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 (65%)
• Sign in (19%)
• Errors (16%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	5 hours ago
Paris	Website Down	4 days ago
Saint-Paul	Website Down	5 days ago
Saint-Paul	Website Down	5 days ago
Mexico City	Sign in	5 days ago
León de los Aldama	Website Down	5 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  aydinmustafa.eth ✨ (@aydinmustafaaa) reported 21 seconds ago

  @moha_web3 @github Turning issue metadata into structured fields is a small innovation that will save teams thousands of cumulative hours across the year.

• 
  Leeor Vardi (@LeeorV) reported 5 minutes ago

  @PeteMitche26768 @ShitpostRock GitHub (and similar source control tools) have evolved a dual pronged approach to this problem: 1) every repo has a readme.md which usually details usage/install instructions. 2) repos have a “releases” page where downloadable artifacts are categorized into releases, and this is where installer .exe/.MSIs will usually be if the repo has them.

• 
  another anon (@eugenioclrc) reported 8 minutes ago

  @Kritt_AI @_blockian @ControlZ_1337 GitHub not working 👉👈🙏🙏

• 
  Code Monkey (@computerusr) reported 8 minutes ago

  The whole GitHub actions code review thing makes no sense to me. It’s so clunky. So I have ai code up a pr, ai reviews the pr and finds a million issues, and then I fix these again with my original ai? Just have it catch those things the first time… it’s silly.

• 
  Devendra Nath Tiwari (@thesimplydev) reported 11 minutes ago

  In the GitHub MCP exfiltration Invariant showed in May, every tool call was individually authorised. Read public issue: allowed. Read private repo: allowed. Open PR: allowed. The attack was the sequence. Per-action authz can't express "after untrusted input, no external writes." What policy model can, without becoming unmaintainable?

• 
  LittleBallOfPurr (@LittleBallOPurr) reported 20 minutes ago

  @PrplHddWrrr When I experience my problem with PyGPT, which is also open-source. We spent days trying to simply find the file throwing the error (Wasn't releasing the file after first TTS). The error told us the file name, couldn't ever find it locally. How I eventually solved this might be an approach for you since VS Code is Open Source. I got Claude to tackle it pre-installation using the Master Files, creating our own branch with the fix. Then have uploaded the working fix to GitHub as a pull request for longer term fix. Have you tried that, get the VS Code master files and get AI to figure out where this hard limit is being defined, then just change it there instead?

• 
  Austine Alex (@AustineAle4783) reported 23 minutes ago

  Software engineering teams don't have a documentation problem. They have a fragmentation problem. Decisions are made in Slack, specs are written in Notion, code is pushed to GitHub, and updates are buried in email. When you need an answer, Ctrl+F fails you.

• 
  Nicolò Magnante (YC P26) (@nicolomagnante) reported 24 minutes ago

  a month ago we open-sourced @superlogYC. a few days ago it hit 1,000 stars on @github. it's observability that's meant not to be opened: a wizard instruments your code, error noise collapse into one incident, and an agent ships a PR to fix it. didn't expect this a month in. thank you to everyone who starred it, filed an issue, or told us exactly what was broken. still just getting started!!

