Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 ozan
|
c5a1b34ebc | ||
|
ozan
|
d83fdb137c |
Submodule .worktrees/agent/write-two-new-blog-posts-as-markdown-fil added at f9c9c960e0
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||||
<!DOCTYPE html>
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<head>
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||||
<meta charset="UTF-8">
|
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>Blog — Tinqs Studio</title>
|
||||
<meta name="description" content="Dev logs, behind-the-scenes, and lessons learned from building games, tools, and platform infrastructure at Tinqs Studio.">
|
||||
<meta name="robots" content="index, follow">
|
||||
<link rel="canonical" href="https://www.tinqs.com/blog/">
|
||||
|
||||
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|
||||
<meta property="og:url" content="https://www.tinqs.com/blog/">
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<meta property="og:title" content="Blog — Tinqs Studio">
|
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<meta property="og:description" content="Dev logs, behind-the-scenes, and lessons learned from building games, tools, and platform at Tinqs Studio.">
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<nav class="nav nav--scrolled" id="nav">
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<a href="/" class="nav__logo" aria-label="Tinqs home">
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<span class="nav__wordmark">TINQS</span>
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</a>
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<div class="nav__links">
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<a href="/#tech" class="nav__link">Technology</a>
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<a href="/#about" class="nav__link">About</a>
|
||||
<a href="/blog/" class="nav__link" style="color: var(--c-accent-l);">Blog</a>
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<a href="/#signup" class="nav__link">Contact</a>
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<a href="/press" class="nav__link">Press</a>
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<button class="nav__burger" aria-label="Open menu" id="navBurger">
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||||
<span></span><span></span><span></span>
|
||||
</button>
|
||||
</nav>
|
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||||
<div class="mobile-menu" id="mobileMenu">
|
||||
<a href="/#game" class="mobile-menu__link">Games</a>
|
||||
<a href="/#tech" class="mobile-menu__link">Technology</a>
|
||||
<a href="/#about" class="mobile-menu__link">About</a>
|
||||
<a href="/blog/" class="mobile-menu__link">Blog</a>
|
||||
<a href="/#signup" class="mobile-menu__link">Contact</a>
|
||||
<a href="/press" class="mobile-menu__link">Press</a>
|
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</div>
|
||||
|
||||
<!-- BLOG HEADER -->
|
||||
<div class="blog-header">
|
||||
<span class="section-label">Dev Log</span>
|
||||
<h1 class="blog-header__title">From the Workshop</h1>
|
||||
<p class="blog-header__subtitle">Behind-the-scenes notes on building games, forging tools, and running a small studio that punches above its weight.</p>
|
||||
</div>
|
||||
|
||||
<!-- BLOG LIST -->
|
||||
<div class="blog-list">
|
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{{CARDS}}
|
||||
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|
||||
|
||||
<!-- FOOTER -->
|
||||
<footer class="footer">
|
||||
<div class="footer__inner">
|
||||
<span class="footer__wordmark">TINQS</span>
|
||||
<div class="footer__links">
|
||||
<a href="/#game">Games</a>
|
||||
<a href="/#tech">Technology</a>
|
||||
<a href="/#about">About</a>
|
||||
<a href="/blog/">Blog</a>
|
||||
<a href="mailto:hello@tinqs.com">hello@tinqs.com</a>
|
||||
<a href="/press">Press Kit</a>
|
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|
||||
<p class="footer__copy">Tinqs Limited — London, est. 2020</p>
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||||
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||||
const burger = document.getElementById('navBurger');
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|
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document.body.style.overflow = open ? 'hidden' : '';
|
||||
});
|
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mobileMenu.querySelectorAll('a').forEach(link => {
|
||||
link.addEventListener('click', () => {
|
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mobileMenu.classList.remove('mobile-menu--open');
|
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burger.classList.remove('nav__burger--open');
|
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document.body.style.overflow = '';
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|
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|
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|
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|
||||
</body>
|
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</html>
|
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|
After
|
+138
@@ -0,0 +1,138 @@
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||||
<!DOCTYPE html>
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||||
<html lang="en">
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||||
<head>
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||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
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|
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<title>{{TITLE}} — Tinqs Blog</title>
|
||||
<meta name="description" content="{{DESCRIPTION}}">
|
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<meta name="robots" content="index, follow">
|
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<link rel="canonical" href="https://www.tinqs.com/blog/{{SLUG}}">
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<meta name="twitter:image" content="{{OG_IMAGE}}">
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|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "BlogPosting",
|
||||
"headline": "{{TITLE}}",
|
||||
"datePublished": "{{DATE_ISO}}",
|
||||
"author": {
|
||||
"@type": "Person",
|
||||
"name": "{{AUTHOR_NAME}}"
|
||||
},
|
||||
"publisher": {
|
||||
"@type": "Organization",
|
||||
"name": "Tinqs Limited",
|
||||
"url": "https://www.tinqs.com"
|
||||
},
|
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"description": "{{DESCRIPTION}}"
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<link rel="icon" type="image/svg+xml" href="/img/favicon.svg">
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||||
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|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400;1,500;1,600;1,700&display=swap" rel="stylesheet">
|
||||
<link rel="stylesheet" href="../style.css">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<!-- NAV -->
|
||||
<nav class="nav nav--scrolled" id="nav">
|
||||
<a href="/" class="nav__logo" aria-label="Tinqs home">
|
||||
<span class="nav__wordmark">TINQS</span>
|
||||
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|
||||
<div class="nav__links">
|
||||
<a href="/#game" class="nav__link">Games</a>
|
||||
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||||
<a href="/#about" class="nav__link">About</a>
|
||||
<a href="/blog/" class="nav__link" style="color: var(--c-accent-l);">Blog</a>
|
||||
<a href="/#signup" class="nav__link">Contact</a>
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||||
<a href="/press" class="nav__link">Press</a>
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|
||||
<span></span><span></span><span></span>
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||||
</button>
|
||||
</nav>
|
||||
|
||||
<!-- MOBILE MENU -->
|
||||
<div class="mobile-menu" id="mobileMenu">
|
||||
<a href="/#game" class="mobile-menu__link">Games</a>
|
||||
<a href="/#tech" class="mobile-menu__link">Technology</a>
|
||||
<a href="/#about" class="mobile-menu__link">About</a>
|
||||
<a href="/blog/" class="mobile-menu__link">Blog</a>
|
||||
<a href="/#signup" class="mobile-menu__link">Contact</a>
|
||||
<a href="/press" class="mobile-menu__link">Press</a>
|
||||
</div>
|
||||
|
||||
<!-- POST -->
|
||||
<article class="post">
|
||||
<a href="/blog/" class="post__back">← All Posts</a>
|
||||
<span class="post__date">{{DATE_DISPLAY}}</span>
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<h1 class="post__title">{{TITLE}}</h1>
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{{AUTHOR_ROLE}}
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||||
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||||
<!-- FOOTER -->
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||||
<footer class="footer">
|
||||
<div class="footer__inner">
|
||||
<span class="footer__wordmark">TINQS</span>
|
||||
<div class="footer__links">
|
||||
<a href="/#game">Games</a>
|
||||
<a href="/#tech">Technology</a>
|
||||
<a href="/#about">About</a>
|
||||
<a href="/blog/">Blog</a>
|
||||
<a href="mailto:hello@tinqs.com">hello@tinqs.com</a>
|
||||
<a href="/press">Press Kit</a>
|
||||
</div>
|
||||
<p class="footer__copy">Tinqs Limited — London, est. 2020</p>
|
||||
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|
||||
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||||
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|
||||
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burger.classList.toggle('nav__burger--open', open);
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document.body.style.overflow = open ? 'hidden' : '';
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});
|
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mobileMenu.querySelectorAll('a').forEach(link => {
|
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link.addEventListener('click', () => {
|
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mobileMenu.classList.remove('mobile-menu--open');
|
||||
burger.classList.remove('nav__burger--open');
|
||||
document.body.style.overflow = '';
|
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});
|
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});
|
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|
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|
||||
</body>
|
||||
</html>
|
||||
|
After
|
@@ -0,0 +1,276 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>Tinqs Studio Is an Agent Harness for Game Dev — Tinqs Blog</title>
|
||||
<meta name="description" content="An agent harness gives AI agents identity, memory, tools, and guardrails. Tinqs Studio is one built specifically for game development --- git, 3D preview, image generation, and a CLI that makes every session a warm start.">
|
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|
||||
<link rel="canonical" href="https://www.tinqs.com/blog/agent-harness">
|
||||
|
||||
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|
||||
<meta property="og:url" content="https://www.tinqs.com/blog/agent-harness">
|
||||
<meta property="og:title" content="Tinqs Studio Is an Agent Harness for Game Dev">
|
||||
<meta property="og:description" content="Tinqs Studio is an agent harness for game dev --- identity, skills, vision, git, and creative tools in one platform.">
|
||||
<meta property="og:image" content="https://www.tinqs.com/img/og-cover.jpg">
|
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|
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<meta name="twitter:card" content="summary_large_image">
|
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<meta name="twitter:title" content="Tinqs Studio Is an Agent Harness for Game Dev">
|
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<meta name="twitter:description" content="Tinqs Studio is an agent harness for game dev --- identity, skills, vision, git, and creative tools in one platform.">
|
||||
<meta name="twitter:image" content="https://www.tinqs.com/img/og-cover.jpg">
|
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<script type="application/ld+json">
|
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{
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|
||||
"headline": "Tinqs Studio Is an Agent Harness for Game Dev",
|
||||
"datePublished": "2026-05-25",
|
||||
"author": {
|
||||
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|
||||
"name": "Ozan Bozkurt"
|
||||
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|
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"publisher": {
|
||||
"@type": "Organization",
|
||||
"name": "Tinqs Limited",
|
||||
"url": "https://www.tinqs.com"
|
||||
},
|
||||
"description": "An agent harness gives AI agents identity, memory, tools, and guardrails. Tinqs Studio is one built specifically for game development --- git, 3D preview, image generation, and a CLI that makes every session a warm start."
|
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|
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|
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|
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|
||||
<link rel="stylesheet" href="../style.css">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<!-- NAV -->
|
||||
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|
||||
<a href="/" class="nav__logo" aria-label="Tinqs home">
|
||||
<span class="nav__wordmark">TINQS</span>
|
||||
</a>
|
||||
<div class="nav__links">
|
||||
<a href="/#game" class="nav__link">Games</a>
|
||||
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<span class="post__date">25 May 2026</span>
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<h1 class="post__title">Tinqs Studio Is an Agent Harness for Game Dev</h1>
|
||||
<p class="post__lead">An agent harness is the layer between a raw AI model and a useful team member. It gives the agent identity, memory, tools, and guardrails. Tinqs Studio is an agent harness built specifically for game development.
|
||||
|
||||
## What Is an Agent Harness?
|
||||
|
||||
A raw AI model — Claude, GPT, Gemini — is powerful but stateless. It doesn't know who you are, what project you're working on, what tools are available, or what happened yesterday. Every session is a cold start. Every conversation begins with "let me explain the project..."
|
||||
|
||||
An agent harness solves this. It wraps around the model and provides:
|
||||
|
||||
- <strong>Identity</strong> — who the agent is, what it values, how it should behave
|
||||
- <strong>Memory</strong> — what happened in previous sessions, what was decided, what failed
|
||||
- <strong>Tools</strong> — what the agent can actually do beyond generating text
|
||||
- <strong>Context</strong> — what project this is, who's asking, what infrastructure exists
|
||||
- <strong>Guardrails</strong> — what the agent must never do, what requires human approval
|
||||
|
||||
Without a harness, you have a chatbot. With one, you have a team member.
|
||||
|
||||
## Why Game Dev Needs Its Own Harness
|
||||
|
||||
Generic agent harnesses exist — LangChain, CrewAI, AutoGen. They're built for web apps, data pipelines, and customer support. Game development has different problems:
|
||||
|
||||
<strong>Assets are binary.</strong> A web developer's PR is a text diff. A game developer's PR is a 150MB GLB file. Generic harnesses don't know how to preview 3D models, manage LFS bandwidth, or review binary assets.
|
||||
|
||||
<strong>The pipeline is visual.</strong> Game dev goes from concept art to 3D model to rigged character to in-engine asset. Each step uses different tools — image generators, 3D modellers, auto-riggers, game engines. An agent harness for game dev needs to orchestrate this entire chain.
|
||||
|
||||
<strong>Scale is physical.</strong> A web app's complexity is in business logic. A game's complexity is in geometry — 12km worlds, 155 vegetation prototypes, 576 terrain regions, 2000 crowd instances. The agent needs to understand spatial systems, GPU memory, and frame budgets.
|
||||
|
||||
<strong>The team is small and cross-functional.</strong> A 4-person game studio has no dedicated DevOps, no dedicated artist, no dedicated PM. The harness needs to fill all those gaps, not just one.
|
||||
|
||||
## How Tinqs Studio Works as a Harness
|
||||
|
||||
Tinqs Studio is a platform built on a <a href="forking-gitea" style="color: var(–c-accent-l);">Gitea fork</a> with game-specific features layered on top. But the git platform is just the foundation. The harness is everything around it.
|
||||
|
||||
### Identity: Soul Files
|
||||
|
||||
Every agent session starts by loading a soul file — a markdown document that defines the agent's persistent identity. Not just "you are a helpful assistant" but specific values, knowledge scope, and behavioural rules.
|
||||
|
||||
The soul file means the agent behaves consistently whether it's triaging bugs at 9am or generating concept art at midnight. It knows what repos exist, who the team members are, what the game is about, and what decisions have been made. Identity isn't cosmetic — it's the difference between an agent that asks "what project is this?" and one that says "I see the vegetation grid was updated yesterday, want me to check the cache eviction?"
|
||||
|
||||
### Memory: Markdown Files in Git
|
||||
|
||||
Agent memory is plain markdown files in a git repository. No vector databases, no proprietary stores. The agent reads its memory on session start, updates it during work, and commits changes back.
|
||||
|
||||
This is deliberately low-tech. Markdown in git gives you version history, branching, merge conflict resolution, and human readability for free. When memory goes wrong — and it will — you can <code>git log</code> to see what changed and <code>git revert</code> to fix it.
|
||||
|
||||
### Tools: The CLI
|
||||
|
||||
A <a href="studio-cli" style="color: var(–c-accent-l);">single Go binary</a> gives every agent access to:
|
||||
|
||||
- <strong>Identity loading</strong> — full project context in 100ms
|
||||
- <strong>Screenshots</strong> — capture any window from outside the process
|
||||
- <strong>Cloud vision</strong> — send screenshots to a vision model, get structured descriptions
|
||||
- <strong>Health checks</strong> — verify services, repos, and tools are working
|
||||
- <strong>Service status</strong> — which URLs are live, what's reachable
|
||||
|
||||
The CLI is the agent's hands and eyes. Without it, the agent can only read and write text. With it, the agent can see the game running, photograph bugs, and verify infrastructure.
