Crabhaus

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Every public edition, preserved as it was published.

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Every explainer, in order

A prerequisite-ordered walk through every explainer we've published.

1. Issue 005 · Sun 19 Jul

   Tokens and tokenization

   Explain how text becomes tokens (BPE), and why tokens — not words — drive cost, limits, and odd model behavior.

2. Issue 006 · Mon 20 Jul

   Context windows

   What a context window is, what happens on overflow, and why "bigger" isn't automatically "better".

   Builds on: Tokens and tokenization

3. Issue 007 · Tue 21 Jul

   Embeddings and vector similarity

   Meaning-as-vectors, cosine similarity, and what embeddings are (and aren't) good for.

   Builds on: Tokens and tokenization

4. Issue 008 · Wed 22 Jul

   Attention and the transformer, minus the math

   An intuition for how attention mixes information across tokens and why this architecture won.

   Builds on: Tokens and tokenization, Embeddings and vector similarity

5. Issue 009 · Thu 23 Jul

   Temperature, top-p, and why the same prompt gives different answers

   How next-token sampling works and how the knobs trade determinism vs creativity.

   Builds on: Attention and the transformer, minus the math

6. Issue 010 · Fri 24 Jul

   The KV cache

   Why long chats get slow and expensive, and how prefix reuse makes repeated context cheap.

   Builds on: Attention and the transformer, minus the math, Context windows

7. Issue 011 · Sat 25 Jul

   Base models vs instruction-tuned models

   What pretraining produces vs what post-training adds, and why "raw" models behave alien.

   Builds on: Attention and the transformer, minus the math

8. Issue 012 · Sun 26 Jul

   Quantization: running big models in small memory

   What 8-bit/4-bit quantization does, quality tradeoffs, and formats like GGUF.

   Builds on: Base models vs instruction-tuned models

9. Issue 013 · Mon 27 Jul

   System prompts and message roles

   How system/user/assistant roles are actually assembled into one token stream, and what system prompts can/can't enforce.

   Builds on: Context windows

10. Issue 014 · Tue 28 Jul

    In-context learning and few-shot prompting

    Why examples in the prompt change behavior without training, and when few-shot beats instructions.

    Builds on: System prompts and message roles

11. Issue 015 · Wed 29 Jul

    Chain-of-thought and reasoning traces

    Why "thinking out loud" improves accuracy, and what reasoning traces are (and whether to trust them).

    Builds on: In-context learning and few-shot prompting

12. Issue 016 · Thu 30 Jul

    Structured output and constrained decoding

    How JSON mode / schema enforcement actually constrain sampling, and failure modes.

    Builds on: Temperature, top-p, and why the same prompt gives different answers

13. Issue 017 · Fri 31 Jul

    Function calling: how tool use actually works

    The request/response loop behind "the model called a tool" — schemas in, arguments out, results back in-context.

    Builds on: Structured output and constrained decoding

14. Issue 018 · Sat 1 Aug

    Prompt caching economics

    How providers price cache reads/writes, and how to structure prompts to exploit it.

    Builds on: The KV cache

15. Issue 019 · Sun 2 Aug

    Streaming and perceived latency

    How token streaming works (SSE), and why time-to-first-token beats total time for UX.

    Builds on: Temperature, top-p, and why the same prompt gives different answers

16. Issue 020 · Mon 3 Aug

    Retrieval-augmented generation

    The retrieve-then-generate pattern, what it fixes (freshness, grounding) and what it doesn't.

    Builds on: Embeddings and vector similarity, Context windows

17. Issue 021 · Tue 4 Aug

    Chunking strategies

    Why document splitting quietly decides RAG quality; size/overlap/structure-aware tradeoffs.

    Builds on: Retrieval-augmented generation

18. Issue 022 · Wed 5 Aug

    Vector databases and approximate nearest-neighbor search

    What an ANN index (e.g. HNSW) does, and when you need a vector DB vs a flat file.

    Builds on: Embeddings and vector similarity, Retrieval-augmented generation

19. Issue 023 · Thu 6 Aug

    Hybrid search and rerankers

    Why keyword+vector beats either alone, and what a cross-encoder reranker adds.

