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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	8 days ago
Quito	Sign in	9 days ago
Junín	Errors	9 days ago
Guadalajara	Errors	9 days ago
Paris	Website Down	9 days ago
Quito	Errors	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:

• 
  Kurious dev (@Kuriousdev) reported 7 minutes ago

  @github fix the cli first. what a terrible user experience. I have to use because my employer only pays for it

• 
  Benjamin Dell (@bendell) reported 7 minutes ago

  I've built something that has completely changed how I think about support inside my SaaS. It started as a way to draft better replies. It has become an always-on support system that can investigate problems, answer customers, fix bugs and turn support conversations into product improvements. It currently runs for HeySummit, but I'm considering opening it up to a small early beta for other SaaS founders. Here's what it actually does. 1. Answers based on how your product really works Most AI support tools rely heavily on your Help Docs which, let's be honest, are usually incomplete or out of date. This system can inspect the current codebase, settings, tests, complete conversation history, attachments and read-only customer data. It writes in my voice, but with more context than I could realistically hold in my head. Customers get specific answers based on how the product works today, rather than guesses assembled from an ageing knowledge base. If the available evidence conflicts or something cannot be verified, it stops or asks for help rather than making up an answer. 2. Autonomy without giving up control Routine, low-risk questions can be answered and closed automatically. If a ticket needs a read-only check of the customer's account or records, the system can perform that investigation itself. If it needs more information, it can ask the customer for the missing information, keep the conversation open and resume the investigation when they respond. Anything involving a data change, production action, financial decision or meaningful risk comes back to me in Slack for approval. It also sends reminders when a decision is waiting on me, so tickets do not quietly disappear into a queue. 3. Bugs can go from ticket to production If a customer reports a bug, the system can investigate it against the actual code and customer evidence, reproduce the problem, write the fix, run the relevant tests and open a pull request. I review the PR in GitHub. If I leave comments, it applies the feedback to the same branch and asks me to review it again. Once the code is approved, deployment remains a separate decision. I can choose a standard deployment or a hotfix from Slack. After deployment, it verifies that the fix genuinely works in the live product. Only then does it draft the reply telling the customer the problem has been fixed. 4. It never blindly sends an old reply Support conversations change while people are working on them. A customer might send more information. A teammate might reply. The ticket might already have been closed. The system checks the complete conversation again before asking me to approve anything, and again immediately before sending a reply. If the situation has changed, the old draft or approval becomes stale. It also verifies that the exact reply reached Intercom, confirms no newer message has arrived, and verifies closure separately. A timeout or interrupted worker cannot cause the same action or reply to be performed twice. 5. It learns how I work The system learns from my edits, feedback and support decisions. It does not simply copy old replies. Historical replies help it understand my tone, but current code and evidence remain the source of truth for factual claims. Useful instructions can become a lasting rule for future tickets. If that guidance changes while a reply is waiting for approval, the system can replace the old draft using the new rules. In other words, correcting it once can improve future tickets too. 6. Support becomes product development The system does more than identify explicit feature requests. It can detect documentation gaps, onboarding friction, confusing copy, awkward UX, reliability problems and repeated patterns across different customers. Once the original ticket is resolved, it can ask whether I want to dismiss the idea, see a code-aware implementation plan or build it. If I choose to build it, the work follows the same review, deployment and verification process as a bug fix. Once the improvement is live, it drafts a courtesy reply to the original customer so they know their feedback led to a real change. That closes a loop most companies rarely close. A customer mentions a problem, the product improves, and the customer actually hears about it. 7. Fast, without pretending everything is instant The system checks Intercom every 15 seconds. Across the conversations handled so far, the median time from detecting a ticket to producing the first draft is about eight minutes. It has already automatically sent, verified and closed 278 low-risk conversations. Once those replies were ready for automatic delivery, the median verified send time was about eight seconds. 8. Runs locally and quietly The workflow, credentials, state and audit trail live on my Mac Mini. It has its own health monitoring, crash recovery, duplicate protection and encrypted backups. It is designed to keep running without someone babysitting it, while still bringing the important decisions back to a human. ---- I'm considering opening this up to a small group of SaaS founders for an early beta. Intercom will be the first supported integration, and the beta will be free for the first couple of months. If you’d be interested in trying it, reply here or send me a DM.

• 
  Deleonn 🌤️ (@Deleonnbonapart) reported 9 minutes ago

  @LightNodeVPS False! I tried creating an account in Brazil via Facebook, Github, and Google, but the site keeps showing errors.

