A personal learning laboratory for becoming a TypeScript engineer who can build agent-native systems
A deliberate path of hands-on projects in TypeScript + Node.js, ramping from backend fundamentals through the context-engineering and AI-agent infrastructure that today's startups are actually built on: tool-calling loops, memory and summarization, MCP servers, agent orchestration, durable execution, evals, and observability.
- Build real backend systems instinct: streaming, concurrency, I/O efficiency, backpressure, observability.
- Master context engineering: tokenization, token budgets, summarization, hierarchical memory, prompt caching.
- Become fluent in the agent stack: tool-calling loops, MCP, orchestration, durable execution, sandboxing, and evals.
- Ship agent-native capstones polished enough to be a remote-hire portfolio.
-
CLI File Analyzer — a robust CLI that analyzes any JSON or CSV file and outputs schema info, row count, null counts, type detection, and basic statistics.
-
Simple Hono API — a basic REST API using Hono with GET routes serving static/mock data, with proper TypeScript and Zod validation.
-
Tokenizer / Lexer — a tokenizer for a slice of JavaScript, built from scratch: scans source character by character into a typed token stream (punctuation, identifiers, keywords, string literals), with whitespace handling and error reporting on malformed input.
Applied across every repo — table stakes, and their absence is what fails take-home reviews:
- Testing — Vitest for unit tests, Testcontainers for integration tests that spin up real Postgres/Redis. Don't mock what you can run.
- CI/CD — ESLint + Prettier + a pre-commit hook + GitHub Actions running lint/typecheck/test on every PR.
- Language/runtime: TypeScript (strict) + Node.js
- Web: Hono
- Data: Postgres + Prisma, Redis, pgvector, SQLite
- Queue: BullMQ
- AI/agents: Anthropic SDK / Vercel AI SDK, MCP,
gpt-tokenizer - Ops: Docker, OpenTelemetry, Prometheus
Later phases will revisit the performance-critical pieces (tokenizer, sandboxing, storage layer) in Go and C++, and expand into other languages. The focus stays on practical systems thinking, performance, and real-world backend + agent engineering.
Status: In Progress
Started: April 2026
Feel free to explore, star, or fork if you find it useful!
Made with ❤️ for deep learning and systems mastery.