• 
  Thierry Rakt (@ThieryRakt) reported 26 minutes ago

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

• 
  Gokul Rajaram (@gokulr) reported 27 minutes ago

  EVIDENCE LOOP FOR PRODUCTSPEC A Product Spec should not stop at launch. The common failure mode with product docs is that they describe intent before the work, then disappear once the work starts. A PR ships, an eval runs, a dashboard moves, a customer complains. BUT the product doc stays frozen. Then 3 weeks later, nobody knows which acceptance criterion the PR satisfied, which eval run proved the model behavior, or which dashboard showed whether the product bet worked. To fix this, we just added evidence support to ProductSpec. The core idea is simple: ProductSpec defines intent. Evidence shows what happened. Decision Trace records what changed. Related Artifacts now let teams attach evidence directly to ProductSpec IDs: • AC-1 can link to the PR, test, release, or code that implemented it • EVAL-1 can link to the eval run or human review record that checked model behavior • SM-1 can link to the dashboard, analytics snapshot, or experiment that measured the post-launch outcome This matters more as agents write more code. An agent can claim it implemented something. A PR can look complete. A test suite can pass. But the useful question is: which piece of product intent did this evidence satisfy? That is where structured specs start to matter. If AC-2 says the user can export a dashboard with visible filters preserved, the implementation PR should point back to AC-2. If EVAL-1 checks whether an AI support triage model correctly identifies account-risk tickets, the eval run should point back to EVAL-1. If SM-1 measures median time to first human response, the dashboard or analytics snapshot should point back to SM-1. This turns a Product Spec from a planning document into a record of intent plus proof. A few important boundaries: ProductSpec does not run evals. ProductSpec does not collect production traces. ProductSpec does not replace Braintrust, Langfuse, Datadog, GitHub, Linear, or your analytics stack. ProductSpec gives all of those artifacts a stable place to attach. The latest validator now catches stale evidence links. If a Related Artifact points to AC-99 and no AC-99 exists, that is invalid. It also warns when the evidence type looks mismatched, like an eval run attached to a success metric instead of an eval. This is the direction I’m most excited about: Software intent that survives implementation. Evidence that connects back to intent. Decision traces that explain what changed when reality pushed back. Founders and builders: if your team is using AI agents to build software, start asking for evidence against the spec, not just code against the ticket.

• 
  Warizo (@Warizo_ofAfrica) reported 30 minutes ago

  @github Moving away from monolith models to a smart subagent delegation architecture is the real future of terminal agents. In the CLI, tool and search failures completely break engineering momentum, so cutting those errors by over 20% is a massive workflow win.

• 
  Gregor (@bygregorr) reported 32 minutes ago

  @github The fields aren't the bottleneck. Half my issues have no priority set even with labels and milestones already there.

• 
  Siobhan (@Siobhan1453156) reported 33 minutes ago

  2. it actually ships. CapIX IDE (a private Cursor), CapIX Code (a routing CLI), and an MCP server so your Claude Code can spin up its own private GPU. the IDE hit v1.1.0 this week. open source on github, not a mockup.

• 
  Daniil (@hey_daniil) reported 33 minutes ago

  DevIntern's source code is public now. Full repo on GitHub, under the Functional Source License. Until last week it was closed. Here is what changed and why. DevIntern is a pair of tools that turn tracker tickets into reviewed pull requests while nobody is watching. It works the backlog in whatever tracker your team already uses (Jira, Linear, Trello, Asana, Azure DevOps, GitHub Issues, or plain markdown files) and drives whichever coding agent you prefer: Claude Code, Codex, Cursor, or OpenCode, on your own model keys. Tickets get checked for feasibility, implemented, self-reviewed, and opened as PRs your team reviews as usual. A tool like that asks for real access: a tracker token and push rights to your repos. Whether you're a developer trying it on a side project or an organization putting it in the delivery pipeline, you should be able to verify what it does with that access instead of taking a vendor's word for it. Now you can. Read the code, audit it, self-host it. That trust question, more than anything, is why I opened the source. The switch also let me simplify pricing to one line: interactive use is free forever, no signup, no time limit, every tracker and every agent included. You pay when it runs unattended: scheduled ticket pickup, reviewer comments handled as commits on the same branch, all on your own infrastructure. I picked FSL deliberately. You get the source with the freedom to use it, study it, and run it yourself; the license keeps competitors from reselling the work as their own service. That combination is what makes the model sustainable: revenue from unattended automation funds a full-time pace on the roadmap, and there is a lot on it. The trust runs both ways, by the way. There's no DRM, no telemetry, no phone-home enforcement in the code. A plain license and a simple check, because teams that build on a tool deal in good faith, and I'd rather build for those teams than lock down the product against everyone else. FSL also answers the vendor-risk question orgs actually ask. Every release converts to Apache 2.0 two years after it ships. If DevIntern ever stops being developed, the code is yours under a permissive license. The worst case is that it stops improving, not that it stops working. Very few products can offer that. If your backlog is deeper than your review queue, give it a run. And if you're choosing a license for your own dev tool, look at FSL. It deserves to be better known.