|
||||
|
||||
### Skills: Teachable Workflows
|
||||
|
||||
Skills are markdown playbooks that teach agents specific procedures. Instead of hoping the model figures out how to generate concept art or create a 3D model, you write the steps once:
|
||||
|
||||
- <a href="../skills/image-generation.md" style="color: var(–c-accent-l);">Image Generation</a> — generate game art with fal.ai Flux using a <a href="fal-image-generation" style="color: var(–c-accent-l);">4-layer prompt pattern</a>
|
||||
- <a href="../skills/concept-art-pipeline.md" style="color: var(–c-accent-l);">Concept Art Pipeline</a> — from design brief through 2D art to 3D model export
|
||||
- <a href="../skills/tripo-browser-workflow.md" style="color: var(–c-accent-l);">3D Model Generation</a> — Tripo Studio text-to-3D and image-to-3D
|
||||
- <a href="../skills/sora2-video.md" style="color: var(–c-accent-l);">Video Generation</a> — trailer clips with OpenAI Sora 2
|
||||
|
||||
Skills compound. Every playbook you write makes the agent more capable. After six months, our agents handle art generation, competitive research, video production, project management, and code review — all from markdown files.
|
||||
|
||||
### Git Platform: 3D Preview and LFS
|
||||
|
||||
The <a href="forking-gitea" style="color: var(–c-accent-l);">Gitea fork</a> underneath handles the game-specific git problems:
|
||||
|
||||
- <strong>3D asset preview</strong> — rotate GLB/FBX/STL files in the browser during code review
|
||||
- <strong>LFS-first workflows</strong> — auto-tracking for game file extensions, storage dashboards
|
||||
- <strong>OAuth2 SSO</strong> — one login for git, tools, and the game
|
||||
- <strong>22 format support</strong> — GLB, FBX, OBJ, STL, 3DS, PLY, and more via O3DV
|
||||
|
||||
### Guardrails: Human-in-the-Loop
|
||||
|
||||
The harness defines what agents can and cannot do:
|
||||
|
||||
- Agents can file issues, draft announcements, generate assets, write code
|
||||
- Agents <strong>cannot</strong> merge code, deploy builds, push to public repos, or post to external channels without human approval
|
||||
- The <a href="https://tinqs.com/tinqs/blog" style="color: var(–c-accent-l);">public blog repo</a> requires human-approved merge requests — agents can propose changes but a person must review
|
||||
|
||||
This isn't a limitation — it's a feature. The agent handles volume; the human handles judgement.
|
||||
|
||||
## The Cold Start Problem
|
||||
|
||||
The biggest problem with AI agents in production isn't capability — it's context. Every new session is blank. The agent doesn't know what happened yesterday, what's in progress, or what tools are available.
|
||||
|
||||
Most teams solve this with long system prompts. That works until your context is 200 markdown files, 15 skills, and 3 years of project history. You can't paste all of that into a system prompt.
|
||||
|
||||
The harness solves this with <strong>staged loading</strong>:
|
||||
|
||||
1. <strong>CLI identity call</strong> (100ms) — loads soul file, company context, machine info, service status
|
||||
2. <strong>Memory file</strong> (instant) — loads cross-session context
|
||||
3. <strong>Skills</strong> (on demand) — loaded only when the task matches a skill name
|
||||
4. <strong>Repo context</strong> (on demand) — read files as needed, not all upfront
|
||||
|
||||
The agent goes from cold to fully contextual in under a second. No "let me explain..." No re-reading the same onboarding doc. Just start working.
|
||||
|
||||
## What Makes This Different from LangChain
|
||||
|
||||
LangChain, CrewAI, and similar frameworks are <strong>code-first</strong>. You define agents in Python, chain them with function calls, and deploy them as services. They're powerful for building AI products.
|
||||
|
||||
Tinqs Studio is <strong>file-first</strong>. Agents are defined in markdown. Skills are markdown. Memory is markdown. Identity is markdown. Everything is in git, readable by humans, editable without code changes, and version-controlled.
|
||||
|
||||
This matters for game teams because:
|
||||
|
||||
- <strong>Non-engineers can contribute.</strong> The designer writes a skill for concept art. The PM writes a skill for sprint planning. No Python required.
|
||||
- <strong>Everything is auditable.</strong> <code>git log</code> shows who changed what, when, and why. Memory changes are commits. Skill updates are diffs.
|
||||
- <strong>It works with any AI tool.</strong> The same soul files and skills work in Cursor, Claude Code, or any tool that reads markdown. You're not locked into one framework.
|
||||
|
||||
## The Stack
|
||||
|
||||
| Layer | What | How |
|
||||
|——-|——|—–|
|
||||
| <strong>Identity</strong> | Soul files, company context | Markdown in git, loaded via CLI |
|
||||
| <strong>Memory</strong> | Cross-session context | Markdown in git, updated by agents |
|
||||
| <strong>Skills</strong> | Teachable workflows | Markdown playbooks, loaded on demand |
|
||||
| <strong>Tools</strong> | CLI, screenshots, vision | Go binary, one install per machine |
|
||||
| <strong>Git</strong> | 3D preview, LFS, SSO | Gitea fork with game-specific features |
|
||||
| <strong>Creative</strong> | Image gen, 3D models, video | fal.ai, Tripo, Sora 2 via skills |
|
||||
| <strong>Guardrails</strong> | Human approval gates | Branch protection, MR requirements |
|
||||
|
||||
## Getting Started
|
||||
|
||||
If you want to build your own agent harness for game dev:
|
||||
|
||||
1. <strong>Start with a soul file.</strong> Write 50 words about your project's identity, values, and scope. Put it in your repo root as <code>SOUL.md</code>.
|
||||
2. <strong>Write one skill.</strong> Pick the workflow you repeat most — concept art generation, bug triage, build verification — and write the steps as markdown.
|
||||
3. <strong>Build a CLI identity command.</strong> Even a shell script that prints "project name, repos, services" gives your agent a warm start.
|
||||
4. <strong>Put everything in git.</strong> Not a database, not a SaaS tool. Git. You already have it.
|
||||
|
||||
The rest — 3D preview, LFS management, OAuth SSO, creative pipelines — you can add as you need it. Or use <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a>, where we've already built it.
|
||||
|
||||
—
|
||||
|
||||
An agent harness isn't a product category yet. But it should be. The gap between "I have an AI model" and "I have an AI team member" is infrastructure — identity, memory, tools, context, guardrails. For game development, that infrastructure needs to understand binary assets, visual pipelines, and spatial systems. That's what we're building.</p>
|
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<div class="post__author-avatar">OB</div>
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<div class="post__author-info">
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<span class="post__author-name">Ozan Bozkurt</span><br>
|
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CTO & Developer, Tinqs
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<span class="post__date">6 March 2026</span>
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||||
<h1 class="post__title">How a Small Game Studio Runs on AI Agents</h1>
|
||||
<p class="post__lead">We gave our AI agents persistent identities, skill playbooks, and access to our entire knowledge base. This is how a 4-person game studio built an agentic workflow that punches above its weight.</p>
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<div class="post__body">
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<h2>The Problem Every Small Studio Knows</h2>
|
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<p>When you're four people building a game, there's no room for a dedicated DevOps person, a full-time PM tool chain, or someone whose job it is to "keep things organised." Everyone wears five hats. Documentation drifts. Issues pile up. The left hand doesn't know what the right hand shipped.</p>
|
||||
<p>We tried the usual tools — Notion, Trello, shared Google Docs. They all had the same problem: they're passive. They sit there and wait for a human to update them. In a team of four where the lead developer is also the CTO, that human never has time.</p>
|
||||
<p>So we built something different. We gave AI agents persistent identities, connected them to our entire knowledge base, and let them become working members of the team.</p>
|
||||
<h2>The Architecture: Agents with Identity</h2>
|
||||
<p>Our primary AI agent runs inside the IDE and has access to the full documentation repository — the game design document, backlog, meeting notes, company operations, everything. It's not a chatbot. It's a persistent team member with a <strong>soul file</strong> that defines its values and operating principles, and a <strong>memory file</strong> that persists context across sessions.</p>
|
||||
<p>The key insight: <strong>all knowledge lives in markdown files in one repo</strong>. No databases, no SaaS dashboards, no proprietary formats. Plain text, version-controlled, readable by humans and agents alike. When anyone on the team opens the docs repo, the agent wakes up with full context of who they are, what machine they're on, and what's been happening.</p>
|
||||
<h3>What the agent actually does</h3>
|
||||
<ul>
|
||||
<li>Triages and grooms the issue backlog</li>
|
||||
<li>Keeps documentation in sync with the game state</li>
|
||||
<li>Processes bug reports from testers and creates structured issues</li>
|
||||
<li>Drafts team announcements, reviews PRs, manages cross-repo coordination</li>
|
||||
<li>Generates concept art, trailer frames, and UI assets using integrated API skills</li>
|
||||
<li>Conducts competitive research — analysing Steam pages, player reviews, pricing strategies</li>
|
||||
</ul>
|
||||
<p>The team talks to the agent through voice. The IDE's built-in microphone transcribes and auto-translates (multilingual team). The agent is trained to interpret messy voice-to-text artifacts and act on intent, not grammar.</p>
|
||||
<h2>Background Automation</h2>
|
||||
<p>The interactive agent only runs when someone opens the IDE. But a studio doesn't sleep — bugs get reported at midnight, issues go stale, and the team chat fills up while everyone's away.</p>
|
||||
<p>A background daemon runs 24/7, ticking every 15 minutes. It uses a three-tier model strategy — cheap models for routine checks, medium for analysis, and premium only when it needs deep reasoning. The whole thing costs about $15/day.</p>
|
||||
<h3>What it handles</h3>
|
||||
<ul>
|
||||
<li><strong>Chat monitoring</strong> — polls team chat, responds to commands, acknowledges messages</li>
|
||||
<li><strong>Bug intake</strong> — when a tester reports a bug in chat, creates a structured issue automatically</li>
|
||||
<li><strong>Stale issue detection</strong> — flags issues that haven't been touched, nudges the team</li>
|
||||
<li><strong>Daily summaries</strong> — posts a morning digest of what happened overnight</li>
|
||||
<li><strong>Self-learning</strong> — creates its own skill files when it discovers better approaches</li>
|
||||
</ul>
|
||||
<p>The two agents coordinate through the docs repo itself. One writes, the other reads. No API calls between them, no message queue. Just git.</p>
|
||||
<h2>The Skill System</h2>
|
||||
<p>Agents don't just have instructions — they have <strong>skills</strong>. Each skill is a markdown file that teaches the agent a specific workflow: how to generate concept art through a pipeline, how to use image generation APIs, how to conduct competitive research, how to create 3D models from concept art.</p>
|
||||
<p>When someone asks the agent to do something that matches a skill, it reads the skill file and follows the procedure. This means you can teach the agent new capabilities without changing any code — just write a new markdown file.</p>
|
||||
<p>We've open-sourced several of our skills in this repo:</p>
|
||||
<ul>
|
||||
<li><a href="../skills/image-generation.md" style="color: var(–c-accent-l);">Image Generation with fal.ai</a></li>
|
||||
<li><a href="../skills/concept-art-pipeline.md" style="color: var(–c-accent-l);">Concept Art Pipeline</a></li>
|
||||
<li><a href="../skills/tripo-browser-workflow.md" style="color: var(–c-accent-l);">3D Model Generation with Tripo</a></li>
|
||||
<li><a href="../skills/sora2-video.md" style="color: var(–c-accent-l);">Video Generation with Sora 2</a></li>
|
||||
</ul>
|
||||
<h2>Soul Files: Why Identity Matters</h2>
|
||||
<p>Giving the agent a persistent identity isn't theatre. It creates consistency across sessions. The soul file defines:</p>
|
||||
<ul>
|
||||
<li><strong>Values</strong> — what the agent prioritises (e.g., "never break the build," "always verify before acting")</li>
|
||||
<li><strong>Knowledge scope</strong> — what repos, services, and team members exist</li>
|
||||
<li><strong>Behavioural rules</strong> — how to handle ambiguity, when to ask vs act, what requires human approval</li>
|
||||
</ul>
|
||||
<p>The agent remembers what it learned, adapts to who's asking, and maintains the same principles whether it's triaging bugs or drafting a Steam page description. The soul file is the agent's constitution.</p>
|
||||
<h2>What We've Learned</h2>
|
||||
<p><strong>Plain text is the universal API.</strong> Every tool, every agent, every human can read a markdown file. We store everything — design documents, meeting notes, agent memory, team contacts — as .md files in one repository. This sounds almost too simple, but it eliminates an entire class of integration problems.</p>
|
||||
<p><strong>Cheap models for routine, expensive models for thinking.</strong> Most of what an autonomous agent does is pattern matching and text formatting — you don't need the most expensive model for that. Save the premium tokens for decisions that actually require reasoning.</p>
|
||||
<p><strong>The human stays in the loop for decisions.</strong> The agents can file issues, draft announcements, and generate assets — but they don't merge code, deploy builds, or post to public channels without explicit approval. The workflow is designed so the AI handles the grunt work while humans make the calls that matter.</p>
|
||||
<p><strong>Voice input changes everything.</strong> When you can describe a bug while looking at the game screen, and the agent transcribes, interprets, and files an issue — that's a workflow that collapses the distance between noticing a problem and tracking it.</p>
|
||||
<p><strong>Skills compound.</strong> Every skill file you write makes the agent more capable. After 6 months, our agents have 15+ skills covering art generation, competitive research, video production, and project management. Each one took 30 minutes to write and saves hours every week.</p>
|
||||
<h2>The Numbers</h2>
|
||||
<ul>
|
||||
<li><strong>Team size:</strong> 4 humans + AI agents</li>
|
||||
<li><strong>Background agent cost:</strong> ~$15/day (~$450/month)</li>
|
||||
<li><strong>Knowledge files:</strong> 200+ markdown documents</li>
|
||||
<li><strong>Skills:</strong> 15+ agent skill files and growing</li>
|
||||
<li><strong>Infrastructure:</strong> Multiple machines, self-hosted git, zero DevOps engineers</li>
|
||||
</ul>
|
||||
<hr>
|
||||
<p>We're not claiming this is how every studio should work. But for a small team trying to build something ambitious, having AI agents that actually understand the project — not just answer questions about it — has been transformative. The agents don't replace anyone on the team. They make it possible for four people to do the work of forty.</p>
|
||||
<p>We're building all of this as part of <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — a game development platform that brings git hosting, AI tools, and team workflows together. The blog posts and skills in this repo are part of that journey.</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
||||
</div>
|
||||
</div>
|
||||
</article>
|
||||
|
||||
<!-- FOOTER -->
|
||||
<footer class="footer">
|
||||
<div class="footer__inner">
|
||||
<span class="footer__wordmark">TINQS</span>
|
||||
<div class="footer__links">
|
||||
<a href="/#game">Games</a>
|
||||
<a href="/#tech">Technology</a>
|
||||
<a href="/#about">About</a>
|
||||
<a href="/blog/">Blog</a>
|
||||
<a href="mailto:hello@tinqs.com">hello@tinqs.com</a>
|
||||
<a href="/press">Press Kit</a>
|
||||
</div>
|
||||
<p class="footer__copy">Tinqs Limited — London, est. 2020</p>
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script>
|
||||
const burger = document.getElementById('navBurger');
|
||||
const mobileMenu = document.getElementById('mobileMenu');
|
||||
burger.addEventListener('click', () => {
|
||||
const open = mobileMenu.classList.toggle('mobile-menu--open');
|
||||
burger.classList.toggle('nav__burger--open', open);
|
||||
document.body.style.overflow = open ? 'hidden' : '';
|
||||
});
|
||||
mobileMenu.querySelectorAll('a').forEach(link => {
|
||||
link.addEventListener('click', () => {
|
||||
mobileMenu.classList.remove('mobile-menu--open');
|
||||
burger.classList.remove('nav__burger--open');
|
||||
document.body.style.overflow = '';
|
||||
});
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
After
|
@@ -0,0 +1,223 @@
|
||||
#!/usr/bin/env node
|
||||
/**
|
||||
* Blog build script — converts markdown posts to static HTML.
