Conceptos que puedes arrastrar y sentir.
Olvídate de las docs de 40 páginas. Cada explicador convierte una idea complicada de IA, Claude Code, MCP o cloud en un diagrama animado en vivo que puedes arrastrar, scrubear y romper — para que el concepto te haga clic en minutos, no en horas.
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Cada concepto empieza con una historia corta y clara. Sin jerga, sin relleno — solo el modelo mental que necesitas.
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Explicadores más queridos
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.
Elige tu próximo concepto
Jailbreaks and Guardrails: The Cat-and-Mouse of LLM Safety
Jailbreaks slip past a model's safety training; guardrails sit outside the model and catch what slips. Both are needed; neither is sufficient.
AI Alignment: Making Models Want What We Want
Alignment is the gap between what we say we want and what the model actually optimises. Get it wrong and the model wins by Goodharting your reward.
Vision-Language Models: How AI Sees and Talks About It
A vision encoder turns pixels into tokens; a language model reads them like text. The whole "image understanding" trick is just adapter-glue.
Diffusion Models: From Noise to a Clear Image
Diffusion learns to undo noise, one tiny step at a time. Reverse the noising process and pure static turns into a photorealistic image.
Speech-to-Text: From Sound Waves to Sentences
Modern ASR is one big neural network: audio in, text out. The pipeline used to be five hand-tuned stages; now it is a single Transformer.
Multimodal Fusion: Joining Text, Image, and Audio in One Model
Multimodal fusion is just: encode each modality separately, project into one shared space, let a transformer mix them. The hard part is the data.
Claude Agent SDK: Building Autonomous Agents With Claude
The Agent SDK is Claude's toolkit for shipping production agents — tool use, file edits, sub-agents, and the loop that ties them together.
Claude Code: AI-Powered Terminal Coding, Explained
Claude Code is an agentic CLI that reads your repo, edits files, runs tests, and ships code. Less autocomplete, more pair programmer.
Model Context Protocol (MCP): The USB-C of AI Tools
MCP is an open protocol that lets any AI agent talk to any tool, database, or app via a uniform interface. Build a server once, every MCP-aware client gets it free.
Claude Prompt Caching: 90% Cheaper, Same Quality
Mark the stable parts of your prompt as cacheable. Claude bills the cache hit at ~10% of the input cost. On chat-shaped workloads, the savings are huge.
Claude Computer Use: AI That Clicks and Types Like You
Computer use lets Claude see a screen, move the mouse, and type — driving any GUI like a human. Powerful for automation; treat with respect.
Claude Opus vs Sonnet vs Haiku: Which Model When
Three sizes, three roles. Opus for hard reasoning, Sonnet for daily production work, Haiku for cheap and fast. Pick by task, not by hype.
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