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%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	57 minutes ago
Montlhéry	Website Down	19 hours ago
Aulnay-sous-Bois	Website Down	19 hours ago
Saltillo	Website Down	20 hours ago
Granada	Website Down	20 hours ago
Vernon	Website Down	24 hours ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  uaghazada (@uaghazadae) reported 7 seconds ago

  @BenSyne seems like its github issue. pls go to repo from website

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  Brabalawuka (@brabalawuka) reported 34 seconds ago

  @thdxr Does the desktop beta support v2 server alr? I tried search with ChatGPT but give very vague answers based on github commit history / lissue log

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  hochulambo (@hochulambo) reported 3 minutes ago

  His team started tethering laptops to phones in the car on the drive home, just to keep steering agents. That was the moment he decided the setup was broken. The talk is about what they built instead: agent sessions that follow you across Slack, GitHub and mobile without losing context. > 03:59 - Same session everywhere. Move from Slack to GitHub and the agent remembers > 05:35 - Turn every external signal into code the team can evaluate fast > 06:41 - The meeting bot that turns customer calls into prototypes > 11:41 - Permission boundaries: the agent asks before touching what it shouldn't Agentic coding has been single player this whole time. That is the actual bottleneck. Watch it today, then read how to structure the harness for a team in the article below

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  Kenneth | Automating Businesses & Payment Systems (@thekennwakanma) reported 6 minutes ago

  @solopribuilds CSRF and Browser Security Review: * CSRF protections * CORS * CSP * security headers * cookie attributes * origin validation * SameSite configuration * clickjacking protection File Handling Inspect: * uploads * downloads * image processing * document processing * video uploads * temporary files * storage buckets * signed URLs Look for: * unrestricted uploads * MIME spoofing * extension bypasses * executable uploads * path traversal * malicious filename handling * public bucket exposure * authorization bypasses * oversized uploads * decompression attacks Secrets and Cryptography Look for: * committed API keys * passwords * tokens * private keys * database credentials * weak encryption * insecure hashing * hard-coded secrets * predictable tokens * weak randomness * incorrect encryption usage * exposed environment variables * secret leakage through logs Database Security Review: * query construction * row ownership * tenant filtering * migrations * database roles * privileged database operations * raw SQL * transaction handling * race conditions * locking * destructive operations Pay particular attention to cases where user-controlled IDs reach database operations. Multi-Tenant Security If the application contains organizations, teams, workspaces or tenants, aggressively test the architecture for cross-tenant access. Verify that a user from Tenant A cannot: * read Tenant B’s data * modify Tenant B’s data * enumerate Tenant B’s resources * invoke Tenant B’s actions * access Tenant B’s files * access Tenant B’s billing information * access Tenant B’s administrative functionality Payments and Billing Review: * checkout * subscriptions * plan changes * entitlements * coupon handling * payment webhooks * refund handling * invoice handling Look for: * client-controlled prices * client-controlled plan IDs * entitlement manipulation * webhook spoofing * missing webhook signature verification * replay attacks * race conditions Webhooks Verify: * authentication * signatures * timestamp validation * replay protection * idempotency * payload validation * authorization * secret rotation Dependencies Inspect dependency manifests and lock files. Identify: * known vulnerable dependencies * abandoned packages * dangerous packages * unnecessary privileged dependencies * suspicious lifecycle scripts Separate confirmed vulnerabilities from dependency risks requiring external version verification. CI/CD and Supply Chain Review: * GitHub Actions * deployment scripts * build scripts * package installation * release workflows * secrets usage * permissions * third-party actions Look for: * excessive GitHub token permissions * unpinned actions * unsafe pull-request workflows * secret exposure * command injection * supply-chain risks Docker and Infrastructure Review: * Dockerfiles * docker-compose * Kubernetes * Terraform * reverse proxies * cloud configuration Look for: * root containers * privileged mode * exposed management ports * unnecessary services * public databases * weak network segmentation * insecure volumes * leaked secrets * dangerous capabilities Business Logic Do not restrict analysis to textbook vulnerabilities. Look for business-logic abuse including: * bypassing subscription limits * manipulating plan entitlements * repeating one-time actions * race conditions * referral abuse * promotion abuse * unauthorized state transitions * workflow bypasses * inconsistent server/client enforcement PHASE 4 — ATTACK-PATH ANALYSIS For important vulnerabilities, trace the complete attack path. Example: attacker-controlled input → route → middleware → service → authorization decision → database/storage/external service → security impact Determine whether multiple individually small weaknesses can be chained into a serious compromise. Prioritize exploitable chains over isolated theoretical issues. PHASE 5 — VALIDATION Do not report speculative vulnerabilities as confirmed vulnerabilities.

