Applied Computer Science — Projects

52 from-scratch systems that implement the concepts taught in the companion book Applied Computer Science. Where a book chapter says "Build it →", it points here.

Each project is built to real engineering standards — trait/interface-driven design, typed errors, and a substantial test suite — and each is honest about its scope. Some are complete, hardened services; others are deliberately CPU-only simulations or teaching implementations (the GPU, OS-kernel, and async-runtime work in particular). Every project's README and BLUEPRINT.md state what is real versus simulated, so nothing here overclaims.

Conventions

• Python (34 projects) — FastAPI + pytest, packaged with pip install -e ..
• Rust (17 projects) — trait-based design, Result error handling, Criterion benchmarks, built with cargo build / cargo test.
• Go (1 project) — gRPC + protobuf.
• Every project ships its own README.md, a docs/BLUEPRINT.md design document, and build/run instructions. Served APIs share an opt-in hardening baseline (API-key auth, in-process rate limiting, request timeouts).

Projects

Foundation & backend (01–10)

#	Project	Lang
01	Distributed Job Queue	Python
02	Microservice Platform	Go
03	High-Performance Cache (redis-lite)	Rust
04	ML Training Orchestrator	Python
05	SaaS Web Platform	Python
06	Async Runtime	Rust
07	Data Lakehouse	Python
08	Streaming Platform	Python
09	Data Observability Platform	Python
10	Warehouse Semantic Layer	Python

Distributed systems (11–20)

#	Project	Lang
11	Distributed Key-Value Store (Raft)	Rust
12	Distributed Log System (Kafka-lite)	Rust
13	Service Mesh	Rust
14	Network Stack (TCP + HTTP)	Rust
15	Minimal OS Kernel	Rust
16	CRDT Collaboration Engine	Rust
17	Columnar Query Engine	Rust
18	Python Subset Compiler & Interpreter	Rust
19	GPU GEMM Optimization (cuBLAS-lite)	Rust
20	SIMD Analytics Engine	Rust

ML / AI core (21–37)

#	Project	Lang
21	Custom Embedding Model	Rust
22	Long-Context Attention	Rust
23	LLM Agentic Runtime	Rust
24	Synthetic Data Generator	Python
25	RAG Baseline	Python
26	Advanced RAG	Python
27	Micro-Model Orchestrated RAG	Python
28	AI Workflow Engine	Python
29	Model Routing Layer	Python
30	Large-Scale Parameter Server	Python
31	ML Compiler	Python
32	Distributed Tensor Algebra	Python
33	RL Physics Engine	Python
34	HDFS-Lite Distributed File System	Python
35	Differentiable Programming	Python
36	Distributed Streaming Analytics	Python
37	Dynamic Graph Execution Runtime	Python

Advanced ML (38–49)

#	Project	Lang
38	Dynamic Graph Execution (DynaGraph)	Python
39	GPU Memory Manager	Python
40	Distributed Autograd System	Python
41	Vector-Quantized LLM	Python
42	GNN Runtime	Python
43	Vector Index	Python
44	Autoregressive Inference Engine	Python
45	Neural Compression Engine	Python
46	Multi-Tenant GPU Scheduler	Python
47	On-Device LLM Runtime	Python
48	Multi-GPU Kernel Scheduler	Python
49	AI Benchmark Suite	Python

Data infrastructure (50–52)

#	Project	Lang
50	Feature Engineering Platform	Python
51	Message Queue	Rust
52	Time-Series Database	Rust

Running a project

Each project is self-contained. From its directory:

# Python
pip install -e ".[dev]"
pytest

# Rust
cargo test
cargo bench

See the project's own README.md for endpoints, configuration, and design notes.

License

MIT © 2026 James Hu

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_{🤖 Built with Claude Code.}