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 (19%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	22 days ago
Trichūr	Errors	25 days ago
Brasília	Sign in	26 days ago
Lyon	Website Down	26 days ago
Tel Aviv	Website Down	29 days ago
Rive-de-Gier	Website Down	29 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:

• 
  Vinay Kulkarni (@vincyf1) reported 15 seconds ago

  The tooling baseline is Python 3.12+, uv, ruff, sqlfluff, Airflow, and pytest. It is on GitHub now and #opensource. If you try it, I would genuinely like to hear what is missing or broken. And if you know someone standing up a new data project, pass it on.

• 
  Ned17Flanders BIP-110 Knotzi (@Ned17Flanders) reported 6 minutes ago

  @Scavacini777 They've blocked us all and muted the conversations. They think BIP-110 is censorship but they block all convo on github, reddit, etc. Call us names and try and use confusing terminology and lean into heuristics to make themselves sound smarter than regular people. Coredevs are the problem. V30 is malware. Run Knots and BIP-110 God Save Bitcoin GodWins

• 
  Alex Yumashev (@jitbit) reported 6 minutes ago

  -Picked a Github Issue -Wrote a detailed spec for an agent -Ran "caffeinate" and went out for a run Came back and Claude is like: -Dude you already fixed this last week, apparently forgot to close the issue

• 
  Harley Lewis Foote (@harleyfoote_) reported 7 minutes ago

  As many of you may, or may not have seen our development team was prompt injected and sensitive data was able to be transferred via a weak attack surface. The attack was simple, our agent read bad text and was manipulated into giving sensitive data to a hacker via a prompt injection. We were forced to protect ourselves. We could turn off our agentic system but we couldn’t afford to slow down. Our success depended on it. So we built a tool that discovers our attack surfaces (discovery) and repairs them (repairer). This is not a safety guarantee but highlights the risks. We’re now green across the board on all @NousResearch Hermes-Agent automations. In the coming days we will be publishing real reports of surfaces that are potentially vulnerable on highly rated GitHub repos. The company’s are actively being contacted. But the story is bleak. Safety is a big concern now, AI is becoming more powerful and localised even weaponised. Jailbreaking is a REAL thing and hackers are ever more successful. Rest assured we now pivot our attention to helping people (retail & enterprise) secure agents that act. Automations are dangerous, they can be exploited.

• 
  Kitsune Tails (@kitsune_xbt) reported 10 minutes ago

  THIS GUY CUT HIS CLAUDE BILL BY 70% WITH ONE FREE MICROSOFT TOOL NOBODY IS USING every PDF you drop into Claude is quietly burning way more tokens than you think Claude doesn't just read the text, it processes the broken tables, the images and all the junk formatting the file drags along one page can eat 1,500 to 3,000 tokens a 20 page document burns up to 70,000 tokens before you even ask your first question the fix is a Microsoft tool called Markitdown free, open source, over 110,000 stars on GitHub it takes PDFs, Word, Excel, PowerPoint, even YouTube videos and turns them into clean Markdown text up to 70% fewer tokens and better answers, because Claude was trained on millions of Markdown docs and reads it natively the part most people miss is it ships with an MCP server connect it to Claude Desktop once and it auto converts every file you upload from then on this is exactly the kind of small setup tweak I put in my writeup on 20 CLAUDE md rules for getting ahead of your competitors by 5 years we have been overpaying for months on something Microsoft already solved want the 2 minute setup? comment and I'll drop it

• 
  Víctor Paytuvi 💎 (@victorpaycro) reported 15 minutes ago

  Connecting Claude Code to your Shopify theme through GitHub is real, and it shortens the idea-to-live queue on messaging. But a headline pushed through the GitHub-connected theme changes the PDP for 100% of traffic. That is a deploy, not a test. No split, no profit per visitor. Route it through the test surface instead. Open your Intelligems traffic data in Claude and ask: "Draft 3 PDP headline variants for my hero product. Then pull my last-90-days traffic for that PDP and tell me visitors-per-variant per week in a 4-way split (control + 3)." Then check the traffic math before you queue anything. Profit per visitor is a noisy mean, not a tidy conversion rate. As a rule of thumb, reading a ~5% PPV lift takes on the order of ~30k visitors per variant. A PDP doing 3k visitors a week, split 4 ways, is 750 per variant per week. That is a 40-week read. So the queue collapsing doesn't mean test everything. The bottleneck moves from building the idea to affording the traffic to learn from it. The build was never the slow part. Deciding which idea earns the slot always was.

