Astronauts Sophie Adenot of ESA (European Space Agency) and Jack Hathaway of NASA, both Expedition 74 flight engineers, look out a window in the cupola, monitoring the automated approach and docking of the SpaceX Dragon cargo spacecraft to the International Space Station on May 17, 2026. The orbital outpost was soaring 259 miles above the Indian Ocean just west of the Maldives at the time of this photograph.
Selçuksports adlı internet sitesinin yöneticisi olduğu iddia edilen Selçuk Yılmaz 2 Haziran’da verdiği ifadesinde kanalla herhangi bir bağlantısı olmadığını söyleyerek suçlamaları reddetti.
Sitenin, Türkiye Süper Lig maçlarını kaçak yayınladığı ve yasa dışı bahis sitelerine yönlendirme yaptığı tespit edilmişti.
Microsoft teknoloji dünyasında işletim sistemlerinin geleceğini şekillendiren yepyeni bir altyapı projesini teknoloji ekosistemine sunuyor. Şirket akıllı cihazlarda geleneksel mobil uygulamaların yerini tamamen yapay zeka ajanlarına bırakan yeni nesil bir donanım ve yazılım platformu olan Project Solara‘yı resmi olarak duyurdu.
Platform, kullanıcıların farklı uygulamalar arasında geçiş yapma zorunluluğunu tamamen ortadan kaldırıyor. Cihazlar doğrudan entegre edilen yapay zeka ajanları üzerinden tüm işlemleri kullanıcı komutlarıyla veya proaktif bir şekilde kendi kendine gerçekleştiriyor.
Bilişim modelinin kalbinde ise donanım devi Qualcomm yer alıyor. İki dev şirketin stratejik ortaklığı kapsamında Project Solara altyapısı doğrudan Qualcomm işlemcilerle desteklenen cihazlarda tam performanslı bir şekilde çalışıyor. Geleneksel uygulama mimarisinin dışına çıkan bu sistem tüm işlem gücünü yapay zeka modellerini yerel cihaz üzerinde çalıştırmak için kullanıyor.
Kullanıcılar artık bir e-posta göndermek takvim planlamak veya karmaşık araştırmalar yapmak için ayrı ayrı uygulamalara ihtiyaç duymuyor. Arka planda çalışan yapay zeka sistemi tüm bu görevleri tek bir merkezden doğal dil işleme yetenekleriyle anlayarak milisaniyeler içinde hızlıca sonuçlandırıyor.
Teknoloji dünyasındaki uygulama mağazası bağımlılığını doğrudan azaltan bu yeni ekosistem donanım üreticilerine yepyeni bir oyun alanı açıyor. Akıllı telefonlar, giyilebilir cihazlar ve ev teknolojileri artık doğrudan yapay zeka asistanlarıyla entegre bir şekilde pazara sunuluyor. Microsoft ve Qualcomm ortaklığı ile şekillenen platformun ilk fiziksel cihazlarda ne zaman yer alacağına veya geliştiriciler için ne tür bir gelir modeli sunacağına dair net bir bilginin de henüz paylaşılmadığını belirtelim.
This article is from Making AI Work, MIT Technology Review’s limited-run yenisletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here.
From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to handle these tasks, but small businesses don’t always have this luxury.
That’s where AI comes in. Today’s AI models do a decent job at these tasks. The trick for small businesses is to understand where AI is good enough and where it’s not.
One place where a “good enough” AI can already be quite valuable to small business owners is in providing secretarial skills and handling basic administrative matters. Let’s take a look at how one private tutor is using it to improve his recordkeeping and free up his time.
Case study
Sam Finnegan-Dehn works in fundraising for a charity, but he moonlights as a math and philosophy tutor for university students from his home in London. Through this part-time business, he can leverage his degrees in philosophy and share his love of the subject with clients.
But meeting with students is only a fraction of the work it takes to be a good tutor. He also plans lessons and finds fresh reading materials, creates assignments, sends invoices, and keeps up with yeni research—all on top of his regular job. Given these demands, Finnegan-Dehn doesn’t have as much time as he’d like to grow his tutoring roster.
