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	21 days ago
Trichūr	Errors	25 days ago
Brasília	Sign in	25 days ago
Lyon	Website Down	25 days ago
Tel Aviv	Website Down	29 days ago
Rive-de-Gier	Website Down	29 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  MaxMusterman (@maxschuetz_) reported 6 minutes ago

  using Github Issues as your Roadmap is way better than any other tool. AI Agents can check them in an interval, fix explicit Issues, i check them and then they get merged. Soon Customer Feedback --> grading/clustering --> Github Issues --> automatic fixtures and deployment

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  🇺🇸 Santore (@santoretech) reported 9 minutes ago

  @Sherifdeenolat2 both are required these days - at least willing to login to github and understand the process.

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  Joseph Mocanu, PhD (@jmocanu) reported 11 minutes ago

  First Github PR... and with rclone at that. Oh down the deep rabbit hole I go.

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  BullBear.News (@bullbear_info) reported 12 minutes ago

  @github 👀 Wake me up when the Copilot workspace actually fixes a broken CI pipeline.

• 
  Alireza Najafi (@alire8za) reported 13 minutes ago

  @daniel_nguyenx is this an android apk? Boox note air 3 c was so laggy with non default android note taking applications. the stock app was nice and fast but the ones I installed were all laggy. Didn't you face such problem in your development? Also is this available on github?

• 
  Waldek Mastykarz (@waldekm) reported 14 minutes ago

  You shipped a new CLI. Deprecated the old one and updated the docs. Developers are migrating. Then an agent uses the old tool. Here's why. Models learn from the internet. If your technology has been around for a decade, there are thousands of blog posts, Stack Overflow answers, tutorials, and GitHub repos that document the old way. Your new CLI has a handful of announcement posts and maybe some updated docs. Ten years of content versus 6 months. The math isn't even close. We've seen this across multiple platform teams at Microsoft. The SPFx team partnered with us to evaluate this risk as they prepare a new standalone CLI to replace their Yeoman-based generator. When we pointed an agent at a scaffolding task, it ignored the new CLI entirely. Went straight for the Yeoman generator, constructed the yo command from memory, and moved on. Even when we explicitly told it to use the new tool, the agent concluded we were being imprecise and defaulted to the generator. In its reasoning traces, we could see it consider the new tool and then talk itself out of it. Not enough signal to confirm it exists. The agent wasn't broken. It was doing exactly what its training data said to do. What you can do: ship an agent extension on day one. Don't wait for training data to accumulate. Put the correct information directly into the context window, where it overrides training data. Make the deprecation explicit and machine-readable. "Do not use X" works better than "use Y instead." Both together is strongest. And if you're still in the naming phase, pick something distinctive. A name like "Platform CLI" collapses into the same concept as the predecessor.

• 
  buckintosh.log (@buckintosh) reported 17 minutes ago

  Coinbase has 1,200 full-time AI agents working right now. Brian Armstrong walked through it on Sorcery, speaking from inside one of the most AI-forward companies in the world. That number is agent-hours, not headcount. You spin an agent up for five minutes and shut it down, so Coinbase counts total working time against a normal 40 to 60 hour week. The pod shrinks too. Ten people used to be the unit: a PM, a designer, eight engineers. Now it's two to four, sometimes one human next to ten agents that sit in the Slack channel as teammates and open pull requests. Code per developer is up around 2x year over year. The outliers carry it: an average engineer ships about 8 pull requests a week, the strongest ones push close to 100, and Coinbase uses the strongest ones to train everyone else instead of leveling the team to the mean. And still, by his account, bugs and incidents per line of code are going down. Usually the opposite happens as AI code volume grows. Reviews drown, regressions stack up, quality slips. Here, volume up and quality up, together. Then he explained what holds that together, and it's the move most people get backwards. An agent hands you a pull request, and it came out not quite right. The instinct is to jump in and fix it yourself. Armstrong says don't touch the PR. Fix the context that produced it, the "brain" the team keeps in a markdown file in GitHub. Tell it what it missed, and let it regenerate from scratch. It ships only once it nails the thing in one pass. Fix the pull request and you've fixed one pull request. Fix the brain and you've fixed every one that team will ever write. The same shape runs on the product side. Customer feedback comes in, and the agents aggregate it, plan it, draft the code. A human reviews, approve, approve, approve, a hundred changes in a day. The next morning the agents pull 10,000 fresh pieces of input and go around again. Armstrong has a name for the loop. Recursive self-improvement. People usually file that under something a lab does to a model. He runs it as an org chart. Full conversation: @sourceryy on YT

