swyx and teams podcast, newsletter and discord has been the highest signal to noise ratio for keeping up and learning.

But the other books (#1, #3, #4) seem like they're intended for those who want to understand all the math. Many people don't want (or need) a full understanding of how all this works. They can provide significant value to their employers with some knowledge of how machine learning works (e.g. the basics of CNNs and RNNs), and some intuitions/vibes about SOTA LLMs, even if they don't understand transformers or other modern innovations.

Let's put it this way: if even half the people who call themselves "AI Engineers" would read the research in the field, we'd have a lot less hype and a lot more success in finding the actual useful applications of this technology. As is, most "AI Engineers" assume the same thing you do and consider "AI Engineering" to be "I know how to plug this black box into this other black box and return the result as JSON! Pay me!". Meanwhile most AI startups are doomed from the start because what they set out to do is known to be a bad fit.

> Just get a feel for how these LLMs respond then build a pretty and user friendly app on top of them.

as you know, "just" is a very loaded word in software engineering. The entire thesis of AI Eng is that this attidude of "just slap a UI on an LLM bro whats so hard" is a rapidly deepening field, with its own stack and specialization (which, yes, some if not much of which is unnecessary, vc funded hypey complexity merchantism, but some of which is also valid), and if you do not take it seriously, others will, and do so running rings around those who have decided to not even try to push this frontier, passively waiting for model progress to solve everything.

i've seen this play out before in underappreciated subfields of engineering that became their own thing, with their own language, standard stack, influencers, debates, controversies, IPOs, whole 9 yards.... frontend eng, mobile eng, SRE, data eng, you name it. you just have to see the level and quality of work that these people are doing that is sufficiently distinct from MLE and product/fullstack webdev to appreciate that it probably deserves its own field of study, and while it will NEVER be as prestigious as AI research, there will be a ton more people employed in these roles than there can be in research and thats a perfectly fine occupation too.

I'm even helping instruct a course about it this week as it happens if you want to see what a practical syllabus for it looks like https://maven.com/noah-hein/ai-engineering-intro

1. The actual deep ML researchers that work on models 2. The "AI engineer" who creates products based on LLM's 3. The "AI researchers" who basically just stack LLM's together and call it something like Meta-Cognitive Chain-of-Thought Advanced Reasoning Inteligence or whatever it is.

Similarly I know how to call cryptography libraries to get my passwords hashed using a suitable cipher before storing them. I don't understand the deep math behind why a certain cipher is secure, but that's fine. I can still make good use of cryptographic functions. I'm not a cryptography engineer either :).

My take on it is that if you should call yourself any kind of "XYZ Engineer", you should be able to understand the inner workings of XYZ.

This reading list is most likely (mostly) for those who want to get a really deep understanding and eventuellt work on contributing to the "foundational systems" (for a lack of a better word) one day.

Hope that helps.

- Actual examples of Fine tuning of LLMs or making merges - usually talked about in r/localLlama for specific use cases like role playing or other scenarios that instruction tuned LLMs are not good at. Jupyter notebook or blog post would be great here.

- Specifically around Agents & Code generation - Anthropic's post about SWE-bench verified gives a very practical look at writing a coding agent https://www.anthropic.com/research/swe-bench-sonnet with prompts, tool schema and metrics.

- The wide amount of Loras and fine tunes available on civitai for image models - a guide on making a custom one that you can use in ComfyUI.

- State of the art in audio models in production - Elevenlabs seems to still be the best for closed platforms, but there are some options for open source voice cloning, TTS, or even text to speech with very small parameter models (kokoro 82M).

I am out of my depth when it comes to reading papers, but I second 'The Prompt Report' from your list.

It gives a great taxonomy that helped me understand the space of prompting techniques better.

I’m curious, is there also some specific existing “AI Researcher Reading List” you would personally recommend? Or do you plan on making and maintaining one?