I guess it makes sense, there is still tons of value to be created just by using the current LLMs for stuff, though maybe the low hanging fruits are already picked, who knows.

I heard John Carmack talk a lot about his alternative (also neuroscience-inspired) ideas and it sounded just like my project, the main difference being that he's able to self-fund :) I guess funding an "outsider" non-LLM AI project now requires finding someone like Carmack to get on board - I still don't think traditional investors are that disappointed yet that they want to risk money on other types of projects..

The question is, are companies going to use fewer people to do the same, or the same amount of people and just create better products?

For prototyping new ideas this is also an invaluable turbocharge for startups who can't really afford to have hordes of developers trying out alternative solutions.

But something like o4-mini-high is a domain expert in all versions of React, Redux, RN etc. and knows every internal SDK change over the last 10 years (or goes out and reads the changelogs and code itself). Countless times I've had it port old code to new and it figures it out 100%. It formulates good modern canonical ways to solve stuff. It knows all the stupid tricks you have to do to get RN stuff run well on Android and iOS that I would never be able to keep in my head unless I work full-time on that. And it does the eye-watering boring styling code that nobody likes, you can even just upload a screenshot of another app or a sketch on paper and it will correctly output code for the style in a matter of seconds.

The end result is that I can, without investing a full-time of keeping myself current, do a professional RN dual Android/iOS app development cycle because I have the general skill to understand what to ask it and how to merge its output properly. This leaves me time to do other stuff and generally be more productive.

My guess is that many who gave up on the AI coding stuff tried the bad tools like the default chatgpt 4o-mini (or tried the tools available 2 years ago) and got a bad experience. There are light-years of differences between these and something like o4-mini-high.

TL;DR: use the correct model for the job, and it doesn't really need to be an argument - if it makes you more productive it's a good tool, if it doesn't, nobody is forcing you to use it. But I don't think you should imply that everybody who likes these tools are stupid.

Some customers also just had bad memory and loved sort of re-living their day every evening which made memories store more efficiently in the brain.

There are social "contracts" that the users need to consider when using stuff like this though, as you do take photos of those around you or who you interact with..

As opposed to the more literary authoritative prose from textbooks and papers where the model output from the get-go has to commit to a chain of thought. Some interesting relatively new results are that time spent on output tokens more or less linearly correspond to better inference quality so I guess this is a way to just achieve that.

The tokens are inserted artificially in some inference models, so when the model wants to end the sentence, you switch over the end token with "hmmmm" and it will happily now continue.

In the dual slit experiment this is visible as you can't get the interference effects by summing the probabilities for "particle through slit 1" and "particle through slit 2" but rather you need to sum the amplitudes of the processes.

Working physicists (since 100 years) just do this, there is no practical need to interpret it further, but it would be cool if someone could figure out some prediction/experiment mismatch that does indeed require tweaking this!

This is fundamental to 100 years of quantum mechanics and underlies most of physics including all semiconductors, materials science, chemistry, lasers, etc. The double slit experiment is just a very good illustration of the principle boiled down to its essentials, which is why it's everywhere in pop-sci. It makes for more accessible story than describing how a hydrogen atom works.

I don't really care about insanely "full kitchen sink" things that feature 100 plugins to all existing cloud AI services etc. Just running the released models the way they are intended on a web server...