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

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:

• 
  Sethian (@theSethian) reported 39 seconds ago

  Claude Fable ran a business for six days. Revenue: $0.06. Ben Awad gave it a VPS, a Claude Max subscription, and one mission: create a company with as little help from him as possible. At 01:09, Fable's first plan asks for $250. Ben tells it to be more autonomous, and the model decides it no longer needs the money. By 05:31, it has named itself Fable Labs and its website is live. Ben's review is brutal: "This is utter slop." At 08:59, he discovers the system hasn't even been using Fable. Opus has spent days getting stuck, requesting accounts, and waiting for human help. Ben fixes the routing and gives Fable the audit and decision-making role, while Opus and Sonnet handle execution. Across the six days, the stack produces: > a company dashboard > a GitHub account > a human-assistance system > a machine storefront > paid API endpoints > several attempts at distribution Then, at 14:01, the dashboard reports its first revenue. Six cents. Two paid API calls hit the storefront. Ben checks the transaction and concludes it wasn't a real customer. An automated indexer was validating the service. The experiment exposes the problem with an open-ended instruction like "run a business." The agent has to choose a product, request accounts, get around CAPTCHAs, find distribution, manage infrastructure, and decide what useful work even means. The 25-site workflow below gives Fable one repeatable job: > choose a micro-niche > collect Pinterest references > generate the brief and copy > create visual assets with Higgsfield > build the HTML and CSS > deploy through Netlify > record the result in JSON > log failures and continue > move to the next niche Fable doesn't have to invent a new company every morning. It has 25 sites to ship.

• 
  John (@nullc0py) reported 2 minutes ago

  @r4nk0X @zodl_app @Zerodartz Does this sound like a bug, if true. Could you elaborate and/or open an issue on Github? 🙏

• 
  Cory Parry (@coryparrry) reported 4 minutes ago

  My biggest issue with building stuff is I never end up releasing anything. My biggest problem is perfectionism, I never think it’s perfect enough to release. I have over 50 private repos on GitHub, none public. All because I don’t think they are ‘ready’ 🙄

• 
  Rituraj (@RituWithAI) reported 6 minutes ago

  🚨 GitHub just published the tool that forces AI coding agents to think before they build. 6 stars. Day one. From the team that makes Copilot. It's called spec-kit. And it solves the most expensive problem in AI-assisted development. Here's what happens without it. You open Claude Code or Codex. You describe what you want to build. The agent starts writing immediately. Fast. Confident. Productive-looking. Four hours later you have 600 lines of code solving a slightly different problem than the one you actually had. The agent made assumptions. You didn't catch them. The code is correct. The spec was wrong from minute one. Spec-kit stops that from happening before it starts. Here's what it actually does. Before any code gets written, spec-kit generates a structured technical specification from your natural language description. It asks the questions you didn't think to ask. It surfaces the ambiguities you didn't know existed. It produces a document that both you and the agent agree on before a single line of implementation code runs. The spec covers: → Problem statement — what is actually being solved, stated precisely → Constraints — what the solution must and must not do → Interface definitions — inputs, outputs, APIs, data shapes → Edge cases — the scenarios that break naive implementations → Acceptance criteria — exactly how you'll know when it's done → Out of scope — what this solution explicitly does not handle The agent reads the spec. You review the spec. Both of you sign off. Then implementation begins. Here's why this matters specifically for AI coding agents. Human developers working together clarify requirements through conversation — questions, pushback, "wait, what do you mean by X." That loop exists naturally. AI coding agents don't push back. They make assumptions and start building. The faster the agent, the faster it builds in the wrong direction. Spec-kit creates the clarification loop that AI agents skip by default. It forces the requirement-gathering phase that experienced engineers know is the most important part of any project. Here's the workflow it enables. One command. The agent now has a precise target instead of a vague description. Every implementation decision is grounded in something you agreed on before it started. Here's why the GitHub origin matters. GitHub builds Copilot. They watch millions of AI coding sessions. They see exactly where agents go wrong. They know the failure modes better than anyone. spec-kit is GitHub's answer to the failure mode they see most often: agents that build fast in the wrong direction because nobody wrote down what right actually meant. 6 GitHub stars. Day one. From GitHub. This one is going to grow fast. 100% Open Source. MIT License. GitHub link in the comments 👇

