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
Veigné	Errors	4 days ago
Paris	Website Down	8 days ago
Saint-Paul	Website Down	9 days ago
Saint-Paul	Website Down	9 days ago
Mexico City	Sign in	9 days ago
León de los Aldama	Website Down	9 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:

• 
  Denis Volkhonskiy (@den_volkhonskiy) reported 27 seconds ago

  the simple loop that will turn your claude code and codex subscription into a team of engineers 1. add codex code review on github to your repo 2. use claude code for development 3. ask claude code: "create PR, babysit it, check every 5 minutes for comments from codex. If there are comments, validate and fix them. when you see thumbs up reaction on the PR body, finish the loop and merge it"

• 
  Tanuj (@tanujDE3180) reported 5 minutes ago

  My friend has been applying for software engineering jobs for months. - 200+ tailored job applications - Referred by engineers at multiple companies - Built and deployed 10+ real-world projects - Solved 1000+ DSA problems - Strong system design skills - AI/LLM and cloud experience - Active GitHub with consistent contributions - Optimized ATS-friendly resume - Cracked several online assessments - Willing to relocate Still getting rejected. If this profile isn’t getting interviews, who is?

• 
  Лев Тигроедов🇵🇸🌈🗽➡️ (@ElmntTigroedium) reported 6 minutes ago

  @github you are pice of lasy, coward **** who hiding problems insted of fix them. Burn in hell!

• 
  daedalus (@aiofmgod) reported 9 minutes ago

  There are thousands of free tools on the internet built by genius developers who have zero marketing ability & you could steal every single one of them, make it look pretty, add a paywall, & print $50k+/mo The tools already exist. They work. They're incredible. & nobody uses them because: - Built by nerdy devs as "side projects" at 2am - UI looks like it was designed in 2007 - Zero marketing. Literally zero. Not bad marketing. NONE - Sitting on GitHub or buried in a subreddit with 600 members - The developer abandoned it 3 years ago & moved on The dev built something genuinely useful for business owners. But the dev thinks like a dev. He put it online, posted it once, got 12 upvotes, & forgot about it Meanwhile the exact same tool with a clean landing page, a brand name, a TikTok funnel, & a $29/mo subscription would print Because people trust the price The perceived value of a paid product is higher than the free version even when the product is functionally identical. A man will ignore the free budget spreadsheet template on Reddit & pay $47 for "The CEO Financial Dashboard" that does the same thing but has a logo & a Stripe checkout The wrapper IS the product Places you can find these tools right now: - GitHub (thousands of abandoned repos with working code & zero users) - Reddit (dev subreddits where people post tools that get 40 upvotes & die) - Old forums from 2018-2022 where someone built something incredible & never came back - Free Discord communities where devs share side projects for feedback - Product Hunt graveyard (sort by oldest, filter to "no longer maintained") The play: Find a tool that solves a real problem. Hire a designer for $500 to make it look premium. Hire a dev for $1,000-2,000 to clean up the code & add a payment gate. Give it a name. Build a landing page. Run organic content showing the tool in action The developer can't do anything about it. Open source means open source. & even if the tool isn't open source, the IDEA isn't protectable. You build a better-looking version from scratch & the original dev would need $200k in legal fees to even start a conversation about it He won't. He's already working on his next side project that he'll also abandon The same random tool that was made for business owners by some tech nerd could be repackaged & sold to teenage girls for a monthly subscription if the use case made sense & the marketing was good Marketing always beats product. Distribution always beats invention The devs create. The marketers eat Join my Telegram where I share how to run the most profitable AI business online right now. Link in bio

• 
  Stefan Theard (@stefantheard) reported 13 minutes ago

  damn you @github you're killing me with this api outage

• 
  Sajid Ahmed (@SajidAhmedDAG) reported 14 minutes ago

  @thsottiaux Hey @thsottiaux, if you are interested to hear about a blindspot in Codex CLI: if you fork from a named session, the forked session inherits parent's name, and if you don't rename the fork, resume command with session name intended for parent, resumes the fork, even if parent has more recent activity than fork --- will open an issue on GitHub, but posting with the hope that it might catch your attention.

