Artificial IntelliTools

  I'll focus on the WAL (Write-Ahead Logging) system from the PostgreSQL architecture diagram,  because its the only thing there that I’d never heard of before and turns out it's crucial for database reliability and ACID compliance and also So What is Write-Ahead Logging? Write-Ahead Logging is a mechanism where changes to the database (modifications to data pages) are recorded in a special log file (WAL file) before they are actually written to the main database files. This log is used to ensure data integrity and to recover the database in case of a crash or failure. Or to put it simply: Imagine you’re building with blocks: Before you add or remove any block, you write down your plan in a notebook: "I'm going to add a red block on top." Then you go ahead and add the block. WAL does the same thing: Before saving changes to the database, it writes down those changes in a special log file. If something crashes, the database reads the log and "replays" t...

In this age of social media that is no longer even truly social, few things are left to us as humans. We all know that most of what you see on Instagram, Facebook, Reddit, Twitter, Threads—mention it—isn’t really what you want to see. I remember when Facebook was just about my friends, what they were doing, and keeping up with people. But these days, every single social media platform is just a tool of addiction—a dopamine-chasing, zombie-like doom-scrolling nightmare. The videos, pictures, posts, and media you see aren’t things you care about anymore. They’re all just designed to keep you addicted to these apps. So many psychologists have done research on this, and so many people have revealed the evil decisions and tricks these apps employ to optimize addiction—and, of course, revenue. But that’s not what this article is about. This is about the platforms that were supposed to be different. There's a very good talke by the CEO of patreon about this. Even the ones that claim to be...

So, I'm sure you've heard of Google's Notebook LM. If you haven't, go listen to this podcast first, then come back. Seriously, I'll wait. Alright, now that you're back, let me give you a brief background on why and how this  started. For months, my teammates Andrea Lacche ,  Pradeep, and I have been working on analyzing New York City's noise landscape as part of a class project. The goal was to analyze noise complaints across New York City to better understand the urban noise landscape. We wanted to identify key hotspots and trends, ultimately providing insights into how noise pollution affects different neighborhoods and what measures might be effective in addressing these issues. We uncovered some pretty interesting insights, such as the Bronx having the highest number of noise complaints, particularly related to loud music, and identifying significant spikes in complaints during the COVID-19 pandemic. If you're curious about those and deeper insights...

Understanding Factless Fact Tables I want to discuss Factless Fact Tables because, to be honest, the name caught my attention—it sounds funny, a "Factless fact?"   lol. And since am a simple person to please, I figured there must be an something interesting here or at least a reason behind it. Plus I figured that by understanding what "factless" means, I could work backward to understand what a regular fact table is… hehe. So, I’ll try to explain this in a way that's easy to understand. So What is a Fact Table?   S o much for working backwards am I right? lol. But bare with me for a sec In data warehousing, a fact table stores measurable data—things that can be counted or summed. For example, a "Sales" fact table might store: Product sold Number of units sold (a measure, or "fact") Sale date Sale amount (another fact) Each row in a fact table represents a measurable event, like a sale, and includes numbers that help us analyze performance. Wh...

  Hi, Emmanuel here. So first off, I know nothing about fantasy football, or much about football for that matter, so I'm going to write my honest take on this model. This will also be me writing in my own style, so I'll exercise some of the writing skills I have been practicing since the start of this year. So brace yourself, and I apologize in advance for the fact that you have to sit there and take valuable time off your day to read my wannabe New York Times bestseller jabbering and ramblings. Let's begin. In general, we have what's called an ERD, an entity-relationship diagram, if I picked up anything from undergrad times. The model consists of 10 entities: League, Owner, Fantasy Team, Player, Position, NFL Team, Fantasy Game, NFL Game, Relevant Play, and Play Type. They are all connected to each other one way or another; we shall try to get into those connections as we go along. Let's start with the Player, who plays a position captured in the Position table. Fo...

  "Alternative Intelligence" We are on the cusp of outsourcing intelligence, yet everyone is focused on the wrong aspects of this seismic change. Plus Why are psychologists, Humans behavioral scientists, and the like, not deeply engaged in these discussions? Anyway. Like most, I have been following developments in artificial intelligence with great interest for some time. As someone fascinated by human psychology, behavior, intelligence, and the workings of the mind, I have been intrigued by the rise of these, because they might not be human-like intelligence, but at the end of the day, they're some kind of "intelligence". But too much of the discussion has centered on the artificial component of AI. Instead, we should rebrand the AI term as “Alternative intelligence” to shift the conversation toward how we can harness its power rather than stoke fears about it. Words matter, names matter; how people think about something matters. and that's greatly influe...