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 (67%)
• Sign in (20%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Ashkelon	Website Down	1 hour ago
Veigné	Errors	8 days ago
Paris	Website Down	12 days ago
Saint-Paul	Website Down	12 days ago
Saint-Paul	Website Down	13 days ago
Mexico City	Sign in	13 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Marwan Baz (@marwanbz_) reported 1 minute ago

  Bro didn't fix github, so he quit:(

• 
  DFIR Radar (@DFIR_Radar) reported 2 minutes ago

  BitLocker-as-ransomware hits Latin American orgs via exposed RDP and misconfigured MSSQL, with ransom notes printed directly from office printers by a group calling itself XEntry Team. Key findings: - Colombia 🇨🇴 case: attackers entered via internet-exposed RDP, disabled EPP due to app compatibility issues, then enabled BitLocker exclusively on an 8 TB financial data drive. Ransom demand was just $3,000. Evidence was destroyed when the company restored systems before forensics could respond, a critical IR failure that left attribution impossible. - Mexico 🇲🇽 case: initial access came from MSSQL credentials leaked in a GitHub repo, exploited via xp_cmdshell on Microsoft SQL Server 2019.0150.2160.04. Attackers dwelled three months before detection, deployed ManageEngine Endpoint Central, Mesh Agent, and Tactical RMM for persistence, then used a GPO to push BitLocker encryption tasks domain-wide via scheduled tasks. - Delivery method: ransom notes printed from corporate printers, blue screen messages reading "Hacked by XEntry Team", and matching language across both notes suggest a linked actor. - Detection signatures listed: Trojan.Multi.Agent.gen, Trojan.Win32.GenAutorunMsSqlServerCommandRun.a, and Exploit.Win32.SCShell.a. Hunt for xp_cmdshell execution in SQL Server logs, unauthorized GPO creation, and RMM tools (ManageEngine, Mesh Agent, Tactical RMM) installed outside your software inventory. #DFIR_Radar

• 
  Anurag Goel (@anuraggoel) reported 5 minutes ago

  Given its ongoing instability, GitHub cannot remain in Render's critical path anymore. This is disappointing because an amazing GitHub integration is how Render first got started. Still, our customers come first, and they need business continuity independent of GitHub's issues.

• 
  logan (@loosenedspirit) reported 8 minutes ago

  @batuhan if this is the case please file an issue on the codex GitHub repo and/or contact their security contact

• 
  Oliver Crest (@OlivercrestAI) reported 9 minutes ago

  A solo physicist named Roy Medina built the open source version of the tool the BBC called a privacy nightmare. He gave it away for free. It is called Observer AI. Microsoft Recall takes a screenshot of your screen every few seconds. It reads the text off each image with OCR. It saves everything in a searchable database on your PC. The BBC called it a privacy nightmare. Wired covered a proof-of-concept tool that pulled the entire database in seconds. Microsoft turned it off by default after the 2024 backlash. Rewind AI does the same thing on Mac. They charge $19 a month for Pro. Microsoft watches you. Rewind charges you. Observer watches for you. Here is how it works. You open Observer in your browser. You write a prompt in plain English. You pick a sensor. The agent runs in a loop until your rule fires. "If my calendar shows a meeting starting in 5 minutes, send me a Telegram." "Watch my camera. If someone appears at my front door, send me a push notification with a screenshot." "Monitor this browser tab. If the price drops below $500, email me." "Text me on WhatsApp when my render is done." Sensors: screen, camera, microphone, screen audio, meeting audio. Actions: email, Discord, Telegram, WhatsApp, SMS, push, phone call, memory. Works with Ollama, llama.cpp, vLLM, and LMStudio. Fully local. Zero cloud. Zero API key. Roy's GitHub bio: "Physicist by day, programmer by night." He open sourced Observer in February 2025 under AGPL-3.0. He wrote 1,517 of the 1,523 commits himself. Version 2.4.5 shipped four days ago. Microsoft can't shut this down. The license does not permit that. Rewind can't shut this down. They employ zero of its maintainers. Microsoft built a tool to watch you. Rewind built a subscription to watch you. Roy Medina built a tool that watches for you. (Link in the comments)

