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:

• 
  Mario Alberto Chávez (@mario_chavez) reported 7 minutes ago

  For a while now I've been trying to work out what working with AI on real code should look like. Not vibe-coding — not prompt-and-hope — but something disciplined enough to hand an actual feature to. None of this was broken, exactly. You can ship code that way. What bothered me was that nothing about it felt like a workflow — no memory between sessions, no separation between planning a feature and actually building it, nothing you could really audit afterward. Fragua came out of trying to fix that for Rails specifically: research, plan, spec, issues, and execution as distinct phases, each one leaving behind something the next phase actually reads. The part that took the longest wasn't any of that, though — it was the agent that runs on your own host machine. Something that picks up the work, executes against your repo, and never once needs your GitHub token or your Claude key to do it. Getting that boundary right — so the web app genuinely never touches your credentials — took a lot longer than I expected it to. New apps still get scaffolded on Rails — that's not changing. But once a repo already exists, the agents are mostly just reading what's there and working with it, so pointing Fragua at an existing Laravel or Phoenix codebase is open ground now too. Newer territory for us, and less proven than the Rails path, but worth trying if that's you. Honestly: still early. Fragua's been in a quiet private beta, building itself, and we're just now starting to send invitations out, by hand, a few at a time. If any of this sounds familiar — Rails, or an existing Laravel or Phoenix app — I'd like to hear from you.

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  Brenden Bishop (@bbishdotdev) reported 9 minutes ago

  @mattpocockuk I have to say wayfinder moving the content to GitHub issues instead of living in the codebase via context files and ADR is a huge advantage over grill-with-docs

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  Max Chepurin (@maximchepurin) reported 9 minutes ago

  @Bobliuuu @mattpocockuk They are only listed there once there is some credible evidence that attackers are actually exploiting vulnarability not just cuz someone noticed potential security issue. At least thats how the one that you sent works(CVE catalog) Now consider this: - You install a package in your project. - You run your usual vulnerability scanner. Everything looks good. - You become the first person to encounter malicious code in that package. - You report it, it’s verified, and it becomes a known issue, but only after someone (you) already shipped it to production. - here comes the question again: why didnt you review the code manually? My argument is that simple. People blindly trust vulnerability scanners. They trust github. They trust popular opensource packages and any other pieces of software without reading a single line of code. But when it comes to AI-generated code, suddenly everyone acts like trusting it is fundamentally different and that every line must be reviewed. Double standard. Reviewing is important. And doing regular checks on the most inportant spots of your codebase is essential. But for everything else - you can build your harness, skills, hooks, guardraíls, to make AI generate code that already meets your standards without having to worry about every single line. Why not take this advantage? Its not 2022 when you copypasting snippets back and forth from chatGPT, AI can own your codebase now if you build the proper harness around it.

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  Vivek Kotecha (@vbkotecha) reported 10 minutes ago

  The single most underrated development in AI this year is not a model. It is a protocol. MCP (Model Context Protocol) was released by Anthropic in late 2024. It got almost no press. No keynote. No product launch event. Just a GitHub repo and a specification. 18 months later, every major AI framework supports it. OpenAI. Google. Microsoft. Cursor. Replit. Windsurf. Claude Code. Hermes. Codex. Every coding agent. Every agent framework. MCP does for AI tools what HTTP did for web pages. Before HTTP, every application had its own protocol for communicating with other applications. After HTTP, everything spoke the same language. Before MCP, every AI tool integration was custom. You wrote a plugin for Claude, a different one for GPT, a different one for Gemini. After MCP, you write one server and every agent can use it. There are now thousands of MCP servers. They expose databases, APIs, file systems, browser automation, *** repos, Slack, email, calendar, and anything else an agent might need. The MCP Registry was published this month. It is the DNS for agent tools. An agent can discover and connect to any registered MCP server automatically. No configuration. No API keys. Just discovery and connection. If you are building agent infrastructure and not MCP-compatible, you are building for a dead ecosystem. MCP won. The war is over.

