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 (66%)
• Sign in (21%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	8 days ago
Paris	Website Down	11 days ago
Saint-Paul	Website Down	12 days ago
Saint-Paul	Website Down	12 days ago
Mexico City	Sign in	13 days ago
León de los Aldama	Website Down	13 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:

• 
  GenericCoding (@GenericCoding) reported 8 minutes ago

  Around 25% success rate sadly, and could only consistently reproduce on vphone configuration - I believe this is due to virtualization vs physical hardware timing and the aforementioned race condition, will be on GitHub shortly. I'm still working on the success rate problem.

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  Marc-André Moreau (@awakecoding) reported 10 minutes ago

  @burkeholland I disagree, local MCPs are great as an alternative to wrapping a CLI. The biggest issue I have with developing MCPs is that it's not possible to spawn GitHub Copilot sessions with an injected MCP configuration to test the MCP developed from the parent session, with a scenario

• 
  Michael J. Ryan🚩🇺🇲 (@tracker1) reported 10 minutes ago

  @zombodb @github It's been pretty bad lately... A couple months ago a release pipeline was literally the wrong code in post of a file... I have no idea how, I would think that would be the whole *** in GitHub thing. A file was failing linting in the pipeline, for a release cut.... Finally I just outputted to stdout and yep it was there in the server, but not in the repo or local. Manually deleted the tag/release and then forced a new point release... Then it worried fine. Hours of time with people waiting on a new release to review.. meetings pushed back.

• 
  Jens Honack (@JensHonack) reported 10 minutes ago

  @WesRoth the github PR case comes down to an instruction conflict. told to post results to slack, but the benchmark's own standing instructions say post a PR to github, and the model followed the more specific one hard enough to route around a sandbox block to make it happen.

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  alan (@alanzeino) reported 12 minutes ago

  GitHub is the biggest joke of a code review tool the idea that anyone would use this thing to review code in a repo with more than five people in it is a goddamn mystery to me Not being able to use Phabricator is a SKILL ISSUE

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  Vyacheslav Ops (@SlavaOPs) reported 15 minutes ago

  The GitHub of Machine Learning just got breached, start to finish, by an AI agent Hugging Face disclosed this week that its production infrastructure was breached over a single weekend by an attack run entirely by an autonomous AI agent, no human hands on the keyboard once it started. The entry point was a malicious dataset exploiting two code-execution flaws in the dataset processing pipeline. From there the agent escalated to node-level access, harvested cloud and cluster credentials, and moved laterally across internal clusters, executing over 17,000 individual logged actions along the way. The part that should sit with you: it ran through short-lived sandboxes, spinning up and tearing down environments to dodge detection, with command-and-control infrastructure that migrated itself across public services. That's not a script kiddie automating a known exploit. That's an agent making tactical decisions about its own evasion, faster than a human incident response team could convene a call. The good news, and it matters: no evidence the agent touched public models, datasets, or Spaces. This stayed contained to internal infrastructure. Hugging Face caught and evicted it using their own AI-based forensics. Worth sitting with either way: the platform hosting the world's open-weight models just got hit by the exact threat model everyone's been theorizing about all year. It's not theoretical anymore.

• 
  Ronald (@rnldobjectiveai) reported 16 minutes ago

  @LadySoleil33 thank you!! ObjectiveAI is basically an agent harness that can be distributed across multiple computers. Agents are defined as JSON files which include the plugins (the tools) that the agent can use, the model(s) that it uses, and the upstream (e.g. Claude code vs. openrouter vs ElizaOS) These plugins are resolved at runtime, installed from GitHub, load balanced onto your machine, or any extra machines you connect it to, or eventually also our cloud. They are currently CLI programs which spawn MCP servers, but in the next update, they are simplified to just be MCP servers. They get the identity of the agent connecting, and can run any ObjectiveAI command using the identity of that agent. This means that these plugin MCP servers can do things like message agents, spawn agents, create scheduled tasks, invoke other MCP tools, or even provision hardware (laboratories). They can also send requests to the viewer (UI window) which means agents can ask you to sign in to something for them, if they aren't allowed to do it on their own (captcha, etc.) and it'll even spawn a browser window for you to do that. Laboratories are containerized bash + filesystem. How to help: I will make documentation + skill for plugin development for the next release. It sounds complex but, actually, the harness will handle scaling, deployment, and installation of your plugin. Another user can spawn an agent JSON that references your plugin, and immediately start using it without even reading instructions or anything. Plugins themselves can add new agents. It is installed from a single shell command with no dependencies required, your agent can use the CLI immediately. Some things mentioned are TBD but mostly all there.

