AI Path

This is Day 14 of the “AI Path: Advanced Upgrade Guide” Week 3 series. Previous post: Day 13 Practice. Project repo: picture-book-pipeline. Introduction In Day 13, I built a skill through six rounds of conversation. A skill is made of role documents, one per role. The agent works from those documents. How you write them directly dictates the quality of the output. A role document has four blocks: identity, input, output requirements, quality requirements. This post goes through each one. What to write, and why. ...

This is Day 13 of the AI Path L1→L2 Upgrade Guide. Before diving in, check out Day 8, Day 9, Day 10, Day 11, and Day 12 first. Day 12 ended with a promise: in the next practice, we’d run the pipeline by hand. Two tools working in relay. Today, I deliver on that promise. I’m not here to show you how to use an off-the-shelf skill; I’ll show you how to build one from scratch. The project is picture-book-pipeline, which I use to batch-generate children’s picture books. It turns a workflow into a complete skill kit: a SKILL.md overview, role prompts, and execution scripts, all in one directory. ...

This is Day 12 of the AI Path L1→L2 Upgrade Guide. Do Day 8, Day 10 and Day 11 first. Day 11 ended with a crucial question: How do we automate these steps? My approach relies on a simple realization: pairing stateless API calls with an AI agent shares the same core principles as breaking down workflows, designing systems, and building software. I’ve been doing that kind of work for years. The AI picture book project I’m building right now is a clean example. I’ll walk through how I designed its pipeline and tasks. ...

This is Day 11 of the AI Path L1→L2 Upgrade Guide, a practice article. Do Day 8 and Day 10 first. Day 10 ended with a note: description and verification are the same coin. If you can’t describe what you want, you can’t check whether you got it. I default to trusting AI output, especially when it sounds confident. It’s well structured, clear, and sounds right. Day 10 mentioned an example: I asked AI to “organize these files by category.” It sorted them alphabetically by name. The result looked organized, but it wasn’t the kind of organization I meant. At the time I thought “I need to describe it better next time,” not “let me check whether what it delivered is actually correct.” ...

This is Day 10 of the AI Path L1→L2 Upgrade Guide. You should complete Day 8 and Day 9 first. I learned this the hard way. “I thought I was clear” is a lie I have told myself more often than I care to admit when prompting AI. I once asked an AI to organize project documents: “Help me sort these files.” What I got back: all .md and .py files mixed together, sorted alphabetically by filename. It did sort them, just not the way I meant. Another time I said: “Show me the directory structure.” I wanted a tree view. The AI gave me ls -lh output: file sizes, timestamps, permissions, everything I didn’t ask for. The same request, described vaguely or clearly, can produce two completely different results. The gap wasn’t in the tool. It was in how I described what I wanted. A vague description and a clear one can mean the difference between three iterations and zero. Here are three exercises from everyday scenarios. Each starts with a vague version, breaks down what’s missing, and builds up to a clear version. If you read these and think “wait, I write prompts like the vague version too,” that is exactly why this article exists. ...

This is a bonus article for the “AI Path L1→L2 Upgrade Guide.” If you haven’t set up Codex yet, this guide gets you from zero to running. On July 9, 2026, OpenAI made two announcements at once: GPT-5.6 went public, and Codex was merged into the ChatGPT desktop app. The new desktop app has three modes: Chat, Work, and Codex, all in one app. If you’ve already got the standalone Codex desktop app, it updates in place. Your projects and settings carry over. ...

This is a bonus article for the “AI Path L1→L2 Upgrade Guide.” If you have not installed OpenCode yet, this guide gets you from zero to running. In Day 8 you learned about autonomous execution AI. The next step is actually installing and configuring OpenCode. OpenCode is an open-source AI coding assistant. It is free, supports multiple models, and has a skill system. Paired with OpenCode Zen, you can use tested models without setting up third-party API keys, including the free DeepSeek V4 Flash. ...

This is Day 9 of Week 2 in the “AI Path L1→L2 Upgrade Guide.” You should have completed Day 7 Exercise: Add Error Handling to Your Script first. Day 7 added error handling to your script, so it’s resilient now. But there’s a bigger cost factor you might have missed: the API provider you picked could cost a lot more than you think. DeepSeek V4-Pro charges $0.435 per million input tokens. OpenAI GPT-5.5 charges $5.00. That’s roughly an 11x difference. Factor in caching and the gap widens further. ...

This is Day 8 of Week 2 in the “AI Path L1→L2 Upgrade Guide.” You should have completed Day 7 first. Day 7 you added error handling to your script. It now runs reliably in real network conditions. But there’s a more fundamental limitation: you still have to write code. Writing code to call APIs is one kind of automation. There’s a lighter one: describe the task, and let AI write the code, run it, and fix the bugs itself. That’s autonomous execution AI. ...

This is the Day 7 exercise for Week 2 of the “AI Path: Level Up Guide” series. Complete Day 6: Batch Processing Practice first. In Day 6, you wrote a batch processing script that works. Pick a scenario, walk the folder, call the API, save the results. It all runs smoothly until you try it for real. But that script runs in an idealized environment. Real networks are not ideal. Have you hit any of these with your script? ...