This repository serves as a personal knowledge base and study companion for mastering AI engineering, model interactions, and core software development foundations. It integrates evidence-based learning strategies with practical resources for maximizing the utility of Large Language Models (LLMs).
The library is organized into the following main sections:
Documentation and best practices for interacting with specific AI models.
- Claude: Guides on interaction, effective harnessing of long-running agents, and prompting best practices.
A collection of optimized prompts and meta-prompts designed to improve the performance of various models.
- Improvement: Meta-prompts specifically crafted to refine and optimize inputs for Claude, Codex, Gemini, and Grok.
Rules written to steer coding agents, organized by the tool that loads them — the guidance is shared, only the activation metadata differs.
- Java Concurrency &
static: decision rule forstaticin high-RPM services on Java 25 — shared state, virtual threads, DynamoDB/Redis clients, and OpenTelemetry. Available for Claude Code and for Cursor as an.mdcproject rule — both scoped by globs to Java sources and build files. Seeai/rules/README.mdfor install paths, the Cursor rule-type/frontmatter mapping, and sourced notes on the.mdcfrontmatter gotchas.
Executable reference implementations for the rules above — a rule the build does not enforce is only a suggestion.
java/static-concurrency-enforcement/: ArchUnit test suites (v1 and v2, the latter frozen against a baseline) plus an Error ProneBugCheckerthat covers thesynchronizedblocks ArchUnit cannot see in bytecode.
Structured study plans and educational resources.
- Basics Study Plan: A comprehensive guide covering Computer Science foundations (Algorithms, Data Structures), Backend Engineering, and System Design. It incorporates neuroscientific learning principles like Active Recall and Spaced Repetition, utilizing tools like NotebookLM as a cognitive partner.
- Tools: Generators for Product Requirement Documents (PRDs) and Technical Review guidelines.
- Active Learning: The study plans encourage active engagement (teaching back, solving problems) rather than passive consumption.
- Prompt Engineering: The repository treats prompts as code, with versioning and optimization strategies for different model architectures.
- Cognitive Amplification: Using AI tools not just to generate code, but to deepen understanding of complex concepts.
- Study: Follow the
basics-study-plan.mdfor a rigorous CS and Engineering curriculum. - Optimize: Use the prompts in
ai/prompts/improvement/to refine your instructions to AI agents. - Reference: Consult
ai/docs/for model-specific interaction patterns and best practices.