course22p2
simpleaichat
course22p2 | simpleaichat | |
---|---|---|
6 | 22 | |
431 | 3,386 | |
2.6% | - | |
0.0 | 8.7 | |
11 days ago | 4 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
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course22p2
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Ask HN: Daily practices for building AI/ML skills?
Practical Deep Learning for Coders: https://course.fast.ai/Lessons/part2.html
- Stanford A.I. Courses
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A quick visual guide to what's actually happening when you generate an image with Stable Diffusion
To me the most important bit is that the diffusion loop turns a noisy latent into an image, does that iteratively, and uses "guidance" in the form of a prompt/controlnet image/etc to do it. The scheduler part, I felt, was needlessly complex for this short explainer, so I hand-wave it away. IF someone wants to dive in deeper, much deeper, they can go through the same thing I'm doing, which is this: https://course.fast.ai/Lessons/part2.html
- Practical Deep Learning for Coders - Part 2 overview
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Courses for an AI beginner
They also recently released a course for more experienced students where they teach you to implement the Stable Diffusion algorithm from scratch.
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From Deep Learning Foundations to Stable Diffusion (Part 2)
The full transcripts are available here in plain text form:
https://github.com/fastai/course22p2/tree/master/summaries
simpleaichat
- Efficient Coding Assistant with Simpleaichat
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Please Don't Ask If an Open Source Project Is Dead
I checked both the issues mentioned, people have been respectful and showing empathy to author's situation
https://github.com/minimaxir/simpleaichat/issues/91
https://github.com/minimaxir/simpleaichat/issues/92
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We Built an AI-Powered Magic the Gathering Card Generator
ChatGPT's June updated added support for "function calling", which in practice is structured data I/O marketed very poorly: https://openai.com/blog/function-calling-and-other-api-updat...
Here's an example of using structured data for better output control (lightly leveraging my Python package to reduce LoC: https://github.com/minimaxir/simpleaichat/blob/main/examples... )
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LangChain Agent Simulation – Multi-Player Dungeons and Dragons
So what are the alternatives to LangChain that the HN crowd uses?
I see two contenders:
https://github.com/minimaxir/simpleaichat/tree/main/simpleai...
https://github.com/griptape-ai/griptape
There is also the llm command line utility that has a very thin underlying library, but which might grow eventually:
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Custom Instructions for ChatGPT
A fun note is that even with system prompt engineering it may not give the most efficient solution: ChatGPT still outputs the avergage case.
I tested around it and doing two passes (generate code and "make it more efficient") works best, with system prompt engineering to result in less code output: https://github.com/minimaxir/simpleaichat/blob/main/examples...
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The Problem with LangChain
I played around with simpleaichat for a few minutes just now, and I really like it. Unlike LangChain, I can understand what it does in minutes, and it looks like its primitives are fairly powerful. It looks like it's going to replace the `openai` library for me, it seems like a nice wrapper.
I'm especially looking forward to playing with the structured data models bit: https://github.com/minimaxir/simpleaichat/blob/main/examples...
Well done, Max!
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How is Langchain's dev experience? Any alternatives?
https://github.com/minimaxir/simpleaichat bills itself as a simpler alternative to langchain. I have not tried it, but it looks interesting.
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Stanford A.I. Courses
I think you are asking specifically about practical LLM engineering and not the underlying science.
Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).
https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.
Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates
Simon W has some good writeups too: https://simonwillison.net/
I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.
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Where is the engineering part in "prompt engineer"?
This notebook from the repo I linked to is a concise example, and the reason you would want to optimize prompts.
- Show HN: Python package for interfacing with ChatGPT with minimized complexity
What are some alternatives?
developer - the first library to let you embed a developer agent in your own app!
lmql - A language for constraint-guided and efficient LLM programming.
playground - Play with neural networks!
langroid - Harness LLMs with Multi-Agent Programming
latentblending - Create butter-smooth transitions between prompts, powered by stable diffusion
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
machine-learning-specialization-andrew-ng - A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
gchain - Composable LLM Application framework inspired by langchain
StableDiffusion-By-Parts - Slice and dice the Stable Diffusion pipeline, saving to a TIFF file in between sections.
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.