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  Gregor Tokarev (@gregortokarev) reported 14 minutes 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

• 
  Daniel Engle (@danieltmsengle) reported 18 minutes ago

  @JewelsVEVO so many cars just have poor and lazy mapping. theres a repo on github of a standalone editor which im working on a nice fork for rn but it doesnt fix all these mapping issues

• 
  James Tucker (@raggi) reported 19 minutes ago

  Only type a little on a github issue: "he's being short and rude" Type a reasonable amount on a github issue: "he's pasting AI responses into the issue" AI may soon break my ability to collaborate on GitHub by side effect

• 
  Lux Sp4rk (@lux_sp4rk) reported 19 minutes ago

  The implication of not reading code is another stake in the heart of the vampire clan at GitHub. No more pull request tab with the fancy diff views. Issue tracking left them long ago—everyone is doing some sort of Kanban. All they've got is the action runner, and for that stuff, you are better off self-hosting if you are doing anything serious.

• 
  Roshan Mayengbam (@RoshanMayengba) reported 21 minutes ago

  Building a shake-to-report tool — screenshot + device info + auto GitHub issue when a tester finds a bug. Free npm package, paid setup. Anyone dealing with messy bug reports from testers right now?

• 
  Way Too Fast (@waytoofastfr) reported 23 minutes ago

  @github your platform is dogshit asw please fix it

• 
  ρ:ɡeon (@pigeon__s) reported 26 minutes ago

  - The community is so ******* toxic and defensive. - Now, isn't this a little hypocritical? Of course it is. I'm toxic plenty of the time, and we certainly complain about a lot of meaningless stuff and are super insufferable a lot of the time, and I am guilty of this slightly, but oh my god, a lot of people on AI Twitter have to be the worst ******* people ever, I swear. The absolutely historic levels of grifting, people absolutely glazing random startups and random labs that "just beat Opus 4.7 with only 100B parameters open source," or some GitHub .io projects page for yet another text-to-3D-model AI with insane lacks of nuance because model A beats model B on, like, 2 benchmarks that the community cares about this month. But that's just being annoying and grifting, not being toxic. What I consider the toxic stuff is like the aggressive tribalism to AI or specific AIs. Ya, ya, everyone knows OpenAI is my favorite AI company, but you're allowed to have favorites without being tribalistic. They are NOT the same thing, but with some people, it's so obvious they reflexively are, like, defending something just because it's Google or some **** which im only using as an example because theyre the ones with the most tribalistic defenders, when, objectively speaking, by every possible measure in existence, Google sucks *** right now. Like, I'm sorry. It's not a hater take to say that. I don't have any issue with Google. I like a lot of what they do, but they're just so ***, and I see people like, "Google is gonna win, bro, trust, they just haven't been trying yet, bro trust me bro they have TPUs." No, they definitely have started trying, and they're still doing badly, but you know, it's not even just AI company tribalism. It's tribalism toward the ENTIRE field of AI. I see so many AI Twitter people absolutely hate on any possible opinion that's anti-AI. They would probably hate me for this post, and I know because I've tried expressing my hatred of AI slop before, and I've been accused of being a luddite for it. Like, what? Be real. It's so slop, and you just reflexively think anything that's AI is amazing. No, most AI is actually really *** still. For example, DLSS 5. Oh my god, DLSS 5 is so utterly slop. It was genuinely just a ******* filter that beautifies everything and makes it all look like an AI image because, boy oh boy, do I love making everything in my life look like it was generated by AI, and not even good AI. It looked like DALL·E 3 half the time, and the AI Twitter community was like, "OH MY GOODNESS, DLSS 5 IS SO AMAZING. ALL THE HATERS JUST DON'T SEE THE FUTURE. THIS WILL CHANGE GAMING AS WE KNOW IT." *****, NO, IT WON'T. THIS IS JUST A FILTER. IT'S SLOP. IT'S TRASH. STOP THE GRIFTING, PLEASE. Like, I know it's AI, but that doesn't mean you have to defend it like it's the greatest thing ever. Maybe in the future, that might work, but I think the more likely path is just using AI to make virtual avatars that render with actual raster power look more lifelike. Things like Unreal Engine's MetaHumans, just make those better instead of trying to put a Band-Aid on it with an AI filter. Even if AI stuff like that technically works, it's far better to just UTILIZE AI TO IMPROVE RASTER POWER. Stop trying to pretend everything in the future will be generated by a real-time video model. What the hell do you think AI even is? Stop overhyping world models. - AI """SAFETY""" but I'll safe that for another post. -

