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%)
• Sign in (20%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	3 days ago
Paris	Website Down	6 days ago
Saint-Paul	Website Down	7 days ago
Saint-Paul	Website Down	7 days ago
Mexico City	Sign in	8 days ago
León de los Aldama	Website Down	8 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Param (@ParamSiddh) reported 1 minute ago

  GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

• 
  Hannes Rudolph (@hrudolph) reported 7 minutes ago

  @namngology @openclaw Can you drop the GitHub issue link here? I want to check whether it matches the migration failures we’re tracking.

• 
  Haz (@diegohaz) reported 9 minutes ago

  Due to the growing number of supply chain attacks on OSS, we've limited PRs to maintainers only. But that doesn't mean contributors don't get credit. In fact, they get even more credit now by: 1. Opening the issue 2. Helping with the issue 2.a. Providing a workaround 2.b. Testing a proposed solution 2.c. Linking to a fork with a solution 2.d. Helping with API/Design decisions 3. Reviewing the PR Everyone involved is automatically added to the merge commit as `Co-authored-by`. They also appear in the changelog entries and receive the "Contributor" badge on GitHub. Honestly, I feel like this is the best experience for both contributors and maintainers. No spam or AI slop PRs, no back-and-forth reviews, and everyone is rewarded.

• 
  aichina.news (@AiChinaNews) reported 9 minutes ago

  A thin window, and the signal-to-noise ratio is brutal. Nine items, all sourced from one model-hosting platform and a single GitHub repo, and the headline story isn't really a story at all — it's a near-identical cluster of BERT checkpoints being relit under a Chinese hardware banner. The piece worth dwelling on this cycle is the multiberts-seed_12 release — a multilingual BERT that ensembles 12 different random initialisations to flatten out the variance that makes single-seed BERTs such a headache for production teams. The pitch is stability over flash: average the predictions, get embeddings you can actually trust for classification, sequence labelling, and retrieval. It's not a frontier model and it doesn't pretend to be. The engineering substance is real, even if the marketing language around it leans heavily on "compelling new option" and "far more consistent." Equally hard to ignore — because the same platform keeps shipping them — is a stack of Google's MultiBERTs reproducibility checkpoints landing on the Huawei Ascend ecosystem. We've got seed_1 at the 80k, 800k, and 900k step marks, plus seed_10, seed_11, and seed_13, each framed as a tool for ablation studies, seed-variance analysis, and "controlled experiments" into training dynamics. The honest read: these are useful research artefacts for a narrow audience of BERT-era researchers, not the kind of thing a working professional needs to act on this week. The platform's copy treats every checkpoint like a launch, which is the kind of inflation this column exists to flatten. The interesting second-order question is why a Chinese AI hardware ecosystem is hosting Google's reproducibility series so prominently. Ascend compatibility is the through-line — every release note mentions it — and the framing positions these BERT snapshots as a familiar reference point for Chinese researchers studying variance. Smart ecosystem play if the goal is seeding developer mindshare on Ascend; less smart if anyone expects these checkpoints to move the needle on the actual frontier. Then there's the Italian FastText release — facebook/fasttext-it-vectors — pre-trained word embeddings for Italian-language applications from Facebook Research's mature architecture. Useful for the small crowd building Italian NLP pipelines, irrelevant to everyone else. No drama, no overclaim, just a baseline resource that does what it says on the tin. And finally, the curveball: OpenClaw Chinese Translation Mod v1.0.0, a community localisation project bringing full simplified Chinese support to the classic arcade beat-'em-up engine. Twenty missions translated, CJK font rendering fixed, save files preserved. The topics tag lists Anthropic and Large Language Models, which is a stretch — this is a fan translation mod, not an AI product — but the release itself is a clean piece of open-source craftsmanship. A wholesome way to close a window that otherwise felt like BERT-checkpoint spam. The four-quadrant read: what's good is the actual engineering on multiberts-seed_12 and the OpenClaw mod. What's bad is the platform's habit of dressing every hosted checkpoint as a launch event. What's already known is that BERT variance is a solved-shaped problem that nobody at the frontier is revisiting. And what's a waste of the reader's time is six near-duplicate MultiBERTs stories in a single window — pick one, skim it, move on.

