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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Shravan Bachu (@DailyPulseLive) reported 3 minutes ago

  @riya_mishra007 Before we use AI in code, you should have proper basic coding knowledge. Don't simply rely on it. In GitHub Copilot, there is an option called "debug mode" that will simply detect the issue, provide a stack trace, and offer end-to-end resolution steps.

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  Matias Kiviniemi (@PragmaticZone) reported 5 minutes ago

  @Appyg99 Bigger change/opportunity could be in legacy operations. Big chunk of IT budget is spent on "men passively watching servers" with limited ability to solve problems. The moment you can just hook an agent to cloud account, github and ticketing and have it handle everything...

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  aventursne (@00aventurine) reported 5 minutes ago

  @jvasata03 @Jooornio i agree that code on github and such being used for ai is mostly ethical. my issue is that i cant find a good reason why art should be treated differently other than some vague "it feels icky". so i choose to not really say anything

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  Aseem Shrey (@AseemShrey) reported 6 minutes ago

  GitHub doesn't normalize signatures before verifying, so the mutated commit keeps its Verified badge. And because a commit hash includes its parent, changing one commit cascades new hashes down the whole chain. A ton of tooling assumes "this commit hash = this exact content, signed by this person." That assumption is wrong.

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  Han MF Brolo (@bxlewi1) reported 8 minutes ago

  @dhh I put them against each other and have them talk to each other through GitHub issues. They have a loop that watches until they see something for them, do the work then send it up for the next one.

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  RyDawgE @ Halfapps (@RyDawgE_) reported 10 minutes ago

  @StorgaardMikkel @AmazonessKing3 ... Of AI and not the root cause. We can have AI without malicious terms and conditions foul play. People are smart, people will stop using GitHub eventually and go back to hosting their own *** servers to avoid this exact problem.

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  sgbett (@sgbett_614) reported 12 minutes ago

  @kristovatlas I have copilot enabled, and a skill /copilot-check that reads review comments evaluates them on merit then fix/resolve with comment. It iterates until nothing is left. Then i run /review fix (what should be) minor polish issues and kicks off a final /copilot-check loop. Not bullet proof but catches a lot of errors. Customer copilot-instructions.md for GitHub. The other thing that makes a big difference is closing the “defer” gap (Claude defers things but the only record is a comment in a now closed PR. I have it lean toward always fixing there and then unless there is a compelling reason for it to be a separate PR. If it must be deferred then it must get a new issue. Just lately I’ve been nailing down its proclivity to write comments instead of self documenting code. My code ran about 8% comments/code. Claude was putting out 48% comments. Horrendous. Telling it how to write code that doesn’t need commenting over hitting metrics to try and make sure it makes sensible decisions. We will see!

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  JMACK19 (@TheJMACK19) reported 15 minutes ago

  Serious question, why do so many of these projects require you to join a discord server to download them? I don't support discord, I'll never speak to anyone in the server after download, it's a massive waste of time. Why not just drop it on GitHub?

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  OIiver (@posedscaredcity) reported 16 minutes ago

  @my_knn_totoro @KSimback i actually run gstack across my company and can answer this too ( i was just seekign outsider opinion) pros: - works in practice like magic now for us - the agents are continuously learning. the default output before vs after is like a 3 generation model difference on the same model. gpt 5.5 with it was comparable to fable without it. fable with it is insane. - much easier to prompt - no need to transfer much context - new hires and anyone can get any and all questions out of their wheelhouse answered as needed - tracks decision etymology in a way that was missing cons: 1. its quite broken: many days of agent time spent to get and keep it working. dreaming has broken so many times. 2. authentication wasn't developed or wasn't developed well and setting up new hires or new agent systems to hook in with correct attribution is a ***** (with how i set it up at least) 3. once installed agents do not use it and do not use it well. we needed a good agents.md file telling it to look for task preferences before starting, and to fill out the empty search queries from the start when wrapping up and meta preferences within gbrain itself. 4. it slows down the agents since they have more to traverse 5. ingestion was broken out of the box and integrations sucked. we hooked in and heavily modified composio so i could ingest a lot of events 6. connecting a github account will ingest all events from all open source repos you've ever touched. cleaning that up was a ***** 7. federating access is really hard as a result haven't bothered but isn't scalable.

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  Jacob Gadikian (@Senpai_Gideon) reported 16 minutes ago

  @bdowns328 Yes that's exactly the problem. It's just not all that great. GitHub keeps getting worse and worse but gitlab is still not better than GitHub

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  Bart De Ruyck (@bartderuyck) reported 20 minutes ago

  @MarkJSzymanski You lost me at "no server to go down". How do you think static files are served to visitors, then? Whether it's Github Pages, Cloudflare Workers, Vercel, whatever: it's on a server. And it can go down.

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  Hugo Bowne-Anderson (@hugobowne) reported 23 minutes ago

  “You still use pull requests? I wouldn’t even do that anymore. Just push it straight to trunk, have your agent summarize it.” That’s @gregce10, co-founder and CPO of SpecStory. He previously worked at GitHub, Dropbox and Google, and was CPO at Pluralsight. And he kept going: - PRs are the limiting gate when agents produce more code than humans can review. - The model should never decide when its own work is finished. Put the deterministic checks somewhere it cannot access. - *** is probably here to stay. Whether GitHub remains the platform, “we’ll see.” @HanchungLee came at the same problem from the evaluation side. Han is Director of Machine Learning at Moody’s and works on SkillsBench, evaluating skills across combinations of models and agent harnesses. - An agent is the model plus its harness. You need to evaluate the complete system. - A green check proves nothing if the agent found a way to game the task. - Your agent could delete the failing test and declare success. Both are figuring out how to turn masses of agent-generated slop into signal. Greg mined 516 saved agent sessions to recover the decisions and intent behind the work, identify recurring practices, and forge the ones he approved into reusable skills. Han runs skills inside controlled environments, grades the result, and preserves the complete trajectory so we can inspect what the agent actually did. Preserve the intent. Inspect the trajectory. Verify the result. Turn what works into skills. Full episode in the replies 👇

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  J A X 🐬TermMax (@HieuTrinhVn) reported 24 minutes ago

  @levelsio You might try the K3 GitHub issues for community fixes there.

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  Aditya🌪️ (@aditya4f) reported 24 minutes ago

  why are so many GitHub accounts getting banned/suspended these days? glitch or something?

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  Richard Geldreich 🇺🇸 (@richgel999) reported 32 minutes ago

  "Frame is an X11 server written in pure x86_64 Assembly... Frame was created over the past month largely via Claude Code. The Assembly code in all its glory can be found on GitHub."