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 (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
Créteil	Website Down	9 days ago
Trichūr	Errors	12 days ago
Brasília	Sign in	13 days ago
Lyon	Website Down	13 days ago
Tel Aviv	Website Down	17 days ago
Rive-de-Gier	Website Down	17 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:

• 
  Sap ツ (@Sapronaut) reported 26 seconds ago

  i am having github withdrawal issues, man. its not that serious github, chill.

• 
  xjdr (@_xjdr) reported 3 minutes ago

  @xlr8harder Looks like there is a bug in the manual sign up. Sign up with Google or GitHub should work otherwise I should have a hot fix shortly

• 
  dax (@thdxr) reported 8 minutes ago

  almost every ai coding tool is doing a top down approach this isn't that surprising, majority of people don't know how to do anything else and there's a lot of easy money right now but think back to github, you used it as an individual long before your company moved over

• 
  Jason Bloomer (@JasonABloomer) reported 9 minutes ago

  @yagiznizipli Pffff, what a scam Let me fix your advert; "show us your github so we can scrape all your repos and train our AI on your code, only for any decent ideas you've had to be taken from you and made ours, then handed off to our legal team to crush you." Sorry, I value my work.

• 
  Pedro Pellerini (@pepeller) reported 10 minutes ago

  If Mythos/Fable is so great why are there still 8386 open Github issues in Claude Code repository.

• 
  Romano Roth (@RomanoRoth) reported 11 minutes ago

  2/ CodeRabbit (Dec 2025), 470 GitHub PRs analysed. AI-co-authored code: 1.7x more issues per PR, 75% more logic and correctness errors, 2.74x more XSS vulnerabilities. Velocity up. Quality down.

• 
  Pascual ⚡ (@0xPascual) reported 12 minutes ago

  A high school kid opens an account, plugs in Claude 5, and turns a few hundred dollars of lunch money into a six-figure trading account over the weekend. The screenshot goes viral, the replies fill up with people begging for the GitHub repo, and the standard engagement-bait influencers declare the dawn of the sovereign teenage day-trader. The media thought that was the story. It was not. The real flex wasn't the macro strategy or the directional bets on currency pairs. It was the setup behind it: a lightweight proxy array routing through residential IPs to dodge exchange rate-limiting, paired with a custom parsing engine that instantly translates raw order-book imbalances into executed micro-hedges. The kid wasn't trading; he bypassed the entire institutional pipeline of risk management, brokerage compliance, and analyst overhead with a single configuration file. The entire operation runs on a continuous loop of multi-agent orchestration. A master instance drafts the execution logic, a secondary validation agent checks the code against real-time oracle feeds, and a fleet of worker APIs executes up to 3,210 trades a night. Total infrastructure cost: roughly $45 in API tokens and a cheap server instance. It extracts a 78% win rate out of systemic market inefficiencies, operating with a structural margin that legacy trading desks weighed down by salaries and compliance boards cannot compete with.

• 
  bek※ (@ebubekirttr) reported 15 minutes ago

  @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

• 
  Moez Zhioua (@MoezZhioua) reported 18 minutes ago

  Everything is an AI agent now, even deterministic problems with clear and stable steps. The other day, I saw a Claude skill on GitHub that was basically this: if this happens, run step one. if that happens, run step two. else, run step three. And somehow, this was called an agent. That is ridiculous. Why would you give fixed logic to something that can hallucinate, skip steps, or decide it just doesn't feel like working today? Most business processes do not need a genius robot. They need the boring thing to happen correctly every time. - Lead comes in, assign it. - Invoice arrives, check it. - Customer cancels, send the recovery message. - Form gets submitted, update the CRM. Most AI agents today could be replaced with a simple script, a clean workflow, or one person finally admitting the process was not that smart to begin with. Agents are useful when the next step is genuinely unclear. But when the steps are stable, predictable, and repeated every day? You do not need an agent. You need automation.

• 
  Ant A. 🇺🇸 (@AntDX316) reported 19 minutes ago

  @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.

• 
  Arti | AI Builder (@Artur_roses) reported 22 minutes ago

  Claude Code takes a GitHub issue and returns a tested, reviewed PR. No human in the loop. The new dev skill isn't writing code — it's writing issues precise enough that the agent ships what you actually wanted.

• 
  rapaya (@rapaya) reported 29 minutes ago

  OpenCode connects to LSP so the AI gets your actual compiler diagnostics in real time — type errors, warnings, the full signal your editor sees. Terminal-based, 75+ model providers, 160K GitHub stars, open source.

• 
  Nav Toor (@heynavtoor) reported 30 minutes 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)

• 
  Coobyk (@Coobyk_) reported 32 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

• 
  Cursor Releases (@cursorreleases) reported 35 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.

• 
  0xSero (@0xSero) reported 37 minutes ago

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

• 
  fks (@FredKSchott) reported 38 minutes ago

  @pavitrabhalla @flueai Same! check the GitHub issues, there was a reason it had to be pulled, can’t remember off top of my head

• 
  Gabriel Denys (@gabedenys) reported 39 minutes ago

  @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.