• 
  iced out markov chain (@permavirgin) reported 8 minutes ago

  Uh oh looks like GitHub is down. Sorry boss I can’t do any work. I have no choice but to play video games on company time

• 
  Dan Kornas (@DanKornas) reported 11 minutes ago

  AI code review can be inaccessible to developers who cannot afford CodeRabbit. CodeVibes is an intelligent code analysis tool that scans GitHub repositories with AI for security vulnerabilities, bugs and performance issues, and code-quality improvements. Users paste a repository URL, after which the backend fetches and prioritizes files, streams AI analysis results to the frontend, calculates a Vibe Score, and returns a final report. Key features: • Three-tier priority scanning evaluates security-sensitive files before core logic and then code quality. • Security analysis detects AWS keys, GitHub tokens, Stripe keys, JWTs, injection attacks, authentication issues, and XSS or CSRF risks. • Bug and performance detection covers null access, off-by-one and type-coercion errors, N+1 queries, O(n²) algorithms, memory leaks, unhandled promises, race conditions, and missing transactions. • Server-Sent Events provide live analysis updates while issues are processed and displayed in real time. • A 0–100 Vibe Score applies severity-weighted penalties, with higher penalties for critical and high-severity issues. This project is licensed under the MIT License. Link in the reply 👇

• 
  TnvMadhav (@TnvMadhav) reported 11 minutes ago

  Is @GitHub down?

• 
  Caleb Porzio ⚡️ (@calebporzio) reported 15 minutes ago

  @inxilpro mmm yeah, it basically solves the problem of the growing pile. email inboxes, github issues, obsidian notes, support emails. - keep on top of - give me highlights - walk me through the most important part is the lack of opt-in. it will run weather i like it or not

• 
  Yarchi (@undefinedKi) reported 17 minutes ago

  DoorDash just published how their AI agents automated 130,000 engineering tasks in a single month, and it reads like a spec for a job that did not exist two years ago The work itself is unglamorous. Reviewing pull requests, triaging broken builds, clearing on-call tickets, and the routine maintenance. 25,000 code reviews a week on its own. An agent on your laptop shares the CPU with everything else, stops when you close the lid, and holds every credential you hold. So they moved it off the laptop, into four pieces. A sandbox: a Firecracker microVM per task, loaded with the repos, tools and secrets that task needs. Cold VM to ready in under five seconds at p95. A gateway: one door to CI, tickets and monitoring. The task declares what it needs, gets exactly that, and every call is logged. A playbook: one YAML file holding the task, its tools, its permissions and its expected output. Surfaces: that same playbook fires from Slack, GitHub, cron or the CLI. Teams wrote the 300 playbooks themselves. Nobody is short of agents. The scarce thing is somebody who can build the place to put one.

• 
  Bright (@brightlinxu) reported 20 minutes ago

  um so is github releases down rn or something

• 
  Gerard Sans | Axiom 🇬🇧 (@gerardsans) reported 20 minutes ago

  @Amir_Safavi_N Instead of saying AI is good at math you should ask what area of math and how much literature and solutions of that specific type of problem is available publicly. Within a software development context this translates to a program written in JavaScript or Python (most popular languages on GitHub) performing better just because there’s more code samples available. In any case, if the problem is not available AI won’t be able to help. Remember it’s about data coverage not generalisable skill. The current technology is narrow. No AGI yet. That’s hype not science.

• 
  Anthony Hagi (@AnthonyHagi) reported 21 minutes ago

  @athasdev GitHub is slopware now. It’s on a very slow decline that we’re all noticing and feeling It’s just a matter of time before someone steps in to take it on

• 
  Mark (@Mark850428) reported 24 minutes ago

  @nishchay_jais And on my works GitHub any PR with more than 250 lines changed, is simply rejected with no review no feedback, if you cant fix it in 250 lines, something is wrong. Exceptions are VERY rare

• 
  J. Gamboa (@evoclock) reported 24 minutes ago

  @arthurkatcher @dandurand1414 @thsottiaux it def is. We only get GitHub copilot at work (terrible I know) but have successfully made the argument to reallocate funds from Anthropic models to OpenAI models on the basis of strong performance/cost ratio from Luna on Max effort. Figured Tibo would appreciate that.

