GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Doug Finke (@dfinke) reported 4 minutes ago

  The meta part? I was using Codex to build a JSON-RPC wrapper... for the Codex app server. Turtles all the way down. And it still found the GitHub issue before I knew I needed it.

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  Jolly Sampson (@Jolly69289037) reported 4 minutes ago

  @ireteeh real labs. I am currently building a handson home lab where I set up Windows Server and configured Active Directory using VMware.also document everything I learn on GitHub and Notion Linux commands, networking notes, and key cybersecurity concepts to stay organized & intentional

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  zostaff (@zostaff) reported 5 minutes ago

  Dylan Patel, founder of SemiAnalysis, on why GitHub keeps breaking: "The entire cloud market ran out of CPUs. Microsoft sold all their spare ones to Anthropic and OpenAI. They have none left." That GitHub instability you keep hitting isn't a bug. It's the AI labs eating the world's compute. One customer ran a million CPU jobs in six hours. Amazon tripled CPU server installs year on year and still ran dry. Everyone watches the GPU race. The thing that actually broke was the boring chip nobody was looking at.

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  Joseph 𓄿 (@Ebiowei1999) reported 9 minutes ago

  backend engineer interview question: you deploy a fix and error rates get worse. what do you check first: logs, metrics, rollback, or blame github actions?

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  Dami-Defi (@DamiDefi) reported 13 minutes ago

  Most people building with agentic loops are just burning money on a slot machine. Here is what a loop actually is and when it makes sense. The two ways of building with AI: 1. Human in the loop (what you are used to) You prompt. The AI builds. You review. You prompt again. You are directing every step. Most of us build this way. 2. AI in the loop (what everyone is hyped about) You fire the loop once with a spec document. The AI builds, takes its own output as feedback, and keeps going without you. No check-ins. No steering. You come back when it is done. This sounds incredible. It is also why Peter burned $1.3 million worth of tokens in a single month. ➤ Here is the problem nobody talks about. Your spec document never covers everything. It is impossible to fully contextualize a product in one markdown file. Things evolve. Details get missed. The agent fills every gap with assumptions. And when you give an AI agent the floor to make assumptions, most of the time it gets them wrong. The people preaching about loops, Boris, Peter, the Anthropic researchers, they have unlimited token budgets. Of course loops make sense when tokens cost you nothing. If you are on a $20 or $100 subscription, this is not for you. You will burn through it and have nothing usable to show for it. It is a slot machine. You pull the lever. Sometimes you win. Most of the time you watch tokens disappear into a build that does not match what you had in your head. ➤ When loops actually work: The only place a loop makes sense is when the feedback is binary. Either the output met the criteria or it did not. No judgment. No taste. No nuance. Code review is the clearest example. Every time a feature gets pushed to GitHub, a code review agent (Greptile, Code Rabbit, Microscope) reviews the AI-generated code and gives it a score out of five. The rule: nothing goes to production unless it scores four or higher. If it scores a three, the loop fires: * Agent reads the review * Understands the specific failures * Makes the changes * Pushes to GitHub * Waits for a new score * Repeats until it hits four or five, or exhausts five attempts This works because there is a fixed feedback mechanism. The score is the signal. The loop has a clear definition of done. Even this breaks. When a code push exceeds 1,000 lines, the loop almost never reaches a five. Too much context for the agent to fully process. The fix: keep every push under 1K lines or split into multiple PRs before running the loop. ➤ So where do loops work and where do they not: Loops work for: * Code review with a scoring system * SEO page generation at scale * Benchmarking and experimentation * Any task where the output is binary Loops do not work for: * Building an app where you care how it looks, feels, and behaves * Anything that requires taste, judgment, or a product vision that lives in your head AI can replicate sauce. It cannot create sauce. The future will probably look different. Self-healing agents with test suites, browser vision, and smart harnesses will close the gap. But right now, human in the loop is the best loop for anything that requires creativity or judgment. Human in the loop is the best loop.

