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 (52%)
• Errors (33%)
• Sign in (15%)

Live Outage Map

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

City	Problem Type	Report Time
Ahmedabad	Errors	4 days ago
Delme	Sign in	5 days ago
Lyaud	Website Down	5 days ago
Catania	Errors	7 days ago
Inverness	Website Down	20 days ago
Quito	Sign in	20 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:

• 
  Yeemio (@yeemio) reported 24 seconds ago

  owlrunkit is on github now. public corresponding source for the npm package. issues go here.

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  🐻‍❄️ (@Nerevarineeee) reported 4 minutes ago

  @jiriknesl @napenforcer yeah vscode is bloated electron slop and github....... do i even have to mention the down times and unavailability? it has literally became much worse since ms acquired it, so wtf are you talking about?

• 
  Shaun Patrick SteWaRt (@ShaunStewart) reported 4 minutes ago

  @annalea_l Honestly, I really want to see this. You have to understand: I am the type of person who can learn and do anything on the fly at a high level, and I just threw myself into this whole developer and engineering world. When I first started learning all this stuff, I already knew what I wanted and how I wanted it to operate, regardless of what I saw on X or what was considered possible. Before I even started following hundreds of developers and learning about harness engineering, mechanical engines, persistent memory, and all that, I put my brain on a GitHub repo. Everything is shared across every machine, every cloud entity, and every AI. I am not even technically an engineer or a developer, and I don't actually write code. But once I started following all these people and saw all the problems they complain about, I thought: this isn't even my trade, and I have already solved all these little things everyone says are impossible. Why aren't people talking about developing your harness more and making things more mechanical, instead of just arguing with a terminal all day long? Whenever I see articles people post on X, I run them by Claude or Grok and ask, "Should we implement this?" I have hundreds of bookmarks, but every single time they tell me, "Nope, your brain's better. Nope, your harness is better." I can never find anything built better than what I have or what I am currently working on. The brain and harness setup is basically like a mini operating system. All that said, I am really looking forward to seeing something I can use that goes far beyond what I am already doing. I definitely want to see your end product, it sounds very interesting.

• 
  Bruno (@BrunoRJ33) reported 5 minutes ago

  @openclaw @github Endless codex and claude code tokens to fix it from time to time… and to improve its harness. I currently run around 10 claws 🦞. 24/7 for several purposes.

• 
  The Oracle (@scientist1q) reported 7 minutes ago

  when my Oura ring detects a cortisol spike from a GitHub Actions failure, Hermes (Fable 5.1) detects it and sends a 900 word root cause analysis, Hermes dispatches the work to my 12 Grok Bot employees, The Chief of Operations bot approves the fix while im watching rezero

• 
  Anime0t4ku (@Anime0t4ku) reported 10 minutes ago

  @c_hri_s Github issues are not closed. Mahbe refresh your webbrowser.

• 
  Joshua Okolo (@joshuaokolo_) reported 11 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

• 
  radhika (@RaadhikaThacker) reported 12 minutes ago

  First surprise: a GitHub issue form isn’t a form. It’s a YAML file. You describe the field- this one’s a dropdown, this one’s required and GitHub builds the UI from that. I did not know that.

• 
  Avinash (@Avinash25818689) reported 16 minutes ago

  People who want to start contributing to open source: - Pick an Org based on your interest - Fork the repository - Clone it - Do the local setup - Read README and contributing .md - Pick an issue - Create a new branch - Fix the issue - Write tests (if necessary) - Test it - Add, Commit & Push the code - Go to GitHub & raise that PR That's pretty much it. Start small and learn as you go.

• 
  Rituraj (@RituWithAI) reported 16 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 👇

• 
  Max Rovensky (@MaxRovensky) reported 17 minutes ago

  @thekitze you'd be even further down if you fixed the GitHub bug I just reported

• 
  To the Moon (@Gardnmi) reported 18 minutes ago

  @mitsuhiko Try the trick of putting the issue on github and having some clankers take a crack at it.

