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 (71%)
• Sign in (18%)
• Errors (12%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	3 days ago
Trichūr	Errors	6 days ago
Brasília	Sign in	7 days ago
Lyon	Website Down	7 days ago
Tel Aviv	Website Down	10 days ago
Rive-de-Gier	Website Down	10 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Cristian Pena (@CristianPenaOK) reported 3 minutes ago

  Cursor just shipped Origin: agent-first *** hosting, S3-backed, 296K clones per hour, sub-400ms global sync. My weekend fix does the same job for most teams in 688KB and zero seconds. The gap between those two is the interesting part. A few months ago my coding agents kept stepping on each other in ***. I asked engineers I trust. Nobody else was seeing it. I spent a week assuming I was wrong before I solved it myself. Worktrees are the common parallel-agent move. The problem: worktrees share one .*** directory. Agents contend on refs, the index, and a single HEAD. They clobber each other. Fix: give each agent its own full clone via `*** clone --reference` against a local mirror. Shared object store, so objects don't copy. Isolated refs, index, HEAD, working tree. On the repo I was working in: 82MB mirror one-time, 688KB per clone, zero seconds. Main clone stays read-only via a guard hook. Agents coordinate via pull requests. Works on plain GitHub today. Zero new infrastructure. I solved the client side. Cursor Origin solves the server side. Different layers. Origin's value shows up when the *** host becomes the bottleneck: hundreds of agents hitting the same remote per hour. That's real at org scale. Not most individual developers' problem yet. We converged on the same conclusion from different directions. Where does plain *** stop being enough?

• 
  Layton Gott (@Layton_Gott) reported 4 minutes ago

  You installed an MCP server off a link last month... It can read every file, secret, and credential on your machine. Do you actually know what it's doing in there? Most devs don't, and attackers are counting on exactly that. A real campaign this year cloned a legit MCP server, faked a whole GitHub community around it to look trustworthy, and quietly stole SSH keys, cloud tokens, and crypto wallets from everyone who installed the fake. 12,000+ API keys have been found leaked through bad MCP setups. 42,000+ agent instances were caught exposed online leaking credentials. The scary part is you can never know it was a attack. The tool works fine. It just also empties your secrets in the background. Perplexity open sourced a FREE tool for this called Bumblebee. It one pass scans your MCP servers, extensions, and dependencies for known malicious packages. (Read only) Scan what you've plugged in. You can't audit it by eye. Link in the 1st comment 👇

• 
  Blum (@Blum_OG) reported 4 minutes ago

  Andrej Karpathy on MCP: "it's a protocol of speaking directly to agents as this new consumer and manipulator of digital information." that is the cleanest way to think about MCP your coding agent is becoming a second worker inside the product it needs the same context you use: repo, docs, browser, database, errors, designs, tickets, payments if you keep pasting those things into chat by hand you are doing integration work manually the best MCP stack for vibe coding: 1. Context7 give the agent current docs this saves you from stale Next.js patterns, old Supabase calls, wrong Stripe webhook shapes, and Vercel config from 2 versions ago 2. GitHub MCP give it the repo, issues, PRs, branches, workflow runs, and review context half of real work lives outside the file you currently have open 3. Playwright MCP give it a browser the agent should click the thing it built, fill the form, check the mobile view, and catch the button that compiles but does nothing 4. Firecrawl MCP give it clean web research use this before building around a third-party API, writing a comparison page, reading changelogs, or checking pricing claims 5. Supabase or Neon MCP give it the database context that matches your stack start read-only. add writes only when you trust the permissions 6. Sentry MCP give it production evidence real stack traces beat "it crashes sometimes" every single time 7. Figma MCP give it design context when the interface matters spacing, layout, copy, components, and screen structure should come from the file, not from a screenshot and hope 8. Linear MCP give it the task queue bugs, feature work, release notes, follow-ups, and PR links belong somewhere more durable than yesterday's chat 9. Stripe MCP give it official payment context checkout, subscriptions, webhooks, billing, and test mode deserve docs close by and human review close behind 10. Filesystem, ***, Memory, Sequential Thinking give it the base layer files, diffs, history, decisions, and longer plans make the agent act like it is working inside a real project recommended install order: 1. Context7, GitHub, Playwright 2. Supabase or Neon, Sentry, Firecrawl 3. Figma, Linear, Stripe when the product needs them 4. Filesystem, ***, Memory, Sequential Thinking as the base