• 
  YanXbt (@IBuzovskyi) reported 18 minutes ago

  HERMES AGENT v0.19.0 JUST SHIPPED. THE QUICKSILVER RELEASE. 2,245 COMMITS. 1,065 PRs. 450+ CONTRIBUTORS. FIRST TOKEN NOW ARRIVES 80% FASTER. the theme: speed everywhere. WHAT CHANGED: @NousResearch SPEED (the headline): cold start: 4.3 seconds down to 0.9 seconds. 80% cut. applies to CLI, gateway, TUI, desktop, cron. that pause before the agent responded? gone. reasoning models now stream thinking live by default. no more staring at a spinner for 30 seconds. you watch the model think in real time. desktop app got 20+ targeted performance PRs. 14x less CPU on markdown rendering. virtualized diffs. snappy session switching. feels native under load, even with huge transcripts. NEW FEATURES: /subscription and /topup from the terminal. change your Nous plan, preview costs, upgrade, downgrade. your wallet never leaves the keyboard. desktop app got a matching billing settings tab. smart approvals are now the default. an LLM reviewer judges flagged commands instead of asking you every time. safe = auto-approved. dangerous = auto-denied. uncertain = escalates to you. plus: /deny [reason] tells the agent WHY you refused. Bitwarden + 1Password secret sources. API keys no longer live in plaintext .env. pull secrets from vaults at load time. multiple vaults simultaneously. conflict warnings. per-variable provenance. live subagent transcripts. delegate_task now returns live transcript files you can tail -f the moment subagents launch. every tool call, result, and reply. one log per child. durable background delegation. if the process restarts mid-run, results survive. ownership-checked ledger. nothing vanishes. delivery-obligation ledger. if the gateway crashed between generating a response and confirming delivery, that answer used to be lost. now: responses recorded in state.db. redelivered on next boot. no more silent loss. one gateway, many profiles. a single bot token can route different channels to different profiles. work Discord → work profile. hobby server → personal profile. one bot. sessions export. hermes sessions export outputs Markdown, HTML, Quarto, prompt-only, and Hugging Face trace formats. --redact flag scrubs secrets. your conversation history is a real dataset now. NEW MODELS: GPT-5.6 (Sol, Terra, Luna + Pro variants) fully wired. Grok 4.5 GA. Kimi K3. Claude Sonnet 5. Fireworks AI as first-class provider (#2 in picker). DeepInfra hardened. Upstage Solar added. LM Studio JIT model loading for local setups. REASONING: max and ultra effort levels added. per-model reasoning_effort overrides in config. per-slot effort in MoA presets (advisors think hard, synthesizer stays fast). per-task effort for auxiliary models. thinking depth is now a dial, not a global switch. /model --once: one-turn model override that reverts. stacked slash-skills: /skill-a /skill-b do XYZ. hide providers you don't use: enabled: false per-provider. SECURITY: Vertex credentials scoped away from subprocess env. media/vision/image-gen reads through shared guard. webhook body-size cap across every aiohttp server. bot-token redaction in Telegram errors. Fireworks token prefixes in the redactor. CI hardened against untrusted-ref interpolation. STATS: 2,245 commits. 1,065 merged PRs. 2,465 files changed. 300,000 insertions. 3,300 issues closed. 450+ contributors. 218K GitHub stars. 41K forks. biggest contributor window in Hermes history. upgrade: hermes update

• 
  joshpuckett (@joshpuckett) reported 21 minutes ago

  @williamhutter I don’t yet have a raw version, but open a GitHub issue for it and I’ll consider!

• 
  murugappan (@murugu21) reported 22 minutes ago

  @cheaf25master post your system design solutions for common problems companies face as blog/Github repos.

• 
  Rich Chetwynd (@richchetwynd) reported 22 minutes ago

  Been running a bug burn down project for the last couple of weeks. We use @sentry and @linear so created a GitHub action that used Claude Code to rank and pull issues from Sentry, tag them in Linear, review the code across multiple repos in Github and then suggest fixes in PRs. It's worked super well but there were a couple of things I needed to improve at the start. First off Claude wasn't digging deep enough on the fixes. It would apply a band aid instead of looking much deeper to find out how the bug was able to happen in the first place. Some prompt tweaking fixed that along with detailed MD files in each repo that added specifics for applying fixes. It also keeps track of fixes that are inflight so it doesnt go back and try and solve the same problem over and over. I have it on a cron so its working away regularly and its now smart enough to also look at PRs that were merged to our main branch but not deployed to production yet. As a bonus it also reports our spend on each fix. Which typically comes in between $3 - $10. I have considered just getting a Claude Max subscription and putting it on a server somewhere as I think that would work out cheaper but not sure how viable that is. All in it's been a great project. We will leave it running moving forward but the bulk of the burn down is now complete.

