progressive disclosure — feeding the AI only what it needs right now, not everything upfront

orchestration — coordinating several AI agents like a conductor leads a band

dumb zone — the stretch where AI has too much context and starts thinking worse, not better

agentic workflows — AI that plans, acts, checks its work, and adapts — multi-step on its own

harness — the scaffolding around the model — files, terminal, tools — that turns a chatbot into a worker

compaction — auto-summarizing old chat so the AI keeps going without hitting its memory ceiling

context window — the AI’s working memory — how much it can “see” at once before older details fall off

ralph wiggum loop — when the AI repeats the same broken step in circles, like a confused kid who can’t stop

MCP — a universal adapter letting AI talk to your tools (GitHub, Slack, databases)

hooks — auto-triggers that run your rules before or after the AI does anything

context rot — quality decay as the conversation drags on and earlier details blur

prompt engineering — the craft of phrasing requests so the AI understands exactly what you mean

AI slop — low-effort, generic AI output that looks polished but says nothing

inference — the moment the model actually runs to produce an answer. Training is when the model learned (once, long ago). Inference is the model answering you, right now

hallucination — when AI confidently makes up facts that sound true but aren’t

context bloat — overstuffing the AI’s memory so it slows down and loses focus

one-shot prompting — giving the AI one example and asking it to follow the same pattern

token burn — wasting expensive AI “words” on unnecessary back-and-forth or bloated prompts

vibe coding — describing what you want in plain English and hoping the AI nails it

agentic engineering — building guardrails so AI acts like a reliable teammate, not a gamble

Qwen3-235B at temperature = 0 — first divergence at token 103 (“Queens, New York” vs “New York City”)

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

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

Uncle Bob warns that “vibe coding” — generating code from prompts without understanding what the LLM produces — is hazardous for novices. LLMs are mathematical functions that predict the next most likely token via matrix multiplications, trained on internet text and GitHub code. They are powerful tools — but, as he puts it, “novices using power tools lose fingers.”

Robert C. “Uncle Bob” Martin — author of Clean Code · Clean Architecture · co-author of the Agile Manifesto.

Source: Robert C. Martin on X

⚠️ Watch out (method 1)

The prompting method sometimes creates the wrong structure. Instead of generating a folder with SKILL.md inside (e.g. weather-fetcher/SKILL.md), it creates a plain weather-fetcher.md file. The wrong form isn’t recognized as a skill by Claude Code.