And yet the monologue is a complete work of fiction, a script delivered by a talented actor that we still find moving. So what are these authentic experiences to you, or does it not matter if we can’t tell the difference?
I joined gdm recently, and previously used (neo)vim exclusively. Begrudgingly Cider-V is very, very good. It might be possible to get by without it, but the system is so locked down you’re going to make a lot of sacrifices. (very few authorised extensions, codebase is so large it’s going to break whatever tools your used to using anyway, no git)
I’m well thinking I may as well trade my brick of an m5 pro for a 13” chromebook, it’s a strange time.
While I large agree, when I rely too much on agentic llm usage I come away feeling that I haven’t really learnt much over the session, and the code wasn’t really “mine”. It’s also easy to let your skills atrophy over time if you’re not careful, and for the hardest / interesting problems I often turn the llm off entirely and write out the code by hand, and come out a lot happier than just guiding Claude
It’s a quite deceptive paper. The main headline benchmarks (math500, aime24 /25) final answer is just a number from 0-1000, so what is the takeaway supposed to be for pass@k of 512/1024?
On the unstructured outputs, where you can’t just ratchet up the pass@k until it’s almost random, it switches the base model out for instruct, and in the worse case on livecodebench it uses a qwen-r1-distill as a _base_ model(!?) that’s an instruct model further fine tuned on R1’s reasoning traces. I assume that was because no matter how high the pass@k, a base model won’t output correct python.