So he’s turned to AI for some help in managing the day-to-day aspects of his business. He says AI has taken on a secretarial role across all of his digital notebooks, where he jots down reminders about his clients’ progress and yeni readings to keep himself up-to-date. He describes using AI as kind of like having a second memory that helps him connect ideas he’s written down in various places.
While he has experimented with different tools like Claude and ChatGPT, he’s now landed on Notion AI because it integrates better with his tutoring notes, which live across his notebook tabs in the Notion app. Finnegan-Dehn doesn’t use AI to create teaching materials, but he does let Notion AI record meetings with his clients (after getting their consent), and then uses its automated summaries to refine his teaching strategy. For example, if he notices from the AI’s summary that it seems like a certain technique was not helping a student, he may change how he approaches the subject next time.
Beyond this, Notion AI also helps him with goal-setting, drafting lesson notes, invoicing, and generating and syncing social media posts. For goal-setting, for example, Finnegan-Dehn says he understands his long-term goals for his business but not always the concrete steps to build to them. He uses AI to help fill in these gaps. He starts by writing down a “North Star” goal—say, to have a certain number of clients by the end of the year. Next, he asks his AI to generate the steps that he needs to take to get there, given the profile he has built up in the app. Then, he can reflect on the results and choose which tasks to tackle first.
The tool
Notion has been a big player in note-taking software for many years. Its AI add-on, released in late 2023, now has tools that enable it to interact with many other online productivity platforms. There’s an email client, calendar integrations, and a yenily released agent. And while this level of access has raised privacy concerns, it can also make for a pretty powerful virtual assistant.
Many of the tasks targeted by Notion AI are less creative and more rote: syncing information across documents or searching through old scribbles, for example. This makes the tool especially appealing to small business owners, who have limited bandwidth, particularly for menial work.
Other companies are developing tools targeted at specific industries. For example, Grandma’s Quilt Shop in Yuma, Arizona, uses Rain, which has a software suite tailored to craft companies, to generate inventory descriptions and pricing for its stock of fabric designs. The owners claim this AI tool cuts the time it takes to list items by 60 to 80%.
There are drawbacks, though, as Finnegan-Dehn described some of Notion AI’s idiosyncrasies as “clunky” at times. And the AI add-on for Notion costs $20 per month. As with all yeni tools, small business owners should carefully assess how the potential gains and headaches measure up against the cost of just doing the job themselves.
User tips
Consider these points when thinking about whether AI might be able to help you run a business, or make any part of your work life just a little bit easier.
Look before you leap. Since LLMs feed on the data you input to answer your queries or complete tasks, you want to give them information in a way that’s convenient for you and for the model. For many of these notebook AI services, this means, for example, using their platform for notetaking so you don’t have to input or upload notes later. Because of this, it’s a good idea to weigh your options carefully before committing to an AI-powered ecosystem.
Work to your strengths. Think about what skills you lack in-house, and see if AI can either help with training or take these tasks on for you. Just be aware: AI hallucinates and makes mistakes, so think about where accuracy is needed and keep humans in charge there.
AI isn’t always the best tool. It’s okay to use something off the shelf when that’s the better choice. It’s going to be safer, for example, to use existing payment processing platforms like Shopify or Square than to vibe-code one using AI.
Consider using local models for any sensitive information. Our reporting has covered the risks that online AI models have in leaking sensitive data, and there have been many reports about how AI companies collect your data when you ask their chatbots questions. Even if your business doesn’t handle personal information, there can still be some things you’d prefer not to share publicly. In these cases, using an open-source model that makes inferences on your prompts locally can be a great option, instead of ChatGPT or Claude or other proprietary models. Thankfully, some LLMs can now be run off of laptops and small desktops. Here’s how to set one up and start using it.
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Travelers built an AI-powered Claim Assistant with OpenAI to guide customers through filing claims, provide 24/7 support, and scale operations during peak demand.
"This is the sixth post in my series on Emacs completion…. This one coins a term for a special case, Incremental Suggesting Read (ISR), where the candidate set produced by incrementally typed input is a suggestion, rather than a literal completion of that input. The ability to generate inferred matches in addition to literal matches vastly expands the scope of what a 'completion' system can do. Two conceptual sources supply the suggestions: 1) semantic retrieval and 2) generative synthesis.