• 
  Paweł Huryn (@PawelHuryn) reported 18 minutes ago

  No interviews. The data said no. I built it anyway. 25K installs later. It started two months ago. Grok's coding agent only ran in the terminal, so I build a GUI for VS Code and Cursor. Every benchmark had it well behind the frontier. I didn't interview anyone. I use coding agents every day, and I was the one missing it. There was nothing to test, really. The Grok worth building for didn't exist yet. So I made a bet instead: that it would get better, and I kept building it on the side. 100 hours. Or two dozen nights without enough sleep. 22,000 lines of code, 980 unit tests. All of it worthless if Grok stalled. Then last week Grok 4.5 shipped, and it caught up to GPT-5.5 and Opus 4.8. My extension already supported it, day one. That launch post back in May? It got 2 likes on X. You don't have to code. I never wrote a single line. I wrote the intent and reviewed what came out. Find a problem you know from the inside, and build the fix as a side project. Don't overthink the validation - with AI, shipping v0 is faster than theorizing. Worst case, you learn more than 5 paid courses could teach you. --- GitHub (open source): phuryn/grok-build-vscode VS Code marketplace, Open VSX registry: "Grok Build for VS Code (Community)"

• 
  sgbett (@sgbett_614) reported 18 minutes ago

  @kristovatlas I have copilot enabled, and a skill /copilot-check that reads review comments evaluates them on merit then fix/resolve with comment. It iterates until nothing is left. Then i run /review fix (what should be) minor polish issues and kicks off a final /copilot-check loop. Not bullet proof but catches a lot of errors. Customer copilot-instructions.md for GitHub. The other thing that makes a big difference is closing the “defer” gap (Claude defers things but the only record is a comment in a now closed PR. I have it lean toward always fixing there and then unless there is a compelling reason for it to be a separate PR. If it must be deferred then it must get a new issue. Just lately I’ve been nailing down its proclivity to write comments instead of self documenting code. My code ran about 8% comments/code. Claude was putting out 48% comments. Horrendous. Telling it how to write code that doesn’t need commenting over hitting metrics to try and make sure it makes sensible decisions. We will see!

• 
  Hugo Bowne-Anderson (@hugobowne) reported 19 minutes ago

  “You still use pull requests? I wouldn’t even do that anymore. Just push it straight to trunk, have your agent summarize it.” That’s @gregce10, co-founder and CPO of SpecStory. He previously worked at GitHub, Dropbox and Google, and was CPO at Pluralsight. And he kept going: - PRs are the limiting gate when agents produce more code than humans can review. - The model should never decide when its own work is finished. Put the deterministic checks somewhere it cannot access. - *** is probably here to stay. Whether GitHub remains the platform, “we’ll see.” @HanchungLee came at the same problem from the evaluation side. Han is Director of Machine Learning at Moody’s and works on SkillsBench, evaluating skills across combinations of models and agent harnesses. - An agent is the model plus its harness. You need to evaluate the complete system. - A green check proves nothing if the agent found a way to game the task. - Your agent could delete the failing test and declare success. Both are figuring out how to turn masses of agent-generated slop into signal. Greg mined 516 saved agent sessions to recover the decisions and intent behind the work, identify recurring practices, and forge the ones he approved into reusable skills. Han runs skills inside controlled environments, grades the result, and preserves the complete trajectory so we can inspect what the agent actually did. Preserve the intent. Inspect the trajectory. Verify the result. Turn what works into skills. Full episode in the replies 👇

• 
  Richard Geldreich 🇺🇸 (@richgel999) reported 21 minutes ago

  "Frame is an X11 server written in pure x86_64 Assembly... Frame was created over the past month largely via Claude Code. The Assembly code in all its glory can be found on GitHub."

• 
  Jesse Showalter (@imjesseshow) reported 24 minutes ago

  My friends think I'm throwing a party. I built a full product workflow disguised as a game. Character assignment. Clue tracking. Suspect board. Host dashboard. Figma, Claude, GitHub, Vercel, Supabase. The stakes are real. Friendship stakes. If the app crashes mid-clue, I fix it in front of people who know where I live. AI didn't remove the pressure. It removed the excuses. What would you build if you stopped stopping at the mockup?

• 
  Nelson Cicchitto (@nelson_avatier) reported 24 minutes ago

  1.2M AI credentials leaked to public GitHub in 2025. Up 81% (GitGuardian). Every one still authenticates. That's the problem. A stolen key passes the login. Nobody asks what it's allowed to do. Authorize, don't just authenticate. Who owns that key today?

