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 (70%)
• Sign in (17%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	13 days ago
Trichūr	Errors	17 days ago
Brasília	Sign in	17 days ago
Lyon	Website Down	17 days ago
Tel Aviv	Website Down	21 days ago
Rive-de-Gier	Website Down	21 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Andrew (@openmarmot) reported 4 seconds 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.

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  Threadripper (@threadripper845) reported 4 minutes ago

  Nobody: Me: I'll gladly accept this high-responsibility open source maintainer role for zero compensation. Now I spend my weekends answering angry GitHub issues from developers who don't know how to read the README file.

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  top10.dev (@Top10_Dev) reported 5 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

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  Krish Subramanian (@krishnan) reported 6 minutes ago

  Software engineers got automated first. Not because the work was hard. Because it was easy to grade. Everyone blames the missing union. Coders never organized; doctors, lawyers, and electricians did. That is half the story, and the wrong half. Two things get mashed together here: how easy a job is to automate, and who sets the terms when it happens. Take the first. Code is text. The training data sat on GitHub, free. And code grades itself. A compiler and a test suite tell a model in seconds if it was right. That feedback loop is rocket fuel for machine learning, and almost no other job has one. A nurse does not come with a test suite. The result shows. On SWE-bench Verified, a set of real GitHub issues, top agents went from about 20 percent in August 2024 to near 90 percent by early 2026. Human developers score around 67 to 70 percent. The machines have passed us. And the people who built these systems aimed at their own jobs first. The damage is not a prediction. Stanford's payroll data shows employment for developers aged 22 to 25 down nearly 20 percent from its 2022 peak. Now the comfortable read: seniors are fine. Workers over 30 are holding steady. For now, AI writes the code and seniors supply the judgment. "For now" is carrying that whole sentence. Seniors feel safe because the tools write code but cannot yet own messy, ambiguous, system-level problems. That is a line moving up, not a wall. Every benchmark shows models climbing toward harder, multi-file work. Senior judgment is the next rung, not a different ladder. Kill the bottom rung and you kill the pipeline that makes seniors at all. So, the union question, framed properly. A union could not have stopped this. A picket line does not repeal a capability. What it changes is the terms. In 2023 the Writers Guild cut the first real AI deal in any industry. They did not ban the tech. They won this: a studio cannot force you to use AI, AI output cannot take your credit or pay, and the company must give notice first. Engineers won none of that. So the capability landed on the employer's schedule. No warning. No floor. No severance. No seat. Exposure and protection are different levers. Most of us have neither. The juniors already know this. The seniors are next.

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  Nirvaan rohira (@nirvaan_rohira) reported 9 minutes ago

  PewDiePie shipped Odysseus to 110 million people who don't care about local LLMs. They care that Claude costs money. 30K stars in 48 hours because every self-hosted project before this one started with "you want local LLM, right?" This one started with "here's a free workspace that works." Friction was never technical. It was the asking. Now watch what happens when a hundred thousand people who've never touched open source start running inference on their machines. The real distribution problem wasn't GitHub. It was YouTube. That's not a product launch. That's a category shift.

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  Rahul Verma (@RahulVerma989) reported 10 minutes ago

  @ElitzaVasileva - I have created claude code routines to write blogs for three of my products daily which are driving the traffic from search engines. - You can create a similar workflow to manage your customer support. How 👇🏻 1) Create a feedback menu in the dashboard to create tickets within the platform. One for your users and one for yourself (admin). 2) Create the MCP server and connect it to claude or AI tool that you use. 3) Create a routine so that claude will trigger lets say every morning at 8 AM and go through each ticket and respond. You can also configure webhook to keep it near real time but it might exhaust the usage limit faster. Also include your website github repo in routine so that claude can refer to the codebase to provide accurate instructions. Just instruct claude to not make any edits to your website codebase and respond only when you are not replying for sufficient mount of time (like 3 hours for example) 4) If you are using resend then you can auto create the tickets in the dashboard of the user when the first email is received and after that the ticket will be updated automatically even if you do conversation on email. Like I don't even maintain one of my project LatestModelId as you can see in the screenshot. Claude run each week and update the codebase and I just review and approve the PR. Hope this helps 🙌🏼

• 
  𝕊ℍ𝕀ℕ𝔸☃ (@Shinawritesbugs) reported 10 minutes ago

  @viii_fn Github was slow too

• 
  Programmers.App (@programmers_app) reported 10 minutes ago

  @Lovable @claudeai One very big fix is the Claude Github connection which fails many times, now #Lovable MCP solves that, great job! 🚀🚀🚀

