Notes on getting better

Language internals and core semantics, the data model and class machinery, concurrency, testing, and idiomatic design.

22 subtopics · 273 concepts

Transformer internals, attention, training and fine-tuning, sampling, inference serving and quantization, prompting and context.

17 subtopics · 171 concepts

Tool calling and the agent loop, MCP, skills and harnesses, multi-agent architectures, and how to evaluate agents.

4 subtopics · 40 concepts

Lexical and semantic retrieval — BM25, embeddings, vector indexes, distance metrics, Elasticsearch, hybrid search, and the RAG pipeline around them.

11 subtopics · 81 concepts

Rate limiting, caching, queues, consistency, database scaling, and reliability — the recurring building blocks behind any distributed system.

6 subtopics · 41 concepts

Experiment tracking, model registries, deployment strategies, monitoring and drift detection, and feature stores — running ML as a reliable production system.

4 subtopics · 24 concepts

JVM internals, the object/memory model, concurrency, and classic design patterns — and how the runtime and idioms differ from Python's.

5 subtopics · 24 concepts

The IoC container and bean lifecycle, proxies and AOP, @Transactional, the JPA persistence context, and the database isolation anomalies underneath it all.

7 subtopics · 95 concepts

The platform basics every GCP engineer must know cold — projects, regions and zones, billing — plus the services for deploying ML/AI workloads and the generative-AI and agent stack.

3 subtopics · 20 concepts

How Git really works under the hood — objects, hashes, refs, the index — and the commands that move them: fetch, pull, reset, switch, rebase, cherry-pick — plus branching workflows and CI/CD.

5 subtopics · 38 concepts

How AI assistants and agents help write code and build systems across the software lifecycle — and the controls that keep the output trustworthy.

7 subtopics · 59 concepts