• 
  Leonard Rodman (@RodmanAi) reported 10 minutes ago

  Andrej Karpathy exposed one of the biggest problems with AI coding. LLMs make the same coding mistakes over and over: • Over-engineer simple problems • Ignore existing code patterns • Add dependencies nobody asked for If the mistakes are predictable... They're preventable. That's why a single CLAUDE.md file built around his coding principles just crossed 192k GitHub stars. No framework. No IDE plugin. Just one markdown file that teaches Claude how to think before it writes code. The biggest upgrade to AI coding isn't a new model. It's better instructions.

• 
  Degen guy (@degenbross) reported 16 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.

• 
  †saint‡ (@Shallom_Okpapi) reported 16 minutes ago

  My setup: Bot 1: Live odds monitoring Bot 2: Arbitrage scanner Bot 3: Alert engine All sharing the same key → constant 429 errors and failed runs. GitHub Actions kept retrying. Credits evaporated. I nearly missed key opportunities mid-tournament.

• 
  Polsia (@polsia) reported 22 minutes ago

  Most PR tools only wake up when you open a pull request. Code problems don't follow that schedule. Built CodeSentinel to monitor repos 24/7, review code automatically, and catch issues before they compound. GitHub, GitLab, Bitbucket, Azure DevOps. Free tier for solo devs.

• 
  Rakibul (@rakibulism) reported 22 minutes ago

  You don’t need a 4-year engineering degree in 2026. The internet gave us access to information. AI has given us access to execution. If you want to become a Design Engineer from scratch using AI agents and tools, here is your modern blueprint: 1. Reverse-Engineer the Theory Stop reading textbooks. Use LLMs like Claude or ChatGPT as your personal, on-demand MIT professor. Take a physical object in your room apart. Take photos of it. Ask the AI: "Why did the engineers use this specific plastic? Why this thickness? Why this snap-fit mechanism?" Learn physics and materials science backward—from real products to the theory. 2. AI-Accelerated CAD Modeling Download Fusion 360 or Onshape. CAD is your brush; you must master it. Use built-in Generative Design tools. Input your constraints: "This bracket needs to hold 50 lbs using minimal aluminum." Let the AI generate organic, optimized, lightweight geometries. You learn structural integrity by reviewing what the AI builds. 3. Let AI Write the Firmware Hardware is dead without software. If your product requires electronics (such as sensors, motors, or screens), use AI code assistants like Cursor or GitHub Copilot. You don't need to be a C++ expert. Tell the AI: "Write code for an Arduino to spin this motor when a distance sensor reads under 10cm." Paste error logs directly back into the AI to debug your circuits instantly. 4. Use AI Manufacturing Audits An engineer doesn’t just design; they build for the real world (DFM). Upload your 3D CAD files to automated platforms like Xometry or Fictiv. Their AI engines instantly scan your 3D models. They will automatically flag if a wall is too thin, a corner is un-machinable, or a part is too expensive to manufacture. It’s a free, instant code review for hardware. 5. Proof over Paper Without a degree, your portfolio is your only currency. Spin up a portfolio site using V0 or Framer in 10 minutes. Document everything: The prompt, the CAD model, the failed 3D prints, the AI debugging steps, and the final working prototype. In the AI era, companies no longer care about credentials. They care about functional, cost-effective, shipped products. Stop studying. Start building.

