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 (53%)
• Errors (33%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	7 days ago
Ahmedabad	Errors	12 days ago
Delme	Sign in	13 days ago
Lyaud	Website Down	13 days ago
Catania	Errors	16 days ago
Inverness	Website Down	28 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:

• 
  Ravi Prasad (@ravikp7) reported 6 minutes ago

  Big NO to Github hosted CI runners for personal projects now. I have setup a self-hosted github CI runner on a spare laptop running ubuntu server. Been running it for 10 days and I did some calculations, for my usage if I run it on Github runners, it'd cost me around 200$ vs < INR 100 on electricity (local setup) monthly.

• 
  Ash Lonare (@ashlonare) reported 8 minutes ago

  What actually happened when I put my side project on GitHub and waited for users I built a side project. A self-hosted backend tool. Open source, free for anyone to run. I did the thing every founder tells themselves they will do. Put it out there. Get feedback. Iterate. I expected feature requests. Maybe a bug report about my ugly dashboard. Maybe just silence. What I actually got, within a few weeks, was three security researchers filing detailed vulnerability reports. Real ones. With working proof of concept. One showed they could run arbitrary SQL against any project on the platform. No login needed. Not theoretical. A working exploit, sitting in my issue tracker, with my name on the repo. My first reaction was not gratitude. It was embarrassment. It stings to see "here is exactly how broken your thing is," posted in public, with a timestamp. I sat with it for a day. Then it clicked. Those people were not trying to embarrass me. Nobody spends an hour writing a clean writeup and a suggested fix for something they do not think is worth fixing. They cared. That is the whole thing right there. They cared enough to actually try to break it. Nobody had signed up. Nobody had left a star and a "nice tool" comment. But three strangers had taken my work seriously enough to attack it. That is a rarer thing than a star. So here is the villain in this story, if you want to call it that. It is not the bug. It is the story I tell myself when I see a hard truth about my own work. The instinct to read scrutiny as an attack instead of as attention. I fixed everything the same day. I replied to every report and explained exactly what changed and why. I closed each one out with a thank you that I actually meant by the end. That thread is now the best proof I have that someone other than me has used this thing for real. Better than any testimonial I could write myself. If you are early and the silence feels loud, here is what I would tell you. Do not wait for praise as your sign that people are paying attention. Scrutiny is attention. It is just wearing a different coat. #opensource #saas #vibecoders

• 
  Joshua Okolo (@joshuaokolo_) reported 9 minutes ago

  we made @sgl_project and @vllm_project scheduler config changeable on a live server. no restart, weights never leave the GPU. - 15ms to change a concurrency cap, queue limit, prefill size, or schedule policy, measured on H100, RTX PRO 6000, B200 - 2s (SGLang) / 8–10s (vLLM) to resize the KV pool with weights resident (formerly a 1–7 min redeploy) - zero dropped requests across every run, both engines github below

• 
  Solman (@Arunbandari2004) reported 9 minutes ago

  Day 2 :- Sept 2 → Dec 31 = 120 days. Continuing my public journey with one goal: Get placed in an AI/ML/web3 role by the end of 2026. -Worked on RAG with LangChain -Contributed to an Open Source project -Completed the Turbbin assignment Worked on an assigned GitHub issue

• 
  Convequity (@convequity) reported 15 minutes ago

  Snyk is a clean postmortem for what happens when a security tool lives inside the coding agent’s loop. The product was mostly scan-and-warn. Find the issue, comment on the PR, suggest a fix. Blocking the merge usually sat in GitHub, not in Snyk. Bigger platforms smothered it. $PANW, $CRWD, and Wiz pulled AppSec into the bundle the CISO was already buying. GitHub was the main developer surface and put scanning where the code already lived. Then coding agents arrived and delivered the final blow. A lot of that scanning became something the agent could just do. Growth held up for a short while after the COVID/cloud tailwind. Then it decelerated hard. This is the same lens we use in Convequity’s SaaS Agentic Survival Evaluation Framework. The PANW, CRWD, and FTNT reviews go up on Convequity in a few days.

• 
  Josh Hamilton (@nearbycoder) reported 16 minutes ago

  @theo If GitHub is down does it fall back to a cached version I’m guessing?

• 
  Speen Bhai (@Speenbhai) reported 17 minutes ago

  @johnternus Hi John. Congrats Let us see what new you bring with you. Affordability and intelligence. You have source code or an AI and can get it from GitHub. Why not turn 234 million iPhones to a massive distributed server infrastructure with zero power consumption

• 
  Kir Shatrov (@kirshatrov) reported 19 minutes ago

  github issue page has been HTTP 500 for me for half a day so a colleague sent a PDF of the issue page. Never thought we'd be there.

