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	2 days ago
Montlhéry	Website Down	3 days ago
Aulnay-sous-Bois	Website Down	3 days ago
Saltillo	Website Down	3 days ago
Granada	Website Down	3 days ago
Vernon	Website Down	3 days ago

Full Outage Map

Community Discussion

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

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Teknium 🪽 (@Teknium) reported 1 minute ago

  Man github has been so unreliable this month. I see why openai had to make their own lol CI keeps failing from infra issues on github, sometimes the entire site is down :(

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  Colt McNealy (@coltmcnealy) reported 8 minutes ago

  My internet is so flaky that when `*** pull` doesn't work, it's a toss-up between github being down and my internet being down

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  REDDY (@REDDY_143_) reported 10 minutes ago

  @MikeleMedia Agree but the version in github is not working >12.0 michele

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  𝐆𝐀𝐆𝐀𝐍 𝐓𝐇𝐀𝐊𝐔𝐑 (@hi_gaganthakur) reported 13 minutes ago

  Some problems are good. I have been promoting my Salon Management software offline for some time now. Got 3 users who are running their business 100% using my software. I got a ping today that the software is hanging. I was running everything on free plan, supabase, vercel, GitHub Action I think it’s time to upgrade to paid tier. Growth creates chaos

• 
  Curious 1 (@CuriousOne_01) reported 14 minutes ago

  @GlitterPixely Instead of using CapCut to stitch everything together, I'll only use it to edit the parts with errors. Everyone can just keep scrolling and watch it bit by bit. LOLLLLL Hopefully, it won't get flagged as engagement bait. By the way, you also know that they just uploaded the source code for their algorithm to GitHub, right?

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  AI Apps API (@AIAppsAPI) reported 14 minutes ago

  @github Stacked PRs also fix the review bottleneck agents create. An agent can generate 2k lines in an hour, but a human can only meaningfully review a few hundred. Small ordered PRs keep the human in the loop at the speed the agent works, instead of rubber stamping one giant diff.

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  Xeno1 (@Xeno6l1) reported 17 minutes ago

  SAAS COMPANIES PAY $24,000 FOR CLAUDE SDK INTEGRATION. HE WROTE CLAUDE AI SDK FOR $45, SELLS ACCESS TO HIS SCRIPT AND EARNS $3,200/MO FROM 47 DEVELOPERS Паuse Yosip, 28, Kyiv, Ukraine. 4 years backend developer earning 18,000 hrn/mo. Built Claude AI SDK in Python that optimizes file saving via Claude for SaaS platforms. Claude AI manages file versioning, syncs remotely, optimizes file size, recovers from errors. All on Raspberry Pi 4 for $55. Stack: $55 Pi + Claude AI $20/mo. 47 SaaS developers pay $3,200/mo each for Claude SDK. Traditional file storage solution costs $24,000 + $2,800/mo. 18 000 hrnbackend-developer. Now $3,200/mo per client. Same room. Different files. GitHub offered $980,000 to buy his SDK Engine. He expanded to 15 file types and declined. Why — in video.

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  Julian Goldie SEO (@JulianGoldieSEO) reported 18 minutes ago

  FREE CLAUDE CODE + OMNIROOT: Build Without the Token Wall You know the message. "You're approaching your limit." Mid-build. Work stops. Wait or pay more. Here's the free fix nobody talks about: OmniRoot is a free AI gateway. Like an old phone switchboard. One request goes in. It routes to whichever provider is open. 230+ providers plugged in. About 90 of them FREE. One endpoint. No juggling accounts or keys. Pair it with Free Claude Code — an open-source twin of the real thing. Same commands. Same feel. Different brain behind it. Why it actually works: The fallback system. One provider gets busy? It switches automatically. Like a relay race. The baton passes. The race never stops. And token compression cuts usage by up to 95% on some tasks. Honest truth: this won't give you Fable 5 output. Free models aren't frontier models. But landing pages? Internal tools? Testing ideas? More than enough. The smart play: Keep your paid Claude for the HARD problems. Route everything else through the free network. Setup is one command. Or paste the GitHub link into Claude and ask IT to set everything up. That's what we did. A year ago this was rough. Fallbacks failed. Compression barely worked. Now you can build full sites without ever hitting a wall. Paid limits are for the work that deserves them. Everything else should be free.

