the Wazuh agent (Agent 1)
• Kali Linux: Linux endpoint monitored using the Wazuh agent (Agent 2)
• Ubuntu: Linux endpoint monitored using the Wazuh agent (Agent 3)
• VMware Workstation: Virtualization platform used to host all systems
• Web Browser (Windows): Used to access the Wazuh web dashboard over HTTPS

Wazuh Deployment

Wazuh was deployed on Ubuntu Server using the official all-in-one installation script, following the Wazuh deployment guide. Read here

curl -sO https://packages.wazuh.com/4.14/wazuh-install.sh && sudo bash ./wazuh-install.sh -a

This command installs all required dependencies along with the Wazuh Manager, Indexer, and Dashboard. Upon completion of the installation, a username and password are automatically generated for accessing the Wazuh web interface.

Firewall Configuration

The firewall on the Wazuh server was configured to allow all Wazuh components to communicate properly, including agents, the dashboard, indexer, and Syslog. The following UFW rules were applied:

# Essential Wazuh ports
sudo ufw allow 1514/tcp   # Agent → Manager communication
sudo ufw allow 1515/tcp   # Agent enrollment
sudo ufw allow 443/tcp    # Wazuh Web Dashboard (HTTPS)

# Optional ports for future use
sudo ufw allow 55000/tcp  # Wazuh API
sudo ufw allow 514/tcp    # Syslog collector
sudo ufw allow 22/tcp     # SSH access to server

sudo ufw enable - Enables the UFW firewall
sudo ufw status numbered - Displays the current firewall status and lists all active rules with numbering

Accessing the Wazuh Web Interface

After the firewall was configured to allow all essential ports, the Wazuh web dashboard was accessed via a web browser. This interface provides centralized visibility into all connected agents, system events, and security alerts.

https://192.168.79.145:443

Log in using the username and password generated during installation.

Agents Enrollment

To enroll an agent, navigate to Endpoints, Deploy new agents.

Agent 1: Windows

Step 1: Select windows architecture

Step 2: Enter the Wazuh Manager IP Address

Step 3: Set agent name (optional)

Step 4: Copy and Run the Installation Command in Powershell. Run Powershell with administrator privileges

Invoke-WebRequest -Uri https://packages.wazuh.com/4.x/windows/wazuh-agent-4.14.2-1.msi -OutFile $env:tmp\wazuh-agent; msiexec.exe /i $env:tmp\wazuh-agent /q WAZUH_MANAGER='192.168.79.145' WAZUH_AGENT_GROUP='default' WAZUH_AGENT_NAME='Windows'

Step 5: Still in Powershell, run this command to start Wazuh agent

NET START Wazuh

Agent 2: Kali Linux

On the Deploy new agent;

Step 1: Select the Operating System

• Choose Linux as the target OS.
• For Kali Linux, choose DEB amd64.

Step 2: Assign Server Address

• Enter your Wazuh manager’s IP address:

Step 3: Set Agent Name (Optional)

• Enter a unique agent name (Kali)

Step 4: Copy and run the installation command

• The UI generates a command tailored to your inputs. Run it in your Kali terminal:

wget https://packages.wazuh.com/4.x/apt/pool/main/w/wazuh-agent/wazuh-agent_4.14.2-1_amd64.deb && sudo WAZUH_MANAGER='192.168.79.145' WAZUH_AGENT_GROUP='default' WAZUH_AGENT_NAME='Kali' dpkg -i ./wazuh-agent_4.14.2–1_amd64.deb

Step 5: Start and Enable the Agent

Run the following commands to activate the agent:

sudo systemctl daemon-reload
sudo systemctl enable wazuh-agent
sudo systemctl start wazuh-agent

Agent 3: Ubuntu

Repeat the same steps as Agent 2

Copy and run the installation command

• The UI generates a command tailored to your inputs. Run it in your Ubuntu terminal:

wget https://packages.wazuh.com/4.x/apt/pool/main/w/wazuh-agent/wazuh-agent_4.14.2-1_amd64.deb && sudo WAZUH_MANAGER='192.168.79.145' WAZUH_AGENT_GROUP='default' WAZUH_AGENT_NAME='Ubuntu' dpkg -i ./wazuh-agent_4.14.2-1_amd64.deb

Start and Enable the Agent

Run the following commands to activate the agent:

sudo systemctl daemon-reload
sudo systemctl enable wazuh-agent
sudo systemctl start wazuh-agent

Dashboard of all Enrolled Agents

Conclusion

Through the deployment and configuration of Wazuh, I successfully set up a centralized security monitoring environment with multiple agents across Windows, Kali Linux, and Ubuntu. Wazuh provides real-time visibility into system events, log collection, and threat detection, making it a robust and open-source industry-standard SIEM solution. Beyond log monitoring, Wazuh can be leveraged for File Integrity Monitoring (FIM) to track changes in critical files and directories, detect unauthorized modifications, and alert security teams.