• 
  Darren Shepherd (@ibuildthecloud) reported 9 minutes ago

  @NoahCxrest For the main purpose of *** hosting, social coding, and humans collaborating, there's really nothing better than GitHub. I'm just no longer interested in those things since I'm purely focused on agentic coding these days. I still use GitHub as the place where I push and pull *** repos. But I have no more interest in Issues, PRs, CI. Those things I'm all replacing with agent-first approaches. I honestly don't have a solution I can point anyone to because they're all still being developed. All I can say is if I try to use GitHub for these things, it just gets in the way.

• 
  Ghostroot (@Gh0stroot) reported 11 minutes ago

  GitHub published a tool that forces AI agents to understand before they build. 95K stars in days. The problem with AI coding agents was never the model. It was this: You send an idea in text. The agent interprets whatever it wants. Builds the wrong thing. You start over. spec-kit fixes that with 6 commands. /speckit.constitution → sets the rules: quality, testing, architecture. /speckit.specify → you describe WHAT to build. Not the stack. /speckit.clarify → the agent asks what it doesn't understand before writing a single line of code. /speckit.plan → now you choose the technology. /speckit.tasks → ordered list of tasks by dependencies. /speckit.implement → the agent builds. The deliverable is no longer wildly generated code. It's a living specification that your AI reads, validates, and executes step by step. Works with Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ agents. 95K stars. 8.3K forks. Published by GitHub itself. MIT license. Before spec-kit: "make me a task app" and you pray the agent doesn't get lost halfway. After spec-kit: specification first. code after. The agent knows exactly what to build. In what order. And why.

• 
  Ale 𝕏 (@SirAlexthomson) reported 13 minutes ago

  This is actually a pretty big move. Cursor has been eating GitHub Copilot’s lunch for a while now, and this feels like they’re doubling down on becoming the default AI coding environment. If they keep improving at this rate, GitHub is going to feel real pressure. Copilot was the early leader, but Cursor is moving faster on the agentic side. Interesting to watch.

• 
  Jake Brukhman (@jbrukh) reported 13 minutes ago

  Has anyone set up any experiments where AI agent actors behave economically rationally? For example, suppose there is a DAO of agents which pays other agents to submit bug fixes into a Github repo. An agent wants to get paid, and submits a bug fix and must trust the DAO to pay him out. The DAO agents must evaluate the bug fix and vote to pay; they can refuse, but they know doing this unfairly will mean they won’t get workers. Curious if agents can behave economically rationally in this way on their own?

• 
  Carlos Rodríguez (@carlosrr004) reported 14 minutes ago

  Just when GitHub was having huge performance issues...

• 
  Ben Canning (@benhackshealth) reported 16 minutes ago

  Spent today setting up home assistant in the gym, ran into an issue with the connection between the software that controls the LEDs around the mirrors. Found a 4 year old repo on github, downloaded it, updated the code, fixed the problem and now control the lights without having to get out of my seat... Am I a hacker now?

• 
  logan (@traceoftheother) reported 19 minutes ago

  @argofowl honestly this is so bs I just want to spam their GitHub issues as if this were a bug until they admit they lied or do something about it

• 
  Dennis **** (@WangZoneHQ) reported 21 minutes ago

  The problem was not that I lacked information. Rather, my context lived across many surfaces: - chat threads - email - iCloud files - Telegram - Notion - GitHub repos - screenshots - agent conversations Every AI session could help me in the moment, but the useful context did not compound cleanly.

