Difficulty in accurately writing code due to the complexity of logic and abstractions, compounded by the limitations of AI models in handling diverse coding tas

Signals over time

2 observed signals across 1 sources, tracked for 7 days. Confidence: low.

What people are saying Observed

• “If you can't get any coding done with 100K context that is either a broken model, a broken harness or a skill issue. I would mostly use Haiku in task or explorer subagents. I'm not saying I stay under that on every task, but I do have quite a few sessions that cap out well below that, so that price difference would be very meaningful. I use Luna for this day in and out and its excellent - if Haiku is that much better I will be changing things up.”

  Hacker News · frustration

• “I completely agree. First, there's the split between the ones using AI to write coder and there's the Ai that gets used in software. Writing code is _hard_ because it's by definition accurate logic math will tons of abstractions that build on each other. Our brains are great at this. Frontier models with lotsa context are too. Let's ignore those though. There are a lot of problems that are simple. Accuracy is important (e.g. is it nudity?), but it's still simple. The difference between Jev and the general idea of small models you described is that Jev (and a million copycats, Jev being a copycat itself) only answers multiple choice. That's clever; why wax poetic when you can ask a question and just get back the multiple choice, standardized tests love them for a reason. So really it's: a) "Big Know it All" Models. Slow and pricey, but they'll handle anything you through at them. b) "Small Domain-Specific" model that still speak answers. They need some amount of "can form eloquent answers" in there with the specialized knowledge so still have a bit of a floor, but totally make sense. c) "Memorized multiple ch”

  Hacker News · frustration