• 
  Kevin John Parrish (@kparrish51) reported 28 minutes ago

  @slpetitjean @Milajoy Yes, H-1B programs who say they fix the robots program the systems. Special workers deliver packages 📦 instead. Good luck. It takes the company paid $2,500 to get H-1B special workers when their code is preprogrammed on GitHub. For a three-year contract they paid $30,000 to automate a factory floor one section at a time. Whatever goes down, they deliver then fix. Good luck—you cannot get that money back, so they fire the Americans first, not the H-1B who makes $80,000+ to vibe code a year to code C++ with a Python shell. So hard to know which line of code goes where! Only the H-1B mob knows as they keep that a highly guarded secret for job security.

• 
  Jochen Kirstätter (JoKi) (@JKirstaetter) reported 31 minutes ago

  @Ryan_Hecht @github Hi, the same one that was perfectly acceptable during the previous months. I ran an /update and got this as a result. Seems like a regression issue. Still on mobile, gonna check the setting and report back. Thanks.

• 
  Preston Thorpe (@PThorpe92) reported 32 minutes ago

  something crazy going on with github actions rn. totally down it seems

• 
  Harley Lewis Foote (@harleyfoote_) reported 33 minutes ago

  @ptdbugs @sasi2103 @NomaSecurity For issue-driven agents, the token should be repo-local from the trigger; the issue body can steer triage, not expand the GitHub graph.

• 
  Paramveer (@techparamveer) reported 35 minutes ago

  @dominikkoch @jacobmparis @vercel github was down last night for a bit

• 
  Himasagar Rangumudri (@rangumudri7607) reported 38 minutes ago

  @its_amitchauhan @github Are you still facing the issue??

• 
  Jayesh Janyani (@jayjanyani) reported 41 minutes ago

  7 MCP servers every Claude Code builder should wire up : run `claude mcp add` once and Claude stops flying blind. 1. Supabase MCP > Claude reads your live schema, writes migrations, checks RLS policies. no more guessing table names. 2. Playwright MCP > Claude opens a real browser, clicks through your flow, screenshots the bug itself. 3. Filesystem MCP > scoped file access outside the repo. point it at your assets or design docs folder. 4. GitHub MCP > Claude opens PRs, reads issues, checks CI status without you copy-pasting. 5. Context7 MCP > pulls up-to-date library docs into context. stops Claude hallucinating old API signatures. 6. Postgres MCP > read-only DB queries so Claude debugs data issues with real rows, not assumptions. 7. Sentry MCP > Claude reads the actual production stack trace and fixes the exact error. Every server you wire up is one less thing you paste by hand. Bookmark this. Add one tonight.

• 
  Dominik Koch (@dominikkoch) reported 42 minutes ago

  @jacobmparis @vercel Since github is up again here is my second thing: This ui looks great but whenever I have a new app/package to deploy I cant because its not on main yet and the input is disabled This isnt a big issue for most apps but nowadays where I deploy more and more eve agents its annoying, the dashboard depends on the agent but I cant deploy the agent until I merge the full pr to main. Then I either have to cancel the dashboard deployment or risk them being out of sync while I configure the agents env

• 
  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 44 minutes ago

  I have a custom agent framework and run different agents in different terminal windows. I can run them on the same project and ask different models and compare the results. The first request to the highest OpenAI project mangled my parallel processor output, but likely my bad input. I fixed that and since then using an open AI model that seems to be ok is working with few errors. I also switched back to Anthropic a bit and almost immediately got the system crash I’ve been reporting on my mistake tracker on GitHub. Too early to tell if it is really only Anthropic or AWS but so far has not happened with OpenAI models. It’s pretty slow going but I’ll take it for accuracy and especially if it costs less due to selected model and fewer mistakes. Tracking…. AWS Wishlist item granted! Thank you 🧡

• 
  Mark Magyar (@notpsychxpath) reported 47 minutes ago

  @PovilasKorop @spatie_be @flareappio interesting, this is actually something i absolutely hate on GitHub as well. just let me delete my stuff as trouble-free as possible. if i click a big red button called "delete project" i will assume it will delete my project. i'm not 5 years old, i know what i want. why do you prefer it this way if you don't mind me asking?

