AI

This is the Day 1 companion exercise for the AI Path L1→L2 Upgrade Guide. Read Part 1 first, then come back here to practice. Today we do exactly one thing: run the hello_api.py from Part 1 and see AI reply in your terminal. Prerequisites Complete these steps from Part 1 (skip if already done): Register a DeepSeek developer account (Part 1, “Register for API Accounts”) Get your API Key and save it to a .env file (Part 1, “API Key Safety”) Install uv and Python 3.12 (Part 1, “Install Python”) Create a virtual environment and install dependencies (Part 1, “Create a Virtual Environment”) Confirm your project directory looks like this: ...

TL;DR: This is Part 1 of the “AI Path L1→L2 Upgrade Guide” series. Four parts total, one per week of practice. This article takes you from chat windows to APIs—automating your AI interactions through code, laying the foundation for batch processing and autonomous task-execution AI. Introduction: From “I Ask AI” to “Programs Ask AI” If you finished the L0→L1 graduation checklist, you might remember one line from the graduation post: “Register for an API account and use Python to print your first AI reply.” Today is that day. ...

📖 This is Part 5 of 5 in the “AI Path L0→L1 Upgrade Guide” series — Series Navigation + Graduation Checklist. Series Navigation Part Topic Core Content Part 1 Understanding Your Tools LLM fundamentals (not a search engine), working memory vs. long-term memory, mainstream platforms and specialized tools Part 2 From Vague Questions to Precise Instructions The RBGO prompt framework, Chain-of-Thought reasoning, format constraints Part 3 Turning AI Into Your Collaboration Partner Iterative follow-up questions, context management (new conversations / progress summaries / chunked processing), role-playing Part 4 Building Your Personal System Prompt library, scenario-to-tool mapping (international and China options), layered knowledge management Part 5 Graduation & Next Steps L1 graduation checklist, L1→L2 dual-path preview ...

The biggest obstacle to building a Prompt library isn’t the tool — it’s knowing how to organize it. Yesterday you picked 5 Prompts; today I’ll show you a complete real template. Directory Structure This structure uses the Markdown folder approach. You can copy it directly: prompt-library/ ├── writing/ │ ├── email.md │ ├── article-summary.md │ └── ... ├── analysis/ │ ├── data-interpretation.md │ ├── case-breakdown.md │ └── ... ├── daily/ │ ├── meeting-notes.md │ └── ... └── README.md (global notes) The record format for each Prompt: ...

Today’s Practice From your recent AI conversations — coding, writing, analysis — pick 5 prompts that actually worked well. Record them using the template from Part 4: original prompt + effectiveness rating + iteration notes. Where you record them doesn’t matter — a notes app, Notion, a plain text file. Don’t overthink the tool. If you can’t find your chat history, spend 20 minutes creating 5 prompts you’ll definitely use at work. For example: “Check the edge cases in this code,” “Rewrite this technical article for beginners,” “Extract the 3 main issues from these 100 user feedbacks.” ...

📖 This is Part 4 of 5 in the “AI Path L0→L1 Upgrade Guide” series. Part 1: Understanding Your Tools · Part 2: From Vague Questions to Precise Instructions · Part 3: Turning AI Into Your Collaboration Partner · Part 4: Building Your Personal System · Part 5: Graduation & Next Steps Over three weeks we’ve picked up follow-up questions, context management, role-playing… the skills are piling up, and here’s the problem: how do you manage all these scattered abilities in one place? Week 4 is about exactly that — building a prompt library, choosing the right tools, and setting up knowledge management. Turning what you’ve learned into a personal system. ...

Series: Classic Theory Meets Agent Practice (Part 3) Part 1: Dual-Pass Review: Why You Can’t Have Both Recall and Precision · Part 2: Strategy Genes: Pruning Review Prompts with Genetic Algorithm Thinking TL;DR: Two controlled experiments. Code review dimensions went from 8 to 11, and known-issue detection went from 1/6 to 6/6. Design review introduced axiomatic design dimensions, and detection also went from 1/6 to 6/6. But the version with a math formula proved that more dimensions are not always better — computation consumed review attention, and findings dropped 35%. Run controlled experiments with known issues as reference, and you learn which dimensions actually work. ...

Yesterday we talked about follow-up questions — three questions to dig out the hidden assumptions behind an AI answer. The catch is, you need to know what to ask. Some blind spots you simply can’t see from your own perspective. That’s when you give AI a different identity. The Devil’s Advocate Please act as a strict reviewer. Go through this plan point by point and identify every risk and weakness. Don’t hold back — the sharper, the better. ...

Series: Classic Theory Meets Agent Practice (Part 2) Previous: Dual-Pass Review: Why Recall and Precision Cannot Both Win TL;DR: A review prompt went from 317 lines to 135 lines (-58%), and review quality improved by 29%. What I removed was not useful procedure, but redundant content the model could infer on its own. What stayed were strategy genes: irreplaceable constraints, negative examples, and tone locks. The previous post covered dual-pass review: splitting one review agent into a “find everything” pass and a “filter hard” pass. Valid find rate went from 75% to 92%. But it left one problem open: what the “find everything” pass chooses to report or ignore is still affected by prompt wording. ...

The previous post was about how long conversations drift. After writing it, I noticed something else: drift does not only happen after a conversation gets long. It can also happen inside any answer that looks complete. AI answers quickly, and its conclusions often sound smooth. But it rarely says upfront: what assumptions does this conclusion depend on? If those assumptions are not checked, I end up accepting them by default. Accept enough unchecked assumptions, and the later analysis may be built on the wrong foundation. ...