Compute Library — Index
Software engineering + computer-science substance: distributed systems, databases, operating systems, networking, ML/AI fundamentals, security, observability. Sister library to
Engineering/(hardware + physics) andLanguages/(the syntactic catalog).
- Status: Tier 1 complete (24 systems/ML deep notes + 4 theory-of-computation notes) + Tier 2 specialty (31 deep notes).
- Library structure: mirrors Engineering / Robotics / Languages — Tier 1 deep notes + Tier 2 specialty + Tier 3 family indexes.
- Schema: see _schema (TBD)
Subdomains
| Subdomain | Examples |
|---|---|
| Distributed systems | consensus (Raft/Paxos), CAP, replication, sharding, gossip, leader election |
| Databases | OLTP, OLAP, key-value, document, columnar, graph, time-series, vector |
| Operating systems | process / threads / scheduling, memory management, virtual memory, IPC, kernels |
| Networking | TCP/IP, UDP, HTTP/2/3, QUIC, DNS, BGP, mesh, gRPC, WebSocket |
| Concurrency | threads, async, actors, CSP, locks, lock-free, transactional memory |
| Compilers + runtimes | parsing, IR, JIT, AOT, GC, type systems |
| ML / AI | transformers, fine-tuning, RAG, embeddings, training, inference, evaluation |
| Cloud / cloud-native | containers, K8s, serverless, IaC, service mesh, CD/CD |
| Observability | metrics (Prometheus), logs, traces (OTel), profiling |
| Security | crypto, auth (OAuth/OIDC/SAML), TLS, secrets, RBAC, attack patterns |
| Performance engineering | profiling, benchmarking, latency, throughput, cache hierarchy |
| Architecture | microservices, monolith, event-driven, CQRS, hexagonal |
| Theory of computation | algorithms & data structures, complexity (P/NP), automata & computability, PL semantics |
Tier 1 — Complete (24 / 24)
| Status | Topic | File | Lines | Subdomain |
|---|---|---|---|---|
| ✅ | Distributed systems fundamentals | distributed-systems-fundamentals | 584 | distributed-systems |
| ✅ | Consensus protocols | consensus-protocols | 490 | distributed-systems |
| ✅ | Database internals | database-internals | 812 | databases |
| ✅ | SQL vs NoSQL design | sql-nosql-design | 564 | databases |
| ✅ | OS scheduling, memory & IPC | os-scheduling-memory | 586 | operating-systems |
| ✅ | Networking foundations | networking-foundations | 605 | networking |
| ✅ | HTTP/2, HTTP/3 & QUIC deep | http2-http3-quic | 520 | networking |
| ✅ | Concurrency primitives | concurrency-primitives | 588 | concurrency |
| ✅ | Compiler design | compiler-design | 592 | compilers |
| ✅ | Garbage collection | garbage-collection | 540 | runtimes |
| ✅ | Transformer architecture | transformer-architecture | 591 | ml-ai |
| ✅ | Fine-tuning & RLHF | fine-tuning-rlhf | 896 | ml-ai |
| ✅ | RAG, embeddings, vector search | rag-embeddings-vector-search | 432 | ml-ai |
| ✅ | LLM inference optimization | inference-optimization | 566 | ml-ai |
| ✅ | Diffusion & generative-media architectures | diffusion-and-generative-media | 610 | ml-ai |
| ✅ | Data engineering & stream processing | data-engineering-and-stream-processing | 691 | data-engineering |
| ✅ | Kubernetes deep | kubernetes-deep | 595 | cloud-native |
| ✅ | Containers & service mesh | containers-service-mesh | 593 | cloud-native |
| ✅ | Observability stack | observability-stack | 580 | observability |
| ✅ | Cryptography fundamentals | cryptography-fundamentals | 1096 | security |
| ✅ | Authentication & authorization | auth-authz | 772 | security |
| ✅ | CPU cache & performance | cpu-cache-performance | 506 | performance |
| ✅ | Microservices patterns | microservices-patterns | 552 | architecture |
🎯 Compute Tier 1 complete: 24 / 24 deep notes, ~13,760 lines.
Tier 1 — Theory of computation (added 2026-06-03)
The classical CS-theory core, complementing the systems/ML Tier 1 above (and the mathematical foundations in Math/ — graph theory, combinatorics, logic, information theory).