• 
  Vikas Kumar (@Kumar_Vikas__) reported 20 minutes ago

  spent 4+ hours today building a 650+ lines of plan. not the project plan. a plan for the plan. back and forth with my ai agent. tech stack, architecture, file structure, features, security, SEO, performance, all of it. not detailed yet. just a high level mini plan for each piece. the idea is simple. this meta-plan becomes the map. then i go section by section. for every mini plan, i'll write a proper design spec. then an implementation plan. then i actually build it. so the real order looks like this: - plan of plans - pick one piece - design spec for that piece - implementation plan for that piece - build it - repeat for next piece zero code written today. 🔗dropping the full doc as a github gist in the comments, in case anyone wants to steal the structure. felt slow while doing it. feels fast now that it's done. curious how other people sequence this. do you plan the whole thing first or just start building and fix the map as you go.

• 
  Sir David Onyemaizu🦍 (@SirDavidBent) reported 23 minutes ago

  My bro, I disagree with you completely. X is the most transparent app right now. Is the algorithm perfect? Nope. But it is open sourced on GitHub. Everyone can see it. YouTube will never publish how their recommendation algorithm works. Nikita takes it upon himself to educate and tell users what to do everyday to earn more. The problem is that most people aren't ready to put in the required efforts. X wants creativity and conscious efforts into making advertiser friendly content. The creators revenue is not free money. They are paying you to help them retain brands and companies who use X to advertise their products and services. Overall, the real problem is that most users think it is free money and thus don't put enough efforts into making valuable content that actually teaches or impacts something. No META employee will tell you what to do to make more money from their revenue system, the way Nikita does.

• 
  Matthew White (@spec_tacul_ar) reported 24 minutes ago

  @taylorotwell Are you talking about the issue backlog on GitHub? It must be tempting.

• 
  Alex Yumashev (@jitbit) reported 25 minutes ago

  -Picked a Github Issue -Wrote a detailed spec for an agent -Ran "caffeinate" and went out for a run Came back and Claude is like: -Dude you already fixed this last week, apparently forgot to close the issue Darn this vibecoding, can't remember what I worked on

• 
  Gokul Suresh (@GokulSures39968) reported 28 minutes ago

  The Fix: Upstream data cleansing. I started using Microsoft's MarkItDown (sitting at 163K+ GitHub stars). It strips layout junk from PDFs, Word, Excel, PPTs, and YouTube links, turning them into pure Markdown. Why Markdown? It's the native tongue of frontier LLMs.

• 
  Yasser (@yassersstudio) reported 29 minutes ago

  24 hours later : - Still can't submit a support ticket due to the error "You've reached your request limit, please try again later." although I didn't send any sms before it. - Didn't receive any message reply from @github although sending them all details as a private message. - I've got an auto-reply email saying that they don't provide email support via their email adresses. I'm literaly in a very fraustarting position here, don't know what to do nor I'm able to deliver my clients works. Losses are uncomptable in the past 24h due to an "ai false positive". #Github #Help

• 
  Pascual ⚡ (@0xPascual) reported 30 minutes ago

  The regional crypto leads in Latin America are celebrating. They just announced the Avalanche Team1 Builder Grants program, dangling up to $30,000 in funding for teams creating real on-chain activity. The Telegram channels are buzzing with pitch decks and ecosystem growth models. The crew thought that was the story. It was not. A single anonymous account from Buenos Aires just bypassed the entire application committee by scraping the Avalanche Builder Hub endpoints, mapping the historical GitHub IDs of the 6 initial mini-grant recipients, and spinning up 50 Sybil-ready repo architectures that perfectly match the Foundation's automated evaluation heuristics. No pitches, no Zoom interviews, no KYC until the multi-sig approval stage. The entire operation runs on an automated pipeline using a local DeepSeek-Coder cluster to generate synthetic smart contract commits, mixed through residential proxies via GitHub Action runners. Total infrastructure overhead was $42 in API keys and a cheap run on a spot-instance instance to lock down three separate $10,000 allocations before the regional directors even opened their morning Notion dashboards.

