LLMs in particular seem well fitted to extracting semantically correct insights from unstructured data. When it comes to observability we’re in a better spot; since we have discrete structured data, which makes it easy to build rules and logic on top of it. I don’t think this kind of tooling will benefit much from recent advances. If anybody has anything worth being shown I’d love to check it out.
On the contrary, I think that the left piece of code is not building constrains prematurely and actually enables you to modularize it away when needed.
Sure, if the logic grows, if it needs to scale, if the team increases in size… then it makes sense to modularize it. But building something from the very beginning to achieve that is going to impose constraints that make it harder to reason about and harder to refactor; you’ll have to break down the previous structures and boundaries built by the function heavy example, which will probably introduce needless indirections.
I get nothing. So after a while I told my bosses I would simply stop doing it, since the work to compensate us was still “in progress”. It helped the rest of the team get a free day per on call week, which I guess is something, but still not enough for me personally.
I told them I wasn’t even sure it was legal in my country (Spain) which I guess they didn’t even discuss with legal, or legal didn’t even blink.
I still don’t really understand how a high-end device can “run out of updates” from a technical point of view. Crossing fingers this will be legislated one way or the other.
I have a few things in my reading backlog about bullshit. I think that it tends to be trivialized in social discurse. It honestly feels like the patterns of bullshit exploit built in biases we have.
This is my future starting point for when I leave some room to this topic: https://en.wikipedia.org/wiki/On_Bullshit