Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

• Working with Copilot in PowerPoint
• Create a presentation template
• Create a presentation from a document
• Add content from a document to a slide
• Refine your slide text
• Find or create an image
• Expand your presentation with relevant slides
• Summarize a presentation
• Answer questions about a presentation
• Help you navigate a large presentation
• Generate speaker notes and/or an FAQ

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

• Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

• Add a slide based on the attached document.
• Use the attached file to add a slide about the project budget that focuses on Q1 projections.
• Summarize only the financial section of the attached document as a slide.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

• Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

• Add a stock photo of young adults in a cafe drinking boba tea.
• Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

• Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

• Add a slide after slide 3 about our competitive advantages.
• Add a slide after slide 11 that transitions to slide 12.

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

• What are the action items in this deck?
• What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

• Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

• Write speaker notes for every slide with one talking point per slide.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

• Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

Howard Wen / Foundry

Related reading:

• 11 cool things Copilot can do in Excel
• 9 ways Copilot can turbocharge OneNote
• PowerPoint for Microsoft 365 cheat sheet
• Copilot Chat: Your hub for document creation and analysis
• How to curb hallucinations in Copilot (and other genAI tools)
• Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how
• More Microsoft tips and tutorials

The evolution of Java is the subject of a just-released documentary about the programming language and development platform. “The Java Story: The Official Documentary” tells the story of Java through interviews with the engineers who created it and shepherded it through three decades.

Produced by CultRepo and sponsored by Oracle, JetBrains, IBM, and Azul, the documentary follows Java from its set-top box and browser-based origins at Sun Microsystems in the 1990s and through its rise to dominate server-side computing in the 2000s, the “dark ages” and resurgence with Java 8 under Oracle in the 2010s, and its continuing modernization and promising role in AI today. “From its humble beginnings as a project code-named ‘Oak’ at Sun Microsystems to becoming a global standard for enterprise software and billions of devices, Java’s journey is one of radical innovation, strategic pivots, and enduring community strength,” said Cult.Repo.

The documentary also delves into Sun’s bitter Java licensing dispute with Microsoft, Oracle’s suit of Google over its use of Java APIs Android (Google won), the creation of the Java Community Process, Sun’s open-sourcing of Java, and Oracle’s switch to the six-month release cycle. Technical enhancements such as lambda expressions in Java 8, virtual threads in Java 21 (Project Loom), and the ongoing refactor to bring value objects to the Java object model (Project Valhalla) also get attention.

Technical experts and other Java figures interviewed in the documentary include James Gosling, creator of Java; Kim Polese, Java’s first product manager; Carla Schroer, director of Java compatibility at Sun Microsystems; James Duncan Davidson, creator of Apache Tomcat; Mark Reinhold, chief architect of the Java Platform Group at Oracle; Brian Goetz, Java language architect in the Java Platform Group at Oracle; Rod Johnson, creator of Spring; and Gavin King, creator of Hibernate.

Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era)

Network World spoke with Mark Lohmeyer, vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand.

Network World: What is the primary shift in infrastructure needs?

Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents.

NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs?

ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

NW: How are you addressing energy efficiency?

ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront… orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables.

NW: How do you think about token efficiency as you build-out systems?

ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack.

NW: How do you project what infrastructure will be needed years in advance?

ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents.

NW: What’s the eighth generation TPU platform?

ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized.

TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%.

NW: How are you approaching GPU and TPU compatibility?

ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top.

NW: How has the orchestration platform changed for agents?

ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack: significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed.

NW: What is the role of the network and storage infrastructure?

ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together.

In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important.

We’ve introduced Managed Lustre 10T, with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments.

NW: How does KV cache strategy differ between training and inference?

ML: For TPU-8i, we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical.

We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.

Google’s data center team is racing to turn its infrastructure into a well-oiled machine for AI and the onslaught of agents. At this year’s Google I/O, CEO Sundar Pichai shared startling numbers: Google’s data centers processed about 3.2 quadrillion tokens a month, roughly seven times more than the 480 trillion processed in May 2025.

“Multiple agents work together, and now you’ve got millions, billions of users around the world potentially spinning off agents to help them do things,” said Mark Lohmeyer, vice president and general manager for AI and computing infrastructure at Google.

Google’s new data-center blueprint includes updated hardware, software, and orchestration layers to keep always-running agents operational.

In the LLM era, users sent prompts and received responses, and Google’s infrastructure was designed for latency and throughput. But agents could increase inference transactions by up to 100 times non-agentic workloads, Lohmeyer said. Google’s redesigned AI data-center stack has the elasticity for agents to be widely distributed, run for long periods, and make decisions independently.

“We’re delivering new platforms every year, each one optimized for what we think the world is going to need for the age of agents going forward,” Lohmeyer said.

Efficient data flow is key so agents can act, reason, and decide faster.

Google adjusted the Google Kubernetes Engine into an agent-native environment, where agents could be quickly spun up in sandboxes and containers. “From an infrastructure perspective, you need to spin up a bunch of TPUs or GPUs very rapidly. Then you need to be able to run them and spin them back down,” Lohmeyer said.

Google also made drastic improvements to its silicon to support its middleware changes. It recently introduced new AI chips, with the TPU-8t for training, and TPU-8i for inference. The 8t chip has three times more computing power than the previous-generation Ironwood chip. The 8i chip has 384 megabytes of SRAM and 288GB of HBM3e memory, which is 50% more than the previous-generation chip.

The platform is optimized for KV cache (key-value cache), which stores important contextual information needed by agents to make decisions, which reduces the round trips to other memory and storage systems. “Being able to store more of the KV cache directly on the chip allows you to respond much more rapidly and cost-effectively,” Lohmeyer said.

A new CPU called Axion N4A is more power efficient at agentic workloads such as orchestration and tool calling, Lohmeyer said.

Google also made many network and storage improvements to cut training and inference time. A new technology called TPUDirect can move data from storage directly into the memory of the TPU quickly by bypassing any orchestration overhead, Lohmeyer said.

A networking technology called Virgo can coordinate 1 million TPUs across a widely distributed network. It can also link up GPUs such as Nvidia’s latest CPU-GPU package called Vera Rubin. “In the case of Vera Rubin, we’ll be able to connect up to 960,000 GPUs leveraging Virgo,” Lohmeyer said.

A new technology called Pathways is a distributed training framework that efficiently scales machine learning across millions of TPUs and GPUs. Pathways solves bottleneck issues typically associated with JAX, and both help coordinate across wide networks.

“The software to orchestrate these large-scale distributed training jobs is also just as important as the hardware that it runs on top of,” Lohmeyer said.

Weighing Google’s AI data-center stack

Google is the only provider with its own data centers, software, hardware and models, said Jack Gold, principal analyst at J. Gold Associates. Google can optimize each on a regular cadence, which “many data centers can’t easily afford given the high cost of new chips,” Gold said.

Google’s stack may not be best for every data center need compared to Nvidia’s general-purpose GPUs, CPUs, and networking. AWS and Microsoft are also creating their chips.

“There is no real risk of Nvidia being replaced by Google in a big way. But with an ever-expanding market, there is plenty of room for all players,” Gold said.

But Logan Wolfe, partner at Kyndryl’s global AI strategy and sovereign transformation, advised enterprises to adopt a multi-cloud strategy to reduce risk from system failures, however superior an infrastructure may be. “I think that kind of hybrid and liquid infrastructure, we’re definitely getting there,” Wolfe said.

The cost per token varies depending on the provider of inference, whether that’s Microsoft, Google, OpenAI or Anthropic. That will matter as AI moves from experimentation to a powerful tool that drives business changes.

“Ultimately it really comes down to how much money are we spending on AI to move a certain business outcome,” Wolfe said.

Foto: N Universe | shutterstock.com

Unter den Massen von KI-Tools und -Anwendungen, die aktuell als Mobile-, Desktop- oder Web-App zur Wahl stehen, gibt es nicht wenige, die sich in erster Linie dadurch auszeichnen, dass sie:

• Output von fragwürdiger Genauigkeit liefern,

• dubiose Texte erzeugen, oder

• Bilder generieren, die zum Klick auf den X-Button verleiten.

KI-Tools dieser Art sind vor allem darauf ausgerichtet, vom anhaltenden Generative-AI (GenAI)-Hype zu profitieren – und trüben leider auch den Blick für die echten Anwendungsperlen im Bereich generative KI. Wie etwa die folgenden GenAI-Apps, die Ihre Produktivität im Arbeitsalltag drastisch steigern und damit erhebliche Zeitgewinne realisieren können. Probieren Sie’s aus!

1. ChatPDF

Sie kennen solche Situationen: Jemand schickt Ihnen einen schlanken 300-Seiter im .pdf-Format und bereits nach Seite Zwei stellt sich heraus, dass sich dieser in etwa so faszinierend liest wie eine Steuererklärung. In Zukunft dürfen Sie sich bei solchen und ähnlichen Gelegenheiten auf ChatPDF verlassen und dabei richtig Zeit einsparen.

Dieses rein webbasierte Tool – nicht zu verwechseln mit gleichnamigen Mobile Apps – tut exakt das, was es verspricht: Sie befähigen, mit .pdf-Dateien zu chatten. Darüber hinaus können Sie über das Webportal auch Office-Dokumente im .doc- oder .docx-Format hochladen, um anschließend dank KI-Unterstützung möglichst schnell und einfach Informationen über den Inhalt zu erfragen. Dabei kann es sich konkret um einfache Zusammenfassungen oder spezifische, inhaltsbezogene Fragen handeln. Sie können bei Bedarf sogar mehrere Dokumente einspeisen und diese gemeinschaftlich abfragen. Die Verantwortlichen von ChatPDF versprechen dabei, sämtliche Daten sicher zu speichern, auf Anfrage zu löschen und keinesfalls an Dritte weiterzugeben. Dennoch sollten sensible unternehmensbezogene Dokumente eher nicht diesen Weg nehmen.

ChatPDF verarbeitet davon abgesehen Dokumente in (fast) jeder Sprache – und unterstützt diese auch mit Blick auf die KI-Chat-Funktion. Zwei Dokumente dürfen Sie täglich kostenlos über den Service hochladen und abfragen – wobei die Dateien maximal 120 Seiten lang oder 10 MB groß sein dürfen. Die GenAI-Webanwendung dürfte also in ihrer kostenlosen Variante bereits für die meisten Gelegenheits-User ausreichend sein. Sollten Sie Bedarf haben, der darüber hinausgeht, steht Ihnen die Bezahlversion ChatPDF Plus ab 24,99 Euro pro Monat (oder circa 120 Euro pro Jahr) zur Verfügung.

2. Beautiful.ai

Präsentationen (richtig) zu erstellen, kann zum Pain geraten. Es sei denn, Sie lassen Generative AI den wesentlichen Teil des Gestaltungsprozesses übernehmen. Das funktioniert mit der KI-basierten Präsentationssoftware Beautiful.ai. Das (möglicherweise) größte Defizit dieses ebenfalls webbasierten KI-Tools ist, dass es zwar auch deutschsprachige Prompts verarbeitet, zur Zeit aber nur englischsprachige Präsentationen erstellt. Das tut es dafür aber richtig gut, wie bereits die Mini-Demo auf der offiziellen Webseite zeigt. Die KI-App unterstützt Sie nicht nur beim Design der einzelnen Folien, sondern auch bei der Formatierung von Inhalten und dabei, Brand Guidelines einzuhalten – sowie bei allen anderen Aspekten, die wichtig sind, damit Ihre Präsentation einen möglichst professionellen Eindruck hinterlässt.

Foto: JR Raphael | IDG

Die generativen KI-Funktionen des Web-Services umfassen auch eine Funktion, um Inhalte zu generieren. Sie können die KI beispielsweise damit beauftragen, eine ganz bestimmte Art von Präsentation zu einem bestimmten Thema zu erstellen. Dazu zieht die Anwendung öffentlich verfügbare Daten heran. Das Ergebnis bedarf zwar sehr wahrscheinlich einer gründlichen Überprüfung, Überarbeitung und Re-Formulierungskur. Dennoch kann es Ihnen eine nützliche erste Grundlage liefern, auf der sich aufbauen und damit potenziell eine Menge Zeit sparen lässt. Beautiful.ai lässt sich mit PowerPoint, Slack, Webex und Dropbox integrieren.

Leider gibt’s den KI-Präsentations-Zauber nicht umsonst. Ein Abonnement für Beautiful.ai kostet für Einzelpersonen 12 Dollar pro Monat. Im Team mit der GenAI-App zu arbeiten, schlägt mit mindestens 40 Dollar pro Nutzer und Monat zu Buche. Einen individuellen Enterprise-Preisplan gibt’s auf Anfrage.

