Sometimes I like to put my thoughts into words. Here's a collection of my thoughts in some of the topics I care about.
While ChatGPT captures headlines, other AI approaches are quietly revolutionizing physics simulation, medical imaging, gaming graphics, music production, and weather prediction. Discover the diverse world of artificial intelligence beyond large language models.
Read article →In autoregressive language models, not all tokens are created equal. The first tokens in a context window shape everything that follows — from attention patterns to generation trajectory. This is why short sessions beat long ones, and why compression exists.
Read article →Peter Gostev's BullshitBench exposes a disturbing truth about LLMs: most models will confidently answer nonsensical questions instead of pushing back. Even worse, reasoning models that "think harder" tend to generate more elaborate bullshit, not less. Here's what the data reveals.
Read article →Emerging research shows that AI tools are not reducing workloads — they are intensifying them. A deep dive into how artificial intelligence amplifies workaholism, erodes boundaries, and reshapes the meaning of "enough."
Read article →A University of Michigan foundation model called Prima can read a full brain MRI and diagnose across 52 neurologic conditions in seconds — reaching up to 97.5% accuracy. Here is why this matters for patients, radiologists, and the future of medical imaging.
Read article →Context windows define the boundaries of what an LLM can see, think about, and respond to. In this article we break down tokens, tokenization, and the invisible walls that shape every AI conversation.
Read article →An in-depth analysis of Claude Opus 4.6, covering its new features like adaptive thinking, 1M-token context, and agent teams, along with detailed benchmark comparisons against OpenAI GPT-5.2, Google Gemini 3 Pro, Meta Llama 4, and DeepSeek R1.
Read article →From 5,000 to 145,000 GitHub stars in two weeks, through a trademark dispute, a crypto scam, and critical security vulnerabilities—the wild story of Clawdbot (now OpenClaw), the self-hosted AI assistant that redefined what personal AI agents can do, and why security experts are warning everyone not to run it.
Read article →In a world obsessed with sleek GUIs and Electron apps, developers are quietly returning to text-based interfaces. From lazygit to btop, TUIs are proving that sometimes the best interface is the one that gets out of your way.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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
Read article →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
Read article →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.
Read article →