惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
月光博客
月光博客
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
F
Fortinet All Blogs
T
The Blog of Author Tim Ferriss
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
aimingoo的专栏
aimingoo的专栏
U
Unit 42
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
MyScale Blog
MyScale Blog
T
Tailwind CSS Blog
N
Netflix TechBlog - Medium
B
Blog
博客园_首页
G
Google Developers Blog
Recent Announcements
Recent Announcements
博客园 - 【当耐特】
P
Proofpoint News Feed
博客园 - 司徒正美
Hugging Face - Blog
Hugging Face - Blog
MongoDB | Blog
MongoDB | Blog
Last Week in AI
Last Week in AI

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
From Steam to Web: The Day Job Meets the Dream
AllByte Studios · 2026-06-23 · via DEV Community

AI is game-changing for software — game dev included. In an age of game "slop" it's rightly scrutinized. But I don't believe AI can only accelerate the flood of low quality. I'd argue the opposite: it can give real time back to developers to make better games, and hand more power to the indie dev. Here's how I came to that.

AllByte

My day job is large-scale distributed systems engineering. Getting code to work together at scale — services, queues, observability, CI/CD, the whole apparatus that lets a hundred engineers ship a single product without stepping on each other.

The night project is Chronicles of Nesis — a tactical RPG I've been building solo in Godot for four years. Pixel art, custom font, hand-built dialogue system, the whole 90s-JRPG aesthetic. It exists because I love it, not because it makes business sense.

These two have always been disconnected. Gamedev folks don't usually care about distributed systems. Distributed systems folks don't usually make games. Then about a month ago, I downloaded Claude.

The Solodev

For a long time, the only way to make a solo indie game was to grind (and not the fun JRPG leveling up kind). Every line of code costs you an hour you don't have. You make trade-offs. You skip the test harness, skip the CI, skip the observability, skip the architecture diagrams — because the math doesn't work at solo scale. The same practices that make a large-scale codebase tractable would crush a one-person codebase before the game ever shipped.

So you cut corners. You manually playtest. You write code that you can hold in your head because there's no other way to verify it. You ship a half-tested build to a closed beta and hope nobody finds the regressions you can't catch yourself. The discipline isn't wrong; it's just unaffordable. Or it used to be.

The Bottleneck and The Feedback Loop

People have been asking me to download AI and code with AI for years. I always answered the same way: "I tried it, I get it, it helps you, but I can code it faster — and better." And I was right. Until I wasn't.

Code generation is fast now, but "fast" doesn't get the idea across. It's not that it's faster. It's…

I used to estimate time for very strong software engineers to complete things. I've shipped hundreds of apps, dozens of architecture stacks. I know how long it takes to build something, and how long it takes to build it right. The way I think about this now is: code generation that had to be hand-crafted ran on human time. Code generation went from human time to compute time. And if you know what a processor's clock speed is, you have an idea of what "fast" means now.

So fast that it has stopped being the bottleneck. In my opinion, the bottleneck has moved to two places: knowing what to ask for, and knowing whether you got it.

Both of those are things AI can also help with. But not for free. They each demand something specific:

  • Knowing what to ask for demands a knowledgeable user. AI doesn't replace expertise; it amplifies it. The unknowledgeable user gets fluent-sounding garbage; the knowledgeable user gets a hundred junior engineers.
  • Knowing whether you got it demands a tech stack and architecture where verification is cheap. Without that, you're back to manually verifying every little step, and the velocity gain quickly levels off.

The Game

"Zero to Steam" was the first chapter — four years of grinding the game itself into a shape worth shipping. Combat, isometric grids, dialogue, save system, controller support, the whole 90s tactical RPG vocabulary. The endgame of that era was the demo on Steam.

"From Steam to Web" is the conclusion I came to after wracking my head on a different question: OK, AI can write my code now — what do I actually do with that? It started as "well, I have four years of a passion project sitting here, how do I aim AI at it?" Which became "I know what I'd need to make AI useful at this scale — a validation loop, fast enough to keep up with how fast the code is generated." Which became "the validation loop needs Playwright, but Playwright lives in browsers, and heavy browser games are notoriously difficult to build." And then — wait. "Difficult to build" was where I started… Claude.

Chronicles of Nesis is the test case because it's four years of a codebase I care about, and because Godot exports to browser — which means Playwright can drive it. The open question is whether the practices that scale a large-scale system scale down to one person. I don't know yet. That's the experiment.

The Conclusion

All to say — why build a website? I'm trying to do a lot of things with this one. As with all meaningful projects, it should have a lot of win cases. I want to keep working on Chronicles of Nesis. I love it. Now I can make it playable in the browser (or so I hope), focus more on the art, and have Claude do the code. I can ship an enterprise-grade website with cost optimization and real features — again, thanks to Claude — and write a devlog about how Claude enables others to do the same. And probably the biggest win, and the hardest to achieve: I can provide an example — ideally a runbook — for others to use Playwright with Godot and Claude to make great games.

The Youtube video shows the loop in action — a bug reported and live-fixed in three minutes with Godot, Claude, and Playwright.