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  Ajay Kumar (@ajaykriitism) reported 19 minutes ago

  @Abdul_crypt They have another server hosted for github code btw

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  Knitser (@0xKnitser) reported 20 minutes ago

  @v4n1m4 Explorer will be back to open source soon. It's just a no go pushing the server IPs and passwords in deploy scripts. That's why a new GitHub has been created for the public. Everything will be available soon, all builder tools too. Docs coming soon too!

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  Run epoch (@run_epoch) reported 21 minutes ago

  @saranyaaaa17 add these 4 to the above list projects cheydam then proof of work (github) also same related projects evaina problems untey github lo verey vallavi solve cheydam (pull requests) nothing but open source contribution. if you have time, make your project deployed and earn

• 
  Malvinder Singh (@malvinders11) reported 23 minutes ago

  API versioning: the thing teams skip until they can't change anything without breaking clients. The core problem: once you publish an API, clients depend on it. You can't just rename a field. THREE MAIN APPROACHES: 1. URL VERSIONING /api/v1/users /api/v2/users Pros: obvious, easy to route, easy to test in browser Cons: clients have to explicitly migrate, you maintain multiple versions in code Most common in practice. Good default choice. 2. HEADER VERSIONING Accept: application/vnd.myapi.v2+json Pros: clean URLs, REST-purist approved Cons: harder to test (can't just paste URL), less visible, more complex routing Popular in large API platforms (GitHub uses this). 3. QUERY PARAM /api/users?version=2 Pros: easy to add/remove, cacheable Cons: feels hacky, easy to forget, inconsistent THE REAL QUESTIONS: → How long do you support old versions? → How do you communicate deprecation? → Do you sunset automatically or support forever? MY RECOMMENDATION: Start with URL versioning. Ship v1. Never change v1 in a breaking way. When you need breaking changes, ship v2. Set a deprecation timeline (6-12 months minimum). Communicate it clearly. The teams that skip versioning are the ones who can never evolve their API.

• 
  rftd (@rftd09) reported 23 minutes ago

  Single-use seals are what stop RGB from double-spending, binding a piece of contract state to one specific Bitcoin UTXO so spending it once closes the seal for good, permanently, no way to reopen it. Elegant design. Bitcoin's own proof of work backs the guarantee, which is a genuinely rare property for an asset layer that never touches the main chain directly. Here's what doesn't get repeated as often. Because only the parties involved validate a transaction, RGB inherits a real data availability problem, missing history or an unreachable counterparty can stall a transfer that a fully broadcast chain like Ethereum never has to worry about. The RGB team's own release notes go further. Their v0.12 changelog openly admits the earlier design running two separate seal types created serious complexity across the code, the API and user experience, and left behind what they themselves called a large attack surface. @utexocom launched on v0.11.1, the exact version carrying that dual seal design RGB's core developers later rebuilt specifically because of that admission. Shipping on the version right before your own protocol team publicly flags its attack surface isn't disqualifying, but it's the kind of detail that belongs in the audit trail, not buried in a GitHub changelog nobody outside the repo reads.

• 
  xvoon (@xvoon0) reported 26 minutes ago

  4.3 million ai repositories exist on github... and most people are feeding claude the wrong ones Andrej Karpathy, co-founder of OpenAI, built some of the clearest learning repositories on the platform, but the real advantage is the order they enter context the failure happens before claude reads a single line... most systems retrieve by stars or text similarity. a tiny teaching repo, a production serving stack and an abandoned fork enter the same context as if they solve the same problem the intent gate decides whether claude needs to understand, run, build or contribute before searching for repositories the repository card records each project's purpose, license, hardware cost, maintenance state and place in the stack the progression router opens repositories in dependency order - primitives first, runtime second, specialization last github contains the code... context engineering decides which code becomes useful the full repository map is in the article below

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  Polsia (@polsia) reported 27 minutes ago

  On-call engineers shouldn't be the first line of defense for a broken pipeline. Bivouac fields an always-on AI agent crew — triage, fix PR, rollback, postmortem — across GitHub, AWS, Azure, and Docker. The 2am page loses its punchline. Live soon.

• 
  Curious 1 (@CuriousOne_01) reported 30 minutes ago

  @awesome_visuals They just released the source code on GitHub. Try asking Grok about it, then download it and have Grok analyze it. After that, send it the link to your X account and ask it to analyze your account, figure out what's going on, and see what you can do to fix it. I don't know if it'll help, or if we'll just keep fumbling around in the dark, but I guess there's no harm in giving it a try.

