Tag: llms

This is a useful bridge for software developers who can build with LLM APIs but want to understand the math vocabulary underneath attention, embeddings, optimization, and probabilities.

I like links like this because they reduce magical thinking. You do not need a PhD to reason better about LLM systems, but you do need enough math to know what the abstractions are hiding.

This is the kind of long historical synthesis that helps engineers stop treating transformers as a sudden miracle. It traces the ideas through distributed representations, language modeling, attention, transformers, and generative pretraining.

Worth keeping because architectural judgment improves when the current stack has a history. The tradeoffs feel less arbitrary when you can see what each generation was trying to solve.

This visual guide is useful because embeddings become easier to reason about when they stop being abstract vectors and start feeling geometric. That matters for RAG, clustering, recommendations, search, and model evaluation.

I would keep it as a teaching aid, not as an implementation reference. The value is intuition: what similarity means, why representation quality matters, and why retrieval errors often start before the model answers.

This is worth keeping because reasoning models change product expectations and evaluation shape. The important question is not only whether a model can think longer, but whether the extra compute improves the user's task enough to justify latency and cost.

It pairs well with the agent links because reasoning is not a magic permission slip for autonomy. The system still needs constraints, tools, and tests.

This is one of the foundational agent explainers: planning, memory, tool use, reflection, and the early patterns that still show up in current products. It belongs in the stream because the best agent discussions need a shared vocabulary.

The piece is also a useful time capsule. Some implementation details have moved quickly, but the core system questions have not gone away.