Quanta Magazine consistently explains mathematics/physics for an advanced lay audience in ways that don't terribly oversimplify / still expose you to the true ideas. It's really nice! I don't know of any other sources like this.
(1) This is awesome. Feels like this wraps enough complexity that it won't just be a toy / for prototyping.
(2) When a schema is provided, is it fully enforced? Is there a way to do migrations?
Migrations are the only remaining challenge I can think of that could screw up this tool long-term unless a good approach gets baked in early. (They're critically important + very often done poorly or not supported.) When you're dealing with a lot of data in a production app, definitely want some means of making schema changes in a safe way. Also important for devex when working on a project with multiple people — need a way to sync migrations across developers.
Stuff like scalability — not worried about that — this tool seems fundamentally possible to scale and your team is smart :) Migrations though... hope you focus on it early if you haven't yet!
I haven’t seen a better solution than remolacha’s #2 (create separate temporary state for the form).
Forms just inherently can have partially-finished/invalid states, and it feels wrong to try and kraal model objects into carrying intermediary/invalid data for them (and in some cases won’t work at all, eg if a single form field is parsed into structured data in the model)
In all the cases in the article it looks like shame plays a big role. I wonder if hikikomori is caused by a loop of [adverse circumstances that cause the person to feel shame] -> withdrawal to avoid shame -> being ashamed of having withdrawn [loop]
How’s PostgreSQL’s code quality? If projects have tons of technical debt or poor abstractions it can often be hard to make significant changes. Is that the case here, or no?
It was certainly an unresolved question before they did this work!
Naively, it seems reasonable to believe that if you adjust all the weights of a neural net towards the behavior you want via SFT and RLHF, that it would compete with/mute/obscure undesired behavior like a back door. But it seems not to be so… Indeed the cute mask does not cover the entire shoggoth— it may still have tentacles (https://images.app.goo.gl/YW9g3BvwGqGwYTgd6)
An LLM can be executed in an “OODA” loop like in AutoGPT and given a goal towards which it takes agentic actions, especially if the LLM is fine-tuned for function calling. So, it can be the main component of an agent that does have goals/de-facto motives! The wrapper code can just be a couple hundred lines.
AutoGPT itself is pretty weak, but it’s possible to write wrapper code that leads to stronger agency. Also, agents formed this way with GPT4 are way stronger than with GPT3.5… so expect this trend to continue.