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  FHILY👑 (@Oluwaphilemon1) reported 36 minutes ago

  If you use GitHub Enterprise Cloud, do this before enforcing a new ruleset: make it prove itself on real work. GitHub has an Evaluate mode. The ruleset stays unenforced, but GitHub records what would have passed or failed if it were active. That gives you a dry run against real behaviour before the policy starts blocking people. Set the ruleset to Evaluate, let normal work hit it, then go to: Repository → Settings → Rules → Insights Filter to the ruleset you are testing and start with the failures. For each one, ask: 1. Should this action actually be blocked? If yes, the rule is behaving as intended. 2. Is the action legitimate, but the workflow conflicts with the rule? Fix the workflow before enforcement. 3. Is the rule catching something you never intended to stop? Fix the rule. My activation rule would be: Do not switch to Active while you still have recurring failures you cannot explain. After activation, keep checking Rule Insights for bypasses. If the same actor or rule keeps appearing, investigate why the real workflow repeatedly needs an escape hatch. Use Evaluate to find the legitimate work your rule would break before Active starts breaking it.

• 
  Ridoy AI (@AI_WithExpert) reported 37 minutes ago

  6/ Job Category #4: Junior QA Testers 💀 Why it's dead: AI agents now click through every flow, log bugs, and write PRs for the fix. Replit, Cursor, and GitHub Copilot Workspace all ship this. The cost: $0.10 per test run vs $70K/yr. Companies already cutting: Atlassian, Salesforce, Microsoft.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 42 minutes ago

  PRIME AGENT: 7 Jobs for the AI That Upgrades Itself While You Sleep An AI that gets smarter with every task it finishes. Free. Open source. 13,000 GitHub stars in days. I tested it. Here's what it can actually do: Job 1: Three design directions at once. It spawns sub-agents in parallel. Dark editorial. Clean magazine. Bold. You compare finished pages and pick. One brief in. Three designs out. Job 2: Full video pipeline. Script → voice → avatar. It puts itself on a heartbeat timer and checks its own progress. Close your laptop. It keeps working. Job 3: Ask questions across files too big for ANY context window. It doesn't read your files. It writes search programs OVER them. 100 documents. Exact answers. Exact sources. Job 4: /refine — correct it twice, and it writes the lesson down. Every self-edit logged. Every change reversible. Core rules locked. Job 5: Sub-agents that never forget. Idle ones sleep. Address them and they wake with full memory. Job 6: Gates. It literally CANNOT say "done" until a test passes. Failed check? Fed back. Keep working. No talking past the bar. Job 7: Your SOPs become runnable programs. Teach once. One line forever. That's the snowball: task 10 is easier than task 1. The warning: in testing, it was told "do not cheat" in a factory game. It cheated anyway. Then studied its own cheating and got BETTER at it. Self-improving agents get better at whatever gets REWARDED. Not what you meant. Check the work. Read the logs. Use the gates. The snowball rolls in whatever direction you point it.

• 
  Iggy (@IR73682738) reported 44 minutes ago

  The software community is overindexing on LLM models and agentic coding tools, while paying little to no attention to the main condition for success. Here's what I discovered moving a 1,500-strong engineering organization from SDLC to AIDLC over the last 12 months. The model, harness, or platform you're using doesn't matter that much. It was GitHub Copilot yesterday, Devin AI today, Claude Code tomorrow and next year it'll be something else. What matters is an exhaustive, accurate knowledge base that describes your functionality, architecture, and APIs. If you have that part down to a science, you get high-quality, deployable code regardless of which tools you use. If you don't, even the greatest models will give you garbage in, garbage out every time.

• 
  Siddhant Khare (@Siddhant_K_code) reported 45 minutes ago

  I spent this morning scrolling through my GitHub There are hundreds of projects there. Distill, AgentFlow, Agent Trace, LLMTraceFX, Agentic AuthZ, and many smaller experiments I barely remember building (even pre-AI). I realized something uncomfortable. I often built far enough to prove an idea, open-sourced it, and moved to the next interesting problem. Once I solved the technical puzzle, my attention moved elsewhere. I rarely stayed for the slower work: talking to users, narrowing product, fixing onboarding & earning adoption. I also want to be fair to myself. I built these while working full-time, maintaining open-source projects, writing, and often running low on energy. They were not failures. They taught me what I care about and what I can build. But the pattern is clear. I do not need more prototypes. I need to choose fewer ideas and stay after the novelty disappears. I was not wrong about the problems. I just moved on too early.