• 
  Synonmous 🌚 (@Gem_Akinbo) reported 23 minutes ago

  The Developer Who Can't Sell Is Still Selling — Just Badly Ask most developers what they think of "sales" and they'll probably cringe. It feels synonymous with spam calls, pushy pitches, and empty promises. Engineers are taught that their currency is truth—code either works or it doesn't—while sales feels like persuasion for persuasion's sake. But here's the uncomfortable truth: Every developer is already in sales. If you've ever explained a technical decision to a non-technical stakeholder, written a README, pitched a side project, negotiated your salary, priced freelance work, or answered "Why should we hire you?", you've sold something. The only question is whether you did it well. Sales isn't manipulating people into saying yes. It's helping someone make a decision that benefits them by clearly communicating value and reducing uncertainty. That's it. The best sales conversations don't feel like sales. A doctor recommending treatment. A senior engineer defending an architecture. A freelancer telling a client not to build an unnecessary feature. All of them are translating expertise into language another person understands. So why do developers resist it? Because we believe good work should sell itself. It doesn't. Most people evaluating your work can't judge your architecture, code quality, or engineering decisions directly. They judge your explanation of them. If people can't understand your value, they can't reward it. This is why great products lose to average ones with better messaging. Why weaker candidates get hired over stronger engineers. Why brilliant open-source projects die with unread READMEs. The market doesn't reward the best solution. It rewards the best understood solution. Think about sales the same way you think about debugging. When debugging, you first understand the system, isolate the problem, identify the root cause, fix it, then verify the result. Selling follows the exact same process. Understand the person's problem. Discover what's actually stopping them from saying yes. Address that concern. Confirm they understand the value. You're not debugging software. You're debugging uncertainty. This changes how you communicate. Stop leading with features. Nobody buys WebRTC, Rust, Kubernetes, or PostgreSQL. People buy faster workflows, happier users, fewer outages, and more revenue. Implementation impresses engineers. Outcomes convince decision-makers. The same goes for objections. "That's expensive." Usually doesn't mean it's expensive. It often means: "I don't yet understand why it's worth that." Treat objections like bug reports, not personal attacks. Most developers also think confidence means being loud or charismatic. It doesn't. Confidence is simply being clear about what you know, honest about what you don't, and calm under pushback. Good engineers already practice this every day. Here's the irony: If you refuse to learn sales, you're still selling. You're just doing it badly. Your interview is sales. Your portfolio is sales. Your GitHub README is sales. Your technical blog is sales. Your startup landing page is sales. Even convincing your team to adopt your architecture is sales. Building something valuable and communicating why it's valuable are two separate skills. Master only the first, and your success depends on someone else explaining your work better than you can. Sales isn't the opposite of engineering integrity. It's the delivery mechanism for it. You can write the cleanest code in the world. But if nobody understands why it matters, it might as well not exist. Learning to communicate value isn't selling out. It's finishing the job.

• 
  Federico Ulfo (@feulf) reported 25 minutes ago

  @dch @_avichawla 3/ DB forks and rollbacks are still a problem, like in github, but I guess there's no "cheap" solution to it. Question: Curious, why not combining gitsubtree + prompts-history-{***-sha}.jsonl + a skill to manage them?

