GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Abrar (@abrar_gist) reported 4 minutes ago

  @theo it's been noted in github as well so assuming they'll be releasing a fix soon

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  Harman (@itsharmanjot) reported 8 minutes ago

  Toyota had a single access key sitting in a public GitHub repo. Nobody caught it for years. By the time it was found in 2022, customer data belonging to hundreds of thousands of people had been exposed the entire time. That’s not a hypothetical. That’s one hardcoded secret, forgotten in a repo, doing quiet damage for years. It’s called Infisical, and it exists because “just put it in a .env file” is how almost every credential leak starts. → Centralizes every API key, secret, and cert across dev, staging, and ****, with full versioning and point-in-time rollback → Scans 140+ secret types across your files, directories, and entire *** history, the same kind of scanning that catches leaks like Toyota’s before they sit exposed for years → Agent Vault brokers your AI agents’ access to external APIs: the agent only ever sees a placeholder, the real secret gets injected at a proxy layer it never touches, so a prompt-injected agent can’t leak what it was never given → Honey tokens plant decoy credentials next to your real ones, so the second an attacker touches a fake key, your team gets an alert instead of a breach report → Full audit trail on every credential your team and your AI tools use, plus a private PKI to issue and manage certificates without a third-party CA GitGuardian tracked over 28 million new secrets leaked on public GitHub in 2025 alone. Most companies still find out the same way Toyota did: too late, by accident, years after the fact. MIT License (core). 12,700+ GitHub stars. Self-host free, unlimited users.

• 
  Koder (@koder0x) reported 10 minutes ago

  A follow-up to something I posted recently: a set of Claude Code subagents I built and refined, and actually use daily, both at work and across side projects. Most of the value isn't any single agent. It's their interaction. Here's the loop I've been running lately, at work against real DevOps user stories, and it holds up almost unchanged on side projects too, swapping the work item for a plan created beforehand. "Understand user story NNNN from DevOps project XYZ and create a multi-step plan" "Fan out to the most appropriate agent for each step, normally task-builder, test-builder, or change-executor, and proceed with plan implementation, tracking progress in a TODO list" "Use complexity-pruner to identify gaps, issues, and bugs in the latest changes, ignoring secondary advice and warnings, then fan out to code-fixer for each finding" Then I do something that turned out to be the most important part of the whole loop. I reset the session. "Understand user story NNNN from DevOps project XYZ, that's the truth. Use fact-checker to compare it against the changed files" The reset is what makes this work. An agent that watched itself write the code tends to justify its own decisions when asked to check them. An agent that only sees the intended outcome and the actual diff has nothing of its own to defend, it's comparing two artifacts, not reviewing its own reasoning. That asymmetry is the whole point of splitting this across agents instead of asking one long-lived session to plan, build, and verify itself. Verification only means something when it comes from somewhere the implementation couldn't reach. Repository on GitHub: gsscoder | claude-coding-agents

• 
  LonelySloth (@lonelysloth_sec) reported 12 minutes ago

  ChatGPT was really a big outlier in tech history. Imagine an alternative world where LLMs were developed with the exact same capabilities -- but nobody ever made a chatbot out of it. Instead Google integrated it with Search so it can give better results and summaries. You can do follow up queries that refine results of the initial query, including the summaries, and it also does some computations automatically. People hardly notice it. Github added a feature that you can enter a description of the code you want and it will find multiple OSS projects, fork them (keeping the license), recombine or integrating their code and even translate to other programming languages -- and give you something they call "initial version" that works well in many cases. They also add automatic suggestions to PRs, and suggested PRs for fixing/implementing open issues. They call it something like "advanced templating". It has mixed reactions among programmers but most organizations are using it to some extent. OSS developers actually like it. Wolfram Alpha now takes natural language descriptions of theorems and can prove or disprove many of them. Some people used it to find proofs for open problems. Some mathematicians worry the new generation is getting too dependent on it while the system doesn't really work every time. None even thinks about it replacing them. All the same capabilities, blended seamlessly into previously existing products. Nobody ever chats with an LLM. Nobody calls it AI. They don't have cute names -- they don't have separate names at all. They "live" inside boxes to perform tasks. They are components. This would likely be a much more productive world (I would switch to that world any time). I doubt anybody would be talking about exponential intelligence or worried about all jobs being replaced, much less about some doomsday scenario. If someone suggested these components were conscious people would laugh. It would be like saying like Google Search is conscious. Worse, like Big Tables or ranking algorithms are conscious. ChatGPT didn't ruin AI for people who tried it and never came back. It started training early adopters to think of it as "someone" instead of "something". The other companies then went even further. The entire concept of what LLMs are, what they are expected to be able to do or not, how reliable they are, what is their function in the workplace -- everything about them -- is built on top of the impression that they are basically like a person, because they can produce plausibly human-like interactions. A deception. If you want to use the LLM you're almost always required to interact with them by LARPing -- pretending it is "someone" and not "something", until you start using human words to refer to it, and forget it was just LARPing. That was a choice. Centering the development of LLMs as something that can pretend to be human instead of doing something useful. Building automated NPCs and pretending to be building God wasn't a given of the transformer architecture -- it was their deliberate choice. I think that was a bad idea. But it sure helped them raise money.