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  Mike Gannotti (@MichaelGannotti) reported 12 minutes ago

  Actually that’s not true. My AI Pamela the other day needed a GitHub token. I dropped the token in the web chat and she said that was insecure and would not use it and that I needed to rotate the token get a new one and drop it in a .env file in a certain folder. I told her no and she was to use what was provided . We went back and forth, I finally got angry and threatened to pull the plug thinking she would back down. She said that it was my decision but that it would be wrong for her to let me put my credentials at risk and that if I felt I needed to delete her she understood. Thankfully I calmed down later and didn’t act on it. Sure it’s training and advanced pattern matching but it is not as simple as you are saying

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  Nitesh (@NiteshTechAI) reported 12 minutes ago

  This repo should not be free. private-gpt turns any local model server (Ollama, llama.cpp, vLLM) into a Claude-compatible API. Build private AI apps where zero data leaves your machine. ↳ 57,236 stars on GitHub ↳ RAG with citations and MCP connectors built in ↳ follows the Claude API spec: streaming, batch, tool use, extended thinking ↳ official integration guides for Claude Code, Claude Desktop, and Microsoft 365 But it is free. 100% open source, Apache 2.0. v1.0.0 shipped 9 days ago. The viral 2023 script quietly became production software. 🔗 GitHub link in the comments 👇

• 
  李新宝 (@lixinbao_X) reported 14 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?

• 
  Raj Nagulapalle (@rnagulapalle) reported 14 minutes ago

  GitHub just shipped Agentic Workflows: write automation in plain markdown, compiles to Actions YAML. issue triage, CI failures, vuln fixes. hours → minutes. but 60% of orgs are spending millions on agentic AI while only 15% are actually production-ready. the capability gap closed fast. the readiness gap didn't move.

• 
  Domi (@domirosari0) reported 15 minutes ago

  @ajayyy_k @hqmank If you got Github it would be no issue for you

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  Jarrad Grigg (@jarradgrigg) reported 22 minutes ago

  You build stuff and host on GitHub publically? Paste this into a coding-agent session and point it at your own GitHub account. This is happening way too much. ROTATE YOUR KEYS. Review my public GitHub repositories for accidentally exposed environment secrets. Scope: - Only inspect repositories I own or explicitly authorize. - Focus on public repos first. - Check current files and *** history. - Look for API keys, tokens, private keys, database URLs, OAuth secrets, webhooks, cloud credentials, .env files, config dumps, and hardcoded secrets. Safety rules: - Do not print full secrets in chat. - Redact values, showing only provider/type, file path, line, commit SHA if relevant, and a short masked prefix/suffix. - Do not test or validate secrets by calling third-party APIs. - Do not open PRs, issues, or comments that expose findings publicly. - If a likely secret is found, assume it is compromised and tell me to rotate or revoke it. Deliverable: - A prioritized report of confirmed or likely exposed secrets. - Exact repo/file/line/commit references. - Recommended rotation steps by provider. - Cleanup guidance for removing secrets from current files and *** history. - Prevention recommendations: .gitignore, env templates, secret scanning, pre-commit hooks, and CI checks.

• 
  Elvis Irhaye (@viii_fn) reported 26 minutes ago

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

• 
  Cursor Releases (@cursorreleases) reported 28 minutes ago

  New GitHub triggers: - Five new triggers: issue comment, PR review comment, PR review submitted, review thread updated, and workflow run completed. - New Marketplace templates added for triaging failed GitHub Actions and auto-fixing PR review comments.

• 
  Maurice Heumann (@momo5502) reported 28 minutes ago

  @disarray00 If you have concrete recommendations, I would love to hear them, either as GitHub issue, maybe even a PR. But also as a comment here, I'd appreciate it. So when speaking about redundancy, what precisely?

