They have gone and described in really simple terms on AI observability. This is the best page on AI observability I've come across. Some other pages are just giving us some pills to digest, like look at these new three pillars as opposed to old three pillars and whatnot.
Even section like "How tools instrument your code" makes sense for an AI forward company employee to understand and act on.
Eye catching - "Open ended problems" claude code session success rate jumped from 20% (pre opus 4.5 release) to 70% after sometime after opus 4.6 was released.
Yeah this seems true. Claude Code are famously dubbed as best AI coding agent, but google doesn't care about that niche I guess. Somehow, I still rely on google search as they have diversified it.
If you ask questions, it will enable "AI overview" , but if we search about particular object/platform like "Google stock" or "bbc news", it will give the old classic search experience and we woulnd't need to swallow "AI overview" pill in that case.
> This is the opposite of the “10x productivity” slop-cannon style of development that most people imagine when they think of vibe coding, but I find it very satisfying.
I can relate to this. When I spend time on writing unit test , even the one which takes 1% of code coverage, it will be honestly wholesome moment for me to ship it confidently.
As you said it's distributed across - People, conversations, AI agents , tooling, etc... , can't the LLM Knowledgebase/ wiki ( a.k.a. org's second brain) solve this ? I think if , second brain exists, no one needs to pay cognitive debt.
I use to do this and then do test manually to validate everything works as expected in my small open source project. But then over the time I saw that some bugs crept in which I was unable track since I was doing manual testing. So I wrote some e2e tests with playwright and I think that gives a bit relief (at least).