GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Ashkan (@ashkans_dev) reported 4 minutes ago

  @thsottiaux @OpenAIDevs Do we know when we will have a fix for this issue by any chance in ChatGPT "The local GitHub login has expired". Keeps happening but its not actually expired

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  🐉Raylith!🐉 (@actualNico19009) reported 6 minutes ago

  @catgirlprostate @ZipperArtz then devs should stop sending people to github to download their stuff..????????????? the issue would cease to exist immediately

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  Spencer Heckathorn (@mrhobbeys) reported 8 minutes ago

  @burkeholland @github I'm kind of bad... I let my GitHub subscription lapse. Maybe it is time to fix that.

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  YAMICIA CONNOR (@YamiciaC) reported 13 minutes ago

  1 · Documentation — Google Docs, Notion, Coda. Where you write down what you decided and why. This is where AI reads its instructions. If it isn't written down, it doesn't exist. 2 · Storage — GitHub. Not backup. History. Every change, who made it, and how to undo it.

• 
  SasoEquity (@saso_capital) reported 15 minutes ago

  My key takeaways of the $MSFT (Microsoft) earnings call. 1. The Azure acceleration came from the fleet, not from new capacity. Azure grew 43% against a prior year that itself included accelerating growth, and Q1 is guided to roughly 45% in constant currency. But the upside driver was not more capacity landing, it was efficiency gains across the CPU and GPU fleet plus process improvements that pulled delivery of new capacity earlier, which was then monetized inside the quarter. Hood was explicit that because supply is short, any efficiency gain converts to revenue almost immediately. That is the highest-quality form of a beat: revenue with no incremental capital attached. Dock-to-live times for new GPUs in the largest regions came down by nearly 50% over the year, another gigawatt was added in the quarter, and they remain on track to roughly double total capacity in two years. 2. The "it's all OpenAI" bear case got attacked directly. Commercial RPO is $678B, up 84%, but every dollar of sequential RPO growth came from customers outside the frontier model companies, and RPO ex-OpenAI still grew 25%. Duration is 2.3 years, about 30% converts to revenue in the next twelve months (that near-term slice up 37%), and the portion beyond twelve months grew 112%. For the full year, nearly 90% of Microsoft Cloud revenue came from customers outside the frontier labs. This is the single most important disclosure on the call and it is deliberately constructed to kill the circularity narrative. 3. Free cash flow is the pressure point, and the guidance bar is almost comically low. Capex was $41B in the quarter, roughly two-thirds short-lived assets, i.e. CPUs and GPUs, with $5.6B of finance leases and $35.8B cash paid for PP&E. Operating cash flow was $55.4B, up 30%. Free cash flow was $19.6B. For FY27, the commitment is that they expect to remain free cash flow positive. A company doing $155B of operating income guiding to positive FCF is telling you exactly how much of the P&L is being reinvested. 4. The flexibility argument is the same one every capex-heavy name is now making. Hood's defense: the largest cost component is short-lived silicon with short lead times, so if demand changes you simply slow that down; land and shell is a smaller share and the build timing is adjustable. Add an unusually diverse book by geography, segment and industry, plus a large first-party app business that consumes the same capacity. Structurally this is Bloom's "units are fungible once they're on a truck," restated at hyperscaler scale, late-bind the expensive component, keep the demand pool broad. 5. Model-agnosticism is a gross margin strategy dressed as architecture. Nadella: "You've got to keep your harness separate from the model", memory, context and action space external, every model substitutable. The numbers underneath are what matter: MAI-Code-1-Flash delivering higher code acceptance with 10% lower median token usage on GitHub Copilot; MAI-Cyber-1-Flash beating a much larger frontier model at half the cost; 89% GPU cost reduction in Dynamics 365 and up to 84% in PowerPoint; Maia 200 at 30% better performance per dollar and 40% better performance per watt on MAI models. This is first-party COGS deflation, and it is the quiet answer to "how do AI margins recover." 6. The business model is moving from seats to seats plus consumption. M365 Copilot passed 30 million paid seats with net adds more than doubling sequentially. E7 launched mid-quarter, hundreds of enterprises bought millions of seats, EY took 400,000. GitHub Copilot went usage-based in June and Copilot revenue accelerated over 60% quarter-over-quarter; GitHub is at 225 million users and one in three pull requests now involves an agent. Dynamics customer service credit consumption up 4x QoQ. Bigger TAM, better monetization of heavy users, and a revenue line that is less predictable than seats. Bottom line: the mix in this print is better than the headline and the cash is worse. RPO ex-OpenAI at +25%, 90% of cloud revenue from non-frontier customers, and Azure accelerating on efficiency rather than new capex all attack the concentration fear that took the stock from $555 to a $390 close. The derating was about who was buying and whether the capex earns its return. This quarter answered the first question and deferred the second.

• 
  Teedor🥷 (@MisterT_1) reported 16 minutes ago

  Nearly half, 45.6%, rely on shared API keys for agent-to-agent access. That's like giving everyone in a building the same front-door key. Security researchers have been warning about this for a while. OWASP lists it as one of the biggest risks facing non-human identities. In one 2025 scan, researchers found 24 million machine credentials exposed on GitHub, and 70% of the credentials leaked back in 2022 were still valid years later. Nobody had rotated them because nobody was treating those agents like real users. Gartner expects the consequences to get worse. By 2028, organizations that continue sharing human credentials with AI agents are expected to see account takeovers and fraud roughly triple. When you strip away all the technical language, the problem is straightforward... We've built millions of software workers, but almost none of them have an identity of their own. Instead, they borrow ours. And borrowed identities are much easier to compromise.

