Sometimes I like to put my thoughts into words. Here's a collection of my thoughts in some of the topics I care about.
How Model Context Protocol servers can accidentally intoxicate AI agents with too much information, and why code execution might be the cure for this self-inflicted cognitive bottleneck.
A comprehensive technical comparison of single-agent and multi-agent AI systems, exploring architecture patterns, performance metrics, cost analysis, and real-world case studies to help you choose the right approach for your AI implementation.
Discover how low-code and no-code ML tools are democratizing machine learning. From Orange3 to PyCaret, explore the best platforms that let you drag, drop, and click your way from raw data to AI predictions—no PhD required.
Everyone hates Comic Sans—until you learn why it might be the most accessible font choice for dyslexic programmers. From undiagnosed developers to Comic Code and Comic Mono, we explore the intersection of typography and neurodiversity in software development.
Vector databases are specialized systems designed to store and search high‑dimensional numerical vectors called embeddings. In this article we'll analyze their structure, philosophy and challenges with this new protocols
Model Context Protocol (MCP) is utilised to provide tools to a LLM model in order to interact with an application. In this article we'll analyze it's structure, philosophy and challenges with this new protocols
Deep Learning has been the choosen tool to analyze complex data in many fields. Heart anomalies is no exception. There is a plethora of articles working on deep learning models capable of identifying anomalies in heart-rate data. This article present a different approach: instead of working with the raw data, we will process the signal to extract a simplified one that will contain enough information to train a simple model to identify anomalies. This approach will allow the simple signal to be recreated using other sensors and monitoring tools that won provide ECG data.