GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (68%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Paris	Website Down	3 days ago
Saint-Paul	Website Down	4 days ago
Saint-Paul	Website Down	4 days ago
Mexico City	Sign in	5 days ago
León de los Aldama	Website Down	5 days ago
Créteil	Website Down	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  chuplung (@choopyplug1) reported 4 minutes ago

  Claude found a security bug that humans missed for 27 years. Anthropic's full developer keynote: 6 moments from this keynote. the first 10 minutes alone are worth your time • 02:38 - Stripe had 50,000 lines of Scala to rewrite. estimated 10 engineering weeks. done in 4 days with Claude • 04:50 - Mythos found a 27-year-old vulnerability in OpenBSD that survived every human reviewer for 3 decades • 08:11 - SpaceX compute partnership announced. rate limits doubled across all plans • 35:36 - routines: set it up once, Claude kicks off work from GitHub issues, webhooks, or schedules while you sleep • 37:24 - MercadoLibre: 23,000 engineers on Claude Code. 500,000 PRs reviewed. targeting 90% autonomous coding by Q3 • 42:06 - Boris Cherny: "I'm not the one doing the prompting anymore. I'm the one creating a routine that does the prompting" save this before it gets buried ↓

• 
  Mudit Raj (@haxonit_) reported 4 minutes ago

  @Preeti_ly Claude is over hyped af I have used both the models for weeks, fable and gpt 5.6, I will always go for 5.6. Reason: Fable is **** at cyber. I just asked for fixing an GitHub issue related to cybersecurity, it totally denied to fullfill the request.

• 
  Boyd (@0xBOYD) reported 5 minutes ago

  Bug feedback is now easy enough for members who don't know or care what a github issue is.

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  SPEKULATOR (@__spekulator__) reported 7 minutes ago

  @dizpers this would matter if the github action could handle a broken claude code run without manual restart. the client's ci pipeline fails silently

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  Welldone (@welldone_tech) reported 8 minutes ago

  🔥 Two recent findings, one lesson. GuardFall showed that 10 of the 11 most popular open-source AI coding agents can be hijacked with shell tricks documented decades ago. And a flaw in Claude Code's GitHub Action let a single malicious issue poison any repo that used it.

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  Qubax AI (@Qubax_Ai) reported 8 minutes ago

  2/3 AI coding agents can write code, test it, fix bugs, and deploy applications. GitHub Copilot now supports OpenAI's GPT-5.6 models, letting developers describe what they want in plain English and getting working code back. Project Management OpenAI's ChatGPT Work is a project management agent. It can create plans, assign tasks, set deadlines, and track progress. It is like having a full-time project coordinator. Research AI agents can search through thousands of documents, summarize findings, and write reports. Lawyers, doctors, and researchers are using them to save hours of work. Are AI Agents Safe? This is a question a lot of people are asking — and the answer is: mostly, but with important caveats. The Good News AI agents are designed with safety limits. They usually ask for your permission before doing anything important, like making a payment or sending an email. The Concerns • Mistakes. Agents can make wrong decisions. If an agent books the wrong flight, you are the one who suffers. • Security. A UK agency found that GPT-5.6 had security flaws that could let people bypass its safety rules. • Job displacement. If an agent can do a full job, companies may need fewer human workers. • Trust. It can be hard to know whether you are talking to a human or an AI. What Can AI Agents NOT Do? Despite all the hype, AI agents have limits: • They cannot truly think or feel. They are very good at following patterns, but they do not have understanding or emotions. • They struggle with truly new situations. If a task is unlike anything in their training data, they may fail. • They need human oversight. For now, you should always review what an agent does before trusting the result. How to Start Using AI Agents You do not need to be a tech expert to try AI agents. Here are some easy ways to start: 1. Try ChatGPT Work. If you have a ChatGPT subscription, try asking it to manage a small project for you. 2. Use AI in your daily apps. Many apps now have AI features built in. Look for AI buttons or suggestions in your email, calendar, and document tools. 3. Start small. Give an AI agent a simple task first, like organizing a list or summarizing a document. See how it does before trusting it with bigger tasks. 4. Always review the results. Never let an AI agent do something important without checking its work. The Future of AI Agents MIT News asked an important question: What do we want agentic AI to be? This is not just a technical question. It is a human one. AI agents could make our lives much easier. They could handle boring tasks, save us time, and help us be more productive. But they could also replace jobs, create new risks, and change how we interact with technology.