This post is more speculative than useful, so carry that pinch of salt with you as you watch the video or read this post."
From the floor of HumanX, Ryan welcomes Songyee Yoon, managing partner at Principal Venture Partners (PVP), to chat about AI development outside the US, from the need to adapt models to local languages and culture to the challenges of the global supply-chain for things like semiconductors to how venture capital is looking at international AI companies.
While the agentic shift has made development faster, it’s also led to disjointed workflows, more context switching, and too much time spent reviewing agent-generated code.
If agents are going to be a durable part of how software gets built, they need a real place in the developer workflow. Yet most developer tools were not designed for directing multiple agents in parallel. Context scatters across windows. You lose track of what’s running. Code lands in pull requests without a clear trail of what the agent tried, what it validated, or where human judgment is needed.
Get started with the GitHub Copilot app today using your existing Copilot Pro, Pro+, Business, or Enterprise plan. Learn more >
Across GitHub, developers are using agents to move from prompt to plan, from issue to pull request, from review feedback to merged code. As agentic workflows become the norm, repository creation, pull request activity, and API usage are all accelerating with no evidence of slowing down. On GitHub alone, commits nearly doubled year over year, crossing 1.4 billion per month, plus over 2 billion GitHub Actions minutes a week.
To meet this demand and continue to be the home for all developers (and now their agents), our focus is scaling our underlying systems and improving resilience and stability across all of our services, at every layer of the stack.
GitHub is building that system for the agentic frontier, and that’s what we’re showing today at Microsoft Build.
Copilot app: A control center for agent-native development
You start the day with three pieces of work already in motion. One agent is investigating a production bug. Another is implementing a backlog issue. A third is working through review feedback on a pull request. Each is running in its own isolated environment, producing changes you can inspect, redirect, test, and merge.
You need an environment that can keep up.
The yeni GitHub Copilot app is the agent-native desktop experience built on GitHub. From a single My Work view, you can see work in motion across connected repositories: active sessions, issues, pull requests, and background automations. The Copilot app is now available in technical preview for existing Copilot Pro, Pro+, Business, and Enterprise users.
The GitHub Copilot app is the latest in a line of AI tooling from GitHub that is transforming our business. Moving beyond AI assistance, the app has provided a much-needed control center for agentic development.
Our Forward Deployed Engineers can dispatch a cohort of agents and manage multiple initiatives, all from one location. Easy access to plans and autopilots with the ability to run interactive sessions or step into code where needed.
David Jobling | Master Technology Architect, Head of Technology & Delivery Futures, Global Solutioning & Delivery, Avanade Inc.
Every session runs in its own git worktree, a real, isolated copy of your branch. This helps parallel agent sessions work without stepping on each other. The app handles every worktree for you: no manual setup, no cleanup, no branch juggling. Whether you start from a prompt or an issue from your inbox, Copilot gets the context it needs from existing issues, pull requests, and the repos you’ve connected.
Then Agent Merge helps carry that pull request through review, checks, and merge. It monitors CI, tracks required reviewers, addresses failing checks, and waits for all conditions to be satisfied. You choose how far Copilot should go: drive CI back to green, address feedback, or merge when your conditions are met. You decide what automation is enabled and what ships.
Canvas: Where intent becomes inspectable work
Chat is powerful for instruction and ambiguity. But once an agent starts doing real work, a chat thread becomes a long scroll of decisions, logs, and corrections. You need a place where the work itself is visible.
Today, we’re also introducing canvases in the GitHub Copilot app. Canvases are bidirectional work surfaces for humans and agents. A canvas might show a plan, pull request, browser session, terminal, deployment, dashboard, or workflow state. Agents update the canvas as they work, and developers can edit, reorder, approve, or redirect that work on the same surface.
This is the beginning of agent experience (AX) in the Copilot app: interfaces where people and agents operate together. Chat is where you instruct, discuss, and reason through ambiguity. Canvases are where that intent becomes visible work you can inspect, steer, and verify.
