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Learn about the progress we’ve made toward our accessibility goals and how you can help make open source more inclusive. The post From pledge to practice: Building a more inclusive open source ecosystem appeared first on The GitHub Blog .

AI agent that controls your computer autonomously Discussion | Link

Google's DeepMind AI lab is teaming up with A24 to develop new movie production technologies that aim to help future filmmakers "expand their storytelling possibilities." As part of this new research and development collaboration, The Wall Street Journal reports that Google is investing "around $75 million" into A24, marking the first time the search giant […]

Tesla touts Autopilot as lifesaving a day after grandmother died in crash.

The Steam Machine is here, but getting one is a little complicated. Valve is taking preorders using a reservation system, which is intended to make the process more fair and harder for bots to exploit. However, it's a bit different than the $5 reservations Valve used for the Steam Deck. Starting today, you can sign […]

If you don't get lucky with Valve's Steam Machine reservation system, you can make your own Steam Machine instead. Valve says that "starting with the SteamOS 3.8 release, you can put together your own Steam Machine using whatever PC parts you want." SteamOS 3.8.10 launched last week with a slew of updates, including "improved compatibility […]

Two years after it turned Marg Monday into a daily, the Ninja Slushi is only $200. That’s the cheapest deal ever.

Valve finally announced the price of the Steam Machine, and like a lot of new gadgets these days, it's not cheap: It starts at $1,049 for a 512GB model, and a 2TB model costs $300 more. Configurations with a bundled Steam Controller cost an extra $79 each. Despite Valve offering a console alternative with the […]

After months of waiting, Valve has finally announced that the Steam Machine, its new living room-friendly PC, will start at $1,049 and go on sale beginning June 29th. You can now register your interest to buy a Steam Machine as part of a reservation system. To offer a fair playing field for people who want […]

My first day with the Steam Machine was a mess. Instead of enjoying a worry-free game console, I spent hours troubleshooting what felt like a finicky PC. That's because the Steam Machine is a PC, with a very important twist. Since the Magnavox Odyssey came out in 1972, game consoles have been built with the […]

Amid concerns about AI models’ cybersecurity capabilities, OpenAI revealed an improved version of GPT-5.5-Cyber and its “Patch the Planet” initiative to fix open-source software bugs.

Reflection AI will pay $150 million a month beginning July 1, 2026 through 2029 for immediate access to Nvidia's latest GB300 AI chips and supporting hardware across SpaceX's Colossus 2 data center near Memphis, Tennessee.

Smart glasses are hitting the mainstream, so companies are now getting into the accessories game.

Smart ring maker RingConn's marketing copy says Lord of the Rings' "enduring narrative highlights a simple but powerful idea: that meaningful transformation often begins with the choices we make each day. RingConn embraces a similar philosophy, believing that lasting change begins with everyday awareness and small, intentional decisions." Of course, the "intentional decisions" Frodo makes […]

A24 is taking $75 million in Google money to help build AI tools for movie production.

AI presentation agent for editable decks Discussion | Link

understand software Discussion | Link

A static-analysis approach to unifying 46 repositories (37 air-closet-side + 9 mall-side) of legacy production code into one knowledge graph. Why simply 'letting AI read the code' isn't enough, why I had to chase down boundary nodes (API endpoints, DB tables, Event topics), how I dealt with framework and library diversity, and what 3 months of trial and error solved or didn't solve — looking back through actual git history.

It is a one-line habit that has saved me more debugging time than any clever abstraction: I log which model actually answered every Claude request. Not which model I asked for. Which one responded. In 2026, with model fallbacks, fast-changing model strings, and routing logic, those are not always the same thing. Here is why the gap exists and what it has caught. The request model and the response model can differ You send model: "claude-fable-5" . You assume Fable 5 answered. But the response object tells you what actually served the request: const response = await client . messages . create ({ model : " claude-fable-5 " , max_tokens : 16000 , thinking : { type : " adaptive " }, messages : [{ role : " user " , content : prompt }], }); console . log ( " requested fable-5, served: " , response . model ); Most of the time they match. The interesting cases are when they do not. The Fable 5 safeguard fallback The reason this matters most in 2026 is Fable 5's safeguards. Fable 5 has hard guardrails in cybersecurity, biology, chemistry, and health. If your prompt trips one, the request does not just refuse. It silently falls back to Opus 4.8 to produce a safe answer. For my security work, this is a sharp edge. I run contract-analysis prompts that can look adversarial to a safeguard. If one trips, I am quietly getting Opus 4.8 output while believing I am getting Fable-tier reasoning. The quality difference on a hard audit is exactly the thing I paid double for, and it vanished without an error. The only way to know is to read response.model : if ( ! response . model . startsWith ( " claude-fable-5 " )) { logger . warn ( { requested : " claude-fable-5 " , served : response . model }, " Fable 5 request fell back, likely a safeguard trip " , ); } Without that log, a fallback is invisible until I notice the analysis got worse and have no idea why. Routing logic and config drift The other source of the gap is my own code. I have routing that picks a model based on task type and

I'm 19. In four months I built 128 projects with AI — 61 GitHub repos, 15 MCP servers, a 7-department agent OS, the works. I shipped 5 . Total stars: 6 . Revenue: $0 . That gap bothered me enough that I did the obvious-but-uncomfortable thing: I had an AI audit everything — every repo, every project folder, 4,239 build sessions, 244 memory notes — and pin it all like specimens in a cabinet. No flattery. Here's what the autopsy found. → The full interactive atlas: https://builder-archive.vercel.app/en The number that explains everything 128 built. 5 shipped. It's tempting to read that as a discipline problem. It isn't. The build velocity is real — I once shipped ~20 vertical SaaS in a single weekend on a shared Next.js + Drizzle + Stripe stack. The code works. The UIs are clean. The problem is the last mile . README writing, deployment, the final 10% that turns a repo into a thing a stranger can use — that's where almost everything died. Not ability. Execution. The AI put it in one line: "Can build anything. Finishes nothing." Strength and weakness are the same coin Here's the part I didn't want to see: the thing that makes me fast is the thing that kills me. Because I can build deep, I lose the stopping point. Because building is cheap, I start the next thing before finishing the last. The audit scored two skill axes: Build (design → implementation → automation): advanced Distribution (publish → ship → monetize): beginner Every problem I have lives in that asymmetry. It's not a motivation gap — total commits across repos: ~4,800. The effort is enormous. It just never crosses the finish line into something public. The hardest thing I made is the one I hid The audit flagged a buried asset: a GCC/ZATCA e-invoicing toolkit — Saudi Fatoora Phase 2, EN16931 + Peppol validation, secp256k1 signing, Go compiled to WASM. The single hardest, most verifiable piece of work I've done. It's been sitting in a private repo. That's the disease in one example: the more valuable the th