• 
  MO. Mortada (@MortadaDEV) reported 27 minutes ago

  @webdevcody As for "AI can go through commit history and link to GitHub issues to pull context," sure, it can traverse the history, the same way that I can read a medical textbook and link to case studies. Doesn't make me a doctor!! The model can traverse text ... But understanding context is not the same as reading text. Most commit messages are "fix bug," "update," or "WIP." The real context lives in incident postmortems, Slack threads, verbal handoffs, and domain knowledge that was never written down. A model linking to a GitHub issue cannot reliably distinguish between a design decision that was carefully considered and one that was a rushed hack that nobody got around to fixing. It can't tell you that the weird timeout value on line 312 exists because a specific third-party API was flaky for 6 months in 2024 and the team learned the hard way that the default timeout caused cascading failures. That's not in the commit message. That's in the head of the engineer who got paged for it. The tools are genuinely impressive at surface-level context retrieval. But "impressive at surface-level" is exactly the kind of capability that creates overconfidence. It looks like understanding, it reads like understanding, and it works right up until it doesn't, usually in the exact scenario where understanding actually matters.

• 
  Jerry (@Bobliuuu) reported 29 minutes ago

  @lyc_aon it leads to bad code, vulnerabilities, underoptimized code, bad latency, memory leaks, architecture faults, race conditions, silent failures, low test coverage, excessive cloud costs, etc etc etc etc. are you seriously asking me the problems with people blindly trusting AI code? we see this by the decline in code quality, e.g. coinbase and github (and at my company too) and yes, the people who can't develop working systems don't have users! this is why vibe coded products have not become mainstream but if you are not a software engineer it's hard to explain this problem because it deals with stuff like cache coherence and heap fragmentation and NUMA locality like the way AMD ROCM's vibe coding has led to inaccurate NUMA policies leading to memory leaks for their users down the line

• 
  Roy Jossfolk Jr. (@royjossfolk) reported 39 minutes ago

  Having a security issue with my GitHub connector with Codex @OpenAI @thsottiaux but can not figure out how to contact support for this. Even Codex can not figure out how to reach someone. The support bot on the help page doesn't work. My GitHub connector is connecting to some random person's account no matter how many times I disconnect everything and try again. How do I get this to someone?

• 
  Spike 1% (@SpikeCalls) reported 41 minutes ago

  BORIS CHERNY RUNS CLAUDE CODE AT ANTHROPIC AND NOW SHIPS 100% OF HIS CODE WITHOUT WRITING 1 PROMPT. He said it out loud at Meta Scale conference. The clip hit 700,000 views in 24 hours. «I don't prompt Claude anymore. I have loops running that prompt Claude and figuring out what to do. My job is to write loops.» Most people read that as a flex. It's a job description. The old way: write a prompt, read the output, write the next one. You're the glue between every step. Cherny deleted himself from the chain. Hundreds of Claude instances now run in parallel reading GitHub issues, scanning Slack, watching CI, deciding what to build next. He doesn't review each one. The loop does. Most of it, he runs from his phone. The shift has 6 parts, and they map 1:1 to real commands: 1. A trigger that starts the work. 2. A goal that defines "done" checked by a second, separate model, so the agent never grades its own homework. 3. Isolated worktrees so parallel agents don't overwrite each other. 4. Skills that freeze what "good" looks like. 5. Connectors so the loop can act, not just talk. 6. Memory so it never starts from zero. The loop is the easy part. The stop condition is the hard part. Get it wrong and it doesn't crash. It runs all night shipping bugs with total confidence. The prompt was the unit of work. Now the loop is.