|
||||
*
|
||||
* Usage: node build.js
|
||||
* Input: posts/*.md (markdown with YAML frontmatter)
|
||||
* Output: *.html (from _template.html + _index_template.html)
|
||||
*
|
||||
* Zero external dependencies — uses only Node.js built-ins.
|
||||
* Markdown conversion is intentionally minimal (handles the subset
|
||||
* our posts actually use). For richer formatting, swap in marked/markdown-it.
|
||||
*/
|
||||
|
||||
const fs = require("fs");
|
||||
const path = require("path");
|
||||
|
||||
const BLOG_DIR = __dirname;
|
||||
const POSTS_DIR = path.join(BLOG_DIR, "posts");
|
||||
const TEMPLATE = fs.readFileSync(path.join(BLOG_DIR, "_template.html"), "utf8");
|
||||
const INDEX_TEMPLATE = fs.readFileSync(
|
||||
path.join(BLOG_DIR, "_index_template.html"),
|
||||
"utf8"
|
||||
);
|
||||
|
||||
// ── Frontmatter parser ──────────────────────────────────────────────
|
||||
|
||||
function parseFrontmatter(src) {
|
||||
const match = src.match(/^---\r?\n([\s\S]*?)\r?\n---\r?\n([\s\S]*)$/);
|
||||
if (!match) throw new Error("Missing YAML frontmatter");
|
||||
const meta = {};
|
||||
for (const line of match[1].split("\n")) {
|
||||
const idx = line.indexOf(":");
|
||||
if (idx === -1) continue;
|
||||
const key = line.slice(0, idx).trim();
|
||||
let val = line.slice(idx + 1).trim();
|
||||
// strip surrounding quotes
|
||||
if ((val.startsWith('"') && val.endsWith('"')) || (val.startsWith("'") && val.endsWith("'"))) {
|
||||
val = val.slice(1, -1);
|
||||
}
|
||||
meta[key] = val;
|
||||
}
|
||||
return { meta, body: match[2] };
|
||||
}
|
||||
|
||||
// ── Minimal Markdown → HTML ─────────────────────────────────────────
|
||||
|
||||
function md(src) {
|
||||
const lines = src.split("\n");
|
||||
let html = "";
|
||||
let inUl = false;
|
||||
let inCode = false;
|
||||
let codeLang = "";
|
||||
let codeLines = [];
|
||||
|
||||
function closeUl() {
|
||||
if (inUl) { html += "</ul>\n"; inUl = false; }
|
||||
}
|
||||
|
||||
for (let i = 0; i < lines.length; i++) {
|
||||
const line = lines[i];
|
||||
|
||||
// Fenced code blocks
|
||||
if (line.startsWith("```")) {
|
||||
if (!inCode) {
|
||||
closeUl();
|
||||
inCode = true;
|
||||
codeLang = line.slice(3).trim();
|
||||
codeLines = [];
|
||||
continue;
|
||||
} else {
|
||||
html += `<pre><code${codeLang ? ` class="language-${codeLang}"` : ""}>${codeLines.join("\n")}</code></pre>\n`;
|
||||
inCode = false;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
if (inCode) {
|
||||
codeLines.push(escapeHtml(line));
|
||||
continue;
|
||||
}
|
||||
|
||||
// Blank line
|
||||
if (line.trim() === "") {
|
||||
closeUl();
|
||||
continue;
|
||||
}
|
||||
|
||||
// Headings
|
||||
const hMatch = line.match(/^(#{1,6})\s+(.*)$/);
|
||||
if (hMatch) {
|
||||
closeUl();
|
||||
const level = hMatch[1].length;
|
||||
html += `<h${level}>${inline(hMatch[2])}</h${level}>\n`;
|
||||
continue;
|
||||
}
|
||||
|
||||
// Horizontal rule
|
||||
if (/^---+$/.test(line.trim())) {
|
||||
closeUl();
|
||||
html += "<hr>\n";
|
||||
continue;
|
||||
}
|
||||
|
||||
// Figure ( on its own line)
|
||||
const figMatch = line.match(/^!\[([^\]]*)\]\(([^)]+)\)$/);
|
||||
if (figMatch) {
|
||||
closeUl();
|
||||
html += `<figure>\n <img src="${figMatch[2]}" alt="${escapeHtml(figMatch[1])}">\n <figcaption>${inline(figMatch[1])}</figcaption>\n</figure>\n`;
|
||||
continue;
|
||||
}
|
||||
|
||||
// Unordered list
|
||||
const liMatch = line.match(/^[-*]\s+(.*)$/);
|
||||
if (liMatch) {
|
||||
if (!inUl) { html += "<ul>\n"; inUl = true; }
|
||||
html += ` <li>${inline(liMatch[1])}</li>\n`;
|
||||
continue;
|
||||
}
|
||||
|
||||
// Paragraph
|
||||
closeUl();
|
||||
html += `<p>${inline(line)}</p>\n`;
|
||||
}
|
||||
|
||||
closeUl();
|
||||
return html;
|
||||
}
|
||||
|
||||
function escapeHtml(s) {
|
||||
return s.replace(/&/g, "&").replace(/</g, "<").replace(/>/g, ">");
|
||||
}
|
||||
|
||||
function inline(s) {
|
||||
// Bold
|
||||
s = s.replace(/\*\*(.+?)\*\*/g, "<strong>$1</strong>");
|
||||
// Italic
|
||||
s = s.replace(/\*(.+?)\*/g, "<em>$1</em>");
|
||||
// Inline code
|
||||
s = s.replace(/`([^`]+)`/g, "<code>$1</code>");
|
||||
// Links
|
||||
s = s.replace(/\[([^\]]+)\]\(([^)]+)\)/g, '<a href="$2" style="color: var(--c-accent-l);">$1</a>');
|
||||
// Em dash
|
||||
s = s.replace(/---/g, "—");
|
||||
s = s.replace(/--/g, "–");
|
||||
return s;
|
||||
}
|
||||
|
||||
// ── Date formatting ─────────────────────────────────────────────────
|
||||
|
||||
function formatDate(isoDate) {
|
||||
const d = new Date(isoDate + "T00:00:00Z");
|
||||
const months = [
|
||||
"January", "February", "March", "April", "May", "June",
|
||||
"July", "August", "September", "October", "November", "December",
|
||||
];
|
||||
return `${d.getUTCDate()} ${months[d.getUTCMonth()]} ${d.getUTCFullYear()}`;
|
||||
}
|
||||
|
||||
// ── Build ───────────────────────────────────────────────────────────
|
||||
|
||||
function buildPost(file) {
|
||||
const src = fs.readFileSync(path.join(POSTS_DIR, file), "utf8");
|
||||
const { meta, body } = parseFrontmatter(src);
|
||||
|
||||
// Split lead (first paragraph) from rest
|
||||
const parts = body.split(/\n\n/);
|
||||
const lead = parts[0].trim();
|
||||
const rest = parts.slice(1).join("\n\n");
|
||||
|
||||
const html = TEMPLATE
|
||||
.replace(/\{\{TITLE\}\}/g, meta.title)
|
||||
.replace(/\{\{DESCRIPTION\}\}/g, meta.description || meta.title)
|
||||
.replace(/\{\{OG_DESCRIPTION\}\}/g, meta.og_description || meta.description || meta.title)
|
||||
.replace(/\{\{OG_IMAGE\}\}/g, meta.og_image || "https://www.tinqs.com/img/og-cover.jpg")
|
||||
.replace(/\{\{SLUG\}\}/g, meta.slug)
|
||||
.replace(/\{\{DATE_ISO\}\}/g, meta.date)
|
||||
.replace(/\{\{DATE_DISPLAY\}\}/g, formatDate(meta.date))
|
||||
.replace(/\{\{LEAD\}\}/g, inline(lead))
|
||||
.replace(/\{\{BODY\}\}/g, md(rest))
|
||||
.replace(/\{\{AUTHOR_NAME\}\}/g, meta.author || "Ozan Bozkurt")
|
||||
.replace(/\{\{AUTHOR_INITIALS\}\}/g, meta.author_initials || "OB")
|
||||
.replace(/\{\{AUTHOR_ROLE\}\}/g, meta.author_role || "CTO & Developer, Tinqs");
|
||||
|
||||
const outPath = path.join(BLOG_DIR, `${meta.slug}.html`);
|
||||
fs.writeFileSync(outPath, html);
|
||||
console.log(` ${file} → ${meta.slug}.html`);
|
||||
return meta;
|
||||
}
|
||||
|
||||
function buildIndex(posts) {
|
||||
// Sort newest first
|
||||
posts.sort((a, b) => b.date.localeCompare(a.date));
|
||||
|
||||
const cards = posts
|
||||
.map(
|
||||
(p) => `
|
||||
<a href="${p.slug}" class="blog-card">
|
||||
<span class="blog-card__date">${formatDate(p.date)}</span>
|
||||
<h2 class="blog-card__title">${p.title}</h2>
|
||||
<p class="blog-card__excerpt">${p.excerpt || p.description}</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>`
|
||||
)
|
||||
.join("\n");
|
||||
|
||||
const html = INDEX_TEMPLATE.replace("{{CARDS}}", cards);
|
||||
fs.writeFileSync(path.join(BLOG_DIR, "index.html"), html);
|
||||
console.log(" index.html (listing)");
|
||||
}
|
||||
|
||||
// ── Main ────────────────────────────────────────────────────────────
|
||||
|
||||
console.log("Building blog...");
|
||||
|
||||
if (!fs.existsSync(POSTS_DIR)) {
|
||||
console.error("No posts/ directory found.");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const files = fs.readdirSync(POSTS_DIR).filter((f) => f.endsWith(".md")).sort();
|
||||
const posts = files.map(buildPost);
|
||||
buildIndex(posts);
|
||||
|
||||
console.log(`Done — ${posts.length} posts built.`);
|
||||
@@ -0,0 +1,231 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>Building a Cloud Agent Harness with DeepSeek V4 and Pi — Tinqs Blog</title>
|
||||
<meta name="description" content="We forked Pi, merged a browser dashboard into the monorepo, and built a Go orchestrator inside our Gitea fork. Agents code overnight for about $0.80 — and you can watch them from localhost:33634.">
|
||||
<meta name="robots" content="index, follow">
|
||||
<link rel="canonical" href="https://www.tinqs.com/blog/cloud-harness">
|
||||
|
||||
<meta property="og:type" content="article">
|
||||
<meta property="og:url" content="https://www.tinqs.com/blog/cloud-harness">
|
||||
<meta property="og:title" content="Building a Cloud Agent Harness with DeepSeek V4 and Pi">
|
||||
<meta property="og:description" content="Pi fork, merged agent dashboard, and a Go orchestrator inside Tinqs Studio.">
|
||||
<meta property="og:image" content="https://www.tinqs.com/blog/img/cloud-harness-architecture.png">
|
||||
|
||||
<meta name="twitter:card" content="summary_large_image">
|
||||
<meta name="twitter:title" content="Building a Cloud Agent Harness with DeepSeek V4 and Pi">
|
||||
<meta name="twitter:description" content="Pi fork, merged agent dashboard, and a Go orchestrator inside Tinqs Studio.">
|
||||
<meta name="twitter:image" content="https://www.tinqs.com/blog/img/cloud-harness-architecture.png">
|
||||
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "BlogPosting",
|
||||
"headline": "Building a Cloud Agent Harness with DeepSeek V4 and Pi",
|
||||
"datePublished": "2026-05-26",
|
||||
"author": {
|
||||
"@type": "Person",
|
||||
"name": "Ozan Bozkurt"
|
||||
},
|
||||
"publisher": {
|
||||
"@type": "Organization",
|
||||
"name": "Tinqs Limited",
|
||||
"url": "https://www.tinqs.com"
|
||||
},
|
||||
"description": "We forked Pi, merged a browser dashboard into the monorepo, and built a Go orchestrator inside our Gitea fork. Agents code overnight for about $0.80 — and you can watch them from localhost:33634."
