There’s been a bit of chatter online about the free opera concert at Fed Square tonight, and honestly, it’s given me a lot to think about. Someone posted about how packed it was, how magical the whole experience felt, and threw in a cheeky reference to Timothy Chalamet being wrong. For those not following the entertainment news cycle (I barely keep up myself), the actor recently made some comments about opera being too elitist or inaccessible – or something along those lines. The irony of his statement being contradicted by a packed Fed Square certainly wasn’t lost on the crowd.

Someone on Reddit just did the investigative journalism that apparently none of our major news outlets bothered to do. They traced $2 billion in nonprofit grants and lobbying records across 45 US states and discovered something that’s simultaneously shocking and utterly predictable: the company behind those age verification bills is the same company that profits from collecting your data.

Let me say that again more clearly: Meta, a company whose entire business model revolves around hoovering up personal information, has been actively lobbying for laws that would require even more data collection. And they’ve dressed it up as “protecting the children.”

There’s something deeply unsettling about reading that a former government employee walked out of the Social Security Administration with the personal data of 500 million Americans on a thumb drive. Not because data breaches are new—we’ve all received those “your information may have been compromised” emails more times than we can count—but because this wasn’t a sophisticated hack or a system vulnerability. This was someone just… taking it. With apparent blessing from above and an expectation of a presidential pardon if caught.

I came across a fascinating discussion online about someone who built a fully self-hosted web scraping infrastructure using 50 Raspberry Pi nodes, and honestly, it’s been rattling around in my head for days now. Not because it’s the most efficient solution—quite the opposite, actually—but because it represents something I find increasingly rare in our field: building something just to see if you can.

The setup is admittedly bonkers. Fifty Raspberry Pis, each running Chrome via Selenium, each with its own VPN connection, all coordinated to scrape job postings. The whole thing is local—no cloud services, just hardware sitting in someone’s home, collecting 3.9 million records over two years. There’s even an IoT power strip that automatically power-cycles nodes when they stop responding. It’s automated chaos, and I kind of love it.

I’ve been watching the 3D printing space evolve over the years, mostly from the sidelines. There’s something satisfying about the idea of being able to fabricate physical objects on demand, though I’ll admit my own maker skills are more oriented toward deploying containers than designing custom brackets. So when I stumbled across a project that essentially gives an AI agent the ability to search, design, slice, and print 3D models through natural conversation, I had that familiar mix of excitement and unease that seems to accompany every significant AI advancement these days.

There’s something deeply cynical about watching a company worth billions announce 1,600 job cuts while simultaneously claiming “our approach is not AI replaces people.” It’s the corporate equivalent of “it’s not you, it’s me” – except it’s very much them, and we all know it.

Atlassian’s latest round of layoffs has been making the rounds in tech circles, and the discussions around it have been fascinating in all the wrong ways. The official line is that AI is making workers more “efficient,” which apparently means they need 1,600 fewer of them. But here’s the thing that really gets under my skin: AI isn’t holding a gun to anyone’s head. These are choices made by executives, pure and simple.

Andrej Karpathy just casually dropped something on Twitter that’s got me sitting here with my third latte of the day, staring at my MacBook screen and feeling that familiar mix of excitement and low-key existential dread that seems to define 2025.

For those who don’t know, Karpathy is one of the godfathers of modern AI – co-founded OpenAI, former head of AI at Tesla, basically the kind of person who forgets more about neural networks before breakfast than most of us will ever learn. So when he posts about an AI agent that ran autonomously for two days and improved his tiny LLM training process by 11%, making it go from 2.02 hours to 1.80 hours to match GPT-2 performance, people pay attention.

I’ve been reading about the rapid deployment of AI in military applications lately, and frankly, it’s keeping me up at night. Not in the melodramatic sense, but in that particular way where you’re scrolling through your phone at 2am and suddenly realize we might be sleepwalking into a future we’ll deeply regret.

The thing that really gets me is how we’re having this conversation after the technology has already been deployed. Someone mentioned that we passed the milestone of technology moving faster than regulation “a while ago,” and that’s the crux of it, isn’t it? We’re not having a preventative discussion here – we’re playing catch-up with systems that are already making life and death decisions.

I’ve been watching the AI tools landscape explode over the past couple of years, and honestly, it’s been both exhilarating and exhausting. Every week there’s a new “game-changing” platform that promises to revolutionize how we work. But here’s the thing that’s been bugging me: we’re still talking about these tools the wrong way.

Someone recently shared a project that maps 137 AI tools and their actual connections – not just another directory, but a visual graph showing how these tools integrate with each other in real workflows. Twenty-five complete workflows, from podcast production to SEO content pipelines, showing exactly which tools feed into which at each stage. The whole thing runs in your browser, no login required, completely free.

There’s a headline doing the rounds that’s got everyone fired up: “40% of teenage boys believe women lie about domestic and sexual violence.” It’s the kind of statistic that makes you want to throw your phone across the room. But before we all collectively lose our minds, we need to talk about something that’s been bugging me for years now – the way survey questions are worded, and how those wordings get spun into inflammatory headlines.