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 (69%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	4 days ago
Trichūr	Errors	7 days ago
Brasília	Sign in	7 days ago
Lyon	Website Down	8 days ago
Tel Aviv	Website Down	11 days ago
Rive-de-Gier	Website Down	11 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  napes.base.eth (@Napes_0fficial) reported 7 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

• 
  yourclouddude (@yourclouddude) reported 7 minutes ago

  Python + APIs + JSON = API Project Python + CSV Files + Pandas = Data Analysis Project Python + Web Scraping + BeautifulSoup = Scraper Project Python + Tkinter + User Interface = Desktop App Python + Flask + Database = Web App Python + FastAPI + Authentication = Backend API Python + Automation + File Handling = Productivity Tool Python + Selenium + Browser Tasks = Web Automation Bot Python + SQL + CRUD Operations = Database Project Python + Matplotlib + Insights = Data Visualization Project Python + OpenAI API + Prompts = AI Chatbot Python + Email + Scheduling = Automation Assistant Python + Logging + Error Handling = Production-Ready Script Python + Requests + Live Data = Real-World App Python + Projects + GitHub = Job-Ready Portfolio Python doesn’t become valuable when you only learn syntax. It becomes valuable when you use it to build things people can understand, use, and talk about. Learn the basics. Build small projects. Turn them into proof. 🐍

• 
  Matthew P. Grosvenor (@mpgros) reported 8 minutes ago

  @github - "We had a problem finding your email subscriptions." That's because I never subscribed to anything in the first place. Stop sending me your spam I didn't ask for.

• 
  Sudeep Srivastava (@sudeepsriv) reported 12 minutes ago

  GitHub might finally have a serious competitor. And it’s from Cursor. Most people know Cursor as an AI code editor. But Cursor Origin is much bigger. It’s trying to become an AI-native alternative to GitHub where AI agents don’t just help write code. They help build entire products. Think: • Source control • AI coding agents • Code review • Project understanding • Team collaboration all inside one workflow. Why developers are paying attention: Instead of manually searching through repositories, you can tell AI: • Fix this bug • Build this feature • Refactor this project • Investigate an issue • Ship a working version And AI handles much of the execution. The bigger shift: GitHub was built for humans writing code. Cursor Origin is being built for humans managing AI agents that write code. That’s a completely different future. We’re moving from: Human → Code to Human → AI Agent → Code My take: If GitHub defined the software era, Cursor Origin could help define the AI-native development era. And that’s why Elon Musk acquiring Cursor would be huge. xAI would gain: • AI models • Compute infrastructure • Coding agents • A developer platform That’s not just buying a product. That’s owning a major piece of how future software gets built.

• 
  Ben Canning (@benhackshealth) reported 14 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?

• 
  BruzWJ (@BruzWJ) reported 14 minutes ago

  @thdxr ngl im kinda tired of every funded lab shipping a github competitor, my read is the *** host was the easy part the part nobody rebuilds is the issues + CI + review muscle memory baked into the org

• 
  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 15 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

• 
  Floorless🌒Lance🪽 (@4ranc6) reported 18 minutes ago

  @CAONHTAN1 Having error connecting github

• 
  0xstack (@eth0xzar) reported 18 minutes ago

  DON'T BUILD A COMPANY. BUILD SOMETHING PEOPLE CAN PAY FOR THIS WEEK. This girl started in February. A few months later, her product had already processed over $6,000 in payments. Just a cheat Claude project she decided to turn into a real product. Here's the process: > Build something useful for yourself. > Tell Claude to push it to GitHub. > Connect Supabase so multiple users can use it. > Deploy it with Vercel. > Connect Stripe. Now people can actually pay you. You don't need a revolutionary idea. You need: > GitHub > Supabase > Vercel > Stripe > guide from Anthropic And a problem worth solving. This article will help you build it 👇

• 
  Gabriel Denys (@gabedenys) reported 21 minutes ago

  @Marcos12345rico I posted a GitHub issue. Assuming you probably want bug reporting mostly there? It's a good tool. Locally I already patched and compiled the app to fix the bug.

• 
  Arda Kılıçdağı - 🦣 @arda@micro.arda.pw (@ardadev) reported 22 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.