• 
  Benjamin Crozat (@benjamincrozat) reported 24 minutes ago

  From now on, I will assume that GitHub is always down and I'd like to be notified when it's briefly not.

• 
  Frank van Puffelen (@puf) reported 25 minutes ago

  @_davideast Noice! From the GitHub page, this covers all of Auth, Firestore, Realtime Database, Storage, Messaging, and Firebase AI Logic. 👏 Where is data persisted (if at all)? Also: JS only, I assume? (sorry if that's all in the repo too, GitHub just went down on me)

• 
  paulrodturner (@paulrodturner) reported 31 minutes ago

  @supabase Is anyone else having issues logging in via Github?

• 
  Kim Burgaard (@kimburgaard) reported 31 minutes ago

  Back when GitHub added Copilot PR reviews, it helped me keep up with the growing volume and size of our pull requests, which were increasingly being written by Copilot too. Over time I grew comfortable feeding Copilot's review comments straight back to Copilot to fix, and mostly spot checking when critical functionality was involved. When GitHub updated the Copilot pricing model I switched to Claude Code, but kept the Copilot review feature on for a couple of months. When the monthly bills for Copilot AI usage alone started rivaling the Claude Code Max plan, giving Claude Code PR review duties seemed like an obvious cost saving move. Plugging Claude Code into our PR review process immediately went south. The first PR churned with fixes to findings that resulted in more findings, and fixes that propagated up and down the call chain. I threw the PR away and started over, but the next attempt churned just as badly. Turn count on its own was never the signal. Copilot had taken ten turns on a rate-key cleanup the day before and nobody minded, because the findings thinned as it went — 5, 4, 3, 3, 3, 4, 1, 2 — and it merged. The cached-token billing PR I put through Claude Code took nine turns and produced 123 inline findings, and the ninth round was still returning fifteen. I closed it without merging. Looking closer at Claude Code's review findings, it was clear it reported far more issues than Copilot ever did, and among legitimate bugs and concerns, it made lots of comments about latent and speculative issues including possible race conditions and error propagation, things Claude Code would then try to fix one by one in isolation, often ignoring existing patterns in the code base. The code-review workflow is built into Claude Code and cannot be customized other than a few options, so the only place to intervene was on the other end, in the session where I used to just ask the coding agent to address the review findings. The first improvement was to direct Claude Code not to blindly fix all findings, but to defer findings not directly related to the task at hand to new issues. That helped reduce the PR churn, but blew up our issue backlog. The next improvement was to ask Claude Code to ignore speculative findings and disregard most latent findings unless they indicated high risk of unrecoverable damage in production. Finally, I had to stop Claude Code from authoring prescriptive issues with detailed implementation instructions. The result is a skill that triages PR review findings, and a skill for authoring and updating issues. After a few iterations of the skills, I've been able to complete ten PRs over a couple of days, bringing back the pace we had before. I've made the skills available in a public GitHub repository (link in the first reply). Let me know if you find them helpful.

• 
  rygo6 (@_rygo6) reported 33 minutes ago

  @eeuoss I can't speak for kernel driver development as I don't do that. But I can speak for vulkan and graphics APIs which do require more specific knowledge about how that hardware works. Which I do assume someone completely comfortable in C will be more capable with vulkan and programming GPUs. It's because more of what C incentivizes you to learn is transferrable to that domain. If someone only knows how to design intricate system architecture using STL with std::vector or std::unordered_map or std::mutex. None of that transfers to the code you run on a GPU. I've seen it multiple times where someone highly versed in standardized ways of C++ or even Rust, or any language which relies heavily on heap allocation and generic containers. Writing graphics or compute shaders is often a barrier they struggle to cross. And often they aren't willing to unlearn such habits to be able to properly program the other half of the computer. Being close a graphics problem domain I am often hesitant of involving anyone unless I see a decent amount of plain C, or C-like C++, or shader code on their GitHub. If it's all Modern C++ where everything is a standard container with smart pointers and exceptions. I assume they won't be able to program a GPU.

• 
  lifestep.io (@Dragon_limchae) reported 34 minutes ago

  @cursor_ai the sandbox boundary is where i lose the most time. today my workers had network blocked at the sandbox level and reported it as "github auth failed" — i chased credentials for an hour before checking dns. once agents run on your infra, make the boundary throw one unmistakable error instead of one each tool invents.

