5 ms·
For what I’m working on, I can also use the wrong approaches. Going through my fail often fail fast feedback loop is a lot more efficient with LLMs. Like A LOT
by valval 2y ago
For what I’m working on, I can also use the wrong approaches. Going through my fail often fail fast feedback loop is a lot more efficient with LLMs. Like A LOT more.
Then when I have a bunch of wrong answers, I can give those as context as well to the model and make it avoid those pitfalls. At that point my constraints for the problem are so rigorous that the LLMs lands at the correct solution and frankly writes out the code 100x faster than I would. And I’m an advanced vim user who types at 155 wpm.
- lolinder 2y ago> And I’m an advanced vim user who types at 155 wpm. See, it's comments like this that make me suspect that I'm working on a completely different class of problem than the people who find value in interacting with LLMs. I'm a very fast typer, but I've never bothered to find out how fast because the speed of my typing has never been the bottleneck for my work. The bottleneck is invariably thinking through the problem that I'm facing, trying to understand API docs, and figuring out how best to organize my work to communicate to future developers what's going on. Copilot is great at saving me large amounts of keystrokes here and there, which is nice for avoiding RSI and occasionally (with very repetitive code like unit tests) actually a legit time saver. But try as I might I can't get useful output out of the chat models that actually speeds up my workflow.
- whackyMax 2y agoI have always thought of it as a way to figure out what doesn't work and get to a final design, not necessarily code. Personally, it's easy to verify a solution and figure out use cases that wouldn't work out. Keep on iterating until I have either figured out a mental model of the solution, or figured out the main problems in such a hypothetical solution.