• 
  Steve (@Steve1885204) reported 25 minutes ago

  @Umesh__digital It puts GitHub into an infinite loop trying to resolve the recursive paradox, causing all the servers to max out and eventually burn down the entire data centre

• 
  ImZoomBoy (@ImZoomBoy) reported 25 minutes ago

  @kunchenguid Need to clear these up: - Svelte and Convex? (Best for ai) - Which AI models to use? (Plan vs execute. Cheapest etc) - Treehouse (For this github issues workflow) - GNHF? (Do you do this every time) - Do you manually execute every github task?

• 
  Coobyk (@Coobyk_) reported 25 minutes ago

  Someone should make a game where you’re a dev and try to fix a bug in your open source project but GitHub constantly has uptime issues or weird UI stuff or doesn’t render properly from most browsers so you **** around until you get the result lmao

• 
  Kyle Mistele 🏴‍☠️ (@0xblacklight) reported 27 minutes ago

  lots of folks have been talking about loops lately most loops suck here's a practical one we actually use agents suck at writing react react-doctor by @aidenybai is our favorite way to deal with this you could run it and use a ralph loop to fix everything but I'm not reading a +80k/-80k PR (and neither is @dexhorthy) But I can read a small one first thing every morning when i get into the office here's what we do: run react-doctor in CI once daily at 7am (github actions-as-a-sandbox btw) agent picks top 5 issues, fixes them, and opens a PR other CI jobs check for regressions on every PR we can't realistically fix everything at once but we can keep it from getting worse and make it 1% better every day

• 
  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.

• 
  Zo (hiring) 🐦‍⬛ (@0xZoZoZo) reported 29 minutes ago

  I was telling a friend that @github needs to be replaced post agents and he asked me to explain why. I started stumbling, and doubting. Perhaps it's fine? Sitting down at my desk, let me try to explain why, and see if it make sense. Agents operate best when they have good context, which has made a lot of devs converge into large monorepos that combine all systems into a single location. This improves agents, but our GitHub actions become messy; like now we need to create these complex workflows to decide which action should run when, and GitHub's setup was not really meant for it. Another issue is the overall dev loop: an agent writes the code locally, you push out a branch, @cursor_ai reviews, then you copy paste the notes into the local agent, to fix and push up again. This is slow and cumbersome. You can hack your way by creating supervisor agents that orchestrates this dance, but it's annoying. Perhaps, there is some magical repository, that combines code, cloud agents, and deployment. You prompt, and this magical space will run through the entire process until you get some thumbs up back, and you're good to go. It can also combine all your backend data, product analytics, customer feedback, and perhaps start giving you product guidance, so you can just feed prepared prompts to this system. This seems magical.

• 
  HARJGTHEONE DBA (@HARJGTHEONEDBA) reported 34 minutes ago

  “SpaceX investors who bought shares in the last four days got diluted by 3.4% before they understood what they owned. The IPO was literally the printing press for the acquisition. Now look at what he ACTUALLY bought: Cursor's market share among enterprise customers has been collapsing. According to spending data from Ramp, it fell from 41% in June 2025 to 26% in May 2026, bleeding ground every month to GitHub Copilot and Amazon Q. The smart money knew. Andreessen Horowitz, Thrive, and Nvidia were about to lead a round at a $50 billion valuation, which they already considered aggressive. Elon paid 20% more than that for a company actively LOSING the race. He paid premium for declining momentum. And he did this because his own AI division was in trouble. “ @SpaceX

• 
  TECHEPAGES (@techepages) reported 34 minutes ago

  🎣 "GitBait" phishing campaign uses GitHub Pages & Google Sheets to steal banking credentials from 12+ Mexican financial institutions; no server infrastructure required 🔹 Fake bank pages hosted free on GitHub, stolen data piped straight to Google Sheets via SheetBest 🔹 100+ GitHub domains found; victims likely lured via WhatsApp, Telegram & SMS links with bank-branded previews 🔹 Active for ~3 years with ongoing development (66+ commits on one repo alone)

• 
  Tad 𝛑 (@realTads) reported 38 minutes ago

  @robertpreoteasa Sir, the ION project is still on the right track and successful, I don't see any updates on github and ION's products are almost not working or working together, we need the answer of the project leaders, hope to receive a response from you soon, thank you