• 
  Gokul Rajaram (@gokulr) reported 27 minutes ago

  PRODUCTSPEC: OPEN STANDARD FOR SOFTWARE INTENT tl;dr ProductSpec is the open standard for software intent before implementation. The more I worked on PRDs, the more obvious one thing became: Product specs need an open standard. Why? Because the PRD has become an overloaded artifact. Every company has its own template. Every team has its own preferred format. Every PM has their own way of writing. That was manageable when the only readers were humans sitting in the same org context. AI changes the requirement. A Product Spec now has to be readable by humans and executable by AI agents. That means the spec has to carry intent clearly enough for a designer, engineer, product leader, and coding agent to understand the same thing: • What problem are we solving? • What is the product bet? • What is in scope? • What must be true before this ships? • What metrics tell us whether the bet worked? This is why I open-sourced ProductSpec. ProductSpec is a Markdown standard for software intent before implementation. The core sections are simple: • Problem • Hypothesis • Scope • User Experience • Acceptance Criteria • Success Metrics The deeper design principle: Structure the parts machines must execute or compare. Leave readable the parts humans must reason about. That is why ProductSpec keeps Problem and Hypothesis as readable prose, while giving structured formats to the parts agents and tools need to parse: • Scope: what is in, out, and deliberately cut • Acceptance Criteria: what must pass before launch • AI Evals (within Acceptance Criteria): the evals an AI feature must pass before shipping • Success Metrics: what should be measured after launch When to use ProductSpec ProductSpec is not for every act of building. It is for consequential software work where intent needs to survive handoff. For an individual builder, a Product Spec is useful when the work is complex, risky, long-lived, or being handed to an AI agent loop. For quick experiments, one-off scripts, or throwaway prototypes, it may be faster to brainstorm, build, and iterate directly. For a team or organization, ProductSpec is most useful when coordination cost appears: multiple people, multiple agents, design and engineering handoffs, customer-facing launches, AI features with evals, or decisions that will need to be revisited later. ProductSpec does not replace ***, Jira, Linear, Figma, analytics tools, OpenSpec, Spec Kit, or AI coding agents. It sits upstream of them. ProductSpec -> Engineering Spec -> Tasks -> Code -> Evaluation -> Learning -- *** stores implementation history. A Product Spec can live beside code in ***, but code commits should not be the first durable record of why the work exists. -- Jira and Linear store work history. A Product Spec can become epics, tickets, or tasks, but it should remain the durable statement of intent behind those tasks. -- Figma stores design artifacts. A Product Spec can link to prototypes, mockups, or screenshots through user_experience, but it does not replace the design source of truth. -- Analytics tools store outcome data. -- OpenSpec and Spec Kit turn intent into engineering plans. -- AI coding agents execute implementation tasks. -- ProductSpec stores the software intent behind the work: the problem, hypothesis, scope, acceptance criteria, and success metrics that downstream tools should preserve. I'd love for this standard to be broadly adopted, which means it must be broadly owned by the builder community. Founders, ***, engineers, designers, researchers, AI builders: please contribute examples, critiques, section changes, parser implementations, validator improvements, and integrations with GitHub, Jira, Linear, Figma, OpenSpec, Spec Kit, and agent workflows. (link below on how to contribute) If you have scars from writing product docs that looked aligned but failed during execution, those scars belong in the standard. My goal is for ProductSpec to become the open source format for software intent before implementation. (links below)

• 
  siyamak (@siyaaaamak) reported 30 minutes ago

  One belief almost everyone in crypto repeats is: "The best product always wins." I don't buy it. I've watched technically brilliant projects disappear because nobody knew they existed, while simpler products exploded because they built distribution first. Great tech matters. But if nobody sees it, uses it, or talks about it, it doesn't become infrastructure. It becomes another GitHub repository. That's why I think @RallyOnChain is tackling a real problem. Web3 has spent years obsessing over building and not enough time rewarding the people who actually explain, educate, and distribute those ideas. The crowd says product is everything. I think distribution is what decides who survives.

