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AI without the hype: models, agents and use cases that work in production.
AI agents have moved from a lab curiosity to serious SDKs from three major providers. A reflection on moving from the flashy demo to an internal use case that shifts a real, measurable metric.
Desde que Microsoft abrió GraphRAG, el patrón de usar grafos sobre tus propios datos ha pasado de experimento académico a técnica con aplicaciones prácticas. Reflexión sobre cuándo compensa, cómo se monta y qué errores se repiten.
The AI features Figma has rolled out since Config 2024 are changing how product design teams work. A look at what each feature delivers, what remains human work, and which habits are taking hold across teams.
Con las primeras obligaciones del AI Act europeo ya en vigor, la gobernanza de la IA en empresa deja de ser teórica. Qué comités montar, qué políticas escribir y qué auditar, desde la experiencia de varias implantaciones.
Anthropic publicó Claude 3.7 Sonnet a finales de febrero con pensamiento extendido opcional y un compañero de consola llamado Claude Code. Reflexión sobre qué cambia de verdad y qué queda para la próxima familia.
A year ago open weights were a gamble; today they are a real production option. I review what has worked, what has not, and how Llama, DeepSeek, Qwen, and Mistral are fitting into enterprise architectures that used to depend on closed APIs.
vLLM remains the reference engine for serving LLMs on GPU in 2025: automatic prefix caching sharply cuts latency for repeated prompts, speculative decoding speeds up large models, and multi-LoRA support lowers the cost of multi-tenant SaaS, though multi-GPU support and non-NVIDIA hardware remain weak points.
GraphRAG has been in real enterprise use for over a year: during indexing, an LLM builds a knowledge graph that answers global questions about a corpus well, precisely where classic RAG fails because no single chunk holds the full answer. Here I compare indexing costs, the cases where it pays off, and the hybrid pattern that teams have settled on.
Three years after RLHF became popular, the model-alignment field is far richer. A review of RLHF, DPO, and newer methods such as KTO and ORPO, with criteria for choosing between them.
Google released Gemma 2 in mid-2024, and it has since seen real production use. A look at how it competes in the open-model ecosystem, which sizes actually make sense, and where its adoption has settled in.
o3-mini, the first public release of OpenAI's o3 reasoning series, clearly improves logic, math, and complex code over GPT-4o, though it answers slower and still hallucinates facts. This analysis, based on weeks of real use, explains where it pays off and where it does not.
Google ha lanzado Gemini 2.0 con un énfasis claro en uso de herramientas y agentes. Repaso de qué aporta, dónde está por detrás de la competencia y en qué tipo de aplicaciones encaja mejor.
Two years running AI-assisted code review in a real team leave a clear balance: AI catches mechanical oversights well and writes useful pull-request summaries, but it struggles with architectural judgment and produces many false positives on subtle bugs. The single decision that helped the most was not blocking merges on its automated comments.
Meta publicó Llama 3.2 con modelos tan pequeños como 1B y 3B, pensados específicamente para ejecutarse en dispositivos. Análisis de qué pueden hacer realmente y cómo se comparan con las alternativas.
Qualcomm, Intel and AMD Copilot+ processors have normalised the presence of an NPU in everyday PCs. A 40 TOPS NPU can run quantised Phi-3 Mini drawing just 5-10 W, versus 40-50 W for a laptop GPU doing the same task. What actually changes for running AI models locally, and when it is worth it.
Measuring RAG quality rigorously takes more than skimming a handful of answers: it requires objective metrics (faithfulness, relevance, context precision, and coverage), a golden set of hundreds of curated questions, and regular human validation of the LLM judge to avoid misleading conclusions.
OpenAI presentó o1 en septiembre de 2024. Un modelo que razona internamente antes de contestar. Qué cambia y cuándo merece la pena el coste adicional.
Hybrid search combines BM25 and vector retrieval to cover what each misses alone. Vectors fail on exact identifiers like SKUs or CVEs; BM25 fails when query and document use different vocabulary for the same idea. Reciprocal Rank Fusion (RRF) merges both rankings without depending on their score scales.
llama.cpp is the C++ library that powers Ollama and much of the local-LLM ecosystem. 2024 added speculative decoding with two- to three-fold speedups, an RPC server for sharding layers across machines, and a stable GGUF format. Ollama covers 90% of cases; going direct pays off with uncommon hardware or specific flags.
Ollama became the standard for running large language models locally in 2024. It wraps llama.cpp in a single binary with Docker-style CLI and an OpenAI-compatible API. Phi-3 Mini runs in 4 GB; Llama 3.1 8B Q4 needs 6 GB. For production traffic at scale, vLLM remains the correct choice.
Model Context Protocol (MCP) is the open standard Anthropic published on 25 November 2024 to connect language models with external data and tools over JSON-RPC 2.0. It does not replace function calling: it standardises the server side, aiming to become for context what the Language Server Protocol is for code editors.
Product-market fit for LLM-powered products still depends on the same classic signals: cohort retention, NPS, and revenue expansion. What changes are the higher quality baseline, faster competitor iteration, and where durable moats come from: proprietary data, workflow integration, and network effects.
LLM applications need three distinct observability planes: prompt and response traces for debugging hallucinations, per-token and per-feature cost tracking, and response quality evaluation. Mature tools like Langfuse, LangSmith, and Helicone cover all three planes with specific instrumentation.
TensorRT-LLM is the NVIDIA inference engine that compiles each model into a binary optimized for the exact GPU and batch size it will serve. It uses hand-written CUDA kernels and native FP8 quantization on H100. Against vLLM it can run 2 to 3 times faster in the best case, at the cost of a 30 to 90 minute build.
