nishantjani10

Very interesting use case of mutation testing and leveraging LLMs for JIT catching tests.

This! Again, like all things in engineering, requires systematic approach of having distributed traces chart out your upstream and downstream dependencies.

you then carve out essential user flows from these system diagrams and only then, why you look at an alert in isolation can you tie the entire story, whether is this alert important, which user workflow does it break, what is the SLO on it.

you are defining the art of setting up SLOs for end user workflows. This is typically achieved with contract monitoring (top down). This article is focused on bottom up approach of fine tuning and setting up alerts

this is the part of the article that I did not sit well with me either. Code is agent generated, agent can debug it but will alway be human owned.

unless anthropic tomorrow comes in and takes ownership all the code claude generates, that is not changing..

a lot of people need to read and understand the message here. Very, very well said!

I primarily use AI for understanding codebases myself. My prompt is:

"deeply understand this codebase, clearly noting async/sync nature, entry points and external integration. Once understood prepare for follow up questions from me in a rapid fire pattern, your goal is to keep responses concise and always cite code snippets to ensure responses are factual and not hallucinated. With every response ask me if this particular piece of knowledge should be persistent into codebase.md"

Both the concise and structure nature (code snippets) help me gain knowledge of the entire codebase - as I progressively ask complex questions on the codebase.

"Processes that rely on unstructured data are usually unstructured processes." - thats a brilliant take, seriously!