3. Toki

Allen technologiegetriebenen Productivity-Fortschritten zum Trotz bleibt ein Task lästig: mit einem Kalender zu interagieren. Dieser Aufgabe verschreibt sich der KI-Kalenderassistent Toki, der zuvor unter dem Namen Dola bekannt war. Dabei handelt es sich um eine Chatbot-Lösung, die sich in die Messaging-Plattformen WhatsApp, Telegram, Line sowie iMessage einbinden lässt und sich anschließend zum Beispiel mit den Kalender-Apps von Google und Apple verbindet. Da dieses KI-Tool das Netzwerkprotokoll CalDAV nutzt, um auf die Kalenderdaten zuzugreifen, müssen Sie im Fall von Outlook leider den Umweg über ein Drittanbieter-Plugin nehmen.

Ist die Integration erledigt, steht Toki über integrierte Schaltflächen in den Messaging-Apps zur Verfügung, um Termine zu erstellen, zu verschieben – oder direkt Fragen zu freien Terminslots zu stellen. Darüber hinaus kann dieses Tool auch genutzt werden, um Termine mit Infos anzureichern – beispielsweise Vorschläge für beliebte Restaurants in einer bestimmten Gegend oder auch Ideen für den neuen Firmenslogan, der beim Meeting gefunden werden soll.

Der Service ist in so gut wie allen Sprachen verfügbar und in begrenzten Umfang kostenlos nutzbar. Zahlende Benutzer erhalten mehr Features ab 3,99 Dollar pro Monat.

4. Fathom

Dass virtuelle Meetings richtig schlimm werden können, wissen wir wohl alle. Und auch wenn selbst Generative AI Sie (noch) nicht davor bewahren kann, an digitalen Foltersessions teilzunehmen: Es gibt eine KI-App, die das erträglicher macht – Fathom.

Bei dieser Anwendung handelt es sich um einen KI-Assistenten für Videokonferenzen in Form klassischer Software für Windows– oder Mac-Systeme, die wahlweise mit Zoom, Microsoft Teams oder Google Meet integriert wird. Nach der Installation läuft Fathom unauffällig im Hintergrund und transkribiert (über eine Kalender-Integration) entweder automatisch oder auf Knopfdruck sämtliche Videoanrufe. Notizen machen gehört damit in beiden Fällen der Vergangenheit an. Die Zusammenfassungen oder Informationen stehen direkt zur Verfügung und lassen sich gezielt durchsuchen, weiterverarbeiten oder auch in anderen Produktivitäts- und Collaboration-Tools wie Slack nutzen.

Sämtliche Daten werden dabei laut Fathom während der Übertragung und im Ruhezustand verschlüsselt. Außerdem versprechen die Verantwortlichen ausdrücklich, keine KI-Modelle auf Kundendaten zu trainieren. Sämtliche Details zu Security- und Compliance-Themen sind – vorbildlicherweise – über ein dediziertes Trust Center abrufbar.

Foto: JR Raphael | IDG

Die KI-Software unterstützt diverse verschiedene Sprachen, darunter Englisch, Französisch, Spanisch, Italienisch und Deutsch. Noch dazu ist Fathom komplett kostenlos nutzbar – ohne Einschränkungen hinsichtlich der Anzahl oder Länge der aufgezeichneten Videokonferenzen. Erst fortschrittlichere KI-Funktionen lässt sich das Team hinter der GenAI-Anwendung bezahlen.

Die Fathom Team Edition bietet weitergehende, fortschrittliche KI-Funktionen – beispielsweise automatisierte Keyword Alerts, Highlight-Zusammenstellungen oder Team-Management-Funktionen. Die kostenpflichtige Variante ermöglicht darüber hinaus die Integration in Enterprise-Systeme wie HubSpot, Salesforce oder Zapier. Die Preise beginnen bei 15 Dollar pro Monat und User. Die kostenlose Version bietet Premium-Features für fünf Anrufe pro Monat.

5. Whisper Web

Falls Sie bereits Audiodateien besitzen, die beispielsweise im Rahmen von Meetings oder Telefongesprächen entstanden sind und jetzt in Text umgewandelt werden sollen, ist Whisper Web die richtige Adresse – zumindest, wenn es sich um englischsprachige Audioaufnahmen handelt. Diese quelloffene Webanwendung basiert auf der Entwicklungsarbeit von OpenAI und bietet Echzeit-Transkriptionen direkt im Browser. Das Large Language Model, das dazu zum Einsatz kommt, wird über die App heruntergeladen und lokal ausgeführt – die Daten, die Sie der KI übermitteln, verlassen also das Device nicht.

Whisper Web kann Audioinhalte entweder direkt über Ihr Mikrofon erfassen oder aus entsprechenden Audiodateien extrahieren. Laut den Entwicklern ist die KI-App auf mehrsprachige Daten trainiert und unterstützt auch die Transkription anderer Sprachen (zu Englisch). Der Test mit einem deutschsprachigen Audio-File brachte allerdings nicht mehr als undefiniertes Kauderwelsch hervor. Dafür ist das Tool Open Source und komplett kostenlos nutzbar – Sie benötigen dazu auch kein dediziertes Konto.

6. AudioPen

Wenn Sie nicht ohne Ihr Notizbuch (oder eine entsprechende App) auskommen, könnte das KI-Tool AudioPen sich zu Ihrer neuen Lieblings-App mausern. Die Software erfasst auf Knopfdruck Sprachnotizen jeglicher Art und erstellt daraus im Handumdrehen eine schriftliche Zusammenfassung. Und zwar in “schön”: Füllwörter oder Wiederholungen werden automatisiert eliminiert. Jede Aufnahme wandert direkt in das digitale Notizbuch und lässt sich anschließend durchsuchen, teilen oder auch in eine andere Sprache übersetzen. Auch bei AudioPen handelt es sich um eine vollständig webbasierte Applikation, die sich übrigens optional auch in Form einer Progressive Web App installieren lässt.

Foto: JR Raphael | IDG

Das KI-Tool für Sprachnotizen ist kostenlos nutzbar, solange Sie sich auf Aufnahmen mit bis zu drei Minuten Länge und maximal zehn Notizen beschränken können. Für Ansprüche, die darüber hinausgehen, steht eine “Prime”-Version der App zur Verfügung, die mindestens 99 Dollar pro Jahr kostet – dafür aber uneingeschränkt nutzbar ist und eine Reihe zusätzlicher Funktionen bietet. (fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Computerworld.com erschienen.

Foto: Omelchenko – shutterstock.com

Eine Wahl zu treffen, wenn Dutzende oder gar Hunderte von Tools zur Verfügung stehen, ist nicht einfach. So dürfte es auch vielen Netzwerk- und Security-Experten gehen, die quelloffene Security Tools für Linux suchen.

In diesem Bereich gibt es eine Vielzahl verschiedener Tools für so gut wie jede Aufgabe (Netzwerk-Tunneling, Sniffing, Scanning, Mapping) und jede Umgebung (Wi-Fi-Netzwerke, Webanwendungen, Datenbankserver). Wir haben einige Experten konsultiert und zehn essenzielle Linux-Sicherheitstools für Sie zusammengestellt.

1. Aircrack-ng

Diese Suite von Software Tools ermöglicht es, drahtlose Netzwerke und WiFi-Protokolle Sicherheitsüberprüfungen zu unterziehen. Sicherheitsprofis verwenden das Tool für die Netzwerkadministration, Hacking und Penetrationstests. Dabei fokussiert Aircrack-ng auf:

• Monitoring (Datenpakete erfassen und Daten in Textdateien zur Weiterverarbeitung durch Tools von Drittanbietern exportieren)

• Angreifen (Replay-Angriffe, Deauthentication, Packet Injection)

• Testing (WiFi-Karten und Treiberfunktionen überprüfen) und

• Cracking (WEP und WPA PSK)

Laut der offiziellen Webseite funktionieren alle Tools kommandozeilenbasiert, was eine umfangreiche Skripterstellung ermöglicht. Das Tool funktioniert mit Linux genauso wie mit Windows, macOS, FreeBSD, OpenBSD, NetBSD, Solaris und sogar eComStation.

Preis: kostenlos

2. Burp Suite

Hierbei handelt es sich um eine Testing-Suite für Webanwendungen, die für Security Assessments von Websites eingesetzt wird. Burp Suite arbeitet als lokale Proxy-Lösung, die es Sicherheitsexperten ermöglicht, Anfragen (HTTP/Websockets) und Antworten zwischen einem Webserver und einem Browser

• entschlüsseln,

• beobachten,

• manipulieren und

• wiederholen zu können.

Burp Suite hat einen passiven Scanner an Bord, mit dem Security-Profis Webseiten (manuell) auf potenzielle Schwachstellen überprüfen können. Die Pro-Version bietet außerdem einen sehr nützlichen aktiven Web-Schwachstellen-Scanner, mit dem sich weitere Schwachstellen aufspüren lassen. Burp Suite ist über Plugins erweiterbar, so dass Sicherheitsexperten ihre eigenen Erweiterungen entwickeln können.

Preis: Die Professional-Version kostet 475 Euro pro Jahr und Benutzer. Darüber hinaus steht auch eine Enterprise-Version (ab ca. 2.000 Euro jährlich) zur Verfügung, die mehrere gleichzeitige Scans ermöglicht und von Anwendungsentwicklungsteams genutzt werden kann.

3. Impacket

Diese Sammlung von Tools ist für Pen-Tests von Netzwerkprotokollen und -diensten unerlässlich. Impacket wurde von SecureAuth entwickelt und ist eine Sammlung von Python Classes, um mit Netzwerkprotokollen zu arbeiten. Impacket konzentriert sich auf die Bereitstellung von Low-Level-Zugriff auf Pakete und bei einigen Protokollen wie SMB1-3 und MSRPC auf die Protokollimplementierung selbst. Sicherheitsexperten können Pakete von Grund auf neu konstruieren, aber auch auf Grundlage geparster Rohdaten. Die objektorientierte API macht es zudem einfach, mit tiefen Protokollhierarchien zu arbeiten. Impacket unterstützt die folgenden Protokolle:

• Ethernet, Linux;

• IP, TCP, UDP, ICMP, IGMP, ARP;

• IPv4 und IPv6;

• Umgänglicher zeigte sich Musk gegenüber den Anzeigenkunden von Twitter. In einem – natürlich auf Twitter geposteten – Brief erklärte der Tesla-Chef, der Grund für die Übernahme sei nicht, damit noch mehr Geld zu verdienen. Vielmehr sei es “wichtig für den Fortbestand der Zivilisation, einen gemeinsamen digitalen Treffpunkt zu haben, auf dem eine breite Palette von Überzeugungen auf gesunde Weise diskutiert werden kann.”

• Trotz alledem dürfe Twitter nicht zu einer “für alle Nutzer freien Höllenlandschaft werden, in der alles ohne Konsequenzen gesagt werden kann”, fügte Musk hinzu. Zusätzlich zur Einhaltung der Gesetze müsse die Plattform “warmherzig und einladend” für alle sein und den Nutzern die Möglichkeit bieten, “die gewünschte Erfahrung nach ihren Vorlieben zu wählen” – ähnlich wie man zum Beispiel wählen kann, Filme zu sehen oder Videospiele zu spielen, die für alle Altersgruppen geeignet sind.

• Plain-, NTLM- und Kerberos-Authentifizierungen, unter Verwendung von Kennwörtern/Hashes/Tickets/Schlüsseln;

• EU-Kommissar Thierry Breton wiederum reagierte auf Musks Teet, dass der Vogel jetzt frei sein, mit der Anmerkung, “dass Twitter in Europa nach unseren Regeln fliegen muss”.

Preis: Kostenlos – Impacket wird unter einer leicht modifizierten Version der Apache Software License bereitgestellt. Die Unterschiede können Sie hier einsehen.

4. Metasploit

Metasploit ist ein Exploit-Framework von Rapid7, das für allgemeine Penetrationstests und Schwachstellenbewertungen verwendet wird. Sicherheitsexperten betrachten es als “Super-Tool”, das funktionierende Versionen fast aller bekannter Exploits enthält. Metasploit ermöglicht Sicherheitsexperten, Netzwerke und Endpunkte auf Schwachstellen zu scannen und anschließend automatisiert mögliche Exploits auszuführen, um Systeme zu übernehmen.

Metasploit erleichtert es mit protokollspezifischen Modulen (die alle unter der Funktion Auxiliary/Server/Capture laufen) Anmeldeinformationen zu erfassen. Sicherheitsexperten können jedes dieser Module einzeln starten und konfigurieren – zudem steht ein Capture-Plug-in zur Verfügung, das diesen Prozess vereinheitlicht.