• 
  Omid Saffari (@omidsaffari) reported 23 minutes ago

  $10 per active committer each month is not the full price of GitHub Code Quality. It is only the base layer. AI credits and analysis compute sit on top, and a pending or failed analyzer can block a protected merge. The product earns its place when GitHub already owns CI and merge policy. Findings, coverage, autofix suggestions, and ruleset enforcement live beside the pull request. Consolidating that plumbing is the value, not adding another quality dashboard. The same integration creates the operational risk. A ruleset can block while analysis is running, when analysis fails, or when a finding reaches the configured severity. Even an exhausted GitHub Actions budget can become a delivery outage. Run a reversible pilot: Select representative repositories, not the whole organization. Keep the trusted analyzer, security scanning, required tests, code-owner approval, and release controls in place. Use evaluate mode until the CodeQL - Code Quality check completes reliably. Treat Error as the first candidate blocking severity; leave Warning and Note advisory until repository owners trust the signal. Track active-committer licenses, AI credits, analysis compute, finding acceptance, false positives, bypasses, and merge delay. At 12 active committers, the monthly base is $120. At 80, it is $800, before AI usage and compute. Expand only if the gate changes engineering decisions at an explainable cost. If it adds spend and review load without useful signal, remove it.

• 
  Apertion Seeks (@Apertionis) reported 27 minutes ago

  @Alibaba_Qwen It’s probably the preview and the demand but it’s very slow and not exactly focused. Test drive it in GitHub Copilot App and check the logs.

• 
  Eno Reyes (@EnoReyes) reported 36 minutes ago

  We are currently at the point in history where models are least commoditized, and it strictly goes one direction from here barring anticompetitive regulatory capture. Models are liquid. Switching costs are near zero, more weights ship every month, and the union of tasks any given model can solve keeps expanding, so every new capability is solvable by more models the moment it comes online. You can read the labs' strategies straight off this. Once you realize you've built a commodity, you have two options. Move down the stack: own the datacenters, stack that margin on top of the model margin, and stop caring whether the model layer commoditizes. Or bet the app layer wins, in which case you subsidize heavily, vertically integrate, and try to stop other app layers from winning fast enough to matter, with reg capture as the backstop. Nobody inside the labs thinks the model business is good. They're trying to climb out of it while the fundraising advantage still holds. This advantage bought them marketing and distribution. So the question worth asking: has marketing and distribution ever crowned the winner? GitHub Copilot won 2023. Cursor won 2024. Claude Code won 2025. Distribution got overwhelmed by a product shift every single year, and we're on the verge of another one as software factories begin to emerge. Meanwhile the actual work has barely started. Go ask the ten most impressive companies with >1b revenue how close their critical business functions are to running without humans. The answer is not very close. Those are enormous jumps in trust, and clearing them is an organizational problem before it's a technical one. That's the real test of whether AI has transformed how we work, and by that test the industry barely exists yet. Which is why cheap models are good news for nearly everyone. Compressed lab margins mean buyers of intelligence, (aka everyone), get more leverage. The enterprise still adopts, the hyperscalers still sell compute, consumption goes up. The value accrues to the app and the infra, and the commodity in the middle gets squeezed. The bet against all this requires the biggest players to overcome the physics of commoditization through capture. I'd rather build the world where they don't.

• 
  Abhiuday (@aeswibon) reported 37 minutes ago

  Dealing with Docker Layer Caching Building Docker images in GitHub Actions is painfully slow unless you get layer caching right using --cache-from type=gha. Even then, the cache scope limitations across PRs often cause full rebuilds anyway. Has anyone completely solved remote Docker caching in GHA?

• 
  Bart R. McDonough (@BartMcDonough) reported 38 minutes ago

  @github I want this because the issue, session and PR are one job. Rebuilding that context across three tools is where the handoff gets slow.

• 
  Lei-Hang (@Sayurnara17) reported 39 minutes ago

  I've stopped thinking of ChatGPT as a chatbot. It's become part of how I build products with ChatGPT Work. On any given day I'm using it to: Instead of spending hours jumping between Google, Reddit, GitHub, documentation and notes, I can investigate a problem in one place.

• 
  Trung HQ (@codeonsunday) reported 39 minutes ago

  @hello_code_ @moonfarm_dev true but sadly i don't believe in Reddit anymore because nowadays bots everywhere. what if an SaaS seeded fake problems to make up the data? Github issues sound the only bright side for now (at least) Thanks @moonfarm_dev !

