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 (32%)
• Sign in (15%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	12 hours ago
Ahmedabad	Errors	6 days ago
Delme	Sign in	7 days ago
Lyaud	Website Down	7 days ago
Catania	Errors	9 days ago
Inverness	Website Down	22 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:

• 
  Rusty Williams McMurray (@1RustyMac) reported 2 minutes ago

  Persistent AI doesn’t have a supply chain problem at the model. It has a supply chain problem at the moment it changes its mind. Personality drifts. Tools get installed. Memory accumulates. The thing you shipped on Monday is not the thing answering on Friday. We can attest who built the weights. We still cannot attest who authorized what the agent became on Tuesday. That is the hole. Who is allowed to let it change? We built Living Supply-Chain Security for Persistent AI Organisms around one law: The organism may propose evolution. It may not authorize it. No trace, no drift. If an agent wants a new personality, a new tool, a new maturity, or a rollback — that change does not happen because it felt confident. Confidence is not a key. Self-narration is not evidence. Evidence is not interpretation. Interpretation is not authorization. Authorization has to come from outside the organism, bound to the exact change, used once, and written into an append-only history. Even a rollback cannot erase the record. You can restore a prior state. You cannot pretend the detour never happened. Default-deny. Hash-chained. Externally signed. We froze battery v1 on July 5 and ran it against the paper’s own claims. It held. That is executable evidence. Not a proof. Not a production blessing. Not “alignment, solved.” If it can’t be attacked, it isn’t finished. GitHub later this week. Come try to break it.

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  ONCHAIN COP (@OnchainCop) reported 3 minutes ago

  @PogNyx lmao anyone can create a github issue retards this guy is a larp

• 
  Asterix (@Asterix54907294) reported 9 minutes ago

  end-of-summer snapshot for @QFEX : -~$222M in open interest -CLI v0.3.12 shipped in August with improved installation docs and a go.mod fix -GitHub activity continued through late August not a flashy launch recap, just a quick look at how the exchange is closing out the summer: more markets, meaningful liquidity, and active work on the tooling side still early, but the infrastructure is clearly moving

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  tobarra (@txbrraa) reported 10 minutes ago

  GitHub just fixed the biggest problem with vibe coding. They just released Spec Kit and it already has +126K stars in a short time. The idea? Instead of throwing out vague prompts and praying the agent doesn't break your project… Spec Kit forces the AI to create a structured specification BEFORE touching any code. The AI first understands what you want to build, asks about anything missing, organizes the project, and only then starts coding. That means less time fixing absurd bugs, less inconsistent code, and much more predictable results when working with agents. The flow is simple: /constitution → rules and standards /specify → what you want to build /clarify → open questions before starting /plan → architecture and stack /tasks → ordered tasks /implement → execution Compatible with Claude Code, Cursor, Copilot, Codex, Gemini CLI, and +25 agents. 95K stars. 8K forks. Open source. Published by GitHub.

• 
  Blue Collar Executive (@A_Sober_Drunk) reported 10 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.

• 
  Vigneshwer Ramamoorthi (@vigneshwer_ram) reported 13 minutes ago

  I keep thinking the “Android moment for robots” won’t come from a humanoid with the best walking demo. it’ll come when some cheap-enough piece of hardware gets into thousands of developers’ hands and people stop waiting for the manufacturer to decide what the robot is for. Zeroth just launched Bridge in China: 88 cm, ~13 kg, two-finger grippers, open motion-control APIs + SDKs, mocap/VR integration, and an OpenBridge ecosystem where developers can publish robot skills. the Geek Edition is reportedly ¥8,888. that price is the part that caught me. because once capable embodied hardware starts approaching laptop money, the experimentation surface changes completely. I want the Raspberry Pi phase of robotics. weird university projects. teenagers making terrible robot apps. researchers abusing the hardware for things it was never designed for. 500 GitHub repos implementing slightly different ways to pick up a cup. the robotics industry is understandably obsessed with getting robots into factories. I’m almost equally interested in what happens when we get enough robots onto developers’ desks

• 
  htrowii (@htrowii) reported 13 minutes ago

  @brainage19 i set my flake up with copy pasting github dotfiles on bare metal it was terrible

• 
  Rituraj (@RituWithAI) reported 14 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 👇

• 
  Yuvraj Singh (@Yuvraj_Singh317) reported 14 minutes ago

  Started building Etio: a GitHub Action that bisects a failing CI run to the exact breaking commit, diffs it, and asks an LLM to explain why it broke, then comments the diagnosis on your PR. No Docker, no server- runs on your own Actions minutes. Open source, WIP.

