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Hey this is Isaac, Welcome to my new weekly newsletter. Each week, we explore one build, one lesson, and one refactor. Only practical ideas to help you build better. This week I jumped from untangling a messy Stripe integration to light creativity generating an AI promo video. The mental whiplash is real. Let's start with the AI Promo Video. The Build: Directing an AI to Make a Promo VideoI created a 74-second promo video draft in about 30 minutes. AI wrote the code, but I was the creative director. Retaining control of AI is everything; You can't just vibe-code it. Context, taste, vision, and critical feedback can't be manufactured. Here’s what I did:
AI is powerful if wielded by someone with expertise and a clear vision. For a full breakdown of the prompts and process, read the full article here. The Learn: A Pattern for Personal AutomationHere’s a powerful pattern for building your own tools (both for you and your agent): combine a
This lets you build powerful workflows. I have a ` Each script is independent, and the Why build your own tools? Because with AI, the creation cost is very low. I created a custom GitHub issue reader script in less than 10 seconds. It was faster than finding, installing, and learning a pre-built tool. And the github MCP has over 90 tools and takes tens of thousands of tokens of context - better to write your own in a few seconds. To see the full workflow and get the scripts, read the full article here. The Refactor: Trading API Calls for SimplicityThe classic approach to Stripe webhooks is a complex state machine. You write separate handlers for every event:
We threw that out for a radically simpler approach for a SaaS I am building. The old way: Listen for a specific event, interpret its payload, and update our database accordingly. The new way: After any subscription-related webhook arrives, we ignore the payload and make one API call to The result? We replaced over 200 lines of complex event-handling logic with a single, idempotent function call. It's a 90% reduction in code in our webhooks file and completely eliminates ambiguity. It might cost an extra API call, but it buys us absolute certainty and drastically simpler code. Until next week, Isaac |
Every post comes from something I've done on a real project. AI tools, development approaches, how I actually build things. You're getting a curation of my taste, not takes on stuff I don't use. Subscribers also get extras: things that went wrong, how my thinking about AI is changing, hacky workflows I use every day, and the occasional personal update. Stuff I share with subscribers because it's a little too personal or unpolished to blast across the internet.
Today's post is about a project that I will take concepts from to improve my own work. So I studied the OSS prototype of it to understand how it works. Point and Talk: How Clicky’s AI Interface Works Clicky is an AI buddy made by Farza that lives on your Mac. You press a key, talk, and a glowing blue triangle flies across your screen, points at whatever you asked about, and talks you through the answer. Most apps with an agent should have something like this. Point and talk is closer to how...
Last week I shared my knowledge base tool, agentkb. This week I am OSSing my pi harness that uses it, Harpy. The most interesting part of the harness is that knowledge base <-> harness integration so that's what this week post dives into. It covers what RLM is, how it drives a python loop with state, how Pi (typscript) extension talks to python, and how that allows for smart delegation and fan-out to smaller models to give more accurate search results. How My RLM Tool Works An LLM writes...
Hey, This was a week of context switching 😅. Product release weeks are always odd. The goal is super specific, and yet the work is scattered small tasks (testing, polish, docs, marketing copy, etc.). We shipped the stoa beta, and I kept wishing my agents could remember more. That's why I'm open sourcing `agentkb`, my work-in-progress knowledge base framework today. The Pi harness built around it will be open sourced next. AgentKB: A Local Knowledge Base for Agents My agent should learn from...