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“Crack the books”—can anybody recommend good books for this shared future of ours? I’m tired of trying to piece it together from blog posts, READMEs, and short

“Crack the books”—can anybody recommend good books for this shared future of ours? I’m tired of trying to piece it together from blog posts, READMEs, and short video tutorials.

simonw 2y ago
I haven't read these all the way through myself but I've seen enough of them that I'm confident suggesting them: - Prompt Engineering for LLMs by John Berryman and Albert Ziegler: https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-Applications/dp/1098156153 https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-A... - AI Engineering by Chip Huyen, which I recommend based on the strength of this extract about "agents": https://huyenchip.com/2025/01/07/agents.html https://huyenchip.com/2025/01/07/agents.html
airstrike 2y ago
I like hands-on learning more than I like textbooks, so in case that matches your requirements, maybe try training your own GPT to have a sense for how it works. I wrote a Rust version of the famous https://github.com/karpathy/nanoGPT https://github.com/karpathy/nanoGPT (which is in Python) so that I could learn how it's built. I wrote it in Rust because I wanted to improve my skills in that language, be forced to write code instead of just reading the existing implementation so that I would truly learn, and test the quality of the nascent Rust AI/ML ecosystem, but you could pick your own language
- brainbag 2y ago
  Would you say more about your experience writing it in Rust? It worked well, what didn't, anywhere you found that you struggled unexpectedly or that was easier than you expected?
  - airstrike 2y ago
    Hey, thanks for asking. I'm the furthest from an authority in this so I encourage you to take everything I say with a grain of salt. I was using the burn[0] crate which is pretty new but in active development and chock-full of features already. It comes with a lot of what you need out of the box including a TUI visualizer for the training and validation steps. The fact that it's so full of features is a blessing and a curse. The code is very modular so you can use the pieces you want the way you want to use them, which is good, but the "flavor" of Rust in which is written felt like a burden compared to the way I'm used to writing Rust (which, for context, is 99% using the glorious iced[1] GUI library). I can't fault burn entirely for this, after all they are free to make their own design choices and I was a beginner trying to do this in less than a week. I also think they are trying to solve for getting a practitioner to just get up and going right away, whereas I was trying to build a modular configuration on top of the crate instead of a one-and-done type script. But there were countless generic types, several traits to define and implement in order to make some generic parameter fit those bounds, and the crate has more proc_macro derives than I'd like (my target number is 0) such as `#[derive(Module, Config, new)]` because they obfuscate the code that I actually have to write and don't teach me anything. TL;DR the crate felt super powerful but also very foreign. It didn't quite click to the point where I thought it was intuitive or I felt very fluent with it. But then again, I spent like 5 days with it. One other minor annoying thing was that I couldn't download exactly what I wanted out of HuggingFace directly. I ended up having to use `HuggingfaceDatasetLoader::new("carlosejimenez/wikitext__wikitext-2-raw-v1")` instead of `HuggingfaceDatasetLoader::new("Salesforce/wikitext")` because the latter would get an auth error, but this may also be my ignorance about how HF is supposed to work... Eventually, I got the whole thing to work quite neatly and was able to tweak hyperparameters and get my model to increasingly better perplexity. With more tweaks, a better tokenizer, possibly better data source, and an NVIDIA GPU rather than Apple Silicon, I could have squeezed even more out of it. My original goal was to try to slap an iced GUI on the project so that I could tweak the hyperparameters there, compare models, plot the training and inference, etc. with a GUI instead of code. Sort of a no-code approach to training models. I think it's an area worth exploring more, but I have a main quest I need to finish first so I just wrote down my findings in an unpublished "paper" and tabled it for now. ________ [0]: https://github.com/tracel-ai/burn https://github.com/tracel-ai/burn [1]: https://github.com/iced-rs/iced https://github.com/iced-rs/iced
senordevnyc 2y ago
It honestly feels like books are a poor fit for this topic, because things are moving so much faster than the publishing industry. A book published in 2024 would have been written in 2023, and would give you a pretty skewed picture of the SOTA.
rramadass 2y ago
Some that i am looking into; these are "practical" books which do not focus on the theory/algorithms but given that they are available (library/models/whatever), how to build your apps using them; 1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen. 2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book). 3) Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster. 4) Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG by Bouchard & Peters.

