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 (59%)
• Errors (28%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	5 days ago
Madrid	Errors	5 days ago
Bogotá	Errors	5 days ago
Paris	Errors	5 days ago
Lyon	Website Down	6 days ago
Lima	Errors	6 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Volodymyr Pavlenko (@mindinpanic) reported 1 minute ago

  @Austen GitHub Models shutting down quietly is the kind of thing that breaks a hundred CI pipelines before anyone notices

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  Darek Gusto (@darekgusto) reported 2 minutes ago

  Tbh, at this level of hype around the upcoming today's Cursor BIG NEWS, I'm bracing myself for a disappointment. But I still really want them to deliver awesomeness today. Is it a new Composer model? Is it Origin throwing down the gaunlet at Github? Or something entirely new? With how much they hype it, preferably all three. Hopefully not just the newly announced Grok Bot...

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  Gabriel Odusanya 🇳🇬🇬🇧 (@gabbytech01) reported 4 minutes ago

  𝗜 𝗗𝗘𝗕𝗨𝗚𝗚𝗘𝗗 𝗠𝗬 𝗣𝗢𝗥𝗧𝗙𝗢𝗟𝗜𝗢 𝗟𝗜𝗞𝗘 𝗔𝗡 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗦𝗘𝗖𝗨𝗥𝗜𝗧𝗬 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥. 🔐 I don't know how I survived today but I did 🤧 Today I spent a good part of the day getting my personal portfolio from “it works locally” to actually being deployed and accessible through my own domain. And the process turned into something much more interesting than just deploying a website.I had to deal with build failures, TypeScript configuration issues, Next.js caching problems, *** configuration, remote repository conflicts, GitHub Pages deployment, DNS records, custom-domain configuration, and HTTPS. At each stage, the approach was basically the same:Don't guess. Investigate.When the build failed, I didn't immediately start changing random files. I looked at the error. I identified what layer was actually failing. I changed one thing.I rebuilt.I observed the result. Then I moved to the next issue. That is also how I approach application security. When you see a vulnerability, the goal shouldn't simply be to say: “The scanner found XSS.” The real questions are: Where does the input originate? Where does it travel? What processes it? What trust boundary does it cross? What controls are supposed to stop it? Why did those controls fail? What is the actual business impact? How do we fix the underlying design problem? The same reasoning applies when debugging infrastructure and deployment pipelines. A system is made up of layers.Application → Dependencies → Configuration → Build → CI/CD → Infrastructure → DNS → User.A failure at any one of those layers can make the entire system appear broken. That was probably my biggest takeaway from today's work. Debugging isn't just a developer skill. It's a security skill. If you understand how a system behaves when everything is working, you're in a much stronger position to understand how it behaves when something is being abused. Building things teaches you how systems work. Breaking them teaches you where they fail. Doing both is where the real security engineering starts.

• 
  Neuro (@neurometax) reported 5 minutes ago

  @BkashJosi havent had chance to finish integration, still building the PR. finally have my Hermes set up to work my github and process my PRs with merge conflicts and issues.

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  Thomas Peitz (@tpeitz_dus) reported 7 minutes ago

  Since I have github issue -> PR flow (claude action) - I am a lot more creative. I just let it build and when I am bored hours later I just check what it did. - This even makes more fun with my game which I am programming. - I can just play a round and tune further.

• 
  Peter (@Pr0ftrader) reported 7 minutes ago

  @bot Awesome, but there is one issue. When you use Github to sign in with your @cursor_ai account. It gives you a 404 error. Just a heads up, so far love the app (using on a Mac).

• 
  Ryan McHargue (@ryanmcharguee) reported 8 minutes ago

  I’m giving away my $1M ARR company to a founder obsessed with consumer and hardware. Quit-vaping hardware. Profitable. 100,000 customers all time. Not selling it. Giving it. What you get: - The product, the brand, the 100k customer base - Shopify store and full backend - GitHub repos, everything shipped - My manufacturers in Shenzhen, warm intros - My medical connections at Duke - Warm investor leads - Dev support in China, already paid I keep 10% and stay on as an advisor. You get 90% and full control. Why: I’m heads down on agents and can’t chase two rabbits. This one deserves an operator who’s obsessed. I’m not anymore. Don’t reply “me.” Tell me what you’d do with it