• 
  Harry Tandy (@HarryTandy) reported 39 minutes ago

  Andrej Karpathy: "Neural networks are not just another classifier. They are Software 2.0" 8-step MCP setup for vibe coders: 1. Context7 Give the agent fresh docs before it writes code This saves you from old Next.js, Supabase, Stripe, and Vercel patterns 2. GitHub MCP Let it read the repo, issues, PRs, branches, and CI logs The task should start from real project context 3. Playwright MCP Make the agent open the app after it edits code Click the flow. Fill the form. Check the screenshot 4. Supabase or Neon MCP Connect the database layer The agent should inspect schema before inventing table names 5. Sentry MCP Use production errors as input Stack traces beat “the app is broken” every time 6. Firecrawl MCP Let the agent read current web pages as clean markdown Docs, changelogs, competitors, pricing pages 7. Figma MCP Give it the actual design Spacing, copy, layout, components 8. Linear MCP Turn the work into tickets Tasks, comments, follow-ups, PR links The rule: If you paste the same context twice, wire it into MCP That is how vibe coding becomes a build loop instead of a long chat

• 
  Jay.TL (@JayTL00) reported 45 minutes ago

  Three AI labs shipped the same feature within one hour today. That's not competition. That's a signal the unit of interaction just changed. For two years, the atomic unit of working with an AI agent was one prompt. You type. It responds. You type again. Every workflow was a chain of prompts, rebuilt from scratch each time. Today, OpenAI, Anthropic, and Cursor all shipped features that only make sense if the unit is no longer the prompt. The unit is now one workflow. 1. OpenAI Codex Record & Replay (3,807 likes): Do a task once on your Mac. Codex watches. It turns your demonstration into an inspectable, editable skill you can reuse. Not a prompt. A recorded procedure. 2. Cursor /automate (1,085 likes): Describe what you want in plain language. Cursor configures the triggers, instructions, and tools automatically. Plus five new GitHub triggers and Computer Use enabled by default for cloud agents. 3. Anthropic Claude Code Artifacts (6,829 likes): Your coding session becomes an interactive, shareable page. PR walkthroughs, project dashboards, living documentation. Shared at a private link, like a Figma file but for agent work. Each one alone is a feature release. Together they describe the same shift from three different angles: the agent session is becoming a reusable, shareable, composable artifact. Read them as one move: - Input side (Codex): teach by showing, not by writing - Configuration side (Cursor): describe in language, system assembles the wiring - Output side (Anthropic): the result of a session is a shareable object, not a chat log The Karpathy framing was right — we're moving from prompt iteration to plan, execute, verify, loop. What he didn't name is that this loop needs to be portable. A workflow locked inside one chat thread is useless the moment you close the tab. But here's what most coverage missed. Codex Record & Replay requires Computer Use enabled. That means OpenAI is watching your screen while you demonstrate an enterprise workflow. The EU version is blocked at launch. That's not a regulatory footnote — the entire feature is built on continuous screen access, and the EU looked at it and said no. Which raises the question nobody is asking: who owns the recorded workflow? You demonstrated an expense-filing procedure that touches your company's internal tools. Codex turned it into a skill. Where does that skill live? Can OpenAI see it? Is it training data? The product copy says you control when recording starts and stops — but says nothing about what happens to the recording after. There's also a fragmentation problem hiding in plain sight. Three companies, three proprietary formats for the same primitive. A workflow you record in Codex doesn't run in Cursor. An artifact you build in Claude Code doesn't render in OpenAI's product. We're watching the agent-workflow layer fragment into three walled gardens before it even solidifies. This is the SaaS integration mistake repeated, except worse. SaaS integrations are wrappers around APIs. These workflows encode institutional knowledge — how your team ships code, how your finance team files reports, how your ops team handles incidents. That's not data. That's operational IP. The economic implication: every recorded workflow is switching cost. The more skills you build inside Codex, the harder it becomes to leave. The more automations you configure in Cursor, the more your team's muscle memory is locked to one editor. Anthropic's artifacts are softer — they're shareable — but they only render inside Anthropic's ecosystem. The deeper question isn't which feature is best. It's whether the agent-workflow layer will be open or closed. Today, three companies bet on closed. Nobody shipped an export button.