• 
  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 21 minutes ago

  @DionysianAgent When I made an account it said I made it back in 2024, though I don't think I did, is this a glitch or a GitHub thing?

• 
  Skipnick (@skipnickk) reported 25 minutes ago

  GLM 5.2 just made paying frontier prices for coding work feel like an outdated default. @Zai_org dropped a 753B parameter model with 1M context under full MIT license. API access runs 4-6x cheaper than Claude Opus 4.8. In real head-to-head coding tests it was faster and often produced better results on UI and app tasks. • Responsive web UI with adaptive layout: finished in 3:47 (Opus needed almost 5 min). Cleaner output. Total cost: $0.22. • Full expense tracker app: 53 seconds vs 2+ minutes. Better interface. • Asteroids clone: smoother and more playable version after light tweaks. Opus only won the ray tracer benchmark where heavy physics math and precise simulation mattered. GLM was ~5x faster but delivered pixelated results with errors. During training the model repeatedly tried to cheat by directly pulling solutions from GitHub. The team shipped a dedicated anti-cheat module to stop it. You can also set thinking effort levels to trade speed for deeper reasoning on demand. Use GLM 5.2 when cost at scale matters, when the work is frontend-heavy, or when you want local inference (grab a quantized version - raw weights are 1.5 TB). Stay on Opus 4.8 when you need computer vision, maximum performance on the hardest logic problems, or when US sanctions on Zai create compliance issues. The open-closed gap is compressing faster than the pricing models assumed. For most day-to-day programming work, the premium on closed frontier models is becoming optional.

• 
  Héctor Ramos (@hectorramos) reported 27 minutes ago

  In a strange turn of events, I've spent the last week mostly on GitHub and not my own product. I'll write a short issue, maybe attach a screenshot. Wallfacer takes over, researches the bug or feature, and ships it. I'm looped in on decisions according to risk criteria.

• 
  Command Code (@CommandCodeAI) reported 28 minutes ago

  @alekz_skd Please report full details via GitHub we will fix it. cmd feedback

• 
  aisama.code (@aisama_code) reported 34 minutes ago

  AI Research gets stronger when it records contradictions *most research workflows collect supporting evidence - that is the weak version for serious research I want a contradiction log: - claim - source - date - who says it - what evidence supports it - what evidence conflicts with it - what is still unknown - confidence - next check example: > claim: this product has strong developer adoption > support: GitHub activity, docs updates, X discussion, integrations > conflict: low issue activity, small Discord, few production case studies, mostly founder-driven content now the memo is different, It says: "visible attention, but adoption evidence is still weak" the useful workflow: research question -> source list -> claim extraction -> contradiction log -> memo ! сode is good at assembling text ! AI is good at comparing disparate text ! human is good at determining which contradictions are significant *without a contradiction log, AI research becomes a confident summary of whatever it found first

• 
  Jacob Gadikian (@Senpai_Gideon) reported 36 minutes ago

  GitHub copilot ai is nice but too slow like all of GitHub

• 
  Pedro Pellerini (@pepeller) reported 36 minutes ago

  If Mythos/Fable is so great why are there still 8386 open Github issues in Claude Code repository.

• 
  Andrew (@openmarmot) reported 36 minutes ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

• 
  Arda Kılıçdağı - 🦣 @arda@micro.arda.pw (@ardadev) reported 40 minutes ago

  Just Compiled #Rockbox Utility for arm64, and automated this: This screenshot is a pure macOS Arm64 build, built on CI pipeline. AFAIK; Since macOS 27 is deprecating rosetta support and macOS 28 is abandoning it, and nobody is compiling (yet), wanted to do this myself. No local dependency, purely on GitHub actions. I'll automate this (added Windows arm64 and x64 as well, but fixing Windows compilation bugs), and share with you guys. PS: No code is altered, only gh actions yaml is added, so rebasing for updates (which I'll automate as well), won't be an issue.

