Chuanxilu for Skilled Homo sapiens

Previous post: Day 16: L2 Complete! Next Stop: Harness Engineering Evolution and the L3 Blueprint. Make sure you have understood or mastered the skills Day 16 covered, and let’s start L3 from here. This is the L3 entry point. Upcoming posts: Day Type Topic Day 1 Exercise Reading the Agent Loop in 120 lines Day 2 Core Contracts and interception: hooks and permissions Day 3 Exercise Building an approval flow with Claude Code hooks Day 4 Core Extensions vs plugins: Pi vs DSH Day 5 Exercise Writing a Pi extension Day 6 Exercise Composing DSH Cordis plugin modes Day 7 Core Agent Teams and task DAGs Day 8 Core Memory and learning: Hermes, Nowledge Mem, EvoMap Day 9 Exercise Designing your own skill system Day 10 Wrap-up L3 graduation check Start by Remembering Your Day 4 Script Think back to Day 4 of L2. In that lesson we wrote a batch script: read files from a folder, call the API on each one, write the results back, and when a step failed, log the error first, then keep going. ...

This is the graduation assessment of the “AI Path: L1→L2 Upgrade Guide” series. Previous post: Part 5: How Non-Coders Use AI to Write Code. Full Navigation Day Type Topic Day 0 Core Part 1 AI Path L1→L2 Upgrade Guide (1): Your First API Call Day 1 Exercise Day 1 Exercise: Run Your First API Code Day 2 Exercise Day 2 Exercise: Run the Same Request on an Aggregator Platform Day 3 Exercise Day 3 Exercise: API Parameter Experiments Day 4 Core Part 2 AI Path L1→L2 Upgrade Guide (2): From One Call to Batch Processing: Let Your Program Do 100 Tasks Day 5 Exercise Day 5 Exercise: Teach Your Script to Read More File Formats Day 6 Exercise Day 6 Exercise: Batch Processing Practice: Pick a Scenario and Run It Day 7 Exercise Day 7 Exercise: Add Error Handling to Your Script Day 8 Core Part 3 Day 8: Autonomous AI, Automation Without Writing Code Day 9 Side Quest Day 9: API Caching Basics and Why Unit Price Isn’t the Whole Story Day 10 Exercise Day 10: Your AI Feels Like an Intern? Try the GCO Framework Day 11 Exercise Day 11: How to Verify What AI Gives You Day 12 Core Part 4 Your AI Toolbox: How Scripts and AI Agent Work Together Day 13 Exercise Day 13: Turning a Workflow into a Skill Kit Day 14 Exercise Automating Skill Documents: Four Core Blocks for Tailored AI Workflows Day 15 Core Part 5 Part 5: How Non-Coders Use AI to Write Code Day 16 Stage Recap Graduation Assessment Graduation Assessment: The L2 Nine-Item Checklist Seventeen days, from your first API call to a complete pipeline of your own. AI tools and features will keep changing, but don’t let that scare you: turning the unchanging methods and ways of thinking into your own capability is the right choice. ...

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?” ...