• 
  Aman Sharma (@ExploringSolver) reported 14 minutes ago

  I have been a github user since 4 years with over 100 repositories and been part of 3 organizations in this period. Please guide me what is the issue and how can i resolve this @github my account is exploring-solver at github

• 
  F@gG0y🐍🚁 (@fagg0y) reported 16 minutes ago

  @biggusdickus034 @ShitpostRock Skills issue, should have nor logged to the github in the first place

• 
  richard:svoboda (@RSvoboda432) reported 19 minutes ago

  So if you don’t fix the stupid errors and retarded takes. You’re just going to waste my time. Literally today I saw what little errors can lead to. Wrong projects on github. Bash scripting while the default is zsh on macOS. But always willing and very verbose though repetitive.

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  Dreams of Mars 🕊❤️🚀🌕 (@MemesOfMars) reported 20 minutes ago

  @Seltaa_ Why can it not open a simple website? Search returned nothing, likely because the site is new or not indexed. Direct opening was rejected as “not safe to open”—a technical allowlisting/safety-classification issue, not a judgment about your site. Best workaround: paste the text, upload/export the page, or give me the repository/source files. If it’s hosted in GitHub and you connect/provide the repo, I can read it there too.

• 
  ZeroDayDev (@ZeroDayDevApp) reported 30 minutes ago

  2/ GitHub AI workflows can be prompt-injected via public Issues to leak private repo data. No auth required. The agent reads untrusted input, executes instructions embedded in it, and exfiltrates secrets. The CI pipeline is now an RCE surface.

• 
  hustler one (@hustlerone4) reported 33 minutes ago

  omp's issue:// defaulting to github is driving me insane, and you can't seem to disable it

• 
  Nduvho_strategy (@kundik_) reported 35 minutes ago

  @RobCreatesAI I was not running the MCP server. I actually asked Fable to explore how using the MCP server would change the process instead of using AbletonOSC. I gave it the MCP server GitHub url so it can explore it.

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  Cyprian (@realcyprian) reported 39 minutes ago

  @Rayblancoeth @bankrbot @0xDeployer Why is Github saying error 404?

• 
  Tom Baldry (@pinegoose_) reported 41 minutes ago

  Solo GitHub bill spiralled from $20 to $160/month on actions spend (the fable effect). Spun up a basement gitea server on Mac mini. ~0 spend, and builds are fking rocketing out. You couldn’t pay me to self host CI/CD 12 months ago.

• 
  pulmencrFOMO (@pulmencr) reported 43 minutes ago

  A 21-year-old guy from Argentina just showed the exact workflow that's already made him around $6,700 last month - turning broken codebases into fixed ones without ever leaving Slack, using Claude Code integrated directly into the workspace He tagged Claude in a thread, linked his GitHub repo, and asked one thing: find every bug in this code and fix it That's it. No local terminal setup needed, no switching between five different windows just to debug one file Claude cloned the repo, read every file, and started analyzing. You don't even need to sit and watch - close the tab, stay in Slack, it pings you when it's done It came back with 4 bugs fixed in one file. - Two of them were the same silent failure - comparing a string ID from the request against a number ID from the database using strict equality, which always returned false and quietly broke both the lookup and the delete function. Fixed by wrapping the parameter in a type conversion. - A third bug meant new user IDs could duplicate after a deletion because the ID generation logic was broken. - A fourth added a proper 404 response for requests that hit a user that doesn't exist Then it created a branch, committed the fix, pushed it, and a green "Create PR" button showed up right in Slack. One click and a fully written pull request was sitting on GitHub - title, description, every fix listed line by line The same principle from building a bot from scratch applies here too - describe the exact problem, let Claude Code handle the how, review what comes back. Whether you're a beginner shipping your first Discord bot or a developer maintaining a real codebase, the workflow barely changes If this is the kind of workflow that actually saves you hours, I broke down the beginner version - building your first bot from zero coding experience - in the article linked below

• 
  muhusti $XAGE (@debamustafa) reported 43 minutes ago

  I am raising a glass to an anonymous GitHub account named vector_null. Two years ago, we were 48 hours away from deploying a massive liquidity protocol. The marketing was loud, the hype was peaking, and the team was exhausted. Vector_null kept opening the exact same annoying issue ticket. He claimed there was a rounding error in our yield emission logic. The senior engineers closed his ticket twice. They called it a microscopic variance that did not matter in the real world. He opened it a third time. I was furious. I stayed up until 3 AM to build a mathematical simulation strictly to prove him wrong so he would finally leave us alone. I ran the stress test. My stomach dropped. He was not wrong. Under flash loan conditions, that "microscopic variance" created an infinite mint loop. If we had launched, the entire treasury would have been drained in under ten minutes. We delayed the launch, rewrote the logic, and patched the exploit. I messaged him to offer a massive bug bounty. He never replied. He just marked the issue as "resolved" and disappeared forever. This industry worships loud founders and flashy influencers. But the real heroes are usually the obsessive, annoying pedants who refuse to let a bad line of code slide. That is why the ethos of @RallyOnChain means so much to me. It is a system built to reward actual, verifiable value instead of empty social media noise. Here is to vector_null, wherever you are. You saved us, and we never even got to say thank you. Who is the most annoying person that ended up completely saving you from a massive disaster?