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Architecture

LLM caches: saving tokens without dropping quality

A caching proxy in front of a language model can cut the token bill significantly, but it introduces subtle risks if the design is not careful. Which cache types work in production, where the usual traps sit, and how to add them without degrading the experience.

Architecture

Inference routers: choosing a model based on the request

Un enrutador de inferencia decide qué modelo atiende cada petición en función de coste, latencia y complejidad. Bien diseñados reducen la factura de tokens sin que el usuario perciba degradación; mal diseñados introducen fallos sutiles difíciles de depurar.

Artificial Intelligence

Testing with AI: the determinism problem

Probar sistemas que incluyen modelos de lenguaje rompe la primera regla del testing: la misma entrada da la misma salida. Analizo las estrategias que han funcionado tras un año largo integrando IA en productos reales, por qué los tests deterministas tradicionales no bastan y cómo plantear un cinturón de pruebas que capture regresiones sin bloquearse en la varianza.

Architecture

Agent OS: the concept shaping the new stack layer

The term Agent OS has spent a year gaining traction across research and product circles. It describes a layer that goes well beyond an agent library: request scheduling, context management, persistent memory, and isolation. A look at the real state of that concept.

Architecture

Model Context Protocol in 2025: from announcement to ecosystem

Model Context Protocol turns ten months old since Anthropic's announcement, and it is no longer just a proposal: hundreds of servers, cross-vendor implementations and a public registry now back it. A look at what has worked, what is still weak, and why 2025 marks the shift from curiosity to basic infrastructure.

Artificial Intelligence

GPT-5: public availability and early impressions

After months of rumors, OpenAI released GPT-5 in early August. The first weeks of real-world use show a picture less spectacular than the marketing suggested and more useful than many expected. It is worth separating what is genuinely new from what is merely incremental.

Artificial Intelligence

RAG 2.0: knowledge graphs, vectors, and hybrid

El RAG de 2023 era búsqueda vectorial con un LLM detrás. El de 2025 es un sistema híbrido que combina vectores, búsqueda léxica y grafos de conocimiento. Qué ha cambiado, dónde funciona cada pieza y qué decisiones marcan la diferencia entre un RAG útil y uno decepcionante.

Artificial Intelligence

Gemini 2.5: context scaling and multimodality

Google publicó Gemini 2.5 Pro en vista previa en marzo y la versión general llegó en junio. El salto respecto a Gemini 2.0 no está solo en puntuaciones sino en dos frentes prácticos: ventana de contexto utilizable en serio y multimodalidad que deja de ser demostración para convertirse en herramienta.

Artificial Intelligence

The knowledge graph era is reborn with LLMs

For a decade, knowledge graphs were an academic idea with few real use cases, held back by the cost of building and maintaining the schema. LLMs have changed that equation: they now extract entities automatically and help anchor answers, audit reasoning, and support agents without hallucinating.