Nerd_

Do you want your name sent around the Moon on the #Artemis I mission? It’s not quite writing it in the stars, but it’s still pretty impressive!

Network switches corrupt bad frames on purpose. A cut-through switch is already sending the frame when the CRC fails, so it writes the inverse of the right CRC and every box after it knows. How CRCs work, what CRC-32 catches, and how PCIe, DDR5 and 400G links use theirs.

I was thinking about my 2+ years experience with teaching STEM subjects to secondary school students, then I realized that some of the challenges women in STEM in Nigeria face have deep cultural roots. My thoughts are in shared below as screen shots from my notepad .

We all die one day but I am not giving up my machine intelligence work to a corporations offering at most $10K or less for an advanced visual perception system codebase I developed while teaching myself computer vision & programming in Zambia back when internet was expensive.

MIT and Harvard built an AI simulation containing 8.3 billion virtual people, roughly the entire population of planet. They call it MatrAIx. It’s a population-scale simulation running on frontier models, GPT and Claude among them. The longer description took me a while to believe. Start with the dataset, Persona 8B. 8.3 billion synthetic profiles, each one specified across 1,290 dimensions. Background, psychology, spending habits, how comfortable they are with technology, what they do on a Tuesday night, the small behavioral quirks that make one 34-year-old nurse in NYC different from another. A spreadsheet of 8 billion rows would be a curiosity. What they did next is the part that matters. They turned the profiles into agents and dropped them into four environments: surveys, AI chat interfaces, live web browsing, and native desktop and mobile apps. Then they ran real products through the crowd. New features, software updates, pricing changes. And they logged everything. How long an agent paused after seeing a price go up. The exact point at which it gave up on a broken checkout. How much lag it would sit through before closing the app. The number I keep coming back to is 91.5%. That’s how often the agents stayed in character, behaving the way their assigned psychology and demographics said they should. Not perfect. But for billions of agents across four environments, closer than I expected. Here’s what that does to how products get built. Right now, if you want to know whether a feature will land, you ship it to a slice of users and wait. Or you pay a research firm and wait longer. Either way the answer arrives in weeks, from a sample you hope is representative. With something like this, you run the feature past 8 billion simulated reactions overnight. One server. Results by morning, broken down by every one of those 1,290 dimensions if you want them. I have questions about how well a synthetic crowd predicts a real one. 91.5% is persona adherence, not forecast accuracy, and those are different things. But the direction is clear, and it’s coming faster than most product teams are ready for.