I wrote a practical field guide for engineers building LLM-powered troubleshooting assistants for real production operations.
Not another “chatbot over docs” walkthrough.
This is about designing dependable evidence workflows: gathering context from logs, metrics, traces, deployments, tickets, and runbooks; connecting retrieval to operational knowledge; defining safe tool contracts and approval gates; evaluating diagnostic quality with realistic incident cases; adding observability, memory, and feedback loops; and rolling out from prototype to production.
Limited-time price: $9.99 / €8.49.
Amazon: https://t.co/wV2P0FB8lU
DRM-free: https://t.co/7sSLoXpPCg
@xy0zhswyqklSMPy@STV_e3 Home, dir-li pijo a algú per no conèixer "una app" és una mica fluixet eh? Això sí, això és liquidació d'estocs. Com a mínim no els llencen com fan els súpers.
@TrippSmith_com@antirez I us a custom troubleshooting harness on top of GH Copilot so I can pretty much switch models on the fly and Opus 4.8 is bad for this use case too. Not as terrible as GPT-5.5, but worse than 4.7 and 4.6
@pierceboggan Not great for my use case, which is NOT coding! (it's troubleshooting) so kinda expected. As a matter of fact, even GPT-5.5 is terrible for my use case and only Opus family are okay-ish.
Do you know when/if MAI-Thinking-1 is landing on GH Copilot?
@jordienr They'll never top Japanese car manufacturers calling their cars "wanker", "the whore" or "snot" in Spanish.
Or the famous American politician from the 90's Internet days "Mike Lapolla". Every Spanish speaker Internet user emailed him back in the 56k days.