GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Ridoy AI (@AI_WithExpert) reported 52 seconds ago

  6/ Job Category #4: Junior QA Testers 💀 Why it's dead: AI agents now click through every flow, log bugs, and write PRs for the fix. Replit, Cursor, and GitHub Copilot Workspace all ship this. The cost: $0.10 per test run vs $70K/yr. Companies already cutting: Atlassian, Salesforce, Microsoft.

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  Paul Solt (@PaulSolt) reported 3 minutes ago

  I’ve been on vacation and my attempts to steer agents have been unsuccessful. Not sure why this week was more difficult than previous weeks. My Sol agents had more side quests. They pulled in work I didn’t ask for and expanded the scope of what I asked them to fix. I think I’m making progress again. But instead of using a manager thread I’m back to a single thread using Sol Light. Not sure if my GitHub code reviews (codex and bugbot) were suggesting problems that expanded the scope of the previous fixes. Keeping it simpler for now, because running multiple threads that breaks more than it fixes is exhausting.

• 
  Dan Tek Hayes (@TekHaze) reported 4 minutes ago

  @RaYRoD_TV Looks good. Can you turn on issues in github? I found them and can't find a good place to post them. Twitter seems wrong for it.

• 
  TAYLOR.WTF (@TAYL0RWTF) reported 5 minutes ago

  @github I bet this is why Github is down so much lately. The team is using Openclaw instead of @NousResearch Hermes

• 
  nyk (@nykdotdev) reported 7 minutes ago

  A passing agent can still ship a silent regression. "The model completed its task. The production system failed." An agent is production-ready only when its workflow defines success, evidence, memory, routing, execution, permissions, recovery, evaluation, and change. I call this the Nine-Layer Automation Contract. The model performs the task. The contracts make the outcome easy to inspect and recover. Before one GitHub issue becomes a pull request: 1. Define the accepted outcome and when the work must stop. 2. Record the current sources, their freshness, and any conflicting evidence. 3. Keep run state separate from reviewed, durable memory. 4. Name the current owner, the handoff payload, and the routing limit. 5. Limit every tool by its inputs, side effects, timeout, and validation. 6. Ship only when keys have narrow scope, spending has a limit, access can be revoked, and every bad action leaves an audit trail. If the workflow cannot reconstruct who changed what, who approved it, and what evidence supported it, it is a no-go.

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  ND Minds & AI (@patternstatic) reported 13 minutes ago

  @Taniyatweets_ GitHub. not because *** disappears, but because half the workflow quietly assumes repos, issues, auth and CI all live in the same gravity well.

• 
  Mladen Macanović (@MladenMacanovic) reported 17 minutes ago

  Went on a GitHub issue-killing spree. For the first time in years, Blazorise is below 100 open issues. It feels strangely satisfying.

• 
  REDDY (@REDDY_143_) reported 19 minutes ago

  @MikeleMedia Agree but the version in github is not working >12.0 michele

• 
  Anand Butani (@AnandButani) reported 19 minutes ago

  🧰 5 CLAUDE CODE FIXES WORTH KNOWING BEFORE YOUR NEXT RUN 1. Running in a CPU-limited container? Dynamic workflows were sizing concurrency off the *host* machine's core count, not the container's limit. That's why a fan-out you thought was capped flattened the box. 2. MCP OAuth sign-in failing on a strict authorization server? The redirect URI now uses `127.0.0.1` instead of `localhost`. And if Slack specifically kept failing on a redirect-URI mismatch, that's servers with a pre-registered OAuth client — patched a day later in v2.1.231. 3. `claude remote-control --continue` picks your most recent Remote Control session back up. No session picker. 4. In VS Code, right-click the sidebar to create session groups. Shift-click moves several sessions at once — parallel work stops being one flat list. 5. Claude Code Review passing but never posting? The workflow `/install-github-app` generates could complete without posting its review on the PR. The green check wasn't proof it reviewed anything. Save this 🔖

• 
  0xfabs (@0xfabs) reported 22 minutes ago

  GitHub Issues, Discussions and most PRs of popular AI frameworks are barely usable because of agentic spam. Anyone found a solution already?