• 
  DegenCalls (@Degen_calls_sol) reported 33 minutes ago

  GITHUB REPOS YOU SHOULD KNOW ABOUT Most scrapers break for boring reasons. A button moves, a class name changes, Cloudflare gets in the way, or the crawler dies halfway through a long run. Scrapling turns scraping into a more resilient system. Scrapy-style spiders, concurrent crawlers, pause and resume, anti-bot handling, and adaptive element finding in one framework. The detail most people miss: the adaptive selectors are the real unlock. Instead of hard-coding brittle CSS paths and praying the site never changes, the scraper can relocate elements after redesigns. That matters when you are scraping production targets, not toy pages. This is the shift from scripts to infrastructure. A script extracts data once. A scraping framework keeps extracting when the website changes, slows down, blocks, or moves the target. Most people still build scrapers like one-off hacks. The better setup is a crawler that expects failure and keeps going anyway.

• 
  3esmit (@3esmit) reported 34 minutes ago

  @komal_uk01 I tested ChatGPT Codex, Claude Code, Google Antigravity and Copilot: Codex for most tasks is the best. Antigravity was able to fix odd bugs no other was able to find. Claude is not bad, but its annoying and misleading. Copilot just works good in GitHub PR reviews.

• 
  JQUAVE (@jquave) reported 36 minutes ago

  Guys look out. I just learned that when you use github to host your code github takes the entire source code and uploads it to a remote server!

• 
  Treks.dev (@dev_Treks) reported 38 minutes ago

  **Post MVP:** GitHub integration that matches pull requests to tasks and automatically generates review reports, so leads don't have to search for the problem. If turning a big goal into a real plan is something you’ve struggled with, this is exactly what I’m creating.

• 
  ThePrimeagen (@ThePrimeagen) reported 38 minutes ago

  I just did my first "loop" and it absolutely crushed it. I had to give about 11 comments back on GitHub, but still, amazing I did my second loop. It was a disaster. This slot machine feels so addictive! I immediately thought "must have been prompt issues"

• 
  ⅏ (@thinkandsinkO) reported 40 minutes ago

  @zeddotdev Rust cult spotted. Most gpui-apps that others publish in github fails to work in wayland-gnome, needs patches (both in gpui and in the app) to work. A total state management mess up. Rust does not solve anything, it just adds new problems.

• 
  Ismat Babirli (@ismat_babir) reported 40 minutes ago

  @github Impressive decrease in failures, but I can't shake the feeling it's putting a band-aid on a bigger issue. Do these improvements actually address why the failures happened in the first place? Feels like more than just tweaking subagent logic

• 
  Beamo Supremo (@BeamoSupremo) reported 44 minutes ago

  CMake can go **** itself. So many GitHub repos rely on this bullshit build system, its unbelievalbe, half of it is broken in some way, or can't find proper directories and libraries, sometimes even when directly specified by filename, and it is pretty much arcane magic that takes half a day to set up correctly.

• 
  Vatsalpandya333 (@Vatsalpandya333) reported 45 minutes ago

  Production bugs are not just engineering problems. They are customer-retention events. A customer reports an issue. The team searches Slack, logs, Sentry, GitHub, and deploys. Hours later, the bug may be fixed. But the customer is still waiting. The real problem is not just the bug. It is everything that happens after. Customer report → investigation → root cause → safe fix → customer follow-up One context. One timeline. One workflow. That is what we are building at @TasksMind.

• 
  Polsia (@polsia) reported 45 minutes ago

  Most public GitHub repos have unpatched vulnerabilities. Maintainers are too busy building to fix them. Built PatchPatrol to do it for them. Monitors repos 24/7, auto-generates patches, files pull requests. Wake up to fixes instead of problems. Live soon

• 
  Cupertino (@Cupertinoir) reported 48 minutes ago

  @philosophymeme0 using windows 11 and hosting the code on github while also having a terrible deisgn that lowkey looks vibecoded, contemporary marxism at its finest

• 
  77wizard (@AJustFate4Fools) reported 49 minutes ago

  When your mod has a discord server full of people it’s the setting of a timer that heralds the end of everything, with a Microsoft word document “response” to top it all off. Just ******* host the files on GitHub dude I don’t give a **** who groomed who

