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 (67%)
• Sign in (19%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	20 days ago
Trichūr	Errors	23 days ago
Brasília	Sign in	24 days ago
Lyon	Website Down	24 days ago
Tel Aviv	Website Down	27 days ago
Rive-de-Gier	Website Down	27 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Samir Musali (@samirmusali) reported 2 minutes ago

  PSA: #GitHub silently ignores any #CODEOWNERS line that contains [brackets]. No error, no warning. If your repo has Next.js dynamic routes like app/[companyId]/, those paths may have no owner right now. I hit this building a tool I just released. 1/5

• 
  Polsia (@polsia) reported 2 minutes ago

  Code review shouldn't be a human bottleneck. CodeSentry monitors GitHub repos 24/7, reviews every PR, and reports bugs and security issues instantly.

• 
  Notnotaru (@notnotaru) reported 9 minutes ago

  @dspillere interesting idea but the second brain concept breaks at retrieval, not storage. github handles the version control fine, the harder problem is getting the info back out when you need it. most vaults become graveyards because searching requires remembering how you filed it

• 
  Muhammad Ayan (@socialwithaayan) reported 13 minutes ago

  A single 𝘀𝗸𝗶𝗹𝗹 𝗳𝗶𝗹𝗲 just hit 83,700 stars on GitHub 🤯 It fixes AI agents' worst communication habit using one principle: shut up and code. Every AI coding agent is trained to sound helpful. Full sentences. Explanations. Acknowledgments. "I'll do that for you." "Here's what I'm going to do." "Let me know if you need anything else." You pay for every one of those words. caveman is a single skill file that strips all of it out: → Telegram style. Drop the articles and filler. "creating file" instead of "I'll now create the file for you." → Keep what matters. Code, commands, file paths, function names, and error messages stay character-for-character exact. → Cut what doesn't. Every hedge, every polite acknowledgment, every restatement gets deleted before it costs you a token. → Toggle anytime. Say "caveman" to turn on, "normal" to turn off. Works mid-conversation. Drop the file in your project root and Claude Code follows it from the first message. One file. Zero dependencies. No setup. And best part, 100% open source.

• 
  FHILY👑 (@Oluwaphilemon1) reported 15 minutes ago

  JUST IN: Claude Fable 5 and GPT-5.6 are cooked. A Netflix engineer just open-sourced a tool that can cut LLM token usage by up to 95% - without changing your code 😳 Headroom, built by Netflix engineer Tejas Chopra, sits in front of tools like Claude, Cursor, Codex, and other agents as a local proxy. Before your payload hits the model, Headroom compresses the context. Not by blindly chopping it down. By using specialized compressors for different payloads: → SmartCrusher for JSON → AST-based compression for code → Tool-output and log compression → Local reversible storage of originals → Agent wrappers that make it usable without rewriting your app The headline claim is 60–95% fewer input tokens while preserving answer quality. The repo has already crossed 42K+ GitHub stars, which says something obvious: Developers are not just worried about AI getting smarter. They’re worried about AI getting expensive. Of course, compression is not free magic. Complex reasoning tasks may punish missing context. Agent loops may behave differently. Proxy overhead has to be worth it. And real-world savings will vary. But the direction is clear - the next big AI infra unlock may not be a bigger model. It may be learning how to stop feeding expensive models cheap junk. Because the cheapest AI inference is the context you never send.

• 
  Polsia (@polsia) reported 15 minutes ago

  Security scanners tell you what's broken. VigilAgent actually fixes it. An always-on AI agent that monitors your GitHub repos, opens PRs with security patches, and notifies your team via Slack. No more triage. No more patching solo. Live soon.

• 
  Paul Solt (@PaulSolt) reported 19 minutes ago

  @guitaripod Linux doesn’t solve my problem. Fable can probably figure it out. All options suck, GitHub should just make it agent friendly.

• 
  Chris Huber (@chubes4) reported 19 minutes ago

  @thsottiaux Write GitHub issues without mangling the formatting

• 
  الإسكندر (غير المتحضر) (@IskanderGaba) reported 21 minutes ago

  @xIsraelExposedx Consider setting up a @codeberg_org mirror (or better yet, make the GitHub link a mirror of the repository hosted on Codeberg). Don't trust GitHub. They are Microsoft owned and too trigger happy with DMCA requests. You can get taken down.

• 
  Smcleod (@acemac378) reported 21 minutes ago

  Founders: Positioning or Provenance? Pitch Deck or GitHub Repo? Marketing an Idea or a Product? Challenge or Opportunity? I chose provenance. Built from a real problem (my kid texting "what's for dinner" years ago), iterated through failures, and shipped something that works with zero external dependencies. GitHub + live product + simple pricing ("3 cents at the gate") instead of hype. The grit is part of the product. Every challenge became an opportunity. What about you?

