henriquegodoy

This is actually really needed, current ai design tools are so predictable and formulaic, like every output feels like the same purple gradients with rounded corners and that one specific sans serif font that every model seems obsessed with, it's gotten to the point where you can spot ai-generated designs from a mile away because they all have this weird sterile aesthetic that screams "made by a model"

This is pretty cool and feels like we're heading in the right direction, the whole idea of being able to hop between devices while claude code is thinking through problems is neat, but honestly what excites me more is the broader pattern here, like we're moving toward a world where coding isn't really about sitting down and grinding out syntax for hours, it's becoming more about organizing tasks and letting ai agents figure out the implementation details.

I can already see how this evolves into something where you're basically managing a team of specialized agents rather than doing the actual coding, you set up some high-level goals, maybe break them down into chunks, and then different agents pick up different pieces and coordinate with each other, the human becomes more like a project manager making decisions when the agents get stuck or need direction, imho tools like omnara are just the first step toward that, right now it's one agent that needs your input occasionally, but eventually it'll probably be orchestrating multiple agents working in parallel, way better than sitting there watching progress bars for 10 minutes.

Looking at this evaluation it's pretty fascinating how badly these models perform even on decades old games that almost certainly have walkthroughs scattered all over their training data. Like, you'd think they'd at least brute force their way through the early game mechanics by now, but honestly this kinda validates something I've been thinking about like real intelligence isn't just about having seen the answers before, it's about being good at games and specifically new situations where you can't just pattern match your way out

This is exactly why something like arc-agi-3 feels so important right now. Instead of static benchmarks that these models can basically brute force with enough training data, like designing around interactive environments where you actually need to perceive, decide, and act over multiple steps without prior instructions, that shift from "can you reproduce known patterns" to "can you figure out new patterns" seems like the real test of intelligence.

What's clever about the game environment approach is that it captures something fundamental about human intelligence that static benchmarks miss entirely, like, when humans encounter a new game, we explore, form plans, remember what worked, adjust our strategy all that interactive reasoning over time that these text adventure results show llms are terrible at, we need systems that can actually understand and adapt to new situations, not just really good autocomplete engines that happen to know a lot of trivia.