Chuanxilu for Skilled Homo sapiens

This is a supplementary part of the AI Path Advanced Guide L2, teaching non-coders how to use AI to write code. L2 has one final step: making AI write code that actually works. How do you make AI write code you can use? Not a demo that runs once. A script you can drop into your project and run. For someone who doesn’t code, the biggest pain points aren’t “how to write code.” They’re three questions: ...

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. ...

If you build with, or are thinking of using, OpenCode.ai (oc, for short) for development or agent work, you’ve probably felt this anxiety: worried the models won’t match the official ones, worried the free tier will hit its limit every day, or puzzled over how the Go subscription quota is even calculated, especially with claims floating around about burning through half a month’s quota in 5 minutes. Once DeepSeek V4 Flash (ds4f, for short) stabilized, the token constraint issues became much easier to manage. I analyzed over 3,000 API call logs from my local machine across three months of active usage and cross-checked them against the numbers online. Here’s the truth behind these five misconceptions. Once you get these straight, your workflow runs steadier, and you stop worrying about burning through your token quota. ...

TL;DR: On August 6, 2026, DeepSeek announced that it plans to raise API prices. No single cause: cost pass-through, user filtering, expectation management, free marketing, a shift to value-based pricing, and open-source ecosystem pressure all point at the same move. Rising compute costs are real, but they don’t explain the timing or the form of the announcement. The market is largely moving from winning share with low prices toward value-based pricing, and seven falsifiable signals before the official plan will confirm or overturn this post’s inferences. ...

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. ...

I’ve been freelancing for three months. At first, lunch was no different from my office days. Open the delivery app, scroll, pick something, wait. Nothing wrong with it. I’d been doing it for years. The shift wasn’t sudden. First I noticed the monthly delivery bill added up fast. One person ordering has to meet minimum order thresholds plus packaging and delivery fees, and a decent meal runs $6–$8. By the end of the month, that wasn’t pocket change. Then I noticed how heavy the oil and salt were. I’d finish eating, only to hit an immediate post-meal slump. Paying money just to feel sluggish felt like a raw deal. ...

This is Day 11 of the AI Path L1→L2 Upgrade Guide; if you haven’t done so yet, complete Day 8 and Day 10 before diving into this practice session. Day 10 ended with a note: description and verification are two sides of 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. The generated text is 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,” rather than asking, “Did it actually deliver what I requested?” ...

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 being clear” is a lie I’ve told myself too many times while writing AI prompts. 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 root cause wasn’t model capability. It was prompt specification. Refining your task description can mean the difference between three back-and-forth prompt iterations and getting it right on the first turn. 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. ...

Prologue: 1,754 Perfect Green Lights The team watched the test panel late on the night Aristotle v1.6.0 shipped. Green indicators lit up like dominoes. Python side: 1,166 assertions. TypeScript side: 588 checks. Total: 1,754 automated checks. All green. In code terms, that’s like having cameras and infrared sensors on every wall. A fly couldn’t sneak through without setting off alarms. The team leaned back. The system looked like an iron fortress. ...