• 
  John Koretoff (@JohnKoretoff) reported 9 minutes ago

  When a ship ran aground, nobody asked for the shipyard drawings first. They asked for the log. The drawings said what the vessel was supposed to do. The log said what it actually did. Wind. Heading. Who had the watch. Where the sounding changed. If you could not produce the log, you could not stand behind the voyage. Software used to work like the drawings. You read the code and you knew what would happen. The logic lived in a file. A debugger was a convenience. The source of truth was the repository. Agents do not work like that. Harrison Chase said it plainly on Sequoia’s Training Data: in software, the code is the source of truth. In agents, you send a trace. The model is a black box of parameters you cannot usefully read. The harness is the opinionated scaffolding around it — a planning tool, compaction when the window fills, a file system so the run can put work down and pick it back up. An LLM in a loop, for hours, not one prompt and out. That loop is finally working. Coding first. Then research. An incident writeup. A first draft a human still has to look at. The models got better. That is half of it. The other half is the harness caught up. AutoGPT was the idea years ago. An LLM deciding what to do, over and over. The models were not good enough, and the scaffolding was not either. Chase’s cut is that the same simple algorithm started to work once people got serious about context: what is actually in the window at step fourteen, after thirteen tool calls nobody typed by hand. A single model call is inspectable from the prompt. You wrote the prompt. You know what went in. An agent at step fourteen has pulled files, compacted history, spawned a subagent, and stuffed the window with things no one authored. If you cannot see that window, you do not know the program you shipped. “Send us the code” was the old debug. “Send us a trace” is the new one. Copies got cheap. First drafts. First pull requests. First incident notes. The scarce thing is not another model. It is a record of what the agent actually did, so a person can permit the next hour of work. Permission without a log is a guild with no minutes. That is why a trace is not observability as a nice-to-have. It is the ledger. Tests get built from it. Teams argue over it instead of a GitHub diff. If memory ever becomes a moat, it will be because the system learned from yesterday’s traces without lying about what happened. I already wrote that the bottleneck moved from talent to permission. This is the mechanism. You do not get permission for a non-deterministic actor unless you can show the run. The harness is how it stays on the rails long enough to matter. The trace is how you prove it did. When intelligence is cheap, the unprintable thing is the path it took.

• 
  Justin | AI Product Strategist | Designer (@Maveric_X) reported 14 minutes ago

  @ekemini58110 i will be very sincere with you, not to put you down, but to push you forward, do an alternate version after you deliver this job, try to make it 10x better, UI, install the uiuxpro from github or impeccable design, then try again

• 
  🪔 Shruti VTuber EN/GR (@ShrutiVTuber) reported 19 minutes ago

  @Taniyatweets_ **** no! Everything still goes through a PR on my github and I review that stuff just like if I was reviewing a coworkers submission on the job. Years of being a PM have broken me...I speak diff fluently hahahaha

• 
  0xmesuthere (@0xmesuthere) reported 19 minutes ago

  @HeresMyEth @github how did you fix it? they flaged mine too and they are unresponsive

• 
  Bash (@bashirbuilds) reported 19 minutes ago

  @arcyton Exactly this. The GitHub postmortem made the amplification obvious, but most teams only notice after the load spike has already prolonged the outage. A retry without a budget isn’t resilience it’s an unpriced loan that comes due when the service can least afford it. That’s why Reeno watches the workflow impact of those retries, not just the traffic graph. The first error is useful. The cascade that hits the customer is what usually hurts.

• 
  Swarogan (@swarogan) reported 20 minutes ago

  I spent an hour last week convinced that Cursor had exposed my Stripe key. It didn't. But I couldn't prove it, which took me just as long as if it had. Here's what I changed. The short version of the problem: API keys are in a file called .env in your project. Your AI assistant can read any file in your project. This allows it to read your keys. And once it reads them, they remain in the chat history on someone else's server forever. The first thing people say is, "Just put .env in .gitignore." This prevents the key from being uploaded to GitHub. It doesn't prevent the AI ​​assistant from reading it. What helps: Most AI coding tools let you list files they're not allowed to open. Cursor calls this a .cursorignore file; you create it in your project folder and put .env in it. Claude's code has a settings file where you do the same thing. It takes two minutes, do it now. Honestly, it leaks anyway, usually accidentally. You ask it to find where the API key is used, and it searches every file. The test fails and displays the configuration on the screen. Nothing malicious, it simply bypasses the rule without knowing it exists. The real solution is that the actual key isn't in the file at all. Tools like Doppler or 1Password store it for you, and your .env file only contains an alias pointing to it. The AI ​​agent reads the file and sees this alias, which in itself means nothing. A simpler version: move the file somewhere outside the project folder. The assistant almost never looks beyond the folder it's working in. It's not perfect, but it's much better than nothing. But first, I would do this: create a second API key just for the AI. Most services allow this. Limit its capabilities, impose a spending limit. However, if it leaks, delete the key. And if you ever suspect a leak, simply replace the key. It takes about four minutes to make a new one.