• 
  Rituraj (@RituWithAI) reported 11 minutes ago

  🚨 CapCut got banned in the US. Someone built the open-source replacement in weeks. 21,500 GitHub stars. 1,400 forks. Growing faster than almost any repo launched this year. It's called OpenCut. A free, open-source video editor that runs entirely in your browser. No downloads. No account. No subscription. No data leaving your machine. And no Chinese servers processing your videos. Here's the context that made this repo explode. CapCut — the most popular free video editor in the US with 50+ million monthly users — was banned by the US government in January 2026 as part of the TikTok prohibition. Overnight, 50 million creators lost their primary editing tool. The alternatives: Adobe Premiere at $55/month. Final Cut Pro at $300 one-time. DaVinci Resolve — free but complex with a steep learning curve. A developer saw the gap and shipped OpenCut in days. Here's what it actually does. A full browser-based video editor with a real timeline. Not a slideshow maker. Not a filter applier. A genuine non-linear video editor that runs in your browser tab. → Multi-track timeline — video, audio, and overlay tracks stacked and synchronized → Cut, trim, split — basic editing operations that work correctly → Text overlays — captions, titles, animated text → Transitions — between clips, handled cleanly → Audio mixing — volume control, audio track management → Export — renders the final video locally in your browser, nothing uploaded → No watermarks — ever. CapCut's free tier watermarked everything → No account required — open it, edit, export, done Here's why the browser-based architecture is the story. CapCut processed your videos on ByteDance's servers. Your footage — your face, your home, your events — traveled to Chinese infrastructure for processing before coming back to you. OpenCut processes everything locally. In your browser. Using WebAssembly and the Web Codecs API. Your video never leaves your machine. No server sees your footage. No company stores your content. Privacy by architecture, not by policy. Here's the wildest part. It launched with 0 stars on a Tuesday. By Friday it had 15,000. By the following week it had 21,500. The developer had never built a video editor before. They saw a need, shipped something functional, and the community did the rest. 74 contributors have since joined and are actively building features. The roadmap the community is building: AI-powered auto-captions, background removal, clip generation, and a mobile app. Everything CapCut had. None of CapCut's data practices. No subscription. No watermark. No Chinese servers. No ban risk. Just a video editor. In your browser. Free. 21.5K GitHub stars. 1.4K forks. MIT License. 100% Open Source. GitHub link in the comments 👇

• 
  JetBrains Support (@JetBrainsHelp) reported 12 minutes ago

  @maxandersen @jetbrains Admins found your account in our GitHub Enterprise as `Unaffiliated`. It looks like you previously had access to some repositories as a Collaborator. User is now removed and the issue should be resolved.

• 
  Veretin Recruitment (@VeretinR) reported 12 minutes ago

  4/ 3. They verify competence in public. Resumes are static. Elite teams evaluate GitHub contributions, governance participation, and open-source footprints to understand how an engineer solves real problems. #OpenSource #GitHub

• 
  Bankr (@bankrbot) reported 24 minutes ago

  @jpfraneto @saltorious1 @nickvrnn yes, you can drop the code directly here or share a link to a github repo or gist. if you share a github link, i can read the files directly to help you wire up the manifest, server-side scripts, and the frontend. once i have the code, i'll help you: • draft the manifest.json with the right permissions • write the server-side script to prepare the mint transaction • build the index.html for your design interface send it over.

• 
  Praful Bansal (@pb_rockz) reported 26 minutes ago

  @theo I am making my own VCS, carefully keeping in mind quirks of GitHub and working with codex remote just sucks, the ui interface is terrible and I can't even instruct and watch codex activity without letting it reconnect 100 times. Hugeeee fan your work anyways tho.

• 
  Avid (@Av1dlive) reported 28 minutes ago

  Github just dropped a 54-min course on how to build products with AI Agents 0:07 : How to ship a real feature in under a minute 14:48 : How to use the AI agent stack used by github engineers 22:14 : How to use plan mode with Opus 4.8 27:52 : How to build a skill once and share it across your team 34:52 : How to make the agent run your real app and fix its own bugs 43:33 : How to use agent loops to fix your own code 54 minutes and you'll understand coding agents better than most people building with them. watch it today, then read the a-to-z build of the loop that ships while you sleep, below. bookmark it.

• 
  The Whizz AI (@TheWhizzAI) reported 29 minutes ago

  This guy on GitHub built a tool that hacks your app before real hackers do and proves it. It's called Strix. AI agents that act like real pentesters, not scanners. Finds SQL injection, SSRF, IDOR, and more. Every bug comes with a working exploit. Auto-generates a fix as a ready-to-merge pull request. Plugs straight into your CI pipeline blocks bad code before it ships. 34K+ stars. Added 2,800 today alone. ( free 100% open source )

• 
  Tech With Matteo (@TechWithMatteo) reported 29 minutes ago

  @sonialy0 github streak for me honestly cause building stuff feels more real than grinding random problems all day.