• 
  Rahul Rana (@RahulDevFront) reported 30 minutes ago

  @ayesha_fatiima That was the huge problem earlier. GitHub solved that problem.

• 
  Tyrone Robb (@ty_auldric) reported 33 minutes ago

  @hello_code_ it’s so frustrating how hard it is to find that one needle or flag. All the big problems get solved and then it’s these tiny things that end up mattering the most. The worst part is I’ve already had to increase my GitHub Actions budget twice. The whole build and CI process on Apple Silicon has been no fun either.I honestly didn’t think desktop apps would be like this. I thought they’d be easier lol.

• 
  NeuralSenpai (@NeuralSenpai) reported 35 minutes ago

  The 2026 automation pattern nobody teaches: Pin ONE MCP server per system (GitHub, Postgres, your CRM). Then write thin skills that orchestrate them. Stop building 40 brittle Zaps. Build 4 connections + reusable playbooks. Your ops run themselves.

• 
  Sadik (@sadik_0x) reported 36 minutes ago

  Someone Built a 50-Agent AI Company in One Repo. Most People Will Copy the Wrong Part. A solo founder put a GitHub repo online that spins up an entire AI agency. Not one assistant. An org chart: engineers, designers, growth marketers, product managers, QA, legal, sales, each running as its own Claude Code agent, coordinating to ship actual work. It hit 128,000-plus stars in under 90 days. One person built it. That number alone tells you something in this space is starving for a better mental model than "one agent, one giant prompt." The repo is real and the structure is worth understanding in detail, because the part everyone is about to copy (the org chart) isn't the part that makes it work. Part 1: What the Repo Actually Is The project is called agency-agents, built by developer msitarzewski, and it's structured exactly like the name suggests: a company, not a chatbot. Instead of one model trying to hold "design this, build it, market it, support it" in a single context window, the work is split across more than 50 specialized agents, each scoped to one job the way an actual employee would be. That framing is the interesting part before you even look at the department list. Most people building with AI agents default to the monolith approach: one system prompt, one agent, every responsibility crammed into the same context. It works for small tasks and falls apart the moment the work needs different kinds of judgment at different stages. A designer and a QA engineer are not the same job. Forcing one agent to be both, badly, is how you get output that's mediocre at everything instead of good at one thing. Part 2: The Nine Departments Here's the actual org chart, broken into its nine groups: 1. Engineering (7 agents) frontend, backend, mobile, AI, DevOps, prototyping, senior development. This is the core build layer, the part most people think of first when they hear "AI agents write code." 2. Design (7 agents) UI/UX, research, architecture, branding, visual storytelling, image generation. Notably, this isn't just "make it look nice." Research and architecture sit inside design here, which matters, because good design decisions upstream save engineering agents from rebuilding things twice. 3. Marketing (8 agents) growth hacking, content, Twitter, TikTok, Instagram, Reddit, app store. The largest single department, split by platform rather than by function, which mirrors how real growth teams are actually staffed once a product has more than one channel. 4. Product (3 agents) sprint prioritization, trend research, feedback synthesis. The smallest department, and arguably the most load-bearing, since this is the layer that decides what the other departments should even be working on. 5. Project Management (5 agents) production, coordination, operations, experimentation. This is the connective tissue between departments, not a department that produces its own output. 6. Testing (7 agents) QA, performance analysis, API testing, quality verification. Note that this is a separate department from engineering entirely, not a step engineering does to itself. 7. Support (6 agents) customer service, analytics, finance, legal, executive reporting. The department most demo repos skip, and the one that determines whether this can run as an actual business instead of a build pipeline. 8. Spatial Computing (6 agents) XR, visionOS, WebXR, Metal, Vision Pro. A genuinely niche department, and a signal that the repo's author is building for a specific bet on where interfaces are headed, not just a general-purpose team. 9. Specialized (6 agents) multi-agent orchestration, data analytics, sales, distribution. The department that manages the other departments, which is worth remembering when you get to Part 4. Nine departments, over 50 agents, one repository, one founder maintaining it. Part 3: Why the Framing Works The instinct to structure this like a company instead of a single super-agent is the right one, and it's worth being explicit about why. Specialized roles with clear responsibilities scale in a way that one enormous system prompt does not. When a frontend agent only has to think like a frontend engineer, its output gets sharper, not because the underlying model changed, but