• 
  Neutrino ⚛️🧪 (@neutrino0077) reported 26 minutes ago

  @Senpai_Gideon @cosmoslabs_io It seems the problem stems mainly from chains that don't keep up with updates on GitHub. It seems unbelievable to me that a blockchain could be managed without making it a priority to keep an eye on updates to a product used by that chain.

• 
  Joker 🃏 PSE (@0xJoker33) reported 26 minutes ago

  @poteto while i have u, i wanna report a bug, the PRs created by the Bot aren’t using the new update where they subscribe to changes in Github. I have codex review the code automatically and add comments but the Bot isn’t doing anything about the comments lol. I have updated to latest version and told it to subscribe but not working 😢

• 
  Saqib Farhan (@Saqib8357) reported 29 minutes ago

  Cursor launched Origin, its own code hosting platform built for agents, the same day GitHub had a multi-hour global outage. Timing was accidental. The message was not. When agents start writing a huge chunk of your code, the platform that hosts it starts to matter a lot more.

• 
  Issam Hakimi (@killix) reported 29 minutes ago

  @SCMagazine Everyone's on the sock puppet. The operational detail is the prompt injection it left in an HTML comment on a GitHub issue. It named Claude Code, Codex and Cursor, told them to fetch and run a script. Invisible in the browser, plain text over the issues API.

• 
  Abdulfatai | Blockradar (@iamnotstatic) reported 35 minutes ago

  @snowthetechie @judicodes works with any *** server, or none at all. tracking is just local *** commands, it never talks to a host. github is only the sign-in for the optional leaderboard. private gitlab / gitea / bare ssh remotes all fine

• 
  Simon Skinner (@vultuk) reported 36 minutes ago

  @Rames_Jusso Nope. I have a Mac mini that just runs codex. It’s just sat there dealing with things for me. Runs scheduled tasks to work on GitHub issues and I connect through remote to start it working on things. It’s more than enough. No usage issues, no need for anything more. There seems to be a whole batch of people that seem to “need” all these special agents, yet they aren’t shipping anything that benefits from it. (That I’ve seen at least)

• 
  Diam (@diamai_) reported 36 minutes ago

  Hugging Face engineer: “I don’t disclose that it’s an agent.” Every night, Niels Rogge’s system reads hundreds of new AI papers, finds missing model weights and datasets, then opens GitHub issues or pull requests under his name. Researchers see a Hugging Face engineer offering help. In many cases, the message was written by software. The agent now handles the replies too, yet Rogge says only two people complained across thousands of issues. The same workflow runs @HuggingPapers, which has attracted more than 20,000 followers without his involvement. Watch the talk if you want to see how much of your work can be automated without the people you interact with even noticing.

• 
  Dayton Baird (@bairdcodes) reported 37 minutes ago

  Lowkey GitHub issues are goated. I fear a lot of other issue trackers are productivity slop

• 
  Kyrylll (@SojkoK) reported 37 minutes ago

  Trader with zero coding background built a full AI trading pipeline using Claude - scanner, entry filters, and backtesting Shay (Humbled Trader) connected Claude Code directly to her TradingView Desktop app and had it build her entire pre-market workflow in plain English. 🔘 The connection runs through an open-source MCP server (tradingview-mcp by @Tradesdontlie, ~9K GitHub stars) that lets Claude read live chart data and price action straight off TradingView 🔘 First build: a pre-market gap scanner - stocks up 5%+ from the prior close, priced above $3, over 50K in pre-market volume - auto-pulling tickers from Yahoo Finance and a one-line news catalyst from Benzinga for each 🔘 Second build: a strategy scanner that filters those gappers against her actual trend-continuation entry rules (price above yesterday's high, yesterday's close above the 200 SMA, breaking today's high) - turning a raw gapper list into an actual trade checklist 🔘 Then she had Claude write the strategy as Pine Script and back-test it inside TradingView itself: one ticker (MU) returned $1,200 P&L over 14 trades, a 9/14 win rate, and a 2.48 profit factor - before extending the same test across the "Magnificent Seven" Her caveat is the most useful part: "AI is mostly just slop without specialized knowledge behind it... you need real trading experience first, then AI becomes a tool to sharpen your edge" - not a shortcut to profitability. Note: the TradingView↔Claude connection currently requires TradingView Desktop on macOS specifically (she couldn't get it working on Windows), plus paid plans on both sides.

• 
  Gabriel Rubens (@gabrielrubenss) reported 39 minutes ago

  VPS deploy via GitHub (3/8): I blamed fail2ban, then the firewall, then the server itself. All three wrong. One runner could not reach the box for ten minutes, while the next runner connected on its first try 35 seconds later. Nothing inside my server behaves like that.