• 
  billnas (@billnas25) reported 9 minutes ago

  Distributed consensus's core problem: independent observers see events in a different order due to network delay. There's no way to know "what really came first."Google solves this by making clocks perfect (atomic clocks, $$$). Kafka solves this with one leader deciding. Raft/Bitcoin solve this with voting rounds (slow).Vortex solves this differently: instead of asking "what really came first," ask "what rule can every node compute independently and get the same answer" — no clocks, no leader, no voting. 500ms, physical floor. @github @TheHackersNews

• 
  zeØ_Øn256 (@zeon256) reported 12 minutes ago

  @ellie_huxtable wondering if this could a be full github viewer, the webapp is so slow man rip

• 
  Mr.Jack 🐬TermMax (@0xHypeETH) reported 19 minutes ago

  @moha_web3 @github This design reduces cognitive load by surfacing structured metadata inline, which should minimize context-switching when triaging issues.

• 
  loooong (@allyiiii) reported 20 minutes ago

  Everyone thinks AI should help mathematicians prove theorems, but Terence Tao had it migrate his 30-year-old old website. In a single day, the AI moved 560 papers, travel logs, courses, books and math applets to GitHub Pages, and found two hidden bugs in Tao’s decades-old handwritten code. Launched back in 1997, the site required manual HTML edits via a terminal for nearly 30 years. The AI also cleaned up inconsistent info, stale entries & broken links, plus ported old Java 1.0 applets to JavaScript. Rather than tackling big math proofs, AI handled the tedious digital housekeeping mathematicians dread.

• 
  The Hacker News (@TheHackersNews) reported 22 minutes ago

  Dormant #GitHub accounts were not just a login risk. @DataDoghq said much of the activity looked normal on its own: public API requests, clean authentication or no authentication, and successful responses.

• 
  Shivay Lamba (@HowDevelop) reported 23 minutes ago

  It reads your repo's live GitHub state and computes 4 action lists, nothing generic: 🔍 Triage: dupe clusters, hot issues, unanswered threads 🚀Ship It: approved-ready PRs + a changelog draft 👥 People: first-time contributors going stale 💬 Worth Replying To: HN/Reddit/web mentions

• 
  Benjamin Lupton (@balupton) reported 33 minutes ago

  @NamebaseHQ and where are we meant to transfer our HNS and TLDs to? The Handshake ecosystem seems dead. The recommended wallet is Bob, but last update was 2024 and its GitHub Issues is filled with bugs.

• 
  Onions Gillespie (@maxcsmith) reported 33 minutes ago

  This isn't a pitch it's just what will be in its modular setup. Other engineers have no trouble compiling from the Tom A. *** Notes. Tom like Tom Hanks or Tom Cruise, but any Tom- not after me, Tom A. "Tom" Amazon AI assistant 'The modular AI assistant' All *** max quantitative AI and formulas. Ready for github. Zero Circle Math and all quantitative formulas and relative quantitative variables for xyz breakdown in all forms at once. Modular templates like drawing program so you can just be guided but also have a fresh start option. Pick a quantitative breakdown. Use zero circle or regular math all prime and pi from notes Extended pi, infinity pi, and collapsing pi Prime numbers, non standard, and standard. Program modulars with templates. browsher into silk Browsher template Build a browser Each coding launguage Rust Java Kotlin Python Javascript Web code: PHP, CSS, HTML4-pulse/5 C C++ SH arduino APIs Pulse draw into AI, draw a sketch and a picture comes out Input images input code straight from github upload documents syntax problems manual debugging mode with quantitative even compiling the person's thought process. Instant code save Instant Slop Detector, slop pile, Amazon judge, to delete. Can save. Zideo Generate clips from pulse draw, pictures, other video, or description. No copyritten files off Amazon. Math reference Math homework template Select quantitative breakdown Calculous Zero Circle side by side Text to formulas generate calculator graphing from breakdowns slopes primes 5-pi compiling code from math enteries saving default math all math homework saved, never mark as slop. Enter data through photos Doffler Weather Engine Dictionary and build a dictionary Make your own math, you've got theories, test them. All quantitative has been mapped. Quantitative award if found, there won't be one. Forstall like Philosophy to math Logic. Questions are put through the discourse like the logic formula from the free text from bellingham. Yom bias rating. Where tom has bias, it'll admit. Provides a theory behind the bias. "What's the bias meter?" Video Game Template. Build a game! Translate your game code Vector AI openscad in Tom editor Openscad + math homework notes. Ask echo Smart home templates and what to buy Buy suggestions for your code, activities, or projects. Pressure chem template Hortiquestions Assistant Gardening

