I like the glasses path, well I do wear glasses, but some elements remain unclear to me:
- are prescription glasses available for display ? I guess not ?
- these glasses need to be online, I guess they do so with a phone and bluetooth connection nearby ? So that's the glasses, the band and the phone, oh and the glasses case, seems a lot to carry.
- pedestrian navigation seems to be rolled out per city, so it's not like having gmaps available right out of the box.
Congrats, this solution resembles AlphaEvolve. Text serves as the high-level search space, and genetic mixing (map-elites in AE) merges attemps at lower levels.
Losing the mental map is the number one issue for me. I wonder if there could be a way to keep track of it, even at a high level. Keeping the ability to dig in is crucial.
Spend time reviewing outputs like a tech lead does when managing multiple developers. That's the upgrade you hust got in your career, you are now bound to how many "team members" you can manage at a single time. I'm grateful to live in such a time.
The code is the mental map. Orchestra conductors read and follow the music sheet as well. They don't let random people comes in and mess with. Neither do film directors with their scripts and their plans.
Hello, very interested in the scrollback! I've used mosh for 10+ years and it still runs my 100+ opened terminals to this day !
Would love to try your alternative
Exactly, for real time applications VTO, simulators,...), i.e. 60+FPS, diffusion can't be used efficiently. The gap is still there afaik. One lead has been to distill DPM into GANs, not sure this works for GANs that are small enough for real time.
I mean it is really hard to push diffusion models down in size so that just makes the speed part hard. I'm not sure diffusion can ever truly win in the speed race, at least without additional context like breadth of generation. But isn't that the thing? The best model is only the best in a given context?
I think the weirdest thing in ML has always been acting like there's an objectively better model and no context is needed.
Plenty of large companies only hire union contractors for electrical, mechanical, and plumbing systems (aka skilled trades, or trades that can damage a building if installation is done poorly)
I'd be interested in what implementation of D3PM was used (and failed). Diffusion model are more data efficient than their AR LLM counterpart but les compute efficient at training time, so it'd be interesting to know whether with more time.to.converge the diffusion approach does succeed. I guess I'll try :)
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