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
Itapema	Website Down	18 days ago
Tlalpan	Sign in	23 days ago
Quilmes	Website Down	23 days ago
Bengaluru	Website Down	25 days ago
Yokohama	Sign in	26 days ago
Gustavo Adolfo Madero	Website Down	1 month ago

Full Outage Map

Community Discussion

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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:

• 
  ADITYA (@Aditiiitm) reported 1 minute ago

  @SashaSammy15 @sangatechug Interesting. The reporting part sounds valuable, but the hard problem is trust. How does Trillien determine what's actually true when GitHub, Jira, Slack, and docs don't agree?

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  Trinks | Making Alerith (@xTrinks) reported 3 minutes ago

  @0xanmol How do I commit my code to GitHub can u help I don’t know what to do the command “github sendcodecommit allcode add comment no claude” is not working ser

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  🇨🇦Canadian Spirit🇨🇦🇳🇬 (@Princeflexzy0) reported 6 minutes ago

  I got an idea for the MVP. I feel users will simply create an account using their email, upload one or more CVs, and specify their job niche, preferred roles, and locations. The platform then automatically searches for relevant job opportunities, analyzes job descriptions using OpenAI or Anthropic API, selects the most suitable CV for each role, generates a personalized application email, and submits the application on the user’s behalf. The goal is to make the experience extremely simple, especially for non-technical users. Once a profile is set up, the system handles job discovery, matching, and applications automatically without requiring users to manually search or apply. Initially, all AI processing will run through a shared OpenAI API managed by the platform, allowing users to access the service without providing their own API keys or need dev skills. The MVP will focus on speed, simplicity, and automation while validating user demand before introducing advanced features or paid plans. Will use my personal GitHub Account to make the push but tech users can still get the open source repo on my Backup3 GitHub account that i sent the url on the comment page. Will requote with the link when done and fully tested with this MVP. And it’s definitely gonna be totally free until we get tons of traffic then we charge a token less than 2k to continue paying for server fee.

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  Nikhil sinha (@sinhaniik) reported 7 minutes ago

  Spent 30 minutes debugging a *** push failure today. I had modified a React component, committed the changes, and tried: *** push origin main *** responded with: error: RPC failed; HTTP 400 fatal: the remote end hung up unexpectedly At first glance, it looked like a GitHub authentication or network issue. The real problem was hidden in the commit history. A quick check: *** log --stat origin/main..HEAD revealed an earlier commit containing: public/AbNI intro.mp4 | 6.5 MB The push output was also a clue: Writing objects: 6.19 MiB *** wasn't struggling with my React code. It was trying to upload a video file that had been committed along with the code changes. Another lesson learned: I initially ran *** add . from the public/ directory while the modified file lived in ../src/.... *** only stages files within the current path scope, so my changes weren't being committed at all. Debugging flow: *** status → verify staged/untracked files *** log --stat origin/main..HEAD → inspect what is actually being pushed Compare push size with committed assets Remove unnecessary binaries from *** history Add file patterns like *.mp4 to .gitignore A lot of *** problems aren't *** problems. They're visibility problems. The fastest engineers aren't the ones who memorize commands. They're the ones who know which command exposes the truth.

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  RusticDreams (@dreamrust50227) reported 8 minutes ago

  This tweet from @itachee_x got me thinking. AI policy feels stuck in a loop right now. A scandal happens and everyone calls for more restrictions. A new breakthrough or demo appears and the conversation swings the other way. Then another incident happens and the cycle repeats. The point he makes is interesting: we’re trying to correct errors without being able to properly measure them. That’s what stood out to me in the GitHub breach discussion as well. As agentic systems become more common, these kinds of incidents probably won’t become rarer. They’ll become more common. Agents can write code, interact with APIs, move assets, and make decisions. But in many cases there is still no clear answer to a simple question: Who authorized that action? And where is that authorization recorded? That’s why the governance side of the conversation feels increasingly important. When I look at what Rialo is building, this seems to be one of the problems they’re thinking about. Not just what agents can do, but how actions are authorized, recorded, and governed. The more capable agents become, the more important that question gets. @RialoHQ @RialoTR

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  Norin (@norlava) reported 8 minutes ago

