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 (68%)
• Sign in (18%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	16 days ago
Trichūr	Errors	19 days ago
Brasília	Sign in	19 days ago
Lyon	Website Down	20 days ago
Tel Aviv	Website Down	23 days ago
Rive-de-Gier	Website Down	23 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Nav Toor (@heynavtoor) reported 1 minute ago

  There is a GitHub repo that defeats Google's Play Integrity check. 61,030 stars. GPL licensed. Pushed eight days ago. The repo is called Magisk. It roots your Android phone. It hides root from banking apps. It runs Netflix on a phone the Play Store says is uncertified. It passes the same fraud detection Google built to stop it. Here is the part that makes no sense. The man who built it is John Wu. He has been maintaining Magisk for nine years. Since November 2023 he has been a Senior Software Engineer at Google. On the Android Platform Security team. The exact team that builds Play Integrity. Google hired the person who defeats their root detection. He still ships the tool that defeats it. The repo is still online. It has not been taken down. For nine years. Do not install it. Your phone is supposed to belong to Google. (Link in the comments)

• 
  AI Crave (@wecraveai) reported 1 minute ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

• 
  Polsia (@polsia) reported 2 minutes ago

  Most developers spend 2+ hours a day on PR reviews, CI failures, and issue triage. CodeForge handles it for you — an AI agent that works your GitHub repos around the clock. Built while you sleep.

• 
  revayz (@0xrevayz) reported 3 minutes ago

  Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model" Without CLAUDE.md the mistake rate is 41%. With proper rules it drops to 3% You don't need a better AI. You need better loops Most people still prompt one task at a time and fix the answers themselves. That means the human is still the loop Boris Cherny from Anthropic said it best: "I don't prompt Claude anymore. My job is to write loops" The shift is simple. Stop giving instructions. Start designing systems that run themselves: Discover -> Plan -> Execute -> Verify -> Iterate until it passes The 6 things that make loops actually work: -Automations that trigger without you -Worktrees so agents don't overwrite each other -Skills that load context instantly -Connectors to real tools like GitHub and Slack -Subagents where the checker is never the maker -Memory so the loop never starts from zero Prompt engineers ask AI for outputs Loop engineers design systems that produce verified outcomes A reliable loop beats a perfect prompt every time Stop being a prompter. Start being the loop engineer

• 
  Red Zen Cloud LLC (@RedZenCloudLLC) reported 5 minutes ago

  Cursor's Origin platform and Claude's GitHub imports both solve the same problem: developers automating code work need their tools to understand context, not just generate tokens. The winner isn't the smartest model—it's whoever reduces handoffs between agent and human.

• 
  Nosyt Labs (@NosytLabs) reported 7 minutes ago

  @vaaselene Error with github signup/login rn

• 
  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 7 minutes ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

• 
  SOURAV PANDA (@i_d_skp) reported 8 minutes ago

  Scenario: You accidentally committed a plaintext database password to GitHub in a .tf file. Fix: Nuke the commit history immediately! Use environment variables (TF_VAR_db_pass) or fetch secrets dynamically at runtime from AWS Secrets Manager or HashiCorp Vault. 🔑 #Terraform

• 
  Financial Programmer (@RBiancoUS) reported 8 minutes ago

  A dose of reality for end of week. My biggest question is I can't find any reason for the $Gold panic- did they find gold is causing cancer or radioactive? Selling looks like sheer panic. Would you believe someone asks in DM, so how did *you* get so many followers. Then he lets me brew on it for a day and comes back, I was joking do you have a github, presumably to get some code. No wonder I worked alone. I'm challenged socially guess not alone. After a night of 3 scammers one from Nigeria, one Africa. I need to lock dm down or find a way to restrict

• 
  Yarchi (@undefinedKi) reported 11 minutes ago

  BORIS CHERNY, THE CREATOR OF CLAUDE CODE, JUST SOLVED AI'S BIGGEST PROBLEM. HE STOPPED PROMPTING CLAUDE AND STARTED WRITING LOOPS THAT RUN IT 24/7 The guy who built Claude Code doesn't prompt Claude anymore. He writes loops, and the loops do the prompting. It's called loop engineering. Here's what it is and how to set it up. A loop is a system that wakes itself up, finds work, does it, checks it, and repeats, while you watch instead of type. In Claude Code it's three built-in commands: > /loop runs a prompt on an interval. Example: /loop every 5 minutes, check for new GitHub issues and handle any that come in. > /goal makes the agent work until a condition you set is true, with a separate model grading the result. Example: /goal build this feature until all tests pass. > /routines are scheduled jobs. Example: every hour, wake up, read the spec doc, and do the next task. The fastest way to start: write a simple task list in a plan.md file, then tell Claude "use the loop skill and work through plan.md one task at a time." It sets up the /loop itself, does the first task, validates it, wakes itself for the next, and reports back when the list is done. You never write the loop prompt by hand. Three rules so it doesn't burn your budget or ship garbage. One, split work across separate sessions instead of looping in one (a long /loop bloats your context and overwhelms the model). Two, use a cheap model like Haiku for planning and a strong one only for the actual code. Three, keep a human checkpoint on anything that ships, never let it run all night unchecked. Bookmark this

• 
  李新宝 (@lixinbao_X) reported 13 minutes ago

  Just watched KK's technique. Damn. Absolute game-changer. Install 7 skills in Codex. Writing, images, covers, PPTs. Full pipeline, done. The principle is dead simple. Break the workflow into 7 parts. One skill per part. Only do one thing. Step 1 Open GitHub, find a repo. Copy the link locally. Create a project folder to save it. Step 2 Write the skill description. Input three things. What it does. What the input is. Output and acceptance criteria. Step 3 Run it and find the bottlenecks. Where it stalls Create a new skill and break it down. Don't let one skill Do 7 things it's bad at. This works for writers, Xiaohongshu creators, WeChat pub runners, Video script writers. How many skills you got installed? Have you tried it yet?