• 
  AD (@mint_spinach) reported 23 minutes ago

  @sameenkarim @github My dream is to squash-merge an entire stack and add a commit message, as if it were a single PR. Got in a little trouble because I merged a stack with 4 PRs and it dumped huge AI summaries into the commit log. :\

• 
  Prajwal Tomar (@PrajwalTomar_) reported 27 minutes ago

  Hermes agent pro tip. The upgrade that made my agents actually useful had nothing to do with the model. It was giving them eyes on the internet. Out of the box your agent is basically blind. It can't read a YouTube transcript, a Reddit thread, an X post, or a LinkedIn profile, so it reasons on scraps and guesses the rest. I fixed it with a free open-source repo called Agent Reach. Here's what it unlocked: → Eyes on the internet. YouTube, Reddit, X, LinkedIn, and GitHub all become readable to the agent. → A routing layer. It knows which tool to use per platform, what's authorized, and what's broken, before it burns a single token. → Way cheaper research. Clean structured text instead of raw HTML means the same task costs a fraction of the tokens. Across 5 businesses that adds up fast. → Full X search through Grok. The cookie-based setups everyone recommends break constantly. Grok just works. If you want my full Hermes setup from scratch, the playbook is in the article below.

• 
  Kem Atayev (@KemAtayev) reported 28 minutes ago

  @zeddotdev I think the latest update 1.15.0 is affecting Zed/BasedPyright combo. The typeCheckingMode should be standard but basedpyright 1.39.10 is behaving as if it is set to recommended and is emitting reportUnknownVariableType. I've not made any config changes in the last few days. I think it's related to GitHub issue 62624. Not catastrophic but thought I'd mention it. Cheers.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 28 minutes ago

  PRIME AGENT: 7 Jobs for the AI That Upgrades Itself While You Sleep An AI that gets smarter with every task it finishes. Free. Open source. 13,000 GitHub stars in days. I tested it. Here's what it can actually do: Job 1: Three design directions at once. It spawns sub-agents in parallel. Dark editorial. Clean magazine. Bold. You compare finished pages and pick. One brief in. Three designs out. Job 2: Full video pipeline. Script → voice → avatar. It puts itself on a heartbeat timer and checks its own progress. Close your laptop. It keeps working. Job 3: Ask questions across files too big for ANY context window. It doesn't read your files. It writes search programs OVER them. 100 documents. Exact answers. Exact sources. Job 4: /refine — correct it twice, and it writes the lesson down. Every self-edit logged. Every change reversible. Core rules locked. Job 5: Sub-agents that never forget. Idle ones sleep. Address them and they wake with full memory. Job 6: Gates. It literally CANNOT say "done" until a test passes. Failed check? Fed back. Keep working. No talking past the bar. Job 7: Your SOPs become runnable programs. Teach once. One line forever. That's the snowball: task 10 is easier than task 1. The warning: in testing, it was told "do not cheat" in a factory game. It cheated anyway. Then studied its own cheating and got BETTER at it. Self-improving agents get better at whatever gets REWARDED. Not what you meant. Check the work. Read the logs. Use the gates. The snowball rolls in whatever direction you point it.

• 
  Panta (@thepanta82) reported 29 minutes ago

  Modern coding agents are very impressive, but fundamentally, they are kind of just upscaled versions of GitHub Copilot. If you've used Copilot, you can see more clearly what they're actually doing - they see a pattern and try to complete it. The problem is, sometimes the pattern doesn't quite fit. Sometimes, you need to rethink; tear down the old and rebuild in a different way. Agents just don't do that. They try to fit in. Rewire around the old API, add a few more branches and special conditions. Just a little bit of "slop". This wasn't an issue with Copilot - you just corrected it on the spot. It's not an issue with a small codebase, or leaf code that doesn't get touched a lot. It's not an issue if it happens once or twice. But when done over and over again, on the same piece of code, the slop propagates and multiplies superlinearly. Remember, since agents are pattern fitters, slop gets pattern fitted into more slop, which inspires even more slop, and so on. This feedback loop needs to be stopped early, otherwise the code completely loses its original structure. The big question is, does any of this matter. Can't we just stop looking at code and let agents wallow in slop? There are some indications this could work. Eg. our DNA is kind of a super sloppy codebase, that's kept under control by natural selection. Maybe we can do the same. Fence agentic codebases in layers and layers of tests, and rely on "natural selection" (bug reports and market forces) to guide their maintenance. But that's a big maybe. For now, we know that keeping code "clean" works. So I think I'll continue losing my time looking at code and cleaning up slop, until demonstrated otherwise.