Readit NewsRecently I made a diet checklist [1] that I've been following more or less to the letter 5 days out of the week. I have a little Android button that just opens right up to the web page. I click, click, click, then move on with my day. If I feel I need to change something I can copy a plain text screenshot of what's on there currently and chat with Gemini about it.
I'm really liking this new wave of technology.
Now the credit card company knows what service I am buying; I would get endless marketing emails from the service for buying additional things; my info as a person willing to pay for such a service would get sold to other companies; my credit card info would get leaked/stolen, ...
If the whole experience was literally as simple as handing someone a $1 bill, I promise I would pay for many many internet services.
I don't know. Paying for streaming services seems very natural nowadays.
1. Learn basic NNs at a simple level, build from scratch (no frameworks) a feed forward neural network with back propagation to train against MNIST or something as simple. Understand every part of it. Just use your favorite programming language.
2. Learn (without having to implement with the code, or to understand the finer parts of the implementations) how the NN architectures work and why they work. What is an encoder-decoder? Why the first part produces an embedding? How a transformer works? What are the logits in the output of an LLM, and how sampling works? Why is attention of quadratic? What is Reinforcement Learning, Resnets, how do they work? Basically: you need a solid qualitative understanding of all that.
3. Learn the higher level layer, both from the POV of the open source models, so how to interface to llama.cpp / ollama / ..., how to set the context window, what is quantization and how it will affect performances/quality of output, and also, how to use popular provider APIs like DeepSeek, OpenAI, Anthropic, ... and what model is good for what.
4. Learn prompt engineering techniques that influence the qualtily of the output when using LLMs programmatically (as a bag of algorithms). This takes patience and practice.
5. Learn how to use AI effectively for coding. This is absolutely non-trivial, and a lot of good programmers are terrible LLMs users (and end believing LLMs are not useful for coding).
6. Don't get trapped into the idea that the news of the day (RAG, MCP, ...) is what you should spend all your energy. This is just some useful technology surrounded by a lot of hype of all the people that want to get rich with AI and understand they can't compete with the LLMs themselves. So they pump the part that can be kinda "productized". Never forget that the product is the neural network itself, for the most part.
Also if you like doing certain tasks, then it is like eating an ice cream vs telling someone to eat an ice cream.
- Mixx: https://mixxx.org/screenshots/
- darktable: https://www.darktable.org/about/screenshots/