machine-learning-specialization-andrew-ng
simpleaichat
machine-learning-specialization-andrew-ng | simpleaichat | |
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4 | 22 | |
455 | 3,386 | |
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6.1 | 8.7 | |
11 months ago | 4 months ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
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machine-learning-specialization-andrew-ng
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Stanford A.I. Courses
I recently completed the specialization with Andrew Ng and think it’s a fantastic introduction to ML. It has a good blend of theory, practical tips, and coding.
If anyone is interested, I’ve published detailed notes and my submissions for the lab assignments:
https://github.com/pmulard/machine-learning-specialization-a...
- Machine Learning Specialization (Andrew Ng) - Course Notes and Lab Assignments
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?
playground - Play with neural networks!
lmql - A language for constraint-guided and efficient LLM programming.
course22p2 - course.fast.ai 2022 part 2
langroid - Harness LLMs with Multi-Agent Programming
developer - the first library to let you embed a developer agent in your own app!
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
simonwillisonblog - The source code behind my blog
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
machine-learning-specialization-a
gchain - Composable LLM Application framework inspired by langchain
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.
griptape - Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.