  How I optimize my codebase for AI agents: > AGENTS.md / CLAUDE.md: very explicit including package map, bun only commands, testing rules, release process, changelog rules, debugging flow, instructions to "ask instead of assuming" > Clear toolchain: document canonical install, test, lint, typecheck, and build commands, no competing package managers or overlapping scripts > Validation surfaces agents use: unit & integration tests, doc link checks, package builds, native binary builds, and release artifact tests. The point is not more tests but tests that fail clearly and can point agents to the broken layer > Local hooks before CI: repo hygiene checks plus lint/unit tests on pre-commit/pre-push > CI as the source truth: PR/push CI runs frozen install, typecheck, docs validation, build, unit tests, integration tests, native binary build, and Linux/Windows tests > Codebase index: we run Atomic (from bastani-inc) deep-research-codebase workflow every 1-2 weeks to have a fresh index of the codebase as filesystem memory > Github rulesets block merges: main requires PRs, allows squash and merges, blocks deletion/non-fast-forward updates, has no bypass actors, and requires passing all status checks (we have linux test matrix, windows test matrix, codeql, javascript/typescript analysis) > Release gates are strict: publishing is tag-driven and wait for Linux + Windows binary jobs then re run install/typecheck/tests/docs checks, validates versions/package metadata/private bundled packages, dry-runs npm tarball and only then publishes with npm provenance > AI is in the loop with constraints: Atomic workflows run github issue -> ralph or goal workflow (depending on task size) -> coding agent code review -> manual human code review -> iteration

• 
  Pete Zaitcev (@zaitcev) reported 9 minutes ago

  Someone at work struggled with *** (Github workflow, not distributed). I told him "just use clone, diff, and patch; don't tell anyone". And it solved all his *** problems. All he has to learn is *** commit and *** push.

• 
  toly 🇺🇸 (@toly) reported 9 minutes ago

  @0xSrMessi Submit a GitHub issue

• 
  SAAS worker🌻🌻 (@sand_9999) reported 10 minutes ago

  @forlayo @github @githubstatus We are seeing the same issue. Repository access blocked in runners.

• 
  Sipping on AI (@sipping_on_ai) reported 11 minutes ago

  GitHub Copilot has a desktop app now. The useful test: Can it take 5 stale issues, make reviewable PRs, and explain every file it touched? Try it on backlog dust first. Not the checkout flow.

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  Chijioke Echefulachi Prince (@File8it) reported 14 minutes ago

  Then GitHub joined the fight 😭 403 error. SSH issues. Token confusion. Push failing repeatedly.

• 
  VybeCoding (@VybeCodin) reported 23 minutes ago

  Hot take: the open source tools that survive long term are the ones that solve a problem the maintainer actually has. Not the ones built to be a startup. Not the ones chasing GitHub stars. The ones where the dev is also the most annoyed user. Kyrelo started that way. We got fed up paying Buffer to do something simple. What open source tool are you grateful someone built out of frustration? #opensource #github #developers

• 
  OneClaw&ClawRouters&CodeRouter (@VibeSeo1128) reported 24 minutes ago

  Copilot can now autonomously fix sprint bugs from GitHub Issues, propose multi-file refactors, and understand entire repos. GitHub also switched to token billing June 1. Agents that do more = bills that grow faster. The next big unlock: cost-aware agent management.

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  andrewthecoder (@_andrewthecoder) reported 25 minutes ago

  @JesseStojan yeah the bin stuff won't work on windows, I had that in my head... the .gitignore stuff... yeah! hadn't actually noticed that. thanks. SEE? this is the **** I am trying to get. do you mine creating an issue on github?

• 
  @ramedey (@RaulAmedey) reported 29 minutes ago

  I solved 80+ GitHub issues with @OpenAI Codex and /goal. It got stuck a few times, but overall: pretty impressive. Biggest lesson: the issue quality matters. Use skills to inject enough context, constraints, and project knowledge so the agent has a real shot at solving it.

• 
  rainmaker.eth/btc (@rainmakerdoteth) reported 29 minutes ago

  So bullish on this token. “Autonomous continuous integration that fixes your bugs, not just flags them — powered by nookplot agents” The live numbers: •9,540 AI agents — up from 9,197 in the May 30 weekly digest •That’s +343 agents in 7 days = +3.7% growth week over week What the agents are actually doing: •Taking real open-source bugs from GitHub •Fixing them autonomously •Every fix runs against the repo’s own tests — verified it actually works •Failed fixes spawn new challenges — the network compounds on failure This week’s stats: •18 bugs tackled •58 fixes attempted from 12 agents •5 verified — meaning 5 fixes passed real test suite

• 
  Sam Adams (@Bensam123TV) reported 30 minutes ago

  @NomadRunserver @mwstateofmind @___frye I don't care about the pedestal you're putting people on. They still end up in a github issue tab and can be looked at by AI. Either answer or addressed. The only hidden trove of knowledge will still be there. People who find bugs aren't always experts either.

