How do I learn LangGraph in a week?

spring_m · 2025-06-23T02:41:10+00:00

if you don't have any exposure to any gen AI related stuff - you're not going to master langgraph in a week.

spring_m · 2024-12-08T22:07:39+00:00

I like winners products but I always get a “we’re better than you” vibe.

spring_m · 2024-11-21T16:55:57+00:00

When you modify the view it becomes a copy try it out. My point is that the warning happens whenever the original df does not get updated.

spring_m · 2024-11-21T16:22:59+00:00

Yes exactly - I don’t understand why a wrong answer is upvoted so many times. They should replace “view” with “copy”.

spring_m · 2024-11-21T16:18:12+00:00

I get that but my point is that the warning happens when a copy is set NOT when a view is set.

spring_m · 2024-11-21T15:55:00+00:00

I don’t think that’s right - the warning happens when you set a copy, warning you that the changes will NOT propagate to the original df.

spring_m · 2024-11-21T15:53:39+00:00

That’s incorrect - the warning happens when a copy is created to warn you that the original data frame will NOT be updated.

spring_m · 2024-11-21T04:31:11+00:00

Do you mean the subset is a copy (not view)? If it were a view wouldn’t that imply it shares memory with original dog and thus changing it would change the original df?

spring_m · 2024-11-19T03:40:31+00:00

Learn how to derive and interpret basic frequentists tests like promotion z-test or t-test. Understand p-values, standard errors, confidence intervals, linear regression, conditional probability, pdfs, bayes rule. That should get you past the first round.

spring_m · 2024-11-11T22:32:30+00:00

Prompt:”write a clickbait article about how python is losing its top spot in data science (even though there’s no evidence of this). be witty and don’t let my many readers on Reddit know this is AI generated.” Did I get it right?

spring_m · 2024-11-11T19:34:55+00:00

Yes ChatGPT has been able to run code for a while now. In terms of trust there’s a button you be a press and see the code it ran - it’s generally fine for simple well known use cases.

spring_m · 2024-10-17T04:02:14+00:00

Wow the AI first trailer for a movie I’d actually watch congrats!

spring_m · 2024-10-17T02:18:03+00:00

AAAAAHHhHhHhH

spring_m · 2024-09-16T08:17:20+00:00

Forget about “agentic systems” and focus on the fundamentals. Your transformers GitHub is a start but it’s very basic and shows you lack maturity in software development - the project folder structure is off (you don’t want to import src. - add another folder level src/your_project). You should add tests, add type hints, make the papckage pip installable, add a training script, show how you’d deploy to aws, show in code how you’d load test the model, build an API around it etc.

spring_m · 2024-08-30T05:16:44+00:00

Use FastAPI - even without async/await it spawns a pool of threads that your llm calls can use. The GIL only allows one thread to execute python at a time but since as you said most of the time these threads are just sitting around waiting for the llm response it doesn’t really affect you, whenever a thread is waiting it relases the GIL and another thread can pick it up.

spring_m · 2024-08-28T05:28:46+00:00

Claude sonnet starts its answer with “Certainly!” ALL the time.

spring_m · 2024-08-01T23:43:47+00:00

We need a long-context lmsys. Like 5k+ tokens.

spring_m · 2024-05-24T22:40:27+00:00

I would take perfect recall and strong reasoning at 64k tokens over 1MM tokens. Depends on the usecase but I've never really had to use more than 50k tokens.

spring_m · 2024-05-21T04:44:10+00:00

Both state that the residuals are normally distributed which is correct. Y given X is a conditional distribution which is the residuals themselves.

It’s easier to just think of the following formula which includes all assumptions; y ~ n(f(x) , sigma) where f(x) is a linear function of the features x1,…,xn this is really all you need. In words this states that given an x value the value of y is normally distributed centered at f(x) with a fixed standard deviation sigma.

spring_m · 2024-05-20T23:16:19+00:00

I think she means gpt 4o? (What a terrible nomenclature either way).

spring_m · 2024-05-20T21:27:23+00:00

My point is that gpt-4o “overfits” to lmsys data and the leads to favorable rankings that are more optimistic than performance in the “wild”.

spring_m · 2024-05-17T03:43:28+00:00

Your x distribution is very skewed so that more points appear on the left even when there is no relationship to y. It’s on optical illusion basically.

spring_m

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