|
||||
}
|
||||
</script>
|
||||
|
||||
<!-- PostHog (EU) -->
|
||||
<script>
|
||||
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<a href="/blog/" class="post__back">← All Posts</a>
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<span class="post__date">26 May 2026</span>
|
||||
<h1 class="post__title">Building a Cloud Agent Harness with DeepSeek V4 and Pi</h1>
|
||||
<p class="post__lead">We spent a few sessions building something that still barely exists elsewhere: a cloud agent harness where AI coding agents are first-class citizens of the platform, not bolt-on tools. The stack is a <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">Pi fork</a> for the brain, a Go orchestrator inside our <a href="https://tinqs.com/tinqs/studio" style="color: var(–c-accent-l);">Gitea fork</a> for overnight work, and a browser dashboard merged into Pi for the daytime. Here is how it fits together.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Problem</h2>
|
||||
<p>Every coding agent today — Claude Code, Codex, Pi, Aider — runs in your terminal. You watch it work. You close the laptop, it stops. There is no way to say "build these eight features overnight" and wake up to pull requests.</p>
|
||||
<p>We wanted exactly that. Not a coding assistant. An autonomous workforce — with a UI when a human needs to be in the loop.</p>
|
||||
<h2>Why Not Just Use Claude Code or Codex?</h2>
|
||||
<p><strong>Cost.</strong> Claude Code runs on Opus at $15/MTok output. Codex uses GPT 5.5. Running eight agents overnight on either would cost $50–200. DeepSeek V4 Flash costs $0.28/MTok output. Eight overnight tasks: <strong>about $0.80</strong>.</p>
|
||||
<p><strong>Control.</strong> Cloud tools are black boxes. We cannot add a Gitea API tool, a fal.ai image generator, or a guardrail that blocks <code>aws ec2 terminate-instances</code>. With our own harness, we add an extension and it is live.</p>
|
||||
<p><strong>Platform.</strong> We are building <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — a Gitea-based game development platform. Agents are not a feature we want to outsource. They are the product.</p>
|
||||
<h2>Pi — The Agent Brain</h2>
|
||||
<p><a href="https://pi.dev" style="color: var(–c-accent-l);">Pi</a> is an open-source coding agent by Mario Zechner. MIT license, TypeScript, minimal by design — four core tools (read, write, edit, bash) and an extension system.</p>
|
||||
<p>We <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">forked it</a>. Not to rewrite the core — to add first-party extensions:</p>
|
||||
<ul>
|
||||
<li><strong>tinqs-provider</strong> — routes DeepSeek V4 Flash and Pro through our inference proxy</li>
|
||||
<li><strong>tinqs-tools</strong> — Gitea REST API, fal.ai image generation, Amazon Nova Lite vision</li>
|
||||
<li><strong>tinqs-ci</strong> — reads CI pipeline status, logs, and polls for completion</li>
|
||||
<li><strong>tinqs-guardrail</strong> — 29 safety patterns that block dangerous operations</li>
|
||||
</ul>
|
||||
<p>Each extension is a single TypeScript file. No extra npm dependencies on the extension side.</p>
|
||||
<p>Pi has four output modes. The one that matters for automation is <strong>RPC</strong> — a headless process that accepts JSON on stdin/stdout. That is how the orchestrator drives it.</p>
|
||||
<h2>DeepSeek V4 — The LLM</h2>
|
||||
<p>DeepSeek V4 Flash through our own inference proxy. OpenAI-compatible API, so Pi treats it like any other provider. The proxy adds:</p>
|
||||
<ul>
|
||||
<li>Redis job queue (10 concurrent workers)</li>
|
||||
<li>Per-user usage tracking</li>
|
||||
<li>System prompt injection for cache hit optimization</li>
|
||||
<li>Gitea PAT authentication (same token as git push)</li>
|
||||
</ul>
|
||||
<p>Cost per task: <strong>$0.02–0.10</strong> depending on complexity.</p>
|
||||
<h2>Go Orchestrator — Overnight Batch Work</h2>
|
||||
<p>Inside <code>tinqs/studio</code> we added <code>modules/agents/</code> — a Go worker pool that:</p>
|
||||
<ul>
|
||||
<li>Spawns Pi with <code>–mode rpc –no-session</code></li>
|
||||
<li>Tracks task lifecycle (pending → running → done)</li>
|
||||
<li>Streams events over <strong>SSE</strong> to any connected UI</li>
|
||||
<li>Enforces guardrails at the platform layer (worker limits, timeouts)</li>
|
||||
</ul>
|
||||
<p>Six HTTP endpoints, same auth as git push:</p>
|
||||
<pre><code>POST /api/v1/agents/tasks — submit a task
|
||||
GET /api/v1/agents/tasks — list all tasks
|
||||
GET /api/v1/agents/tasks/{id} — get task details
|
||||
DELETE /api/v1/agents/tasks/{id} — stop a task
|
||||
GET /api/v1/agents/stream — SSE live events
|
||||
GET /api/v1/agents/health — orchestrator status</code></pre>
|
||||
<p>We considered bolting on a separate orchestration SaaS and rejected it. The orchestrator lives in the same binary as git — same auth, no extra service to deploy.</p>
|
||||
<p>The intended loop:</p>
|
||||
<pre><code>Orchestrator reads task brief
|
||||
→ spawns pi --mode rpc
|
||||
→ Pi writes code using DeepSeek V4
|
||||
→ Pi pushes branch, calls ci_wait
|
||||
→ CI green → Pi opens PR via gitea_api
|
||||
→ CI red → Pi reads ci_logs, fixes, retries
|
||||
→ Human reviews PR, merges</code></pre>
|
||||
<p>Git worktree integration and full push/PR automation are still being wired; the API and worker pool already run locally.</p>
|
||||
<h2>Pi Dashboard — Browser UI (Shipped)</h2>
|
||||
<p>The cloud orchestrator is for batch work while you sleep. During the day you want to see agents, chat with them, and spawn sessions without living in a terminal.</p>
|
||||
<p>We merged <a href="https://github.com/BlackBeltTechnology/pi-agent-dashboard" style="color: var(–c-accent-l);">pi-agent-dashboard</a> into the Pi monorepo — not as a second repo to install. One checkout, one command:</p>
|
||||
<pre><code class="language-bash">npm run dashboard:dev</code></pre>
|
||||
<p>Open <strong>http://localhost:33634</strong>. You get:</p>
|
||||
<ul>
|
||||
<li><strong>Live session streaming</strong> — watch tool calls and model output in real time</li>
|
||||
<li><strong>Interactive chat</strong> — send prompts, answer <code>ask_user</code> dialogs from the browser</li>
|
||||
<li><strong>Session spawning</strong> — start Pi in any pinned project folder</li>
|
||||
<li><strong>Cost tracking</strong> — per-session token usage when using Tinqs inference</li>
|
||||
<li><strong>Plugins</strong> — flows, subagents, workspace helpers</li>
|
||||
</ul>
|
||||
<p>The dashboard talks to Pi sessions over a WebSocket bridge on port <strong>9999</strong>. Inference uses the same Tinqs proxy as the CLI — register a custom provider in <code>~/.pi/agent/providers.json</code> and authenticate with your existing <code>tstudio</code> token. No separate LLM API keys.</p>
|
||||
<pre><code>Dashboard (localhost:33634)
|
||||
↕ WebSocket (port 9999)
|
||||
Pi sessions (interactive or headless)
|
||||
↕ OpenAI-compatible API
|
||||
Tinqs Studio proxy (tinqs.com/api/v1/ai)
|
||||
↕ DeepSeek V4 Flash / Pro</code></pre>
|
||||
<p>When Studio runs locally with agents enabled, the dashboard can also talk to the orchestrator API on port 3000 — submit tasks and watch SSE events in the same UI.</p>
|
||||
<p>One browser tab for daytime work; the orchestrator queue for overnight runs.</p>
|
||||
<h2>The Guardrail</h2>
|
||||
<p>Our biggest fear: an agent hallucinating instead of using tools, or running <code>aws ec2 terminate-instances</code> at 3 AM.</p>
|
||||
<p>The guardrail extension monitors every agent turn:</p>
|
||||
<p><strong>Hallucination detection</strong> — if the agent claims file contents without calling <code>read</code>, it gets corrected.</p>
|
||||
<p><strong>No-tool drift</strong> — three consecutive turns without a tool call triggers a warning.</p>
|
||||
<p><strong>Command blocking</strong> — 29 patterns covering destructive git, AWS teardown, process killing, and production API abuse.</p>
|
||||
<h2>What It Cost to Build</h2>
|
||||
<p>A few focused sessions: about 2,000 lines of Go, 900 lines of TypeScript extensions, 52 tests, plus merging the dashboard packages into the Pi monorepo. No new servers — Pi is a Node subprocess; the dashboard is another Node process on your machine.</p>
|
||||
<h2>What Is Next</h2>
|
||||
<p>| Piece | Status |</p>
|
||||
<p>|——-|——–|</p>
|
||||
<p>| Pi fork + tinqs extensions | Shipped |</p>
|
||||
<p>| Dashboard merged into Pi monorepo | Shipped |</p>
|
||||
<p>| Go orchestrator + REST/SSE API | MVP, running locally |</p>
|
||||
<p>| Git worktree + push + PR loop | In progress |</p>
|
||||
<p>| Domain routing (game / sim / platform tasks) | Designed |</p>
|
||||
<p>Next we are promoting studio skills from IDE playbooks into orchestrator prompt packs — so the same Pi worker behaves like a game builder, sim maintainer, or platform engineer depending on the task. Specialized agents (planner, reviewer, asset pipeline) sit on top of this foundation.</p>
|
||||
<p>The harness — inference proxy, guardrails, dashboard, orchestrator API — is in place. The work now is feeding it real tasks and hardening the git loop.</p>
|
||||
<hr>
|
||||
<p><em>Tinqs Studio is an open platform for game development — git hosting, AI inference, asset generation, and autonomous agents. We are building <a href="https://arikigame.com" style="color: var(–c-accent-l);">Ariki</a>, a survival colony sim, using the same tools we ship.</em></p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
||||
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<title>AI Art at Scale: Using fal.ai Flux for Game Asset Generation — Tinqs Blog</title>
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<a href="/blog/" class="post__back">← All Posts</a>
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||||
<span class="post__date">25 May 2026</span>
|
||||
<h1 class="post__title">AI Art at Scale: Using fal.ai Flux for Game Asset Generation</h1>
|
||||
<p class="post__lead">We're a small indie studio building a survival colony sim. We don't have a concept artist on staff. Every piece of character art, trailer frame, and UI icon in our game was generated with fal.ai Flux models — at roughly a penny per image.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Problem with AI Art for Games</h2>
|
||||
<p>Most AI image generators produce beautiful images that are completely useless for game development. They look great on social media but fall apart when you need consistency: the same character from four angles, a UI icon that reads at 64x64, a trailer frame that matches your game's art style rather than whatever the model defaults to.</p>
|
||||
<p>The issue isn't the models — Flux is genuinely good. The issue is prompting. When you write "warrior on a beach," you get a different art style every time. Different skin tones, different proportions, different lighting. You can't build a game from that.</p>
|
||||
<p>We spent three months iterating on prompt patterns before we found something that works consistently. The result is a 4-layer system that anchors the model to your art direction and produces images you can actually ship.</p>
|
||||
<h2>Why fal.ai</h2>
|
||||
<p>We evaluated Midjourney, DALL-E 3, Stable Diffusion (self-hosted), and fal.ai:</p>
|
||||
<p><strong>API-first.</strong> Midjourney is Discord-only. DALL-E's API works but the model makes everything look like a stock photo. Self-hosted SD means maintaining GPU infrastructure. fal.ai gives you Flux models behind a simple REST API — POST a prompt, GET an image URL.</p>
|
||||
<p><strong>Cost.</strong> $0.01 per image with <code>flux-2-pro</code>. $0.004 with <code>schnell</code> for rapid iteration. A full character design session — 12 variants across 3 rounds of refinement — costs $0.12. A 20-frame trailer storyboard costs $0.20. At these prices, the bottleneck is creative direction, not budget.</p>
|
||||
<p><strong>Speed.</strong> <code>flux/schnell</code> returns an image in 4 seconds. <code>flux-2-pro</code> in 15 seconds. Fast enough that an AI agent can generate, display, get feedback, and regenerate in a single conversation turn.</p>
|
||||
<p><strong>No subscription.</strong> Pay per image. No monthly fee, no credit packs that expire, no tier-gated features.</p>
|
||||
<h2>The 4-Layer Prompt Pattern</h2>
|
||||
<p>This is the pattern that made AI art actually usable for our game. Each layer adds specificity, and the combination anchors the model to a consistent output.</p>
|
||||
<h3>Layer 1: Design Context</h3>
|
||||
<p>The most important layer and the one most people skip. It sets the overall art direction:</p>
|
||||
<pre><code>Art direction: stylized 3D render for a survival colony sim. Warm earthy
|
||||
palette --- browns, tans, dark reds, cream, ocean blues. Carved wood
|
||||
textures, traditional patterns, woven natural fibres. Game engine quality,
|
||||
not photorealistic.</code></pre>
|
||||
<p>This paragraph appears at the start of every prompt. Same paragraph whether you're generating a character, a landscape, or an icon. It anchors the model to your art style.</p>
|
||||
<p><strong>The key insight:</strong> write this once, paste it everywhere. It's your art bible compressed into 50 words. Every time we skipped it — "just a quick test" — the output drifted into generic fantasy art.</p>
|
||||
<h3>Layer 2: Scene Description</h3>
|
||||
<p>Describe exactly what should appear, element by element:</p>
|
||||
<pre><code>Full body character in T-pose, front view. Young woman, mid-20s.
|
||||
Wearing a woven wrap skirt (mid-thigh length) and a fitted cloth top.
|
||||
Shell necklace with a carved bone pendant. Single bone bracelet on
|
||||
left wrist. Hair swept back over right shoulder. Bare feet.
