Disclosure: I’m the creator of PyNuxt.
What you get
Durante años hemos desarrollado habilidades que van mucho más allá de encontrar respuestas. Aprendimos a interpretar contextos, identificar riesgos, validar información y tomar decisiones. Esas capacidades siguen siendo profundamente humanas y continúan siendo uno de nuestros principales diferenciales profesionales.
Soy una entusiasta de la inteligencia artificial. Llevo años dedicando ...
The Problem Nobody Wants to Say Out Loud
Most LLM agent deployments have a quiet assumption baked into their architecture: the model will behave.
Not because anyone decided this explicitly. It happened by default. You write a system prompt. You test it. The model behaves correctly in your test cases. You ship it. And then, in production, under real inputs from real users with real inten...
There is an old habit in web development that still feels attractive: build everything yourself.
Custom boilerplate. Custom admin panels. Custom authentication. Custom deployment scripts. Custom validation layers. Custom logging. Custom everything.
It feels professional. It feels like engineering. It feels like control.
But in many modern web projects, I think this appro...
We have a growing problem in the autonomous AI agent space: Garbage in, garbage out, and no proof of when it happened.
When your AI agent generates source code, analyzes market data, or creates a financial report, how do you mathematically prove that this specific artifact was generated at a specific time? How do you prove to your clients that the output wasn't retroact...
When AWS announced Cost Optimization Hub at re: Invent 2023, my first reaction was: finally.
For years, AWS savings recommendations had been scattered across at least four different consoles. Compute Optimizer, for instance, right-sizing. Trusted Advisor for general checks. The Reservations and Savings Plans pages are for commitment planning. Cost Anomaly Detection for spikes. Each one with...