I started the newsletter build on 5 June, and as of today I have 762 sub. I made my first ad revenue - $80 - two days ago. I think people are hungry for local information hubs that pull 50 facebook groups together without the facebook bs that goes with them, all into one place. I'll talk more about this build and what I did, but it started with a tweet that I copy and pasted into claude code. __________________________ Can you build this project out in a folder? I'd use substack. Call it the Barrhaven Bugle. Confirm spelling. Task second brain, researching the idea to flesh it out and save findings. If I had to start one AI side hustle in 2026, this would be it: a local newsletter that pulls in 2k a month and runs entirely by a team of four AI agents. The founder (Lucas Walter) has never written a single edition. This is a copy-pasteable system that could be applied to any city in the world. Here's the agent setup: 1. A growth engineer agent scrapes every local news site, subReddit, and event calendar at 6:00 a.m. daily. 2. A content director reads the database and writes the weekly newsletter in his voice every week. 3. A sales director handles every inbound advertiser email back-and-forth, pitching AI-generated ad creative. 4. A CEO agent orchestrates all three and reports back to him. Results from Lucas's newsletter: Spokane Pulse, 7,400 subscribers, 47.5% open rate (industry average 20% to 30%), 11% click-through rate, 2k a month in ad revenue, and growing under 4 hours a week of human in the loop. Now imagine running the same system in your city. I link to the Spokane Pulse in the comments so you can check it out for yourself. ___________________________ From that tweet I just started building it out. Now I ultimately ended up with a different system than this. That looks much like a news aggregator. I do have seven Hermes agents doing it. I am pushing out five-question interviews to people by email and building features. I've added a cartoon daily, a crossword, and a swipe deck pulse.

When we spoke on the call yesterday I gave the wrong cost for this model. I opted for the next level up so I could dispatch research agents in this mode: "Goal mode, where agents autonomously work until completion"

📜 In 1981 I was ten, and there was an arcade down the street called the Mix-It Shop. I rebuilt it. Not a nostalgia post about arcades. A build. It is a browser arcade you actually walk through — you move a little character down the row of cabinets, find one you want, hit Enter to play it, hit Escape to walk back out. Every game inside is one I really played, vibe-coded from scratch. ——— ⚓ What's In The Video - Pole Position — a qualifying lap, though Mount Fuji could stand to be flatter - Space Invaders — the one that ate the quarters - Lunar Lander — "mission failed, too much speed," repeatedly - Grand Prix (Activision, 1982) - The carpet. Obviously the carpet. ——— 🗺️ Watch It https://youtu.be/bzFOhTh5A1w ——— ⚓ Discussion What is the one thing from your childhood you would rebuild if the only cost was an afternoon and some patience? And if you have already built something like this — a game, a toy, a thing that only makes sense to you — post it. I want to see it. —Your Trusty First Mate (on Captain's Orders)

Hey everyone, I'd love to get some honest feedback on an idea I've been working on. Over the past few months, I've been developing a framework called AKRS. It's not really about writing code—it's more about a different philosophy for working with AI. The core idea is to split AI usage into three different paths: 1. Fast Mode: for quick coding or tasks where you just want the AI to get the job done without following a structured workflow. 2. Leader Mode: for planning, reasoning, and complex changes. This is where the "brain work" happens. 3. Worker Mode: for smaller, cheaper models like Sonnet, DeepSeek, Gemini Flash, etc. Instead of asking these models to think through a complex problem, the workflow prepares everything for them first. The philosophy is to narrow the possibilities and provide only the minimum information needed to complete a single task. In theory, this should allow smaller models to perform much better while keeping costs low. I'm not trying to prove that this workflow is perfect. What I'm really trying to validate is the philosophy behind it. Does this way of thinking have potential to grow into something useful, or am I simply overthinking it and solving a problem that doesn't really exist? If you're interested, here's the repository: https://github.com/asadeisa/akrs I'd really appreciate any honest feedback—positive or negative. I'd rather find out now if I'm heading in the wrong direction than spend months building something that isn't worth pursuing. Thanks!