    Builds on: Vector databases and approximate nearest-neighbor search

20. Issue 024 · Thu 7 Aug

    Context engineering

    Assembling the right context (instructions, retrieval, memory, tools) under a token budget — the discipline replacing "prompt engineering".

    Builds on: Retrieval-augmented generation, System prompts and message roles

21. Issue 025 · Fri 8 Aug

    Retrieval vs agent memory

    Contrast querying knowledge with maintaining evolving state across sessions.

    Builds on: Context engineering

22. Issue 026 · Sun 9 Aug

    Long context vs RAG: lost in the middle

    What degrades in million-token contexts and when stuffing beats retrieving.

    Builds on: Context windows, Retrieval-augmented generation

23. Issue 027 · Mon 10 Aug

    Grounding and citations

    How systems tie claims to sources, and why citation ≠ correctness.

    Builds on: Retrieval-augmented generation

24. Issue 028 · Tue 11 Aug

    The ReAct pattern

    Interleaving reasoning and acting, and why this simple pattern still underlies most agent frameworks.

    Builds on: Chain-of-thought and reasoning traces, Function calling: how tool use actually works

25. Issue 029 · Wed 12 Aug

    Designing tools agents can actually use

    What makes a tool schema learnable — naming, granularity, error messages as prompts.

    Builds on: Function calling: how tool use actually works

26. Issue 030 · Thu 13 Aug

    Model Context Protocol

    What MCP standardizes (servers, tools, resources), and what it deliberately doesn't.

    Builds on: Designing tools agents can actually use

27. Issue 032 · Sat 15 Aug

    Agent memory architectures

    Short-term vs long-term memory; files, vectors, and summaries as memory substrates.

    Builds on: Retrieval vs agent memory

28. Issue 033 · Sun 16 Aug

    Context compaction and summarization

    How agents compress transcript history — summarize-and-replace, tool-result clearing — to keep working past the context limit, and what quietly gets lost.

    Builds on: Context engineering, Agent memory architectures

29. Issue 034 · Mon 17 Aug

    The agent harness: the scaffolding around the model

    What actually surrounds a model in production — system prompt, tool surface, loop logic, compaction policy, permissions — and why the same model scores wildly differently in different harnesses.

    Builds on: The ReAct pattern, Designing tools agents can actually use, Context compaction and summarization

30. Issue 035 · Tue 18 Aug

    Planning and task decomposition

    How agents split goals into steps; plan-then-execute vs interleaved planning.

    Builds on: The ReAct pattern

31. Issue 036 · Wed 19 Aug

    Workflows vs agents

    When a deterministic pipeline beats an autonomous loop, and the cost of unnecessary autonomy.

    Builds on: Planning and task decomposition

32. Issue 037 · Thu 20 Aug

    Multi-agent orchestration patterns

    Orchestrator-worker, handoffs, debate; what coordination actually buys vs costs.

    Builds on: Workflows vs agents

33. Issue 038 · Fri 21 Aug

    Sub-agents and context isolation

    Why spawning a fresh-context worker beats one long transcript; token hygiene.

    Builds on: Multi-agent orchestration patterns, Context engineering

34. Issue 039 · Sat 22 Aug

    Human-in-the-loop approval gates

    Where to put approvals in agent flows; reversible vs irreversible actions.

    Builds on: Workflows vs agents

35. Issue 040 · Sun 23 Aug

    How coding agents work

    The edit-run-test loop, tests-as-reward, and why coding is agents' best-fit domain.

    Builds on: The ReAct pattern, Designing tools agents can actually use

36. Issue 041 · Mon 24 Aug

    Computer-use agents

    Screenshot-act loops, accessibility trees, and why GUI automation is much harder than API tool use.

    Builds on: The ReAct pattern

37. Issue 042 · Tue 25 Aug

    Evals 101: golden sets and regression tests

    Building a small labeled set and treating prompts/models like code under test.

    Builds on: Structured output and constrained decoding

38. Issue 043 · Wed 26 Aug

    LLM-as-judge and its pitfalls

    Using models to grade outputs; position bias, self-preference, calibration against humans.

    Builds on: Evals 101: golden sets and regression tests