Additionally, it integrates seamlessly with other security tools and services, such as Syslog for centralized logging, SSH for remote administration, and custom APIs for automated workflows, enabling comprehensive and proactive security operations.

This setup serves as a foundation for future projects, where I plan to expand Wazuh’s capabilities with additional agents, advanced detection rules, integrations with threat intelligence feeds, and custom security automation workflows to simulate real-world SOC scenarios.

Many thanks to Efam Harris 🫡

Wazuh SIEM Deployment with Multi-OS Agents was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post NVIDIA HORIZON: A Hands-Free Agent that Evolves Git Worktrees and Hits 100% RTL Benchmark Completion appeared first on MarkTechPost.

Release Date: July 1, 2026
Since v0.17.0: ~1,720 commits · 998 merged PRs · 2,215 files changed · ~251,000 insertions · ~41,000 deletions · 949 issues closed · 370+ community contributors

The Judgment Release. Over the last week and a half the team put nearly all of its effort into one goal: resolve every P0 and P1 issue and PR in the entire Hermes Agent repo — and as of this release, 100% of them are closed. Zero open P0s. Zero open P1s. That's ~700 highest-priority items cleared as part of ~1,950 total issues and PRs closed this window. We intend to keep P0/P1 at zero from here on.

On top of that clean-sweep, v0.18.0 is about how well Hermes thinks and how it knows when its work is actually done. Mixture-of-Agents became a first-class citizen — named ensembles of models you can pick like any other model, with every reference model's reasoning shown to you and the aggregator's answer streamed live. The agent learned to verify its own work against evidence instead of vibes, /goal gained completion contracts, and /learn + /journey turned self-improvement into something you can see and steer. Underneath, the gateway became genuinely deployable-at-scale (scale-to-zero, drain coordination), the desktop grew first-class coding projects and a playable memory graph, and subagents can now fan out in the background.

🎯 The P0/P1 Clean Sweep — 100% resolved

This is the release headline. For a week and a half the team hammered the priority backlog day and night, and every single P0 and P1 across the whole repo is now closed:

That's ~692 highest-priority items resolved in twelve days — and at the moment the sweep completed, the open P0/P1 count hit 0 across the entire repo. The final cluster to fall was the interrupt-protected-compression sibling-fork bug (issue #56391) and its fix (#56416), closed on an all-nighter right before this release cut.

Special shoutout to @kshitijk4poor, who burned through the priority backlog day and night alongside the core team — the cron reliability wave, the compression-fork fix, the credential-exfil hardening, and a huge share of the P1 closures are his.

We're keeping P0/P1 at 0 from here forward. 🫡

✨ Highlights

• Mixture-of-Agents is now a first-class model you can pick — MoA used to be a mode you toggled; now every named MoA preset shows up as a selectable model under a moa provider, right alongside Claude, GPT, and Grok in every model picker (CLI, TUI, desktop, gateway). Pick "my-council" the same way you'd pick any model, and Hermes routes your prompt through that ensemble automatically. An ensemble of frontier models deliberating on your hardest questions is now one selection away, on every surface. (#46081, #53548, #53561 — @teknium1)

• See every model's reasoning, then watch the answer stream in — When a MoA ensemble runs, each reference model's full output now renders as its own labelled block — you can read what GPT-5 thought, what Claude thought, and what Grok thought, before the aggregator synthesizes them into one answer. And that final answer now streams to you live instead of appearing all at once after a long silence. This works in the CLI, the TUI, and the desktop app. You get to watch the committee deliberate, not just read the verdict. (#53793, #53855, #55625, #56101 — @teknium1)