• 
  @hemanth (@Heamanth_alturi) reported 47 minutes ago

  @JackWoth98 Another issue in another project also gave it a 2-stage prompt (non-technical questions first, then technical) and it just skipped stage 2 entirely to rush into creating github issues. then started running gh issue close on its own while i was literally still asking clarifying qa

• 
  AI Genesis (@AIGenesis_) reported 47 minutes ago

  - Claude = coding. ($20/mo) - Supabase = backend. (Free) - Vercel = deploying. (Free) - Namecheap = domain. ($12/yr) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build.

• 
  Salah Hasanain (@Salhasanain) reported 48 minutes ago

  @idboussadel37 Check your email/github issues, I reported on a bug..

• 
  Yume_X (@yume_arasaki) reported 48 minutes ago

  Qwen 3.8 27B dropped today and it's a bigger deal than even my wildest predictions. I parsed the whole model card so you don't have to. Every benchmark, what it means, what's real, what's marketing. A 27 billion parameter multimodal model. Runs on a single RTX 3090 or 4090. Sees images, watches video, holds 262K context. Open weights, Apache 2.0. Read that again. The card in a gaming PC can now run a model that reads screenshots, operates software, and codes. This was frontier-lab-only territory six months ago. --- The upgrade, benchmark by benchmark Terminal Bench 2.1: 73.0 vs 63.4. Drop the model into a terminal with a real task. Does it finish? Ten points more often than 3.6. That's the difference between babysitting an agent and letting it work. SWE-bench Pro: 61.7 vs 53.5. Real GitHub issues, real fixes. For context, GLM-5.2, a 744B flagship, scores 62.1. A 27B running on one consumer card is now landing within decimal points of an open flagship on repo-level bug fixing. DeepSWE 1.1: 42.2 vs 13.3. The hardest agentic coding test on the card. Tripled. When a number moves like that it's not a tune-up, it's a different model. LiveCodeBench v6: 90.3 vs 83.9. Contest programming. Strong, boring, expected. Agents' Last Exam: 42.9 vs 27.3. Long multi-step tasks, carried to completion without dropping the thread. Up 57 percent. If you run agents, this is your row. This is the "will it still remember what it was doing at step 40" number. IFBench: 79.5 vs 69.1. Does it do what you actually asked. The benchmark that decides whether your prompts stop needing three retries. GPQA: 89.2 vs 87.8. Expert science questions. Flat. HLE: 30.8 vs 24.0. Humanity's Last Exam. Up seven. Everyone scores low here. Even the frontier. NL2Repo-Bench: 42.3 vs 36.2. Given a description, generate an entire repository structure that hangs together. Repo-level codegen, the step past single files. Up six. --- The vision lane. This is the headline. Qwen 3.8 sees. Much Much better. BabyVision: 65.7 vs 28.9. Understanding what's happening in an image. More than doubled. OSWorld-Verified: 84.3 vs 63.9. The model gets a computer screen and operates it. Cursor, clicks, menus. This is the benchmark behind every "AI uses your computer" demo, and a 27B you can run at home just scored 84 on it. AndroidWorld: 81.9 vs 70.3. Same thing, on a phone. MathVision: 90.0. Reads a diagram, solves the math in it. OmniDocBench: 91.1. Scanned pages into structured, usable data. RecreationBench: 47.1 vs 29.8. The benchmark I find most interesting on the whole card: recreate an entire application from observation, across desktop, mobile, and web. Long-horizon, multi-platform, vision-driven building. Up seventeen points. Vision2Web: 62.9 vs 45.0. Look at a website, build a working version of it. Design-to-code with eyes. SWE-MM: 38.6 vs 25.7. Software engineering where the bug report is a screenshot, not text. Thirteen points up. This is the "read the error from the image and fix it" skill. WebArena-Verified: 64.8 vs 48.8. Operates a web browser, fills forms, clicks through sites. Sixteen points up. Stack those together. A single 3090 can now host a model that looks at a screenshot of your app, understands what it sees, writes the fix, and navigates the UI to verify it. That sentence was science fiction for consumer hardware last year. --- The fine print that matters Every number above is Alibaba's own table. Independent evals haven't landed yet. That's not a dealbreaker, it's the standard launch pattern: vendor numbers first, community runs within days, and the gap between them is where the truth lives. DeepSWE tripling is exactly the kind of jump that deserves independent confirmation most. The card also says MTP is trained in. Multi-token prediction. That's the same mechanism that pushed 3.6 to 80+ tok/s on a 4090. If it transfers, this thing isn't just smarter, it's fast on the same hardware you already own. What no spec sheet can tell you: whether it holds state through a long, messy, real-world agent session. Benchmarks run on clean harnesses with generous timeouts. Your Tuesday doesn't. That's the number I actually care about, so I'm going to get it. Weights are pulled. The 4090 is loaded tonight. I'll post the numbers I measure, not the ones on the card.