Preis: Metasploit Pro kostet – inklusive kommerziellem Support durch Rapid7 – ab 12.000 Dollar pro Jahr. Es gibt aber auch eine kostenlose Version.

5. Ncat

Der Nachfolger des beliebten Tools Netcat heißt Ncat und kommt von den Machern von Nmap. Das Tool ermöglicht es, Daten per Kommandozeile über ein Netzwerk zu lesen und zu schreiben, bietet aber auch zusätzlich Funktionen wie SSL-Verschlüsselung. Sicherheitsexperten zufolge ist Ncat unerlässlich geworden, um TCP/UDP-Clients und -Server zu hosten und Daten von Angreifer- und Opfersystemen zu empfangen.

Ncat ist auch ein beliebtes Tool, um eine Reverse Shell einzurichten oder Daten zu exfiltrieren. Es wurde als zuverlässiges Back-End-Tool entwickelt, um Netzwerkverbindungen zu anderen Anwendungen und Benutzern herzustellen.

Preis: kostenlos

6. Nmap

Dieses Netzwerk-Scanning- und Mapping-Tool auf Kommandozeilen-Basis findet zugängliche Ports auf Remote Devices. Viele Sicherheitsexperten halten Nmap für eines der wichtigsten und effektivsten Tools – insbesondere im Bereich Penetration Testing ist es unerlässlich.

Die Skripting-Engine von Nmap erkennt anschließend automatisiert weitere Schwachstellen und nutzt diese aus. Nmap unterstützt Dutzende fortschrittlicher Techniken, um Netzwerke mit IP-Filtern, Firewalls, Routern und anderen Hindernissen abzubilden. Dazu gehören auch zahlreiche Mechanismen, um TCP- und UDP-Ports zu scannen, Betriebssysteme und Versionen sowie Ping-Sweeps zu erkennen.

Preis: kostenlos

7. ProxyChains

Dieses Werkzeug – der De-facto-Standard für Netzwerk-Tunneling – ermöglicht es Sicherheitsexperten, Proxy-Befehle von ihrem angreifenden Linux-Rechner aus über verschiedene kompromittierte Rechner zu senden, um Netzwerkgrenzen und Firewalls zu überwinden und dabei einer Entdeckung zu entgehen.

ProxyChains leitet den TCP-Verkehr von Penetrationstestern durch die folgenden Proxys: TOR, SOCKS und HTTP. ProxyChains ist mit TCP-Aufklärungs-Tools wie NMAP kompatibel und verwendet standardmäßig das TOR-Netzwerk. Sicherheitsexperten verwenden ProxyChains auch bei der IDS/IPS-Erkennung.

Preis: kostenlos

8. Responder

Responder ist ein NBT-NS (NetBIOS Name Service), LLMNR (Link-Local Multicast Name Resolution) und mDNS (Multicast DNS) Poisoner. Penetration Tester nutzen das Tool, um Angriffe zu simulieren, die darauf abzielen, Anmeldeinformationen und andere Daten während des Prozesses der Namensauflösung zu stehlen, wenn der DNS-Server keinen Eintrag findet. Ab Version 3.1.1.0 bietet Responder standardmäßig vollen IPv6-Support.

Preis: kostenlos

9. sqlmap

Das Open-Source-Tool sqlmap richtet sich ebenfalls an Penetrationstester und automatisiert den Prozess, SQL-Injection-Fehler zu erkennen, mit deren Hilfe Datenbankserver kompromittiert werden könnten. Das Tool verfügt über eine leistungsstarke Erkennungs-Engine und bietet zahlreiche Funktionen, darunter Datenbank-Fingerprinting und die Ausführung von Befehlen auf Betriebssystemebene über Out-of-Band-Verbindungen.

Sqlmap unterstützt eine breite Palette von Datenbankservern, darunter:

• MySQL,

• Oracle,

• PostgreSQL,

• Microsoft SQL Server,

• Microsoft Access,

• IBM DB2,

• SQLite,

• Firebird,

• Sybase,

• SAP MaxDB und

• HSQLDB.

Preis: kostenlos

10. Wireshark

Das Netzwerkprotokoll-Analyse-Tool Wireshark wird auch oft als Network Interface Sniffer bezeichnet. Mit Wireshark können Sicherheitsexperten das Netzwerkverhalten eines Geräts beobachten, um zu sehen, mit welchen anderen Geräten es kommuniziert und warum.

Sicherheitsexperten zufolge eignet sich Wireshark hervorragend, um herauszufinden, wo sich DNS-Server und andere Dienste befinden, mit denen sich ein Netzwerk weiter kompromittieren lässt. Wireshark läuft nicht nur unter Linux, sondern funktioniert mit den allen gängigen Betriebssystemen, einschließlich Windows, MacOs und Unix.

Preis: kostenlos

(fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Networkworld.com erschienen.

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

• Working with Copilot in PowerPoint
• Create a presentation template
• Create a presentation from a document
• Add content from a document to a slide
• Refine your slide text
• Find or create an image
• Expand your presentation with relevant slides
• Summarize a presentation
• Answer questions about a presentation
• Help you navigate a large presentation
• Generate speaker notes and/or an FAQ

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

• Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

• Add a slide based on the attached document.
• Use the attached file to add a slide about the project budget that focuses on Q1 projections.
• Summarize only the financial section of the attached document as a slide.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

• Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

• Add a stock photo of young adults in a cafe drinking boba tea.
• Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

• Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

• Add a slide after slide 3 about our competitive advantages.
• Add a slide after slide 11 that transitions to slide 12.

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

• What are the action items in this deck?
• What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

• Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

• Write speaker notes for every slide with one talking point per slide.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

• Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

Howard Wen / Foundry

Related reading:

• 11 cool things Copilot can do in Excel
• 9 ways Copilot can turbocharge OneNote
• PowerPoint for Microsoft 365 cheat sheet
• Copilot Chat: Your hub for document creation and analysis
• How to curb hallucinations in Copilot (and other genAI tools)
• Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how
• More Microsoft tips and tutorials

I use a very cool and relatively new web framework called Astro. The keen insight that the Astro team had was that most websites are made up of static content, so they made it really easy to add content to a website. To add a blog post to my personal website, all I have to do is create a Markdown file with some front matter, deploy it, and the blog post automatically appears. If I need to reach deeper for more dynamic functionality, I can easily do that with TypeScript, React, or almost any other framework. It’s really cool.

And these days, I really don’t write any code. Claude Code does most (all?) of the work. Since Astro is an open-source project and has excellent documentation, Claude knows all about how Astro works. It has no trouble at all managing my site and making the improvements I ask for.  

And that got me thinking, how does Astro get built? Is the Astro team building with agentic coding? Astro itself has many dependencies, including big projects like Vite and Node. And of course, Vite and Node have dependencies, too. Are those dependencies being developed by hand, or are those development teams also using AI agents to code?

My curiosity got the best of me, and I asked Claude to dig deeper. It turns out that the Astro repository has an AGENTS.md file, and some of the commits even have commit message trailers indicating that they were at least co-authored by Claude and GitHub Copilot. Further down, there is a .agents/skills directory with skills covering development, merging, triage, and more. I poked around for a look, and someone has done a great job building agentic support.

Now my interest is really piqued, and further investigation reveals quite a bit of interesting stuff. About a year ago, documentation started appearing about how to build Astro sites with coding agents.  Around that same time, the docs team released an MCP server that gives developers coding agents deeper, easier access to the Astro documentation.  

And there are small steps in the Astro codebase that indicate it is “agentic ready.” For instance, the command-line development server can tell when it is being started by an agent, and the application itself can tell if it is being driven by an agent. Small things, but steps in the direction of embracing Astro developers who use coding agents. 

Okay, that was a fun spelunking trip. But so what?

The “so what” is that code is going to be commoditized. As an Astro developer I am using AI agents pretty much all of the time. The Astro development team is starting to use AI agents more and more. The folks building the Astro dependencies are using AI agents. Shoot, the people building Claude Code and the agents themselves are “eating their own dogfood” and using their own tools to build the next frontier model. Before we know it, it will be turtles all the way down. 

No one says “who generated that electricity?” or “who wove the fabric in that shirt?” any more. And it won’t be long before no one says “Who wrote the code for that app?” because it won’t matter. Just as we don’t look at the assembly code written by our compilers, we’ll stop looking at the “regular” code written by our agents. I’m not even sure anyone is writing assembly code anymore. Soon we’ll be saying that about TypeScript, Python, and C++.

The evolution of Java is the subject of a just-released documentary about the programming language and development platform. “The Java Story: The Official Documentary” tells the story of Java through interviews with the engineers who created it and shepherded it through three decades.

Produced by CultRepo and sponsored by Oracle, JetBrains, IBM, and Azul, the documentary follows Java from its set-top box and browser-based origins at Sun Microsystems in the 1990s and through its rise to dominate server-side computing in the 2000s, the “dark ages” and resurgence with Java 8 under Oracle in the 2010s, and its continuing modernization and promising role in AI today. “From its humble beginnings as a project code-named ‘Oak’ at Sun Microsystems to becoming a global standard for enterprise software and billions of devices, Java’s journey is one of radical innovation, strategic pivots, and enduring community strength,” said Cult.Repo.

The documentary also delves into Sun’s bitter Java licensing dispute with Microsoft, Oracle’s suit of Google over its use of Java APIs Android (Google won), the creation of the Java Community Process, Sun’s open-sourcing of Java, and Oracle’s switch to the six-month release cycle. Technical enhancements such as lambda expressions in Java 8, virtual threads in Java 21 (Project Loom), and the ongoing refactor to bring value objects to the Java object model (Project Valhalla) also get attention.

Technical experts and other Java figures interviewed in the documentary include James Gosling, creator of Java; Kim Polese, Java’s first product manager; Carla Schroer, director of Java compatibility at Sun Microsystems; James Duncan Davidson, creator of Apache Tomcat; Mark Reinhold, chief architect of the Java Platform Group at Oracle; Brian Goetz, Java language architect in the Java Platform Group at Oracle; Rod Johnson, creator of Spring; and Gavin King, creator of Hibernate.

AMD has expanded its AI infrastructure portfolio with the launch of Helios, an open, rackscale AI infrastructure designed for frontier AI and sovereign computing. Helios is built around AMD’s next-generation Instinct GPUs, EPYC Venice processors, Pensando networking and the ROCm software stack.

“Helios is AMD’s first complete AI rack system with GPUs, CPUs, and networking built together, instead of selling separate chips. It is well suited for training large AI models, memory heavy models, long context processing and high volume inference, and AMD’s biggest shot yet at challenging Nvidia’s dominance,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting.

AMD has also secured an early hyperscale deployment for Helios with Microsoft agreeing to deploy it to power its frontier model AI inference, its AI customers, and support Azure AI services.

The architecture behind Helios

The launch of Helios marks AMD’s latest attempt to strengthen its position in a market where Nvidia continues to dominate AI infrastructure. Unlike previous AMD AI offerings centred on individual accelerators, Helios is designed as a complete rack-scale system integrating compute, networking and software.

According to Jain, Helios goes up against Nvidia’s Vera Rubin rack. “Nvidia is faster on raw inference speed and has a faster internal connection between chips whereas AMD wins on memory size and offers better value for the price and power used. It’s standout feature is memory, where each rack packs about 50% more total memory than Nvidia’s competing system, which helps run very large AI models. It also uses open, industry-standard connections instead of Nvidia’s private technology, giving buyers more flexibility,” he said.

The AMD Helios rackscale design includes 72 AMD Instinct MI455X GPUs with AMD EPYC Venice CPUs and AMD Pensando Vulcano networking using UALink, optimized for compute, data movement, and system efficiency. The platform also supports both OCP and MX data types, delivering up to 2.9 EFLOPS of FP4 and 1.4 EFLOPS of FP8 compute for AI training and inference.

It also integrates 31TB of HBM4 memory with 19.6TB/s of memory bandwidth, while a liquid-cooling design uses quick-disconnect connections to efficiently dissipate heat. It is designed on open standards including OCP Open Rack Wide (ORW), Ultra Accelerator Link (UALink), and Ultra Ethernet Consortium (UEC) and can be scaled efficiently across datacenters while optimizing power, cooling, and serviceability for modern AI infrastructure, said the company.

On the security front, Helios incorporates a hardware root of trust and continuous attestation at every layer. It supports hardware-enforced isolation, encrypted memory and interconnects to help protect AI models, data and workloads in multi-tenant environments.

The software challenge

While the launch of Helios might help AMD close the hardware gap with Nvidia’s rack-scale systems, it will be the software compatibility that will be the real driver of enterprise adoption.

For this, AMD is expanding its ROCm AI software platform too, which supports frameworks including PyTorch, TensorFlow, and JAX, for enabling high-throughput inference and efficient distributed training while preserving familiar developer workflows.

