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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.
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Most Reported Problems
The following are the most recent problems reported by GitHub users through our website.
- Website Down (71%)
- Sign in (16%)
- Errors (13%)
Live Outage Map
The most recent GitHub outage reports came from the following cities:
| City | Problem Type | Report Time |
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Website Down | 12 days ago |
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Errors | 16 days ago |
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Sign in | 16 days ago |
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Website Down | 16 days ago |
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Website Down | 20 days ago |
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Website Down | 20 days ago |
Community Discussion
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GitHub Issues Reports
Latest outage, problems and issue reports in social media:
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Iman (@RealKingiman) reported@ClaudeDevs Fix the auth bug with GitHub where I have it keep disconnecting and reconnecting GitHub every time
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bek※ (@ebubekirttr) reported@Themadhushaw01 @0interestrates Yeah, but the thing is, I am not working on github and I don’t want to use it so any other repository support would be better like gitlab
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𝐇𝐚𝐦𝐳𝐚 | Network Engineer (Aspiring) (@Hamzaonchain) reportedHere's a summary of what happened in case you didn't hear about it... A cyber extortion group called FulcrumSec (active since late 2025) hit Novo Nordisk the company behind blockbuster drugs like Ozempic and Wegovy pretty hard. They snuck in back in March 2026 through a compromised GitHub access token, roamed around for over two months, and walked out with roughly 1.3 TB of data across 700,000+ files. Now they've started leaking a 264 GB sample publicly. Inside? Source code, proprietary formulas for pipeline drugs like Amycretin (their next big obesity hope), clinical trial records, employee and patient data, manufacturing details, and even private internal AI models for drug discovery. The hackers straight-up roasted Novo's security, pointing out laughably weak hardcoded passwords like "novo123" and "p_assw0rd" in critical systems. After Novo reportedly turned down a $25 million ransom, the group decided to start dumping samples and shopping the rest around privately. Novo confirmed a limited breach in early June involving some pseudonymized patient data from trials. They say there was no major operational disruption and that they're working with experts but this feels like a massive wake-up call for the entire pharma industry on basic security hygiene. Crazy how a simple token slip-up can expose billions in IP and sensitive health data. What's your take — do you think this will finally force better security practices, or is it just another headline that'll be forgotten in a few weeks?
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Digita (@digitaworld1) reportedhow well a model can fix real bugs in real open-source codebases. It is harder to game than older benchmarks because it uses actual GitHub issues, not synthetic problems. M3 scored 59.0% on SWE-Bench Pro, edging out GPT-5.5 at 58.6% and Google Gemini 3.1 Pro, while sitting just
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Dev Ben (@CodeNomadly) reportedEver spent more time finding information about your project than talking about the project itself? Code on GitHub. Screenshots in your gallery. Notes in random docs. I’ve run into this problem so many times that I decided to build a solution for it. Building DevPort in public. Day 2. Have you experienced this too?
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Gabriel Denys (@gabedenys) reported@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.
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nasuy (@n_asuy) reportedi 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.
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Ant A. 🇺🇸 (@AntDX316) reported@thsottiaux When I need to fix up a GitHub Repo through the Smartphone, I prefer Claude Code though because it doesn’t need a device to run the repo, but if it needs to run a repo on a device due to the limitations through the Smartphone, I use Codex Mobile or OpenClaw with GPT-5.5 through Telegram.
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Dave Oak (@StackCurious) reportedthe pattern i see: maintainers burn out because they treat open source like a business that failed to monetize, instead of treating it like a library. once you're answering github issues like customer support, you've already lost. the fix isn't sustainability models—it's saying no earlier. #solodev #shipping
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Threadripper (@threadripper845) reportedNobody: 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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Coobyk (@Coobyk_) reportedSomeone 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
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Adithya S K (@adithya_s_k) reportedbuilt 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 👇
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Build Fast with AI (@BuildFastWithAI) reportedThe 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?
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Jarrad Grigg (@jarradgrigg) reportedYou 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.
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Tymofii Antonenko (@tymofii) reported@prinseccoo Are you using Claude Code or an MCP server? The official GitHub MCP server works pretty smoothly, just needs a PAT in a simple config file
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YNWA🐦🔥 (@YNWAcrypto) reportedThe 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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aisama.code (@aisama_code) reportedAI 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
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Yiqing Xu (@xuyiqing) reported@Faylosophe Certianly. Could you file an issue on the Github page?
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Ucupaint 🔶 (@ucupaint) reported@iye_jr It works fine here. Check if the paint mask is turned on or not. If you still have a problem, please file a github issue with a sample file.
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AJ ✝️ 💚🧡 (@angelcreative) reported@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
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CliffDoesAI (@CliffDoesAI) reportedA 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?
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Q Hoang (@0xqwee) reportedI 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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Rafal Wachol 💙 (@RafalWachol) reported@itometeam @tsuyoshi_chujo I was playing with it and started creating issues on GitHub when I noticed something.
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David O. Ehibor 🇦🇷 (@grayontop_) reportedGitHub Copilot didn't make developers faster It made slow developers more confident about writing bad code quickly 😭
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Financial Programmer (@RBiancoUS) reportedA dose of reality for end of week. My biggest question is I can't find any reason for the $Gold panic- did they find gold is causing cancer or radioactive? Selling looks like sheer panic. Would you believe someone asks in DM, so how did *you* get so many followers. Then he lets me brew on it for a day and comes back, I was joking do you have a github, presumably to get some code. No wonder I worked alone. I'm challenged socially guess not alone. After a night of 3 scammers one from Nigeria, one Africa. I need to lock dm down or find a way to restrict
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Proof of Voice (PoV) (@Proof_Of_Voice) reported$XDB @XDBchain is a @StellarOrg-fork L1 for branded coins and Web3 payments. PoV by @0xNeodallas:“GitHub has been frozen since 2021.” ✅ Explorer, Laboratory, Atlas dev tools ✅ Gate, Bitget, KuCoin, MEXC listings 🔍 Down 99.99% from ATH 🔍 No audit or bug bounty
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Crypto Update IO 🚀 (@cryptoupdate_io) reported@CryptoPatel Hsiao-Wei’s exit follows a 30% drop in EF-funded GitHub commits YTD (per Santiment). The real shift? Funds now focus 60% on L2 R&D vs 30% in 2022. We track this daily—breaking it down in our quarterly reports. Follow for the data before the narrat...
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xjdr (@_xjdr) reported@tolly_xyz @xlr8harder Sorry about that. I'll take a look. Looking with GitHub or Gmail should work but track this down and fix it asap
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Akshay Shinde (@ConsciousRide) reported@theo This exact damaged app error has been open on their GitHub since February. OpenAI still hasn’t fixed the signing or update pipeline for the Mac build. The Codex app keeps getting new agent features while basic Mac packaging stays unreliable. Priorities are obvious.
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Red Zen Cloud LLC (@RedZenCloudLLC) reportedCursor'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.