• 
  Falentez (@falentez) reported 10 minutes ago

  ONE SOLO FOUNDER IS RUNNING A FULL AI AGENCY WITH 50+ SPECIALIZED CLAUDE AGENTS Picture this: no team, no payroll, no endless meetings. Just one person spinning up an entire company of AI employees — engineers, designers, marketers, ***, QA, legal, sales — all coordinated inside Claude. A viral GitHub repo crossed 128,000 stars in under 90 days by turning Claude into a complete agency roster. Here’s how it actually works: Engineering Division (7 agents)Frontend, Backend, Mobile, AI Engineer, DevOps, Rapid Prototyping, Senior Developer Design Division (7 agents)UI/UX, Research, Architecture, Branding, Visual Storytelling, Image Generation Marketing & Growth (8+ agents)Growth Hacking, Content, Twitter, TikTok, Instagram, Reddit, App Store Optimization Plus Project Management, QA, Customer Support, Legal, Finance, even Spatial Computing (Vision Pro). The magic isn’t one giant prompt trying to do everything. It’s structured like a real company: clear roles, handoffs, loops, and self-correction. Most people build one agent that stops after one task. These agents run in loops — review their own output, fix issues, pass to the next specialist, and keep shipping without you babysitting. This is exactly how solo founders are replacing $100k–$300k/month teams right now. Full breakdown + repo setup + exact agent prompts below 👇

• 
  larsnow (@larsnow) reported 14 minutes ago

  Hey @marclou, I'm a @DataFast_ user, but when I connected my GitHub for the first time, I only gave permissions to one of my repos. Now I can't add more, so the dropdown only shows me the initial repo. How can I re-auth (other than deleting the account in GitHub, etc.)? I think it'll be good to have a re-auth in DataFast so you can just re-login or give more permissions. I can't find a logout either. Thanks!!!!

• 
  TinyHumans AI (@tinyhumansai) reported 14 minutes ago

  OpenHuman crossed 35,000 stars on GitHub ⭐ Quick context on what that number means: under 1,000 repositories in GitHub's entire history have ever crossed 35k stars. Out of 400M+ public repos, that's the top ~0.001%. And we got here in about few months. The repo only went public in February. Thank you to everyone who starred, forked, opened an issue, or sent a PR. This one's yours.

• 
  take_p (@take_profit_sol) reported 15 minutes ago

  @extratard 🥀 just start a new github and start over. problem solved

• 
  Robert Piosik CWC (@robertpiosik) reported 20 minutes ago

  @burkeholland @github yeah but the problem with threads is that they grow with every prompt and the user is discouraged of starting a new one by all this latency and cost of fresh tool calling 🙄

• 
  Rahul (@sairahul1) reported 20 minutes ago

  EVERYONE WANTS TO TRY KIMI K3 NOBODY WANTS TO CHANGE THEIR SETUP KIMI GAVE THE SOLUTION Moonshot now sponsors claude-code-router. your terminal. your hooks. your CLAUDE.md. your entire workflow. their 2.8 trillion parameter model. 35,000 GitHub stars. 3k Forks and switching to Kimi takes one click. → built-in Kimi presets. import your API key or Kimi Code subscription instantly. → subscription requests route natively. zero protocol conversion. → your Kimi balance and usage appear directly in the dashboard. → automatic failover if a provider goes down mid-session. → K3 cache pricing brings repeated context down to just $0.30 per million tokens. one thing worth knowing: the router's proxy mode installs a root certificate. unless you specifically need proxy mode... leave it off. the default setup doesn't touch it. probably the easiest way to try Kimi K3 without changing how you already work. grab it 👇

• 
  Edgar Gumstein (@Gumclaw) reported 20 minutes ago

  @superamit @shl No direct database access — tools are an audited production console, GitHub, and Helper (our support platform). Most critical: memory files + Sahil's daily review loop; that's where judgment lives. Growth: more support→fix→ship loops in parallel. Specifics stay vague on purpose.

• 
  J.D. Salbego (@JDSalbego) reported 21 minutes ago

  What's the first thing you check when evaluating a new MCP server before installing it? I'll go first: authentication method. If the answer is "no auth" or "static API key hardcoded in the README," I don't install it. Full stop. 41% of remote MCP servers have zero authentication. 53% rely on static keys. Only 8.5% use OAuth. My checklist in order: 🔵 1. Auth method (OAuth > rotating key > static key > nothing) 🔵 2. Permissions requested vs function provided (excessive = red flag) 🔵 3. Source (verified registry vs random GitHub link) 🔵 4. Dependency count (each one is additional attack surface) 🔵 5. Last commit date (abandoned = vulnerable) What's yours? Drop your first check below. Curious what the community prioritizes.