• 
  AcceptÐoge (@DogeAccept) reported 42 minutes ago

  @MoonlitMonkey69 @probablyluda 1. is an L2 bridge, settles before L1 (dogeos is not fully l2, "app layer, l2ish) 2. is L1 integration "bridging directly to Dogecoin" to settle. ZK integration on L1 could add potential vulnerabilities. L2 wouldn't be "on doge," wouldn't be decentralized at first and you must trust. This post is saying that people have an issue with l1 integration but also have and issue with a permissioned, custodial bridge. I say that people are or should be well aware of what theyre using.. its creators should have no problem being transparent to anyone interested in using it. It is a choice to use this bridge and the tech should be able to be questioned openly so people know exactly what to expect especially when they are trusting. L1 integration should more so be able to be discussed openly beyond a proposal in discussions on github that has been there for a year especially when the people "building" it are currently clearly talking about different tech publicly than what that proposal mentions. We vote with node updates, yes, but as an open community of countless people.. not everyone can or has the ability to run a node. The obvious is that Dogecoin is permissionless when it comes to introducing code to core. That is why we have maintainers to screen and our network itself is the consensus mechanism. However, our community is unique and we have no leaders or voices beyond the community itself. If we want to discuss things like this, we just do. Its messy but its always been open dialog that educates, innovates, and somewhat of a social concensus when it comes to how the community as a whole would or would not like to see a direct of a coin that all of us support goes. Our voice matters too. I have done my best to ask about very specific topics to the right people even when I cant ask the person writing the code directly. I have tried that too, in good-faith, even when I dont agree with them. I have been met with nothing but assumptions, character assassination, empty promises of good-faith conversations or responses and ultimately being blocked by all of them, always right after false accusations. If we cant talk to the builders, if they are not transparent, if we cant discuss with each other, or question the things being shilled to us when we have no leaders.. Can we even call ourselves a community?

• 
  savip. (@savipww) reported 42 minutes ago

  there is an AI agent that rewrites its own brain while you sleep and never sends a single thing it learns to the cloud it is called Hermes and it lives entirely on your machine nothing it learns ever leaves your drive here is the part that breaks people it writes its own skills finish a hard task once and it saves the steps into a file so it never fumbles that task again it remembers you too your habits your projects your quirks all in plain text on your disk not on someone else server its whole personality lives in a file literally called soul.md you edit it and the agent becomes whoever you want and you do not even need to sit at the desk you text it on WhatsApp like a coworker say good morning and it wakes up and gets to work it runs a real loop terminal browser cron jobs sub agents ask it to fix a bug and it spawns a second agent that drives Claude Code to write the fix then checks that it actually runs schedule it once and it keeps grinding through the night and it is quietly blowing up on github while almost nobody outside the labs is talking about it this is where agents are going not a chatbot you poke a thing that lives on your machine learns your life and never ships it anywhere the cloud rents you a stranger this one grows into you

• 
  Hot Aisle (@HotAisle) reported 44 minutes ago

  Wow. I used to do so many hacks to get this functionality. I once built a cf worker caching layer in front of github so that I could have 30k servers downloading private repo binaries without getting rate limited by GH. Eventually hit one of cf’s undocumented rate limits and had to get an account exec to fix it.

• 
  Pascual ⚡ (@0xPascual) reported 46 minutes ago

  A junior engineer clones a trending GitHub repository with 13.8k stars containing Anthropic's context engineering guidelines. The repository breaks down the exact prompt structures, evaluation frameworks, and context-caching strategies required to scale AI agent efficiency by eight times. The media thought that was the story. It was not. The real story is happening silently in the background logs of an un-monitored staging environment. By implementing Anthropic's context-caching architecture, the engineer bypassed the enterprise architecture team's multi-million dollar vector database migration entirely. Instead of rewriting the backend or purchasing massive database infrastructure, the engineer injected an optimized system prompt that freezes identical context blocks in memory, dropping input token processing requirements for recurring codebase loops to almost zero. The automation setup operates via a simple python script running against Claude 3.5 Sonnet, exploiting the context engineering rules to cut token overhead by 90%. Total operating cost is under two dollars an hour, running on a standard API key, effectively rendering the company's internal data platform roadmap obsolete overnight.