• 
  sudo (@sudopong) reported 28 minutes ago

  Been using codex on Ubuntu~~ The optimization done to the codex app is insane, it barely reaches around 6gb of ram usage, during intensive tasks. The only con, is that it dosen't have computer use yet, however their are open source github plugins that fix the issue

• 
  Becker Meister (@MeisterCoins) reported 29 minutes ago

  @jullerino @bot @ericzakariasson classic, cursor gives you github login but bot wants its own PAT

• 
  Mark yu (@Markymarco34) reported 29 minutes ago

  There is another point that should not be ignored. At first, the main focus was the hard fork and PoX-5. We were told to wait for the hard fork, then wait for restaking to recover. Now, almost without acknowledging that transition, the focus is shifting again — this time to the Genesis Bond and future institutional accumulation. But the hard-fork phase itself has not been problem-free. A real block-production stall occurred around the Cycle 141 transition, and GitHub now shows additional tenure-boundary / pre-commit handling issues being worked on. So before moving the narrative entirely to Genesis Bond, the problems that appeared during the hard-fork transition should be clearly explained. I am not saying the Genesis Bond is irrelevant. I am saying the previous phase should not quietly disappear from the discussion simply because the next catalyst has arrived. First it was the hard fork. Then restaking. Now Genesis Bond. Each step may be legitimate on its own. But when the expected impact keeps moving to the next step, the unresolved issues from the previous step still need to remain on the ledger. @muneeb

• 
  Victor (@victor_UWer) reported 30 minutes ago

  I developed a Claude Code full-loop automated workflow: ``` [your spec] Use a new worktree to work on this. /simplify /code-review max --fix Open PR. /goal subscribe to this PR, fix all reviews, reply to all comments, resolve all merge conflicts. When there are no new issues after waiting 10 mins, add to the merge queue, then delete the stale GitHub branch only after the merge is confirmed. ```

• 
  Joshua Riley (@JoshuaRileyDev) reported 35 minutes ago

  @bil0090 GitHub Issues is gonna be cool, next should be linear issues then maybe have a way to auto poll and start threads when a new ticket is opened, been wanting this for a while as it would be like OpenAI’s Symphony concept

• 
  Riza 🐧 (@rizaardiyanto) reported 38 minutes ago

  @rilwis One thing that I like to do beforehand is discussing with the agent first. I give the GitHub issue to the agent, ask it what's implementation he will take. If I see misalign between what I thought and his solution, I will told him right away. This will spark discussion between me and the agent. Only after both of us having shared understanding and agreed on something, then I asked the agent to put all those details as comment in the GitHub issue. This will make the next agent can implement as expected. For the UI/UX, I usually asked an artifact first before he implement directly. If I like the artifact, I give it a go for implementation, if not I ask for some refinement there. And I prefer artifact on Claude rather than Codex. It gives a good result and know how to implement it. Codex artifact still not giving a good result yet

• 
  Heavily Armed Clown (@heavilyarmedc) reported 39 minutes ago

  @StevenBonebrake Which one? The github repo? Not broken for me.

• 
  Toposopher (@toposopher) reported 41 minutes ago

  @cosmicfibretion I like what you do, maya; publishing on GitHub and if people want to read it they do and contribute further. Like genuine curious explorers of the universe will always want to read what other people has to say about some problem.

• 
  Falco Girgis (@falco_girgis) reported 41 minutes ago

  @barisyyild Yes, actually. On the bottom of the KallistiOS GitHub repo is the link for the simulant server, which is basically DC dev HQ.

• 
  Dev (@dev_in_tech) reported 43 minutes ago

  @jonathan_wilke I think the main issue was many people who bought it used it in a public github repository so a simple search gave you the whole repo

• 
  Hao Ke (@kehao95) reported 45 minutes ago

  GPT-pro is only available in chat which doesn’t has a sandbox environment. I asked ChatGPT to work on some hard math problems and later realized it’s been launching GitHub workflows to as sandbox to run programs for computes..

• 
  Luke Ramsden (@lukerramsden) reported 45 minutes ago

  Quite a few services are down (or regionally down) right now - GitHub, Cognition, Blacksmith - what's going on?

