8 posts tagged with "Full Stack"

One plugin standard, shared across seven tools.

Fig: Open Plugins — install once, works across seven tools

AI coding tools are multiplying: Cursor, Claude Code, Codex, Grok Build — each with its own plugin format. Then came Open Plugins.

It's an open standard maintained by Vercel Labs. Install one plugin, it runs across seven tools.

To be honest, I rarely think a "perk" is worth a dedicated post. This one is different.

In July 2026, three things lined up:

• WeChat DevTools opened up AI agent Skills, so the Cursor / Claude Code you use can directly read DevTools console logs, look at simulator screenshots, auto-deploy cloud functions.
• The mini program growth plan's second phase bumped Token from 100M to 1B, image generation from 10K to 100K, and upgraded the model to Hunyuan Hy3.
• CloudBase flattened the backend — AI tools connect directly to its models, Token comes out of your plan, and you can deploy right after writing.

Put together, the hardest hurdles to "one person building a complete mini program alone" were all cleared. Below is my hands-on note. I walked every step, and I'll tell you what's good and what's not.

This article covers four things: ① What Codex is and what it can do → ② Five-minute hands-on: build a playable game → ③ Core usage and features → ④ Connect low-cost Chinese open-source models and deploy your app so others can access it.

Short on time? Codex now supports DeepSeek, GLM, and other affordable models. Want to save money + one-click deploy? Jump to Step 4 and copy one config. Want the full picture? Read on.

A question I get a lot: how do you add AI to a mini program?

Most people asking aren't stuck on the coding. They're stuck on the concrete parts: where to put the API key safely, why the mini program can't call the model API directly, how streaming replies work, whether switching models is a hassle.

This post covers the pitfalls I hit and the standard way I do it now. The core idea is one sentence: let the cloud platform be the middleman — the frontend never touches a secret key.

Ever run into this: AI generates your app, then it breaks and you don't even know what question to ask? AI gives you N solutions but you have no idea which one is right?

This is the pain point for many non-technical people using AI to code:

• Debug hell: page shows something wrong, but no idea which layer is the issue (frontend? backend? deployment?)
• Can't ask the right question: don't know how to describe the problem so AI understands and gives accurate answers
• Unclear learning path: know you need some basics, but don't know where to start or what's enough

Today I'll share a systematic approach — from foundational concepts to debugging techniques to backend solutions — to take you from "able to generate apps" to "able to build real, usable apps."

I've been helping friends review their AI-built projects recently, and I noticed something interesting:

The code runs. The features are there. But somehow... it's not usable.

Not a technical issue. Something else.

After a few months of coding with the Spec workflow, I kept asking myself one question: why does this "write requirements first, design next, then break down tasks" process actually work?

Then I came across Gojko Adzic's Specification by Example: How Successful Teams Deliver the Right Software, and it clicked. Behind Kiro's Spec workflow is exactly the SBE (Specification by Example) methodology. This article explains the relationship between the two — and why your AI coding keeps ending up in rework.

After more than a year of AI-assisted coding, my biggest lesson is this: vibe coding feels great in the moment, but it falls apart the moment a project gets complex. A one-line requirement, AI starts coding on its own assumptions, and what comes out is always a bit off. You ask it to fix something, it fixes that and breaks something else. A few rounds of this and your codebase has tripled in size while the number of working features barely grew.

Eventually I figured it out: the problem isn't that AI writes bad code. It's that I don't say clearly what I want.

Kiro's Spec workflow is built to solve exactly that. Let's walk through it from scratch.