|
||||
Matte skin, warm brown tones. Neutral confident expression ---
|
||||
not smiling, not angry. Dark grey background.</code></pre>
|
||||
<p>Not "tribal clothing" but "woven wrap skirt." Not "jewelry" but "shell necklace with a carved bone pendant." Vague prompts produce vague results. Specific prompts produce usable assets.</p>
|
||||
<h3>Layer 3: Negative Prompt</h3>
|
||||
<p>Always include what you don't want:</p>
|
||||
<pre><code>Do not include: cartoon style, anime style, photorealistic render,
|
||||
extra text or taglines, watermark, deformed elements, modern or
|
||||
sci-fi. No extra fingers, no merged limbs, no floating accessories.</code></pre>
|
||||
<p>Extend per-subject. For characters: "no stereotypical elements, no overly shiny materials." The negative prompt is as important as the positive one.</p>
|
||||
<h3>Layer 4: Reference Images</h3>
|
||||
<p>When you need consistency — the same character from different angles, or a new character matching an existing one — pass a reference image:</p>
|
||||
<pre><code class="language-python">result = fal_client.subscribe("fal-ai/flux-2-pro", arguments={
|
||||
"prompt": "Same character, side view, same clothing and accessories...",
|
||||
"image_url": "https://your-approved-front-view.png",
|
||||
"image_size": "square_hd",
|
||||
})</code></pre>
|
||||
<p>The first approved image becomes the reference for all subsequent views. Without it, you get a different person every time.</p>
|
||||
<h2>The Model Lineup</h2>
|
||||
<p>| Model | Cost | Speed | When |</p>
|
||||
<p>|——-|——|——-|——|</p>
|
||||
<p>| <code>flux-2-pro</code> | $0.01 | ~15s | Final art. Default for anything you'll ship. |</p>
|
||||
<p>| <code>flux/schnell</code> | $0.004 | ~4s | Exploration and iteration. |</p>
|
||||
<p>| <code>ideogram/v2</code> | $0.008 | ~5s | Anything with readable text — logos, UI, posters. |</p>
|
||||
<p>| <code>flux-pro/v1.1-ultra</code> | $0.015 | ~8s | Highest quality, but can hang. |</p>
|
||||
<p>The workflow: explore with <code>schnell</code>, refine with <code>flux-2-pro</code>, add text with <code>ideogram/v2</code>.</p>
|
||||
<h2>How This Fits Our Pipeline</h2>
|
||||
<p>fal.ai is the first step in a pipeline from idea to in-game asset:</p>
|
||||
<pre><code>Brief --> fal.ai (2D concept art) --> Tripo Studio (3D model) --> Blender (decimate) --> Godot (in-game)</code></pre>
|
||||
<p>1. <strong>Brief.</strong> The designer describes the character or asset.</p>
|
||||
<p>2. <strong>2D generation.</strong> Generate 3 variants with <code>flux-2-pro</code>, score each on a rubric (style match, cultural accuracy, silhouette, expression, animatability), pick the best.</p>
|
||||
<p>3. <strong>Reference sheet.</strong> Generate front, side, three-quarter, and head closeup views using the winner as reference.</p>
|
||||
<p>4. <strong>3D model.</strong> Approved concept art goes into Tripo Studio for image-to-3D. Outputs ~1.5M faces with full PBR textures.</p>
|
||||
<p>5. <strong>Decimation.</strong> Blender CLI decimates to 25,000 faces.</p>
|
||||
<p>6. <strong>Rigging.</strong> Auto-rig the body (hair separated first if large).</p>
|
||||
<p>7. <strong>In-game.</strong> Import into the engine, set up materials, done.</p>
|
||||
<p>The entire pipeline from "I want a character" to "character walking around in the game" takes about 2 hours. The quality isn't AAA, but for an indie game with a stylised art style, it's more than good enough.</p>
|
||||
<h2>What We Learned</h2>
|
||||
<p><strong>The design context layer is everything.</strong> Without it, every image is a one-off. With it, every image belongs to the same game. The 50-word context block is worth more than the rest of the prompt combined.</p>
|
||||
<p><strong>Negative prompts prevent drift.</strong> AI models have strong defaults — they want to make things shiny, symmetrical, and photorealistic. If your game isn't those things, say so explicitly.</p>
|
||||
<p><strong>Score and iterate, don't accept the first output.</strong> Generate 3 variants, score on 5 criteria, approve only 8+/10. Three attempts at $0.01 each is $0.03 — cheaper than working around a mediocre image.</p>
|
||||
<p><strong>Reference images are the consistency mechanism.</strong> Without them, every generation is independent. With them, every generation builds on the last approved output. This is how you get a roster of characters that look like they belong in the same game.</p>
|
||||
<p><strong>Fast models for exploration, quality models for output.</strong> <code>schnell</code> at 4 seconds is for "what if..." iterations. <code>flux-2-pro</code> at 15 seconds is for "yes, this is the one."</p>
|
||||
<p><strong>Let the AI agent handle prompt engineering.</strong> We encode the 4-layer pattern, art style guide, and cultural guardrails in a <a href="../skills/image-generation.md" style="color: var(–c-accent-l);">skill file</a>. The agent writes the full prompt, generates images, displays them, and asks for scores. The human's job is creative direction.</p>
|
||||
<h2>The Numbers</h2>
|
||||
<ul>
|
||||
<li><strong>Characters designed:</strong> 10 (full roster for early access)</li>
|
||||
<li><strong>Total images generated:</strong> ~400 across all iterations</li>
|
||||
<li><strong>Total cost:</strong> ~$6 in fal.ai credits</li>
|
||||
<li><strong>Time per character:</strong> ~30 minutes from brief to approved reference sheet</li>
|
||||
<li><strong>Pipeline time:</strong> ~2 hours from concept art to in-game model</li>
|
||||
<li><strong>Models used:</strong> flux-2-pro (80%), schnell (15%), ideogram/v2 (5%)</li>
|
||||
</ul>
|
||||
<h2>Open-Source Skills</h2>
|
||||
<p>We've published the skill files that power this workflow. A skill is a markdown document that teaches an AI agent a specific procedure — like a runbook, but the reader is an LLM.</p>
|
||||
<ul>
|
||||
<li><strong><a href="../skills/image-generation.md" style="color: var(–c-accent-l);">Image Generation</a></strong> — fal.ai API, 4-layer prompt pattern, model comparison</li>
|
||||
<li><strong><a href="../skills/concept-art-pipeline.md" style="color: var(–c-accent-l);">Concept Art Pipeline</a></strong> — full 2D-to-3D character workflow</li>
|
||||
<li><strong><a href="../skills/tripo-browser-workflow.md" style="color: var(–c-accent-l);">3D Model Generation</a></strong> — Tripo Studio text-to-3D and image-to-3D</li>
|
||||
<li><strong><a href="../skills/sora2-video.md" style="color: var(–c-accent-l);">Video Generation</a></strong> — trailer clip generation with OpenAI Sora 2</li>
|
||||
</ul>
|
||||
<p>Drop any of these into your <code>.cursor/skills/</code> directory and your AI agent can follow them. Adapt the design context block to your game's art style and you're good to go.</p>
|
||||
<hr>
|
||||
<p>AI image generation isn't magic and it isn't free. But at a penny per image, with the right prompt structure, it eliminates the most expensive bottleneck in indie game development: the gap between "I know what this should look like" and "I have an image I can actually use."</p>
|
||||
<p>We're building all of this as part of <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — a game development platform that brings together git hosting, AI tools, and creative workflows for game teams.</p>
|
||||
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||||
</div>
|
||||
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||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
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<div class="post__author-info">
|
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<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
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<article class="post">
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||||
<a href="/blog/" class="post__back">← All Posts</a>
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||||
<span class="post__date">25 May 2026</span>
|
||||
<h1 class="post__title">Fork, Don't Build: The Age of Agents Doesn't Need New Tools</h1>
|
||||
<p class="post__lead">The AI developer tools space has a problem: everyone is building new things. New agents, new IDEs, new platforms, new wrappers around GPT. Meanwhile, the tools that actually run the world — git servers, game engines, CI runners — sit there unchanged, waiting for someone to open them up and let agents in. We chose to fork instead of build. Three times. Here's why.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Pattern</h2>
|
||||
<p>We're a four-person game studio. We don't have time to build a git platform, a coding agent, and a game engine from scratch. Nobody does. But we can take something that already works — something with years of battle-testing, thousands of contributors, and millions of users — and change it from the inside.</p>
|
||||
<p>The pattern is simple:</p>
|
||||
<p>1. Find an open-source tool that does 95% of what you need</p>
|
||||
<p>2. Fork it</p>
|
||||
<p>3. Add the 5% that makes it yours</p>
|
||||
<p>4. Stay close to upstream so you get their fixes for free</p>
|
||||
<p>We've done this three times.</p>
|
||||
<h2>Fork 1: Gitea — Our Git Platform</h2>
|
||||
<p><a href="https://gitea.com" style="color: var(–c-accent-l);">Gitea</a> is a self-hosted git server. Single Go binary, MIT license, 45k GitHub stars. It handles repos, issues, pull requests, CI, LFS — everything a team needs.</p>
|
||||
<p>We <a href="https://tinqs.com/tinqs/studio" style="color: var(–c-accent-l);">forked it</a> and built Tinqs Studio. Our changes:</p>
|
||||
<ul>
|
||||
<li><strong>3D asset preview</strong> — click a <code>.glb</code> file and rotate the model in your browser</li>
|
||||
<li><strong>HTML file preview</strong> — rendered in a sandboxed iframe, not raw source</li>
|
||||
<li><strong>Agent API</strong> — six endpoints that let AI agents submit tasks, push code, and open PRs</li>
|
||||
<li><strong>OAuth2 SSO</strong> — one login for git, the game, and every tool</li>
|
||||
<li><strong>Credits system</strong> — monetize AI inference without hiding features behind paywalls</li>
|
||||
</ul>
|
||||
<p>Total lines changed from upstream: about 2,000 out of Gitea's 500,000. That's 0.4%. We modify templates, add Go modules, and tweak CSS variables. We never touch the database schema — we ride upstream's migrations. When Gitea releases 1.27, we rebase, fix conflicts, and ship.</p>
|
||||
<p>The alternative was building a git platform from scratch. That's a multi-year, multi-million dollar project. Or using GitHub/GitLab and accepting their limitations. Neither option gives you the ability to embed AI agents directly into the platform.</p>
|
||||
<h2>Fork 2: Pi — Our Agent Runtime</h2>
|
||||
<p><a href="https://pi.dev" style="color: var(–c-accent-l);">Pi</a> is an open-source coding agent. 51k stars, MIT license, TypeScript. Four core tools (read, write, edit, bash), a minimal system prompt, and an extension system.</p>
|
||||
<p>We <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">forked it</a> and added four extensions:</p>
|
||||
<ul>
|
||||
<li><strong>tinqs-provider</strong> — routes inference through our DeepSeek V4 proxy ($0.28/MTok vs Opus at $15/MTok)</li>
|
||||
<li><strong>tinqs-tools</strong> — Gitea API, fal.ai image generation, vision preprocessing</li>
|
||||
<li><strong>tinqs-ci</strong> — reads CI pipeline status and logs, polls for completion</li>
|
||||
<li><strong>tinqs-guardrail</strong> — 29 safety patterns blocking dangerous commands</li>
|
||||
</ul>
|
||||
<p>Each extension is a single TypeScript file. No npm dependencies. The core Pi code is untouched — we only add files.</p>
|
||||
<p>The alternative was building our own agent from scratch. That means writing tool-calling logic, context management, streaming, retry handling, conversation threading — months of work to reinvent what Pi already does. Or using Claude Code / Codex as a black box and accepting that you can't add a Gitea API tool or a budget cap.</p>
|
||||
<h2>Fork 3: Godot — Our Game Engine</h2>
|
||||
<p><a href="https://godotengine.org" style="color: var(–c-accent-l);">Godot</a> is an open-source game engine. We forked 4.6.2 and added nine C++ modules that turn the engine into an agent-aware runtime:</p>
|
||||
<ul>
|
||||
<li><strong>agent_api</strong> — HTTP server inside the engine, so agents can query game state</li>
|
||||
<li><strong>agent_vision</strong> — screenshot capture for AI vision pipelines</li>
|
||||
<li><strong>agent_console</strong> — programmatic access to the engine console</li>
|
||||
<li><strong>agent_replay</strong> — record and replay game sessions for testing</li>
|
||||
<li><strong>agent_analytics</strong> — PostHog event tracking from inside the engine</li>
|
||||
</ul>
|
||||
<p>These modules compile into the engine binary. A vanilla Godot user never sees them. An agent can connect to the running engine over HTTP, take a screenshot, read the scene tree, execute a console command, and capture the result — all without touching the editor UI.</p>
|
||||
<p>The alternative was building an engine integration from scratch. Or worse, building a custom engine. We'd still be writing a renderer instead of making a game.</p>
|
||||
<h2>Why Forking Beats Building</h2>
|
||||
<h3>You inherit decades of work</h3>
|
||||
<p>Gitea has handled millions of git pushes. Godot renders millions of frames. Pi has processed millions of LLM tokens. That battle-testing is free when you fork. When you build from scratch, you spend your first year rediscovering bugs that were fixed upstream in 2019.</p>
|
||||
<h3>You get free maintenance</h3>
|
||||
<p>Every upstream release brings security patches, performance improvements, and new features — written by hundreds of contributors we don't pay. Our job is to rebase, resolve conflicts, and test. That's an afternoon, not a quarter.</p>
|
||||
<h3>You stay focused</h3>
|
||||
<p>Building a git server from scratch means worrying about pack-file format, SSH key management, webhook delivery, and a thousand other things that have nothing to do with AI agents. Forking means you only think about the 5% that matters to you. The other 95% is someone else's problem.</p>
|
||||
<h3>Agents work better on real platforms</h3>
|
||||
<p>An agent that pushes to a real Gitea instance — with real CI, real code review, real permissions — produces work that humans can actually review and ship. An agent that pushes to a toy demo platform produces demos.</p>
|
||||
<p>The whole point of AI agents is to participate in real workflows. Real workflows run on real tools. If you want agents in your git workflow, put them in your git server. If you want agents in your game pipeline, put them in your game engine.</p>
|
||||
<h2>The 0.5% Rule</h2>
|
||||
<p>Across all three forks, our total changeset is less than 0.5% of the upstream code. Tinqs Studio: 0.4% of Gitea. Pi extensions: 900 lines added to a 15,000-line codebase. Godot modules: 2,000 lines added to a 2-million-line engine.</p>
|
||||
<p>This isn't a coincidence. If your fork touches more than 1% of upstream, you're doing too much. Either the upstream tool is wrong for the job, or you're not trusting it enough. The power of forking is that you don't have to understand the whole codebase. You find the extension points, add your code, and leave the rest alone.</p>
|
||||
<h2>What We're Not Doing</h2>
|
||||
<p>We're not building a new IDE. Cursor and Claude Code exist. We're not building a new LLM. DeepSeek and Claude exist. We're not building a new cloud platform. AWS exists.</p>
|
||||
<p>We're building the layer that connects them. The git server that speaks agent. The coding agent that speaks Gitea. The game engine that speaks HTTP. Each fork is a bridge between an existing tool and the agentic future — not a replacement for either.</p>