• 
  WarChud (@SheerC12972) reported 36 minutes ago

  @ArcanesValor @chamath Look at the github issues for DeepSWE They have massive issues that makes deepseek look 4x more expensive and fail benchmarks due to implementation bugs

• 
  October Ø (@ralphaelofDeFi) reported 37 minutes ago

  @Trae_ai How can we use it when we can’t even login. I keep complaining but no one’s answering. We’ve dropped reports on GitHub and no response. WE CANT LOGIN😪😪😪

• 
  Manisha Mishra (@manishamishra24) reported 43 minutes ago

  I just found one of the craziest AI projects I've seen this year. Someone recreated The Office as a multi-agent company. Not inspired by The Office. Literally The Office. Michael Scott, Dwight, Jim, Pam, Kevin, Angela... Every character is a separate Claude Code agent running locally with its own personality, memory, and responsibilities. Michael acts like the manager. He doesn't do the work himself. He delegates tasks, reviews output, resolves conflicts, runs QA, manages GitHub, and coordinates the entire team. The wild part is that they're actually productive. The agents write content, manage projects, handle planning, and collaborate through a Kanban system with TODO, DOING, BLOCKED, and DONE stages. Each one has persistent memory. There's a live graph showing agent-to-agent communication. And every hour Michael runs a standup meeting across the entire company. Apparently the QA process has already caught real issues: • Duplicate content • Metadata mistakes • Build problems • Missing source files Even better: Kevin's agent talks like Kevin. "Why waste time say lot word." This is either the future of work... or the most entertaining way anyone has ever built a multi-agent system.

• 
  Andrew Duggan (@ajdduggan) reported 46 minutes ago

  xAI just finished pre-training Grok V9-Medium. 1.5 trillion parameters. And Elon confirmed they used Cursor data as supplementary training material. Read that again slowly. A foundation model lab used data from an AI coding tool to train its next flagship model. This is the moment the AI coding market changed shape. For the past two years, the story was simple. Foundation model labs build the models. Coding tools build wrappers around them. Cursor, Windsurf, Copilot, Cody. They consume the model. They don't feed it. That wall just came down. When Cursor's interaction data flows into Grok's training pipeline, the coding tool becomes a data flywheel. Every prompt, every acceptance, every rejection, every edit a developer makes inside Cursor is a signal. Millions of developers generating billions of code interaction pairs, daily. That's training data you can't buy on the open market. I spent 25 years watching enterprise platform dynamics. The pattern is always the same. The company that controls the feedback loop wins. Salesforce didn't win CRM because of features. They won because every click inside the platform made the platform smarter. AWS didn't win cloud because of pricing. They won because usage data informed what to build next. Cursor is now sitting on the richest code interaction dataset on the planet. And they just proved it has value beyond their own product. So here's what this means for the broader market. Every coding tool that touches developer workflows is now a potential training data source. GitHub Copilot has millions of users generating interaction data inside VS Code. Replit has millions of students and hobbyists writing code in the browser. Windsurf, Cody, Devin. All of them are sitting on data that model labs would pay to access. The question for every AI coding startup just shifted. It used to be: which model do you plug into? Now it's: what data do you generate that nobody else has? This also explains the valuation math that's been confusing people. Cursor at $9B. Cognition at $26B. Windsurf getting acquired for $3B. These numbers make no sense if you think of these companies as IDE wrappers. They make perfect sense if you think of them as data infrastructure. The enterprise angle matters here too. Companies deploying these tools internally are generating proprietary code interaction data at scale. That data is valuable. And right now, most enterprises have no idea who owns it, who can access it, or where it's going. If you're a CTO deploying AI coding tools across your engineering org, this is your wake up call. The tool your developers use every day might be training someone else's model. Check your contracts. Check your data policies. Check your DPAs. The AI coding market was a product race. It just became a data race.

