thedreammachine

At this point, extrapolating from the speed of AI progress we're seeing, can we expect to see 10x as many reports like this in a single day before the end of the year?

I've been naively assuming that most senior+ engineers were doing this too?

Here in Australia, magpies do something similar but in a bad way. During nesting season they'll swoop you, remember your face, and keep targeting you. My daughter got attacked two weeks ago.

https://www.abc.net.au/news/2022-08-30/magpie-swooping-seaso...

Is it mostly an artifact of the proof or does the algorithm actually need anything close to it?

I started with a curated backbone across several fields, drafted with frontier models and reviewed by hand. The automatic (and fun) part happens when you pick a goal and a quest gets generated with its own prerequisites, mapped onto that backbone.

It's def not as robust as Math Academy's graph yet, they spent years hand-building and testing it on real students. But I bet a few more iterations of frontier models get there, especially once there's learner data to correct it.

Agreed. An always-on agent focused solely on improving your knowledge/skills could ask you questions daily, build a picture of the gaps in your understanding and learn where to push you next.

I'm experimenting with this at https://miletus.app

I'm also curious about training models with that singular goal. If anyone is exploring similar ideas, I'd love to chat.

That's a good point, but if you have an always-on agent focused solely on helping you improve your knowledge/skills, it can ask you questions every day and gradually identify gaps in your knowledge and it gets really good at knowing where to push you.

I'm experimenting with exactly that: https://miletus.app

Probabilistic Robotics (https://robots.stanford.edu/probabilistic-robotics/) and Driven: The Race to Create the Autonomous Car (https://www.goodreads.com/en/book/show/52767920-driven)

I've been tracking the same for a few months. All open source and available here: https://preseason.ai/

Related and a great read: "The Joy of X: A Guided Tour of Math, from One to Infinity" by Steven Strogatz:

https://www.goodreads.com/en/book/show/13356649-the-joy-of-x

I love that they won as a remote team and no constant hype about revenue.

Optionality. This positions Stripe well to compete in AI compute, model development, apps... This is not a simple 1+1=2 merger.

Two open-source initiatives:

1. https://preseason.ai Basically a benchmarking platform that measures how the top LLMs recommend devtools when prompted to build different types of web apps.

2. https://widen.dev A native Mac desktop Postgres GUI with text-to-SQL. You can even use the LLM that already comes installed in your MacOS.