| Topic | File | Subdomain |
|---|---|---|
| Algorithms & data structures | algorithms-and-data-structures | algorithms |
| Computational complexity (P/NP, the class hierarchy) | computational-complexity | complexity-theory |
| Automata, formal languages & computability | automata-formal-languages-and-computability | automata-computability |
| Programming language theory & semantics | programming-language-theory-and-semantics | programming-languages |
Tier 2 — Specialty (in progress)
| Topic | File |
|---|---|
| Compilers & program analysis | compilers-and-program-analysis |
| Type theory & type systems | type-theory-and-type-systems |
| Databases internals (deep) | databases-internals-deep |
| Differential privacy & privacy tech | differential-privacy-and-privacy-tech |
| Distributed systems (deep) | distributed systems deep |
| FPGA & hardware acceleration | fpga-and-hardware-acceleration |
| iOS — Swift 6 + SwiftUI deep | ios-swift-and-swiftui-deep |
| Android — Kotlin 2 + Jetpack Compose deep | android-kotlin-and-jetpack-compose-deep |
| React Native — New Architecture + Expo deep | react-native-deep |
| Flutter + Dart — Impeller + WASM deep | flutter-and-dart-deep |
| Capacitor, PWA, and cross-platform web | capacitor-pwa-and-cross-platform-web |
| CRDTs & distributed data types (state/op-based, SEC, causal) | crdts-and-distributed-data-types |
| CUDA + Triton + GPU programming | cuda-triton-gpu-programming |
| eBPF & kernel observability (XDP, kprobes, Cilium) | ebpf-and-kernel-observability |
| Formal verification & fuzzing (model checking, SAT/SMT, AFL) | formal-verification-and-fuzzing |
| Lock-free data structures & RDMA (atomics, DPDK, kernel-bypass) | lock-free-data-structures-and-rdma |
| Model serving infrastructure (vLLM / TGI / Triton) | model-serving-infrastructure |
| Model routing & LLM gateways | model-routing-and-llm-gateways |
| Voice & realtime AI architecture | voice-and-realtime-ai |
| Prompt engineering & LLM agent systems | prompt-engineering-agent-systems |
| Agent memory & context engineering | agent-memory-and-context-engineering |
| LLM evaluation (benchmarks, LLM-as-judge, eval harnesses) | llm-evaluation |
| Distributed LLM training (FSDP/ZeRO, 5D parallelism, Megatron) | distributed-llm-training |
| Multimodal model architectures | multimodal-architectures |
| Quantization & model compression | quantization-and-model-compression |
| MLOps & ML platform engineering | mlops-and-ml-platforms |
| Infrastructure as code (IaC) | infrastructure-as-code |
| WebAssembly: component model & WASI | webassembly-and-wasi |
| LLM & AI application security | llm-and-ai-security |
| Software supply-chain security | supply-chain-security |
| Confidential computing & TEEs | confidential-computing-and-tees |
| Randomized & approximation algorithms | randomized-and-approximation-algorithms |
🎯 Compute Tier 2: 30 / 30 done — original 10 (5 specialty + 5 mobile/cross-platform @ 2026-05-25) plus 7 systems/ML notes (CRDTs, eBPF, formal-verification + fuzzing, lock-free + RDMA @ 2026-05-18; CUDA/Triton GPU, model serving, prompt-engineering + agent systems @ 2026-05-17); plus LLM evaluation + distributed LLM training @ 2026-05-30; plus 4 additions @ 2026-05-30 (multimodal architectures, IaC, WebAssembly + WASI, software supply-chain security). (The “distributed systems deep” row above is a planned placeholder with no note yet.)
Planned (next batch): Raft implementation details, BTrees vs LSM trees (now partly covered in databases-internals-deep), FRP, TPU + accelerators.
Tier 3 — Family indexes (deferred)
- Database engine taxonomy (Postgres, MySQL, MongoDB, Cassandra, ClickHouse, DuckDB, etc.)
- Cloud provider service mapping (AWS / GCP / Azure)
- ML framework comparison (PyTorch / JAX / TF / Keras)
- LLM landscape (open / closed model catalog)
- Container orchestrators (K8s / Nomad / ECS)
- Observability tools (Prometheus / Grafana / Datadog / Honeycomb)
- Auth providers (Auth0 / Clerk / WorkOS / Stytch)
- Languages-vs-runtimes catalog
Vendor sub-libraries
Vendor-specific deep dives sit as sub-folders. Each has its own _index.md.