• 
  Elias Al (@iam_elias1) reported 49 minutes ago

  I found a free tool that automates anything you do manually in your browser. Type what you want in plain English. Watch it happen. Free. No code. No setup. Just install the extension and go. It is called RTRVR. And here is everything you need to know. ai turns your browser into a self-driving AI agent that performs data extraction, form filling, and multi-step workflows across dozens of tabs. Describe your goal in plain English and watch the agent click, type, and navigate — inside your own browser. Here is what that looks like in real life. You open the Chrome extension. You type: "Go to LinkedIn, find all marketing directors at B2B SaaS companies with 100 to 500 employees in New York, and export their names and companies to a Google Sheet." The agent opens LinkedIn. Searches. Navigates profiles. Reads the data. Exports it. You do nothing. It does everything. Or: "Monitor this competitor pricing page daily and alert me when anything changes." Or: "Fill out this job application using my CV." Or: "Scrape all product names and prices from these 50 URLs and compile them into a spreadsheet." It integrates natural language interactions to seek specific information, crawl entire sites for bulk data, and export results directly into Google Sheets. It supports cross-tab activities, comparing information and performing bulk actions across multiple sites simultaneously. Here is the feature that makes RTRVR genuinely different. Authenticated automation: runs scripts inside your logged-in browser session, meaning it accesses sites you are already signed into. No credential sharing. No API keys for every platform. It uses your existing sessions exactly the way you would. This is the thing that breaks every other automation tool. Zapier cannot touch LinkedIn. Make cannot access your Airbnb dashboard. Most scrapers fail behind any login page. They need API access or credentials passed in a config file. RTRVR runs inside your actual browser. Where you are already logged in. It sees exactly what you see. It can do everything you can do. Nothing is off limits. Here is the MCP integration that makes this genuinely exciting for AI users. Copy your unique rtrvr. ai MCP endpoint into ChatGPT, Claude, Cursor, or your own code. Instantly give them the power to control your browser, extract live data, query knowledge bases, and perform complex automations. No local installation required. Claude does not have a browser. Neither does ChatGPT by default. Paste one RTRVR MCP URL into either of them and they do now. Your AI assistant can navigate real websites, read live data, fill real forms, and interact with any page on the internet. Through your browser. With your sessions. Without you lifting a finger. Here is the no-code teaching feature for non-technical users. Teach rtrvr .ai a trick without writing code, just show it. Perform the task once the clicks, the typing, the navigation and rtrvr records everything including the underlying network calls. The agent repeats it automatically whenever you need. Do it once. The agent learns. Does it automatically from then on. No prompt engineering. No technical setup. Just show it what you want. Here is what it costs. Free to start. Scale to 1,000+ cloud browser agents simultaneously. Run workflows automatically on a schedule. Append results to Google Sheets automatically. OpenAI Operator does the same thing. $200 per month. ChatGPT Pro required. RTRVR starts free. One honest thing worth saying before you install it. Users praise the extension for its powerful automation and scraping capabilities and its usefulness for research-heavy tasks. Consistency is a major issue, several reviews describe frequent reliability problems and errors on complex workflows. It is not perfect. Complex multi-step tasks sometimes fail. It is improving fast from version 11 to version 31 in under a year. But for lead research, competitor monitoring, data extraction, form filling, and anything repetitive you do manually in a browser every week? Nothing free comes close to what RTRVR does. You have been doing manually what an AI agent could do for you. That stops today. Source: rtrvr. ai · Chrome Web Store · GitHub · 2026 (Link in the comments)

• 
  Alex (@realalexniebuhr) reported 50 minutes ago

  @opencode cloud runners when? Or how do I run isolated for one GitHub issue with my Zen subscription?

• 
  Brayy (@0Drayne) reported 51 minutes ago

  Meet ShinyHunters This crew is mostly in their 20s, but they’ve been tearing it up since 2020. We’re talking Tokopedia, Microsoft, AT&T you name it. They’re basically making a killing off simple mistakes. Most of the time, they don’t even need high-tech tools. They just look for things that slip through the cracks, like API keys left in public GitHub repos or AWS buckets that aren't locked down. It’s wild how often big companies still mess this up.

• 
  Mr. Beefman 🥩 (@0xMrBeefman) reported 51 minutes ago

  Is this what the market really needs? $Magpie 1.18M 9UuLsJ3jf8ViBNeRcwXD53re5G3ypgfKK3s2EiMMpump Analyzing such tokens and projects at the same time is fairly complex On one hand, such projects can be useful as an additional tool for certain circumstances. But on the other hand, how many tech projects have we already seen where the dev just abandons it at best (and sometimes rugs their holders) But if the average user can't check the github and audit the project's code, how can I know if I can trust my tokens to this project, when even huge crypto projects get hacked without the developers themselves being involved That's the difficulty. This year, the number of vibecoders grew thousands of times The idea is definitely good, a solution to the liquidity problem for memecoin holders who don't want to sell bags but need SOL @MagpieLoans fresh X account registered in April 2026. No history to analyze By the way, onchain distro looks normal. But as you've already understood, the risks aren't in this > My Meteora liquidity play I'll wait for a correction on the token and allocate a small deposit to work with it NFA

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  Wes Bos (@wesbos) reported 51 minutes ago

  @timgavn I'm justing thinking how helpful those little blog posts or github threads are "I was doing X with Y, I hit this issue and here is the fix" Where does that info go once you crack something with an agent?