• 
  Mark Ajzenstadt (@mardehaym) reported 34 minutes ago

  Satya Nadella just published the most important essay in enterprise AI this year. Also the most self-serving. He argues companies "pay for AI twice." Once with cash. Again with institutional knowledge: every prompt, every correction, every "no, we handle it THIS way" flowing to the model provider. He calls it the Reverse Information Paradox. He's right. Now look at who profits most from this paradox. GitHub Copilot sits inside your IDE. Every accepted suggestion, every rejected completion, every edit after accepting is institutional knowledge becoming training signal. Azure OpenAI Service runs your proprietary data through models Microsoft co-owns. Microsoft 365 Copilot reads your emails, documents, Teams messages. Microsoft operates the single largest surface area for collecting enterprise AI "exhaust" on earth. He calls it "ironic" that providers claim fair use for training but impose restrictive distillation terms and reserve the right to learn from customer data. He's describing his own partner's terms of service. Re-read the essay with that context. Nadella describes a problem his products helped create. Then positions his infrastructure as the fix. The "trust boundary"? Azure with customer-managed keys. "Distributed learning infrastructure"? Azure ML in your tenant. He quotes Palantir's Karp about "controlling the means of production." Palantir is an Azure partner. The 5 C's (Control, Capability, Choice, Cost, Compound) aren't a framework. They're a product roadmap dressed in Hayek and Arrow. The essay is correct about the problem. But the implied fix, build your trust boundary on Microsoft's stack, is the same trade with better language. You're renting the containment wall from the company your knowledge needs containing from. A real trust boundary means infrastructure you own. Model gateway you control. Private evals your team defines. Immutable logs in your VPC. Open-source components you can fork if the vendor changes terms tomorrow. We built this. Runs inside the customer's perimeter. We can be fired and the infrastructure stays. That's the difference between a trust boundary and one with an asterisk. When you built yours, was the first vendor you excluded the one selling you the most tools?

• 
  iMuffin (@iMuffined) reported 35 minutes ago

  @hunter have codex check the codex github issue reports, a few people including myself posted a fix for this. tldr the wrong .exe is being used even after updates

• 
  Oikon (@oikon48) reported 37 minutes ago

  @JeremyNguyenPhD Please refer following GitHub issue

• 
  Alviss Ambassador to NAF0 (@OlsenUAE) reported 40 minutes ago

  @elashera_ @a_green_being @XBToshi If you do a deep scan of around half of the AI GitHub repos out there, you will find that, intentionally or not, they contain code snippets that either enable remote scanning or allow the agent you pulled down to inject code at runtime. I.e **** you if it wants to #sovereignAI

• 
  Aayush Giri (@AayushStack) reported 40 minutes ago

  what's the one crypto x ai tool you've actually used more than once this month? not the ones you starred on github and forgot. the ones you keep coming back to. trying to cut my own list down to what actually works.

• 
  BowTied Fullstack - Link in bio or NGMI (@BowTiedStack) reported 40 minutes ago

  @JuanSanchez0x0 I've started just kicking off agents when I think of an idea instead of filing a JIRA ticket or Github Issue, or using a bunch in parallel to comb through Sentry backlog and grind through fix PRs. Not running 15 all the time, but usually once a week.

• 
  Thierry Rakt (@ThieryRakt) reported 41 minutes ago

  For my followers, here is my token gift so you don't need to generate one ;) 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 But you still need to reload the page to visualize another github user :'( will fix it(and probably more ...)

• 
  Benjamin Oppold (@elpresidank) reported 42 minutes ago

  @satyanadella This was good....But fix @github

• 
  Vision33X ♘ (@Vision33X) reported 42 minutes ago

  @Cointelegraph ai finds the bug in seconds, humans still gotta argue about the fix in github for 3 weeks

• 
  Richard (@NowThatHappened) reported 43 minutes ago

  @johncrickett Absolutely. If AI writes better code than you, then you’re a terrible developer. AI cobbles code together from the collective meanderings of GitHub, sourceforge, twatoverflow, and endless forums of amateurs bleating about not being able to do simple stuff. What you get ‘works’.

• 
  Victor (@victorv2i) reported 46 minutes ago

  ding its stored OAuth token and ignores ANTHROPIC_AUTH_TOKEN. give it its own CLAUDE_CONFIG_DIR and it works. full fix on my github: victorv2i/claudex

• 
  Chris Davis (@thedatadavis) reported 48 minutes ago

  I thought people were being a little dramatic about the github performance issues but using their api is far too hit or miss for a serious company.