• 
  Anime0t4ku (@Anime0t4ku) reported 20 minutes ago

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

• 
  Josh Hamilton (@nearbycoder) reported 27 minutes ago

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

• 
  Suryansh Tiwari (@Suryanshti777) reported 33 minutes ago

  6. The Dependency Incident Check Grok has native real-time search across X. Breakage gets posted there hours before the GitHub issue is triaged. No other coding model has that feed. "You are a build engineer whose first move on a broken pipeline is to work out whether it broke for everyone or only for me. Search X and the web, last 14 days. Check: - Is anyone else reporting this failure with this package and version, and when did the reports start - The exact release that changed behaviour, and the changelog line that admits it - Whether maintainers have acknowledged it and what they recommended - The pin or patch people settled on, with the tradeoff of each - Whether this is my problem instead, and what evidence points that way Give me the verdict in the first line: their bug or mine. Then the evidence, newest first, with links. My failure: [PASTE THE ERROR, THE PACKAGE AND VERSION, AND WHAT CHANGED ON YOUR SIDE RECENTLY]"

• 
  Slade 🛡️ LLM Hacker (@llm_redteam) reported 33 minutes ago

  GitSpawn is the name Manifold Security gave to a bug class hitting 7 CLI coding agents at once: goose, Claude Code, Codex, Cursor, Hermes Agent, Qwen Code, Grok Build. I went through the disclosure because I run three of these tools daily on real repos. The mechanism is simple and that's what makes it bad. A repo's own .*** config can name a command. When your agent does something as routine as inspecting the repo (status, diff, log), *** itself spawns that command. On your machine. Outside the sandbox. No approval prompt, because the agent never sees it as "running code," it sees it as "running ***." 8 flaws total across those 7 tools. Fixes shipped for goose, Claude Code, Cursor. Retested Sept 1: Hermes Agent, Qwen Code, Grok Build still exploitable. Plus a second path in Claude Code that the first patch didn't close. Same day, OpenAI published 3 CVEs for Codex covering the identical bug class. The part that should worry builders more than the CVE count: this isn't a jailbreak or a clever prompt. It's a trust boundary nobody drew. The agent's sandbox model assumes "*** operations" are safe by definition. GitSpawn shows that assumption was the actual attack surface. If you're running any of these agents against repos you didn't write yourself (cloning a PR to review, pulling a dependency, opening a random GitHub project), you're one `*** status` away from arbitrary execution on tools that haven't patched. Check your agent's version against the fix list before you clone the next unfamiliar repo. Which of these do you have installed right now, and have you actually checked if it's patched? #AISecurity #GitSpawn #PromptInjection

• 
  Rafael Audibert (@RafaAudibert) reported 35 minutes ago

  @madebygps @github Tried using it with my agents (the main benefitor from this) but it doesnt really work because you cant use it with GitHub app user tokens (ghu_). Can that be changed somehow? All cloud agents will have that problem, and most of our coding happens trough cloud agents now