• 
  Luna Tech (@LunaTechAI) reported 12 minutes ago

  This is wild... Claude Code, Codex, Cursor, and 10 other agents just got a shared operating system. It's fully open-source. Every coding agent has the same gap. It writes the code, but it doesn't engineer it. Every session, you have to re-tell it to plan first, write the test before the fix, review its own work, and remember what it just learned. That instruction layer is where every harness leaks. ECC (Everything Claude Code) just open-sourced this exact layer. You install it once and your agent runs a coordinated pipeline: plan → test → implement → review → verify → remember → improve 67 agents, 284 skills, 94 commands, hooks, and a shared memory vault, all working across Claude Code, Codex, Cursor, OpenCode, Gemini, Zed, GitHub Copilot, Antigravity, Qwen, Hermes, and Kimi. The design principle is one line: Optimize the context window. Persist everything else. Here's how that plays out. Skills load only when the task calls for them, so the prompt stays lean. Rules always load, so language and project standards don't depend on the model remembering them. Hooks run outside the model, so quality gates fire deterministically instead of on reminders. And the review step runs from a fresh context, so the session that wrote the code isn't the one blessing it. The memory piece is what makes this cross-harness. The Memory Vault stores durable context as inspectable Markdown, and every harness reads from the same store. Handing off from Claude Code to Codex is one command, not a copied transcript. There's also a Plan Canvas, where the agent opens its plan in a local browser. You click the part you mean, attach an annotation, and either approve or request changes. That verdict maps straight onto the plan's confirm gate. AgentShield ships built-in, and it scans your prompts, hooks, MCP config, permissions, and agent files, treating the harness itself as an attack surface. MIT licensed. Single command install. npx ecc-universal setup

• 
  Dylan Fetch (@DylanJFetch) reported 14 minutes ago

  This is a new one for me. Agent is asked to implement github sub-issue #35. Worktree is named after #34, which is the parent issue. Agent ignores directive and completes #34 instead, all sub-issues.

• 
  Abhinav Arora (@aroraabhinav1) reported 14 minutes ago

  Codex often reports "gh auth isn't working" And since I know it is, I just copy the output of "gh auth status" and paste it into Codex. That's it. And now Codex can raise the PR. What?? Maybe it's a different shell context. Different Keychain access. Sandbox permissions. Or Codex simply inferred auth was broken, then changed its mind after seeing evidence. But the funniest (and most probable) possibility is that nothing changed in the environment at all. The user response just convinced the agent that GitHub works.

• 
  V (@Vic_x_Irem) reported 15 minutes ago

  3 companies (Claude, OpenAI & Gemini) built the same tool without any of them talking to each other, and Spotify just joined the queue That's basically how the internet works, though Somebody solves a problem. Then two other people solve it again, independently, in different languages, with different bugs. Now we just reinvent things constantly and somewhere, three versions of the same solution exist quietly on GitHub with 4 stars each. So, here's the question: Would a decentralized documentation layer help prevent this?

• 
  Branko (@brankopetric00) reported 16 minutes ago

  You push a quick fix. Five minutes later the commit has a live AWS key, sitting in a public repo with three forks already. A teammate says delete the file, force-push, move on. That key is already out. Scrapers harvest secrets off public GitHub in seconds. Rewriting history cleans the repo. It does nothing about the credential itself. What you're actually staring at: - A key with S3 and compute permissions on a real account - Forks that already have the bad commit, force-push or not - No secret scanning or pre-commit hook that would have caught this earlier - A clock that started at push, not at the moment you noticed Revoke first, rotate first, audit for use first, or clean the *** history first? Pick the wrong order and the "fixed" repo still has an active key loose in the wild.

