diff --git a/presentation/claude-code-best-practice/index.html b/presentation/claude-code-best-practice/index.html index bed2a97..c6ca716 100644 --- a/presentation/claude-code-best-practice/index.html +++ b/presentation/claude-code-best-practice/index.html @@ -125,6 +125,7 @@ .pillar-mini-card .pmc-body { font-size: 0.72rem; line-height: 1.35; color: #333; margin-top: 5px; display: -webkit-box; -webkit-line-clamp: 5; -webkit-box-orient: vertical; overflow: hidden; } .pillar-mini-card .pmc-badge { display: inline-block; font-size: 0.65rem; font-weight: 600; padding: 2px 7px; border-radius: 999px; margin-top: 6px; white-space: nowrap; align-self: flex-start; } .pillar-mini-card.inactive { opacity: 0.55; } + @@ -402,6 +403,7 @@ Boris Cherny (creator of Claude Code) — different teams use Claude Code completely differently.
There is no single “correct” way. But there are patterns worth understanding. +

Source: Boris Cherny on X — tweet 1 · tweet 2 · tweet 3

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Each run the model samples — temperature controls how widely it samples.

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Bender, Gebru, McMillan-Major, Mitchell — On the Dangers of Stochastic Parrots (2021)

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stochastic @@ -484,12 +483,86 @@ confident pattern-matching
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Source: Bender, Gebru, McMillan-Major, Mitchell — On the Dangers of Stochastic Parrots (2021)

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Even temperature = 0 isn’t deterministic.

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You set it to zero. You expect the same answer every time. You’re wrong.

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The data point
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Without fix
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80
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unique completions
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out of 1,000 calls
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→
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With fix
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1
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unique completion
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out of 1,000 calls
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Qwen3-235B at temperature = 0 — first divergence at token 103 (“Queens, New York” vs “New York City”)

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Why it happens
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Server load varies → batch size varies → kernel reductions reorder → numerics shift. Not GPU randomness — arithmetic order.

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The fix
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Batch-invariant kernels → consistent reduction order → identical numerics every run.

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Determinism is engineered in — at every layer.

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Source: Thinking Machines — Defeating Nondeterminism in LLM Inference (2025)

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🧠 Models — e.g. Opus, GPT, Gemini

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🧠 Limitations

The raw model has no real-time access — no internet, no files, no clock.

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⚡ Tool Calling — how the harness reaches the world

Turn — one round from the user’s view: you ask, the assistant answers. The entire flow above — your request, the assistant’s tool calls, and the final reply — is one turn.

Inference — one call to the language model. The model wakes up, reads the input it was given, writes a reply, then forgets everything. Every arrow touching the “Language Model” column above is a separate inference. One turn can contain many inferences.

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Source: Anthropic — Claude Code in Action: What is a coding assistant?

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💪 Harness — the body around the brain

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💪 Harness — the body around the brain

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🎉 Yayyyyy! Problem solved with harness

The harness reaches out via WebSearch and fetches a real answer from live sources.

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Really?

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💪 Non-determinism — Doesn’t always use its tools

Similar prompt — but this time the model decided not to use the tool.

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💪 Non-determinism — Tools can fail

The model first tried one source — it failed (403) — so it fell back to another.

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🚨 Problem Statement

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Vibe Coding

Andrej Karpathy's Feb 3 2025 tweet coining 'vibe coding' — 'fully give in to the vibes, embrace exponentials, and forget that the code even exists'

Andrej Karpathy — OpenAI founding team · former Director of AI at Tesla · founder of Eureka Labs.

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Source: Andrej Karpathy on X (Feb 3, 2025)

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Vibe Coding vs Agentic Engineering

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👤 Agents

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Create your first agent — /agents

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Demo