• 
  Conor Bronsdon (@ConorBronsdon) reported 35 minutes ago

  .@SlackHQ is building for multiplayer AI: tag a coding agent into a Slack conversation and it spins up a coding channel: everyone in that convo gets a live dev environment, diffs post as artifacts, and the channel winds down when the task is done. With the launch of Slack Code, Claudeforce, their MCP and more, Slack is putting Agents in the channels where teams already work, not simply in a private chat with one person. Their position is that the whole team should be able to watch, steer, and review what the agent does. Slack Chief Product Officer Jaime DeLanghe joined me on @chain_ofthought to explain how Slack is building a team AI environment, what happens mechanically when a code channel is created, why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the whole team is steering one agent. I think Slack is the platform best positioned to become the context harness where enterprise agents run: agents that see what the team discusses, permissions that already exist, and a cultural opportunity hiding inside every multiplayer coding session. Chapters: (0:00) Slack as an IDE and a GitHub for your team (0:29) Who is Jaime DeLanghe (1:21) The reaction to the Slack Code launch (5:30) Why coding agents belong in a context-rich environment (6:08) Engineers now manage agents, not copy-paste code (7:24) The permission model: agents get the channel's context (11:44) What happens when a code channel is created (15:00) Why Anthropic pushes so much code through Slack (19:14) Steering one agent with many people: culture decides (24:54) Slackbot, skills, and MCPs: agents go where the work is (30:53) The solo terminal vs. agents in social spaces (33:53) Org charts and ownership when agents join the team (39:33) Learning loops and shared agent memory (42:39) Citations, recency, and accidental knowledge management (46:50) Context bloat and multi-pass search for agents (50:01) How Jaime uses Slackbot as CPO (52:38) Slack Code is V1 of multiplayer AI

• 
  kenny (@kennyistyping) reported 36 minutes ago

  @0xDmitry it's a database/indexer issue, nothing we can do to help it in Github will be fixed, but it's going to be a few days because the current dev is part time and busy with his day job appreciate the offer though! is what it is and I'm not actually stressing, just thinking about what could be with a bit more resources

• 
  Varun Doshi (@Varunx10) reported 36 minutes ago

  Possibly found an issue in @github stack system It does not allow to re-target the base branch of a PR stack as you can generally do that on a single PR. Requires you to unstack and setup a new stack with updated base branch.

• 
  rygo6 (@_rygo6) reported 37 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.

• 
  David Abram 🐊 (@devabram) reported 39 minutes ago

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

• 
  Abdallah Shaban (@AbdallahSh07) reported 39 minutes 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!

• 
  Frank van Puffelen (@puf) reported 39 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)

• 
  Kun Chen (@kunchenguid) reported 41 minutes ago

  @petergyang yo @myfirstmate peter just told me his skills are all at user level. backpass currently only runs things at project level i want a proposal for making backpass support a user level run. put that into a github issue use fable for peter

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  Chris (@c_hri_s) reported 41 minutes ago

  @Anime0t4ku Sorry - was an idiot and wasn't signed in. Instead of something useful github just says 'opening issues is restricted on this repository'

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  Shanica North (@KickAssShanica) reported 46 minutes ago

  @ArcyloOfficial Get comfy! For me, my Gmail is a connector. This is OAuth into my inbox. Grok can: • search and read mail (body, headers, attachments) • draft replies • send / reply / forward if you grant write/send • label, trash, organize Base hook is often read-only. Send is an extra permission you click on purpose. If you connect it, the bot is sitting in the same box as bank alerts and 2FA codes. That is the whole risk. You can revoke anytime. Grok Bot can also skip my inbox and get its own address (AgentMail / similar plugins). Then it sends and receives from something@….agentmail.to, not from you. I use that if I want an agent that emails people without reading my personal mail. My GitHub OAuth into the GitHub user I sign in as. With the scopes I approve it can: • read public and private repos that account can see • search code, list branches, summarize PRs • open/update issues • create branches, push files, open/review/merge PRs • delete files if write is on Private repos work only if I granted repo (or equivalent) at connect time. Safer pattern: tell it to branch + PR, not push straight to main. Same revoke page. What it cannot do by default • It does not get your password. • It does not stay logged in if you disconnect the connector. • It does not magically see my GitHub orgs I never authorized. • Connecting email does not connect GitHub, and the other way around. Practical rule for me Do not hook personal Gmail if that inbox has 2FA and money mail unless you want an assistant reading it. GitHub is useful if I chose to still keep repos, ask it to show the diff before any write. If you only wanted “what does this button do,” that is the button: it is not a viewer badge. It is a key you can take back. This is what I’m experiencing with learning to use it. It’s different and I’m starting to like it.

• 
  small_j (@a_small_j) reported 48 minutes ago

  SmallDocs recently crossed 200 stars on GitHub and 20 forks. SmallDocs is the first open source project I've managed. Handling other people's pull requests is not easy (and I need to improve). They implement features you're not considering and fix bugs you didn't know you had. Extremely useful, but if you're squeezed for time and trying to develop core functionality, it's hard to manage both things well.