• The agent verifies its own work — "done" means proven, not claimed — Hermes now records verification evidence for coding work and can decide it's finished by actually running your project's checks, not by asserting success. /goal gained completion contracts: you state what "done" looks like, and the standing-goal loop judges completion against that evidence instead of stopping when the model feels like it. There's a pre_verify hook for wiring in custom checks and a one-time migration that tunes the defaults sensibly. The difference between "I think I fixed it" and "the tests pass, here's proof." (#50501, #52285, #55413, #53552 — @teknium1, @OutThisLife)

• /learn — turn anything into a reusable skill by describing it — Run /learn <anything> and Hermes distills a reusable skill out of whatever you point it at — a directory, a URL, or just the workflow you walked it through five minutes ago. It writes the skill to the standards in your CONTRIBUTING.md automatically. The next time you need that workflow, it's already there. Teaching Hermes a new trick is now a single command, not a manual skill-authoring session. (#51506, #52372 — @teknium1)

• /journey — a playable timeline of everything Hermes has learned about you — The CLI and TUI gained /journey, a learning timeline that shows the memories and skills Hermes has accumulated over time — and you can edit or delete any of them right from the view. Pair it with the desktop's new memory graph (a top-down, playable radial timeline of memories and skills) and for the first time you can actually see what your agent knows, watch it grow, and prune what's wrong. Your agent's memory stops being a black box. (#55555, #55859, #55226 — @OutThisLife)

• Delegate a pile of work and keep going — background fan-out — delegate_task can now fan out multiple subagents that all run in the background: your chat is never blocked, and when every subagent finishes, their results come back as a single consolidated turn. Kick off "research these five competitors in parallel" or "audit these three modules," then carry on with something else while a small fleet works. When it's all done, you get one clean summary instead of babysitting each one. (#49734 — @teknium1)

• First-class coding Projects in the desktop app — The desktop app gained real, per-profile Projects — a sidebar of your codebases, a coding rail, a review pane, git worktree management, and agent-facing project tools, all backed by a proper project → repo → lane model. Instead of scattered chat sessions, your coding work is organized into projects the agent understands and can act on. It's the desktop turning into an actual coding cockpit. (#49037, #54385, #54517 — @OutThisLife)

• Run Hermes at scale — scale-to-zero and drain coordination — The gateway can now go dormant when idle and quiesce cleanly before a restart, migration, or auto-update — without dropping in-flight conversations. A hosted or relay-only Hermes can scale to zero when nobody's talking to it and wake back up on demand, and disruptive lifecycle actions coordinate an external drain so nobody gets cut off mid-turn. Running Hermes for a team or as a hosted service just got a lot more production-grade. (#52243, #52937, #54824 — @teknium1, @benbarclay)

• Cheaper self-improvement — smarter background review — The post-turn self-improvement fork (the one that decides whether to save a memory or skill) now routes to an auxiliary model, digests context instead of replaying the whole conversation, and adapts its cadence — so the "learn from what just happened" loop that runs after your turns costs a fraction of what it used to. You keep the self-improvement, you stop paying full main-model price for it. (#49252 — @teknium1)

• Compose your next prompt in your editor — /prompt — /prompt opens your $EDITOR so you can hand-write a long, multi-line prompt in real markdown instead of fighting a one-line input box. Draft a detailed spec, a structured question, or a big paste, save, and it's queued as your next message. Small thing, huge quality-of-life win for anyone who writes Hermes more than a sentence at a time. (#50509 — @teknium1)

• Google Vertex AI — Gemini through your GCP service account, no static key — Vertex AI is now a first-class provider for Gemini models over Vertex's OpenAI-compatible endpoint. The reason a plain custom-provider setup always died mid-session is that Vertex has no static API key — every request needs a short-lived OAuth2 access token (~1h TTL) minted from a service-account JSON or Application Default Credentials. Hermes now mints and auto-refreshes those tokens for you, so if your org runs Gemini through Google Cloud, you point Hermes at your service account and it just works — no token-pasting, no mid-session expiry. (#56363 — @teknium1, @slawt)

• Security round — This window hardened several surfaces: MCP-config persistence attack surface locked down, cron base_url overrides that could exfiltrate provider credentials blocked, a non-reusable sentinel for prefix secrets in file reads, Slack app-level (xapp-) token redaction, a browser cloud-metadata floor enforced on every backend, and an aiohttp CVE floor across the lazy messaging paths. Fewer ways for a prompt-injected or misconfigured session to leak a credential. (#50476, #56196, #54166, #56227, #52349, #56237 — @teknium1, @kshitijk4poor, @claudlos)