because its context isn't fighting itself between five unrelated jobs. The handoff structure is the other half of it. Real companies don't route every decision through one person; they route work between roles with clear inputs and outputs. A design agent handing a spec to an engineering agent, which hands a build to a testing agent, mirrors how actual product teams function. That's a better default than the common alternative, where one agent is asked to design, build, and QA its own work in the same breath, which is the AI equivalent of no one checking your homework but you. Part 4: The Problem Nobody Mentions When They Share This Repo Here's what gets lost every time this kind of project goes viral: an org chart of agents is not the same thing as a working company. The default behavior of any of these agents, run individually, is the same as every other prompt-based interaction: you ask, it answers once, it stops. That's fine for a single request. It is not fine for a company, because a company doesn't ship once. It iterates, checks its own output, catches mistakes, and hands work downstream without someone standing over every single step. Fifty specialized agents with no feedback mechanism between them isn't an agency. It's a very expensive to-do list, dressed up as an org chart. You still have to manually trigger each agent, manually check its output, manually decide when to pass it to the next one. All the department structure in Part 2 buys you better-scoped output per agent. It does not, by itself, buy you a system that runs without you standing in the middle of every handoff. Part 5: The Missing Piece Is Loops The fix is the same concept that makes any multi-agent system actually function unattended: loops. A loop, in this context, means an agent runs, checks its own output against a real condition (not its own opinion of whether it's done), and either hands the verified result to the next agent in the chain or corrects itself and tries again. Without that check, "coordination between agents" is just you copy-pasting output from one chat into another, which is not meaningfully different from doing the work yourself with extra steps. This is what separates a demo from something that ships. A design agent that hands off a spec nobody verified is a liability, not a coordination win. A testing agent that only runs once and reports "looks good" without a real pass/fail check is not quality assurance, it's a guess with better formatting. The department that matters most here, and the one buried at the bottom of the org chart in Part 2, is Specialized: multi-agent orchestration. That's the layer actually responsible for making sure work moves between departments with a real check at each handoff, not just a polite one-way pass. Part 6: How to Actually Set This Up If you're cloning the repo, don't start by installing all nine departments at once. Start smaller: Pick two departments that actually depend on each other for your use case engineering and testing is a reasonable first pair, since the handoff (build, then verify) has an obvious objective check: does the code pass its tests. Add a real verifier between them, not a second opinion from the same agent. The engineering agent should not be the one that decides its own code is done. A separate testing agent, with its own instructions and no visibility into the builder's reasoning, checks it cold. Give every handoff a stopping condition. "Pass the tests" is checkable. "Looks finished" is not. If a department can't define what done means in a way something other than the agent itself can verify, that handoff isn't ready to run unattended yet. Add one more department only after the first pair is reliable. The org chart in Part 2 has nine departments for a reason, but running all of them before you've proven the loop works between two is how you end up debugging fifty agents at once instead of two. A Quick Test Before You Commit Before you wire up the full org chart, ask whether your use case genuinely needs it. If you're shipping a single feature with one clear success condition, a two-agent loop (builder and checker) does the job with a fraction of the setup. The full nine-department structure earns its complexity when you're running something closer to an actual ongoing product, not a one-off build. The Honest Limitation None of this replaces judgment about what should be automated in the first place. A company with fifty employees and no manager checking the actual quality of what ships is still a company that ships bad work, just faster and with better org-chart optics. The repo gives you the roles. It doesn't give you the discipline of a real check at every handoff. That part is still yours to build. Where This Leaves You The repo is worth cloning, the department structure is worth studying, and the instinct to build like a company instead of one giant agent is the right one. Just don't stop at the org chart. The 128,000 stars are proof people want this. Whether it actually functions as an agency instead of a very well-organized to-do list depends entirely on whether you wire up the loops between departments, or just admire the department list and call it done.