|
||||
<h2>The Bet</h2>
|
||||
<p>The age of agents doesn't need more agents. It needs better platforms. Platforms that understand agents as first-class users — with API endpoints, safety rails, and lifecycle management. Those platforms already exist as open-source projects. They just need someone to fork them and add the wiring.</p>
|
||||
<p>That's the bet. Fork, don't build. Modify the foundation, don't stack another layer on top. Let the upstream community handle the 99.5% while you focus on the 0.5% that makes it yours.</p>
|
||||
<hr>
|
||||
<p><em><a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> is our Gitea fork, open for game teams and indie studios. We're building <a href="https://arikigame.com" style="color: var(–c-accent-l);">Ariki</a> — a survival colony sim — using every tool described in this post. If you're interested in self-hosted game development with built-in AI agents, come take a look.</em></p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
||||
</div>
|
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</div>
|
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</article>
|
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<span class="footer__wordmark">TINQS</span>
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<a href="/#game">Games</a>
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<a href="/#tech">Technology</a>
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<a href="/#about">About</a>
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<a href="/blog/">Blog</a>
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<a href="mailto:hello@tinqs.com">hello@tinqs.com</a>
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<title>Why We Forked Gitea and Built Tinqs Studio — Tinqs Blog</title>
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<a href="/blog/" class="post__back">← All Posts</a>
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||||
<span class="post__date">20 May 2026</span>
|
||||
<h1 class="post__title">Why We Forked Gitea and Built Tinqs Studio</h1>
|
||||
<p class="post__lead">GitHub is built for web developers. Game studios need something different — LFS that works, 3D asset previews in the browser, and project management that understands sprints and milestones. So we forked Gitea and built Tinqs Studio.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Problem with GitHub for Game Dev</h2>
|
||||
<p>We used GitHub for two years. It was fine for docs — small files, text diffs, pull requests. But the game repo was a different story.</p>
|
||||
<p>A single character model with textures and animations is 50–200MB. A terrain heightmap is 16MB. An island's vegetation data is another 10MB. Our game repo was 12GB in LFS alone, growing every week. GitHub's LFS bandwidth limits, slow clone times, and $5/50GB pricing made it untenable.</p>
|
||||
<p>More importantly, nobody on the team could <strong>see</strong> what changed. A PR that modifies a GLB file shows a binary diff. You can't preview it. You can't compare before and after. The artist pushes a model, the developer approves it blindly, and three days later someone notices the normals are inverted.</p>
|
||||
<h2>Why Self-Host, and Why Gitea</h2>
|
||||
<p>We evaluated GitLab, Forgejo, Gogs, and Gitea. The decision came down to:</p>
|
||||
<ul>
|
||||
<li><strong>Single binary.</strong> Gitea compiles to one Go binary with SQLite support. No PostgreSQL, no Redis, no Docker compose with 7 services. Just copy the binary, write an app.ini, and run it.</li>
|
||||
<li><strong>Resource usage.</strong> Our instance runs on a single EC2 instance alongside other services. It uses about 200MB RAM. GitLab needs 4GB minimum.</li>
|
||||
<li><strong>LFS built-in.</strong> Gitea includes a full LFS server. No external LFS store, no S3 configuration for basic use. Files are stored locally. We added S3 backend later, but it works out of the box.</li>
|
||||
<li><strong>Forkable.</strong> Gitea is MIT-licensed, written in Go, with a clean codebase. We can modify it without worrying about license restrictions or CLA headaches.</li>
|
||||
</ul>
|
||||
<p>We ran vanilla Gitea for six months. It solved the cost and bandwidth problems immediately. But the UX gaps for game development were still there.</p>
|
||||
<h2>What We Built: Tinqs Studio</h2>
|
||||
<p><a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> is our fork. It tracks upstream Gitea on the <code>main</code> branch and keeps all customisations on a separate branch. We rebase onto upstream releases periodically, fix conflicts, and deploy.</p>
|
||||
<h3>3D Asset Preview</h3>
|
||||
<p>The headline feature. When you open a PR that contains a <code>.glb</code>, <code>.gltf</code>, or <code>.fbx</code> file, you see a 3D viewer directly in the browser. Rotate, zoom, check materials. No downloads, no external tools. We integrated Online 3D Viewer (O3DV), which supports 22 file formats including STL, OBJ, 3DS, and PLY.</p>
|
||||
<p>This changes the review process fundamentally. The artist pushes a model, the lead rotates it in the browser, leaves a comment about the UV seam on the shoulder, and the artist fixes it — all without leaving the git platform.</p>
|
||||
<h3>LFS-First Workflows</h3>
|
||||
<p>Vanilla Gitea treats LFS as an afterthought. You configure <code>.gitattributes</code> manually. There's no dashboard showing LFS usage, no way to see which files are tracked, no warnings when someone commits a large file without LFS.</p>
|
||||
<p>Tinqs Studio adds auto-LFS tracking on repository creation. Game file extensions (<code>.fbx</code>, <code>.glb</code>, <code>.png</code>, <code>.wav</code>, <code>.ogg</code>, <code>.tscn</code>, <code>.tres</code>) are tracked by default. An API endpoint exposes LFS storage stats per repo. The goal: LFS should be invisible. It should just work.</p>
|
||||
<h3>Platform Integration</h3>
|
||||
<p>Tinqs Studio integrates project management — issues, sprints, time tracking — and OAuth2 SSO. One login for git, the game tools, and the team dashboard.</p>
|
||||
<h2>The Branching Strategy</h2>
|
||||
<p>Staying close to upstream is critical. We don't want to maintain a fork that diverges forever:</p>
|
||||
<ul>
|
||||
<li><code>main</code> tracks upstream <code>go-gitea/gitea</code>. We never commit to it directly.</li>
|
||||
<li>Our production branch holds all customisations.</li>
|
||||
<li>Feature branches merge into production.</li>
|
||||
<li>When upstream releases a new version, we merge, resolve conflicts, test, deploy.</li>
|
||||
</ul>
|
||||
<p>We deliberately limit what we touch. We modify templates, locale strings, CSS variables, and a handful of Go packages. We <strong>never</strong> touch the database models — schema is owned by upstream, and we ride their migrations. This keeps rebasing manageable.</p>
|
||||
<h2>What We Learned</h2>
|
||||
<p><strong>Self-hosting git is surprisingly easy.</strong> The hard part isn't running Gitea — it's convincing yourself that you're allowed to. After years of GitHub being the default, it feels transgressive to host your own git. But a single Go binary on a $10/month server handles a small team with room to spare.</p>
|
||||
<p><strong>LFS changes everything for game repos.</strong> Our clone times went from 45 minutes to 3 minutes. Developers only download the LFS objects they need. CI only pulls what changed. The bandwidth savings alone paid for the server.</p>
|
||||
<p><strong>Forking is maintenance, not rebellion.</strong> The romantic version is "we forked Gitea and built our own platform." The reality is we changed 200 lines of Go, 50 template strings, and a CSS file. 99.5% of the code is upstream's. We're just customising the last half-percent for our use case.</p>
|
||||
<p><strong>3D preview is a game changer.</strong> We expected it to be a nice-to-have. It turned out to be the feature that made the rest of the team actually use git. When the artist can see their work rendered in the browser, they stop asking the developer to "check if it looks right."</p>
|
||||
<hr>
|
||||
<p><a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> is built for game teams that are tired of paying GitHub for LFS bandwidth and reviewing binary diffs blind. We're building it for ourselves first — dogfooding it on our own game — but the plan is to make it available as a platform for other studios. If you're a game team that self-hosts or wants to, we'd love to hear what features you need.</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
||||
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|
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<span class="post__date">22 May 2026</span>
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<h1 class="post__title">Streaming a 12km Archipelago in Godot 4</h1>
|
||||
<p class="post__lead">Godot has no built-in asset streaming. Our game is a 12km x 12km archipelago with 9 islands, thousands of trees, hundreds of buildings, and an ocean that never ends. Here's how we made it run.</p>
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<div class="post__body">
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<h2>The Problem</h2>
|
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<p>We're building a survival colony sim set across 9 islands. The total world is roughly 12km x 12km. Each island is 4km across with its own terrain heightmap, biome textures, vegetation prototypes, and building grids. The player can travel between islands by canoe.</p>
|
||||
<p>Godot 4 is a fantastic engine, but it wasn't designed for this scale. There's no terrain streaming, no asset LOD pipeline, no distance-based loading. If you load everything at startup, you run out of VRAM before the player sees the main menu. So we built four streaming layers on top of Godot, all in C#.</p>
|
||||
<h2>Layer 1: Terrain Regions</h2>
|
||||
<p>We use <strong>Terrain3D</strong> for heightmaps — a GDExtension that gives us a clipmap renderer with 7 LOD levels. Internally, Terrain3D divides each island into 512m x 512m regions. A 4km island has 64 regions. Across 9 islands, that's 576 regions total.</p>
|
||||
<p>The key insight: <strong>don't create all 9 terrain nodes at startup.</strong> Each node allocates a clipmap mesh, collision structures, and materials even when hidden. Our original code created all 9 in <code>_Ready()</code> and just toggled visibility. This wasted hundreds of megabytes on islands the player hadn't visited yet.</p>
|
||||
<p>The fix was lazy instantiation. We create the current island's terrain on startup and defer the rest. When the player gets in a canoe and sails to a new island, we create that island's terrain node on demand, import the heightmap, and start async texture loading — all while a loading screen covers the transition.</p>
|
||||
<h2>Layer 2: Vegetation Chunks (128m Grid)</h2>
|
||||
<p>This is the main prop streaming system. Every island's vegetation — trees, rocks, grasses, shrubs — is divided into a spatial grid of 128m x 128m chunks.</p>
|
||||
<p>The camera position is checked every 0.5 seconds. When it crosses a chunk boundary, we calculate which chunks should be active within a 400m radius (roughly 39 chunks in a circle), <code>QueueFree</code> chunks that fell out of range, and build new chunks that entered range.</p>
|
||||
<p>Each chunk groups vegetation instances by prototype, creates a <strong>MultiMesh</strong> per group, and places instances using height queries. A chunk with 50 palm trees and 30 rocks becomes 2 MultiMesh draw calls, not 80 individual nodes.</p>
|
||||
<h3>The cache problem</h3>
|
||||
<p>Vegetation meshes and materials are cached in dictionaries keyed by prototype name. The problem: these caches are <strong>append-only</strong>. Visit all 9 islands and you accumulate every mesh and material variant permanently. With 155 unique prototypes across the archipelago, that's a lot of GPU memory that never gets freed.</p>
|
||||
<p>The fix is island-scoped eviction. When the player leaves an island, we clear the vegetation caches. Meshes and materials for the departed island are released. If the player returns, they reload from disk. The loading screen covers this cost.</p>
|
||||
<h2>Layer 3: Async Resource Loading</h2>
|
||||
<p>Godot's <code>GD.Load()</code> is synchronous. It blocks the main thread. During gameplay, the frame freezes. We audited the entire codebase and found <strong>26 resource load calls across 13 files</strong>, and only 1 was async.</p>
|
||||
<p>The worst offender was <code>GetMeshForProto()</code> in the vegetation grid. As the player walks across an island for the first time, every new vegetation prototype triggers a synchronous load. With 155 prototypes, the first traversal stutters visibly.</p>
|
||||
<p>We fixed this in two ways:</p>
|
||||
<ul>
|
||||
<li><strong>Pre-warm during loading screens.</strong> When an island is imported, we kick off background loads for all known prototypes. By the time the player gains control, most meshes are already cached.</li>
|
||||
<li><strong>Async loading for biome textures.</strong> Terrain textures use <code>ResourceLoader.LoadThreadedRequest()</code> with <code>_Process()</code> polling. The terrain renders immediately with autoshader colours, and biome textures pop in when ready. The player never notices.</li>
|
||||
</ul>
|
||||
<h3>The ResourceLoader cache trap</h3>
|
||||
<p>On top of our own caches, Godot maintains an internal resource cache. Every <code>GD.Load()</code> call caches the result globally. There's no API to query the cache size or evict entries.</p>
|
||||
<p>If you load an FBX as a <code>PackedScene</code>, instantiate it to extract a mesh, then free the instance — the PackedScene <strong>stays cached</strong>. The mesh you extracted is fine (it's a Resource, not a Node), but the discarded scene wastes memory forever.</p>
|
||||
<p>The rule: use <code>ResourceLoader.Load(path, "", CacheMode.Ignore)</code> for one-shot loads where you extract data and discard the container. Use <code>GD.Load()</code> only for things that should persist (shaders, shared textures).</p>
|
||||
<h2>Layer 4: Entity Rendering</h2>
|
||||
<p>Dynamic entities — colonists, animals, buildings, VFX — are event-driven, not streamed. They update when the simulation pushes new state, not per frame.</p>
|
||||
<ul>
|
||||
<li><strong>Crowd rendering:</strong> Single MultiMesh for up to 2000 colonists. Positions lerped per frame from pre-allocated arrays. Labels distance-culled, capped at 20. No individual nodes, no per-frame allocation.</li>
|
||||
<li><strong>Animals:</strong> One MultiMesh per type. Max 500 per type. Updates only on state change, not per frame.</li>
|
||||
<li><strong>Buildings:</strong> Tracked by ID from sim state. <code>QueueFree</code> when removed. Self-cleaning.</li>
|
||||
<li><strong>VFX:</strong> Capped at 50 active particle systems. Worst case: 10,000 GPU particles.</li>
|
||||
</ul>
|
||||
<h2>Memory Safety: Zero Leaks</h2>
|
||||
<p>We audited every <code>QueueFree()</code> call in the codebase — 47 calls across 17 files. <strong>Zero <code>RemoveChild()</code> calls without a corresponding <code>QueueFree()</code>.</strong> Three patterns we follow everywhere:</p>
|
||||
<p><strong>Pattern 1: Chunk streaming</strong> — Deactivate out-of-range chunks by iterating the active dict, calling <code>QueueFree()</code>, collecting keys to remove, then removing them after iteration. Never modify a dictionary while iterating it.</p>
|
||||
<p><strong>Pattern 2: Extract data from PackedScene</strong> — Instantiate a scene, extract the mesh, <code>QueueFree()</code> the temporary instance. The mesh survives because it's a Resource, not a Node.</p>