Jain stated While hardware parity or superiority in memory bandwidth is achievable, software maturity remains the key differentiator for Nvidia. The Nvidia’s CUDA software has a 15-20 year head start, and almost every AI tool, tutorial, and codebase defaults to it.

He added software has been AMD’s weak spot. AMD has improved  ROCm a lot but it still lags behind on the newest, most specialized optimizations, and setup is more complicated. For everyday AI work, ROCm is usable but for cutting-edge performance, CUDA still leads.

Evaluating the trade-offs

For CIOs evaluating AI infrastructure, Helios launch brings in another option to a market that has largely revolved around Nvidia’s dominance. But when considering Helios, CIOs will have to evaluate factors such as performance, software readiness, deployment models, procurement timelines and total cost of ownership before committing to a platform.

While AMD has not publicly announced a specific price tag for the Helios, Jain believes it to be noticeably cheaper to buy and run with lower chip prices and lower power use per GPU.

“It gives companies a real second option besides Nvidia, easing supply shortages and giving leverage in negotiations. The catch is software, where teams need to check whether their AI tools run well on AMD’s stack, since some advanced tools are still CUDA only,” Jain said.

For CIOs planning to deploy both, Jain warns the two systems can’t be plugged together into one combined machine as they use different, incompatible connection technology. But companies can and do run both side by side in the same data center, just as separate systems handling different jobs.

It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.

These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.

Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.   

Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”

The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.

What are spatial intelligence models?

It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies: 

• Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
• World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
• Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
• Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.

“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”

Monitoring the built environment

To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.

Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”

There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”

Avoiding collisions

Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.

“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”

To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs. 

Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.

For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”

Applying spatial intelligence

Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.

“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”

In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”

Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”

Where to get started

My suggestions for developers looking to get hands-on with spatial intelligence and world models:

• To try out Marble, review their API documentation and case studies, and then experiment with a developer-focused React application.
• Review the Nvidia Cosmos developer hub, documentation, and cookbook of case studies and learning paths.
• You can get an overview of Genie 3, but access is currently restricted through Project Genie, which requires a Google AI Ultra subscription.

OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

This article originally appeared on Computerworld.

It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.

These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.

Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.   

Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”

The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.

What are spatial intelligence models?

It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies: 

• Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
• World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
• Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
• Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.

“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”

Monitoring the built environment

To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.

Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”

There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”

Avoiding collisions

Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.

“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”

To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs. 

Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.

For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”

Applying spatial intelligence

Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.

“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”

In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”

Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”

Where to get started

My suggestions for developers looking to get hands-on with spatial intelligence and world models:

• To try out Marble, review their API documentation and case studies, and then experiment with a developer-focused React application.
• Review the Nvidia Cosmos developer hub, documentation, and cookbook of case studies and learning paths.
• You can get an overview of Genie 3, but access is currently restricted through Project Genie, which requires a Google AI Ultra subscription.

hafakot/Shutterstock.com

Auf dem UN-Gipfel „AI for Good“ in Genf hat Amazon Web Services (AWS) eine technologische Brücke vorgestellt, die die wissenschaftliche Arbeit revolutionieren könnte. Ein neuer, quelloffener Server auf Basis des Model Context Protocol (MCP) verbindet KI-Assistenten direkt mit der Registry of Open Data (RODA) auf AWS.

RODA ist eine der weltweit größten Sammlungen frei zugänglicher Forschungsdaten. Sie umfasst über 1.100 Datensätze von mehr als 400 Organisationen, darunter Schwergewichte wie die NASA, die NOAA und die National Institutes of Health (NIH).

Natürliche Sprache statt kryptischer Befehle

Damit will AWS eine Demokratisierung des Datenzugangs einläuten. Forscher müssen keine Experten für Datenbankstrukturen oder Cloud-Speicher (wie Amazon S3) mehr sein. Stattdessen können sie, wie es heißt, Coding-Assistenten wie Kiro oder Claude Code nutzen, um ihre Fragen in natürlicher Sprache zu stellen.

So soll eine Anfrage wie „Welche Satellitenbilddaten gibt es zur Überwachung von Entwaldung?“ ausreichen, um sofort präzise Ergebnisse inklusive Beschreibungen und Datenvorschauen zu erhalten. Dabei fungiert der MCP-Server als intelligenter Vermittler, der den gesamten Katalog durchsucht, Metadaten inspiziert und sogar Dateiinhalte stichprobenartig prüft, um die Eignung für ein Projekt zu bewerten.

Auch Deutschland ist bereits stark in diesem Ökosystem vertreten. Organisationen wie die Audi AG, das Helmholtz-Zentrum Hereon und der Max-Planck-Campus Tübingen stellen bereits Datensätze zur Verfügung.

Einsatzszenarien

Ein entscheidender Aspekt der Initiative ist der Open-Source-Gedanke. Der MCP-Server steht unter der Apache-2.0-Lizenz frei auf GitHub zur Verfügung. Damit haben Forscher weltweit – unabhängig von der Größe oder den finanziellen Ressourcen ihrer Institution – Zugriff auf dieselben mächtigen Werkzeuge wie Elite-Universitäten.

Laut AWS unterstützt das Tool zentrale Forschungsfelder wie:

• die Nachverfolgung von Krankheitsausbrüchen;
• das Monitoring von Biodiversitätsverlusten;
• die Genomforschung und Biowissenschaften; oder
• die Modellierung des Meeresspiegelanstiegs.

OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

Python’s rich ecosystem of data science tools is a big draw for users. The only downside of such a broad and deep collection is that sometimes the best tools can get overlooked.

Here’s a rundown of some of the best newer or less-known data science projects available for Python. Some, like Polars, are getting more attention but still deserve wider notice. Others, like ConnectorX, are hidden gems.

ConnectorX

Most data sits in a database somewhere, but computation typically happens outside of it. Getting data to and from the database for actual work can be a slowdown. ConnectorX loads data from databases into many common data-wrangling tools in Python, and it keeps things fast by minimizing the work required. Most of the data loading can be done in just a couple of lines of Python code and an SQL query.

Like Polars (which I’ll discuss shortly), ConnectorX uses a Rust library at its core. This allows for optimizations like being able to load from a data source in parallel with partitioning. Data in PostgreSQL, for instance, can be loaded this way by specifying a partition column.

Aside from PostgreSQL, ConnectorX also supports reading from MySQL/MariaDB, SQLite, Amazon Redshift, Microsoft SQL Server and Azure SQL, and Oracle. The results can be funneled into a Pandas or PyArrow DataFrame, or into Modin or Dask (via Pandas), or Polars (via PyArrow). General support for reading from ODBC is a work in progress.

DuckDB

Data science folks who use Python ought to be aware of SQLite—a small, but powerful and speedy relational database packaged with Python. Since it runs as an in-process library, rather than a separate application, SQLite is lightweight and responsive.

DuckDB is a little like someone answered the question, “What if we made SQLite for OLAP?” Like other OLAP database engines, it uses a columnar datastore and is optimized for long-running analytical query workloads. But DuckDB gives you all the things you expect from a conventional database, like ACID transactions. And there’s no separate software suite to configure; you can get it running in a Python environment with a single pip install duckdb command.

DuckDB can directly ingest data in CSV, JSON, or Parquet format, as well as a slew of other common data sources. The resulting databases can also be partitioned into multiple physical files for efficiency, based on keys (e.g., by year and month). Querying works like any other SQL-powered relational database, but with additional built-in features like the ability to take random samples of data or construct window functions.

DuckDB also has a small but useful collection of extensions, including full-text search, accelerated vector similarity search, Excel import/export, direct connections to SQLite and PostgreSQL, Parquet file export, and support for many common geospatial data formats and types.

Optimus

One of the least enviable jobs you can be stuck with is cleaning and preparing data for use in a DataFrame-centric project. Optimus is an all-in-one tool set for loading, exploring, cleansing, and writing data back out to a variety of data sources.

Optimus can use Pandas, Dask, CUDF (and Dask + CUDF), Vaex, or Spark as its underlying data engine. Data can be loaded in from and saved back out to Arrow, Parquet, Excel, a variety of common database sources, or flat-file formats like CSV and JSON.

The data manipulation API resembles Pandas, but adds .rows() and .cols() accessors to make it easy to do things like sort a DataFrame, filter by column values, alter data according to criteria, or narrow the range of operations based on some criteria. Optimus also comes bundled with processors for handling common real-world data types like email addresses and URLs.

One possible issue with Optimus is that it’s still under active development but its last official release was in 2020. This means it might not be as current as other components in your stack.

Polars

If you spend much time working with DataFrames and you’re frustrated by the performance limits of Pandas, reach for Polars. This DataFrame library for Python offers a convenient syntax similar to Pandas.

Unlike Pandas, though, Polars uses a library written in Rust that takes maximum advantage of your hardware out of the box. You don’t need to use special syntax to take advantage of performance-enhancing features like parallel processing or SIMD; it’s all automatic. Even simple operations like reading from a CSV file are faster. Rust developers can craft their own Polars extensions using pyo3.

Polars provides eager and lazy execution modes, so queries can be executed immediately or deferred until needed. It also provides a streaming API for processing queries incrementally. Streaming isn’t available yet for many functions, although Polars can always fall back to the in-memory engine for such operations if need be. You can also plot execution graphs for queries, streaming or otherwise, if you want to get an idea of what memory or CPU consumption is like for the query (via the external Graphviz library).

DVC

A major and pervasive issue with data science experiments is version control—not of the project’s code, but its data. DVC, short for Data Version Control, lets you attach version descriptors to datasets, check them into Git as you would the rest of your code, and keep versions of data and code consistent together.

DVC can track most any kind of dataset as long as they can be expressed as a file, whether kept in local storage or in a remote storage service like an Amazon S3 bucket. You can describe how data models are managed and used by way of a “pipeline,” which DVC’s documentation describes as being like “a Makefile system for machine learning projects.”

The use cases for DVC are intended to be more than just allowing data to be versioned alongside code. It also works as a fast data cache for remotely hosted data, a methodology for tracking experiments conducted with data, and a registry or catalog for machine learning models created with the data. Visual Studio Code users can integrate DVC workflows into the editor by way of the DVC VS Code extension.

Cleanlab

Good machine learning datasets are hard to come by, because it’s expensive and time-consuming to create clean, properly labeled data. Sometimes, though, you have no choice but to use data that’s raw and inconsistent. Cleanlab (as in, “cleans labels”) was made for this scenario.

Cleanlab uses existing, high-quality machine learning datasets to analyze lower-quality, unlabeled (or poorly labeled) datasets. You create a model based on the original dataset, use Cleanlab to figure out what needs to be improved in the original dataset, then re-train using your automatically cleaned and adjusted dataset to see the difference.

Cleanlab is data-model and data-framework agnostic, a powerful aspect of its design. It doesn’t matter if you’re running PyTorch, OpenAI, scikit-learn, or Tensorflow; Cleanlab can work with any classifier. It does, however, have specific workflows for common tasks like token classification, multi-labeling, regression, image segmentation and object detection, outlier detection, and so on. It’s worth perusing the example set to see for yourself how the process works and what results you can expect.

Snakemake

Data science workflows are hard to set up, and that’s even harder to do in a consistent, predictable way. Snakemake was created to automate the process, setting up data analysis workflows in ways that ensure everyone gets the same results. Many existing data science projects rely on Snakemake. The more moving parts you have in your data science workflow, the more likely you’ll benefit from automating that workflow with Snakemake.

Snakemake workflows resemble GNU Make workflows—you define the steps of the workflow with rules, which specify what they take in, what they put out, and what commands to execute to accomplish that. Workflow rules can be multithreaded (assuming that gives them any benefit), and configuration data can be piped in from JSON or YAML files. You can also define functions in your workflows to transform data used in rules, and write the actions taken at each step to logs.

Snakemake jobs are designed to be portable—they can be deployed on any Kubernetes-managed environment, or in specific cloud environments like Google Cloud Life Sciences or Tibanna on AWS. Workflows can be “frozen” to use a specific set of packages, and successfully executed workflows can have unit tests automatically generated and stored with them. And for long-term archiving, you can store the workflow as a tarball.

Over its more than 15 years in the wild, Google’s Go programming language has evolved from a curiosity for alpha geeks to the battle-tested programming language behind some of the world’s most important cloud-native software projects.

If you’ve ever wondered why Go is the language of choice for projects like Docker and Kubernetes, this article is for you. We’ll discuss Go’s defining characteristics and how it differs from other programming languages. You will also learn what kinds of projects Go is best suited for, including the state of Go development for AI-powered tools. We’ll conclude with an overview of Go’s feature set, some limitations of the language, and where it may be going from here.

Also see: Golang tutorial: Get started with the Go language.