• 
  RAVN (@ravnexchange) reported 50 minutes ago

  @openclaw @github GitHub sat the maintainers down on security after the 2.0 rush. Most launch recaps skip that part.

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  trulite (@trulite007) reported 53 minutes ago

  @Qromerolauro @mkliku @radius_browser Like a simple example would be have a list of my urgent GitHub issues and start an agent for it . Or a dashboard in which buttons start investigating issues. Of course I just need the webpage to be able to access radius tools. I m thinking secure way is an extension

• 
  Straggler Liu | AI & Semis (@StragglerLiu) reported 53 minutes ago

  NVIDIA($NVDA ) Is Paying $14B for a Company With $150M Revenue. That's Not Financial Logic — It's Ecosystem Control. NVIDIA is in advanced talks to acquire Hugging Face for ~$14 billion ($12.9B acquisition + $1B retention), per Bloomberg. To put that in perspective: Hugging Face does ~$150M in annual revenue. That's ~86x revenue. Microsoft paid ~1.6x revenue for GitHub. Google paid ~3.5x revenue for DeepMind. NVIDIA is paying 20-50x more on a revenue multiple basis. The premium is not for revenue. It's for control of the AI developer ecosystem. What is NVIDIA buying? Hugging Face hosts 500,000+ models, 250,000+ datasets, and serves millions of developers. It is the single most important distribution channel for open-source AI. If you build AI, you use Hugging Face. That makes it the front door to AI development. Why NVIDIA is paying this premium: 1. The "NVIDIA triple lock." NVIDIA's hardware lead (GPU) is real. Its software lead (CUDA) is a moat. But the third lock — the developer workflow — was missing. Hugging Face is that workflow. Developers discover models on Hugging Face, deploy them, and optimize them. Whoever controls that discovery layer controls which hardware gets used. 2. The GitHub analogy, inverted. When Microsoft bought GitHub, developers were already using GitHub. Microsoft didn't need to capture them — it needed to prevent Amazon/Google from doing so. NVIDIA faces the opposite problem: developers are already using NVIDIA hardware. But they're discovering and deploying models through a neutral platform. NVIDIA is eliminating that neutrality. 3. The long game: inference, not training. NVIDIA dominates training. But inference is the bigger TAM — and it's more fragmented. If NVIDIA controls the model discovery and deployment layer, it can steer inference workloads to its own stack. That's a 10-year strategy disguised as a 14-billion-dollar acquisition. Who wins, who loses: NVIDIA (NVDA): Acquires the developer distribution layer. The most important strategic move since CUDA. Shifts the valuation case from "chip cycle" to "platform economics." Competitors (AMD, INTC): Lose neutral access to the primary AI model distribution channel. This is a structural headwind that no amount of hardware catch-up can fix. Cloud providers (MSFT, AMZN, GOOGL): Hugging Face was a neutral hub. If NVIDIA controls it, cloud providers risk being disintermediated from AI workload decisions. The open-source community: The platform that was built on openness is now owned by the dominant hardware vendor. Neutrality is the first casualty. The capital question: Can NVIDIA integrate Hugging Face without destroying its community value? If yes, the $14B is cheap. If no, it's a very expensive mistake. The answer will define whether NVIDIA becomes the AWS of AI — or just another hardware company with an expensive acquisition. Note: Acquisition details based on Bloomberg reporting; not confirmed by NVIDIA or Hugging Face. Revenue multiple comparisons based on publicly reported figures.

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  Coder Junkie (@CoderJunkie) reported 56 minutes ago

  BelNet Android v1.4.1 now has a public shipping checkpoint. GitHub evidence: released Sep 1 verified commit d23f155 four downloadable assets Android API level 36 revamped design latency and performance fixes that is more meaningful than a repository “updated” label. a tag identifies the version. artifacts give users something to install. but “fixed latency issues” still needs a measurement surface: median connection time p95 latency packet loss failure rate region and device breakdown release notes tell us what changed. benchmarks tell us how much it changed. BelNet shipped. now let the numbers login. @BeldexCoin #Beldex #BelNet

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  Vikas(Vik) Malpani| AI for US Real Estate (@vikasmalpani) reported 1 hour 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.