eachro

Anyone got a list for the agentic LLM age?

Do you think this would be appropriate for a command line tool that hits various apis as the function calls? Ex: "what's the weather in SF tomorrow?" Or "daily price change of apple, Tesla stock for past week"? (Let's assume I have documented the apis thoroughly somewhere that the model has access to or fine tuned it on this data)

Does anyone know what the state of the art industry solvers do for these problems? I had dabbled a bit in ml approaches to combinatorial optimization with great interest a few years back, but I don't think any of these rl based methods ended up being used in production.

Didn't Amazon aquihire Adept Labs?

If you wanted to train it from scratch, how long would it take on a reasonable GPU setup?

I'm reminded of the nixon quote: "When the president does it, that means it's not illegal."

What would it take to make NYC more like Tokyo where you have consumer/retail level things on the not-ground floor level.

From what I've heard, the llama3 models are fairly easy to fine-tune (please correct me if I'm wrong or if there are more amenable models here). How easy is it to finetune smollm3? I know a lot of the MoE LLMs have been quite fickle in this regard.

"And 50% of the time they work 50% of the time."

I think this is still an incredible outcome given how many dice rolls you can take in parallel with multiple claude/o3/gemini attempts at a problem with slightly different prompts. Granted, each rollout does not come for free given the babysitting you need to do but the cost is much lower than going down the path yourself/having junior colleagues make the attempt.

Yeah haha I got autocorrected

Is there a reason to use data classes over pedantic base models anymore?

A lot of people are saying 12gb is too small to do anything interesting with. What's the most useful thing people __have__ gotten to work?

During the openai gym era of RL, one of the great selling pts was that RL was very approachable for a new comer as the gym environments were small and tractable that a hobbyist could learn a little bit of RL, try it out on cartpole and see how it'd perform. Are there similarly tractable RL tasks/learning environments with LLMs? From the outside, my impression is that you need some insane GPU access to even start to mess around with these models. Is there something one can do on a normal MacBook air for instance in this LLM x RL domain?

For those in quant finance, how much of this is useful in your day to day?

This looks great! Is there going to be exercises or a project based component as well?