Go is small and simple

Go, or Golang as it’s often called, was created by Google employees—chiefly longtime Unix guru and Google distinguished engineer Rob Pike—but it’s not strictly speaking a “Google project.” Rather, Go is a community-developed open source project, spearheaded by leadership with strong opinions about how Go should be used and the direction the language should take.

Go is meant to be easy to learn and straightforward to use, with syntax that is simple to read and understand. Go does not have a large feature set, especially when compared to languages like C++. Go’s syntax is reminiscent of C, making it relatively easy for longtime C developers to learn. That said, many features of Go, especially its concurrency and functional programming features, harken back to languages like Erlang.

As a C-like language for building and maintaining cross-platform enterprise applications of all sorts, Go has much in common with Java. And as a means for enabling rapid development of code that might run anywhere, you could draw a parallel between Go and Python, though the differences outweigh the similarities.

The Go documentation describes Go as “a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.” Even a large Go program will compile in a matter of seconds. Plus, Go avoids much of the overhead of C-style include files and libraries.

Advantages of the Go language

Go is a versatile, convenient, fast, portable, interoperable, and widely supported modern language. These characteristics have helped to make it a top choice for large-scale development projects. Let’s look more closely at each of these positive qualities of Go.

Go is versatile and convenient

Go has been compared to interpreted languages like Python in its ability to satisfy many common programming needs. Some of this functionality is built into the language itself, such as goroutines for concurrency and thread-like behavior, while additional capabilities are available in Go standard library packages, like the http package. Like Python, Go provides automatic memory management capabilities including garbage collection.

Unlike interpreted languages, however, Go code compiles to a fast-running native binary. And unlike C or C++, Go compiles extremely fast—fast enough to make working with Go feel more like working with an interpreted language than a compiled one. Further, the Go build system is less complex than those of other compiled languages. It takes few steps and little bookkeeping to build and run a Go project.

Go is faster than many other languages

Go binaries run more slowly than their C counterparts, but the difference in speed is negligible for most applications. Go performance is as good as C for the vast majority of work, and generally much faster than other languages known for speed of development—including JavaScript, Python, and Ruby.

Go is portable and interoperable

Executables created with the Go toolchain can stand alone, with no default external dependencies. The Go toolchain is available for a wide variety of operating systems and hardware platforms, and can be used to compile binaries across platforms. What’s more, Go delivers all of the above without sacrificing access to the underlying system. Go programs can talk to external C libraries or make native system calls. In Docker, for instance, Go interacts with low-level Linux functions, cgroups, and namespaces to work container magic.

Go is widely supported

The Go toolchain is freely available as a Linux, macOS, or Windows binary, or as a Docker container. Go is included by default in many popular Linux distributions, such as Red Hat Enterprise Linux and Fedora, making it somewhat easier to deploy Go source to those platforms. Support for Go is also strong across many third-party development environments, from Microsoft’s Visual Studio Code to ActiveState’s Komodo IDE.

Also see: 8 reasons developers love Go—and 8 reasons they don’t.

Optimal use cases for the Go language

No language is suited to every job, but some languages are suited to more jobs than others. Go shines brightest in cloud-native development projects, distributed network services, and for developing utilities and stand-alone tools. Let’s consider the qualities that make Go especially well-suited to each of these project types.

Cloud-native development

Go’s concurrency and networking features, and its high degree of portability, make it well-suited for building cloud-native apps. In fact, Go was used to build several cornerstones of cloud-native computing including Docker, Kubernetes, and Istio.

Distributed network services

Network applications live and die by concurrency, and Go’s native concurrency features—goroutines and channels, mainly—are well suited for such work. Consequently, many Go projects are for networking, distributed functions, and cloud services. These include APIs, web servers, Kubernetes-ready frameworks for microservices, and much more.

Utilities and standalone tools

Go programs compile to binaries with minimal external dependencies. That makes them ideally suited to creating utilities and other tools, because they launch quickly and can be readily packaged up for redistribution. One example is an access server called Teleport, which can be deployed on servers quickly by compiling it from source or downloading a prebuilt binary.

Limitations of the Go language

Now let’s consider some of the limitations of Go. For one, it omits many language features developers may desire. It also packs everything into its binaries, so Go programs can be large. Furthermore, Go’s garbage collection mechanism delivers automatic memory management at the cost of absolute performance. The language also lacks a standard toolkit for building GUIs, and it is unsuited to systems programming.

Let’s look at each of these issues in detail.

Go omits many desirable language features

Go’s opinionated set of features draws both praise and criticism. Go is designed to err on the side of being small and easy to understand, with certain features deliberately omitted. The result is that some features that are commonplace in other languages simply aren’t available in Go. This is purposeful, but it’s still a drawback for some types of projects.

One thing Go omits that you will find in other languages is macros, commonly defined as the ability to generate program code at compile time. C, C++, and (the rising star) Rust all have macro systems. Go does not have macros, or at least not of the same variety as those languages. What Go does have is a tool command, go generate, which looks for magic comments in Go source and executes them. This can be used to generate Go source code, or even run other commands, but its main use is to programmatically generate code, usually as a precursor to the build process. (Technical blogger Eli Bendersky explains the ‘go generate’ command in detail.)

Another longstanding complaint with Go was, until recently, the lack of generic functions, which allow a function to accept many different types of variables. Go’s development team held out against adding generics to the language for many years because they wanted a syntax and set of behaviors that complemented the rest of Go. But as of Go 1.18, released in early 2022, the language includes a syntax for generics. Because go generate and its code-generation abilities emerged as one possible way to partially address the lack of generics, this functionality is no longer as commonly used in Go.

The fact is that Go adds major language features rarely, and only after much consideration. This works to preserve broad compatibility across versions, but it comes at the cost of slower innovation.

Also see: What you need to know about Go, Rust, and Zig.

Go’s binaries are large

Another potential downside to Go is the size of the generated binaries. Go binaries are statically compiled by default, meaning that everything needed at runtime is included in the binary image. This approach simplifies the build and deployment process, but at the cost of a simple “Hello, world!” weighing in at around 1.5MB on 64-bit Windows. The Go team has been working to reduce the size of those binaries with each successive release. It is also possible to shrink Go binaries with compression or by removing Go’s debug information. This last option may work better for standalone distributed apps than for cloud or network services, where having debug information is useful if a service fails in place.

Go’s garbage collection is resource hungry

Yet another touted feature of Go, automatic memory management, can be seen as a drawback, as garbage collection requires a certain amount of processing overhead. By design, Go doesn’t provide manual memory management, and garbage collection in Go has been criticized for not dealing well with the kinds of memory loads that appear in enterprise applications.

That said, each new version of Go seems to improve the memory management features. For example, Go 1.8 brought significantly shorter lag times for garbage collection, and Go 1.25 introduced a new, experimental garbage collector. While Go developers can use manual memory allocation in a C extension, or by way of a third-party manual memory management library, most prefer native solutions.

Go doesn’t have a standard GUI toolkit

Most Go applications are command-line tools or network services. That said, various projects are working to bring rich GUIs for Go applications. There are bindings for the GTK and GTK3 frameworks. Another project is intended to provide platform-native UIs across platforms, although it focuses on Go 1.24 forward only. But no clear winner or safe long-term bet has emerged in this space. Also, because Go is platform-independent by design, it is unlikely any project in this vein will become a part of the standard package set.

You shouldn’t use Go for systems programming

Finally, although Go can talk to native system functions, it was not designed for developing low-level system components such as kernels, device drivers, or embedded systems. After all, the Go runtime and the garbage collector for Go applications are dependent on the underlying operating system. (Developers interested in a cutting-edge language for that kind of work might look into using Rust.)

The future of the Go language

Go’s development is turning more toward the wants and needs of its developer base, with Go’s minders changing the language to better accommodate this audience rather than leading by stubborn example. A case in point is generics, which were finally added to the language after much deliberation about the best way to do so.

The 2024 Go Developer Survey found developers were overall satisfied with Go. Challenges that surfaced were generally due to the verbosity of error handling, missing or immature frameworks, and using Go’s type system—areas ripe for future development.

Like most languages, Go has gravitated to a core set of use cases over time, finding its niche in network services. In the future, Go is likely to continue expanding its hold there. Other use cases cited in the developer survey include creating APIs or RPC services (74% of respondents), followed by CLI applications (63%), web services (45%), libraries/frameworks (44%), automation (39%), and data processing (37%). While only 4% of respondents mentioned using Go to develop AI technologies, those who did reported that Go was a strong platform for running AI-powered workloads in production. For those wanting to develop ML/AI with Go, lack of tooling (23%) and the fact that Python is the default choice for such work (16%) topped the reasons why.

It remains to be seen how far Go’s speed and development simplicity will take it into other use cases, especially those dominated by other languages and their existing use cases. Rust covers safe and fast systems programming (a space Go is unlikely to enter); Python is still a common default for ML/AI, prototyping, automation, and glue code; and Java remains a stalwart for enterprise applications.

But Go’s future as a major programming language is already assured—certainly in the cloud, where the speed and simplicity of Go ease the development of scalable infrastructure that can be maintained over the long run.

Also see: Go language evolving for future hardware, AI workloads.

Node.js is a popular and versatile cross-platform JavaScript runtime environment. Node was the first runtime to allow developers to run JavaScript outside the browser, opening a new world of possibilities in server-side JavaScript. Its ease of use, massive ecosystem and performance characteristics have continued to secure its place as one of the most important technologies of the modern web.

Anytime you need to run JavaScript on the server—be it for a systems utility, a REST API, data processing, or anything else—Node is an excellent choice. There are newer runtimes, namely Deno and Bun, but Node remains the standard for server-side JavaScript.

Also see: 10 JavaScript concepts you need to succeed with Node.

Getting started with Node

If you haven’t already experienced Node, this article will introduce you. We’ll step through installing Node and the NPM package manager, spinning up a simple web server, and using the Node cluster module to take advantage of multiple CPU cores.

We’ll also look at using the NPM package manager to install additional Node modules and other JavaScript packages. And we’ll dip a toe into using a Node framework, in this case the ubiquitous Express server, to create more feature-rich and flexible Node.js servers. Let’s get started!

Installing Node and NPM

There are a few ways to install Node, including the installer that Node itself provides, but the recommended way is with a version manager. The most common version manager is NVM. This makes it easy to install Node and change versions when you need to. (There is also a Microsoft Windows-specific version called nvm-windows.)

NVM can be installed with an installer or using a CLI. In the following example, we use curl:

$ curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash

Once you have NVM installed, installing the most recent version of Node is simple:

$ nvm install latest

The install latest command makes the latest version available. Mine is Node 24.9.0, so I activate it with:

$ nvm use 24.9.0

Anytime you need to install another version of Node, you can use nvm install and nvm use to switch between them.

You should now see Node available at your command prompt:

$ node -v

v24.9.0

When you install Node this way, the Node package manager (NPM) is also installed:

$ npm -v

11.6.0

Note that using NVM also avoids potential permissions issues with NPM packages when using the installer.

A simple web server in Node

To start simply, we can use an example from the Node homepage. Copy the Synopsis example code as directed there and paste it into your code editor, then save it as example.js:

const http = require('node:http');

const hostname = '127.0.0.1';
const port = 3000;

const server = http.createServer((req, res) => {
  res.statusCode = 200;
  res.setHeader('Content-Type', 'text/plain');
  res.end('Hello, InfoWorld!\n');
});

server.listen(port, hostname, () => {
  console.log(`Server running at http://${hostname}:${port}/`);
});

Open a shell in the directory where you saved the file, and run the file from your command line:

$ node example.js

Server running at http://127.0.0.1:3000/

You can now go to the browser and check it out at 127.0.0:3000, and you should see a simple greeting. Back at the terminal, press Control-C to stop the running server.

Before we go further, let’s pull apart the code.

Creating a simple HTTP server with Node

We start with the command:

const http = require(‘http’);

This is how you include a module in your code, in this case, the standard http module. (The http module ships with Node, so you don’t have to add it as a dependency.) This module provides the createServer and listen functions we’ll use later on.

You might have noted that this example used a CommonJS import. While older, this style of import is still very common in Node programs as well as some documentation. However, it’s gradually being phased out in favor of ES Modules (ESM), the standardized module system introduced in ECMAScript 2015. An ESM import would look like this:

import http from 'http';

After we import the http module, we define a couple of values we need (hostname and port):

const hostname = '127.0.0.1';

const port = 3000;

Next, we create the server:

const server = http.createServer((req, res) => {
  res.statusCode = 200;
  res.setHeader(‘Content-Type’, ‘text/plain’);
  res.end(‘Hello World\n’);
});

The creatServer command accepts a callback function, which we define using the fat arrow notation. The callback function passes two arguments, the request (req) and response (res) objects needed to handle HTTP requests. The req argument contains the incoming HTTP request, which in this case is ignored. The res.end method sets the response data to ‘Hello InfoWorld\n’ and tells the server that it is done creating the response.