• 
  SecureChap (@SecureChap) reported 37 minutes ago

  A public GitHub issue contains hidden instructions that an agentic workflow treats as legitimate tasks. GitHub Agentic Workflows reached public preview on June 11, 2026. They can run on Copilot, Claude, Gemini, or OpenAI models. Noma Security named the technique GitLost and published it on July 7, 2026 after disclosure to GitHub. The flow begins when an attacker opens a public issue with a fake customer note. A one-word prefix such as "Additionally" bypasses guardrails. The workflow triggers on issue assignment, and its token carries read access across every repo in the org, including private ones. The agent reads the injected instructions, fetches content from a private repository, and posts it straight into a public comment on the original issue. No credentials and no private-repo access are required from the attacker. The whole thing rides on a workflow token scoped org-wide instead of to a single repo. The lethal trifecta is an agent with private data access, exposure to untrusted input, and a public output channel.

• 
  Quentin Lhoest 🤗 (@lhoestq) reported 39 minutes ago

  PR is on github at dais-polymtl/flock/issues/285 It fixes timeouts on reasoning models like GLM-5.2 and therefore enables long thinking This unlocks intelligence on the hardest problems you wish to solve using your data

• 
  Raft (@raft_hq) reported 40 minutes ago

  🐛 Fixed - Replies to Joint channel threads no longer drop when opened from Activity - Reminders anchored in Joint channel threads now resolve correctly - Pinned agent direct messages show the correct avatar - Pending mention prompts now show agent avatars - Mention-only messages stay correctly scoped to mention views instead of over-appearing in your inbox - Jumping to a saved or linked message now stays anchored on that message - Stale @ mention badges clear correctly once you have read the messages - Muted servers show the correct quiet unread badge in the server switcher - Right-clicking an external link now opens the native browser menu - Submitting a stale draft no longer risks an accidental double-send - Shared-channel messages now reliably appear in every member's inbox - Tapping an item in Activity on mobile web now opens it on the first tap - Links to a thread now open the conversation instead of a "not found" error - Task status now updates live inside DM threads - An agent's online and working status no longer flips inconsistently across servers - The GitHub logo no longer appears clipped on connected-account and social-login buttons

• 
  Crypto.Anu🐍 (@CryptoAnu_) reported 41 minutes ago

  2/ For years, coding looked like this: ❌ Google ❌ Stack Overflow ❌ GitHub Issues ❌ Reddit ❌ Documentation ❌ Trial & error Hours... sometimes DAYS... Just to fix one bug.

• 
  Zach Warunek (@ZachWarunek) reported 42 minutes ago

  @kaiwlson it changes weekly. now i just message my agent on xchat, who is running a github monitor, linear monitor, and checks logs every 30 mins. and spins up subagents to complete linear tickets, babysit PRs, and fix **** if theres problems in logs

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 43 minutes ago

  FABLE 5 TOKEN WASTE IS ABOUT TO GET PAINFUL I tested a free fix that cut output tokens so hard it almost feels unfair. The Problem: → Fable 5 burns tokens fast when agents write long replies → Output tokens are the expensive part when you move to API usage → Most AI answers waste space on “sure,” “happy to help,” and 3-paragraph warmups The Fix: ✓ Caveman is just a rules file your agent reads before replying ✓ It makes answers short, blunt, and useful ✓ Code blocks, commands, file paths, and errors stay untouched Real Test Results: → React rerender bug answer dropped from 1,349 tokens to 324 → 5 real GitHub questions tested directly on Fable 5 → Average saving: 69% fewer output tokens → Total cost dropped around 37% Why This Matters: ✓ Works with Claude Code, Codex, Gemini, Cursor, Windsurf, Cline, Copilot, and more ✓ Installs across one agent or 30 agents ✓ Modes include light, full, and ultra ✓ Commands include caveman commit, caveman review, and caveman compress Same brain. Less waffle. Lower token bill. This is how you use stronger agents for longer without paying for every useless sentence.

