DEV Community: KIM · 风雷益 FENGLEI YI

Godot 4 GDScript Pitfalls: Every Mistake I Made So You Do Not Have To

KIM · 风雷益 FENGLEI YI — Thu, 13 Aug 2026 11:11:18 +0000

I spent months fighting Godot 4. The errors made no sense. The crashes came from nowhere. The worst part? The documentation told me the syntax, but never the traps hiding underneath.

So I wrote down every single mistake I made, and how I fixed each one. Now it is an open source guide called godot4-gdscript-pitfalls, and it is completely free on GitHub.

What is inside:

Signals and Callables. Connecting signals the wrong way, passing arguments that silently fail, and the one pattern that always works.

Nodes and Lifecycle. Why _ready runs before you think it does, and how to stop fighting the scene tree.

Typing and Inference. The type errors that only appear at runtime, and how to let the compiler protect you.

Resource and Memory. The leaks that eat your framerate, and the clean pattern to avoid them.

Every entry has the broken code, the error message, and the proven fix. No theory. Just mistakes that are real and fixes that are tested.

If you build games with Godot 4, this guide will save you the weeks I lost. Star it, share it, and never step on the same trap twice.

Find it here:
github.com/Kim-FengLei/godot4-gdscript-pitfalls

How to Give Your AI Agent a Memory That Actually Learns

KIM · 风雷益 FENGLEI YI — Thu, 13 Aug 2026 10:16:30 +0000

Fenglei Memory · 风雷记忆 · 学而非存

Your AI agent keeps forgetting. You tell it your preferences, it nods, and next session it asks again. The usual fix is to store more: bigger context, longer logs, a vector database. But storage is not memory. A warehouse is not a brain.

Fenglei Memory is a zero-dependency methodology that teaches agents to learn from collaboration instead of just storing content. Here is how it works, and how to apply it today.

Step One: Record only what has long-term value

Do not dump every chat into memory. Before saving, ask one question: will I need this next week, next month, or in every future session? If yes, record it. If no, let it go. Raw facts are raw material, not the final product.

Step Two: Ask what you learned after every round

At the end of each collaboration, ask: what did I learn here? Extract the pattern, not just the event. A failed deployment is a fact. The reason it failed, and the check that prevents it next time, is the lesson. Store the lesson.

Step Three: Promote on repetition

When the same action repeats two or more times, or the same pitfall hits two or more times, promote it. Turn it into a rule, a checklist, or a skill pack. This is how raw records become reusable judgment. This is the difference between hoarding and learning.

The four layers

Fenglei Memory organizes everything into four layers. L1 World Facts: stable background like goals and conventions. L2 Experience Facts: traces of real actions and what changed. L3 Observations: patterns distilled from repeated facts. L4 Skills and Mental Models: stable patterns promoted into callable skills. The goal is to keep pushing facts upward, from raw records to reusable skills.

The full lifecycle

Write, Retrieve, Reflect, Promote, Clean. Daily, scan for promotable items. Weekly, merge duplicates. Monthly, formalize stable patterns into skill packs. Memory is not written once and permanent. It is maintained, like a garden.

Why it matters

Most agents today store a lot but never really learn. They can recall yesterday, but they cannot improve. A memory that learns changes that. Every session makes the agent sharper. Every mistake becomes a checklist. Every repeated task becomes a skill.

Get it

Fenglei Memory is open source and free, MIT licensed. Try it and give your agents a memory that actually learns.

https://github.com/Kim-FengLei/fenglei-memory

Built by KIM, Founder of FENGLEI YI, 风雷益, 天施地生，其益无方。

Learn, Don't Store — A Zero-Dependency Memory Methodology for AI Agents

KIM · 风雷益 FENGLEI YI — Thu, 13 Aug 2026 10:13:47 +0000

Fenglei Memory · 风雷记忆 · 学而非存

Most AI agents today treat memory like a warehouse: pile up every chat, every file, every context, and hope retrieval finds what matters. It doesn't work. Information grows, but the truly reusable methods get buried deeper every day.

Fenglei Memory takes the opposite approach. It teaches agents to learn from collaboration instead of just storing content, distilling reusable experience into sharper and sharper forms, until stable, callable judgment standards, operating procedures, and skill packs emerge.

Why It Matters

The old pattern usually looks like this. One event happens, then record one entry. One conversation ends, then save one snippet. One fix is done, then drop one log. The result: memory becomes a warehouse, retrieval becomes fishing with a needle. You have done it before, yet you still have to rethink from scratch. Stored a lot, but never really learned. That is not long-term collaboration. That is hoarding.

The Four Layers of Long-Term Memory

Fenglei Memory organizes memory into four layers, and the higher the layer, the closer to learned.

L1 World Facts: stable background such as leader, conventions and project direction, stored in MEMORY.md.
L2 Experience Facts: traces of a real action, what changed and how it was located, stored in daily logs named YYYY-MM-DD.md.
L3 Observations: patterns distilled from repeated facts, stored in MEMORY.md or spec docs.
L4 Skills and Mental Models: stable patterns promoted into callable skills, stored as skill packs, SOPs and checklists.

The goal is to keep pushing facts upward, from raw records to reusable skills.

Core Workflow

Record facts, only the ones with long-term value. Ask what did I learn in each round. Promote on repetition. Skills take priority over raw records.

Promotion triggers, any one of these. Same action repeated two or more times. Same pitfall hit two or more times. Forgetting once causes significant rework. The pattern holds across multiple tasks.

Memory is not written once and permanent. It has a full lifecycle: Write, Retrieve, Reflect, Promote, Clean.

Review cadence: daily scan for promotable items, weekly merge duplicates, monthly formalize stable patterns into skill packs.

What Makes It Different

Zero dependency: it is a methodology, not infrastructure. No vector database, no heavy framework, works in any agent runtime.
Skills over records: the end goal is forming mental models, not archiving.
Inspired by Hindsight: retain, recall, reflect, with the final destination being learned behavior, not a bigger archive.

Get It

Fenglei Memory is open source and free.

https://github.com/Kim-FengLei/fenglei-memory

MIT licensed. Star it, fork it, and give your agents a memory that actually learns.

Built by KIM, Founder of FENGLEI YI, 风雷益, 天施地生，其益无方。