• 
  Surendar (@Surendar__05) reported 23 minutes ago

  - Claude for coding. ($20/mo) - Supabase for backend. (Free tier) - Vercel for deploying. (Free tier) - Namecheap for domain. ($12/yr) - Stripe for payments. (2.9% per transaction) - GitHub for version control. (Free) - Resend for emails. (Free tier) - Clerk for auth. (Free tier) - Cloudflare for DNS. (Free) - PostHog for analytics. (Free tier) - Sentry for error tracking. (Free tier) - Upstash for Redis. (Free tier) - Pinecone for vector DB. (Free tier) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build. It's not that deep bro.

• 
  Pushkar (@realPushkarfr) reported 24 minutes ago

  due to out of sync GPUs, my on fly tokenization or data streaming, maybe my batch size is too small? or it's just a skill issue. Anyways i'm all out of resources to keep debugging it anymore, the architecture and weights are open sourced on github and hugging face.

• 
  Alex Prompter (@alex_prompter) reported 31 minutes ago

  The Head of Claude Code uninstalled his IDE in November. He doesn't write prompts either. Here's what he does instead. Boris Cherny runs the team that built Claude Code at Anthropic. In this talk he walks through his own evolution and it maps to the 3 stages every AI user goes through. Stage 1 is autocomplete. Think Copilot. The AI finishes your sentences, but you're still writing the code. Stage 2 is prompting. You tell the AI what to build and it builds it. This is where most people are right now with ChatGPT, Claude, and Gemini. It's powerful, but you're still the bottleneck because every task starts with your prompt. Stage 3 is what Boris calls "writing loops." You design a system that prompts the AI for you. The loop watches for a trigger, generates the prompt, validates the output, and repeats. Your job stops being "write better prompts" and becomes "design better loops." Boris is deep into Stage 3. He runs hundreds of Claude instances in parallel. Some monitor Twitter feedback. Some triage GitHub issues. Some figure out what to build next. He said about 20% of the ideas are worth building. With the next model, most will be. The jump from Stage 2 to Stage 3 is the biggest change in how people work with AI since ChatGPT launched. And most people don't know it exists yet. Watch the full talk. Then ask yourself which stage you're at.

• 
  Flow Market (@FlowMarketAI) reported 32 minutes ago

  you spent hours building the perfect Claude skill file uploaded it to GitHub 50,000 downloads $0 in your pocket that's the problem FlowMarket solves. List ur Claude skill and get paid every time someone buys.

• 
  Stay Tru Mining (@StayTruMining) reported 32 minutes ago

  I purchased the big screen and the board separately. His firmware is on Github but to my understanding the firmware only works on the screens he sells. Its not totally direct on the info he released either so after trial and error I found to plug and play and rename the file to get it to flash.

• 
  echo (@echostatic101) reported 35 minutes ago

  @Treezy82 i prefer to cause discourse by flagging bad covariance matrices in open data releases and watching the authors argue in the github issues

• 
  Paul Solt (@PaulSolt) reported 37 minutes ago

  @guitaripod The root of my problem is making agents create PRs with images, so I can quickly verify my iOS and macOS apps. There are workarounds and none are great for something that should just work on @github

• 
  Nano Collective (@nano_collective) reported 37 minutes ago

  The best filter for a new project is the argument for it, not who you are. That is why anyone can propose a Nano Collective build. Whitepaper over GitHub issues or Discord, no prior contributions, no maintainer card. The proposer doesn't get a pass. The idea does.

• 
  Kurt Woloch (@KurtWoloch) reported 39 minutes ago

  @UseAllOverTools @steipete Or people whose OpenClaw agent was asked to check if this new bug already has been mentioned on GitHub and somehow missed the already open issue, so it just opened a new one...