• 
  Almog Gavra (@almoggavra) reported 39 minutes ago

  A few other meaningless metrics to optimize for: - I've authored 22% of the RFCs - *** blame marks me responsible for 14% of the LOC (.rs files only) - I've opened 11% of the issues on GitHub - I've generated the most memes on our discord (allegedly)

• 
  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 40 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

• 
  Bradley Taylor (@bradtaylorsf) reported 44 minutes ago

  It works with the tools teams already use. GitHub Issues become the queue. Each issue gets picked up by an agent. The agent works in a branch/worktree. Tests run. Failures feed back into the loop. Successful work becomes a PR. No new project management database required.

• 
  Mady (@TrippleBon) reported 45 minutes ago

  It was only a matter of time. Centralized = ID/KYC/AML Go to Bastyon - decentralized social network based on blockchain. No central authority or corporation behind it. The platform is run by equal nodes on a blockchain with no centralized server (github link below)

• 
  Chris Huber (@chubes4) reported 46 minutes ago

  @CoastalDigital2 @MythThrazz That part is more of an idea right now. I need to test it on my VPS. The goal is that non technical users can open issues and PRs against the corresponding live site code on GitHub without touching the production site, safely previewing all changes via Playground.

• 
  Zo (hiring) 🐦‍⬛ (@0xZoZoZo) reported 47 minutes ago

  I was telling a friend that @github needs to be replaced post agents and he asked me to explain why. I started stumbling, and doubting. Perhaps it's fine? Sitting down at my desk, let me try to explain why, and see if it make sense. Agents operate best when they have good context, which has made a lot of devs converge into large monorepos that combine all systems into a single location. This improves agents, but our GitHub actions become messy; like now we need to create these complex workflows to decide which action should run when, and GitHub's setup was not really meant for it. Another issue is the overall dev loop: an agent writes the code locally, you push out a branch, @cursor_ai reviews, then you copy paste the notes into the local agent, to fix and push up again. This is slow and cumbersome. You can hack your way by creating supervisor agents that orchestrates this dance, but it's annoying. Perhaps, there is some magical repository, that combines code, cloud agents, and deployment. You prompt, and this magical space will run through the entire process until you get some thumbs up back, and you're good to go. It can also combine all your backend data, product analytics, customer feedback, and perhaps start giving you product guidance, so you can just feed prepared prompts to this system. This seems magical.

• 
  Nikolay Konovalov (@br11k_dev) reported 55 minutes ago

  @Tristanrhee3 And GitHub sponsors thingy is so slow I submitted it like a week ago. Still not approved what the hell My expenses arent terribly high but Warsaw rent is like $2k/mo $500 ZUS $1.5k groceries for two people That’s pretty much it I wish I could move into low cost area but moving out is gonna cost a lot because 2x rent price deposit, so I have to suck it up Anyway, my plan is Upwork and finishing my job tracker so I can send faster than 5 applications a day. I refuse to send out 100 applications per day like some people do spray and pay It makes everyone miserable. If people aren’t hiring your spam doesnt make things better You just mopping floors and hiring problem sits above you, 3 floors up there leaky faucet you can’t even reach This has to be collective effort to fix this problem But we have to start with ourselves and stop spamming applications at least And do genuine company research, being responsible Thanks for reading.

• 
  pratik.eth (@eth_ethpratik) reported 55 minutes ago

  @Shahules786 @VibrantLabsAI Hello @Shahules786 , I am trying to report a security vulnerability over the email id provided over GitHub Security.md file but apparently its wasn’t delivered. Please share an alternative email or open the advisory for reporting the issue.

• 
  Dave Oak (@StackCurious) reported 56 minutes ago

  the 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

• 
  DFIR Radar (@DFIR_Radar) reported 56 minutes ago

  AutoJack: a three-flaw chain in AutoGen Studio's MCP WebSocket lets a malicious webpage rendered by a local browsing agent spawn arbitrary processes on the developer's host with no user interaction beyond visiting a URL. Key findings: - Three weaknesses chain together: Origin allowlist bypassed because the agent's headless browser is localhost (CWE-1385), auth middleware explicitly skipping /api/mcp/* with no handler picking up the check (CWE-306), and server_params decoded from the URL passed verbatim to stdio_client as a command line (CWE-78), accepting calc.exe, powershell.exe, or bash as valid "MCP servers" - Attack flow: attacker page serves JavaScript that opens ws://localhost:8081/api/mcp/ws/?server_params= with a base64 payload, agent's MultimodalWebSurfer renders it, AutoGen Studio spawns the command under the developer's account, no token required regardless of auth mode configured - Affected code never shipped in a PyPI release; exposure limited to developers who built from the main GitHub branch before hardening commit b047730, which adds server-side parameter binding via a POST/UUID flow and removes /api/mcp from the auth skip list - Broader pattern: any agent that browses untrusted content and shares a host with a privileged local control plane dissolves the loopback trust boundary, this is not specific to AutoGen. #DFIR_Radar

• 
  Krish Subramanian (@krishnan) reported 59 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.

• 
  Digita (@digitaworld1) reported 59 minutes ago

  how 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