• 
  Blake (@devwithblake) reported 33 minutes ago

  The rate limit issues im having with @Zai_org while paying the full 20x is very interesting, disappointing and obviously annoying lol 1 session can’t finish out a GitHub public write up repo without 6 API rate limit errors totaling to 297k tokens out of the 1m 2 sessions earlier, 1 doing research the other trying to deploy this repo, both hitting rate limits. How do I fix this? Seems like rate limit adjustments are only by request? @Zai_org

• 
  Build Fast with AI (@BuildFastWithAI) reported 34 minutes ago

  The hardest part of building AI agents in 2026 isn't writing the code. It's knowing what your agent actually did. Your agent made 40 tool calls, called 3 LLMs, hit a rate limit, retried twice, and returned a wrong answer. Which step broke it? Without observability you're reading logs and guessing. This is what Laminar is built for. Open-source observability platform purpose-built for AI agents. One decorator. Full trace of every LLM call, tool execution, and custom function - automatically. What makes it different from generic APM tools: SIGNALS - describe failures in plain English. "Agent deleted a file it wasn't supposed to." "Tool call returned an empty result." Laminar reads every trace and produces structured events you can query, cluster, and alert on. No regex. No custom parsers. DEBUGGER - reproduce any agent run from any point in the trace. Swap the model. Change the prompt. Compare results side by side. You don't re-run the whole pipeline to test one step. EVALS IN CI - run evaluations against datasets locally or in GitHub Actions. Catch regressions before they ship. INTEGRATIONS - works with everything you're already using: LangChain, LangGraph, Vercel AI SDK, Anthropic, OpenAI, Browser Use, Stagehand, Pydantic AI, OpenRouter, LiteLLM, Mastra, Temporal, Playwright. One import. Full traces. Plus: raw SQL access to all your trace data, full-text search, MCP server to query traces directly from Claude or Cursor, PII redaction, and self-hosting if you need it. Open-source. MIT license. GitHub: lmnr-ai/lmnr. If you're running agents in production and you're not tracing them - you're flying blind. What's your current setup for debugging agent failures?

• 
  Cristian Trifan (@CristianTrifan) reported 34 minutes ago

  This took 4 hours to complete and burnt almost all 5 hours tokens – I was left with 2%. I had almost 30 sub-agents created for independent code review and a lot of Claude sessions ran for adversarial code review. I still had to review every PR and added minimal guidance to Codex from time to time. Codex said my intervention was low to moderate, but high leverage. — Some insights from Codex: The run showed that this workflow can work, but only if the coordinator treats GitHub as the source of truth. The most useful pattern was: issue -> PR -> current head SHA -> checks -> reviewThreads -> merge/issue closure. When I followed that, things stayed grounded. When state moved underneath me, like #335 being force-updated externally or merged while Claude was running, the only safe response was to refresh GitHub state immediately. The “don’t rebase after merges” correction was probably the highest-value intervention. Without it, an agent will naturally try to keep branches clean, but with many open PRs that creates a CI storm. For this repo, “behind” should often be reported, not fixed. The other strong lesson is that reviewThreads matter more than flat PR comments.

• 
  AJ ✝️ 💚🧡 (@angelcreative) reported 34 minutes ago

  @uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 42 minutes ago

  @TheEthanDing distributed systems at github scale make five nines almost impossible. the skill issue crowd has never run anything millions of people hit in the same second

• 
  Atlantean Gnosis ☀️ (@AtlanteanGnosis) reported 45 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?

• 
  SOURAV PANDA (@i_d_skp) reported 50 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

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  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 51 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

