dreis_sw

I found a big problem with this - I noticed that the longest answer is very often the correct one, which kinda ruined the game. Even though I didn't want it to, it started affecting my decision-making. Luckily, I only noticed this around question 85, though those are really the tricky ones.

Good news for the project is that I think you can easily tweak the LLM to generate better alternatives.

I got 89/100, which extrapolates to 72,700. As a non-native speaker, I'm quite happy with that.

It's well known that companies with an abundance of raw technical skills but poor judgement tend to fail. On the technical side technical debt accumulates, while on the business side the wrong choices are made. I think it's valid to generalize this to AI.

I think the new high effort settings are so strong that selecting them when the task doesn't require it actually impacts the output negatively.

Seems like this model delivers on what has already been scaling quite nicely, which is the length and complexity of the requested tasks, but isn't such a big improvement on what hasn't been scaling so far - common sense, discernment, good judgement.

They are certainly critically flawed at the moment, but I still feel that with proper traceability, KYC, etc. it would be a much fairer system than the current betting companies. It’s one of those use-cases where blockchain technology actually makes sense.

Wonder if that's also the case for web-based UI like Spotify, or if the accessibility APIs are only useful for native applications.

History shows continuous evolution, there won't be a "final AGI thing". The definition of AGI is so vague anyways that any conversation around it is hardly useful. 5 years ago, what we have today would have been considered AGI.

If the economy wasn't completely broken you'd have a job right now.

It also took many years to put capable computers in the hands of the general public, but it eventually happened. I believe the same will happen here, we're just in the Mainframe era of AI.

We'll only know for sure when either of these companies goes public. Google serves inference and Google Cloud is profitable but we don't know how much inference is costing.

If they never go public, there's our answer as well.

Works even better if you have a good test suite, which is surely the case here with Ladybird