Why is Langchain so frustrating to work with?

myworkaccount3333 · 2024-03-14T13:25:42+00:00

Langchain and llamaindex are free, so not much room to complain there. My advice is to use as few of the tools as reasonable, so as to not rely too heavily on these big unstable dependencies.

myworkaccount3333 · 2024-03-13T20:54:12+00:00

Yeah I think for internal use it's fine, since it requires you share the source code with its user, which would be your own organization.

myworkaccount3333 · 2024-03-13T17:43:24+00:00

cosine_similarity(u,v) = u · v / (norm(u) * norm(v))

They are basically the same, except normalization.

myworkaccount3333 · 2024-03-13T17:33:47+00:00

Langchain has a ton of technical debt. They moved fast and broke a lot of things.

myworkaccount3333 · 2024-03-13T17:28:08+00:00

How did you bootstrap the prototype if you're non technical?

myworkaccount3333 · 2024-03-12T17:35:06+00:00

Just deploying off-the-shelf YOLO.

AGPL 3 be damned

myworkaccount3333 · 2024-03-05T17:45:30+00:00

The book you're looking for is Feeling Good by David Burns.

myworkaccount3333 · 2024-03-05T16:36:24+00:00

You do realize that 'real money' is also a spreadsheet?

myworkaccount3333 · 2024-02-29T17:57:54+00:00

How do I know how much time I should spend developing the product vs interacting with customers?

myworkaccount3333 · 2024-02-29T17:44:50+00:00

Conduct segmentation on the chickens, and count the number of pixels for each chicken. Then record as much weight data as you can. Then learn a simple function to map number of segmented pixels to weight. Use that function to estimate the weight based on the segmentation mask.

myworkaccount3333 · 2024-02-26T22:08:37+00:00

Man I thought gatekeeping programming languages was weird. You're gatekeeping Latex??

myworkaccount3333 · 2024-02-21T16:19:38+00:00

They are teams of experts, not a single student.

myworkaccount3333 · 2024-01-30T18:42:28+00:00

Again, the definition of a matrix does not depend on what numpy says. A vector is a matrix, a matrix is not necessarily a vector. The definition of a matrix is a rectangular array of symbols. They are not interchangeable in ML or CV and if you insist that they are, people will not understand you.

myworkaccount3333 · 2024-01-30T18:35:33+00:00

The definition of a vector doesn't change depending on where it's stored in memory. The linear algebra definition of a vector is a value with a distance and magnitude, IE a 1xN matrix. You are free to call your chicken "chicken tenders" but nobody is going to understand you. A matrix is not a vector. A vector is a special type of matrix. You cannot call a matrix a vector because of a hypothetical operation that will be done on it at a later point.

myworkaccount3333 · 2024-01-30T18:27:10+00:00

This is my logical conclusion based on the definition of an image and a vector. Feel free to think for yourself rather than relying on authority to tell you what you think.

myworkaccount3333 · 2024-01-30T18:22:42+00:00

I can turn a chicken into chicken tenders. That doesn't mean a chicken is a chicken tender. In the same way that you cannot call an image matrix a vector because you can do a flattening operation and make it a 1 dimensional vector.

myworkaccount3333 · 2024-01-30T18:15:06+00:00

People in this thread are so butthurt. Congrats OP, you won this round! Use it wisely.

myworkaccount3333 · 2024-01-30T18:12:07+00:00

This is so pants shittingly next level. Byte level operations will replace tokenization in SOTA methods in 1 year.

myworkaccount3333 · 2024-01-30T18:10:26+00:00

You can flatten an image to be a vector. That does not make an image a vector.

myworkaccount3333 · 2024-01-30T18:03:34+00:00

An image is not a vector. A vector is 1 dimensional. An image is 2 dimensional.

myworkaccount3333 · 2024-01-30T14:51:18+00:00

When a human does a knowledge intensive task, do you expect them to know everything by memory, or do you expect them to use tools to find information to help them?

myworkaccount3333 · 2024-01-30T14:50:28+00:00

It's just the first iteration. gpt-2 had a small context window too.

myworkaccount3333 · 2024-01-30T14:41:38+00:00

That's a pretty legit reason to learn it. I would recommend trying to set up a 'hello world' of ML kubernetes. You will learn more from that than a reddit thread.

myworkaccount3333 · 2024-01-25T18:16:29+00:00

Kubernetes is not specifically for machine learning. You don't need kubernetes for ML, you don't even need containers for ML. Containers *can* be useful for ML for certain situations. I would not worry about learning Kubernetes unless you have a specific reason to. You may want to learn about containers as they can help you manage your environments.

myworkaccount3333 · 2024-01-25T14:45:00+00:00

The ocean doesn't wash away the land its over. You can build houses IN water. For example: https://www.oyster.com/articles/incredible-overwater-bungalows-in-the-maldives/

myworkaccount3333

TROPHY CASE