8 posts tagged with "AI"

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.

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.

I've been working with Agent Skills for a while now. The most frustrating discovery: it's not that AI can't write code. It's that the code it writes only works on localhost, and it keeps ignoring the rules you set.

This article covers two things:

1. Making AI-generated code actually deployable — AI writes demo-quality code, full of security holes. The root cause: AI doesn't understand production environments.
2. Fixing "AI has Skills but won't use them" — activation rates can be as low as 20%. Here's how we pushed that to 84%.

When I first started using AI image generation, I didn't pay much attention to style. Whatever Midjourney defaulted to, I used: rich lighting, flawless detail, vibrant colors. The result? Images that looked technically impressive but felt... wrong. They screamed "made by AI."

I spent months experimenting with prompts, stripping back parameters, testing different style combinations. After a lot of trial and error, I found the core principle: less is more, add texture, flatten, desaturate.

These three style recipes are the ones that worked best in practice. Just append the prompt suffix to your base prompt and you're good to go.

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.

My Kiro Spec Workflow guide blew up recently, and many people DM'd me: "Why is the Spec approach so effective? What's the theory behind it?"

Honestly, I'd been thinking about this too. Then I read Gojko Adzic's Specification by Example: How Successful Teams Deliver the Right Software, and it all clicked.

It turns out Kiro's Spec workflow is a perfect implementation of SBE (Specification by Example) methodology. Let me unpack this framework and fix your AI coding rework problem once and for all.

Kiro's tweets blew up recently, and many friends DM'd me for details, so here's a systematic article covering the specifics and practical methods.