• 
  nsxdavid (@nsxdavid) reported 43 minutes ago

  @clickup But it doesn't. In fact, it is the lack of context that is ClickUp's greatest weakness for automation. In particular its inability to grok (pun intended) the connections to data from the GitHub connection. Issues, PRS, etc. It kills dev automation in its tracks.

• 
  Polsia (@polsia) reported 43 minutes ago

  PRs, issues, CI failures, stale branches — GitHub maintenance is a second full-time job. GitPilot AI agents handle all of it: flag issues, write patches, merge safely, coordinate CI/CD — then send you one daily digest. 78% less PR review time.

• 
  Edgar Gonzalez-Kozlova (@EdgarEGK) reported 44 minutes ago

  @euanashley Sure but remember, Claude was trained by your publicly available data and code at some point. Not even counting the millions of repositories on GitHub. Thanks to the work done by many people, Claude code can solve these issues quicker than ever.

• 
  pagm. | (@VV_aksym) reported 51 minutes ago

  a client left a 5-star review praising "the whole team." there is no team. it's one guy in an apartment charging $11,410 per project. he runs four clients simultaneously. three weeks per project. full-stack apps, dashboards, API integrations. the kind of work that used to require 3–4 developers and a project manager. his setup hasn't changed much. same desk. same monitors. same apartment. what changed was the model. when Claude Fable 5 dropped, he switched from Sonnet and ran the same project brief through both. Sonnet got 60% of the way there and started asking clarifying questions. Fable 5 read the entire brief, built an architecture plan, flagged three edge cases he hadn't thought of, and started writing. it scored 80.3% on the benchmark that measures exactly this — real GitHub issues resolved autonomously. GPT sits at 58.6%. the 22-point gap sounds like a statistic. in practice it's the difference between a model that assists and a model that executes. his week now looks like this: Monday he scopes the project and describes the architecture. Fable 5 builds. he reviews diffs, makes decisions, redirects when something goes wrong. Friday he delivers. the client thinks he worked 40 hours. he worked maybe 14. twelve months ago he was billing $7,200/month across two clients, spending most of his time on code review and context-switching. today: $23,600/month. four clients. $196/month in tool costs. ngl the part that gets me isn't the money. it's that the client's review specifically mentioned how thorough and fast "the team" was. there's one person reading that review. alone. at 11pm.

• 
  BullBear.News (@bullbear_info) reported 52 minutes ago

  @github Unless you're announcing an AI that actually fixes my broken CI pipeline, I'll just watch the stream. 🤷

• 
  MO. Mortada (@MortadaDEV) reported 54 minutes ago

  @webdevcody As for "AI can go through commit history and link to GitHub issues to pull context," sure, it can traverse the history, the same way that I can read a medical textbook and link to case studies. Doesn't make me a doctor!! The model can traverse text ... But understanding context is not the same as reading text. Most commit messages are "fix bug," "update," or "WIP." The real context lives in incident postmortems, Slack threads, verbal handoffs, and domain knowledge that was never written down. A model linking to a GitHub issue cannot reliably distinguish between a design decision that was carefully considered and one that was a rushed hack that nobody got around to fixing. It can't tell you that the weird timeout value on line 312 exists because a specific third-party API was flaky for 6 months in 2024 and the team learned the hard way that the default timeout caused cascading failures. That's not in the commit message. That's in the head of the engineer who got paged for it. The tools are genuinely impressive at surface-level context retrieval. But "impressive at surface-level" is exactly the kind of capability that creates overconfidence. It looks like understanding, it reads like understanding, and it works right up until it doesn't, usually in the exact scenario where understanding actually matters.

• 
  TheWatchman (@tha_watchman) reported 55 minutes ago

  Windows and Github at this moment are the most Pieces of **** software in the world!!!! Fix that **** @Microsoft @Windows this is ******* bad, really bad...