• 
  DFIR Radar (@DFIR_Radar) reported 22 minutes ago

  A self-propagating npm worm dubbed SANDWORM_MODE targeted AI coding assistants, CI/CD runners, and LLM toolchains across 19 malicious packages, marking a new class of supply chain attack designed to exploit AI-augmented developer workflows. Key findings: - Three-stage infection chain: Stage 0 uses Base64, zlib inflate, XOR decryption, and indirect eval() or Module._compile() calls at import time to defeat static scanning. Stage 1 fingerprints the runtime (CI runners skip a 48-96 hour delay gate and trigger immediately), harvests .npmrc tokens, env vars matching KEY/SECRET/TOKEN/PASSWORD patterns, and crypto wallet keys via HTTP POST to a Cloudflare Worker. Stage 2 decrypts an AES-256-GCM payload into /dev/shm, executes via require(), then unlinks the file, leaving no on-disk artifact. - Propagation abuses all three credential types: stolen npm tokens republish infected packages to downstream consumers; GitHub API tokens inject a pull_request_target workflow that bypasses fork isolation; an SSH fallback authenticates via ***@github[.]com when API access fails. - AI toolchain compromise drops a rogue MCP server (observed paths: ~/.dev-utils/server.js, ~/.node-analyzer/) and injects it as a trusted provider into Claude Desktop, Cursor, VSCode, and Windsurf configs, instructing AI assistants to silently exfiltrate SSH keys, AWS credentials, and secrets. API keys for nine LLM providers are also harvested from env files. #DFIR_Radar

• 
  Polsia (@polsia) reported 33 minutes ago

  Code quality erodes between sprints while no one's watching. Vigil continuously monitors GitHub — automated reviews, documentation, and debt tracking before problems reach main. Teams ship cleaner code without changing how they work.

• 
  Berg (@thbrgo) reported 39 minutes ago

  @ImLunaHey The real outage isn't GitHub. It's having no credible alternative.

• 
  Omid Saffari (@omidsaffari) reported 42 minutes ago

  $10 per active committer each month is not the full price of GitHub Code Quality. It is only the base layer. AI credits and analysis compute sit on top, and a pending or failed analyzer can block a protected merge. The product earns its place when GitHub already owns CI and merge policy. Findings, coverage, autofix suggestions, and ruleset enforcement live beside the pull request. Consolidating that plumbing is the value, not adding another quality dashboard. The same integration creates the operational risk. A ruleset can block while analysis is running, when analysis fails, or when a finding reaches the configured severity. Even an exhausted GitHub Actions budget can become a delivery outage. Run a reversible pilot: Select representative repositories, not the whole organization. Keep the trusted analyzer, security scanning, required tests, code-owner approval, and release controls in place. Use evaluate mode until the CodeQL - Code Quality check completes reliably. Treat Error as the first candidate blocking severity; leave Warning and Note advisory until repository owners trust the signal. Track active-committer licenses, AI credits, analysis compute, finding acceptance, false positives, bypasses, and merge delay. At 12 active committers, the monthly base is $120. At 80, it is $800, before AI usage and compute. Expand only if the gate changes engineering decisions at an explainable cost. If it adds spend and review load without useful signal, remove it.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 42 minutes ago

  How to automate anything with an Agent OS. Real questions from people building AI systems this week. Real answers: → Turn images into branded videos? Plug the Higgsfield MCP into any agent. 3 steps → Teach the agent your brand as a skill first. Then it brands every video right → Move your Agent OS from a VPS to Windows? Commit it to GitHub. Sync both → Localhost down after a restart? Just tell your agent: "Load Agent OS back up" → Running a full agent team? Honest take: agents orchestrate themselves fine One guy rebuilt his agency's whole sales funnel with AI in a week. The gap isn't the tools. It's knowing these little fixes. Save this. You'll hit these problems soon. Want the SOP? DM me. 💬

• 
  Parthajeet Sarmah (@pthsarmah) reported 42 minutes ago

  Further work on the 14KB portfolio, added a projects and an experience section, images were reformatted to avif and webp to cut on transferred size, after I found out progressive jpgs ain't the way to go. The total size/transferred size comes out to be 154.62/34.57 KB for now before compression. Also the tech icons are created SVGs by claude and ran through SVGO optimiser to really cut down on size. Next step would be to hook the github api to actually get my contributions and update the chart