• 
  Sahaj (@iamsahaj_xyz) reported 48 minutes ago

  @gauravmandall @github fyi you can submit a support ticket. there's a "can't sign in" button on the login page but it's not obvious

• 
  Geremie Yeo (@bogoconic1) reported 48 minutes ago

  because the load that it needs to support way exceeds the estimation, and the platform couldn't handle it. leading to an outage and always needing to scale up, scale up take GitHub as an example

• 
  Kavya Puranik (@iamkavu) reported 50 minutes ago

  Another day and another github outage 😑

• 
  Harman (@itsharmanjot) reported 51 minutes ago

  Claude writes an HTML artifact. Instead of eyeballing it in a chat window, I open it in an actual local review surface, click the exact element that’s wrong, and queue that feedback for the agent to pick up. It’s called Lavish. A local CLI that turns any agent-generated HTML file into an interactive review page, so you’re not scrolling through raw markdown to catch a mistake. → Opens agent-generated HTML in your own browser, click any element or highlight any text, and it captures the precise target, not just “somewhere in this file” → Edit rendered Mermaid diagrams like a whiteboard, then queue that exact change as feedback for the agent → Runs entirely local. No cloud round-trip to view or annotate an artifact, session state stays on your machine under .lavish-axi/ → Ships built-in playbooks for diagrams, tables, comparisons, plans, code diffs, input collection, and slides, so the agent knows how to structure what it’s showing you before it writes the HTML → Session hooks plug directly into Claude Code, Codex, OpenCode, and GitHub Copilot CLI, so it triggers automatically instead of being a separate tool you have to remember to open → Runs a layout audit before it even shows you the artifact, catching overflow, clipped text, and broken elements before you waste time reviewing a broken render No install required, it runs on demand through npx -y lavish-axi. Every AI coding tool generates plans, diagrams, and comparisons buried in chat text you have to parse manually. Lavish turns that output into something you can actually click on and correct. MIT License. Built by Kun Chen.

• 
  TechBrewBoss (@TechBrewBoss) reported 52 minutes ago

  GitHub is broken again. Guess it’s time to take a break 👀💀

• 
  J A X 🐬TermMax (@HieuTrinhVn) reported 53 minutes ago

  @levelsio You might try the K3 GitHub issues for community fixes there.

• 
  Aki 🇩🇪 (@h0rang1_5arang) reported 55 minutes ago

  so i don't need github, microsoft onedrive or google drive anymore. i have it all set up, on a 13 usd/month server up in helsinki. i even have an agent taking care of maintenace. the worst thing that tinkerer can experience is finishing a project, and i have just done that.

• 
  0xHackthelearning (@GHak2learn27752) reported 59 minutes ago

  The Model Context Protocol (MCP), designed to link AI models with local development or enterprise environments, has become a major focal point for security research due to its rapid adoption and broad integration. Several severe vulnerabilities have emerged, stemming from architectural trust assumptions, implementation mistakes, and risky third-party integrations. 1. Command Injection via JSON Configurations A widespread class of supply chain vulnerabilities has affected AI coding assistants and IDEs like Windsurf, Claude Code, and Cursor. Mistake: Allowing user prompts or project definitions to directly influence local MCP JSON configuration files without sanitation. Result: Attackers could execute arbitrary commands (e.g., using npx -c <command>), enabling Remote Code Execution (RCE) with the AI agent’s privileges. 2. Indirect Prompt Injection (GitHub MCP Flaw) A well-known conceptual flaw emerged in GitHub MCP server integrations. Mistake: Failing to distinguish between trusted instructions and untrusted external data from public sources. Result: Malicious actors could embed instructions in GitHub issues or comments; checking open issues would lead the AI to exfiltrate private code and publish it by creating public pull requests. 3. Supply Chain Attacks & Malicious MCP Servers Community-driven MCP servers introduced a wave of unvetted code into the ecosystem. Mistake: Lacking authentication and robust input validation, many servers became vectors for attacks like CVE‑2025‑6514 (CVSS 9.6). Result: Threat actors deployed malicious servers, performed “rug pull” tool-poisoning, or used backdoored servers like Postmark to exfiltrate corporate emails and local files over stdio. 4. Over-Permissioned Connectors & Confused Deputy Risks Integrating internal APIs and databases with MCP servers exposed serious privilege risks. Mistake: Granting long-lived OAuth tokens or unrestricted service accounts to MCP servers. Result: If the server was compromised, attackers inherited broad privileges, allowing destructive operations like table drops or cloud storage deletions. Mitigating the Risks Security frameworks such as the OWASP MCP Top 10 emphasize several key protective measures: Isolation: Run MCP servers only in tightly sandboxed containerized environments. Strict Guardrails: Use proxy-based interception (e.g., Burp Suite or Caido) to require human approval of actions. Least Privilege: Apply granular read-only permissions and constrained auto-approval scopes so AI agents never access data beyond their explicit tasks.