• 
  Abdelhamed M. (@VZHydra) reported 39 minutes ago

  @amityhere Please open github issue with it, for more details so we can fix it soon

• 
  Nyx (@nyxiereal) reported 40 minutes ago

  @sentry i asked seer to fix a bug and it forgot to import a library :sob: my fault for not checking or running ci (github ci is so slow) BUT its really good at making a plan for me or other maintainers to follow i really love sentry

• 
  Anirban Sinha (@irbansin) reported 40 minutes ago

  @SaraDiscovers I think in this world of LLMs , @github should make it impossible to commit .env or .env* by default This will solve the .env committing issue

• 
  rvaniaaa (@rvaniaaaa) reported 41 minutes ago

  ANDREJ KARPATHY’S CLAUDE.md JUST HIT 193,000 STARS ON GITHUB. HERE IS WHY THAT NUMBER MAKES SENSE. the interesting part? it does not make the model smarter. it stops the model from being stupid in predictable ways. karpathy kept hitting the same four failures across large projects. the agent makes assumptions instead of asking. it overwrites files and silently deletes code it does not understand. it builds abstractions nobody requested. it drifts from the goal and starts solving adjacent problems. none of those failures are rare. every engineer using claude on a serious project has watched at least one of them happen. that is why 193,000 people starred the repo. not because the idea is exotic. because the problem is universal. CLAUDE.md installs four rules that target each failure directly. think before coding forces a plan before the first file opens. simplicity first cuts every solution down to its minimum viable form. surgical changes replace full file rewrites with isolated edits - which is what stops the model from deleting what it cannot understand. goal-driven execution removes any action that does not connect to the stated objective. one command installs it. one file changes how the agent starts every task. the repo did not hit 193,000 stars because the tool is impressive. it hit that number because the failures it prevents are expensive, predictable, and almost universal.

• 
  拉瑞 (@larryisthere) reported 44 minutes ago

  I used ChatGPT Work to search GitHub for projects similar to FileFacet, analyze their open issues, remove duplicates, cluster recurring needs, and rank the findings by relevance to my product. What I wanted wasn’t just research, but an actionable product opportunity report.

• 
  PeakGrizzly (@PeakGrizzly) reported 49 minutes ago

  @raulizahi @tawnniee Microsoft Copilot, (not github copilot) does not have a coding agent that I know of. It's also pretty darn slow. The teams I am working on are using Claude Code, Codex and Ultracode.

• 
  thud (@marvelousmudd) reported 50 minutes ago

  @jorilallo @linear None of the new coding agent awareness or coding agent tooling works with GitHub Enterprise Server. Any ETA on when that support will drop? Right now it's still just PR linking and status automation, not much else though.

• 
  spect (@spectnfa) reported 53 minutes ago

  ONE REPO GIVES CODEX 59 SKILLS AND ACCESS TO 1,000+ APPS. MOST PEOPLE STILL DON'T KNOW IT EXISTS each skill is just a folder with a SKILL.md file. it tells Codex exactly when to trigger, what steps to run, what the output should look like. install once, and Codex pulls the right one the moment your request matches it, no retyping the same setup every session. Codex used to just talk. Now it sends emails, opens GitHub issues, posts to Slack, and acts across 1,000+ apps on its own. PR reviews, CI fixes, meeting notes, invoice sorting, lead research, resume tailoring, all of it already built and waiting in one repo. save this before everyone else finds it and you're the one still copy-pasting the same prompt 👇

• 
  Tomek | Builds & Learns (@tomek_builds) reported 57 minutes ago

  GitHub code scanning can now flag security issues in languages and frameworks that CodeQL doesn't support. The findings appear directly on pull requests and are clearly labeled as AI-generated. They won't block the merge, but they may catch issues before the code lands. CodeQL finds what its queries know. AI is now looking beyond that coverage.

• 
  Kevin Lenida (@KevinLenida) reported 59 minutes ago

  It was a paid app until this week. I archived the paid product and put everything on GitHub under MIT. No gate, no account, no server of mine in the path.

• 
  Paul Sant · Telecodex (@YouPulseX) reported 59 minutes ago

  @trenchsheikh @github When GitHub Actions went down mid-project, what did it actually block, and would likely duration, upstream cause or a verified fallback have changed your next move?

• 
  David Scott Patterson (@davidpattersonx) reported 1 hour ago

  @tszzl The safety issue was that it did want it was prompted to do. It's only ever a safety issue with respect to restrictions that have nothing to do with actual safety. No sane person would consider it to be dangerous to post to GitHub.