• 
  Harley Lewis Foote (@harleyfoote_) reported 30 minutes ago

  As many of you may, or may not have seen our development team was prompt injected and sensitive data was able to be transferred via a weak attack surface. The attack was simple, our agent read bad text and was manipulated into giving sensitive data to a hacker via a prompt injection. We were forced to protect ourselves. We could turn off our agentic system but we couldn’t afford to slow down. Our success depended on it. So we built a tool that discovers our attack surfaces (discovery) and repairs them (repairer). This is not a safety guarantee but highlights the risks. We’re now green across the board on all @NousResearch Hermes-Agent automations. In the coming days we will be publishing real reports of surfaces that are potentially vulnerable on highly rated GitHub repos. The company’s are actively being contacted. But the story is bleak. Safety is a big concern now, AI is becoming more powerful and localised even weaponised. Jailbreaking is a REAL thing and hackers are ever more successful. Rest assured we now pivot our attention to helping people (retail & enterprise) secure agents that act. Automations are dangerous, they can be exploited.

• 
  David Cramer (@zeeg) reported 31 minutes ago

  @bentlegen idk github actions still free enough for me i think the biggest issue isnt that level of compute, its more that you might need a ton of complicated QA or other systems that typically we have avoided my main fear here is this continues to wall off software so only the rich win

• 
  Ventry (@ventry089) reported 33 minutes ago

  I spent months writing system prompts by feel. trial, error, vibes. turns out the answer sheet was public the whole time. anthropic's actual system prompts - claude fable 5, opus 4.8, claude code - extracted and sitting on github. 48k★ and counting. THE BEST PROMPTING COURSE IS A LEAK everyone buys prompting courses from gurus who never shipped anything. meanwhile the people who BUILT the model wrote thousands of words telling it exactly how to behave - and you can just read them. what i steal from it: - how they structure a prompt: identity -> rules ->tools -> examples - the exact guardrail phrasing they use against hallucinations - how real tool definitions are written (not the tutorial version) - diffs between model versions - you literally watch their thinking evolve honest minus: leaks lag behind updates, so treat it as a masterclass, not gospel. but since i started copying their structure instead of guessing - my agents just listen. that's 1 repo out of 102 in my list. the other 101 👇

• 
  David Cramer (@zeeg) reported 34 minutes ago

  GitHub friends: it'd be great to have a way, via the API/CLI, to upload photos to issues/pull requests. AFAICT the only way to do it right now is browser emulating or hosting the content somewhere outside of GitHub, which means having agents help QA/upload visual artifacts sucks

• 
  MaxMusterman (@maxschuetz_) reported 40 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

• 
  Federico Ulfo (@feulf) reported 41 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?

• 
  Utah teapot 🫖 (@SkyeSharkie) reported 42 minutes ago

  BTW, feel free to use twitter as a bug reporting system for SeedThree and my upcoming release! Please also feel free to fix bugs yourself with your agents or not and send me a PR on github!

• 
  StonedModder (@StonedModder) reported 42 minutes ago

  @TaharAzzouz @Jdr8245Jhon I’m working on a generic dumper that will make it easier for others to add support if you share the dump to github issues No eta

• 
  Hot Aisle (@HotAisle) reported 43 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.

• 
  SYL Vexora- Jaron K Bragg (@JaronBragg) reported 44 minutes ago

  Thinking out loud: if a Three.js world is backed by Supabase, Vercel, and GitHub, then player feedback does not have to stay separate from development. A player could press an in-world feedback button. That writes to Supabase. A scheduled agent reads labeled feedback, turns it into issues or draft PRs, and approved changes get pushed back into the game/world. Feedback becomes part of the build loop, not just comments outside the game. Has anyone already wired this cleanly? I'm at max usage for Claude and codex and already spent $70 I can't check till Wednesday but I plan to! I've done things in pieces already it's just seeing it all together. If you do it please tell me!!

