machiaweliczny

I think it's best ranking of models so far

But with good computers you can create simulation of biology and verify if via known and described effects. That's how it's gonna be done IMO

Anyone knows it Nautilus Biotechnology could really be this useful for decoding exactly that?

Man I would love the superhuman math AI when I was at uni, there were some problems I couldn't even grasp and this always bothered me.

I guess motivated state actors might still find a way to cause issues

My layman intuition is that given how easy is to make Turing machine and how good NNs are at fitting function I suspect they don't have hard time imitating whole logic via "stochastic process" and recent math results kinda hint at it. Animals are using more efficient algorithm, they infer better the causality but I thought that bayesian algorithm is stochastic.

Thinking has to generalise somehow (likely via internal world modeling) or it's just random search with better and better heuristics that you can verify with real word. The heuristic you get is likely why there might be such big differences between humans for example in abstract problems as for baseline survival we are kinda similar as it got pruned to baseline thing that works.

I think LLMs are missing explicit world generation to be "self-contained" but world models + LLMs are basically the same thing that allows this kind of bayesian causality discovery training but maybe less efficient.

Most people for example when thrown some weird before them like cow from tornado while driving will likely just freeze instead of nicely routing around it etc.

It likely hints that it's so much out of distribution that some heavy computational process is starting trying to model what happened or simply there's no good action to chose as you are "unprepared" for this distribution of event at all and likely both are true. I think that's why self driving cars also have problems with these weird edge cases as you need perfect word modeling to know WTF is happening in these edge cases and they simply won't so it has to be very simple heuristic like stop or try not hitting thing whatever it is in the end. We already have good modeling of human and animal behaviour built-in so driving is a bit simpler from computing these aspects at least but IMO perception is really underappreciated how hard it is but recent vision models seems to be approaching human ability.

If it's cheaper then I guess it will might get practical. Also more likely that mind uses simpler techniques more similar to these.

I wonder though if someone tested mutating training objective though, like keeping original loss/goal and somehow defining loss differently and then comparing against original. This intuitively feels like how mind tries to handle difficult tasks.

In general I like Svelte and SvelteKit although there's 3 problems I don't fully like:

1) The getter trap - if you forget to export hooks via getters you don't get reactivity and it's not easy to figure out (Svelte)

2) The async data fetching - for example fetching data async is currently coupled to views which it shouldn't be - was frustrating a lot (SvelteKit)

3) That reactivity system is not runtime only (Svelte)

I am also not a fan of adding these new things like remote functions - most of apps need multiple frontends and this coupling isn't needed. This will be fad same as GraphQL

Agree, with UA stopping Russia western capital will finally flow into Poland because previous strategy was to defend on Vistula river which blocked a lot of heavy industry investments that are starting now.

> It is horrible until AI gets as good as a perfect project from a prompt or we need to abandon it.

Getting close to it with my recent test of cursor cloud workflow.

I've spent 300M tokens in a day and it achieved what I wanted. Manually would take me a month instead of day so it's not 2x it's 20x faster but project was quite simple rewrite of 20k lines of C++ and there was reference implementation. It also improved on original implementation perf wise.

The problem is with fuzzy ideas for new development and with catching up with comprehension when working on something new - I guess we need better visualization tools for code. Product manager work seems like bottleneck currently and monitoring because coding can move quite fast.

Good news is that HTML in canvas might bring back these cool days :)

Yes it's still bad there's no viable headless UI in browser one can really style and it has all the a11y etc. but need extra library for selects that work etc. Invented work for no good reason. The real complexity is in diversity of devices though nowadays in the frontend.

At current rates you are losing when renting (excluding possible appreciation), that's why it's hard to sell and easy to rent. Due to low rates before homes went big in value cause you were earning even not counting appreciation, so whoever had credit was maxing it to full for easy money but now situation starts to stabilise / reverse?

The thing with home is that it's leveraged investment for normal people. You can pay 10% of value and resell later. Buying home for cash is less optimal but for credit it makes sense usually due to leverage and little downside risk. Plus usually every now and then you get money printing and you 2x your leveraged money or more rarely get hyperinflation and home for free. This was mostly issue of too low interest rates but that's how I see it from the past 10 years (non US resident)