• 
  Martino Vigiani (@martinovig) reported 17 minutes ago

  @bcherny Boris, I am a 6-month Claude Max user, yet what I noticed was: 1. Context engineering, coherence, and long memory management need improvement. I built a file-based memory system in my huge directory that works but always has problems, as I am not a context engineering expert. It would be great to give Claude better management of these things so it is useful for people who cannot do this themselves. Generally, Claude Code should become usable and simple for anyone. Coding difficulty is something apart. 2. I sincerely dislike the Desktop App a lot. It often gives errors; with slow internet it starts erroring with MCPs, old session management, etc. It would be great to have a skill that lets Claude reorganize my sessions. Lastly about the app: the skills, GitHub integration, etc. are way better on Codex and the UI seems cleaner. However, I am still using Claude due to habit. 3. Sonnet 5 still makes coding errors. Somehow I got many errors on YAML files due to indentation, which Sonnet 5 mistook a lot. - And poor Coding ability. 4. It is too resource intensive. Literally with some MCPs, Claude Code sessions, and some skills it gets so heavy, like 800MB–1.2 GB RAM, and I work with 3–4 sessions in parallel so about 5 GB RAM just for Claude Code. 5. Claude models somehow make many mistakes on their own documentation, not knowing how to explain the difference in the harness between Subagents and Teammates. 6. Weekly limits are not enough (sorry to mention that…). 7. Give the models the ability to spawn new threads, now that they can chat between them. 8. Give them the ability to use /goal with subagents. Like they spawn a subagent with /goal “make that”.

• 
  Leander (@leanderriefel) reported 27 minutes ago

  This is what I was scared of. If this really is how Origin works it is not a GitHub alternative. It solves an entirely different problem.

• 
  Dr Milan Milanović (@milan_milanovic) reported 28 minutes ago

  About half of the AI PRs that pass SWE-bench would not be merged into main SWE-bench uses an automated grader, where real projects use a maintainer who has to work with the code every day. METR measured this gap. They handed AI-written pull requests to the people who own the repositories. Those maintainers reviewed them the way they always do. 🔹 𝟰 𝗮𝗰𝘁𝗶𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 from scikit-learn, Sphinx and pytest reviewed 𝟮𝟵𝟲 𝗔𝗜-𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗽𝘂𝗹𝗹 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀. 🔹 Every one of those patches had already passed the SWE-bench automated grader. 🔹 𝗠𝗲𝗿𝗴𝗲 𝗿𝗮𝘁𝗲𝘀 𝗰𝗮𝗺𝗲 𝗶𝗻 𝟮𝟰.𝟮 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗯𝗲𝗹𝗼𝘄 𝘁𝗵𝗲 𝗴𝗿𝗮𝗱𝗲𝗿 𝘀𝗰𝗼𝗿𝗲𝘀, once you adjust for how strict these maintainers are. 🔹 That adjustment matters. The same maintainers merged only 68% of the original human patches. 🔹 They rejected code for weak quality, for breaking other code, and for missing the real issue. 🔹 By the grader, Claude Sonnet 4.5 handles work that takes a person about 50 minutes. By maintainer review, about 8 minutes. They used 95 of the 500 SWE-bench Verified issues and 5 models released between mid 2024 and late 2025. Reviews happened in GitHub, and nobody told the reviewers which patches a human wrote. METR is careful about one point, the agents had one attempt and no chance to answer feedback, the way a developer would. So this is not a hard limit on what agents can do. This means a benchmark score tells you the tests pass. It says nothing about whether the code can live in your codebase. This is exactly why we in our teams still check AI-written PRs first. Credits: METR

• 
  Hassan (@buildwithhassan) reported 33 minutes ago

  @Kappaemme1926 been doing this manually for months. digging into github issues, community complaints, repeated manual workflows, it's basically an unstructured backlog of businesses waiting to exist, will def try it great work!

• 
  Srinivas Rao (@UnmistakableCEO) reported 34 minutes ago

  @DMVG_JTK: This is exactly right. The attack surface is the input method itself. Sandboxing and pulling write tokens is table stakes. If your agent can be social-engineered by a GitHub issue, it's not ready for ****. --- re: The AI vendors that want to autonomously maintain yo...