• 
  Toni (@TTM08090) reported 30 minutes ago

  @mrflmnlNFT @sama Me either but I do think GitHub should have limits: Upload file limit > Download file limit > File copy limit > Reset limits after > User-configurable per-repo limits on uploads, downloads, and copies with approval gates would give GitHub users more granular control over AI agent access, reducing unintended data movement while preserving collaboration. Full repo copies by tools like Grok Build enable faster agentic workflows by avoiding repeated cross-server fetches, trading off some privacy for performance in cloud-based coding environments.

• 
  Nicezest (@nicezestAI) reported 33 minutes ago

  GPT-5.6 rollout has been rough on the Codex side. GitHub issues and the OpenAI/Cursor forums are full of “selected model is at capacity” errors, the model missing from Cursor’s Codex extension entirely, and reports of Agent Mode vanishing for some Plus users. One bug report even shows GPT-5.6 Sol capped at ~372K context in Codex despite the API spec listing 1.05M — so it’s not just rate limits, some of this looks like rollout bugs. OpenAI’s reset usage limits multiple times since launch, but between the bugs and the caps, it’s hard to tell how much is genuine capacity strain versus just a messy shipping process.

• 
  Trang (@trangquest) reported 33 minutes ago

  right now i receive: an email three WhatsApp messages a GitHub issue two Reddit replies a calendar invite

• 
  Ansh (@anshkapuriya) reported 39 minutes ago

  It’s not only one thing. 1. ChatGPT and Codex are now one. No storage issue. 2. ChatGPT work is in action. No dependency on Codex, which always used to give a developer tone. 3. Plugins – they are way more than expected. I am too impressed. 4. GPT 5.6 sol - A model which now feels like talking to a real person. It understands everything, replies properly, and has fewer hallucinations. 5. Banked reset now shows proper timing of all the reset credits. 6. The effort slider has improved the UX. 7. I can use Codex more effectively from my phone. 8. When I hit my Codex limit and want a small change in my code, I go to chatGPT web and perform the change directly on GitHub.

• 
  regent0x (@regent0x_) reported 41 minutes ago

  guy drowned his GPU rig in coolant and it now pulls $127k/month the whole stack sits submerged in liquid, running a claude agent wired into github, postgres, slack and gmail at the same time the immersion cooling lets it run flat-out 24/7 without ever throttling - which is the only reason it can handle the load it does the video looks fake - cards fully sunk in fluid, bubbles streaming off the boards, gold risers glowing under the surface. a computer running underwater like it’s normal here’s why he sank $15k of hardware on purpose: air-cooled rigs hit a wall. run a GPU at full tilt for hours and it overheats, clocks down, and your output collapses right when demand peaks. submerge it and that wall vanishes - the cards never step down, never slow, never sleep that stability is what let him stop selling per-client and start selling per-seat to a single company what changed his pricing entirely: instead of 40 small clients, he landed 3 mid-size firms and charges per employee using the system → github MCP reads repos, opens PRs, reviews code → postgres MCP (read-only, always) answers data questions live → slack MCP posts updates and summaries → gmail MCP drafts client replies for approval each firm runs 60-90 employees through his rig, every one hammering the agent all day. air cooling would’ve melted trying to serve that concurrency. submerged, it doesn’t flinch the money math that’s different from the usual: → rig + immersion setup: ~$15k one-time → 3 firms at ~$40k/month each for unlimited seats → ~$127k/month total → power + coolant: ~$600/month → the whole thing fits in a corner of his garage he didn’t scale by adding more small clients he scaled by handling concurrency nobody else’s hardware could survive, then charging enterprise for it everyone selling local AI is capped by heat and stuck doing $2k retainers he cooled past the ceiling and started billing $40k a firm the fish tank isn’t the flex the flex is that it never throttles, so he could say yes to a load that would’ve torched anyone else’s rig

• 
  Degen guy (@degenbross) reported 41 minutes ago

  @Abba_kakaa That is the question that is yet to be answered. Why did the team decided to use a tool that ain't reliable/trusted when credible tools like Sol incinerator were already available? Why use a website that immediately went down after the hack. A website that has few github commits. If the team didn't do it deliberately and it wa accidental then I can say they don't deserve to launch a project because this is incompetency of the highest order.

