> Your profile reads like a 'Hacker News Bingo' card: NASA, PhD, Python, 'Ask HN' about cheating, and a strong opinion on Reddit's community. The only thing missing is a post about your custom ergonomic keyboard made from recycled space shuttle parts.
I end up asking the same question when experimenting with tools like Cursor. When it can one-shot a small feature, it works like magic. When it struggles, and the context gets poisoned and I have to roll back commits and retry part of the way through something, it hits a point where it was probably easier for me to just write it. Or maybe template it and have it finish it. Or vice versa. I guess the point being that best practices have yet to truly be established, but totally hands-off uses have not worked well for me so far.
Why commit halfway through implementing something with Cursor? Can you not wait until it’s created a feature or task that has been validated and tests written for it?
Why wait until everything is finalized before committing? Git is distributed/local, so while one philosophy is to interact with it as little as possible, the other one is to commit early and commit often, and easily be able to rollback to a previous (working) state, with the caveat that you clean-up history before firing off a PR.
Well, same statement applies. Rolling back commits is also O(1) and just as easy. And if you branch to start with it's not even a "rollback" through the commit history, it's just a branch switch. Feel like OP has never used git before or something.
Another reason why the idea of AI agents for science hasn't made much sense to me. Research is an extremely collaborative set of activities. How good would a researcher be who is very good at literature review, but never actually talks to anyone, goes to any conferences, etc?
You and I have matching stories, unfortunately. I've made a point of sending my sons to public school for this reason. Now, of course this also means that I shopped for a district that aligned with my expectations. But would never want a repeat of my own experience.
I think being accountable for your work to a person who isn't in your family is actually an important thing to learn.
It also turns out, parents aren't really qualified to be teachers just because they believe that they are.
I mean, to some extent, the non-aeronatics part also has some of this. SpaceX picked up propulsive landing and friction stir welding expertise from NASA, to name just two things. (This is not to imply there is anything underhanded about this.)
I think the way to make this work would be to ease transition of technical experts to/from private industry, carrying the know how in their heads.
Isn't gradient descent basically PID over parameters?
And tricks like momentum basically a low-pass filter integrated in the PID loop? It's quite weird how not that many concepts from analog electronics domain have gotten carried over to ML.
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