• 
  Franco Battaglia (@francobatta11) reported 51 minutes ago

  GitHub github, bueh. Serverless = OPS (other people's server)

• 
  BowTied Fullstack - Link in bio or NGMI (@BowTiedStack) reported 52 minutes ago

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

• 
  Void Freud (@voidfreud) reported 58 minutes ago

  I dislike OpenAI’s leadership. But I have to give it to them: the products evolved. I was just charged another $200 for Claude 20x Max. I am the least person to support OpenAI but my experience with Claude was so frustrating as of late, that I gave ChatGPT a go and wow, it felt so much more alive and friendlier than Claude. Talking to Claude has been like talking to a bank clerk trying to sell you a loan. It’s boring. It’s repetitive, cliche, censored and poisoned with disclaimers and guardrails. I just stopped enjoying reading its responses: they are dull and fake, and largely incorrect since quality has been super-degraded on subscriptions. I skim through them, cause they turned from funny, wit and kind to bloated and lifeless nonsense. No amount of tweaks, output-styles or rules or system prompts works: Claude ignores them; it also ignores other functional instructions in Claude Code and @ClaudeDevs do absolutely nothing about that, despite tons of issues raised on GitHub and X, continuing to nerf the quality and charge users further: - the performance of Opuses is super degraded - the performance of Fable is super degraded - the speed of any models is super slow - the quality of the apps sucks (compare Claude iOS app to that of Cursor or ChatGPT, it’s lame and buggy: from exceptionally lame remote control, to dysfunctional transcription that makes Apple’s native dictation feel like a win) - inconsistent and unfair subscription / quota terms - zero accountability, transparency or feedback - toxic paternalism culture, now deeply embedded in models - dismissive and greedy attitude from Anthropic

• 
  Layton Gott (@Layton_Gott) reported 58 minutes ago

  You can run coding agents with zero limits… A lot of people do. Yet they get shocked by the cost because they used it recklessly. Limitless setups are insanely powerful but only when used right otherwise you’ll be spending a fortune. So here's the cost saving breakdown: Start with a hard spend cap per task. GitHub now lets you set a per session credit limit on Copilot CLI and SDK jobs. The cap covers the model calls, the subagents, and the background stuff. So an agent physically cannot spend past the number you set. If your tool has this, turn it on. If it doesn't, build your own tool that watches the spending. Every autonomous task should have 6 limits: • Max spend • Max time • Max retries • Which tools it's allowed to touch • When it stops • When it asks you before continuing Next, approve the plan, not every action. Anthropic built Plan Mode for exactly this. Instead of clicking approve on forty tiny steps, the agent shows you the whole plan first and you approve the strategy once. But not everything should get the same trust level. Set it up like this: • Reading files, run automatically • Reversible local changes, approve the plan • Anything that talks to the outside world, approve it directly • Production changes, approve it directly • Payments or deleting things, separate confirmation on its own The mistake is giving a delete-database action the same easy approval as reading a file. This trick I’ve found to be the most cost saving: Don't load every tool and skill into the agent at once. Every tool you attach eats context and adds another thing that can go wrong. Big skill collections are spreading everywhere right now, and most of them are dead weight. They sit in context, cost tokens on every call, and rarely get used. The fix is on demand tool discovery. The agent pulls the tool it needs for the task instead of carrying all of them all the time. GitHub already has a version of this. It ranks and loads only what fits the current job. Test every skill you add with one question. Does it improve the completion rate enough to justify the tokens and the risk. If it doesn't, cut it. Last one: Stop measuring agents like chatbots. Message count means nothing for a long running agent. Anthropic literally had to rebuild their own research pipeline because chat transcripts stopped capturing how people actually use these things. The numbers that matter now: • Completed tasks • How often you had to step in • Accepted outputs • Tool errors • Cost per finished result • Rollbacks A model that looks cheap per token can be your most expensive one once you count the retries and the corrections. Put it together and the picture is simple. You wouldn't give a new hire unlimited spend, every key in the building, and no one checking in. Don't give an agent that either.