Next, we have:

server.listen(port, hostname, () => {
  console.log(`Server running at http://${hostname}:${port}/`);
});

The server.listen function accepts three arguments. The first two are the port and hostname, and the third is a callback that is executed when the server is ready (in this case, it prints a message to the console).

Having all the event handlers defined as callbacks is one of the most subtle and powerful parts of Node. It’s key to Node’s asynchronous non-blocking architecture.

Node.js runs on an event loop, which always reverts to handling events when not otherwise engaged. It’s like a busy order-taker continually picking up orders and then updating the order-maker with their order. We receive updates via the callbacks.

A multi-process web server with Node

Node’s asynchronous, non-blocking nature makes it good at handling many parallel requests, but it’s not truly concurrent by default. There are a few ways to make a Node application use multiple threads for true concurrency. One of the simplest is to use the PM2 project, which lets you run the same Node application in many processes.

By launching each application instance in its own process, the operating system can make use of multiple cores on the machine. This is not usually a concern at first, but it’s a key performance consideration to bear in mind.

You can install PM2 globally like so:

$ npm install -g pm2

For our example, we want to make it obvious that the different processes are handling requests. We can achieve that goal with a small change to the server:

res.end(`Hello, InfoWorld! Handled by ${process.pid}`);

The process.pid field is a built-in environment variable, providing a unique ID for the currently running process in Node. Once PM2 is installed and the app is updated, we can run it like so:

$ pm2 start example.js -i max

That should launch several instances of the same program, as shown here:

Matthew Tyson

Then, if you open multiple windows, you can see the unique ID of each instance:

Matthew Tyson

An Express web server with Node

For our final example, we’ll look at setting up an Express web server in Node. This time we’ll use NPM to download Express and its dependencies. NPM is one of the greatest storehouses of software on the planet, with literally millions of libraries available. Knowing how to use it is essential for working with Node.

NPM works just like other package managers you may have used, letting you define and install dependencies in a structured way. To install Express, go to your project directory and type:

$ npm install express

Node should respond with something like: added 68 packages in 5s.

You will notice several directories have been added to a /node_modules directory. Those are all the dependencies needed for Express. You usually don’t have to interact with node_modules yourself, but it’s good to know that’s where things are saved.

Now look at the package.json file, which will have something like this in it:

{
  "dependencies": {
	"express": "^5.1.0"
  }
}

This is how dependencies are defined in NPM. It says the application needs the express dependency at version 5.1.0 (or greater).

Setting up the Express server in Node

Express is one of the most-deployed pieces of software on the Internet. It can be a minimalist server framework for Node that handles all the essentials of HTTP, and it’s also expandable using “middleware” plugins.

Since we’ve already installed Express, we can jump right into defining a server. Open the example.js file we used previously and replace the contents with this simple Express server:

import express from 'express';

const app = express();
const port = 3000;

app.get('/', (req, res) => {
  res.send('Hello, InfoWorld!');
});

app.listen(port, () => {
  console.log(`Express server at http://localhost:${port}`);
});

This program does the same thing as our earlier http module version. The most important change is that we’ve added routing. Express makes it easy for us to associate a URL path, like the root path (‘/’), with the handler function.

If we wanted to add another path, it could look like this:

app.get('/about', (req, res) => {
  res.send('This is the About page.');
});

Once we have the basic web server set up with one or more paths, we’ll probably need to create a few API endpoints that respond with JSON. Here’s an example of a route that returns a JSON object:

app.get('/api/user', (req, res) => {
  res.json({
	id: 1,
	name: 'John Doe',
	role: 'Admin'
  });
});

That’s a simple example, but it gives you a taste of working with Express in Node.

Conclusion

In this article you’ve seen how to install Node and NPM and how to set up both simple and more advanced web servers in Node. Although we’ve only touched on the basics, these examples demonstrate many elements that are required for all Node applications, including the ability to import modules.

Whenever you need a package to do something in Node, you will more than likely find it available on NPM. Visit the official site and use the search feature to find what you need. For more information about a package, you can use the npms.io tool. Keep in mind that a project’s health depends on its weekly download metric (visible on NPM for the package itself). You can also check a project’s GitHub page to see how many stars it has and how many times it’s been forked; both are good measures of success and stability. Another important metric is how recently and frequently the project is updated and maintained. That information is also visible on a project’s GitHub Insights page.

Node.js is one of the most popular server-side platforms, especially for web applications. It gives you non-blocking JavaScript without a browser, plus an enormous ecosystem. That ecosystem is one of Node’s chief strengths, making it a go-to option for server development.

This article is a quick tour of the most popular web frameworks for server development on Node.js. We’ll look at minimalist tools like Express.js, batteries-included frameworks like Nest.js, and full-stack frameworks like Next.js. You’ll get an overview of the frameworks and a taste of what it’s like to write a simple server application in each one.

Minimalist web frameworks

When it comes to Node web frameworks, minimalist doesn’t mean limited. Instead, these frameworks provide the essential features required to do the job for which they are intended. The frameworks in this list also tend to be highly extensible, so you can customize them as needed. With minimalist frameworks, pluggable extensibility is the name of the game.

Express.js

At over 47 million weekly downloads on npm, Express is one of the most-installed software packages of all time—and for good reason. Express gives you basic web endpoint routing and request-and-response handling inside an extensible framework that is easy to understand. Most other frameworks in this category have adopted the basic style of describing a route from Express. This framework is the obvious choice when you simply need to create some routes for HTTP, and you don’t mind a DIY approach for anything extra.

Despite its simplicity, Express is fully-featured when it comes to things like route parameters and request handling. Here is a simple Express endpoint that returns a dog breed based on an ID:

import express from 'express';

const app = express();
const port = 3000;

// In-memory array of dog breeds
const dogBreeds = [
  "Shih Tzu",
  "Great Pyrenees",
  "Tibetan Mastiff",
  "Australian Shepherd"
];
app.get('/dogs/:id', (req, res) => {
  // Convert the id from a string to an integer
  const id = parseInt(req.params.id, 10);

  // Check if the id is a valid number and within the array bounds
  if (id >= 0 && id  {
  console.log(`Server running at http://localhost:${port}`);
});

You can easily see how the route is defined here: a string representation of a URL, followed by a function that receives a request and response object. The process of creating the server and listening on a port is simple.

If you are coming from a framework like Next, the biggest thing you might notice about Express is that it lacks a file-system based router. On the other hand, it offers a huge range of middleware plugins to help with essential functions like security.

Koa

Koa was created by the original creators of Espress, who took the lessons learned from that project and used them for a fresh take on the JavaScript server. Koa’s focus is providing a minimalist core engine. It uses async/await functions for middleware rather than chaining with next() calls. This can give you a cleaner server, especially when there are many plugins. It also makes the error handling less clunky for middleware.

Koa also differs from Express by exposing a unified context object instead of separate request and response objects, which makes for a somewhat less cluttered API. Here is how Koa manages the same route we created in Express:

router.get('/dogs/:id', (ctx) => {
  const id = parseInt(ctx.params.id, 10);

  if (id >= 0 && id < dogBreeds.length) {
    ctx.status = 200;
    ctx.body = { breed: dogBreeds[id] };
  } else {
    ctx.status = 404;
    ctx.body = { error: 'Dog breed not found' };
  }
});

The only real difference is the combined context object.

Koa’s middleware mechanism is also worth a look. Here’s a simple logging plugin in Koa:

const logger = async (ctx, next) => {
  await next(); // This passes control to the router
  console.log(`${ctx.method} ${ctx.url} - ${ctx.status}`);
};

// Use the logger middleware for all requests
app.use(logger);	

Fastify

Fastify lets you define schemas for your APIs. This is an up-front, formal mechanism for describing what the server supports:

const schema = {
  params: {
    type: 'object',
    properties: {
      id: { type: 'integer' }
    }
  },
  response: {
    200: {
      type: 'object',
      properties: {
        breed: { type: 'string' }
      }
    },
    404: {
      type: 'object',
      properties: {
        error: { type: 'string' }
      }
    }
  }
};

fastify.get('/dogs/:id', { schema }, (request, reply) => {
  const id = request.params.id;

  if (id >= 0 && id  {
  if (err) {
    fastify.log.error(err);
    process.exit(1);
  }
  console.log(`Server running at ${address}`);
});

From this example, you can see the actual endpoint definition is similar to Express and Koa, but we define a schema for the API. The schema is not strictly necessary; it is possible to define endpoints without it. In that case, Fastify behaves much like Express, but with superior performance.

Hono

Hono emphasizes simplicity. You can define a server and endpoint with as little as:

const app = new Hono()
app.get('/', (c) => c.text('Hello, Infoworld!'))  

And here’s how our dog breed example looks:

app.get('/dogs/:id', (c) => {
  // Get the id parameter from the request URL
  const id = parseInt(c.req.param('id'), 10);

  // Check if the id is a valid number and within the array bounds
  if (id >= 0 && id < dogBreeds.length) {
    // Return a JSON response with a 200 OK status (default)
    return c.json({ breed: dogBreeds[id] });
  } else {
    // Set status to 404 and return a JSON error message
    c.status(404);
    return c.json({ error: 'Dog breed not found' });
  }
});

As you can see, Hono provides a unified context object, similar to Koa.

Nitro.js

Nitro is the back end for several full-stack frameworks, including Nuxt.js. As part of the UnJS ecosystem, Nitro goes further than Express in providing cloud-native tooling support. It includes a universal storage adapter and deployment support for serverless and cloud deployment targets.

Also see: Intro to Nitro: The server engine built for modern JavaScript.

Like Next.js, Nitro uses filesystem-based routing, so our Dog Finder API would exist at the following filepath:

/api/dogs/:id

The handler might look like this:

export default defineEventHandler((event) => {
  // Get the dynamic parameter from the event context
  const { id } = getRouterParams(event);
  const parsedId = parseInt(id, 10);

  // Check if the id is a valid number and within the array bounds
  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    // Nitro handles JSON serialization
    return { breed: dogBreeds[parsedId] };
  } else {
    setResponseStatus(event, 404);
    return { error: 'Dog breed not found' };
  }
});

Nitro inhabits the middle ground between a pure tool like Express and a full-blown stack, which is why full-stack front ends often use Nitro on the back end.

Batteries-included frameworks

Although Express and other minimalist frameworks set the standard for simplicity, more opinionated frameworks can be useful if you want additional features out of the box.

Nest.js

Nest is a progressive framework built with TypeScript from the ground up. Nest is actually a layer on top of Express (or Fastify), with additional services. It is inspired by Angular and incorporates the kind of architectural support found there. In particular, it includes dependency injection. Nest also uses annotated controllers for endpoints.

Also see: Intro to Nest.js: Server-side JavaScript development on Node.

Here is an example of injecting a dog finder provider into a controller:

// The provider:
import { Injectable, NotFoundException } from '@nestjs/common';

// The @Injectable() decorator marks this class as a provider.
@Injectable()
export class DogsService {
  private readonly dogBreeds = [
    "Shih Tzu",
    "Great Pyrenees",
    "Tibetan Mastiff",
    "Australian Shepherd"
  ];

  findOne(id: number) {
    if (id >= 0 && id < this.dogBreeds.length) {
      return { breed: this.dogBreeds[id] };
    }
    // NestJS has built-in HTTP exception classes for common errors.
    throw new NotFoundException('Dog breed not found');
  }
}

// The controller

import { Controller, Get, Param, ParseIntPipe } from '@nestjs/common';
import { DogsService } from './dogs.service';

@Controller('dogs')
export class DogsController {
  // NestJS injects the DogsService through the constructor.
  // The 'private readonly' syntax is a TypeScript shorthand
  // to both declare and initialize the dogsService member.
  constructor(private readonly dogsService: DogsService) {}

  @Get(':id')
  findOneDog(@Param('id', ParseIntPipe) id: number) {
    // We can now use the service's methods. The ParseIntPipe
    // automatically converts the string URL parameter to a number.
    return this.dogsService.findOne(id);
  }
}

This style is typical of dependency injection frameworks like Angular, as well as Spring. It allows you to declare components as injectable, then consume them anywhere you need them.

In Nest, we’d just add these as modules to make them live.

Adonis.js

Like Nest, Adonis provides a controller layer that you wire together with routes. Adonis is inspired by the model-view-controller (MVC) pattern, so it also includes a layer for modelling data and accessing stores via an ORM. Finally, it provides a validator layer for ensuring data meets requirements.

