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?