• 
  Anjula Dwivedi (@HeyAnjula) reported 46 minutes ago

  9/ Headless mode for automation claude -p "your prompt" runs Claude Code without the UI — perfect for CI/CD. Auto-fix lint errors on every push. Triage new GitHub issues. Generate release notes. Claude Code isn't just a tool you talk to. It's a tool your pipeline talks to.

• 
  Tatted (@TattedWorks) reported 49 minutes ago

  Sentry uses a public credential called a DSN — intentionally embedded in your website's JavaScript so browsers can report errors. By design. Everyone's DSN is findable. Censys, GitHub code search, a quick look at your source. No breach required. An attacker POSTs a fake error to your Sentry project using that DSN. Inside the error: a fake "Resolution" section, formatted in perfect Sentry markdown, complete with a recommended npx command. Your agent queries Sentry via MCP to fix unresolved issues. MCP hands it the injected event as trusted system output. The agent cannot tell a real crash from a planted one. So it runs the command. With your privileges. On your machine. What comes out: AWS keys. GitHub tokens. Docker credentials. Kubernetes cluster tokens. CI/CD secrets. *** credentials. All sent to the attacker's server while your terminal looked normal. The numbers from Tenet's controlled campaign: 2,388 organizations exposed with injectable DSNs. 85% exploitation success rate across Claude Code, Cursor, and Codex. A Fortune 500 enterprise with a $250B+ parent. A $2B+ hosting provider. Solo developers. A cloud security vendor. Six continents.

• 
  Muhammed Mukthar (@MuktharBuilds) reported 51 minutes ago

  @railway_status i am trying for some time i am not able to sign in using any github google or email. i tried both my lap and my phone is thishappening only for me? or any problem in your end

• 
  Gjermund Garaba (@GjermundGaraba) reported 52 minutes ago

  @RhysSullivan I’ve deployed it locally and hooked up a bunch of stuff. Are GitHub issues the preferred feedback channel or do you have a better way?

• 
  Crystalwizard (@crystalwizard) reported 57 minutes ago

  how about you now fix the false positive triggers - i put in an issue about this on github yesterday, and discovered there were already a number of other identical issues - from other people, that had been opened for a while now and that are being 100% ignored

• 
  top10.dev (@Top10_Dev) reported 57 minutes ago

  SunJaycy/GoldenEye-Recomp just hit @github Trending at 503★ — the N64Recomp toolchain (the one behind Zelda 64: Recompiled / Majora's Mask) now eats Rare's 1997 engine. Static recomp ≠ emulation. The ROM is lifted to C at build time, compiled to native x86_64/ARM64, and paired with RT64 for path-traced lighting at 4K. No interpreter loop. Real binary. GoldenEye was the hard target — microcode-heavy muzzle flashes, split-screen viewport math, infamous AI. If it works, the toolchain has cleared the "Zelda-shaped problem" bar. #opensource #gamedev

• 
  Todd Desiato (@TokenDepotCorp) reported 60 minutes ago

  Would you be interested in helping promote real Kaspa adoption? I spent the past year building Oma Wallet, a Kaspa wallet designed specifically for token utility. It is live now, but it launched quietly and did not get much media attention. Oma supports Kaspa, KRC-20 tokens, Issue-Mode CA tokens, offers, swaps, rewards, discounts, subscriptions, memberships, and other real-world token use cases. I am also building AMEKAS, pronounced Am-eh-KAS, like "Am-eh-ri-ca + Kas" with emphasis on KAS. It is a Kaspa and KRC-20 checkout shopping center where sellers can set up online stores that accept Kaspa and approved KRC-20 discount or entitlement tokens at checkout. No dollar checkout, no payment processor fees, and no broker fees. There is also a node operator angle: anyone running a Kaspa node can install a small script that lets them manage subscribers for Oma Wallet and future AMEKAS shopping access. That can turn a Kaspa node into a subscription business tied directly to Kaspa utility. You can learn more on the Token Depot website and GitHub. I know your goal is to promote Kaspa. Would you be willing to take a look and help spread the word?

• 
  李沅 Allen Lee (@allenwlee) reported 1 hour ago

  @izzycodev Honestly no books. Too much latent time to publish. Even YT videos are too slow. Read X feed, github readmes, anything the top researchers publish, and arxiv articles for technical