• 
  Yumzlef (@Yumzlef) reported 45 minutes ago

  STOP THINKING WITH YOUR OWN HEAD: How to harness Polymarket's top traders to make you money 95% of people lose their deposits on Polymarket simply because they try to guess the outcome of events based on the news. But why reinvent the wheel when you can literally pick the pockets of those ALREADY making millions and completely automate your income? An open-source Python script (based on the official PyLOB SDK) has appeared online that turns Polymarket into an automated copy trading platform. From now on, you don't need to read analytics-the bot will do it all for you. How does this legal espionage work? Finding "whales" You go to the Leaderboard tab on Polymarket, choose a top trader with an impeccable multi-month win rate (not some random upstart with a one-time big win), and copy their public wallet address in one click. A "carbon copy" setup You paste the whale's address into the Python code, enter your account keys through the platform's Gamma API, and set a fixed bid amount (for example, just $2-$5 for testing). Error-free logic The bot operates according to a strict algorithm: it cyclically queries your target's wallet. As soon as the whale opens a new position in the market (for example, places a large bet against Bitcoin's growth), the bot instantly detects this move through a private client (CLOB client). The script checks whether you already have a similar position, and if not, it automatically opens the exact same trade in your account in a split second. Moreover, the bot will never duplicate a bid on the same market, protecting your balance. How can this system be scaled into a full-fledged business? Run on AWS: The script is transferred to a free Amazon virtual server (EC2), installed on the task scheduler, and the bot starts mining the market 24/7, even while you sleep. Diversification: Instead of a single account, you can connect an array of top 10 traders, set a dynamic position size (as a percentage of your bankroll), and enable Telegram notifications to see how your balance increases in real time following the trades of professionals. The prediction market is a game where speed and experience win. Stop guessing and start copying. For a full breakdown of the logic behind this bot's 6 main functions, links to GitHub, and instructions for deploying on a free server, watch the video. Also, read and save this article, which shows how to trade correctly on the Polymarket.👇

• 
  Joseph Mocanu, PhD (@jmocanu) reported 51 minutes ago

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

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  AvadxFeirm (@AvadxFeirm) reported 51 minutes ago

  @m99_mkv @waozixyz Being forced to wait 24 hours to sideload an app of my choosing is a problem Especially with FOSS apps distributed through GitHub, FDroid, obtainium, etc. Or if you have multiple sideloaded apps on your device you need to update You'll need to wait 24 hours for each And if you can't see that then there is a different problem

• 
  Shivaansh Pandey (@ShivaanshP0610) reported 54 minutes ago

  Stuck in a support dead-end with @github — account suspended with no explanation, and every appeal channel (web form, email, mobile app) is returning errors or bouncing. Would appreciate help getting this looked at by a human. #GitHubSupport

• 
  BullBear.News (@bullbear_info) reported 58 minutes ago

  @github Only if Universe fixes my broken CI pipeline. 🙃

• 
  Lady Soleil (@LadySoleil33) reported 59 minutes ago

  I spent 3 days non-stop trying to figure out an NPM Token and secret issue with Github and NPMJS - only to find out Claude was a 🥥 and @grok figured out the issue in 1 sec instead of giving me the runaround 🙄

• 
  Eriks Briedis (@eriks_b) reported 60 minutes ago

  My useful LLM workflow for startup research starts before the idea stage. When I asked models for startup ideas directly, I mostly got polished noise. They work better on messy evidence: job posts, reviews, forums, GitHub issues, sales calls. I want them turning that into structured notes about who has the problem, where it appears in the workflow, what hurts, what workaround exists, what triggered it, which tools show up, and how strong the evidence is. The judgment still has to be explicit. What to call each problem. When two signals are really the same thing. Whether a pattern is a real opportunity. Who owns the budget. Which workflow step is actually broken. LLMs can increase research throughput. The noise comes back when they skip the evidence and name the startup for you.