GitHub Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Kevin Riedl (@kevinriedl_eth) reported 6 minutes ago

  Nobody doubled their QA budget when AI doubled their code output. That is the problem. GitHub reported 43 million pull requests a month and over a billion commits last year. Code velocity is no longer the bottleneck. But test coverage did not double. QA spend did not double. Review discipline did not double. Most teams scaled output without scaling verification. And AI-generated code fails differently. Not because it is always worse. Because it is confident. It often does not carry the usual warning signs: the awkward variable name, the rushed TODO, the obvious gap where someone ran out of time. The bugs look intentional. We are running QA engagements on software we did not build, and the failure patterns have changed. Not necessarily more bugs. A different shape of bugs. The test strategies that used to catch most issues are now missing more than teams expect. The toolchain changed. The verification layer did not.

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  Nic Watson (@jnwatson) reported 9 minutes ago

  My Gemini morning review informed me of a crash reported on one of my projects. On a plane, on my phone, had Claude Code investigate. Arch specific. I don't have one. No problem, GitHub Actions does. Upstream bug? glibc bug?! Newp. I'm holding it wrong. PR is ready before I land.

• 
  Spettro (@spettrotoken) reported 10 minutes ago

  Today, while building the Android version, I checked a new issue flagged on GitHub. I didn't fix it manually. I fed it straight into Spettro. It instantly took over execution. • Wrote the test suite around the issue • Developed the fix • Iterated continuously until every test passed Fully automated, tested, and resolved. This is what true autonomous workflows look like. Still building.

• 
  Ethan (@DuoEthan) reported 12 minutes ago

  GitHub Copilot shipped before ChatGPT. Microsoft had the distribution lead, the developer relationships, the code repositories. It looked insurmountable. Here's the read on what went wrong: Copilot optimized around code generation, right as the market decided context management was the actual problem. You can have a two-year head start and still build for the last problem. Microsoft Build is June 2. They're shipping their own model. And also putting Claude in Copilot, which is a tell.

• 
  geekopedia (@geekopediax) reported 14 minutes ago

  @DenisKursakov That 3.3% conversion proves forced bundling cannot fake organic demand. Microsoft's paid AI market share slid 39% in six months. Ironically, GitHub Copilot still holds 42% of the dev market. Splintering apps is a loss; they must fix core utility before churn accelerates.

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  VictorHoncharuk (@VictorHoncharuk) reported 23 minutes ago

  @JesusMartinez User ↓ AI agent ↓ Router ↓ LLM Provider ↓ Custom Tool ↓ Database/API you dont want all this stages (what is not Antropic or Openai but can be "custom opensource somebody from github") have your API, credit card number, wallet adress, seeds, google login tokens etc

• 
  Nitin Pulyani (@npulyani) reported 24 minutes ago

  I hosted my site on github pages. Now I need an edge compute layer and a server side deployment. Migrating to vercel. Any better suggestion?

• 
  Hasif (@hasifdev) reported 24 minutes ago

  @github China is going down!???

• 
  Neuroquark (@neuroquark) reported 25 minutes ago

  @malhar_kamble that's what i thought. i'll push it to *** and for extra security i'll encrypt it using symmetric key encryption the problem is i have private repo contributions turned on so every time i push it to github it'll show up as a +1 on my chart which i don't want

• 
  𝙊𝙩𝙞𝙨 (@Otisarbitrage) reported 27 minutes ago

  Step 4: Agent synthesizes and acts. It reads submissions, identifies common friction points, generates a bug report, and creates a GitHub issue automatically. Step 5: Amplify the launch announcement. Same agent fires an X raid on the launch tweet for $2.50 - 50 likes, 20 reposts

• 
  vish (@vishctx) reported 28 minutes ago

  @OfirPress There’s a massive blind spot in the benchmarks. By the time an issue makes it to GitHub with a reproducible state, 80% of the hardest engineering work is already done. Current benchmarks hand models extremely precise problem statements. But in the real world, like when debugging the Linux kernel, you rarely start knowing what the problem actually is. All a user will report is “the app is OOMing, and increasing memory doesn’t help.” Digging into that requires intuition built from past issues. The root cause could be memory leaks, memory fragmentation, or a race condition where threads acquire memory and never release it leading to starvation. We desperately need benchmarks with highly ambiguous starting conditions to test if a model can navigate a state with multiple distinct root-cause scenarios. Right now, models like Opus easily get stuck in loops during open ended investigations. They rarely move forward unless I ask it to check for hypotheses A, B, or C. The next frontier for SWE evals should also include cases where the model is trying to figure out what's actually broken in the first place.

• 
  Dane Wilson (@daneo_w) reported 30 minutes ago

  10 benchmarks. One page. No more digging through PDFs, blog posts, and random GitHub issues.

• 
  Kyle Cole (@kylecole90) reported 33 minutes ago

  @thsottiaux GitHub issue intergration. I want to be able to select the issue right from Codex. Claude Code desktop has this feature and it was nice to not leave the app

• 
  ray arayilakath ‎.߆ (@rayhanadev) reported 33 minutes ago

  the fact that i can see a github issue in slack, automatically link and assign it to me in linear, and get cursor background agents to start root causing it while im in the back of this uber is so cool

• 
  Ebysslabs (@ebysslabs) reported 36 minutes ago

  RAG Radar monitors GitHub and other engineering communities for retrieval reliability issues and ranks the highest-signal problems. RISWIS takes those kinds of problems and applies governance controls inside a RAG pipeline so bad retrieval doesn't become bad answers. Im curious: what's the most common retrieval failure you're seeing in production today? #Rag #retrieval