civicsquid

I like this, it feels like the right balance. I still struggle to define when the handoff point is, but I think maybe it's at the same point as when I would hand it off to a junior engineer: when I've sketched almost everything I need to be quite confident in my approach (and if I'm wrong, I'll find out and try again).

Right, I do use AI for review and find it helpful most of the time (assuming it doesn't decide to be unnecessarily pedantic). I have found it struggles to reason through architectural implications or subtle performance problems when there isn't a specific linter for it, though.

For example, I'm instantiating something N times with this change. Each instantiation is fast, but this is a hot path. It's not clear whether that's safe or if it needs to be gated which is something a reviewer would ideally flag.

I suppose the counter-argument most people would make now is that if the AI didn't call it out, it's probably not likely enough of an issue to focus on -- even if it does wind up becoming a problem later.

Do you find yourself still trying to use AI tools for the synthesis portion of writing/coding, or have you gone back to the 'manual' ways?

I've pretty much completely dropped it for writing (but still use it for catching issues with clarity or logical flow afterwards). I've yet to get away from it for coding. Perhaps I keep trying with coding because I feel I'm doing something wrong (and because for a a little my performance was tied to usage of it...).

Really cool. I'm wondering: what background did you need to be able to think of the question that resulted in this project?

I know you said you're involved in some retrogaming and were experimenting, but as someone who works in a world where hardware is pretty heavily abstracted away, even if I got into retrogaming I don't know that I'd consider that there may be a systems improvement lying around. Beyond the creative aspect, it feels like there is some systems and hardware background that helped put the idea together (and I'd be interested to go learn about of that systems/hardware knowledge myself).

I think one of the biggest challenges for a team lead is understanding the team's priorities, followed by identifying and acting on the leading indicators of success towards those priorities.

By understanding priorities I mean: the tech lead has to be in sync with management (of the team and often other leaders of the org) about what needs to get done and what can be cut if there isn't enough bandwidth. Weak tech leads in my experience don't have a sufficient grasp on changing priorities, which results in the team working on things that don't get rewarded properly / don't pay off and/or loading up the team with work that could have been deferred. Some of this is the manager's job, but often it falls to the tech lead to estimate the true technical 'size' of what is being asked.

By acting on leading indicators of success, I mean: the tech lead will ideally not be doing the majority of execution on a well-staffed team. They should be doing some execution work to ensure the codebase is sufficiently easy to work in etc, but most importantly they need to know how to figure out whether or not something is on track without sinking too much of their time to do so. Setting up milestones and some target date helps with this, but it's often uncomfortable to do that with folks that were recently your peers (it still needs to be done).

I don't have books or other resources, but this has been my experience as I transitioned into similar roles. I also think my experience may skew more towards a 'manager-tech-lead' than a pure tech lead, so take that with a grain of salt. Good luck!

Hm, I’m not sure what you saw in the post before my edits, but I think this answers “did AI motivate / help discover the breakthroughs we saw 20 years ago?” which I definitely agree would be a “no”.

Either way, before and after my edits the intent was to identify areas in which distributed systems researchers moved their focus to support areas such as (but not exclusively) AI.

The question comes from me supposing that “pure” distributed systems research has slowed.

Good suggestion! I’ve seen federated learning come up before but have not explored it much.

This seems like a good direction for sure.

This is the sort of trivialization attitude that I’ve come to associate with people who only care about the “big picture”. It is really irksome if you work on the lower-level stuff that (in some sense) makes the big picture possible.

I think there is a point here that user-facing innovation stagnated and OpenAI helped break that, but it’s wild to me that there is no acknowledgement at all of the giants whose shoulders they stand on. Although I guess that’s what he meant about the arrogance…

I would be curious to understand the "How to Prompt" section more. As someone who does not interact with LLMs regularly I have no idea why this looks like a templating language.

Would anyone be able to explain what's going on in that section or point to resources that explain what the goal is / why this looks so programmatic?