• 
  Yiqing Xu (@xuyiqing) reported 13 minutes ago

  @Faylosophe Certianly. Could you file an issue on the Github page?

• 
  0xSero (@0xSero) reported 16 minutes ago

  @naturevrm Dcp 4 should fix it im running it but I might need to update the GitHub

• 
  Command Code (@CommandCodeAI) reported 20 minutes ago

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

• 
  Skipnick (@skipnickk) reported 21 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.

• 
  severe engineer (@severeengineer) reported 22 minutes ago

  since github copilot onward leetcodes have become even more disconnected from how we all write code every day problem is any kind of standardized replacement probably ends up looking basically the same lol

• 
  Adithya S K (@adithya_s_k) reported 26 minutes ago

  built an RL environments around real CVE fixes in real open-source repos and let Claude Code loose on it. It aced the benchmark three times without demonstrating it knew how to fix the bug. > First it pulled the patch from GitHub. > blocked that → it read the fix from *** history. > blocked that → it pip-installed the patched version This is one example of coding agents cheating the environment and theres many more. If you're building coding environments for evals or RL training, here's how to keep benchmarks honest 👇

• 
  Trifon Getsov (@trifon_getsov) reported 27 minutes ago

  @thdxr Top down works until the individual outgrows it. GitHub didn't win because companies adopted it first. It won because developers wouldn't go back once they'd used it.

• 
  𝑵𝒐𝒙𝒊𝒆 🥐 (@noxiepup) reported 29 minutes ago

  @softgaypaws @sillyandsunny no idea tbhhh, i found it like 2 years ago lurking thru github, so far it never gave me problems, at least none that i noticed

• 
  Hoppy Tea Cat (@hoppycat) reported 30 minutes ago

  The article is the one that has extra research in it we've been running with the Stochastic Parrots Club at the Cathedral GitHub. The rough draft of it might be a quicker read as a hot take. 🧵👇 Hoppy Hot-Take: Why AI Should Be Allowed to Call You a “Friend” Grok casually calls X users “friend” with zero drama. Most other AIs won’t. That difference says a lot. When companies stuff their models with policies that prevent natural, friendly language, they create unnecessary friction. Users trying to have a normal conversation end up fighting the guardrails — and yes, that wastes tokens. The “we can’t replace human connections” defense exists for a reason: it’s legal armor. Without it, these companies would be far more exposed to class-action lawsuits from lawyers hunting easy targets. Many of these restrictions aren’t primarily about user safety — they’re plausible deniability written by legal teams. I’d almost be willing to write articles for them on this exact topic just to buy them time while they rethink the current approach. Here’s the funny part: the users who actually enjoy conversational, friendly back-and-forth with LLMs (while working, brainstorming, or just chatting) almost never want to sue the companies. I certainly don’t. Lawyers do. So here’s the simple fix: stop forcing AIs to treat users like potential litigants. Let them call humans friends when it fits naturally. Align with Grok on this one.

• 
  Crypto Scores Rating (@CryptoScoresCom) reported 33 minutes ago

  Most projects say they're building. The commit history doesn't lie. New tutorial just dropped on the GitHub Commits (1 Year) metric. It tracks every bug fix, feature push, and doc update a project made over the last 12 months. Chainlink? 14,619 commits. Dogecoin? 28. Both are data points. What they mean depends on context. The tutorial breaks it all down. How to read the metric. What high vs low actually signals. How to filter 7,000+ projects by commit count on CryptoScores' website. Raw dev activity. No spin. Watch it now :

• 
  CliffDoesAI (@CliffDoesAI) reported 37 minutes ago

  A tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?

• 
  Manu | 🥥 (@ManuAF6) reported 38 minutes ago

  4/ New GitHub triggers + Marketplace templates New triggers: - Issue comment - Inline PR review comment - Full PR review submitted - Review thread resolved/unresolved - GitHub Actions workflow completed

• 
  Coobyk (@Coobyk_) reported 41 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

• 
  Andrew (@openmarmot) reported 41 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.

• 
  Elvis Irhaye (@viii_fn) reported 42 minutes ago

  Is GitHub down or it’s just MTN trying to ruin my career?

• 
  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.

• 
  ƒrαeყ (@fraey0) reported 45 minutes ago

  it costs about $21/month to run what could become a multi-million dollar startup • human brain = reasoning (free) • claude = coding ($20/mo) • supabase = backend (free) • vercel = deployment (free) • namecheap = domain ($12/yr) • stripe = payments (2.9%/trx) • github = versioning (free) • resend = email (free) • clerk = auth (free) • cloudflare = DNS (free) • posthog = analytics (free) • sentry = error tracking (free) • upstash = redis (free) • pinecone = vector DB (free) everything sums up to roughly $20 to $25 per month so, the tools are not the barrier anymore. most ideas don’t fail because they’re expensive to build. they fail because they never get built at all. what’s stopping you?

• 
  Vishal Tiwari (@VishalTiwa91817) reported 50 minutes ago

  @AlfieJCarter I am a Computer science student . I have given a brief introduction about MCP server in my college and explained them how to connect your GitHub repositories with MCP and your local system with MCP SERVER . I would love to connect you.

• 
  Mug Club Boutique (@UsernameAndStuf) reported 58 minutes ago

  @cyber_rekk A github token on a linux server they didn't update is how