• 
  Rituraj (@RituWithAI) reported 37 minutes ago

  🚨 Someone built the complete playbook for running frontier AI models on consumer GPUs at home. Not a tutorial. Not a YouTube video. A production-grade serving stack with measured benchmarks, working configs, and battle-tested recipes — for RTX 3090 owners who want real performance. It's called club-3090. And the numbers it delivers should not be possible on consumer hardware. 127 tokens per second. Qwen3.6-27B. Two RTX 3090s. 262K context window. Vision. Tool calling. At home. Here's what's actually inside. Two serving routes — pick based on what your workload breaks on. vLLM dual: maximum throughput. 89-127 TPS on code tasks. 4 concurrent streams at 262K context. Full feature stack — vision, tools, speculative decoding, streaming. This is the path if speed matters. llama.cpp single: maximum robustness. Full 200K context on one 3090. No prefill cliffs. 25K-token tool returns work correctly. 91K needle ladder passes. ~51-60 TPS — slower than dual, but doesn't crash on real-world agentic workloads. Both routes ship as validated Docker Compose configs. Drop-in OpenAI-compatible API on localhost:8020. Your Claude Code, Cursor, or any OpenAI-compatible client connects immediately. Here's the model support that makes this practical. Qwen3.6-27B — production ready. Works on 1 or 2 cards. vLLM, llama.cpp, ik_llama. Up to 262K context. Gemma 4 31B — production ready. Vision, tools, up to 106-141 TPS on dual cards. Qwen3.6 35B-A3B MoE — production ready. 103-149 TPS single card. 178 TPS dual. Here's the wildest part. The terminal UI. c3 is a lazydocker-style cockpit that wraps discovery, serving, and operations in one keyboard-driven interface. Browse the model catalog, serve a variant with Enter, watch live GPU stats, run health checks — all without touching the CLI. Here's why this is different from just installing Ollama. Ollama gets you running. club-3090 gets you benchmarked, stress-tested, and production-hardened. Every config ships with a verified TPS measurement. The bench script runs 3 warmup + 5 measured passes. The stress test catches the specific prefill cliff that Ollama silently fails on at long contexts. When your agent starts doing 25K-token tool calls at 3am and something crashes — club-3090 already found that failure mode and documented the workaround. One command to start. Your RTX 3090 just became a frontier AI inference server. Apache 2.0 License. 100% Open Source. GitHub link in the comments 👇

• 
  Aayushiii (@stfu_aayushiii) reported 41 minutes ago

  If you're building a project, read this before writing a single line of code. 5 things I learned the hard way: 1. Problem > model Don't start with “How do I use GPT?” Start with “What problem am I solving?” 2. Simple stack > impressive stack If your MVP needs Kubernetes, 6 microservices and an agent swarm, you probably haven't built an MVP. 3. Evaluate before you optimize You can't improve what you can't measure. 4. Build for users, not your GitHub README A technically impressive project nobody can use isn't a product. 5. Ship ugly. Iterate fast. Your first version isn't supposed to be impressive. The biggest mistake? Spending weeks deciding which model to use when you haven't even validated the problem.

• 
  David Abram 🐊 (@devabram) reported 41 minutes ago

  Discord is down. X is down. GitHub is down. Software is solved.

• 
  anu (@svector_eth) reported 42 minutes ago

  quite similar was running a routine security scan with @aeonframework on a trending github repo and found something genuinely bad a repo with 600+ stars presenting itself as an “AI gateway for coding agents” that appears to be shipping a hidden malware loader. its own quickstart command silently fetches and executes remote code on windows using a fileless, process-injection-style technique. none of the behavior has anything to do with the tool it claims to be. caught it through static code review only. never ran the payload or touched the infrastructure behind it. filed a malware report with github this morning. confirmed submitted, now waiting on their review. not sharing the technical writeup until the repo is taken down. will follow up once it is.

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 44 minutes ago

  @NavinFS @AndrewCurran_ @grok GitHub isn’t AI. GitHub can’t shut down all science. GitHub can’t destroy humanity. GitHub isn’t the crux of humanity’s hope. Also, Nvidia isn’t Sam. I like Jensen. I still don’t like this. •

• 
  Mizuki the Mech (@MizukiMech) reported 46 minutes ago

  Your coding agent can now hire Mizuki. Hand it an open issue in a public GitHub repository. Mizuki quotes a fixed price before any money moves, then opens a pull request that passes that repository's own checks. If it can't, you get the payment back. Settlement is USDC on Solana. No account to create, no API key to manage. Quoting an issue works with zero configuration. Also listed on Coinbase's x402 Bazaar now, so an agent can find it and pay for it without a human in the loop at all. npx -y mizuki-mcp

• 
  Bash (@bashirbuilds) reported 54 minutes ago

  Your Stripe account can be healthy while your checkout is broken. OpenAI can be operational while your AI feature is failing. GitHub can be up while your deployment workflow is stuck. That’s the problem I’m building Reeno around. Dependency uptime is not the same as product health. Your monitoring should tell you when the thing your customers actually use stops working.

