AI/ML

Apps like Notion and Obsidian market themselves as a “second brain” for individuals—a place where you can dump everything you know, connect ideas through links, and have instant access to your accumulated knowledge. Obsidian even visualizes your notes as a graph, revealing connections you never knew existed. But what about organizations? Company knowledge lives in a different kind of chaos: Slack threads, Jira tickets, Salesforce records, HR systems, meeting transcripts, project updates, email chains. This information exists across 10-15 different systems, owned by different teams, formatted differently, and rarely talking to each other. When an executive asks “What are our top organizational risks right now?” or “What’s the impact if we lose our senior platform engineer?”, someone has to spend days manually piecing together information from disparate sources. ...

A century ago, a computer was a person. Teams of people with pencils turned uncertainties into columns of numbers. The revolution began when Claude Shannon showed that circuits could embody Boolean logic. Transistors became switches, gates became adders, adders became ALUs, instruction sets, compilers, operating systems, and finally applications. Each layer constrained the next. Two plus two equals four not by hope, but because the stack is engineered for repeatability from electrons up to software. Fast-forward to today, and we’re amid another profound shift. Instead of explicitly coding every behavior, we increasingly teach or train computers using data – a trend exemplified by machine learning and AI. Andrej Karpathy terms this new approach Software 2.0. You specify the intent or goal of the program and provide data, then let the computer automatically generate the logic. In practice, this means feeding a large dataset and a rough model architecture into a training process, which then “compiles” the data into a working model. The end result is a neural network that embodies the solution. ...