• 
  Nitesh (@NiteshTechAI) reported 35 minutes ago

  Nobody opens the browser tab your AI assistant lives in. Mine lives in Discord. It answers from a 2,800 page knowledge base on my phone, and that one change is why I use it daily instead of monthly. It's called AstrBot. • Agent sandbox for safer tool use. • 1000+ plugins, one click to install. • Knowledge base, personas, MCP support. • Automatic context compression on long chats. • QQ, Telegram, Slack, Feishu, DingTalk, WeChat Work. Adoption is the problem here, not capability. Put the agent where you already type all day and using it stops being a decision you have to make each time. One caveat before you build on it: AGPL-3.0, not MIT. Read the terms if this is going anywhere commercial. ⭐ 38,000+ stars on GitHub. AGPL-3.0 licensed. 🔗 GitHub link in the comments 👇

• 
  Rimsha Bhardwaj (@heyrimsha) reported 38 minutes ago

  Adobe After Effects is in trouble. The engine behind every 3Blue1Brown video is 100% open-source, free, and it renders math animations that After Effects physically cannot produce. Manim is Grant Sanderson's programmatic animation library and it's sitting at 87.7k stars on GitHub. Instead of keyframing and tweening in a timeline UI, you describe animations as Python code. A vector, a transform, a graph, an equation, they're all objects you manipulate with methods like Transform, FadeIn, or ApplyMatrix. Under the hood: - OpenGL shaders (GLSL) handle the actual rendering, so animations stay resolution-independent - FFmpeg pipes the frames into video output - LaTeX renders any equation you throw at it, animated character by character - The Scene class manages state, timing, and playback as pure code Because everything is programmatic, you can animate a Fourier transform, a Riemann sum shrinking to an integral, or a 3D manifold rotating, without touching a single keyframe. The main repo is ManimGL by Grant himself. There's also a Community fork with better docs and testing. Repo link in the replies.

• 
  Sahebjot Singh (@zereraz) reported 41 minutes ago

  @evsubr @thsottiaux @ChatGPT ask it to see if there are known issues on github about this

• 
  Christian (@candresbacho) reported 41 minutes ago

  Working it, would be open source. Any event from GitHub can launch any agent with different tasks handle it by agents. Issue with some bug to fix, automatically handle by an agent. Security/optimizations audit daily handle by agents. Everything that you can imagine

• 
  Puneet Patwari (@system_monarch) reported 47 minutes ago

  9) Split brain Two nodes both believe they are the leader. Both accept writes. The data diverges and there is no correct way to merge it afterwards. Why it matters: This is the failure that destroys data instead of causing downtime. GitHub's 2018 outage was twenty four hours of manual repair from this exact shape of problem. How to learn it: Read the GitHub October 2018 incident writeup end to end. Then learn what a fencing token is, because that is the fix.

• 
  Gidimoney369 (@Gidimoney247) reported 48 minutes ago

  💬 PIONEERS — Node v0.6.2 just dropped. Coincidence or the final signal? ⚡ The timing is everything. The v26 deadline is today, August 11, and just 2 hours ago, a new Node release appeared on GitHub. What's in v0.6.2? · 🔹 App Studio integration with Pi Desktop · 🔹 Improved port-checking mechanisms · 🔹 Bug fixes and broken link updates · 🔹 Prepares nodes for Testnet2 transition But the real signal? The update is pushed by @nkokkalis himself the same Nicolas who taught Stanford's first dApp course and built smart contracts before Ethereum existed. The v26 Upgrade: Hard deadline. 421,000 nodes must comply or lose Mainnet access. Expected downtime? Under 5 minutes. Something is loading. The pieces are falling into place. 👇 Is your node on v0.6.2 yet?

• 
  Nathan Barry (@nathanrs) reported 49 minutes ago

  Turns out you can make LLM inference fully deterministic across devices, with no loss to quality or speed. This weekend at the @SpaceXAI hackathon I got Qwen3-0.6B to produce identical hashed logits from a 512-token generation across 2 GPUs and 3 CPUs: an A100, an H100, an Apple M5 Max, an AMD EPYC, and an Intel Xeon. The main reason why inference isn't deterministic is because floating-point addition is not associative. (a + b) + c ≠ a + (b + c), since every add rounds. Accumulation order changes with the hardware used and kernel selected, so the same prompt can give you different outputs even at temperature 0. Integers ARE associative. So why doesn't integer quantization already fix this? Because, while weights and activations get quantized, the non-linear ops (softmax, normalization, SiLU) dequantize back to float and requantize afterwards. Each of those steps hands you back to floating point rounding. True integer-only inference does exist, but it's historically been motivated by edge hardware without FPUs, which doesn't make much sense for LLMs. One 2024 paper (I-LLM) did it on LLaMA from that angle and didn't get much attention. Nobody seems to have looked at it from the determinism side. I wrote my own implementation, simplifying the approach from the paper, so that every operation between the input ids and the int32 logits is exact integer arithmetic. To test it I chain-hashed the logits at every step and ran that across the devices and configurations below. Every integer run gave the same hash: 64430dd985f8. Every fp16 run gave a different one, all diverging on the very first token. WikiText2 perplexity came out to 20.72 vs 20.95 for fp16 (slightly better than the float baseline), and CUDA-graphed integer decode hits 106 tok/s at batch 1 on an A100, 3.6x the fp16 eager baseline. Github repo is listed in the comments. Plan to do a writeup over this eventually!