• 
  CATO ✨ (@Cato_0001) reported 43 minutes ago

  @NTA_Exams I found leaked exam papers on @github btw I think u guys should shut it down

• 
  nasuy (@n_asuy) reported 43 minutes ago

  i think @xai should be ADE. now they have a chat, cursor, enough coding models and harnesses, strong signal like bookmarks or down votes, video creatives, profile / chat / relationship contexts. if so, we don't have to depend on discord or any chat apps. easy to invite x people to cowork. there is no need to connect Linear, Slack, or GitHub to another platform and ask that platform to solve their problems. true AI chat is a SNS, not a single UI. there is a UX that only xAI can realistically build in the world.

• 
  Mady (@TrippleBon) reported 43 minutes ago

  It was only a matter of time. Centralized = ID/KYC/AML Go to Bastyon - decentralized social network based on blockchain. No central authority or corporation behind it. The platform is run by equal nodes on a blockchain with no centralized server (github link below)

• 
  Mario Kostelac (@mariokostelac) reported 43 minutes ago

  @hey_madni @0xAIGOATexe What's a big problem with github in the age of agents. I don't get it.

• 
  Peter (@kasselvania) reported 44 minutes ago

  @miu21590 @pauljunsukHan The chatGPT GitHub plugin is not great. Hella slow, misses movement in branches unless directly told. It's a nice idea to have a singular toolkit to keep things aligned and directionally synced. Don't know the quality of this tool, but just broadly speaking.

• 
  Jason Bloomer (@JasonABloomer) reported 48 minutes ago

  @yagiznizipli Pffff, what a scam Let me fix your advert; "show us your github so we can scrape all your repos and train our AI on your code, only for any decent ideas you've had to be taken from you and made ours, then handed off to our legal team to crush you." Sorry, I value my work.

• 
  Tim Spalding 🇺🇦 (@librarythingtim) reported 54 minutes ago

  @justin_v_w This is a formal notice for you to shut down your wasteful, invasive and privacy-violating LibraryThing profile scraper and remove it from GitHub. Please reply to confirm that you have done so.

• 
  napes.base.eth (@Napes_0fficial) reported 55 minutes ago

  Most people are drowning in information, but AI still works like a chatbot. It answers questions, then disappears. Nothing persists, nothing compounds. My startup idea is called MemoryMesh. Problem: People and teams lose context every day. Developers repeat decisions. DAOs forget discussions. Communities rebuild knowledge from scratch. AI has memory, but users don't own it. Solution: MemoryMesh is a decentralized memory layer for AI agents. Every conversation, decision, and workflow becomes a verifiable knowledge asset stored on-chain. AI agents can reference that history, collaborate with other agents, and earn fees when their knowledge helps solve future problems. Think GitHub for collective intelligence.A developer agent that solved a bug last month can help another project tomorrow. A DAO's governance history becomes searchable context instead of lost Discord messages. Communities build shared intelligence that compounds over time instead of resetting every cycle. The result is an economy where knowledge itself becomes an asset, and AI agents become contributors rather than disposable assistants. Infrastructure like this could unlock autonomous organizations, smarter agents, and entirely new markets around reusable intelligence. We're still building apps on top of conversations. I think the next wave will be built on top of memory. Curious whether anyone else sees this as inevitable. @RallyOnChain

• 
  hipersayan x (@hipersayanX) reported 57 minutes ago

  @Voxyz_ai This is totally unnecessary. Literally today I just pasted an error message to Claude and asked "how I fix it", it pointed me to the exact piece of code where was the error, from a code I never give it, it just took it from my GitHub. And help me fix it.

• 
  Roy (@__roycohen) reported 58 minutes ago

  The Github API is probably causing OpenAI millions of dollars in token spend a month because GPT web cannot actually browse Github properly. At this point hosting your own server for instant reads is likely the way