“Crack the books”—can anybody recommend good books for this shared future of ours? I’m tired of trying to piece it together from blog posts, READMEs, and short video tutorials.

simonw 2y ago

I haven't read these all the way through myself but I've seen enough of them that I'm confident suggesting them: - Prompt Engineering for LLMs by John Berryman and Albert Ziegler: https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-Applications/dp/1098156153 https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-A... - AI Engineering by Chip Huyen, which I recommend based on the strength of this extract about "agents": https://huyenchip.com/2025/01/07/agents.html https://huyenchip.com/2025/01/07/agents.html

airstrike 2y ago

I like hands-on learning more than I like textbooks, so in case that matches your requirements, maybe try training your own GPT to have a sense for how it works. I wrote a Rust version of the famous https://github.com/karpathy/nanoGPT https://github.com/karpathy/nanoGPT (which is in Python) so that I could learn how it's built. I wrote it in Rust because I wanted to improve my skills in that language, be forced to write code instead of just reading the existing implementation so that I would truly learn, and test the quality of the nascent Rust AI/ML ecosystem, but you could pick your own language

brainbag 2y ago
Would you say more about your experience writing it in Rust? It worked well, what didn't, anywhere you found that you struggled unexpectedly or that was easier than you expected?
- airstrike 2y ago
  Hey, thanks for asking. I'm the furthest from an authority in this so I encourage you to take everything I say with a grain of salt. I was using the burn[0] crate which is pretty new but in active development and chock-full of features already. It comes with a lot of what you need out of the box including a TUI visualizer for the training and validation steps. The fact that it's so full of features is a blessing and a curse. The code is very modular so you can use the pieces you want the way you want to use them, which is good, but the "flavor" of Rust in which is written felt like a burden compared to the way I'm used to writing Rust (which, for context, is 99% using the glorious iced[1] GUI library). I can't fault burn entirely for this, after all they are free to make their own design choices and I was a beginner trying to do this in less than a week. I also think they are trying to solve for getting a practitioner to just get up and going right away, whereas I was trying to build a modular configuration on top of the crate instead of a one-and-done type script. But there were countless generic types, several traits to define and implement in order to make some generic parameter fit those bounds, and the crate has more proc_macro derives than I'd like (my target number is 0) such as `#[derive(Module, Config, new)]` because they obfuscate the code that I actually have to write and don't teach me anything. TL;DR the crate felt super powerful but also very foreign. It didn't quite click to the point where I thought it was intuitive or I felt very fluent with it. But then again, I spent like 5 days with it. One other minor annoying thing was that I couldn't download exactly what I wanted out of HuggingFace directly. I ended up having to use `HuggingfaceDatasetLoader::new("carlosejimenez/wikitext__wikitext-2-raw-v1")` instead of `HuggingfaceDatasetLoader::new("Salesforce/wikitext")` because the latter would get an auth error, but this may also be my ignorance about how HF is supposed to work... Eventually, I got the whole thing to work quite neatly and was able to tweak hyperparameters and get my model to increasingly better perplexity. With more tweaks, a better tokenizer, possibly better data source, and an NVIDIA GPU rather than Apple Silicon, I could have squeezed even more out of it. My original goal was to try to slap an iced GUI on the project so that I could tweak the hyperparameters there, compare models, plot the training and inference, etc. with a GUI instead of code. Sort of a no-code approach to training models. I think it's an area worth exploring more, but I have a main quest I need to finish first so I just wrote down my findings in an unpublished "paper" and tabled it for now. ________ [0]: https://github.com/tracel-ai/burn https://github.com/tracel-ai/burn [1]: https://github.com/iced-rs/iced https://github.com/iced-rs/iced

senordevnyc 2y ago

It honestly feels like books are a poor fit for this topic, because things are moving so much faster than the publishing industry. A book published in 2024 would have been written in 2023, and would give you a pretty skewed picture of the SOTA.

rramadass 2y ago

Some that i am looking into; these are "practical" books which do not focus on the theory/algorithms but given that they are available (library/models/whatever), how to build your apps using them; 1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen. 2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book). 3) Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster. 4) Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG by Bouchard & Peters.