Concepts you can scrub & feel.
Skip the 40-page docs. Every explainer turns a tricky AI, Claude Code, MCP, or cloud idea into a live, animated diagram you can drag, scrub, and break — so the concept finally clicks in minutes, not hours.
Three steps. The idea sticks.
Read the 60-second analogy
Every concept opens with a short, plain-language story. No jargon, no fluff — just the mental model you need.
Scrub the live animation
Press play, drag the timeline, or tap the arrow keys. Watch each step fire frame-by-frame until the flow makes sense.
Push the sliders to the edge
Tweak every parameter. The diagram updates instantly so you feel the trade-offs and remember the limits.
Most-loved explainers
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
Pick your next concept
Chain-of-Thought Prompting: Get LLMs to Show Their Work
Add "think step by step" and accuracy on multi-step problems jumps. Hide the scratchpad in production. Free quality, almost always.
ReAct Pattern: Reasoning + Acting in AI Agents
ReAct interleaves a Thought, an Action, and an Observation at each step. The "talk to yourself, then do, then look" loop powers most modern agents.
Tree of Thoughts: When LLMs Need to Branch and Backtrack
Tree of Thoughts explores multiple reasoning branches, prunes bad ones, and backtracks. Use it when the right path is not the first one the model picks.
Self-Consistency: Voting Across Multiple LLM Samples
Run the same prompt N times at non-zero temperature, take the majority answer. A few extra calls, big accuracy gains on hard reasoning.
Prompt Chaining: Breaking Complex Tasks Into Steps
Instead of one mega-prompt, chain N small prompts where each step's output feeds the next. Easier to debug, easier to evaluate, easier to evolve.
Reflexion and Self-Critique: AI That Reviews Its Own Work
Reflexion adds a critique-and-revise loop. The model produces output, criticises it, revises. A few cents extra; meaningful quality gain on the right tasks.
LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Latency: P50, P99, and Why TTFT Matters Most
Users feel TTFT (time to first token), not total time. Optimise for it. P99 hides the customers who actually churn — track it like your job depends on it.
Semantic Caching: Cache LLM Responses That Mean the Same
A normal cache matches exact keys. A semantic cache matches *meanings* — return the cached answer when the new query is close enough by embedding similarity.
LLM Routing: Right Model for Right Task, With Fallbacks
A router classifies each call and sends it to the cheapest model that handles it. Add fallbacks for outages and you get cheaper *and* more reliable than a single-model setup.
Stop reading about it. Start scrubbing it.
Stuck on an AI, Claude Code, or cloud concept? Tell me what's not clicking — I'll ship a free interactive explainer with the analogy, the animation, and the sliders, usually inside a week.
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