In the world of 10-digit error codes and countless hours spent troubleshooting, tech support professionals are the unsung heroes who keep our digital lives running smoothly. They are the patient, compassionate frontline warriors who, despite facing a relentless barrage of problems, never shy away from going the extra mile to fix them. There’s no denying,…
Andrej Karpathy Just Described a Phase Shift. Most Engineers Aren’t Ready for What Comes Next. If you follow AI at all, you probably saw Andrej Karpathy’s appearance on the No Priors podcast this week with Sarah Guo. If you haven’t listened to it yet, you should. Not because it covers new product announcements or benchmark…
The Speed Trap: Why AI’s Real Value Has Nothing to Do With Typing Faster Most engineers I know are chasing the wrong metric right now. They’re asking how to use AI tools faster, how to generate more code per hour, how to shorten the time between prompt and output. That framing is wrong, and I…
The $500K Engineer Who Isn’t Burning Tokens Is Leaving Money on the Table There’s a quote circulating right now that stopped me mid-scroll: “If your $500K engineer isn’t burning at least $250K in tokens, something is wrong.” Sunny Madra posted it this week and the reactions were split almost perfectly between people who got it…
The $500K Engineer and the $250K Token Bill “If your $500K engineer isn’t burning at least $250K in tokens, something is wrong.” That quote, posted by Sunny Madra on X earlier this week, has been living rent-free in my head. My first reaction was to roll my eyes. My second reaction was to realize I…
Evals First. Everything Else Second. Most engineers I know treat evals like cleanup work. Something you wire up after the “real” engineering is done, right before you push to production and hope nobody notices the weird edge cases. I have done this. You have probably done this too. It is the wrong order of operations,…
A 400 Billion Parameter Model on a MacBook. Let That Sink In. I’ve been doing AI/ML work long enough to remember when running a 7B model locally felt like a party trick. This week, someone ran a 397 billion parameter model on a laptop. Not a workstation. Not a rack-mounted inference server. A MacBook with…
Context Is the Moat. Not the Model. Most teams building AI products are optimizing the wrong thing. They debate model size, chase benchmark scores, and spend weeks on fine-tuning runs. Then they ship something that feels hollow. Generic. Like a customer support bot that clearly has no idea who it’s talking to. I’ve seen this…
Google Stitch Just Leveled Up. Here Is What Designers and Builders Should Actually Think About That. The design community had a collective moment this week, and I think the reaction is telling us something more interesting than the tool itself. Google’s Stitch dropped what can only be described as a significant overhaul. Five major upgrades,…
The Study Nobody Is Reading Correctly Anthropic just published what they’re calling the largest qualitative study of AI users ever conducted. 81,000 people. One week. And most of the coverage I’ve seen misses the point entirely. Everyone is focused on the number. 81,000 is a big number, so people write about the number. But the…
Google’s AI Ecosystem Is Building the Operating System Nobody Else Can Copy Min Choi said it plainly this week on X: “The next AI war won’t be Claude vs ChatGPT. It’s who will own the entire AI operating system.” I think he’s right. And I think most developers building on these platforms right now are…