Agents that can only suggest code leave you do a lot of the work. To be more effective, agents need to run code, inspect results, test changes, and iterate, without touching production.
Cloud and local sandboxes for GitHub Copilot give agents a bounded place to act. Choose where Copilot runs—on your local machine or in the cloud—and begin unlocking agent-driven workflows while prioritizing security and enterprise policy enforcement, and without local resource constraints.
With local sandboxing, Copilot runs in an isolated environment directly on your machine, with restricted access to filesystems, network connectivity, and system capabilities. Local sandbox policies can be centrally configured and enforced.
In the cloud, each sandbox runs in a fully isolated, ephemeral Linux environment hosted by GitHub. Organizations define their own policies. From the cloud, you can pick up Copilot sessions anywhere, on any device, with remote control.
Code review that scales with agentic output
As agents produce more pull requests, the pressure on code review compounds. Copilot code review brings an adaptable, agentic system to filter through the noise, allowing you to focus your energy where it matters most while Copilot conducts code reviews.
You can now extend Copilot so every review reflects your own standards, internal systems, and engineering context via custom agent skills, MCP server connections, and configurable actions workflows.
Copilot code review now offers medium tier review, which routes pull requests to a higher-reasoning model for better precision and recall. Admins can set guidelines for individual repositories to “low” or “medium.” This lets you assign lighter, cost-efficient models for low-risk code and save more robust model use for repos with higher impact.
The /security-review skill gives Copilot a dedicated path for security-focused evaluation. The /rubberduck skill is now generally available to use multiple model families to critique your implementation and find novel issues.
And if you’re working on Azure DevOps, you can now use Copilot code review natively. Get the same one-click review, inline comments, and committable fix suggestions you expect, and admins can enable code review on whichever repos they want.
One runtime for apps, tools, and agents
The same agentic capabilities work across the terminal, the cloud, and even your own tools, on the same foundation.
You can now build your own tools with the GitHub Copilot SDK. Now generally available in Node.js/TypeScript, Python, Go, .NET, Rust, and Java, it exposes the same agentic runtime that powers the Copilot app. If your team needs an internal code analysis tool, a custom release-notes generator, or an agent embedded in a support workflow, you build it on the same foundation instead of wiring together a bespoke stack. One runtime, many surfaces.
For developers who prefer to work in the terminal, Copilot CLI now has a redesigned interface, voice input, and scheduled tasks to keep you there.
Copilot CLI has a redesigned TUI in /experimental mode with tabbed access to pull requests, issues, and gists from the terminal. Voice mode uses on-device speech-to-text, so audio never leaves your machine. /every schedules recurring prompts and background tasks.
Cloud automations let agents run on a schedule, respond to GitHub events, open issues, and leave comments. By default, the cloud agent asks permission before each write action. Switch to autopilot once you have established trust.
Engineering doesn’t end with writing code. It includes filing the issue, kicking off the discussion, and replying to reviewers. Copilot cloud agent can now handle every one of those steps.
Memory++ and /chronicle give Copilot continuity across devices and over time. Query context from sessions started in the app, CLI, VS Code, or on GitHub.
Partner-built agent apps integrate with GitHub Copilot to help automate tasks, generate code, analyze context, and execute actions. Use your favorite tools without leaving GitHub. Assign issues to yeni agents that fit your workflow. Partners include LaunchDarkly, Bright, Amplitude, Sonar, Endor Labs, Octopus Deploy, Packfiles, PagerDuty, and Miro. Start using these agent apps today. And join the waitlist so your company can also bring its own agent apps to GitHub.
What we’re building toward
Professional software demands judgment, verification, and accountability. That is why the GitHub Copilot app, sandboxes, code review, automation, context, and partner ecosystem are coming together as one system: agents can do more of the work, while developers keep control of quality, policy, and delivery.
As agentic workflows grow across GitHub, from repository creation to pull request activity and API usage, the platform has to grow with them. We will continue to focus on availability first. We are committed to hardening these systems so agent-native development is fast, available, and reliable enough for teams to depend on every day.
GitHub is where that system lives, because it is already where the code, the reviews, the issues, and the teams are.
Learn how Googlers used AI to produce Google I/O 2026.
