Del Hardware al Aprendizaje Automático Como especialista dedicado a la administración de servidores, redes WAN y bases de datos, siempre he creído que la infraestructura es el cimiento de cualquier Continue reading Mi primer paso en la IA de AWS: Cómo obtuve la insignia de Machine Learning Foundations
Nuxt Studio is now OPEN-SOURCE. It is a self-hosted Nuxt module for editing content directly on production sites. The module moved from paid platform to open-source MIT release. Content editors Continue reading Nuxt Studio: Self-Hosted Content Editing for Production Sites
If you’re a fan of the Quantified Self movement, you know the struggle: your Oura Ring tracks your sleep, your Whoop analyzes your recovery, and your Garmin logs your runs. Continue reading Stop Drowning in Wearable Data: Build a Unified Health Data Lake with DuckDB and Apache Arrow
For a while now, LogeekMind has been a trusted companion for students seeking an AI-powered edge in their academic journey. We launched with a vision to simplify complex topics, automate Continue reading The Next Chapter: Introducing LogeekMind 2.0 – Smarter Learning, Evolved.
Bringing order to the chaos of the race condition. Chapter 15: Sharing by Communicating The archive was unusually loud that Tuesday. Not from voices, but from the rain hammering against Continue reading The Secret Life of Go: Concurrency
If you are running a Windows message loop from Python (for example via ctypes) and notice that pressing Ctrl+C does not terminate the program immediately, you are not imagining things. Continue reading Why Ctrl+C “Doesn’t Work” in a Windows Message Loop: `GetMessageW()` Blocking, and Two Practical Fixes
Why fluent AI output drifts, sounds generic, and fails to compound — even with good prompts and tools. This is Post 2 in the series Designing Systems That Understand People. Continue reading Why Most AI Systems Fail at Context, Not Generation
In the previous post, I focused on how to build a Retrieval-Augmented Generation (RAG) pipeline on AWS using DynamoDB as a low-cost vector store. The goal there was simple: prove Continue reading Running a RAG Pipeline in a Production Full-Stack Application (Without a Vector Database)
Django Static Files: The Complete Guide Managing static files in Django becomes complex when you add Tailwind CSS compilation, AWS S3 storage, Docker containerization, and CI/CD pipelines. This guide explains Continue reading Django Static Files: The Complete Guide – From Local Dev to S3 Production with Tailwind CSS
“Run your GitHub Actions locally!” — ACT “Why would you want to do that?” — Me, genuinely wondering We live in an era where Continuous Integration (CI) — one of Continue reading If You Run Your CI Locally, You’ve Already Failed
Originally published on February 10, 2025 by Matías J. Magni (Sr. SDET | International Speaker). In today’s dynamic software development environment, selecting the appropriate load testing tool is crucial for Continue reading k6 vs. JMeter: Comparing Load Testing Tools and AI-Enhanced Scenario Generation
Why responsibility cannot be delegated to systems Automation promises efficiency. Intelligence promises accuracy. Scale promises leverage. But none of these promise safety. What ultimately determines whether an AI system can Continue reading Automation Without Accountability Is Structurally Unsafe
Why controllability collapses without explicit power structures Most discussions about AI control focus on behavior. What the system outputs. How it reasons. Whether it follows instructions. Yet controllability does not Continue reading Authority, Boundaries, and Final Veto in AI Systems
The Vision: Pure Temporal, AI-Enhanced Authentication Over the past few days, I’ve been immersed in building an authentication system that combines Temporal.io workflows with AI-enhanced security. The goal was ambitious: Continue reading Building an AI-Powered Authentication System with Temporal.io
Why execution legitimacy matters more than intelligence Modern AI discourse focuses obsessively on capability. Bigger models. Longer context windows. More sophisticated reasoning chains. Yet most failures in real-world AI systems Continue reading Five Non-Negotiable Principles for Controllable AI Systems
