theodorewiles

yeah moats are bad for everyone who isn't a shareholder or employee so i don't get the concern for whether or not there's a moat, its like watching shark tank

I have a simplify skill built from a rich hickey talk that basically solves the erosion and verbosity metrics SCB flagged (i.e. erosion and verbosity are way better and improve over time). However, after running the SCB benchmark I cannot find any evidence that it actually improves test pass rate trajectories. Food for thought on maintainability metrics.

i'm testing to see if doing new feature roll-outs can help me eval whether a refactor was good or not - very similar to approach here. my intuition is that good refactors should reduce tokens used by downstream coding agents. haven't seen a big difference yet but it might just be that i need to do more rollouts (lots of variance in tokens used per run). in my experience you have to intentionally 'mow the lawn' or things get out of hand so i'm always looking for slop signals.

this. system 1 thinking needs complementary really good system 2 thinking.

Yes the fascinating thing is:

1. It will take much longer to understand the output of the machine that it takes to prompt and create it. 2. The only? best? one? way to /verify/ that you /in fact/ understand the output of the machine is to explain it to someone else.

So there will be a machine generating koans which need to be meditated upon and discussed with human social back-pressure validating understanding. I think this could be much more cooperative and at a minimum this will be a way different math social construct.

I think AI CEOs are dumb but AI 'enterprise decision-makers' could be very very interesting.

Imagine how quickly your meetings could get resolved if you had teams submit prompts / contexts, had a clear, documented set of overarching objectives, etc.?

Other thoughts:

- I think the other mistake I see here is trying to over-engineer a deterministic learner path instead of giving the AI more free reign on best next interaction and a set of goals it needs to accomplish through the session; it can feel more responsive and free-form that way from the learner's POV. - If you had voice here you could also make the screen optional. In my experience typing out long answers to questions can take a while too - so a voice mode might be helpful for learners. It would also be cool if people could take a 'photo of their work' for e.g. math equations done by hand. - To the earlier point on family end-market, an interesting idea is modeling bloomy - have some grown-up oriented courses so you can learn with / side-by-side with your child? Just an idea.

I am here to tell you HELL YES and TAKE MY MONEY. I am such a fan of the idea of using AI to help give personalized and structured AI lessons in the hands of students and let them cook!

- It looks like you are gating family access to K-3 for now and I think that's right. I wouldn't really be comfortable giving my first-grader a live chatbot. Maybe I would think about whether there are other non-persona modalities that could still be self-directed (i.e. I am uncomfortable with a chatbot interface on this for a six year old but gamified flash cards with options could be different).

- I think the other issue is with motivation. I have various duct-tape versions of these types of agents and the thing about it is if you're doing the learning right it can be HARD. So I would think about using motivational interviewing or other techniques to help keep the user coming back and motivated.

- I would really think about the assessments here too. Many people are worried about LLMs ruining student evaluations, but if you could bake in reliable, flexible exams that gauge user progress (even for something like a "Did you read this" quiz) I would bet teachers would like it. There is likely so much you could do on student progress observability and e.g. structuring team-based projects or having targeted student working groups to hash out hard concepts in a targeted way, etc.

This is such an interesting market and use case too because the educational system might be very structurally set up to the current pedagogical staffing model (think about the incentives for teacher's unions and administrators). If you think it will be hard to change that system as quickly as you want I would also try to have an offering direct to families / home-schoolers. I think there is also a cottage industry of tutors that might benefit. Maybe partnering with the textbook publishers? I'm sure there are some "Teach your kids better" influencers that would get you into some feeds?

IME LLMs are kind of like a projection of your current expertise - your prompting and guidance etc. biases LLM plans kind of 'in the direction' of your thinking. I think this is one reason why it seems like senior engineers get more lift vs. juniors.

What I am exploring is another step to the classic 'research / plan / implement' pattern: 'research / plan / LEARN / implement' where LEARN involves the human doing AI tutoring sessions to ensure a deep understanding the concepts etc. that the LLM is planning to implement so you can refine / iterate on plans and direct the LLM in ever more effective ways. My idea is that this then compounds your human capital and reduces the occurance of 'sounds smart, doesn't work' pattern.

AI psychosis

I haven't but looks cool!