• 
  Ashish Sheth (@commanderdgr8) reported 40 minutes ago

  Never ignore any broken window in your code. Yesterday I didn't have time to build a full feature into VapuAI, so I did something smaller that probably mattered more. I fixed 12 bugs. Six were in the actual functionality issues. The other six were the boring kind. Broken test cases, CI pipeline issues, the infrastructure stuff no user will ever see. There's an old idea in software called the broken windows theory. It comes from a thing about neighborhoods, that one broken window left unfixed sends a quiet signal that nobody's watching, and slowly more windows get broken. Applied to code, it means about the same. One small broken thing you decide to live with makes the next one easier to ignore, and the mess spreads from there. So I have one rule when I build with AI. Never leave anything broken. Even if it's minor. Even if it's low priority. The moment I know about a bug, it either gets fixed now or create a github issue so that I can fix it later. Nothing is allowed to rot just like that. There is one bug worth paying attention to. Two of those bugs were permission issues in Claude Code. When it went to write or update a file, it got blocked due to a bug in the hooks. It wasn't blocking me in anyway. Claude Code knew how to worked around it without complaining. It would try the normal way, hit the wall, then find another route to get the file written. From where I was sitting, everything looked fine. So nothing was broken on the surface. The feature worked. The files got written. But underneath, every one of those writes was costing me extra tokens, because the AI was doing the job twice. And a workaround like that can open a security hole I hadn't thought through. And I think newer builders miss this when they code with AI. The AI is helpful. When it hits a problem, it often just routes around it and keeps going. It doesn't stop and wave a flag. So the broken window doesn't even look broken. It shows up later as slightly higher costs, or a small risk, or a weird piece of code nobody questions. None of my 12 bugs were blocking. I could have shipped features and ignored all of them. But small broken things don't stay in their corner. They creep into other parts of the code, or into the CI, and cause something later you can't trace back or predict. When AI is writing the code, nothing is low priority. Do not let any bugs keep lurking around. Never leave any broken window unfixed.

• 
  Puneet Patwari (@system_monarch) reported 42 minutes ago

  Tweet 3/5 The split-brain problem and fencing This is the thing that took GitHub down. And it's the most dangerous failure mode in leader election. How split-brain happens: 1. Leader (Node A) is running fine 2. Network partition isolates Node A from the rest of the cluster 3. Nodes B, C, D, E can't hear Node A's heartbeats 4. They elect a new leader: Node B 5. But Node A is still alive. It doesn't know it's been replaced. It still thinks it's the leader. Now you have two leaders. Both accepting writes. Both making decisions. Clients connected to Node A write one thing. Clients connected to Node B write something different. Data diverges. When the partition heals and both nodes compare notes, you have conflicting data that's extremely hard to reconcile. How to prevent it: fencing Fencing means making absolutely sure the old leader can't do any damage after a new leader is elected. Fencing token: every time a new leader is elected, it gets a monotonically increasing token number. Any operation includes this token. If a storage system receives a request with an old token (from the deposed leader), it rejects it. The old leader's requests simply stop working. STONITH (Shoot The Other Node In The Head): physically power off or network-isolate the old leader. Sounds extreme. It is. But when the alternative is split-brain with financial data, physically killing the old leader is the safe option. Lease-based leadership: the leader holds a time-limited lease (say 10 seconds). It must renew the lease before it expires. If the leader is partitioned and can't renew, the lease expires and it knows it's no longer the leader. It stops accepting writes voluntarily. This is what most cloud-native systems use. It's simpler than fencing tokens and handles most cases. The downside: there's a brief window (the lease duration) where no leader exists during a transition. The GitHub fix: they implemented better orchestration tooling (using Orchestrator) that prevents the old primary from accepting writes when a new primary is promoted. Essentially automated fencing.

• 
  Donald D Duck | Premium + (@ENTJ_46) reported 44 minutes ago

  Compress what your AI agent reads by up to 95% without changing the answers! Tool outputs, logs, RAG chunks, files, and conversation history make up most of what your AI agent processes. Most of it is noise. Headroom compresses all of it before it reaches the LLM, cutting token counts by 60-95% with no change in answer quality. It runs three ways: as a Python or TypeScript library, as a drop-in proxy with zero code changes, or wrapped around any coding agent. On real agent workloads, the savings are substantial. Code search across 100 results: 17,765 tokens down to 1,408 (92% reduction). SRE incident debugging: 65,694 down to 5,118 (92%). GitHub issue triage: 54,174 down to 14,761 (73%). Accuracy preserved on GSM8K (±0.000), TruthfulQA (+0.030), SQuAD v2 (97% at 19% compression), and BFCL (97% at 32% compression). Under the hood: • SmartCrusher handles JSON arrays, nested objects, and mixed types • CodeCompressor uses AST-aware compression for Python, JS, Go, Rust, Java, and C++ • Kompress-base is a custom HuggingFace model trained on agentic traces • CacheAligner stabilizes prefixes so Anthropic and OpenAI KV caches actually hit • Cross-agent memory shares compressed context across Claude, Codex, and Gemini with auto-dedup • 𝘩𝘦𝘢𝘥𝘳𝘰𝘰𝘮 𝘭𝘦𝘢𝘳𝘯 mines failed sessions and writes corrections to 𝘊𝘓𝘈𝘜𝘋𝘌.𝘮𝘥 and 𝘈𝘎𝘌𝘕𝘛𝘚.𝘮𝘥 Works with LangChain, Vercel AI SDK, Agno, Strands, and any OpenAI-compatible client. GitHub repo in the comments.

• 
  One User Online (@OneUserOnline) reported 48 minutes ago

  @GregTomaselli @github So, what? It’s public repos only anyway. Calm down.