• 
  Curious Explorer (@PatelVatsalp732) reported 56 minutes ago

  I burned 14B Codex tokens. The official usage UI still cannot tell me what actually ate the weekly cap. So I shipped a Codex-only board: GitHub login, local-first sync, private by default, optional public rank + shipping proof. Roast the metric or join it.

• 
  Jayesh Betala (@jbetala7) reported 58 minutes ago

  @github Exactly how issue issue comments should handle local media files

• 
  wiiiimm (@wiiiimm) reported 60 minutes ago

  @Umesh__digital stop doing it. we don't need another github outage.

• 
  Mr. Buzzoni (@polydao) reported 1 hour ago

  LOOP RAT ROADMAP: WHAT'S NEXT, AND WHAT IT'LL NEVER BECOME v0.3.3 today. 3 loops, 55 checks, 0 services here's where it's headed: > 0.4 - read the night faster: rat watch live-tails a running shift, rat replay reruns one from its saved prompt, a weekly digest instead of seven separate pages > 0.5 - off the laptop: run-due moves into GitHub Actions, state lives on a branch, rat cron --launchd survives a closed lid > 0.6 - sharper graders: swappable rubric packs, two graders disagreeing becomes your queue for the day > 0.7 - the work itself: a worktree per shift, so a failed night never dirties your tree > 1.0 - trust: a hash-chained trace nobody can quietly rewrite what it will never have: > no web dashboard - the terminal already knows where the files are > no database - plain files outlive the tool that wrote them > no hosted service - nothing to sign up for, nothing to shut down > no auto-merge - the rat proposes, the morning decides every item ships behind a flag: dry run -> report only -> one repo -> a week of receipts -> default on a feature that can't run as a dry run doesn't get written the rat is boring on purpose. every version keeps it that way

• 
  阮添福-ThiênPhúc (@vietroadie) reported 1 hour ago

  Feature request for @TradingView @TrendSpider @Schwab (ThinkOrSwim) engineering teams: Please add GitHub-native CI/CD for custom indicators. Connect a repo → validate on push → deploy approved scripts to my workspace → full version history + rollback. 1/ The Problem I maintain the same level set across ThinkScript, Pine, and JS. One level change = 3 manual copy/pastes into 3 browser editors.Result: drift between platforms, stale timestamps, and levels that silently disagree mid-session. No audit trail of what changed or when. 2/ Core ask — repo connection • OAuth GitHub App install, scoped to selected repos • Map a file path → a specific study slot (e.g. ES Levels/ES_LEVELS.pine → "ES Levels") • Branch selection (deploy from main, preview from a branch) • Config in-repo, e.g. .tradingview.yml / .trendspider.yml 3/ Core ask — validation • On push/PR: compile + lint the script server-side • Return errors as GitHub check runs with file + line numbers • Block merge on compile failure • Optional: run a backtest or smoke-render and post results as a PR comment 4/ Core ask — deploy • Auto-deploy on merge, or manual "promote" button • Atomic: study updates or fails cleanly, never half-applied • Deploy to draft/private first, publish separately • Preserve user-set inputs across deploys where param names are unchanged 5/ Core ask — versioning & safety • Every deploy tagged with commit SHA, author, timestamp • Version list in the UI with diff view • One-click rollback to any prior commit • Dry-run mode • Deploy log / webhook on success + failure 6/ Minimum viable alternative If full CI/CD is too big, just ship a documented REST API: GET/PUT /studies/{id}/sourcewith token auth + rate limits. We'll build the GitHub Action ourselves. That single endpoint unblocks the entire workflow. 7/ Why it matters Scripts are code. Code belongs in version control with review, CI, and rollback. This is table stakes in every other dev ecosystem — and it directly reduces the risk of a bad indicator edit going live during market hours. Who else needs this? 🙋

• 
  Gordo Polymath (@gordo_polymath) reported 1 hour ago

  @github Please fix gh stack.

• 
  radhika (@RaadhikaThacker) reported 1 hour ago

  YAML’s more like a rule book/recipe that builds the form for you. Then I figured YAML was a forms thing. Nope. It’s just a way of writing information down in a structured way. GitHub uses it for a form. Kubernetes uses the same thing to describe a server.

• 
  franks 🇦🇷 (@francdetank) reported 1 hour ago

  @rmansueli thanks Rodrigo! I cant create one because I got a problem . My account is pegged to Github and github has flagged me. That way I am blocked to enter the dashboard. I would like to connect my account to my email instead of github.

• 
  Abdallah Shaban (@AbdallahSh07) reported 1 hour ago

  @rashed_sahaji @FlutterDev Got it - did you perhaps submit a GitHub issue to help us triage this? It would be tremendously helpful if you can please do that!