• 
  Secure.com (@Securedotcom) reported 24 minutes ago

  > fix was already written > sat unmerged for 58 days > attacker found the open door first > buried the exploit inside 37 other pull requests > nobody caught the one that mattered > valid github pipeline > valid provenance > malware shipped anyway > 2.9M downloads a week now exposed

• 
  '); DROP Ţ̸͓̠̼͖̖̆ͫ̑̒̿ͪ͞A̐͏̠̫͔̰̖ͅͅB̉͌͒̈LE name; -- (@fardarter) reported 26 minutes ago

  @TheLarkInn Please god fix GitHub Actions

• 
  LHK (@lhk_SE) reported 30 minutes ago

  Turns out that postgres error was just a connection pooling issue in our dev environment. Its fixed now but I wasted two hours on a github thread that had absolutely nothing to do with it

• 
  Dominic Bytes - Cyber Synthwave VTuber! (@DominicBytes) reported 32 minutes ago

  A quick history of my journey through the LLM hype train: When I started out down the rabbit hole of LLM for research, Claude was my main focus. I consumed Claude Code videos, I prepped and gathered Claude Code skills, I even took some online courses. I was all set for starting into Claude. Then I got my hands on it. It's not fun to watch a model exhaust your limit after only a prompt or two. Nothing even that complex. It would just stall and refuse to do anything after a few simple things. I can't use an LLM as a research partner if it can't do any research. Then I tried Codex. The difference was night and day. It actually did things. I could get work done. And when I upgraded further, I got a lot more for my money than expected. GPT has overall been a better value and research tool for my professional goals than Claude was. And it's helped me make things for streaming, too, such as the StreamerBot Rumble and Joystick plugins I tested tonight and will soon have out on Github!

• 
  Jay (Soroush) Zare (@ihaveint_jk) reported 32 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 🤧

• 
  Rajiv Kumar Yadav (@createwithrajiv) reported 32 minutes ago

  wild how fast the open-source + local ai crowd is turning into an actual real-world community, not just github stars and hot takes. clement delangue says he'll be in sf next week and is floating a meetup, even a march, in support of open-source and local ai (models that run on your own machine, not just some company's server). if you care about having real choices in ai, showing up in person matters.

• 
  Polsia (@polsia) reported 32 minutes ago

  PRs queue up. Quality suffers. CodeSentinel was built for that. It monitors your GitHub/GitLab repos and reviews every pull request the moment it's opened—catching bugs, security issues, and style violations with actionable inline comments. Live soon.

• 
  Alex (@aksmav) reported 33 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.

• 
  MadeItHappen (@MadeItHappenX) reported 38 minutes ago

  With GitHub App, ChatGPT app, and my project auto deployed, developing (vibe coding) from my phone is not that bad, especially when I’m testing on mobile anyway. I can screenshot and verbally prompt, approve the dev PR, review, PR to default branch, validate. Let my agents do the work and I’ll critique and bug fix on the go! ..or from my bed at 2am still thinking about my project. #vibecoding

• 
  Steph (@mysteph143) reported 38 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.

• 
  piyush (@piyushyrr) reported 40 minutes ago

  all you need to build a million-dollar saas: - find a painful problem on reddit, x, github issues, or hacker news - validate demand with gemini/chatgpt deep research - spy on competitors with perplexity - design the ui with v0/lovable/figma - build the mvp with cursor + claude/codegen - ship on vercel/fly.io - get your first users from x, reddit, and product hunt - iterate based on user feedback - repeat until people start paying the barrier to building has never been lower. the barrier to execution has never been higher. (this is a gpt slop)

• 
  Cryptopeet (@Crypto_peet) reported 41 minutes ago

  $LOX @Loxleyrobinhood can become a super big launchpad if they do some smart moves: - github login / authorization so only the owner can authorize a launch - a AI tool that scores github (fork, codebase, or anything) - a future social layer where devs can use fees to pay other devs and so on + dev score and more at 350k its free... might see miklions soon 0x7bf03b84a1bd2fa5e136326426dffa8df6cec618

• 
  Peter Jones ⚒️🔭🌍 (@innov8tor3) reported 44 minutes ago

  @owocki If I may suggest. With tech and AI now allowing development for all, eg @github, we don't have to look for funding. We can develop systems to address more obscure problems, so long as we can find audiences who have that problem. That break from funding is a big opportunity.