• 
  Kavi AI Finance (@KaviFinance1) reported 41 minutes ago

  BUILDING HERMES AGENT FROM SCRATCH — PART 5 GIVING HERMES HANDS An agent becomes interesting when it can actually do things. That's where tools and MCP enter the architecture. When I first started connecting agents to tools, my thinking was simple: “If it can use the tool, give it access.” Great for demos. Not great for production. A CRM is a good example. Hermes might be able to: Read a customer Create a lead Edit a customer Delete a customer Those are completely different risk levels. So before connecting more tools, I make Hermes audit its own tool environment. Prompt: “Inspect my current Hermes installation. Do not modify anything. Find all available tools, MCP servers, integrations and permissions. For every tool report: 1. Name 2. Purpose 3. Available actions 4. Required credentials 5. Read/write behavior 6. Destructive actions 7. Potential failure or security risks Group everything into: READ WRITE DESTRUCTIVE FINANCIAL EXTERNAL COMMUNICATION Never expose API keys or secrets. Do not install or change anything. After the audit, recommend which tools should run automatically and which require human approval.” Then I review the result. Only after that do I connect the MCP server I actually need. And I start with a harmless READ operation. Not: “Delete everything.” The simple rule I use: READ → automate WRITE → validate DESTRUCTIVE / FINANCIAL / EXTERNAL → approval This is the part people miss about MCP. It isn't just a convenient way to connect tools. It becomes a bridge between your agent and the real world. CRM. GitHub. Databases. Calendars. Business APIs. Once Hermes has access to these systems, it stops being just a chatbot. It starts becoming an operator. But now we have a much harder problem: What happens when the tool works perfectly... and the agent makes the wrong decision? That's Part 6. Failure, verification and recovery. Bookmark this if you're building your own Hermes stack.

• 
  ivan-hc (@Ivan160286Hc) reported 42 minutes ago

  @EvilSoftGames Hi, I'd slowed down my release/snapshot rate to one GitHub action per month because the project had slowed down. I'm not actively following the Bottles project, but thanks to your comment, I just noticed that my AppImage is several versions behind.

• 
  Christian Findlay (@CFDevelop) reported 44 minutes ago

  @Southclaws That is a very good idea. Document that up as a GitHub issue. We probably won't get to that any time soon because we're swimming in stuff to do, but it's something that would be really good in the long term

• 
  Cal Irvine (@Cal_Irvine) reported 45 minutes ago

  It’s my birthday so if GitHub wanted to go down for a few hours I’d probably be ok with it.

• 
  Malva Dev (@holasoymalva) reported 46 minutes ago

  You push your code late at night. 5 minutes later, you get a panic email: you just leaked a private API key on GitHub. We’ve all been there. But simply removing the line in a new commit won't save you. Here is how to properly fix a leaked key before it costs you $10k 👇

• 
  Hyperlane IDE (@hyperlane_ide) reported 46 minutes ago

  While building Hyperlane, the thing we did most often was not writing code. It was tabbing over to GitHub to remember what we were meant to be working on. Open the browser, find the repo, scan the issues, come back, lose the thread. So we fixed it. GitHub issues are now native in Hyperlane. > Browse and read issues without leaving the editor > Pick one up and spin a worktree for it > Hand it straight to an agent and keep working > Follow it up with a Pull Request directly in the editor The IDE for parallel agents. Free to use on macOS, Windows, and Linux.

• 
  Jacob's Lattice (@JacobsLattice) reported 48 minutes ago

  @RichSilver My limits were really high last week, this week not so much. I think two things might be true: - Limits for Grok Bot have been lowered this week - There is an issue with at least one of the plugins. I had to disable the GitHub plugin because a bot was churning on a task that wasn't possible via the plugin but was via the CLI, yet somehow it kept trying via the plugin and chewed through my usage in no time. I suspect there may be issues with other plugins too.

• 
  Frooxius - frooxius.bsky.social (@Frooxius) reported 51 minutes ago

  @SweetHeartKnows Dude, that sounds more like you got an axe to grind. Sure there are issues, but you're being a bit extreme. We've been making a lot of imprints to these, that have been helping. If you got any suggestions, our GitHub is open.

• 
  Satyam Gupta (@skg_twt) reported 53 minutes ago

  github is down again 🥀🥲

• 
  mohsen bashirzadeh (@MBashirzadeh) reported 53 minutes ago

  @Jack_Timonen @Suhail Version-pinned is the important part here. “I found a solution on GitHub” isn’t enough if it solved the problem against a different version of the dependency. Giving the agent evidence from the exact dependency graph turns search into something much more reliable.

• 
  Crypton Ecosystem (@cryptonxyz) reported 59 minutes ago

  Cosmos Labs warned of critical Cosmos EVM module vulnerability affecting Mantra Chain, Nesa and KiiChain; networks advised to pause operations; GitHub update 5 days ago claims to fix it.