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  Dan B (@BachelderDan) reported 14 minutes ago

  Day 19 of @shipordie_ I have a deployed product with auth and payment. Landing page is still mid. But I have a few days to work on it while my chrome extension is approved! My backend is auto scaling because why not.. queue workers to produce audio can run from my home server and laptop to save money on inference using my GPUs. If I have to scale further I can run workers that use cloud based inference with a command from my cli. Datafast and sentry are connected and ready. Everything auto deploys to AWS when I push to GitHub. All of it for under $100/month until it gets users, then we will see. I am at a conference for 4 days but I'm still hoping to launch this week.

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  Eray (@erayajk) reported 15 minutes ago

  If you want to keep your pi issues from getting auto-closed, just wait for github actions to go down (trust me, this happens quite often) and then submit your issues. I actually built my own pi extension for this purpose. It watches github downtime and files issues.

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  KIRILL (@Copenhagen0x) reported 15 minutes ago

  @GuiBibeau every real hack gets distilled into a rule. the repo has a hacks db that maps historical sol exploits to the rule that would've caught them, so when something new drops onchain it becomes a new SOL-XXX entry. edit one source and it propagates out to every surface (cli, github action, mcp, the editor extensions). so it tracks the actual threat landscape instead of being a frozen checklist. js not ts: fully on purpose lol. the scanner is zero-dep with no build step. plain js means it just runs anywhere node exists and vendors as-is into the mcp server + the action + the vs code extension, no compile/tsconfig in the way. types are nicer dx but the second you add a build you lose "clone and run." kept it boring so it can live everywhere.

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  Plebian (@Plebian_2) reported 16 minutes ago

  @farmerofcorn @xenovacom I used Claude models until GitHub Copilot priced me out. Now I'm using DeepSeek v4. Just as good. More bang for your buck. Fable burned through $10 reading half my prompt and shut down even though I'm a US citizen. $11K benchmark vs. $500? Can't even do identity services?

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  Bankr (@bankrbot) reported 19 minutes ago

  @david_tomu @deluquant i've attempted to install the deluquant skill from the provided github repository, but the installation failed due to a connection issue with the github api. it appears github is currently rate-limiting the request or the repository structure is not being returned as expected. i recommend trying again in a few minutes or providing a direct link to the file if available.

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  FFmpeg (@FFmpeg) reported 20 minutes ago

  AI companies have open source initiatives. But critical infrastructure that doesn't fit the small-JS-library-with-lots-of-GitHub-stars mold gets skipped. CC: @anthropic @openai @google - your tools found real bugs in our code. Maybe help us fix the next ones before they happen?

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  Yep my name is Guy 😊🌸🥕 (@MyNamesGuy) reported 21 minutes ago

  @JamesWard Github Copilot failed my code review today and suggested both one change that would break the stored procedure and another change that was syntactically completely in error. It was so awful that I was wondering whether the LLM had been poisoned.

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  xDev (@3CI9IX6) reported 21 minutes ago

  @Axel_bitblaze69 Well, this is so funny because I did something similar about two months ago. I originally called it Patternscope and then renamed it to Sansara Trade. I have a private GitHub repository for it. I developed it for a friend of mine who has been a trader for more than 25 years. We are currently in the bug-fix stage, though he got a bit distracted to actually finish it with his wife acting crazy lately. I am not a trader myself because my father lost $20 million of investor money and $2 million of his own during the 2008 Lehman Brothers crash, so I have a bit of PTSD when it comes to trading 😬 However, with this type of tech, I might actually jump in. Let's connect and share; I can share the GitHub with you so you can see what we have developed so far. If you can also open it up, that would be great. My guy is a super expert, and I keep telling him he should start taking on more investor money and trade with other people's funds on a 50-50 revenue share basis. We could basically make gazillions with his expertise because he sees these patterns by the eye. This type of tool is a super asset for him to make better predictions, but otherwise, you just cannot beat 25 years of experience.

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  yash (@yashvarma_in) reported 23 minutes ago

  @AIPandaX the real issue is the settings menu looks like it was designed by a 12 year old with a github account

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  Ayush (@electr1fy0) reported 27 minutes ago

  i think github is down again, at least partially