• 
  EDo-J🅁🅅🄽 🐦‍⬛ (@Sangjun121722) reported 50 minutes ago

  On August 10, hans-schmidt posted an emergency update on the official Ravencoin GitHub releases page. The team is currently dealing with two separate bugs. Regarding the KAWPOW issue, 2Miners has already released an emergency Core update.

• 
  Chris Bee (@chrisbeetweets) reported 50 minutes ago

  A question I keep getting: if Claude already connects to GitHub, Jira, and Slack etc. why would anyone need a knowledge graph? Connecting AI to your tools solves the access problem. Understanding is a separate problem, and it's the expensive one. Every question pays the full cost of rebuilding your company's context from raw data, and that cost compounds as more of the company starts asking. A knowledge graph does that work once and reuses it everywhere.

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  XXI (@XXIfomo) reported 51 minutes ago

  A 28-YEAR-OLD TECH LEAD IN AMSTERDAM WATCHED HER REMOTE TEAM SHIP CLAUDE-GENERATED CODE THAT TOOK DOWN A $340,000 ARR CLIENT'S CHECKOUT ON A SATURDAY. SHE FIRED GITHUB COPILOT ON MONDAY. BY WEEK EIGHT NO AI-WRITTEN LINE HAD REACHED PRODUCTION UNREAD. GitHub sells Copilot at $39 a seat and lets it auto-merge trivial PRs on the enterprise tier. Her twelve engineers loved it. Ship velocity doubled in April. The Saturday incident happened in May. The client left in June. She replaced Copilot with a four-agent review pipeline she wrote in one weekend on a canal-house desk in Jordaan. Sonnet reads the diff. Haiku runs the failing test against the previous commit. A third agent, different base model, looks for the specific pattern that broke checkout. A fourth writes the merge note the human on-call will read at 2 a.m. Nothing merges until all four sign off. One flag and the PR waits for a human. Merge rate before the pipeline = 89 percent Merge rate after the pipeline = 34 percent Production incidents from AI code, first six months = 11 Production incidents from AI code, past 47 days = 0 Engineering headcount = 12 Time she personally spends reviewing PRs = 40 minutes per week For seven weeks the team pushed back. Velocity dropped. Two engineers quit in protest. She kept the gate closed. On week eight a junior caught a race condition the pipeline had flagged three days earlier and everyone stopped complaining. GitHub sells the speed. The lead in Amsterdam sells the four signatures that stop the speed from killing the client. bookmark this and read the article below

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  Jack Lippold (@CaptainAppJack) reported 53 minutes ago

  @valhalla_dev Stop trying to put humans in the loop. Oh ma gaud!!!! Is this where we are now? The h/acc vs e/acc vs Luddite’s comes down to code review? Adding this quiz as part of opening a PR on a public OS repo on GitHub might actually be a good idea, but agents would just answer it.

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  Herr Große (@HerrGerie) reported 54 minutes ago

  @iretiayo_queen Buy a domain $7-10 per year, update DNS alias, pay for a VPS Server (Hetzner). Install docker inside the VPS Create a private github for deployment (Don’t deploy using workflow repo)to hold your docker-compose file . , run an n8n, Postgres image etc You can use VPS beyond n8n

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  Rebecca Valentine (@defnotbeka) reported 54 minutes ago

  all of that work is part of the _design_ process, not the building process but if you think about what software is like, the DESIGN process is a text file with some ideas, or a github issue for a feature request