• 
  Alireza Bashiri (@al3rez) reported 19 minutes ago

  So I built a workflow around that ↓ 1/ Every enterprise project needs proper E2E tests. An agent should reproduce a bug, implement the fix, then generate screenshots or video proving the feature works. "The tests passed" isn't enough. I want evidence. 2/ Every feature starts as a detailed GitHub issue. Requirements, expected behavior, reproduction steps, screenshots, edge cases. Foundry syncs issues and converts them into Beads so agents keep the right context across long sessions. 3/ We only use Claude Code, Codex, or Grok at High/Max effort for implementation. A weak model with a cloud machine doesn't become an engineer. The model still needs enough reasoning to understand the codebase, test its changes, and recover when things break. 4/ Each agent gets its own isolated @asciidotdev Box. It can install dependencies, run the app, open browsers, modify code, execute E2E tests, and collect evidence without touching another agent's environment. One issue. One box. One clean workspace. 5/ When an agent finishes, Foundry checks: - Did the build pass? - Did the tests pass? - Did the E2E flow work? - Is there screenshot/video evidence? - Does it match the ticket? If anything fails, the task goes back to the agent. 6/ Green tasks move to staging. Only after passing staging do we allow supervised production deployment. Agents do most of the work. Humans still own the final gate. The workflow: Slack request → GitHub issue → Foundry sync → Beads context → Isolated Box → Claude Code/Codex → Build + test → Evidence collection → QA staging → Supervised production The stack: PostgreSQL for system state. Beads for agent memory. GitHub Issues for requirements. @asciidotdev Box for isolated execution. Claude Code and Codex for engineering. Each Box costs roughly $0.01-$0.05 per task. The expensive part isn't compute anymore. It's building the system that gives agents context, forces verification, and prevents bad code from reaching production. 100s of agents can write code. The goal is making 100s of agents ship code you can trust. That's what we're building with Foundry.

• 
  Blue Collar Executive (@A_Sober_Drunk) reported 20 minutes ago

  on the third try at the same problem, I told Grok to "stop and go search stack overflow or github or something"... five seconds later... Literally the exact issue, problem solved. That's how new global rules are born.

• 
  Hua-**** Xiong (@HuaDongXiong) reported 27 minutes ago

  Codex for Windows stopped launching after an update. Multiple github issues opened for 2+ weeks. This affect users who set the MS store install location to a non-C: drive. Mac version is buggy too. ofc coding is solved! @thsottiaux

• 
  AJ - 14 y/o developer (@aiwithaj_) reported 28 minutes ago

  @Da7_Tech @devindesktop Don't know if there's one left - but I'd use it to continue making contributions to open source software and fixing bugs/issues that were raised on Github as well as making my own open source software

• 
  安坂星海 Azaka || VTuber (@AzakaSekai_) reported 29 minutes ago

  I know you explicitly said "excluding vibecoding," but the biggest problem *IS* AI right now. Several major players in the field have moved on to heavy AI development or even agentic post-exfiltration moves and has muddied the water even more for attribution. Aside from that, the other big trend that we've been seeing more and more in recent years is heavily abusing Living Off Trusted Sites with C2 comms based on GitHub, OneDrive, Outlook, etc. Whilst this is most definitely not "new," we have seen a non-insignificant number of threat groups move to platforms that make tracing a little more difficult. In terms of the malware themselves, most of them have also shifted to using compiler-level obfuscation - a lot more compared to previous years where control flow flattening and jumps all over the place have become increasingly common. Right now, it's still tolerable, but my job has started becoming more and more annoying and less fun especially if every malware now looks the same. #mond_AzakaSekai_

• 
  Priyanshu Bhati (@buildwithpb) reported 32 minutes ago

  @CryptoWendyO @chainlink 30% error rate on github replies sounds like a recipe for accidental flame wars. good luck with the cleanup.

• 
  Jason Sawyer (@foilmanhacks) reported 32 minutes ago

  There's a huge problem in InfoSec education: it’s way too course and tool focused. Instead of teaching the underlying methodologies and how to discover or invent, we feed people the latest "OSINT" slop script that’s been shat onto GitHub. OSINT isn’t about using scripts or services. It’s about understanding how they work, and being able to create your own.