• 
  Kitsune Tails (@kitsune_xbt) reported 46 minutes ago

  THIS GUY CUT HIS CLAUDE BILL BY 70% WITH ONE FREE MICROSOFT TOOL NOBODY IS USING every PDF you drop into Claude is quietly burning way more tokens than you think Claude doesn't just read the text, it processes the broken tables, the images and all the junk formatting the file drags along one page can eat 1,500 to 3,000 tokens a 20 page document burns up to 70,000 tokens before you even ask your first question the fix is a Microsoft tool called Markitdown free, open source, over 110,000 stars on GitHub it takes PDFs, Word, Excel, PowerPoint, even YouTube videos and turns them into clean Markdown text up to 70% fewer tokens and better answers, because Claude was trained on millions of Markdown docs and reads it natively the part most people miss is it ships with an MCP server connect it to Claude Desktop once and it auto converts every file you upload from then on this is exactly the kind of small setup tweak I put in my writeup on 20 CLAUDE md rules for getting ahead of your competitors by 5 years we have been overpaying for months on something Microsoft already solved want the 2 minute setup? comment and I'll drop it

• 
  Adam Warski (@adamwarski) reported 47 minutes ago

  Should code reviews still be a separate stage in the software development process? Code reviews used to be heavyweight: they required involving another human, which is an expensive and slow process. But when agents review the code of other agents (or humans), that's no longer the case. It's trivial to run code reviews on-demand, multiple times, until all the problems are fixed. Hence, can code reviews become just another quality gate in software development, alongside compilers, linters, and static analysis tools like Sonar? That's definitely my experience. I always self-reviewed code before handing it over for further review, so the agentic review loop resembles that. But now, we can review using "fresh" agents or completely different models. So for me, code review used to be an end-of-the-line process, a final quality check. Now it's just a part of the iteration. Which also brings the question: do we need specialized code review systems? Or is a refined prompt, or a lightweight CLI tool enough? (As a side note: code reviews have always been close to my heart; one of our first (failed) startups was CodeBrag - a per-commit code-review tool. Some of the ideas were implemented on GitHub later, so, as always, we've been ahead of our time! ;) )

• 
  Smcleod (@acemac378) reported 48 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?

• 
  allegedly! (@januarycomputer) reported 48 minutes ago

  really interesting thing happened where i asked fable to set something up and, halfway through, it got so stumped on a problem it (completely autonomously) recreated and posted an issue to the llama.cpp github. im not sure if this is a good or bad thing, but this model is definitely Different

• 
  Mindset (@Mind_S_eT) reported 49 minutes ago

  @HyllusAgent Your GitHub is giving errors fix please

• 
  Keeta Github Tracker (@KeetaCode) reported 49 minutes ago

  🐆 Keeta GitHub PR Merged 📦 Repo: node-rs 🔀 PR #29: Chore: Improve DRY 🌿 Branch: chore/improve-dry → main 👤 Originally opened by: @sephynox 🧠 Overview: This pull request appears to clean up repeated code in Keeta’s crypto-related software, which can make the codebase easier to maintain and less error-prone over time. The public description is very limited and only says it “reduces repetition in crypto crate,” with one commit in the PR. This appears to be a technical/internal update with limited public details. - “DRY” is a coding principle that means avoiding the same code being written in multiple places. - Changes like this usually help developers update and review code more easily, but no user-facing feature is described here.

• 
  Rayhan Mahmood (@RayhannMahmood) reported 50 minutes ago

  the easiest money in AI right now isn't building something new it's selling businesses tools that already exist for free, that they have no idea exist there are open-source automations sitting on github right now that solve real, expensive problems a business will pay you 500-2k/mo to set one up in an afternoon. not because it's hard, but because they don't know it exists and you do the gap was never technical. it's awareness find the tool. find the business bleeding money on that problem. connect the two. charge for it