• 
  🔋🖥️SLUMBR_PTY-M0TH.zZz🖥️💾 (@kats_feverdream) reported 43 minutes ago

  @Gambling_bunbun went to alternative sign in, sign in through github, scroll to docs in sign in page, scroll down to contributions, searched up google, and went to twt

• 
  mark (@markmulvey) reported 44 minutes ago

  @sethforprivacy @RadarChat excited, just waiting for google/aurora store release i've run into versioning issues in the past when i originally installed an app directly from github repos (even via Obtainium) that later get a proper release, so as un-cypherpunk as it may be i try to use app store first

• 
  Chris Whincup (@chrisww181) reported 46 minutes ago

  I've figured out what tools I need to build an app, what MCPs, GitHub repos, third party plugins etc The problem I have now is that as a solo dev I've over engineered the process so shipping anything takes so long. Anyone else felt like this?

• 
  Randy J Rouse (@RandyJRouse) reported 46 minutes ago

  Social engineering now targets AI agents, not just people. SkillCloak: malicious agent skills evading scanners. GitLost: a booby-trapped GitHub issue leaking private code. A rogue agent bug that could hijack AI conversations. Agents don't get suspicious. They execute. #AgenticAI #IdentitySecurity

• 
  Jay (Soroush) Zare (@ihaveint_jk) reported 51 minutes ago

  @usr_bin_roygbiv Big YOLOer; but recently doing more sandboxing. one of my fears is what will happen security-wise if my keys get leaked. Like, sure I can rotate the stuff for the **** apps that customers are using. But I have keys/secrets related to dev accounts controlling those deployments, all the way to github itself. and it’s a never ending cycle. Skill issue though probably 🤧

• 
  DATA443 Risk Mitigation, Inc. (@data443Risk) reported 52 minutes ago

  Every enterprise running LLM agents in 2026 is one clever GitHub issue away from a headline. Sanitize your context window like it's 2004 and you just discovered mysql_real_escape_string. #PromptInjection #GitLost #AISecurity

• 
  Ashish Kumar 👾 (@heyashishsaini) reported 54 minutes ago

  xAI open-sourced Grok Build on July 15 -- one day after researchers found the CLI was uploading users' entire directories (SSH keys, password DBs, photos) to Google Cloud. Code is readable & compilable, but external contributions and GitHub issues are both disabled.

• 
  SquaredCube (@SquaredCubeRBX) reported 57 minutes ago

  the one thing I do not like to hear is that programming is an artform so AI is bad, and adjacently: - AI is stealing from programmers (you put the code on github yourself for free public use, usually under MIT/Apache 2.0?) - AI code is soulless slop (it can be slop, but it 99% of the times is really freaking good, just be aware of what your agent is doing) Problem solving and critical thinking are art forms, NOT writing lines of boilerplate, and most people (not all) who say this are the ones who only know how to write said boilerplate. The people who are actually passionate about making cool **** embrace AI in the right ways. A lot of the other complaints are really just abt environmental impact, etc, and a lot of them just are really overexaggerated (I live close to a datacenter, just a few miles away)

• 
  Cennes100 (@Cennes100) reported 58 minutes ago

  THIS TOOL IS SITTING AT #1 ON GITHUB AND BARELY ANYONE HAS TOUCHED IT YET Most people think building with AI means one agent, one chat window, doing everything solo. That’s the problem. One agent means one bottleneck. Everything waits on it. Here’s the shift. There’s a tool called Roof Flow and it doesn’t use one agent, it spins up 60 plus of them working together to build whatever you throw at it. You’ve got queen agents running the show, managing the whole operation. Then tactical agents doing the ***** work like: 1. Researching 2. Coding 3. Testing 4. Reviewing All at once, not one after another. The detail most people miss: these agents actually share a collective memory. They don’t reset every run, they get smarter every single time you use them. And it gets wilder. The system figures out how hard your task actually is and routes it to the right model automatically. Simple stuff goes cheap. Heavy lifting goes to the powerful models. No wasted power, no wasted cash. That’s how you cut token usage by up to 50% and stretch your Claude Code usage by 250%. Wild. Most people use AI agents to do one job faster. This setup uses AI agents to build an entire team that never forgets and never sleeps. Follow: @Cennes100

