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AI without the hype: models, agents and use cases that work in production.
The idea of UI generated on the fly instead of pre-built reached production in 2025. After a year of real-world use, the balance is more nuanced than the initial enthusiasm suggested.
Skills package reusable capabilities; subagents isolate bounded-task execution. Together they form the most effective pattern for composing complex agents in 2026.
Synthetic data has moved from a precarious substitute for real data to a central component of modern model training: the most reliable pattern expands a real core of 500 examples with thousands of synthetic paraphrases, provided you validate diversity, correctness, and distribution, and keep at least 30% real data to avoid model collapse.
Claude Sonnet 4.6 is the default model for most 2026 production workloads: it covers 80% of traffic with quality indistinguishable from Opus 4.7 in blind tests, at roughly 60% of Opus per-token price. Opus is still needed for complex reasoning and agentic coding on large codebases.
The first invoice for a production agent usually runs double or triple the estimate. This article walks through five real levers, in priority order, caching, routing, context control, batching, and telemetry, to cut cost without touching perceived quality.
La factura de IA en las empresas ha dejado de ser anecdótica. Entre tokens de modelos frontera, GPUs reservadas que nadie usa y pipelines RAG con cachés mal configuradas, muchos equipos pagan diez veces lo que deberían. Guía de FinOps específico para IA sin relatos promocionales.
Six months after A2A landed at the Linux Foundation, and after several implementation cycles from Google, Microsoft, and open projects, what version 1 of the protocol means and whether it is safe to build on yet.
With MCP solving the agent-to-tool layer, a parallel problem surfaces: how do two agents from different vendors communicate with each other. Google's Agent2Agent protocol, donated to the Linux Foundation in June 2025, tries to fill that gap with an open standard.
Guardrails frameworks promise to filter language-model inputs and outputs to block data leaks, harmful content, or hallucinations. After evaluating four of the most popular ones in production, I cover what they actually do, what latency and billing cost they add, and when they pay off over simpler controls.
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.
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.
Anthropic presentó Claude Opus 4 y Claude Sonnet 4 el 22 de mayo de 2025, el primer salto grande de nomenclatura desde la serie 3.5. Un mes de uso real en código, documentación técnica y agentes para separar lo que ha mejorado de lo que sigue igual.
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.
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.
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.
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.
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.
OpenAI published Swarm as an experimental, educational framework for multi-agent systems. It reduces coordination to two concepts — agents and handoffs — and fits in under 500 lines of Python. A comparison with CrewAI and LangGraph.
Installing Ollama on an Apple Silicon Mac is as simple as running one Homebrew command. Then pick a model based on available RAM (Phi-3 for 8 GB, Llama 3.1 8B for 16 GB) and expose the local, OpenAI-compatible HTTP API on port 11434 to plug it into your own applications.
SGLang adds a Python DSL for controlling LLM generation with constrained decoding, parallel branching, and RadixAttention, the structure that indexes the KV cache as a radix trie to reuse shared prefixes across requests. When that pattern exists, speedups over vLLM reach up to 5 times; without it, the advantage shrinks.