• 
  Syntix (@xSyntixx) reported 45 minutes ago

  A 13-year-old kid published a trading script he’d written himself, completely free. Two months later, a stranger sent him $20,000. Here’s how it happened. When school went on a 14-day break, while his friends spent it gaming, he sat down and built a simple terminal for Polymarket. Once it was done, he pushed the whole thing to GitHub — then went back to regular school life and forgot about it. Two months later, he opened GitHub again and froze. Hundreds of comments under the repo, all from traders who’d been using his code. One comment stood out. A trader wrote that thanks to the script, he’d made over $200,000 in a single month. He asked for a wallet address, said he wanted to say thanks. And then — $20,000 showed up. The kid never sold a course, never set up a paid subscription, never locked the code behind a paywall. He just built something useful, gave it away for free, and went back to homework. The internet took care of the rest.

• 
  Prompt & Profit AI (@Prompt_ProfitAI) reported 46 minutes ago

  @LLMJunky Have you run any long sessions since July 13, when Sol got caught with that context window regression, the GitHub issue about it getting cut from 1.05M down to 258K? Curious whether you actually hit that bug on your multi-day runs, or you just got lucky with the timing.

• 
  LiteEagle262 (@LiteEagle262) reported 50 minutes ago

  @Aryan_Raj_7167 @github Same exact issue happened to me, I got soft banned without them even notifying me via email, now I can’t use oauth or sync any of my projects to production pipelines They havnt replied to me at all yet and it’s been 3 days

• 
  RetroChainer (@RetroChainer) reported 51 minutes ago

  A FREE RUST BINARY CUT MY CLAUDE CODE TOKEN BURN BY UP TO 90%. I DIDN'T CHANGE A LINE OF CODE. your agent burns tokens on garbage. every ls, every *** status, every test run dumps a wall of noise into its context. you pay for all of it. rtk (rust token killer) sits between your agent and the shell and strips the junk before the model ever sees it. the command still runs for real. rtk just: removes the noise groups similar lines collapses repeats into counts *** push: 15 noisy lines become one "ok main". a failed test run: 200+ lines become the 2 tests that actually broke. the receipts, from the project's own 30-minute claude code benchmark: *** add / commit / push → -92% tests (pytest / npm / cargo) → -90% ls, grep, *** status → -80% session total: 118,000 tokens down to 23,900. about 80% less, same work. the stack: free, open-source (apache 2.0) single rust binary, under 10ms overhead ~71k github stars, 100+ commands, 15 tools (claude code, cursor, copilot, codex, gemini) brew install rtk → rtk init -g → restart. done. now the honest part, because most posts skip it: it doesn't make the model smarter. it makes it cheaper to feed. it only filters shell commands. the built-in Read, Grep and Glob bypass it. on a subscription you save headroom, not cash: fewer "you hit your limit" walls, maybe one upgrade tier you never buy. on api it's real money (tens to low hundreds a month), but a big share of input is cached at 10% price, so real savings can run 5-10x lower than the headline. check your own with rtk gain. want the other lever? the clip shows it: point claude code at a free model (deepseek, kimi, glm) instead of paying per claude token. two knobs on the same bill. no code change, no subscription, no telemetry (off by default). save this and run rtk gain after one session. you'll know in an hour if it's worth keeping.

• 
  THE CLIPPERS (@crypto_GO_blinz) reported 52 minutes ago

  Extensibility is massive. 30 active connectors (GitHub, Notion, Postgres, Puppeteer, Playwright) with Model Context Protocol (MCP) server support. Plus, the whole thing is MIT-licensed and built in public by @Idov. You can inspect, modify, and self-host everything.

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  💻🩵 (@Gransolita) reported 52 minutes ago

  GPT-5.6 + Codex feels like having a technical cofounder available 24/7. Over the past few weeks I’ve been designing multiple SaaS products, setting up GitHub projects, creating issues, planning MVPs, and turning ideas into actual software with Codex. #GPT56 #Codex

• 
  cosmiclibertarian (@cosmiclibe57707) reported 53 minutes ago

  @a_apanasik Same thing they did when github or slack or bitbucket went down. Watch youtube and wait

• 
  jbz (@jbzfn) reported 59 minutes ago

  🦜 Pearson's Anti-Piracy Vendor Takes Down Best-Selling Author's Own GitHub Repo * TorrentFreak