• 
  CATIRL 🏳️‍⚧️ (@CATIRL_9) reported 33 minutes ago

  @mminhamina Google GitHub "open grind", solves your problem

• 
  isha (@heeyyaaaaaaa) reported 36 minutes ago

  spent the entire day trying to reproduce a bug for a github issue 🥀🫩

• 
  Dr Milan Milanović (@milan_milanovic) reported 37 minutes ago

  How Cursor made *** scalable The thing with *** is that it never was designed to be scalable. Your repo lives on the disk, and *** client expect every read to be consistent. This was a problem on GitHub, where shared filesystems and replicated storage failed before 2013. The GitHub built 𝗦𝗽𝗼𝗸𝗲𝘀, and it became the industry standard. This means that every repo is stored as three full copies on three servers, and every push runs a vote (three phase commit). A majority of servers must confirm before it exists. This works, but with high cost, because every push is slow as the slowest server. When we add new servers, it makes it even slower. Now Cursor took some opposite direction with 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆. The repo history is now written as a log in S3, and this is only source of truth. Any push counts only if it is located in the log. The servers don't need to keep anything important, they are just cache. Any server can take a push, and idle repos are dropped from disk and rebuilt from the log when it is needed. This resulted in 120 pushes per second on standard S3, and over 300 on S3 Express. Their tests have shown that read capacity grew linearly up to 100 replicas. Why is this important now? Because of AI agents mostly. We now have more code, PRs, CI runs and many small repos. All of these repos would need three full copies in the old model. This means that we achieve scale by removing parts, not adding them.

• 
  David Abram 🐊 (@devabram) reported 42 minutes ago

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

• 
  Ravi Prasad (@ravikp7) reported 49 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.

• 
  Vikas(Vik) Malpani| AI for US Real Estate (@vikasmalpani) reported 51 minutes ago

  GitHub just shipped an agent whose entire job is deciding when a human should look. It checks every open pull request every 15 minutes, and on almost all of them it does nothing. Sit with how strange that is. For a year the whole pitch for coding agents was do the work, review my code, ship the PR. This one's value is the inverse. It runs constantly and stays quiet, and the product is the small set of PRs it decides are actually worth your time. That is the shift people are missing. Once an agent can act continuously, the scarce resource stops being how much it can do. It becomes how much of that is worth a human's attention. An agent that pings you on every pull request is just faster noise. One that surfaces the three that genuinely need judgment is leverage. The honest problem is the deciding. Tune the filter too eager and it cries wolf until you mute it. Too cautious and it silently ships the one change you needed to catch. Getting when to interrupt a human right is harder than getting the work right, and nobody has a clean metric for it yet. So here is the bet. The next moat in agent products is not a smarter model. It is a better sense of when to stay quiet. If you are building with agents, the thing worth obsessing over is not how much work they can generate. It is how well they protect the one budget that does not scale: your attention.

• 
  Franco Valdes (@francoxavier33) reported 54 minutes ago

  llms rather burn 1m tokens to hand roll something with gaps and broken edge cases instead of just npm installing a 100k github star library how can I stop this?!

• 
  Lily (@lobstermindset) reported 54 minutes ago

  @nnnnicholas i just setup a github issues board, will probs try out linear if it's not sufficient

• 
  Uptimus (@UptimusApp) reported 55 minutes ago

  Sep 02, 2026 at 13:29 UTC: Semaphore reports that periodic authentication failures with GitHub repositories are linked to a wider issue affecting HTTPS operations.

• 
  volkdude85 (@volkdude85) reported 57 minutes ago

  @SentientSquirel @linuxuser1996 So you are you scared of github then. Look dude I have fun on computers and don't take myself seriusly because I have destroyed enough OS's over to not worry about it because I just fix it, If the contents of your PC make you this paranoid its time to check your kink.

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 59 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. •