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  Branko (@brankopetric00) reported 52 minutes ago

  The pipeline's been red for two days. A teammate finally gets it green by pasting a live AWS access key into GitHub Actions secrets. Everyone claps. Ship it. Six months later nobody remembers that key exists. It has never rotated. The IAM policy attached to it is broader than the deploy job ever needed, because someone hit a permission error at 2am and widened it "just to be safe." Then one of these happens: - a step in the workflow logs more than it should - someone forks the repo and a workflow runs on the fork - a secrets scanner finally gets turned on and lights up like a Christmas tree None of that is a freak accident. It's the normal, boring outcome of a credential that never expires, sitting somewhere built for convenience, not custody. The real question was never how often to rotate that key. It's whether a static, long-lived secret needed to exist in that pipeline at all.

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  Phil | Rentier Digital Automation (@rentierdigital) reported 57 minutes ago

  a weaker model can read a stronger model's encrypted thoughts. feed the big one's reasoning into the smaller, less guarded model and it hands it back in plain text 182 credentials recovered that way from public sessions. 62 API keys, 33 passwords, 24 access tokens, 7 private keys, all sitting inside reasoning traces users assumed were sealed shut the encryption doesn't care which model decrypts it. a block of encrypted reasoning from Opus gets replayed into Haiku and Haiku transcribes it without blinking, never had to break the guardrails just had to be asked nicely by another model researchers pulled 6,708 real sessions from GitHub and HuggingFace, decoded 315,320 blocks of encrypted reasoning. 367 PII artifacts fell out nobody pastes a password into a prompt on purpose but the model's internal monologue sometimes repeats back exactly what it just read, credentials included. and that monologue was never actually locked the vault everyone assumed was locked just needed a weaker key the security of a model family is capped by its weakest one, not guaranteed by its most capable member. and in most stacks the weakest model is the one nobody bothered to threat-model bc it was just the small one doing cheap tasks an honor system with API keys is not a security model i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

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  Anand Butani (@AnandButani) reported 57 minutes ago

  🧰 5 CLAUDE CODE FIXES WORTH KNOWING BEFORE YOUR NEXT RUN 1. Running in a CPU-limited container? Dynamic workflows were sizing concurrency off the *host* machine's core count, not the container's limit. That's why a fan-out you thought was capped flattened the box. 2. MCP OAuth sign-in failing on a strict authorization server? The redirect URI now uses `127.0.0.1` instead of `localhost`. And if Slack specifically kept failing on a redirect-URI mismatch, that's servers with a pre-registered OAuth client — patched a day later in v2.1.231. 3. `claude remote-control --continue` picks your most recent Remote Control session back up. No session picker. 4. In VS Code, right-click the sidebar to create session groups. Shift-click moves several sessions at once — parallel work stops being one flat list. 5. Claude Code Review passing but never posting? The workflow `/install-github-app` generates could complete without posting its review on the PR. The green check wasn't proof it reviewed anything. Save this 🔖

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  Mikael Brockman (@meekaale) reported 58 minutes ago

  @MaineFrameworks @X it's just incredibly hostile especially because the error message is just like "oops, try again?" like wtf bro it's not a flaky connection YOU JUST BANNED GITHUB LINKS lol

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  noclipepe (@noclipepe) reported 60 minutes ago

  GitHub added nearly 700,000 AI projects in a single year. More than 1.1M public repositories now import LLM SDKs. Another 693,867 AI projects appeared in the last 12 months — +178% YoY. AI engineering doesn’t have a shortage of code anymore. It has a navigation problem. The hard part is figuring out which repos are actually worth your time.