| Sub-library | Scope | Index |
|---|---|---|
| Claude product ecosystem | Anthropic’s full Claude line — models (Opus 4.7 / Sonnet 4.6 / Haiku 4.5), Messages API + SDKs, Claude Code (CLI / IDE / desktop / web), Claude Agent SDK, MCP protocol, prompt caching, tool use, vision, batches, files, citations, computer use, code execution, memory, web search, hooks, subagents, skills, and the hidden-tricks compendium | _index |
| OpenAI product ecosystem | OpenAI’s full platform — models (GPT-5 / GPT-4o / o-series reasoning), Responses API + Chat Completions + SDKs, automatic prompt caching, function calling + structured outputs (strict mode), vision + audio + image generation, Realtime API (voice, WebSocket / WebRTC), Agents SDK + Codex CLI, batch + fine-tuning (SFT / DPO / RFT), and the hidden-tricks compendium | _index |
| Google Gemini product ecosystem | Google’s full Gemini line — models (Gemini 3.x / 2.5 / 2.0 / 1.5 Pro 2M-context), Gemini Developer API + Vertex AI + google-genai unified SDK, explicit + implicit context caching, function calling + structured output, native multimodal (text + image + audio + video + PDF), 1M / 2M long context, code execution + Google Search grounding, embeddings (multimodal gemini-embedding-2), Vertex AI Search + Model Garden, and the hidden-tricks compendium | _index |
| AWS Bedrock | The AWS-managed gateway to Anthropic Claude, Meta Llama 3/4, Mistral, Cohere, AI21, Amazon Nova / Titan, and Stability AI — full model catalog with pricing + regions, unified Converse API + tool use, Bedrock Agents (multi-agent collaboration + action groups + memory), Knowledge Bases for RAG (OpenSearch Serverless / Aurora pgvector / Pinecone / Neptune Graph + Cohere Rerank 3.5), Guardrails (content filters + PII + contextual grounding + automated reasoning), Prompt Management + Flows, cross-region inference profiles, and the hidden-tricks compendium | _index |
| Vercel | The “AI cloud” — Projects + Deployments (Preview / Production / Skew Protection / Rolling Releases / Instant Rollback), Vercel Delivery Network (126+ POPs + ISR + Image Optimization), Vercel Functions (Fluid Compute concurrency model + Edge vs Node.js + Routing Middleware), the Vercel AI SDK (Core + UI + RSC + 16+ provider plugins + AI Gateway), storage (Blob + Edge Config + Marketplace databases — Neon Postgres / Upstash Redis / etc.), Observability + Speed Insights, Cron Jobs, v0, and the hidden-tricks compendium | _index |
| Cloudflare Workers | V8-isolate serverless on 330+ POPs — Workers runtime + APIs + Wrangler, full bindings catalog (KV / R2 / D1 / DO / Queues / Vectorize / AI / Workflows / Hyperdrive / Browser Rendering / Service Bindings), Durable Objects deep dive (SQLite-in-DO + WebSocket Hibernation + alarms), Workers AI + Vectorize + AI Gateway + Cloudflare Agents SDK, Workflows (durable execution) + Queues + Cron Triggers + Pipelines + Tail Workers, and the hidden-tricks compendium | _index |
| Meta Llama | The open-weight default — full lineup (Llama 2 → 3 → 3.1 405B → 3.2 vision + edge → 3.3 → Llama 4 Scout/Maverick MoE; Behemoth announced-but-unreleased), the Llama Community License (700M-MAU clause, “open weight” vs OSI-open), Llama Stack, Purple Llama safety tooling (Llama Guard / Prompt Guard / Code Shield / LlamaFirewall / CyberSecEval), deployment paths (self-host vLLM/SGLang, on-device llama.cpp/ExecuTorch/MLX, managed via Bedrock/Azure/Together/Fireworks/Groq), and the derivative-model ecosystem | _index |
| Azure AI (OpenAI + Foundry) | The enterprise model gateway — Azure OpenAI Service (GPT-5.x / o-series / image / audio) + Models-as-a-Service for Llama/Mistral/Cohere/DeepSeek/xAI/Phi via Microsoft Foundry, deployment types (Standard / Global / Data Zone / Provisioned-PTU / Batch / Developer), PTU economics, regional data residency (EU Data Boundary, Azure Government), content filtering + Responsible AI + Prompt Shields, Entra ID + Private Link + managed identity, the Foundry platform (catalog / prompt flow / evaluations / Agent Service / AI Search), and an honest Azure-vs-Bedrock-vs-Vertex comparison | _index |
| xAI (Grok) | The frontier lab with real-time X grounding — lineup (Grok-1 open-weight → 1.5 → 2 → 3 Think/DeepSearch → 4 / 4 Heavy → 4.1), OpenAI/Anthropic-compatible API + Live Search + function calling + structured outputs + vision, availability (api.x.ai / grok.com / X / Azure Foundry), benchmark positioning, and the Colossus training-cluster story | _index |
| Open-weight providers | The build-vs-buy / self-host-vs-API economy — open-weight model families (Mistral, DeepSeek, Qwen + Gemma/Phi/OLMo) and their licensing taxonomy, the inference-provider landscape (Together / Fireworks / Groq LPU / Cerebras / DeepInfra / Replicate / Baseten + the OpenRouter & LiteLLM routing layer) compared on speed/price/quantization/data-policy, and the self-host vs managed-provider vs hyperscaler decision framework | _index |
Adjacent libraries
- Engineering — physical disciplines (hardware that runs the compute)
- Robotics — applied robotics (uses compute for perception + planning + control)
- Languages — language syntactic + library catalog
- Math — mathematics + statistics (underlies ML + algorithms)
How to study this library
- Learning paths — _learn_next — “if you’ve read X, learn Y next” recommendation graph plus five named multi-step tracks (Distributed-systems engineer, Database engineer, ML platform engineer, Cloud-native SRE, Security engineer).
- Cross-cutting comparisons — two
_compare_*notes that span the library: _compare_consistency_models, _compare_service-architectures.
How to use
For a software-engineering question:
- Identify the subdomain (distributed / DB / OS / network / concurrency / ML / cloud / security / observability / arch).
- Read the relevant Tier 1 deep note.
- Use Tier 3 family indexes to find specific tool/product options.
- Cross-reference Languages for syntactic / library detail.
- Cross-reference Math for theoretical foundations (probability, complexity, linear algebra, info theory).