MIT's Books on AI & ML (DOWNLOAD FREE):

1. Foundations of Machine Learning
https://cs.nyu.edu/~mohri/mlbook/

2. Understanding Deep Learning
https://udlbook.github.io/udlbook/

3. Introduction to Machine Learning Systems
❯ Vol 1: https://mlsysbook.ai/vol1/assets/downloads/Machine-Learning-Systems-Vol1.pdf
❯ Vol 2: https://mlsysbook.ai/vol2/assets/downloads/Machine-Learning-Systems-Vol2.pdf

4. Algorithms for ML
https://algorithmsbook.com/

5. Deep Learning
https://t.co/vCHVIZQYTI

6. Reinforcement Learning
https://t.co/JNWhFCuCkH

7. Distributional Reinforcement Learning
https://direct.mit.edu/books/oa-monograph-pdf/2111075/book_9780262374026.pdf

8. Multi Agent Reinforcement Learning
https://marl-book.com/

9. Agents in the Long Game of AI
https://direct.mit.edu/books/oa-monograph-pdf/2471103/book_9780262380355.pdf

10. Fairness and Machine Learning
https://fairmlbook.org/

11. Probabilistic Machine Learning

❯ Part 1 : https://probml.github.io/pml-book/book1.html
❯ Part 2 : https://probml.github.io/pml-book/book2.html

Serverless vs on-prem vs edge deployment.

(read before your next deploy)

These are three different answers to the same question of where your model actually runs, and each one costs you something different.

1) Serverless means you hand your model to a provider that only turns on a machine when a request arrives. Between requests the container shuts down, so you pay nothing while idle. The weights get unloaded along with it, so the next call has to pull gigabytes back into memory before it can answer, which adds up to 90 seconds. A reranker sitting in the middle of a search cannot spend that long waking up, so you keep instances warm, and you are back to paying for idle GPUs.

2) On-premise means you rent or own the GPU yourself and keep the server running inside your own network. Nothing leaves your walls, there is no cold start because the engine already holds the weights, and you pay a flat hourly rate instead of a per-token meter. The card runs whether traffic arrives or not.

3) Edge means the model runs on the user's own device, on its NPU or CPU, through a small local runtime. The model has to be shrunk down to fit, so it works offline with nothing ever leaving the hardware, but it handles one narrow task rather than a full pipeline.

On-premise is where serious teams land, and it only pays off if several models share one card.

That part rarely happens, because vLLM pre-allocates 90% of the GPU at startup and ignores every other vLLM instance on it, while HuggingFace TEI (Text Embeddings Inference) takes one model-id per process. Neither one knows the other is there.

So four small models end up on four separate cards. You switched to small models to spend less, and you are now renting four GPUs to run what one could hold.

The serving engine box in that middle row is what decides whether on-prem is cheap or just differently expensive.

The card was never the constraint. The missing piece is one server that holds all the models and shifts memory between them as traffic moves, instead of four servers each convinced they own the whole GPU.

The Superlinked Inference Engine (SIE) is the open-source project built for that slot. One server handles embeddings, reranking, extraction, and generation, loads a model the first time a request needs it, and drops the least recently used one when memory runs short.

Check it out on GitHub: https://github.com/superlinked/sie

(don't forget to star 🌟)

My co-founder wrote a detailed breakdown on what SIE is and how to get started with it.

The article is quoted below.

youtube-skills enables AI agents to retrieve YouTube transcripts, search videos, and browse channels via API calls. It removes the need for headless browsers or local setup and supports integrations with OpenClaw, Hermes Agent, and Claude Code.

https://github.com/ZeroPointRepo/youtube-skills https://x.com/tom_doerr/status/2081893239955554329

Claude Opus 5 is a monster.

But 99% of people are sleeping on what it can actually do.

I've used it to build apps, automate things, fact-check content, and design

Here are 10 ways to use Opus 5 that feel like cheating: https://x.com/socialwithaayan/status/2081760047936459024

this is a PowerPoint deck made with a fund's own template, using their own data room. nobody touched the keyboard.

every figure has its citation, and there's an audit view to check any number. two minutes, done.

@lukeaschenbrand just launched this