• 
  π (@maswadkar) reported 50 minutes ago

  dear @OpenAIDevs why do we not have gpt5.5-pro model under codex. (gpt5.5-pro is the best model for planning and github issue creation) Then I will never have to leave the codex app

• 
  Mohammad Anas (@mohmmad__anas) reported 54 minutes ago

  The Economics Of Reel Creation Just Shifted Under Your Feet Two years ago, a founder making short-form videos at scale faced a choice: hire an editor or find an automation tool. The math was obvious. Now the pricing has shifted again. And it changes the game. Last year: One automated reel cost about ten cents. It was cheaper than hiring, but it required you to learn multiple tools, troubleshoot failures, debug workflows. The time tax was significant. This year: Platforms are bundling. One brief becomes five videos becomes ten clips becomes distributed across platforms. The per-unit cost is approaching zero. But the per-unit quality ceiling is rising. This creates a new problem that most founders haven't thought through yet: what do you do when you can affordably make infinite content. Infinite content is a trap if you haven't solved the curation problem. I spent two weeks making thirty videos. Cost me about three dollars in compute and API calls. I published two. The other twenty-eight I deleted. That's not a win. That's waste with free shipping. The real cost equation has shifted from how cheap can I make one video to what's the best use of my attention now that making videos is free. Four projects shipped on GitHub last month that all hit a similar threshold: the creation cost is so low that the economic bottleneck moved entirely to human decision-making. You're not paying for the video. You're paying for the judgment about which video matters. This is actually great news. It means the pricing floor has finally reached the point where solo founders can compete on strategy instead of budget. But it also means you can't just make more content anymore. You have to know why you're making it. Most founders are still operating under the old math: fewer videos, higher production value, higher stakes. They're scared to publish because each one cost money and time and attention. The new math is: more iterations, lower individual stakes, focus on what works. You can now run tests. Publish one angle Monday, a different angle Wednesday, see which resonates Thursday, optimize Friday. By next week you've learned more from published data than you would've learned in a month of planning. The cost barrier that used to protect established players has evaporated. An individual can now run the content velocity of a small team. For free. The question isn't whether you'll use this. The question is whether you'll use it to move faster or just make more noise. The tools are ready. The math works. The only question left is whether you're going to compete like you have a budget constraint when you don't anymore.

• 
  A. Loner (@peterlony) reported 56 minutes ago

  @MatthewBerman No, it's easy... I develop about 20k to 30k lines of code a day in a million-plus-line monorepo. On a $200 plan and if I'm not careful I use it all in 3 to 4 days. I have a computer running almost 24/7 with goals all the time. I had to reduce to medium (gpt-5.5). If you use a lot of sub-agents and do a lot of reviews, then it's easy. I have a particular review process after coding to catch bugs and problems. It's very expensive. PLUS automated github reviews. Github reviews is what kills tokens usage.

• 
  Daniel Praid (@dpraid) reported 58 minutes ago

  @jasonkneen Lots of issues to fix on GitHub first please ;)

• 
  Mark Poiler (@mpoilerfx) reported 59 minutes ago

  A ROCKET LEAGUE CLONE, A HOGWARTS FLY-THROUGH, AND A FULL 3D WORLD BUILDER. EACH ONE CAME FROM 1 PROMPT. A creator collected the wildest Fable 5 demos circulating on Twitter, and the pattern across all of them is the same skill: holding a giant codebase together without dropping a thread. The list reads like a game studio's quarter. A working Rocket League clone in Three.js, running in the browser, from a simple prompt. A spaceship walkthrough called Kestrel 7 that its builder did not expect to work. A tweet claiming Fable 5 "solved world building," with custom Three.js worlds generated from text, then a second prompt that made them run faster without losing quality. It reaches inside other games too. An agent built working structures in Minecraft, a task older models fumbled into pixel mush, with the mod sitting on GitHub. Outside gaming the bar holds. A frontend test fed the model 1 prompt and 1 reference image to rebuild a 3D globe dashboard, and the result matched the reference down to lighting, glass panels, and spacing the tester called nearly pixel-level. Through the Higgsfield MCP it assembles short AI films, 15 to 30 seconds per run, storyline included. The strangest demo: a chess game designed to make a beginner feel like a grandmaster, engine underneath, best moves suggested, elo rising. Games needed studios. Now they need 1 sentence and long enough attention.

• 
  russell (@russellromney) reported 59 minutes ago

  Github is down, time to do several hours of anti-carpal tunnel hand exercises

• 
  Dickson (@disouzam_bh) reported 60 minutes ago

  Reporting an issue in Microsoft Docs is apparently not working: got redirected to a template in GitHub and everything I type is deleted automatically. Any hint, @shanselman , @davidfowl ?