Konzepte, die du fühlen kannst.
Spar dir die 40-seitige Doku. Jede Erklärung verwandelt ein kniffliges KI-, Claude-Code-, MCP- oder Cloud-Konzept in ein animiertes, scrubbares Diagramm, das du ziehen und brechen kannst — bis die Idee in Minuten sitzt, nicht in Stunden.
Drei Schritte. Die Idee bleibt.
Die 60-Sekunden-Analogie lesen
Jedes Konzept beginnt mit einer kurzen Geschichte in Klartext. Kein Fachjargon — nur das mentale Modell, das du brauchst.
Die Live-Animation scrubben
Drücke Play, ziehe die Timeline oder benutze die Pfeiltasten. Sieh jeden Schritt Bild für Bild, bis der Ablauf klick macht.
Die Slider an die Grenze treiben
Justiere jede Variable. Das Diagramm reagiert sofort — so spürst du die Trade-offs und merkst dir die Grenzen.
Beliebteste Erklärungen
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.
Wähle dein nächstes Konzept
AI Coding Assistants Compared: Cursor vs Copilot vs Claude Code
Three philosophies — Copilot completes, Cursor edits, Claude Code drives. Pick by how much agency you want the AI to have.
GitHub Copilot vs Claude Code: Inline vs Agentic
Copilot speeds up typing. Claude Code finishes tasks. Different jobs, different tools — and increasingly, the same engineer uses both.
AI Code Review: How LLMs Catch Bugs Humans Miss
AI code review is a tireless first-pass reviewer. It misses architectural nuance but rarely misses an off-by-one. Use it as the floor, not the ceiling.
Agentic Coding: From Autocomplete to Autonomous Engineer
Agentic coding is when the AI plans, edits, runs tests, and iterates — without holding your hand at every step. The unit of work shifts from "line" to "task."
Cursor: The AI-Native Code Editor Pattern
Cursor takes VS Code and rebuilds the editor around AI — multi-file edits, tab-to-accept, repo-aware chat. The "editor as AI surface" approach.
Spec-Driven Development With AI Coding Agents
When the AI is the implementer, the spec is the lever. Tight specs ship fast and clean; vague specs ship vague code. Treat the spec like the new keyboard.
Function Calling: How LLMs Use Your APIs
You describe a function in JSON Schema; the model decides when to call it and produces typed arguments. The bridge from "pretty text" to "real action."
Structured Outputs and JSON Mode: Reliable LLM Responses
Stop parsing prose. Constrain the model's output to a schema and your downstream code stops guessing. The single biggest reliability lever in LLM apps.
LLM Streaming: Why First-Token Latency Beats Total Time
Streaming sends tokens as the model produces them. Total wall-time is similar; perceived speed is dramatically better — and lets you cut off when the answer is good enough.
API Rate Limits, RPM, and TPM Explained
Two budgets you cannot ignore: requests per minute and tokens per minute. Understanding both — and the burst behaviour around them — keeps your prod stable.
LangChain vs LlamaIndex: When to Pick Which
LangChain is the kitchen-sink agent framework. LlamaIndex is the RAG-focused data framework. Same neighbourhood, different specialities.
OpenAI SDK vs Anthropic SDK: API Patterns Compared
Same family of APIs, different shapes. Knowing the differences saves an afternoon of head-scratching when porting code between providers.
Hör auf, davon zu lesen. Fang an zu scrubben.
Festgefahren bei einem KI-, Claude-Code- oder Cloud-Konzept? Sag mir, was nicht klickt — ich liefere eine kostenlose interaktive Erklärung mit Analogie, Animation und Slidern, meist innerhalb einer Woche.
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