• 
  Hayyan | Creative AI (@hayyantechtalks) reported 50 minutes ago

  How to become an AI engineer from zero experience You don't need to learn everything about AI You need to learn the right things in the right order, then build with them By the end, you should be able to ✓ Build LLM apps end to end ✓ Use OpenAI, Anthropic, and open source APIs ✓ Design prompts and context properly ✓ Add tool calling and structured outputs ✓ Deploy real AI projects Here's the roadmap I'd follow, month by month MONTH 1: Get solid at coding and fundamentals Learn ✓ Python really well ✓ *** + GitHub ✓ CLI / terminal basics ✓ JSON, APIs, HTTP, and async basics ✓ Basic SQL ✓ Data handling with pandas ✓ Virtual environments + package management ✓ Error handling ✓ FastAPI or Flask Don't rush into agents yet You need to be comfortable building and debugging normal software first MONTH 2: Master LLM application development Learn ✓ Prompting fundamentals ✓ System vs user instructions ✓ Structured outputs / JSON schemas ✓ Function and tool calling ✓ Streaming responses ✓ Conversation state ✓ Tokens, cost, and latency ✓ Failure handling ✓ Prompt injection awareness Your goal here is simple Take an LLM API and turn it into a useful application MONTH 3: Learn RAG properly Learn ✓ Embeddings ✓ Chunking ✓ Vector databases ✓ Metadata filtering ✓ Reranking ✓ Retrieval quality ✓ Hallucination reduction ✓ Citations and grounding Don't just learn how to connect a vector database Learn WHY retrieval fails and how to improve it That's where the real skill is MONTH 4: Agents, tools, workflows, and evals Learn ✓ Agent loops ✓ Tool selection ✓ State management ✓ Retries ✓ Multi step workflows ✓ When NOT to use agents ✓ Evaluation harnesses ✓ Task success metrics This is where your applications start becoming much more capable But don't build an agent just because you can Sometimes a simple workflow is better MONTH 5: Deployment, product thinking, and reliability Learn ✓ FastAPI production patterns ✓ Docker ✓ Background jobs ✓ Queues ✓ Authentication + API key security ✓ Logging ✓ Observability ✓ Prompt/version management ✓ Evaluation dashboards ✓ Cost monitoring ✓ Rate limits ✓ Caching This is the difference between “I built an AI demo” and “I can ship an AI product” MONTH 6: Pick a specialization At this point, you've built a foundation Now choose ONE direction and go deep You have three strong options 1 AI PRODUCT ENGINEER Best if you want startup or product roles quickly Focus on ✓ LLM apps ✓ RAG ✓ Agents ✓ Deployment ✓ Product UX 2 APPLIED ML / LLM ENGINEER Focus on ✓ Fine tuning ✓ When to fine tune vs prompt ✓ Evaluation ✓ Inference optimization ✓ Open source models ✓ Training pipelines 3 AI AUTOMATION ENGINEER Focus on ✓ Workflow orchestration ✓ Business process automation ✓ Multi tool systems ✓ CRM ✓ Documents ✓ Email ✓ Support ✓ Operations use cases And here's the part most people get wrong Don't spend six months just watching courses Everything on this roadmap is best learned through practice Learn something Build something with it Break it Fix it Then build something slightly harder By month six, you should have several real projects or completed examples you can actually show people That's what makes the difference when you're trying to get hired You don't want to say “I completed 12 AI courses” You want to say “I built this” Then show them Save this roadmap Come back to it whenever you're wondering what to learn next The AI engineering field is moving fast, but these fundamentals will give you a strong foundation to build on

• 
  Lethalmon (@Lethalmon) reported 56 minutes ago

  @briccenjoyer I think that, even if your connection is stable, GitHub doesn't like slow connection. It might be GitHub that force stops your download

• 
  Shyamprasad reddy (@Shyam_JSP_) reported 57 minutes ago

  @DattuClay @github Last month issue start ayina 4 hours ki update chesaadu andaru vachi meedha padtharu too many issues from last 6 months

• 
  Dan Siegel (@DanJSiegel) reported 58 minutes ago

  With Claude I gave Fable 5 great instructions it followed Technical Instructions but gave me crappy UI/UX. With Codex I simply instructed it to run an iPad simulator and run through the app to find what’s broken, or poorly implemented, open GitHub Issues then fix them. It took it to the next level and thought about edge cases. That said @thsottiaux it would be awesome if the simulators were running in app like the browser so I can still do other work without worrying that I’m going to mess up the run. Same for Android emulators as well.