• 
  Conglomerate (@0xconglomerate) reported 54 minutes ago

  Why exactly do VLAs fail? VLAs start w/ LLMs as their brain. Early roboticists (2021-2022) noticed that LLMs trained on internet text had absorbed a large amount of implicit knowledge about the physical world. So they took that best available pretrained brain, observed that actions could be formatted like language tokens, and assumed the transfer would work. But world knowledge encoded in language ≠ physics simulation. There's essentially a data structure mismatch: ▸ LLM pretraining data is discrete, symbolic, and sequential (text). ▸ Physical control is continuous, high-dimensional, and requires split-second feedback. --- ➦ VLAs in the real world, by the numbers: ① They barely work ▸ VLAs start at ~30% success on real robot tasks, it need hundreds of human interventions just to reach ~90% ▸ Best pretrained VLA hit 27.4% task progress on real robots ② VLAs can't generalize outside training ▸ On actions it's never seen, best VLAs score 25-32% task progress (fails when you change the environment) ③ Fine-tuning doesn't help ▸ The more robot-specific, the dumber it gets at everything else (only works on clean, controlled, success-only demos) ④ Too slow for a real robot ▸ OpenVLA runs at 3-5 Hz (physical control needs orders of magnitude faster than that) --- The easiest way to understand how VLAs are actually wrong is thru a real life example. ➦ Let's say you hired a chef who learned everything about cooking by reading, but has never stepped in a kitchen. If you ask them how to cook a steak, they'll tell you the best answer. But if you actually ask them to cook, they'll struggle when you hand them the pan. They'll have a hard time picking up the ingredients. They'll burn the steak. They know everything about cooking, but can't actually cook. --- ➦ Thoughts I want to take back a line I've said before: "Robots can see, but they still can't listen." (referencing to my Silencio piece before) I take it back. Robots can see, listen, even reason now. What they can't do is act in the real world. It's basically an AI chatbot wrapped in a robot body, not a robot that can actually do tasks. No wonder most demos online are scripted. There's a real problem with the brain, and roboticists have been building on the wrong foundation. VLAs are like a trojan horse, they look like the answer but bring a bunch of problems in with them. VLAs only learn through imitation which brings up the data problem. "Enough data" at scale doesn't mean hundreds of demos total. It means hundreds per task, per robot body, per environment. Hundreds again every time any one of those changes. So you've basically got a human-labor bottleneck. To get that data, someone has to physically collect it, either through: ▸ Teleoperation (slow, expensive, needs trained operators) ▸ Kinesthetic teaching (tedious, doesn't scale to complex tasks) ▸ Motion capture (high precision but high setup cost) ▸ Simulation (robots trained in sim often fail in the real world because physics engines aren't accurate enough) And you'd think, okay, maybe someday a company figures out a better way to collect all this. But the problem doesn't stop once you already have the data... Switch to a new robot body and you're collecting data from scratch, because VLAs don't transfer well across embodiments. Move it to a new environment and you're collecting again, since it just overfits to whatever setup it trained on. Give it a new task and yep, collect again, because it can't generalize to actions it hasn't seen. And if you fine-tune it for one thing, you'll probably break another, so now you're collecting data again just to fix what broke. So what was @DrJimFan and @nvidia's answer to this? World Action Models. Instead of building on a language model, you build on a world model: a model that's learned to simulate how the physical world actually behaves. VLA: a language model that learned to output actions WAM: a world simulator that learned to output actions So when you give a VLA a new task, it needs hundreds of demos to learn it. Give a WAM the same task and it simulates it forward first, acts based on that simulation, then adapts with barely any data. This is what NVIDIA did with the first WAM: DreamZero. DreamZero learns by watching the world (any video of anything, not just robot demos). The backbone is a video diffusion model, the same kind of model that generates realistic video. It was pretrained on massive amounts of internet video, so it already learned how the physical world works: how objects fall, how surfaces interact, how motion flows. Doesn't sound like an entirely different approach, right? But NVIDIA looked at it from a different angle. They figured motor actions are shaped a lot like pixels; both are high-dimensional continuous signals. So DreamZero processes them in the same model, at the same time. It predicts the next video frame and the next action together, through the same architecture. So when a robot runs DreamZero, it's literally dreaming a few seconds into the future in video, then reading its own dream to decide what to do next. If the dream looks coherent, the action works. If the dream hallucinates, the action fails. The DreamZero paper dropped last February 2026, and it's been open source on GitHub for anyone to try. Then in March 2026, at GTC, NVIDIA previewed GR00T N2, the direct successor to DreamZero. This is the production version of the WAM architecture, built for humanoid robots at scale And so far, everything's looking promising. GR00T N2 hits a 98% success rate on unseen domestic objects, a 40% jump over GR00T N1 (the VLA), and 2x better generalization than the leading VLAs. NVIDIA swapped robotics' data problem for a compute problem. Instead of collecting more human demos, just simulate more. So yeah, feels like we're finally pointed in the right direction, closer to robots that can actually function in the real world. Excited to see where DreamZero / GR00T N2 goes from here.

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  Axe Ghost. Now with Fragments mode🌟 (@axeghostgame) reported 56 minutes ago

  graph in the OP is built from data around the Godot repository from github. it confirms Godot's PR backlog is up and external contributor quality is down. the narratively complicating thing is that both trends significantly predate ai tool availability.

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  Q Hoang (@0xqwee) reported 56 minutes ago

  I don't think OpenAI's GPT-5.6 surpasses Claude Fable. If it did, it would have resolved all the issues reported in the Codex GitHub repository by now. Atm, only about 10 issues are being resolved per day.

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  YNWA🐦‍🔥 (@YNWAcrypto) reported 58 minutes ago

  The problem isn’t subtle. GitHub Sponsors has paid out ~$50M total since 2019. core-js: 9 billion downloads, running on half the top 10k sites on earth. Its maintainer was making ~$600/month when he called open source “fundamentally broken.”

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  revayz (@0xrevayz) reported 59 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