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.
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Backpropagation: How a Network Actually Learns
Backprop is just credit assignment — blame each parameter for the error, in proportion. Tune learning rate and batch size to see training stabilise or diverge.
Neurons, Layers, and Why Depth Matters
A neuron is a weighted sum followed by a kink. Stack a million in layers and you get a function that approximates almost anything.
Gradient Descent: Rolling Downhill to a Smarter Model
Training is a marble rolling down a wrinkled hill — the loss landscape. Tune learning rate and momentum to see it slide, oscillate, or get stuck.
Fine-Tuning vs RAG: When to Teach, When to Look Up
Fine-tuning changes what the model knows; RAG gives it a reference shelf at query time. Most "make the LLM know our docs" jobs are RAG jobs.
LoRA: Cheap Fine-Tuning Without Touching the Whole Model
LoRA freezes the giant model and trains tiny rank-r adapters next to it. 7B-param model, ~1% of the trainable weights, 99% of the quality.
Knowledge Distillation: Teaching a Small Model to Imitate a Big One
Distillation trains a small student model to mimic a big teacher's soft outputs. You ship the small one — much cheaper, surprisingly close in quality.
Quantization: Shrinking Models Without Killing Them
Store every weight in 4 bits instead of 16, fit a 70B model on one GPU, and lose almost no quality. Tune precision to feel the trade-off.
KV Cache: Why the Second Token Is Faster Than the First
Without a KV cache, every new token re-computes attention over the whole sequence. With it, you reuse all previous work. This is most of LLM serving.
Batching: How Inference Servers Serve a Thousand Users at Once
GPUs are starved on a single request — most of the chip is idle. Batching packs many requests into one forward pass for huge throughput wins.
Speculative Decoding: A Cheap Model Guessing for an Expensive One
A tiny draft model proposes 5 tokens at once; the big model verifies them in a single forward pass. Net effect: 2–3× faster decode at identical quality.
Hallucinations: Why LLMs Make Stuff Up Confidently
Hallucinations are not bugs — they are the model doing exactly what it was trained to do. Plausibility is the loss; truth is not. Understand the trap, then engineer around it.
AI Evals: How to Tell If Your Model Is Actually Better
Without evals, "the new prompt feels better" is just vibes. A good eval suite catches regressions before users do — here is how to build one.
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