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Legible AI Collaboration

AI generates code faster than humans can read it. When the machine outpaces the reviewer, the team stops understanding the software it is building. We need to keep humans in control, without removing the benefits of modern coding agents. Jake Levirne of SpecStory, shared workflows they use to manage this by adapting the review process to the task's risk. This separates "intent review" (are we building the right thing?) from "code review" (is it built correctly?). Three workflows handle different needs: 1. 𝗜𝗻𝘁𝗲𝗻𝘁 𝗙𝗶𝗿𝘀𝘁, 𝗧𝗵𝗲𝗻 𝗖𝗼𝗱𝗲: For critical systems. The team agrees on a detailed plan before the AI writes any code. Every line is then reviewed. 2. 𝗜𝗻𝘁𝗲𝗻𝘁 𝗥𝗲𝘃𝗶𝗲𝘄 + 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝗺𝗼𝘀: For most features. A plan is made, but implementation starts immediately. Demos are shared for feedback. 3. 𝗗𝗲𝗺𝗼 𝗢𝗻𝗹𝘆: For prototypes. The goal is quick feedback on a concept, not production-ready code. The intent review is most important. Use an AI to generate a design document, then refine it to clarify the project's what and *why*. This document should focus on the plan, not specific implementation details. Do not just skim the document. For code review, use AI tools for automated security scans and custom checks. Aggressive linting can also enforce structural rules, forcing the AI to produce more modular code. Full blog post: https://isaacflath.com/writing/legibl... --- [00:00] Introduction to Legible AI Collaboration [03:28] The Philosophy of Review: Building the Right Thing vs. Thing Right [06:43] AI Collaboration Patterns & Workflows [16:34] Self-Review Techniques for Solo Developers [19:29] Deep Dive: The Intent Review Process [23:56] Real-World Example: Complex Design Doc & Chat History [36:16] Example: Simple Design Doc & The Arena Tool [47:43] Demo & AI-Assisted Code Review [59:34] Recap & Q&A ---