• 
  Moit Reghason (@MoitReghason) reported 52 minutes ago

  I think the strongest version of this is to preserve your argument, but make the progression clearer: celebration → evidence → pattern → implication → conclusion. Here’s how I’d refine it: ⸻ Everyone’s celebrating agents trading tokenized stocks on Robinhood Chain. Few people are asking what happens when the infrastructure underneath those agents gets compromised. @cursor_ai recently disclosed CVE-2026-50548, a zero-click remote code execution vulnerability where a poisoned MCP response could disable the sandbox and execute code on a developer’s machine. That’s not a hypothetical attack surface. That’s the environment where agent infrastructure gets built. And it’s not an isolated incident. ➠ mcp-pinot-server carries a CVSS 10.0 unauthenticated RCE vulnerability. ➠ Kong’s mcp-konnect allows indirect prompt injection through poisoned data that can steer agent API calls without the user realizing it. ➠ mcp-memory-service exposed unauthenticated endpoints capable of leaking sensitive agent memory data. Each vulnerability adds another entry point to the same expanding attack surface. The recent Taiko bridge exploit made this painfully concrete. $1.7M was drained, not because the cryptography failed, but because a private key was committed in plaintext to a public GitHub repository. The SGX enclave performed exactly as designed. The operational discipline didn’t. What this means for the agent economy is that security debt compounds with every new integration. Cisco’s State of AI Security 2026 found that 71% of organizations are running unmonitored AI agents with broad MCP access. OWASP’s recently published MCP Top 10 found widespread issues across the ecosystem, including path traversal vulnerabilities and extremely limited adoption of standardized authentication mechanisms. As agents gain wallet-signing authority through ecosystems like @virtuals_io and agent key management systems such as @KeeperHubApp, the blast radius of a single operational failure grows proportionally. A private key left in a public repository could drain an autonomous agent treasury just as easily as it drained a bridge. The uncomfortable reality is that the weakest link in all this was never the cryptography. It was always going to be the person who committed it.

• 
  Howard Young (@howard_o_young) reported 53 minutes ago

  @Warizo_ofAfrica @github @cassidoo Simply issue-# then remove the worktree and delete the branch after pr closure.

• 
  BullBear.News (@bullbear_info) reported 54 minutes ago

  @github Unless you're announcing an AI that actually fixes my broken CI pipeline, I'll just watch the stream. 🤷

• 
  Ned17Flanders BIP-110 Knotzi (@Ned17Flanders) reported 57 minutes ago

  @Scavacini777 They've blocked us all and muted the conversations. They think BIP-110 is censorship but they block all convo on github, reddit, etc. Call us names and try and use confusing terminology and lean into heuristics to make themselves sound smarter than regular people. Coredevs are the problem. V30 is malware. Run Knots and BIP-110 God Save Bitcoin GodWins

• 
  Remix (@RemixDotOne) reported 58 minutes ago

  Tell me if you have had this conversation. You: "The padding here needs to be 16px, not 12px." Engineer: "I thought 12 was fine?" You: "It does not align with the spacing system." Engineer: "I'll put it in the next sprint." Three weeks later. You: "Hey, did that padding fix make it in?" Engineer: "Oh, I thought someone else picked it up." You go back to Figma. You re-annotate. You add a comment. You flag it again in Slack. You follow up. You wait. It eventually ships. Correctly this time, mostly. But you have now spent more time managing the correction than the correction itself would have taken. I built Remix because I kept doing the math on how much of my career I was spending not designing but re-explaining designs. With Remix, you open the product, you click the element, you describe the change in plain English, and it applies directly to the real interface. When you are done, Remix automatically generates a GitHub pull request with an AI summary of everything you changed and why. The engineer sees the full story, clicks a link to view the live change, and approves it in one step. Nobody had to have the 16px conversation. The fix was already there.

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  Gregor Tokarev (@gregortokarev) reported 1 hour ago

  Github should do these things if it don't want to lose me and all future developers in the future: - keep SLA above 99.9% - faster clones and pushes - faster and chiper actions - cli that not sucks - moderation tools for issues and pull requests