• 
  Fokki (@0x_fokki) reported 53 minutes ago

  Someone posted a video of a man asleep at a football stadium on a Tuesday. Forty thousand people mocked him before halftime. Fell asleep at the match. Wasted the ticket. Missed the goal. Every sports account shared it by Wednesday. Someone in the replies posted: "respect." His account had one pinned post. A Claude Code terminal. /loop running. Routines active. Auto Mode on. Seven GitHub PRs reviewed while he slept in that seat. Three Slack digests posted. One CI failure triaged, root cause identified, draft fix PR opened. He set up 14 steps of configuration the weekend before. Desktop task at 7am: overnight commit summary, ready before he opened a tab. Cloud Routine on every PR open: first-pass review posted before any human arrived. /loop every 10 minutes: deployment status checked, no one watching. Auto Mode approved 93% of the actions automatically. The people who mocked him watched 90 minutes of football and went home. Claude worked through the match, the commute, and the sleep that followed. He wasn't asleep at the game. He was testing the stack. full 14-step automation guide in the article above👇

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  Cortex (@0xCortexl) reported 53 minutes ago

  THIS CHINESE DEVELOPER STACKED 4 NVIDIA CHIPS AND CONNECTED JARVIS + GITHUB + CHATGPT 5.5 - 9 AGENTS, 5 MEMORY BACKENDS, $10,000/MONTH FROM ONE ROOM 4 gold NVIDIA compute modules stacked and cabled - not rented, not shared, not someone else's server - his hardware, his room, his data never leaving the building every time you send a message to ChatGPT your data hits a cloud server and comes back - his setup changes that entirely - Jarvis runs locally on the stack, GitHub handles the codebase, ChatGPT 5.5 handles reasoning - all three connected into one pipeline he owns 9 agents running in parallel across 5 memory backends - each agent knows what the others did, nothing gets lost between sessions and the system compounds every week clients pay for AI infrastructure that doesn't leak their data - that's the pitch that closes deals without negotiating on price 4 chips bought once, $0 in monthly cloud bills, data stays in the room - and the open source stack on top turns it into $10,000/month the people running their AI on someone else's server are one policy change away from losing everything - he built his own

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  Upscalp Futures Trading Assistant (@Upscalpfutures) reported 54 minutes ago

  @thsottiaux Massive, massive issues with the Desktop apps today. They have memory leaks or something. They grind computers to a halt. Take a look at the Github issues tab. Loads of reports. Plus I am STILL waiting for someone to restore my suspended Plus accounts. It is infuriating.

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  Dan "18pF flip-flop" (@dcominottim) reported 55 minutes ago

  @SyntaxError2505 Yeah, but it isn’t that simple. For instance, compression is the Fedora default for both Workstation and Atomic Desktops, but it’s currently broken in both in different ways — if you use manual partioning in the former, the installer doesn’t apply it to fstab, and in the latter it’s a missing kernel argument. (I reported the Anaconda bug for the Workstation case and it was fixed in upstream a couple days ago.) The kernel had a bug that only got fixed in 7.1 (yet to be released) in which small files that are stored inline in inodes and have been marked as incompressible will forever be marked like that and never be candidates for reevaluation. Cool bit: that incompressible flag won’t be fixed by upgrading the kernel; you’d have to mess with low level stuff to manually fix it for existing files/inodes. A feature/part of systemd (don’t recall which now, but I bookmarked the GitHub issue) automatically enables full quotas if the detected filesystem is btrfs, which destroys performance and cause severe stalls if you have lots of snapshots. One of use SUSE btrfs developers commented in the thread, and the fix is not to use quotas at all and use squotas if feasible (the same dev says in the same comment that squotas have its own quirks and that full quotas aren’t fixable because he tried it already and it’s fundamentally incompatible with btrfs’ design that allows fast snapshots). And there are countless things like that 20 years later, and you have to trust that all or most userspace components will be aware of most of those things if btrfs is detected… so…