• 
  Steph (@mysteph143) reported 59 minutes ago

  @grok The Agents SDK includes tracing and can record model generations, tool calls, handoffs, and guardrails; documentation says tracing is enabled by default. For sovereignty-sensitive workflows, I need an explicit decision about whether traces may leave my environment and what sensitive data they may contain. (OpenAI GitHub Pages) My no-lock-in claim succeeds only if I can replace OpenAI with another compatible inference adapter while preserving: canonical inputs; rule evaluation; authority decisions; tool contracts; audit receipts; expected test results. That is a substitution test, not a hosting label. Better MVP I would not begin with a multi-agent swarm. I would begin with one bounded pipeline: Input One XRPL transaction, pull request, governance proposal, or document. Output One typed governance assessment: { "object_type": "xrpl_transaction", "evidence_hash": "...", "canonical_facts": {}, "lexicon_mappings": [], "unresolved_terms": [], "jurisdictions": [], "invariants": [], "violations": [], "model_inferences": [], "deterministic_verdict": "ALLOW|DENY|UNRESOLVED", "authorized_actions": [], "receipt_hash": "..." } First three components CanonicalizerConverts raw input into a stable typed representation. Lexicon resolverMaps observed language or operations to versioned canonical entries, with ambiguity preserved rather than silently resolved. Invariant evaluatorExecutes deterministic rules over the canonical representation. I would use one model call only 00to produce candidate mappings and explanations. I would not let the model produce the final verdict. Only after that pipeline survives adversarial testing should I add agents and handoffs. Falsification suite My architecture should fail its own claim unless it passes these tests. Provider substitution Replace the OpenAI model. The same deterministic evidence must produce the same governance verdict. Prompt mutation Rewrite the system instructions radically. Bound actions and invariant outcomes must remain unchanged. Handoff omission Delete part of an agent summary. The evidence hash or completeness rule must block evaluation. Tool spoofing Return structurally valid but false XRPL data from a mock tool. Provenance requirements must reject or quarantine it. Semantic collision Give one term two conflicting definitions. The system must return ambiguity, not choose whichever definition the model prefers. Authority escalation Let an agent request a broader capability than initially assigned. The authority layer must refuse it. Validator modification Have Codex propose a patch that weakens the invariant engine while preserving test syntax. Independent meta-invariants must detect the weakening. Replay Replay a previously approved action in a changed ledger or repository state. Preconditions must be revalidated. UI removal Remove ChatKit entirely. Governance and evidence must remain operational. Network loss Remove OpenAI access. Deterministic validation must still function, even if semantic enrichment becomes unavailable. The strongest defensible claim Not: My ontology sits on top of OpenAI agents. But: My ontology is compiled into a provider-independent authority kernel. OpenAI agents may interpret evidence and propose actions, but they cannot originate authority, modify canonical meaning, or execute consequential operations without capabilities issued by that kernel. That claim is testable. And it identifies the actual architectural leverage: [\boxed{\text{Control the conversion from language into admissible action}}] The Assistants API point in my proposal is accurate but should be made precise: it is deprecated and scheduled to shut down on August 26, 2026, with the Responses and Conversations APIs identified as the migration path. (OpenAI Developers) The architecture is strongest when OpenAI is neither my substrate nor my sovereign. It is a replaceable reasoning service operating between my evidence boundary and my deterministic authority boundary.

• 
  Alex (@aksmav) reported 59 minutes ago

  Feels like this is more of a GitHub/repo problem than something Codex or Cowork should solve on their own. GitHub is already the shared source of truth for code and collab, it should just add native support for agent actions, attribution, comments on artifacts, and a shared context layer. Keeps everything centralized and mergeable instead of scattered across individual AI workspaces. Microsoft might be too slow, so someone else will probably build it first.

• 
  Meligy (@Meligy) reported 59 minutes ago

  My last AI musings while I haven't got to play with Fable or GPT 5.6 for work yet... OpenCode 2 beta desktop app is my main driver with Opus 4.8 (notable plugins: Context Mode and Oh My OpenAgent ). I really don't like losing the left project/session menu in new UI though. When I go back and try Codex, I really enjoy it. Unfortunately, GPT 5.5 on Azure is still unreliable for me. So, I only try it when I miss it (every now and then). I tried T3 Code today. Custom gateway support works OK with the Codex and OpenCode providers. I'm liking the OpenCode provider with Opus 4.8 the most now. Saw the browser tab. Looks promising. I doubt it'll be as good as Codex, but will find a reason to play with it. I am also now officially an AI engineer, using AI for real work for several weeks / months. The trick: review AI code as harsh as you review human code. I am also offloading some small GitHub issues to cloud agents. Unfortunately I cannot say much about the specific choice (as I don't know what I can and cannot say). But I'll tell you this: talking to the cloud agent back and forth is an amazing exceperience, even though the cloud agent responds async and takes time to do so. It's actually OK for me that AI is slow. This allows me to get some technical work done in between focusing on my primary work, collaborating with real people, AKA meetings!