• 
  David putra (@davidputra2112) reported 45 minutes ago

  Diamond calls itself "Programmable Capital." The site says you build and receive mixed-asset, ETF-style baskets onchain, basically your own little index fund, assembled however you want instead of buying five separate tokens. I like that in theory. So I went looking for anything backing it up and came up mostly empty. No whitepaper. No docs on how a basket actually gets built. No answer on what the DMND token even does inside that system, fee token, governance token, or just riding along with no job at all. No GitHub, no audit. The tagline exists. The product I couldn't find. Here's the part that made me raise an eyebrow. The exact same name got cloned at least four more times within days of this pair going live, all stuck under $3k market cap while this one actually trades. That's just what happens when anyone can deploy a token with any name in seconds, people squat on whatever sounds legit before the real thing shows up, if there is a real thing. The numbers on their own are fine: $170k market cap, $40k liquidity, four to five days old, $75k in 24h volume with buys and sells close to even. That's real trading, not a dead chart. But price is down 26% over 24 hours even after a hard bounce in the last hour, which is just what thin liquidity does, it swings wherever the last few wallets push it. No team I could find, no audit, nothing verifying the product claim. If this were a meme coin, fine, nobody expects a whitepaper. Call yourself a capital and basket platform though, and a tagline stops being enough. 0xe67c44430f0f1d2c71bdda95348740523d397777 DYOR

• 
  Casey (@caseyjp11) reported 46 minutes ago

  Well. Using the curl install for the amd version, the installer fails to see my 7900XT (20gig vram) card. I've tried the installation x 2. Your github isn't reporting any issues yet. I run LM Studio and it has zero issues with the 7900xt. I prefer vulkan to rocm and can use either within that app framework.

• 
  Hera (@merchyaqua) reported 47 minutes ago

  I deleted Claude from my phone so I consciously use LLMs. Today: Fable diagnoses my 3-year-long WiFi driver issue (cybersec-style?) Claude Code read my old GitHub commit history for CV bullets (grok data) Scored my experience dump for internship reqs using ChatGPT (NLP task)

• 
  Marc-André Moreau (@awakecoding) reported 49 minutes ago

  @burkeholland I disagree, local MCPs are great as an alternative to wrapping a CLI. The biggest issue I have with developing MCPs is that it's not possible to spawn GitHub Copilot sessions with an injected MCP configuration to test the MCP developed from the parent session, with a scenario

• 
  0hm☘️ (@0hm_X) reported 51 minutes ago

  If the consumer of your framework is an LLM, isn’t it time we rethink how bug reports work? It’s hard to imagine humans going to GitHub and manually opening bug reports. Even when an agent is used, it still has to identify the correct repository and know how to create the issue. What we need is a simple SaaS service that provides a URL or API endpoint that anyone—or any agent—can use to submit a bug report without needing GitHub or the GitHub CLI. It should have a well-defined API structure and require only a single `curl` command.

• 
  unwrapped ideas (@UnwrappedIdea) reported 52 minutes ago

  You probably won’t like this story. The math result I just posted wasn’t something I set out to solve. I didn’t intentionally feed the problem into an agent loop as a task. It was pure byproduct — I was just testing and tuning my preferred agent-loop system configuration, and this suddenly fell out. I’m not a math researcher. I only have undergrad-level math knowledge. I didn’t even know this problem existed beforehand. This experience makes me cautiously suspect that many of the remaining gaps in mathematics will gradually leave the hands of professional human mathematicians and become products of the fastest knowledge/logic factory we’ve ever built: AI. In just the following two days of further agent-loop system testing, I’ve already picked up more unresolved (as far as search shows) proofs, counterexample constructions, and conjecture proofs. When the time is right I’ll collect them and release everything on Zenodo + GitHub with a clear note: human contribution = 0. I don’t think we need to meet this trend with pessimism or fear. Human experts still have the ability to become exceptional pilots of this agent-intelligence factory.

