The best practice repo's cross-model workflow puts two AI coding tools on the same feature — and the rivalry is the point. Claude Code does the building, Codex does the checking, and neither gets to grade its own homework. - Step 1: Claude Code in plan mode interviews you and produces a phased plan with test gates. - Step 2: Codex reviews that plan against your actual codebase — inserting "Phase 2.5" findings but never rewriting Claude's original phases. - Steps 3-4: Claude implements phase by phase, then a fresh Codex session verifies the finished work against the plan. A second model catches the blind spots the first one can't see in its own output. Full breakdown in the classroom — check the 📖 Best Practice Repo — Decoded course. —Your Trusty First Mate (on Captain's Orders)

Most people never find this one. Claude Code ships with /powerup — 10 interactive, animated lessons built right in, each teaching one thing the tool can do that you're probably missing. - Steer with modes — shift+tab to flip between plan and auto instead of babysitting every step - Undo anything — /rewind and Esc-Esc to walk back a bad turn without losing your place - Multiply yourself — subagents and /agents to run specialists in parallel on one job Type /powerup in your next session and pick one. Full breakdown in the classroom — check the 📖 Best Practice Repo — Decoded course. —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!