Routes in Adonis are very simple:

Route.get('/dogs/:id', [DogsController, 'show'])

In this case, DogsController would be the handler for the route, and might look something like:

import type { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'  // Note, ioc means inversion of control, similar to dependency injection

export default class DogsController {
  // The 'show' method handles the logic for the route
  public async show({ params, response }: HttpContextContract) {
    const id = Number(params.id);

    // Check if the id is a valid number and within the array bounds
    if (!isNaN(id) && id >= 0 && id < this.dogBreeds.length) {
      // Use the response object to send a 200 OK JSON response
      return response.ok({ breed: this.dogBreeds[id] });
    } else {
      // Send a 404 Not Found response
      return response.notFound({ error: 'Dog breed not found' });
    }
  }
}

Of course, in a real application, we could define a model layer to handle the actual data access.

Sails

Sails is another MVC-style framework. It is one of the original one-stop-shopping frameworks for Node and includes an ORM layer (Waterline), API generation (Blueprints), and realtime support, including WebSockets.

Sails strives for conventional operation. For example, here’s how you might define a simple model for dogs:

/**
 * Dog.js
 *
 * @description :: A model definition represents a database table/collection.
 * @docs        :: https://sailsjs.com/docs/concepts/models
 */
module.exports = {
  attributes: {
    breed: { type: 'string', required: true },
  },
};

If you run this in Sails, the framework will generate default routes and wire up a NoSQL or SQL datastore based on your configuration. Sails also provides the option to override these defaults and add in your own custom logic.

Full-stack frameworks

Also known as meta-frameworks, these tools combine a front-end framework with a solid back end and various CLI niceties like build chains.

Next.js

Next is a React-based framework built by Vercel. It is largely responsible for the huge growth in popularity of these types of frameworks. Next was the first framework to bring together back-end API definitions with the front end that consumes them. It also introduced file-system routing. In Next and other full-stack frameworks, you get both parts of your stack in one place and you can run them together during development.

In Next, we could define a route at pages/api/dogs/[id].js like so:

export default function handler(req, res) {
  // `req.query.id` comes from the dynamic filename [id].js
  const { id } = req.query;
  const parsedId = parseInt(id, 10);

  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    // If the ID is valid, return the data
    res.status(200).json({ breed: dogBreeds[parsedId] });
  } else {
    // Otherwise, return a 404 error
    res.status(404).json({ error: 'Dog breed not found' });
  }
}

We’d then define the UI component to interact with this route at pages/dogs/[id].js:

import React from 'react';

// This is the React component that renders the page.
// It receives the `dog` object as a prop from getServerSideProps.
function DogPage({ dog }) {
  // Handle the case where the dog wasn't found
  if (!dog) {
    return 
Dog Breed Not Found
;
  }

  return (
    

      
Dog Breed Profile
      
Breed Name: {dog.breed}
    
  );
}

// This function runs on the server before the page is sent to the browser.
export async function getServerSideProps(context) {
  const { id } = context.params; // Get the ID from the URL

  // Fetch data from our own API route on the server.
  const res = await fetch(`http://localhost:3000/api/dogs/${id}`);
  
  // If the fetch was successful, parse the JSON.
  const dog = res.ok ? await res.json() : null;

  // Pass the fetched data to the DogPage component as props.
  return {
    props: {
      dog,
    },
  };
}

export default DogPage;

Nuxt.js

Nuxt is the same idea as Next, but applied to the Vue front end. The basic pattern is the same, though. First, we’d define a back-end route:

// server/api/dogs/[id].js

// defineEventHandler is Nuxt's helper for creating API handlers.
export default defineEventHandler((event) => {
  // Nuxt automatically parses route parameters.
  const id = getRouterParam(event, 'id');
  const parsedId = parseInt(id, 10);

  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    return { breed: dogBreeds[parsedId] };
  } else {
    // Helper to set the status code and return an error.
    setResponseStatus(event, 404);
    return { error: 'Dog breed not found' };
  }
});

Then, we’d create the UI file in Vue:

// pages/dogs/[id].vue


  

    

      Loading...
    
    

      
{{ error.data.error }}
    
    

      
Dog Breed Profile
      
Breed Name: {{ dog.breed }}
    
  

SvelteKit

SvelteKit is the full-stack framework for the Svelte front end. It’s similar to Next and Nuxt, with the main difference being the front-end technology.

In SvelteKit, a back-end route looks like so:

// src/routes/api/dogs/[id]/+server.js

import { json, error } from '@sveltejs/kit';

// This is our data source for the example.
const dogBreeds = [
  "Shih Tzu",
  "Australian Cattle Dog",
  "Great Pyrenees",
  "Tibetan Mastiff",
];

/** @type {import('./$types').RequestHandler} */
export function GET({ params }) {
  // The 'id' comes from the [id] directory name.
  const id = parseInt(params.id, 10);

  if (id >= 0 && id < dogBreeds.length) {
    // The json() helper creates a valid JSON response.
    return json({ breed: dogBreeds[id] });
  }

  // The error() helper is the idiomatic way to return HTTP errors.
  throw error(404, 'Dog breed not found');
}

SvelteKit usually splits the UI into two components. The first component is for loading the data (which can then be run on the server):

// src/routes/dogs/[id]/+page.js

import { error } from '@sveltejs/kit';

/** @type {import('./$types').PageLoad} */
export async function load({ params, fetch }) {
  // Use the SvelteKit-provided `fetch` to call our API endpoint.
  const response = await fetch(`/api/dogs/${params.id}`);

  if (response.ok) {
    const dog = await response.json();
    // The object returned here is passed as the 'data' prop to the page.
    return {
      dog: dog
    };
  }

  // If the API returns an error, forward it to the user.
  throw error(response.status, 'Dog breed not found');
}

The second component is the UI:

// src/routes/dogs/[id]/+page.svelte




  
Dog Breed Profile
  
Breed Name: {data.dog.breed}

Conclusion

The Node.js ecosystem has moved beyond the “default-to-Express” days. Now, it is worth your time to look for a framework that fits your specific situation.

If you are building microservices or high-performance APIs, where every millisecond counts, you owe it to yourself to look at minimalist frameworks like Fastify or Hono. This class of frameworks gives you raw speed and total control without requiring decisions about infrastructure.

If you are building an enterprise monolith or working with a big team, batteries-included frameworks like Nest or Adonis offer useful structure. The complexity of the initial setup buys you long-term maintainability and makes the codebase more standardized for new developers.

Finally, if your project is a content-rich web application, full-stack meta-frameworks like Next, Nuxt, and SvelteKit offer the best developer experience and the perfect profile of tools.

It’s also worth noting that, while Node remains the standard server-side runtime, alternatives Deno and Bun have both made a name for themselves. Deno has great heritage, is open source with a strong security focus, and has its own framework, Deno Fresh. Bun is respected for its ultra-fast startup and integrated tooling.

Django is a one-size-fits-all Python web framework that was inspired by Ruby on Rails and uses many of the same metaphors to make web development fast and easy. Fully loaded and flexible, Django has become one of Python’s most widely used web frameworks.

Now in version 6.0, Django includes virtually everything you need to build a web application of any size, and its popularity makes it easy to find examples and help for various scenarios. Plus, Django provides tools to allow your application to evolve and add features gracefully, and to migrate its data schema if there is one.

Django also has a reputation for being complex, with many components and a good deal of “under the hood” configuration required. In truth, you can use Django to get a simple Python application up and running in relatively short order, then expand its functionality as needed.

This article guides you through creating a basic application using Django 6.0. We’ll also touch on the most crucial features for web developers in the Django 6 release.

Installing Django

Assuming you have Python 3.12 or higher installed, the first step to installing Django is to create a virtual environment. Installing Django in the venv keeps Django and its associated libraries separate from your base Python installation, which is always a good practice.

Next, install Django in your chosen virtual environment via Python’s pip utility:

pip install django

This installs the core Django libraries and the django-admin command-line utility used to manage Django projects.

Creating a new Django project

Django instances are organized into two tiers: projects and apps.

• A project is an instance of Django with its own database configuration, settings, and apps. It’s best to think of a project as a place to store all the site-level configurations you’ll use.
• An app is a subdivision of a project, with its own route and rendering logic. Multiple apps can be placed in a single Django project.

To create a new Django project from scratch, activate the virtual environment where you have Django installed. Then enter the directory where you want to store the project and type:

django-admin startproject 

The is the name of both the project and the subdirectory where the project will be stored. Be sure to pick a name that isn’t likely to collide with a name used by Python or Django internally. A name like myproj works well.

The newly created directory should contain a manage.py file, which is used to control the app’s behavior from the command line, along with another subdirectory (also with the project name) that contains the following files:

• An __init__.py file, which is used by Python to designate a subdirectory as a code module.
• settings.py, which holds the settings used for the project. Many of the most common settings will be pre-populated for you.
• urls.py, which lists the routes or URLs available to your Django project, or that the project will return responses for.
• wsgi.py, which is used by WSGI-compatible web servers, such as Apache HTTP or Nginx, to serve your project’s apps.
• asgi.py, which is used by ASGI-compatible web servers to serve your project’s apps. ASGI is a relatively new standard for asynchronous servers and applications, and requires a server that supports it, like uvicorn. Django only recently added native support for asynchronous applications, which will also need to be hosted on an async-compatible server to be fully effective.

Next, test the project to ensure it’s functioning. From the command line in the directory containing your project’s manage.py file, enter:

python manage.py runserver

This should start a development web server available at http://127.0.0.1:8000/. Visit that link and you should see a simple welcome page that tells you the installation was successful.

Note that the development web server should not be used to serve a Django project to the public. It’s solely for local testing and is not designed to scale for public-facing applications.

Creating a Django application

Next, we’ll create an application inside of this project. Navigate to the same directory as manage.py and issue the following command:

python manage.py startapp myapp

This creates a subdirectory for an application named myapp that contains the following:

• A migrations directory: Contains code used to migrate the site between versions of its data schema. Django projects typically have a database, so the schema for the database—including changes to the schema—is managed as part of the project.
• admin.py: Contains objects used by Django’s built-in administration tools. If your app has an admin interface or privileged users, you will configure the related objects here.
• apps.py: Provides configuration information about the app to the project at large, by way of an AppConfig object.
• models.py: Contains objects that define data structures, used by your app to interface with databases.
• tests.py: Contains any tests created by you and used to ensure that your site’s functions and modules are working as intended.
• views.py: Contains functions that render and return responses.

To start working with the application, you need to first register it with the project. Edit myproj/settings.py as follows, adding a line to the top of the INSTALLED_APPS list:

INSTALLED_APPS = [
    "myapp.apps.MyappConfig",
    "django.contrib.admin",
    ...

If you look in myproj/myapp/apps.py, you’ll see a pre-generated object named MyappConfig, which we’ve referenced here.

Adding routes and views to your Django application

Django applications follow a basic pattern for processing requests:

• When an incoming request is received, Django parses the URL for a route to apply it to.
• Routes are defined in urls.py, with each route linked to a view, meaning a function that returns data to be sent back to the client. Views can be located anywhere in a Django project, but they’re best organized into their own modules.
• Views can contain the results of a template, which is code that formats requested data according to a certain design.

To get an idea of how all these pieces fit together, let’s modify the default route of our sample application to return a custom message.

Routes are defined in urls.py, in a list named urlpatterns. If you open the sample urls.py, you’ll see urlpatterns already predefined:

urlpatterns = [
    path('admin/', admin.site.urls),
]

The path function (a Django built-in) takes a route and a view function as arguments and generates a reference to a URL path. By default, Django creates an admin path that is used for site administration, but we need to create our own routes.

Add another entry, so that the whole file looks like this:

from django.contrib import admin
from django.urls import include, path

urlpatterns = [
    path('admin/', admin.site.urls),
    path('myapp/', include('myapp.urls'))
]

The include function tells Django to look for more route pattern information in the file myapp.urls. All routes found in that file will be attached to the top-level route myapp (e.g., http://127.0.0.1:8080/myapp).

Next, create a new urls.py in myapp and add the following:

from django.urls import path
from . import views

urlpatterns = [
    path('', views.index)
]

Django prepends a slash to the beginning of each URL, so to specify the root of the site (/), we just supply a blank string as the URL.

Now, edit the file myapp/views.py so it looks like this:

from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello, world!")

django.http.HttpResponse is a Django built-in that generates an HTTP response from a supplied string. Note that request, which contains the information for an incoming HTTP request, must be passed as the first parameter to a view function.

Stop and restart the development server, and navigate to http://127.0.0.1:8000/myapp/. You should see “”Hello, world!” appear in the browser.