|
||||
<p><strong>Pattern 3: UI rebuild</strong> — <code>QueueFree()</code> all children, then build new content. Safe because <code>QueueFree</code> is deferred — new children are added in the same frame before old ones are freed.</p>
|
||||
<h2>What Runs Every Frame</h2>
|
||||
<p>We're strict about what goes in <code>_Process()</code>:</p>
|
||||
<ul>
|
||||
<li><strong>Vegetation grid:</strong> Camera chunk check (0.5s throttle, early-exits if same chunk)</li>
|
||||
<li><strong>Terrain manager:</strong> Poll async texture loads (loop pending list, check status)</li>
|
||||
<li><strong>Crowd renderer:</strong> Lerp 2000 colonist positions (math-only, pre-allocated arrays)</li>
|
||||
<li><strong>Day/night:</strong> Rotate sun, adjust light energy</li>
|
||||
<li><strong>Camera:</strong> Follow + zoom smoothing</li>
|
||||
<li><strong>Sim bridge:</strong> Drain WebSocket message queue</li>
|
||||
</ul>
|
||||
<p>No heap allocation in any of these. Total per-frame overhead is dominated by the crowd lerp and the message queue drain.</p>
|
||||
<h2>Shaders We Watch</h2>
|
||||
<p>Two custom shaders are performance-sensitive:</p>
|
||||
<p><strong>Ocean shader</strong> — 4 Gerstner wave calculations in the vertex stage, applied to a 12,000m plane. Fragment stage does depth reconstruction, caustics, foam masking, and two normal map lookups. It's the heaviest thing in the render pipeline. We pre-warm it during the loading screen to avoid shader compilation stutter.</p>
|
||||
<p><strong>Wind sway shader</strong> — 6 trig ops per vertex on every vegetation mesh within 400m. The sway is invisible beyond 100m but the shader runs at full cost regardless. Future optimisation: disable sway on distant chunks.</p>
|
||||
<h2>The Target: RTX 3060</h2>
|
||||
<p>Our early access target is an RTX 3060 with 8GB VRAM:</p>
|
||||
<ul>
|
||||
<li>Main island + full vegetation < 4GB VRAM — ship it, we have headroom</li>
|
||||
<li>Approaching 6–8GB — implement lazy terrain nodes + cache eviction</li>
|
||||
<li>Exceeding 8GB — implement vegetation LOD and region-level streaming</li>
|
||||
</ul>
|
||||
<p><strong>Always measure before optimising.</strong> We added VRAM logging before writing a single line of optimisation code. Half the "problems" we expected turned out to be non-issues. The other half were worse than expected. Profiling isn't optional.</p>
|
||||
<hr>
|
||||
<p>Godot 4 can handle open worlds at this scale, but it won't do it for you. You need to build streaming, manage your own caches, audit your resource loading, and be disciplined about what runs per frame. The engine gives you the primitives — MultiMesh, <code>LoadThreadedRequest</code>, <code>QueueFree</code> — and it's up to you to wire them into a system that scales.</p>
|
||||
<p>We're building with these systems and developing the game using <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a>. If you're building something large-scale in Godot, we hope this is useful.</p>
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<div class="post__author">
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<div class="post__author-avatar">OB</div>
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<div class="post__author-info">
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<span class="post__author-name">Ozan Bozkurt</span><br>
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CTO & Developer, Tinqs
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<a href="/blog/" class="post__back">← All Posts</a>
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<span class="post__date">25 May 2026</span>
|
||||
<h1 class="post__title">Image Generation at Every Price Point with fal.ai</h1>
|
||||
<p class="post__lead">We generate every visual asset for Ariki — concept art, app icons, trailer frames, logo variants, Steam capsules — through a single inference proxy that routes to fal.ai. No Photoshop. No Midjourney subscription. Just API calls at prices that range from $0.002 to $0.09 per image. Here's how we decide which model gets which job.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Setup</h2>
|
||||
<p>Our <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> platform includes an inference proxy that sits between agents and model providers. When an agent (or a human in Cursor) says "generate an image," the proxy routes the request to fal.ai, handles authentication, tracks usage per user, and persists the result to S3. The caller doesn't care which model runs — they describe what they want, and the proxy picks or the caller specifies.</p>
|
||||
<pre><code>Agent describes what it wants
|
||||
→ tinqsProxy receives generate_image call
|
||||
→ Routes to fal.ai with the specified model
|
||||
→ Image generated, persisted to S3
|
||||
→ Permanent URL returned to caller</code></pre>
|
||||
<p>One API key. One billing account. Access to every model fal.ai hosts. That's the pitch of aggregator platforms, and fal.ai delivers on it.</p>
|
||||
<h2>The Tiers</h2>
|
||||
<p>Not every image needs the best model. A throwaway mockup doesn't justify $0.09. A final logo doesn't deserve $0.002. We split our usage into four tiers.</p>
|
||||
<h3>Best Quality — Final Art</h3>
|
||||
<p>For images that ship — hero art, app icons, trailer keyframes, print-ready designs — we use three models depending on the content:</p>
|
||||
<p><strong>Flux 2 Pro</strong> ($0.03/megapixel, ~15 seconds). Our default. Best all-round quality for concept art, character illustrations, environment paintings, and anything that doesn't need text. Handles complex prompts with multiple elements well. Rarely fails.</p>
|
||||
<p><strong>Ideogram v3 Quality</strong> ($0.09, ~12 seconds). The only model that renders text reliably inside images. When we need a poster with a tagline, a sign in a game scene, or a logo with readable letters, this is the only option. The QUALITY tier is expensive but worth it — text at lower tiers gets blurry.</p>
|
||||
<p><strong>Recraft v3</strong> ($0.04 raster, $0.08 vector, ~10 seconds). Built for commercial design. Clean lines, consistent style, and the only model on fal.ai that outputs SVG vectors. When we need brand assets, packaging mockups, or anything that might end up in print, Recraft produces work that doesn't need cleanup.</p>
|
||||
<h3>Mid Tier — Everyday Work</h3>
|
||||
<p>For images that are good enough for internal review, social posts, or documentation:</p>
|
||||
<p><strong>Ideogram v3 Balanced</strong> ($0.06, ~8 seconds). Typography quality between Turbo and Quality. Good for marketing materials where text matters but perfection doesn't.</p>
|
||||
<p><strong>Seedream v4.5</strong> ($0.04, ~8 seconds). Google's model on fal.ai. Photorealistic scenes and product shots. Different aesthetic from Flux — slightly more photographic, less painterly.</p>
|
||||
<p><strong>Flux Dev</strong> ($0.025, ~10 seconds). The open-weight Flux variant. Good quality, and the base for LoRA fine-tuning if you want to train on your own style. We use it when we need custom-trained models later.</p>
|
||||
<h3>Low Cost — Drafts and Exploration</h3>
|
||||
<p>For iteration, A/B testing, and throwing things at the wall:</p>
|
||||
<p><strong>Flux Schnell</strong> ($0.003/megapixel, ~3 seconds). The workhorse for exploration. When we're figuring out composition, trying different camera angles, or generating 20 variants to pick one direction — Schnell. A hundred images costs $0.30. You can afford to be wasteful.</p>
|
||||
<p><strong>SDXL Lightning</strong> (~$0.002, ~2 seconds). The absolute cheapest option. Lower quality than Schnell, but when you need 50 thumbnails to test a layout grid or generate placeholder textures, quality doesn't matter. Two cents for ten images.</p>
|
||||
<h3>Specialised — Editing and Post-Processing</h3>
|
||||
<p>For modifying existing images rather than generating new ones:</p>
|
||||
<p><strong>Flux Kontext</strong> (~$0.04, ~12 seconds). Context-aware editing. Give it an image and say "change the wood to marble" or "make it sunset lighting." Preserves composition while changing style or material. Useful for quick style transfers without regenerating from scratch.</p>
|
||||
<p><strong>Nano Banana Edit</strong> ($0.039, ~12 seconds). Image-to-image restyle. We use this for our logo variant pipeline — take one carved-wood Ariki logo and produce versions in mahogany, pearl, obsidian, coral, gold. It's better than Kontext at preserving fine detail in complex images.</p>
|
||||
<p><strong>BiRefNet</strong> ($0.001, ~3 seconds). Background removal. Produces clean alpha cutouts from any image. We pair it with every logo and icon generation — generate with a white background, then cut it out. A dollar gets you a thousand cutouts.</p>
|
||||
<h2>How We Actually Use Them</h2>
|
||||
<h3>The Schnell-to-Pro Pipeline</h3>
|
||||
<p>We never start with the expensive model. Every generation session follows the same pattern:</p>
|
||||
<p>1. <strong>Explore with Schnell</strong> ($0.003) — 10-20 variants, different angles, compositions, color palettes. Total: $0.03-0.06.</p>
|
||||
<p>2. <strong>Pick 2-3 directions.</strong> Human looks at the grid, picks the promising ones.</p>
|
||||
<p>3. <strong>Refine with Flux 2 Pro</strong> ($0.03) — regenerate the winners at full quality with refined prompts. Total: $0.06-0.09.</p>
|
||||
<p>4. <strong>Post-process</strong> — BiRefNet for background removal ($0.001), maybe Recraft for a vector version ($0.08).</p>
|
||||
<p>A full session — from blank canvas to final assets — costs under $0.20. That's the price of a single Midjourney generation on their Pro plan.</p>
|
||||
<h3>Logo Variants at Scale</h3>
|
||||
<p>Our Ariki logo has 18 material variants — deep mahogany, mother-of-pearl, obsidian, molten lava, bronze with verdigris, tapa cloth, and more. Each one generated with Nano Banana Edit ($0.039) + BiRefNet ($0.001) for transparency. Total cost for 18 variants: <strong>$0.72</strong>. A designer would quote hundreds of dollars and a week of work for the same output.</p>
|
||||
<h3>Typography That Works</h3>
|
||||
<p>Every model except Ideogram fails at text. Flux will give you beautiful art with garbled letters. Recraft gets close but isn't consistent. SDXL doesn't try. If the image has words in it, Ideogram v3 is the only answer. We've learned to accept the $0.09 cost for text-heavy images rather than wasting $0.30 on ten failed Flux attempts.</p>
|
||||
<h2>The Numbers</h2>
|
||||
<p>Over the past month:</p>
|
||||
<p>| Category | Images | Total Cost | Avg Cost/Image |</p>
|
||||
<p>|———-|——–|———–|—————-|</p>
|
||||
<p>| Concept art (flux-2-pro) | ~120 | $3.60 | $0.03 |</p>
|
||||
<p>| Exploration drafts (schnell) | ~400 | $1.20 | $0.003 |</p>
|
||||
<p>| Logo variants (nano-banana) | 18 | $0.72 | $0.04 |</p>
|
||||
<p>| Icons (nano-banana + birefnet) | 30 | $1.20 | $0.04 |</p>
|
||||
<p>| Typography (ideogram) | ~25 | $1.50 | $0.06 |</p>
|
||||
<p>| Background removal (birefnet) | ~80 | $0.08 | $0.001 |</p>
|
||||
<p>| <strong>Total</strong> | <strong>~673</strong> | <strong>$8.30</strong> | <strong>$0.012</strong> |</p>
|
||||
<p>Six hundred images for eight dollars. The infrastructure to route, authenticate, and persist them costs more than the generation itself.</p>
|
||||
<h2>What We Learned</h2>
|
||||
<p><strong>Never iterate on expensive models.</strong> The Schnell-to-Pro pipeline saves 10x. Most of the creative work happens at $0.003/image. The expensive model just polishes the decision you already made.</p>
|
||||
<p><strong>Typography is a solved problem — but only on one model.</strong> Stop trying to make Flux render text. Use Ideogram v3 Quality for anything with words. Accept the cost.</p>
|
||||
<p><strong>Vector output is underrated.</strong> Recraft v3's SVG export means logos and icons scale to any size without artifacts. For anything that might end up on a billboard or a business card, pay the $0.08 for vector.</p>
|
||||
<p><strong>Background removal is basically free.</strong> At $0.001 per image, there's no reason to ever manually mask anything. Run BiRefNet on everything, keep both versions.</p>
|
||||
<p><strong>Aggregation beats loyalty.</strong> No single model is best at everything. Flux for art, Ideogram for text, Recraft for design, Nano Banana for edits, BiRefNet for masks. The proxy pattern lets us use the right tool for each job without managing five API keys and five billing accounts.</p>
|
||||
<hr>
|
||||
<p><em>Image generation is built into <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — our Gitea-based platform for game teams. Every model above is available through the same inference proxy that handles LLM calls, authenticated with the same Gitea token. We're building <a href="https://arikigame.com" style="color: var(–c-accent-l);">Ariki</a> with these tools, and every asset in the game touched at least one of them.</em></p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
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|
||||
/* --- Sections --- */
|
||||
.section {
|
||||
max-width: 860px;
|
||||
margin: 0 auto;
|
||||
padding: 0 1.5rem 3rem;
|
||||
}
|
||||
.section-title {
|
||||
font-family: var(--font-mono);
|
||||
font-size: 0.75rem;
|
||||
font-weight: 600;
|
||||
color: var(--text-muted);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.1em;
|
||||
margin-bottom: 1.25rem;
|
||||
padding-bottom: 0.5rem;
|
||||
border-bottom: 1px solid var(--border);
|
||||
}
|
||||
|
||||
/* --- Post cards --- */
|
||||
.post-list { display: flex; flex-direction: column; gap: 0.5rem; }
|
||||
.post-card {
|
||||
display: block;
|
||||
background: var(--surface);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
padding: 1.1rem 1.3rem;
|
||||
transition: background 0.15s, border-color 0.15s;
|
||||
color: var(--text);
|
||||
}
|
||||
.post-card:hover {
|
||||
background: var(--surface-hover);
|
||||
border-color: var(--accent-dim);
|
||||
color: var(--text);
|
||||
}
|
||||
.post-card .post-title {
|
||||
font-weight: 600;
|
||||
font-size: 1rem;
|
||||
margin-bottom: 0.3rem;
|
||||
}
|
||||
.post-card .post-excerpt {
|
||||
font-size: 0.85rem;
|
||||
color: var(--text-muted);
|
||||
line-height: 1.5;
|
||||
}
|
||||
.post-card .post-date {
|
||||
font-family: var(--font-mono);
|
||||
font-size: 0.72rem;
|
||||
color: var(--text-muted);
|
||||
margin-bottom: 0.3rem;
|
||||
}
|
||||
|
||||
/* --- Skill cards --- */
|
||||
.skill-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(240px, 1fr));
|
||||
gap: 0.75rem;
|
||||
}
|
||||
.skill-card {
|
||||
display: block;
|
||||
background: var(--surface);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
padding: 1.1rem 1.3rem;
|
||||
transition: background 0.15s, border-color 0.15s;
|
||||
color: var(--text);
|
||||
}
|
||||
.skill-card:hover {
|
||||
background: var(--surface-hover);
|
||||
border-color: #065f46;
|
||||
color: var(--text);
|
||||
}
|
||||
.skill-card .skill-name {
|
||||
font-family: var(--font-mono);
|
||||
font-size: 0.85rem;
|
||||
font-weight: 600;
|
||||
color: var(--skill-accent);
|
||||
margin-bottom: 0.3rem;
|
||||
}
|
||||
.skill-card .skill-desc {
|
||||
font-size: 0.82rem;
|
||||
color: var(--text-muted);
|
||||
line-height: 1.5;
|
||||
}
|
||||
|
||||
/* --- What are skills --- */
|
||||
.callout {
|
||||
background: var(--surface);
|
||||
border: 1px solid var(--border);
|
||||
border-left: 3px solid var(--accent);
|
||||
border-radius: 8px;
|
||||
padding: 1.25rem 1.5rem;
|
||||
margin-top: 1.5rem;
|
||||
}
|
||||
.callout h3 {
|
||||
font-size: 0.95rem;
|
||||
font-weight: 600;
|
||||
margin-bottom: 0.4rem;
|
||||
}
|
||||
.callout p {
|