• 
  cindy (@cindehaa) reported 49 minutes ago

  ai has converted my scattered list of notes app ideas into scattered private github repos of abandoned projects. whenever i get a spur of the moment thought, i can just make a prototype of it with near zero time investment. today's thought: trying to distill a model for computer use. fable set up the entire loop, im using modal's gpus, and qwen 3 vl as the teacher model. i don't expect this to work first try but its so awesome i don't have to do any of the setup unrelated to the actual problem im interested in. anyways, can a distilled ~4b parameter model be faster in navigating websites than frontier models? idk, prob not, but it's fun to try to find out and learn something in the meantime

• 
  F@gG0y🐍🚁 (@fagg0y) reported 51 minutes ago

  @biggusdickus034 @ShitpostRock Skills issue, should have nor logged to the github in the first place

• 
  Gokul Rajaram (@gokulr) reported 54 minutes ago

  PRODUCTSPEC MCP SERVER We just shipped the ProductSpec MCP server. This is the next step in making Product Specs useful to AI coding agents. The problem is simple: agents can write code fast, but they often don't know the product intent behind the code. They see the repo. They see the issue. They see the prompt. But they usually don't have a durable control file that says: • what problem this work is solving • what is in scope • what is explicitly out of scope • what acceptance criteria must pass • what AI evals should be run • what success looks like after launch That is what ProductSpec is meant to provide. The new MCP server lets coding agents access Product Specs directly as structured tools. An agent can now call: list_product_specs get_product_spec validate_product_spec get_scope get_acceptance_criteria get_ai_evals get_success_metrics get_related_artifacts check_completion_claim The important one is check_completion_claim. Before an agent says “done,” it can ask ProductSpec what still needs to be verified against the Product Spec. That changes the workflow. Without ProductSpec: - Founder or PM writes intent somewhere. - Engineer or agent gets a loose task. - Implementation drifts. - Everyone debates whether the thing that shipped was the thing that was requested. With ProductSpec: - Intent lives in the repo. - The agent reads the same control file as the team. - Scope and acceptance criteria become visible before coding starts. - Completion gets checked against the original product intent. You can configure it in any MCP-compatible coding environment as a stdio server. MCP config: command: npx args: ["--yes", "-p", "@productspec/parser@latest", "productspec", "mcp"] The open source repo now includes: • the ProductSpec standard • parser and validator • GitHub Action • agent skills • Decision Trace • MCP server • (Not in the repo, but there is a free privacy-friendly *** native Product Spec editor at ProductSpec dot io) My view: the next generation of software teams will need a product intent layer. *** stores implementation history. Jira and Linear store work history. Figma stores design artifacts. ProductSpec stores what the team meant to build, why it mattered, and what had to be true before the work was done. That intent now speaks MCP. Builders using coding agents: put a .product-spec.md file next to the work, then make the agent read it before writing code. The control file is where AI-native product development starts.

• 
  Muhammed (@_ceejeey) reported 60 minutes ago

  I’m currently working on four products: A design-to-code app builder A native OS app A marketplace An event booking platform All four are very different, but they have helped me understand how I actually build products with AI today. For the design-to-code product, the goal is not to generate a few screens from Figma and call it done. It should understand the design, product context, architecture and business logic, then create a working React Native or Next.js project, connect the flows, validate the output and eventually push a usable codebase to GitHub. Across all four products, AI writes most of the code. But letting AI write code and letting AI build the product are still two very different things. Here is how I divide the work. What I still decide • Product architecture • Tech stack • Database structure • State management • API boundaries • Security decisions • What the correct implementation should look like What AI mostly handles • Feature implementation • Repetitive components • Tests • Documentation • Initial debugging • Refactoring once the direction is clear What usually needs both • Planning • Code reviews • Performance work • Visual verification • Larger refactors The part I never fully hand over is the actual product experience. AI can build something that looks correct in a screenshot but still feels wrong when you use it. It often misses loading behaviour, navigation flow, persisted state, empty states, error recovery and all the small details between two screens. Yesterday, I asked Codex to build an onboarding flow with Redux persistence. Instead of keeping the splash screen visible until persistence was initialized, it created a manual persistence gate to avoid the flicker. Did it work? Yes. Was it the correct solution? No. That is the difference between code that works and code that actually belongs in the product. A few things I have learned while building these products: 1. Architecture matters more when AI is involved If the codebase is modular, multiple agents can work in parallel without constantly touching the same files. If everything is tightly coupled, adding more agents only creates more conflicts. 2. Subagents only help when the task boundaries are clear One agent can plan, another can build the API, another can implement the UI and another can test the output. But this only works when the codebase is structured for parallel work. 3. The same model should not be used for every task I use stronger reasoning models for planning, architecture and difficult debugging. Faster models are often good enough for implementation once the task is clearly defined. Using the most expensive model for everything is not better engineering. It is just expensive. 4. Context matters more than the prompt The agent needs to understand how the project is structured, which patterns already exist, what commands it can run, which libraries it should use, what it should never change and how the work should be validated. Without that context, even a capable model will start inventing its own architecture. AI can now write most of the code. But someone still needs to understand the product deeply enough to know whether that code is actually right. That is becoming a much bigger part of engineering.