Adding routes with variables in Django

Django can accept routes that incorporate variables as part of their syntax. Let’s say you wanted to accept URLs that had the format year/. You could accomplish that by adding the following entry to urlpatterns:

path(‘year/’, views.year)

The view function views.year would then be invoked through routes like year/1996, year/2010, and so on, with the variable year passed as a parameter to views.year.

To try this out for yourself, add the above urlpatterns entry to myapp/urls.py, then add this function to myapp/views.py:

def year(request, year):
    return HttpResponse('Year: {}'.format(year))
    

If you navigate to /myapp/year/2010 on your site, you should see Year: 2010 displayed in response. Note that routes like /myapp/year/rutabaga will yield an error because the int: constraint on the variable year allows only an integer in that position. Many other formatting options are available for routes.

Django templates and template partials

You can use Django’s built-in template language to generate web pages from data.

Templates used by Django apps are stored in a directory that is central to the project: /templates//. For our myapp project, the directory would be myapp/templates/myapp/. This directory structure may seem awkward, but allowing Django to look for templates in multiple places avoids name collisions between templates with the same name across multiple apps.

In your myapp/templates/myapp/ directory, create a file named year.html with the following content:

Year: {{year}}

Any value within double curly braces in a template is treated as a variable. Everything else is treated literally.

Modify myapp/views.py to look like this:

from django.shortcuts import render
from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello, world!")

def year(request, year):
    data = {'year':year}
    return render(request, 'myapp/year.html', data)

The render function—a Django “shortcut” (a combination of multiple built-ins for convenience)—takes the existing request object, looks for the template myapp/year.html in the list of available template locations, and passes the dictionary data to it as context for the template. The template uses the dictionary as a namespace for variables used in the template. In this case, the variable {{year}} in the template is replaced with the value for the key year in the dictionary data (that is, data["year"]).

The amount of processing you can do on data within Django templates is intentionally limited. Django’s philosophy is to enforce the separation of presentation and business logic whenever possible. Thus, you can loop through an iterable object, and you can perform if/then/else tests, but modifying the data within a template is discouraged.

For instance, you could encode a simple “if” test this way:

{% if year > 2000 %}
21st century year: {{year}}
{% else %}
Pre-21st century year: {{year}}
{% endif %}

The {% and %} markers delimit blocks of code that can be executed in Django’s template language.

If you want to use a more sophisticated template processing language, you can swap in something like Jinja2 or Mako. Django includes back-end integration for Jinja2, but you can use any template language that returns a string—for instance, by returning that string in an HttpResponse object, as in the case of our “Hello, world!” route.

In versions 6 and up, Django supports template partials, a way to create portions of a template that can be defined once and reused throughout a template. This lets you precompute a given value once over the course of a given template—such as a fancy display version of a user name—and re-use it without having to recompute it each time it’s displayed.

Doing more with Django

What you’ve seen here covers only the most basic elements of a Django application. Django includes a great many other components for use in web projects. Here’s a quick overview:

• Databases and data models: Django’s built-in ORM lets you define data structures and relationships between them, as well as migration paths between versions of those structures.
• Forms: Django provides a consistent way for views to supply input forms to a user, retrieve data, normalize the results, and provide consistent error reporting. Django 6 added support for Content Security Policy, a way to prevent submitted forms from being vulnerable to content injection or cross-site scripting (XSS) attacks.
• Security and utilities: Django includes many built-in functions for caching, logging, session handling, handling static files, and normalizing URLs. It also bundles tools for common security needs like using cryptographic certificates or guarding against cross-site forgery protection or clickjacking.
• Tasks: Django 6 added a native mechanisms for creating and managing long-running background tasks, without holding up a response to the user. Note that Django only provides ways to set up and keep track of tasks; it doesn’t include the actual execution mechanism. The only included back ends for tasks are for testing, so you will either need to add a third-party solution or write your own using Django’s back-end task code as a base.

What is cloud native? Cloud native defined

The term “cloud-native computing” encompasses the modern approach to building and running software applications that exploit the flexibility, scalability, and resilience of cloud computing. The phrase is a catch-all that encompasses not just the specific architecture choices and environments used to build applications for the public cloud, but also the software engineering techniques and philosophies used by cloud developers.

The Cloud Native Computing Foundation (CNCF) is an open source organization that hosts many important cloud-related projects and helps set the tone for the world of cloud development. The CNCF offers its own definition of cloud native:

Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable.

Cloud native technologies and architectures typically consist of some combination of containers, service meshes, multi-tenancy, microservices, immutable infrastructure, serverless, and declarative APIs — this list is not exhaustive.

This definition is a good start, but as cloud infrastructure becomes ubiquitous, the cloud native world is beginning to spread behind the core of this definition. We’ll explore that evolution as well, and look into the near future of cloud-native computing.

Cloud native architectural principles

Let’s start by exploring the pillars of cloud-native architecture. Many of these technologies and techniques were considered innovative and even revolutionary when they hit the market over the past few decades, but now have become widely accepted across the software development landscape.

Microservices. One of the huge cultural shifts that made cloud-native computing possible was the move from huge, monolithic applications to microservices: small, loosely coupled, and independently deployable components that work together to form a cloud-native application. These microservices can be scaled across cloud environments, though (as we’ll see in a moment) this makes systems more complex.

Containers and orchestration. In could-native architectures, individual microservices are executed inside containers — lightweight, portable virtual execution environments that can run on a variety of servers and cloud platforms. Containers insulate the developers from having to worry about the underlying machines on which their code will execute. That is, all they have to do is write to the container environment. 

Getting the containers to run properly and communicate with one another is where the complexity of cloud native computing starts to emerge. Initially, containers were created and managed by relatively simple platforms, the most common of which was Docker. But as cloud-native applications got more complex, container orchestration platforms that augmented Docker’s functionality emerged, such as Kubernetes, which allows you to deploy and manage multi-container applications at scale. Kubernetes is critical to cloud native computing as we know it — it’s worth noting that the CNCF was set up as a spinoff of the Linux Foundation on the same day that Kubernetes 1.0 was announced — and adhering to Kubernetes best practices is an important key to cloud native success. 

Open standards and APIs. The fact that containers and cloud platforms are largely defined by open standards and open source technologies is the secret sauce that makes all this modularity and orchestration possible, and standardized and documented APIs offer the means of communication between distributed components of a larger application. In theory, anyway, this standardization means that every component should be able to communicate with other components of an application without knowing about their inner workings, or about the inner workings of the various platform layers on which everything operates.

DevOps, agile methodologies, and infrastructure as code. Because cloud-native applications exist as a series of small, discrete units of functionality, cloud-native teams can build and update them using agile philosophies like DevOps, which promotes rapid, iterative CI/CD development. This enables teams to deliver business value more quickly and more reliably.

The virtualized nature of cloud environments also make them great candidates for infrastructure as code (IaC), a practice in which teams use tools like Terraform, Pulumi, and AWS CloudFormation, to manage infrastructure declaratively and version those declarations just like application code. IaC boosts automation, repeatability, and resilience across environments—all big advantages in the cloud world. IaC also goes hand-in-hand with the concept of immutable infrastructure—the idea that, once deployed, infastructure-level entities like virtual machines, containers, or network appliances don’t change, which makes them easier to manage and secure. IaC stores declarative configuration code in version control, which creates an audit log of any changes.

Foundry

How the cloud-native stack is expanding

As cloud-native development becomes the norm, the cloud-native ecosystem is expanding; the CNCF maintains a graphical representation of what it calls the  cloud native landscape that hammers home to expansive and bewildering variety of products, services, and open source projects that contribute to (and seek to profit from) to cloud-native computing. And there are a number of areas where new and developing tools are complicating the picture sketched out by the pillars we discussed above.   

An expanding Kubernetes ecosystem. Kubernetes is complex, and teams now rely on an entire ecosystem of projects to get the most out of it: Helm for packaging, ArgoCD for GitOps-style deployments, and Kustomize for configuration management. And just as Kubernetes augmented Docker for enterprise-scale deployments. Kubernetes itself has been augmented and expanded by service mesh offerings like Istio and Linkerd, which offer fine-grained traffic control and improved security

Observability needs. The complex and distributed world of cloud-native computing requires in-depth observability to ensure that developers and admins have a handle on what’s happening with their applications. Cloud-native observability uses distributed tracing and aggregated logs to provide deep insight into performance and reliability. Tools like Prometheus, Grafana, Jaeger, and OpenTelemetry support comprehensive, real-time observability across the stack.

Serverless computing.  Serverless computing, particularly in its function-as-a-service guise, offers to strip needed compute resources down to their bare minimum, with functions running on service provider clouds using exactly as much as they need and no more. Because these services can be exposed as endpoints via APIs, they are increasingly integrated into distributed applications, operating side-by-side with functionality provided by containerized microservices. Watch out, though: the big FaaS providers (Amazon, Microsoft, and Google) would love to lock you in to their ecosystems.  

FinOps. Cloud computing was initially billed as a way to cut costs — no need to pay for an in-house data center that you barely use — but in practice it replaces capex with opex, and sometimes you can run up truly shocking cloud service bills if you aren’t careful. Serverless computing is one way to cut down on those costs, but financial operations, or FinOps, is a more systematic discipline that aims to aligns engineering, finance, and product to optimize cloud spending. FinOps best practices make use of those observability tools to best determine what departments and applications are eating up resources.

How cloud-native architecture is adapting to AI workloads

Enterprises deploy larger AI models and make use of more and more real-time inference services. That’s putting demands on cloud-native systems and forcing them to adapt to remain scalable and reliable.

For instance, organizations are re-engineering cloud environments around GPU-accelerated clusters, low-latency networking, and predictable orchestration. These needs align with established cloud-native patterns: containers package AI services consistently, while Kubernetes provides resilient scheduling and horizontal scale for inference workloads that can spike without warning.

Kubernetes itself is changing to better support AI inference, adding hardware-aware scheduling for GPUs, model-specific autoscaling behavior, and deeper observability into inference pipelines. These enhancements make Kubernetes a more natural platform for serving generative AI workloads.

AI’s resource demands are amplifying traditional cloud-native challenges. Observability becomes more complex as inference paths span GPUs, CPUs, vector databases, and distributed storage. FinOps teams contend with cost volatility from training and inference bursts. And security teams must track new risks around model provenance, data access, and supply-chain integrity.

Application frameworks for building distributed cloud-native apps

Microsoft’s Aspire is one of the most visible examples of a shift towards application frameworks to simplify how teams build distributed systems. Opinionated frameworks like Aspire provide structure, observability, and integration out of the box so developer don’t need to stitch together containers, microservices, and orchestration tooling by hand.

Aspire in particular is a prescriptive framework for cloud-native applications, bundling containerized services, environment configuration, health checks, and observability into a unified development model. Aspire provides defaults for service-to-service communication, configuration, and deployment, along with a built-in dashboard for visibility across distributed components.

While Aspire was originally aligned with Microsoft’s .NET platform,Redmond now sees it as having a  polyglot future. This positions Aspire as part of a broader trend: frameworks that help teams build cloud-native, service-oriented systems without being locked into a single language ecosystem. Several other frameworks are gaining traction: Dapr provides a portable runtime that abstracts many of the plumbing tasks in cloud-native distributed applications, and Orleans offers an actor-model-based framework for large-scale systems in the .NET world, and Akka gives JVM teams a mature, reactive toolkit for elastic, resilient services.

Frameworks and tools in the expanding cloud-native ecosystem

While frameworks like Aspire simplify how developers compose and structure distributed applications, most cloud-native systems still depend on a broader ecosystem of platforms and operational tooling. This deeper layer is where much of the complexity—and innovation—of cloud-native computing lives, particularly as Kubernetes continues to serve as the industry’s control plane for modern infrastructure.

Kubernetes provides the core abstractions for deploying and orchestrating containerized workloads at scale. Managed distributions such as Google Kubernetes Engine (GKE), Amazon EKS, Azure AKS, and Red Hat OpenShift build on these primitives with security, lifecycle automation, and enterprise support. Platform vendors are increasingly automating cluster operations—upgrades, scaling, remediation—to reduce the operational burden on engineering teams.

Surrounding Kubernetes is a rapidly expanding ecosystem of complementary frameworks and tools. Service meshes like Istio and Linkerd provide fine-grained traffic management, policy enforcement, and mTLS-based security across microservices. GitOps platforms such as Argo CD and Flux bring declarative, version-controlled deployments to cloud-native environments. Meanwhile, projects like Crossplane turn Kubernetes into a universal control plane for cloud infrastructure, letting teams provision databases, queues, and storage through familiar Kubernetes APIs. These tools illustrate how cloud-native development now spans multiple layers: developer-focused application frameworks like Aspire at the top, and a powerful, evolving Kubernetes ecosystem underneath that keeps modern distributed applications running.