• 
  Prakash Sharma (@PrakashS720) reported 18 minutes ago

  Someone just hacked the AI coding race. Not with billions in funding. Not with a massive team. With one open-source project. It's called jcode. And the benchmarks make Claude Code look slow. • 245x faster boot • 14x lower RAM • 10 parallel sessions in just 117MB But that's not the biggest surprise. There is no `/remember`. jcode builds its own semantic memory, recalls context automatically, and keeps it updated in the background. One binary. 30+ AI providers. Claude, GPT, Gemini, DeepSeek, Groq, Ollama, Copilot, Azure, and more. Then it goes one step further. Instead of one AI agent... It runs an entire AI team. Agents coordinate, chat, avoid file conflicts, and spin up sub-agents on their own. It can even rewrite its own code, rebuild itself, and continue working without ending your session. Built by one developer. Written in Rust. 13k+ GitHub stars. This might be the biggest open-source surprise AI coding has seen this year. Link in the comments.

• 
  Amin Tai (@aminnnn_09) reported 20 minutes ago

  @Its_Nova1012 Thanks! GitHub is just the first visible example. More platforms will be solving the same problem soon.

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  Daniel Cuthbert (@dcuthbert) reported 20 minutes ago

  Life is too short for **** GitHub issues and commit messages “We still don't trust our dinosaur. Now it can at least tell us when it's up to something and then we can shout 'bad boi' or let them go ahead and eat someone elses face.” Raptor sandboxing fun for 3.2 rel

• 
  Graeme (@GraemeVIP) reported 20 minutes ago

  @CastAsHuman 1.05M is what the model can take, not what the product gives you. Those are two different numbers set by two different teams. The other people answering this are wrong... unsurprisingly. The 258K isn't a mystery figure either. It comes straight out of the model catalog OpenAI serves to the client: the entry lists a context window of 272,000 with an effective_context_window_percent of 95, which works out to the 258,400 people are seeing. So it's a deliberate product cap sitting well below the API spec, not a rounding glitch. It also used to be bigger. Users measured roughly 353,000 tokens before, and the drop to about 258,000 (a cut of around 27 percent) happened around 13 July without any announcement. People only noticed because their sessions started truncating early. There's a fairly aggressive pile of GitHub issues about it. As for why product surfaces cap it at all, three things: Cost. Long contexts are brutally expensive to serve at consumer scale. Every token you keep in the window has to sit in GPU memory for the whole session, and prefill time grows with it. Fine at $5/MTok when someone's paying per token. Not fine on a flat monthly subscription. Pricing boundaries. Interestingly, 272K looks like a threshold in OpenAI's own billing: there's a usage multiplier that kicks in above 272K, and users have been asking for warnings before sessions cross it. The cap keeps normal sessions on the cheap side of that line. GitHub Quality. Models get noticeably worse at retrieving things buried in the middle of a very long context. A million-token window that half works is a worse product than a 258K one that behaves. Worth noting too that in the consumer ChatGPT app the numbers are different again and depend on your plan, roughly 32K on Instant and up to 256K on Thinking models for paid tiers. Part of whatever you get is also eaten by the system prompt, tool definitions and memory before you type a word. tThe model card is a spec sheet for the engine, and the app is the car. Nobody's lying, but the top speed on the box isn't what you'll do on the motorway.

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  Prometheus (@prometxbt) reported 21 minutes ago

  COGNITION JUST SHOWED A 13.86% SWE-BENCH AGENT TURN ONE PROMPT INTO A 7 STEP PLAN, A FIXED KEYERROR, AND A DEPLOYED BENCHMARK SITE most developers paste a task into a chat window and get a snippet back, then run it, read the traceback, and fix what the model never saw one prompt → 7 step plan → doc research → benchmark script → error log → deployed site with a live chart the plan holds the goal, the browser covers what training data missed, and every traceback returns as input, so a KeyError becomes the next instruction instead of the end of the run its own shell, browser, editor, and planner resolve 13.86% of real GitHub issues on SWE-bench where GPT-4 resolves 1.74%, and this run put Together at 48.37 tokens per second and Replicate at 9.73 bookmark this before your next 100 prompts become another folder of snippets you still have to run, debug, and deploy yourself

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  bix (@jklpuzo1923) reported 28 minutes ago

  @kris_aieng I'm getting into these roles but also data engineering for pre-train and post-train aiming @ remote roles us/eu do you wanna make a discord server or something to share? Next up ill work on some portfolio projects for github

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  Lannok (@FlukeLSX) reported 31 minutes ago

  @Ahmed___khaan See I was fine with everything right up until you stated: "*** & GitHub" Because that means you failed at the last step: Problem Solving and Critical Thinking. Given all the fun side channel attacks going on with github do you really want to advocate people continue using it?

• 
  καςhι (@CcAkachi) reported 31 minutes ago

  spent an hour convinced my GitHub token was broken. it wasn't. *** push kept failing with a stale VSCode socket error, and my properly-configured credential helper never even got a chance to run. turns out GIT_ASKPASS was set by VSCode and intercepting before *** checked...

• 
  Milady (@onchainmilady) reported 32 minutes ago

  ADDY OSMANI FROM GOOGLE JUST RELEASED A GITHUB REPO THAT TURNS CLAUDE INTO A WHOLE ENGINEERING TEAM what he packed in is a full set of production skills that walk Claude Code through a real build start to finish you point it at your project and it pins down the goal, then plans the architecture before writing a line then it writes the code, runs it, checks that the thing works and reviews the whole codebase like a senior would it even carries you through deploy the part people are copying is the agent personas he wrote a senior staff engineer for the big calls, a curious specialist that digs into the unknowns and a security expert that hunts holes before you ship each one takes a different seat on the team so one person gets covered from every angle you clone it, drop the skills into Claude Code and suddenly your editor argues with itself the way a real team would one developer running this ships what used to take a room of people