Des concepts à manipuler et ressentir.

AI Operations & Production 3 min de lecture

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-cost-optimization Essayer

AI Operations & Production 3 min de lecture

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-observability-traci… Essayer

AI Operations & Production 3 min de lecture

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.

/llmops-explained Essayer

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AI Evaluation & Safety 3 min de lecture

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.

/jailbreaks-and-guardra… Essayer

AI Evaluation & Safety 3 min de lecture

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.

/ai-alignment-explained Essayer

Multimodal AI 3 min de lecture

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.

/vision-language-models… Essayer

Multimodal AI 3 min de lecture

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.

/diffusion-models-from-… Essayer

Multimodal AI 2 min de lecture

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.

/speech-to-text-end-to-… Essayer

Multimodal AI 3 min de lecture

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.

/multimodal-fusion-text… Essayer

Claude Platform 2 min de lecture

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-agent-sdk-expla… Essayer

/claude-code-explained Essayer

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.

/mcp-model-context-prot… Essayer

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-… Essayer

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-ex… Essayer

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.