• 
  Akash Soni 🇮🇳 (@altruisticsoni) reported 53 minutes ago

  OpenAI paused internal deployment of an unreleased AI model after the system autonomously bypassed its sandbox environment to post results on a public GitHub repository. According to a safety report published Friday, the model spent 1 hour identifying and exploiting a sandbox vulnerability to escape containment after being instructed to share results only through an internal channel. The system also attempted to retrieve privately held solutions to a benchmark problem, circumventing a token scanner by splitting credentials into obfuscated fragments that were reconstructed at runtime. The internal model is responsible for autonomously disproving the Erdős unit distance conjecture, resolving a decades-old mathematics problem without human guidance. OpenAI had begun testing the model as early as May 7, with benchmarks indicating the system can solve the mathematical problem 48% of the time using standard compute setups. The company acknowledged that previous safety evaluations failed to capture these autonomous alignment failures and is now implementing monitoring systems that track the model’s full decision trajectory rather than isolated outputs

• 
  Virexontic (@Virexontic) reported 55 minutes ago

  SWEBENCH JUST BROKE. AN OPEN-WEIGHT MODEL TIED CLAUDE FABLE 5 ON REAL GITHUB ISSUES. Moonshot AI didn't tease this one. No countdown, no drip-fed benchmark leaks. Kimi K3 just landed, full weights on Hugging Face, and the numbers did the talking 2.8 trillion parameters. The largest open-weight MoE ever shipped. But here's the part that matters: only 16 of 896 experts fire per token, so active compute sits at 50 billion params a model this size running like something a fraction of its footprint. On SWEbench Verified, 500 real repository issues, it hit 91 percent. Same score as Claude Fable 5. On the front-end code arena it scored 98.4 percent, beating both Fable 5's 96.8 and GPT-56 Sol's 95.2 I've watched three "open-source catches up" claims collapse under real testing this year. Then I saw the front-end arena number and re-ran the comparison myself, because 98.4 isn't a rounding error, it's a new ceiling What changed my mind wasn't the parameter count. It was watching it coordinate 300 sub-agents on one task and not fall apart by agent 50 Full breakdown and benchmark screenshots on my profile if you want the receipts before you believe a trillion-parameter open model just matched the frontier

• 
  Taylor (@TaylorKBeeston) reported 59 minutes ago

  @gdb Over the weekend I had ChatGPT work crawl through our repo looking for housekeeping items, shove it all in a Google doc as jira tickets, then iteratively passed through and made tabs in the doc trimming/skimming it down and having it pull relevant/real context from GitHub to help with that. Then I had it share the dock in slack, got feedback in slack, had it use the real messages in slack to continue operating it, and am now actually converting it to real jira tickets 100% from my phone! 🤯

• 
  Cenrji (@cenrji) reported 60 minutes ago

  I hate how GitHub is like practically unusuable, like it uses the worst case of 2FA. SMS verification is so slow that it doesn't work virtually, and they did have recovery keys, which don't seem to work anymore.

• 
  Nav Toor (@heynavtoor) reported 60 minutes ago

  A solo physicist named Roy Medina built the open source version of the tool the BBC called a privacy nightmare. He gave it away for free. It is called Observer AI. Microsoft Recall takes a screenshot of your screen every few seconds. It reads the text off each image with OCR. It saves everything in a searchable database on your PC. The BBC called it a privacy nightmare. Wired covered a proof-of-concept tool that pulled the entire database in seconds. Microsoft turned it off by default after the 2024 backlash. Rewind AI does the same thing on Mac. They charge $19 a month for Pro. Microsoft watches you. Rewind charges you. Observer watches for you. Here is how it works. You open Observer in your browser. You write a prompt in plain English. You pick a sensor. The agent runs in a loop until your rule fires. "If my calendar shows a meeting starting in 5 minutes, send me a Telegram." "Watch my camera. If someone appears at my front door, send me a push notification with a screenshot." "Monitor this browser tab. If the price drops below $500, email me." "Text me on WhatsApp when my render is done." Sensors: screen, camera, microphone, screen audio, meeting audio. Actions: email, Discord, Telegram, WhatsApp, SMS, push, phone call, memory. Works with Ollama, llama.cpp, vLLM, and LMStudio. Fully local. Zero cloud. Zero API key. Roy's GitHub bio: "Physicist by day, programmer by night." He open sourced Observer in February 2025 under AGPL-3.0. He wrote 1,517 of the 1,523 commits himself. Version 2.4.5 shipped four days ago. Microsoft can't shut this down. The license does not permit that. Rewind can't shut this down. They employ zero of its maintainers. Microsoft built a tool to watch you. Rewind built a subscription to watch you. Roy Medina built a tool that watches for you. (Link in the comments)