It's more like we just invented ball bearings. We just jumped from standard to industrial grade, and precision grade is on the horizon. All kinds of new possibilities have opened up, cars can go a mile a minute on these things! Surely if we keep increasing the precision at this rate, we'll defeat friction once and for all.
Gemini models - at least via some interfaces - have tool calling API access to various Google integrations. flights.google.com, maps.google.com, etc.
The info isn't in the model weights.
Because of where I live, there are three viable airports for any given flight I might want to take, which historically has made shopping a real pain. But Gemini (and only Gemini) has greatly simplified it. Pramble plus date range plus destination and it very quickly generates potential itineraries with costs, total travel time (driving included), etc.
Why not set a global instruction that their direct outputs to you should be in your native language?
For a long time I had Claudes (in the 4.0-4.5.x range) use only French in the chat, while keeping English for working docs (and the code, obviously). Works just fine.
edit: I can guess that any right-to-left languages would likely break claude-code rendering?
Before OpenAI / situational-awareness he made a lot of money on societal-shift type investments and shorts during the lead up to Covid economic impacts.
His "main thing" is success in calling economic impacts of undervalued large shifts, and the premise of the fund is basically that the same thing is occurring around AI, where he also has specific subject matter expertise.
Well, I don't buy it. There's probably a lot of people who picked out those trends, after all they are just what's in the news. Most of them didn't get 45B to invest.
I'm going to need better evidence to believe he has any skill.
From what I can see, his investors could have just bought AI related stocks themselves.
That depends on how long the trend lasts for. You could have got into stocks like Apple, Amazon or NVidia very "late" and still made tons of money.
Aschenbrenner only created his Situational Awareness fund 2 years ago, so he wasn't exactly prescient in predicting the rise of AI - he just had enough conviction to go all in, with leverage, on an investing theme that was already pretty obvious.
Years ago I also did some experimentation w/ midi-device and SRS ( https://www.youtube.com/watch?v=a6tvHMvF8Mo ), where the focus was on ear-training rather than score-learning.
Clef seems to be a pretty strong attempt at a high difficulty UX. I've created an account and will be giving it a go. Wishing you luck, and thanks for sharing.
A feature whose absence I've found more and more conspicuous over time is interactive-compact. Given a current context, and impending context overflow, I know the directions that my mind is heading, and where I expect the development flow should be focused on.
But naive-compact is forced to just sort of guess at what is and isn't relevant from the prior work.
The harnesses have gotten better at some JIT ui stuff, throwing interview questions / forms at users. Compact is the ideal time for this:
Where are we headed here? (2-5 viable options, sourced from current context and imagination)
Then potential follow-up questions as required, but honestly I expect the single guiding answer there to improve post-compact performance pretty dramatically!
Not so long ago, I was good enough for many coding tasks. But I found that things can change in a hurry.
Yes, a cheap and fast Opus4.6 can drive a lot of value in current context. But if we continue to craft bigger-and-bigger balls of mud, Opus 4.6 may end up hitting its conceptual ceiling and unable to contribute.
Winding the clock back on your statement gives:
> I'd gladly pay for a Claude Sonnet 3.5 in silicon and use it for 1-2 years.
Assuming moore's law like progress, which I'm 100% sure isn't going to happen - I think we're at the top of the S curve already. But assuming dramatically increased intelligence every year this is still the exact same position as anyone who bought a computer in the last 5 decades. Yet, people did very much buy computers.
How are the open-weight Chinese models staying ~6-12 months behind on widely distributed / commodified hardware, and serving for even lower prices?
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