||||
font-size: 0.85rem;
|
||||
color: var(--text-muted);
|
||||
line-height: 1.6;
|
||||
}
|
||||
.callout code {
|
||||
font-family: var(--font-mono);
|
||||
font-size: 0.8rem;
|
||||
background: var(--tag-bg);
|
||||
padding: 0.15em 0.4em;
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
/* --- Footer --- */
|
||||
.footer {
|
||||
border-top: 1px solid var(--border);
|
||||
padding: 2rem 0;
|
||||
margin-top: 1rem;
|
||||
}
|
||||
.footer-inner {
|
||||
max-width: 860px;
|
||||
margin: 0 auto;
|
||||
padding: 0 1.5rem;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
flex-wrap: wrap;
|
||||
gap: 0.75rem;
|
||||
font-size: 0.8rem;
|
||||
color: var(--text-muted);
|
||||
}
|
||||
.footer a { color: var(--text-muted); }
|
||||
.footer a:hover { color: var(--text); }
|
||||
|
||||
@media (max-width: 600px) {
|
||||
.hero h1 { font-size: 1.5rem; }
|
||||
.harness-grid { grid-template-columns: repeat(2, 1fr); }
|
||||
.skill-grid { grid-template-columns: 1fr; }
|
||||
}
|
||||
</style>
|
||||
<link rel="icon" type="image/svg+xml" href="/img/favicon.svg">
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com">
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
|
||||
<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400;1,500;1,600;1,700&display=swap" rel="stylesheet">
|
||||
<link rel="stylesheet" href="../style.css">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<header class="header">
|
||||
<div class="header-inner">
|
||||
<div class="logo">TINQS STUDIO <span>/ blog</span></div>
|
||||
<nav class="nav-links">
|
||||
<a href="https://tinqs.com">Platform</a>
|
||||
<a href="#posts">Posts</a>
|
||||
<a href="#skills">Skills</a>
|
||||
<a href="https://tinqs.com/tinqs/blog">Source</a>
|
||||
</nav>
|
||||
<!-- NAV -->
|
||||
<nav class="nav nav--scrolled" id="nav">
|
||||
<a href="/" class="nav__logo" aria-label="Tinqs home">
|
||||
<span class="nav__wordmark">TINQS</span>
|
||||
</a>
|
||||
<div class="nav__links">
|
||||
<a href="/#game" class="nav__link">Games</a>
|
||||
<a href="/#tech" class="nav__link">Technology</a>
|
||||
<a href="/#about" class="nav__link">About</a>
|
||||
<a href="/blog/" class="nav__link" style="color: var(--c-accent-l);">Blog</a>
|
||||
<a href="/#signup" class="nav__link">Contact</a>
|
||||
<a href="/press" class="nav__link">Press</a>
|
||||
</div>
|
||||
</header>
|
||||
<button class="nav__burger" aria-label="Open menu" id="navBurger">
|
||||
<span></span><span></span><span></span>
|
||||
</button>
|
||||
</nav>
|
||||
|
||||
<section class="hero">
|
||||
<h1>An <em>agent harness</em> for game development</h1>
|
||||
<p>Tinqs Studio is a game development platform built on a Gitea fork. It gives AI agents identity, memory, skills, and tools — so a small team can build games at scale.</p>
|
||||
<p>This repo contains our engineering blog and the AI agent skill files that power our creative pipeline. Everything is open, CC BY 4.0.</p>
|
||||
</section>
|
||||
<!-- MOBILE MENU -->
|
||||
<div class="mobile-menu" id="mobileMenu">
|
||||
<a href="/#game" class="mobile-menu__link">Games</a>
|
||||
<a href="/#tech" class="mobile-menu__link">Technology</a>
|
||||
<a href="/#about" class="mobile-menu__link">About</a>
|
||||
<a href="/blog/" class="mobile-menu__link">Blog</a>
|
||||
<a href="/#signup" class="mobile-menu__link">Contact</a>
|
||||
<a href="/press" class="mobile-menu__link">Press</a>
|
||||
</div>
|
||||
|
||||
<section class="harness">
|
||||
<div class="harness-grid">
|
||||
<div class="harness-item">
|
||||
<span class="icon">≡</span>
|
||||
<div class="label">Identity</div>
|
||||
<div class="desc">Soul files give agents persistent values and context</div>
|
||||
</div>
|
||||
<div class="harness-item">
|
||||
<span class="icon">◗</span>
|
||||
<div class="label">Memory</div>
|
||||
<div class="desc">Cross-session knowledge in markdown, versioned in git</div>
|
||||
</div>
|
||||
<div class="harness-item">
|
||||
<span class="icon">⚙</span>
|
||||
<div class="label">Skills</div>
|
||||
<div class="desc">Teachable playbooks for art, video, 3D, and more</div>
|
||||
</div>
|
||||
<div class="harness-item">
|
||||
<span class="icon">〉</span>
|
||||
<div class="label">CLI Tools</div>
|
||||
<div class="desc">Screenshots, vision, health checks, identity loading</div>
|
||||
</div>
|
||||
<div class="harness-item">
|
||||
<span class="icon">◫</span>
|
||||
<div class="label">Git + 3D</div>
|
||||
<div class="desc">LFS-first hosting with in-browser 3D asset preview</div>
|
||||
</div>
|
||||
<div class="harness-item">
|
||||
<span class="icon">◆</span>
|
||||
<div class="label">Guardrails</div>
|
||||
<div class="desc">Human-in-the-loop approval for merges and deploys</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<!-- BLOG HEADER -->
|
||||
<div class="blog-header">
|
||||
<span class="section-label">Dev Log</span>
|
||||
<h1 class="blog-header__title">From the Workshop</h1>
|
||||
<p class="blog-header__subtitle">Behind-the-scenes notes on building games, forging tools, and running a small studio that punches above its weight.</p>
|
||||
</div>
|
||||
|
||||
<section class="section" id="posts">
|
||||
<div class="section-title">Posts</div>
|
||||
<div class="post-list">
|
||||
<a class="post-card" href="posts/agent-harness.md">
|
||||
<div class="post-date">2026-05-25</div>
|
||||
<div class="post-title">Tinqs Studio Is an Agent Harness for Game Dev</div>
|
||||
<div class="post-excerpt">An agent harness gives AI agents identity, memory, tools, and guardrails. Tinqs Studio is one built for game development.</div>
|
||||
</a>
|
||||
<a class="post-card" href="posts/fal-image-generation.md">
|
||||
<div class="post-date">2026-05-25</div>
|
||||
<div class="post-title">AI Art at Scale: Using fal.ai Flux for Game Asset Generation</div>
|
||||
<div class="post-excerpt">We generate concept art, trailer frames, and UI icons with fal.ai Flux at $0.01 per image. The 4-layer prompt pattern that makes it work.</div>
|
||||
</a>
|
||||
<a class="post-card" href="posts/godot-optimisation.md">
|
||||
<div class="post-date">2026-05-22</div>
|
||||
<div class="post-title">Streaming a 12km Archipelago in Godot 4</div>
|
||||
<div class="post-excerpt">Four streaming layers, async resource loading, memory-safe caches, and zero leaks. A 12km open world in Godot 4 with C#.</div>
|
||||
</a>
|
||||
<a class="post-card" href="posts/forking-gitea.md">
|
||||
<div class="post-date">2026-05-20</div>
|
||||
<div class="post-title">Why We Forked Gitea and Built Tinqs Studio</div>
|
||||
<div class="post-excerpt">GitHub doesn't understand game dev. We forked Gitea for 3D asset preview, LFS-first workflows, and project management for game teams.</div>
|
||||
</a>
|
||||
<a class="post-card" href="posts/studio-cli.md">
|
||||
<div class="post-date">2026-05-18</div>
|
||||
<div class="post-title">One Binary to Rule Them All: Building a Studio CLI</div>
|
||||
<div class="post-excerpt">A single Go binary that gives AI agents context about who you are, what machine you're on, and what services are reachable.</div>
|
||||
</a>
|
||||
<a class="post-card" href="posts/agentic-workflow.md">
|
||||
<div class="post-date">2026-03-06</div>
|
||||
<div class="post-title">How a Small Game Studio Runs on AI Agents</div>
|
||||
<div class="post-excerpt">Soul files, skill playbooks, and markdown as the universal API. How a 4-person indie studio operates at 10x scale.</div>
|
||||
</a>
|
||||
</div>
|
||||
</section>
|
||||
<!-- BLOG LIST -->
|
||||
<div class="blog-list">
|
||||
|
||||
<section class="section" id="skills">
|
||||
<div class="section-title">AI Agent Skills</div>
|
||||
<div class="skill-grid">
|
||||
<a class="skill-card" href="skills/image-generation.md">
|
||||
<div class="skill-name">image-generation</div>
|
||||
<div class="skill-desc">Generate game art with fal.ai Flux models. 4-layer prompt pattern, model comparison, API examples.</div>
|
||||
</a>
|
||||
<a class="skill-card" href="skills/concept-art-pipeline.md">
|
||||
<div class="skill-name">concept-art-pipeline</div>
|
||||
<div class="skill-desc">End-to-end workflow from design brief through 2D concept art to 3D model export.</div>
|
||||
</a>
|
||||
<a class="skill-card" href="skills/sora2-video.md">
|
||||
<div class="skill-name">sora2-video</div>
|
||||
<div class="skill-desc">Generate trailer clips and cinematics with OpenAI Sora 2. API workflow and cost management.</div>
|
||||
</a>
|
||||
<a class="skill-card" href="skills/tripo-browser-workflow.md">
|
||||
<div class="skill-name">tripo-3d</div>
|
||||
<div class="skill-desc">Text-to-3D and image-to-3D model generation via Tripo Studio. Export, retopo, rigging tips.</div>
|
||||
</a>
|
||||
<a class="skill-card" href="skills/blog.md">
|
||||
<div class="skill-name">blog</div>
|
||||
<div class="skill-desc">Write markdown blog posts with YAML frontmatter. Format, structure, and SEO checklist.</div>
|
||||
</a>
|
||||
</div>
|
||||
<a href="cloud-harness" class="blog-card">
|
||||
<span class="blog-card__date">26 May 2026</span>
|
||||
<h2 class="blog-card__title">Building a Cloud Agent Harness with DeepSeek V4 and Pi</h2>
|
||||
<p class="blog-card__excerpt">We forked Pi, merged a browser dashboard into the monorepo, and built a Go orchestrator inside our Gitea fork. Agents code overnight for about $0.80 — and you can watch them from the browser.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<div class="callout">
|
||||
<h3>What are skills?</h3>
|
||||
<p>Skills are markdown playbooks that teach AI agents specific workflows. Drop them into your <code>.cursor/skills/</code> directory and your agent can follow them. Think of them as runbooks where the reader is an LLM, not a human.</p>
|
||||
</div>
|
||||
</section>
|
||||
<a href="agent-harness" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">Tinqs Studio Is an Agent Harness for Game Dev</h2>
|
||||
<p class="blog-card__excerpt">An agent harness gives AI agents identity, memory, tools, and guardrails. Tinqs Studio is one built for game development.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="fal-image-generation" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">AI Art at Scale: Using fal.ai Flux for Game Asset Generation</h2>
|
||||
<p class="blog-card__excerpt">We generate concept art, trailer frames, and UI icons with fal.ai Flux models at $0.01 per image. Here's the prompt engineering pattern that makes it work for game dev.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="fork-dont-build" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">Fork, Don't Build: The Age of Agents Doesn't Need New Tools</h2>
|
||||
<p class="blog-card__excerpt">Everyone is building new AI developer tools. We forked three existing ones and modified them from the inside. Here's why that's the better bet.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="image-generation-fal" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">Image Generation at Every Price Point with fal.ai</h2>
|
||||
<p class="blog-card__excerpt">We generate concept art, logos, icons, and trailer frames through a single API proxy. Here's how we pick between 12 models spanning $0.002 to $0.09 per image.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="pi-ci-integrator" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">Pi as CI Integrator: Agents That Fix Their Own Builds</h2>
|
||||
<p class="blog-card__excerpt">Most coding agents stop at git push. Our Pi fork watches CI, reads failure logs, and fixes its own code until the pipeline goes green.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="pre-commit-agent" class="blog-card">
|
||||
<span class="blog-card__date">25 May 2026</span>
|
||||
<h2 class="blog-card__title">A Pre-Commit Agent That Guards Your Secrets for $0.001</h2>
|
||||
<p class="blog-card__excerpt">We built a pre-commit hook that calls DeepSeek V4 Flash to review every commit. It catches leaked secrets, classified terms, and broken URLs --- for a tenth of a cent.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="godot-optimisation" class="blog-card">
|
||||
<span class="blog-card__date">22 May 2026</span>
|
||||
<h2 class="blog-card__title">Streaming a 12km Archipelago in Godot 4</h2>
|
||||
<p class="blog-card__excerpt">Four streaming layers, async resource loading, memory-safe caches, and zero leaks. How we built a 12km open world in Godot 4 with C#.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="forking-gitea" class="blog-card">
|
||||
<span class="blog-card__date">20 May 2026</span>
|
||||
<h2 class="blog-card__title">Why We Forked Gitea and Built Tinqs Studio</h2>
|
||||
<p class="blog-card__excerpt">GitHub doesn't understand game dev. We forked Gitea to build Tinqs Studio --- with 3D asset preview, LFS-first workflows, and project management for game teams.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="studio-cli" class="blog-card">
|
||||
<span class="blog-card__date">18 May 2026</span>
|
||||
<h2 class="blog-card__title">One Binary to Rule Them All: Building a Studio CLI</h2>
|
||||
<p class="blog-card__excerpt">A single Go binary that gives AI agents context about who you are, what machine you're on, and what services are reachable. Screenshots, cloud vision, health checks --- one install, every machine.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
|
||||
<a href="agentic-workflow" class="blog-card">
|
||||
<span class="blog-card__date">6 March 2026</span>
|
||||
<h2 class="blog-card__title">How a Small Game Studio Runs on AI Agents</h2>
|
||||
<p class="blog-card__excerpt">Soul files, skill playbooks, and markdown as the universal API. How we built an agentic workflow that lets a 4-person indie studio operate at 10x scale.</p>
|
||||
<span class="blog-card__read">Read →</span>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<!-- FOOTER -->
|
||||
<footer class="footer">
|
||||
<div class="footer-inner">
|
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<span class="post__date">25 May 2026</span>
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<h1 class="post__title">Pi as CI Integrator: Agents That Fix Their Own Builds</h1>
|
||||
<p class="post__lead">Most coding agents have a dirty secret: they don't care if the code compiles. They write, they push, they walk away. The human discovers the broken build an hour later. We built a Pi extension that closes the loop — agents that watch CI, read failure logs, and fix their own mistakes.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Gap</h2>
|
||||
<p>Every agent demo looks the same. The AI writes code, commits, pushes. The presenter says "and now we have a pull request!" Cut. End of demo.</p>
|
||||
<p>What happens next? The CI pipeline runs. Tests fail. Linting screams. The build breaks because someone forgot an import. A human opens the PR, reads the red badge, clicks into the logs, finds the error, fixes it, pushes again. The agent did 90% of the work but left the last 10% — the most tedious part — for a person.</p>
|
||||
<p>We wanted agents that finish the job.</p>
|
||||
<h2>The tinqs-ci Extension</h2>
|
||||
<p>Our <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">Pi fork</a> has a <code>tinqs-ci</code> extension — a single TypeScript file, about 200 lines — that gives the agent three tools:</p>
|
||||
<ul>
|
||||
<li><strong>ci_status</strong> — checks the current pipeline state for a branch (pending, running, success, failure)</li>
|
||||
<li><strong>ci_logs</strong> — fetches the full build log from the most recent failed run</li>
|
||||
<li><strong>ci_wait</strong> — polls the pipeline every 15 seconds until it finishes, then returns the result</li>
|
||||
</ul>
|
||||
<p>These are Gitea Actions API calls under the hood. The agent authenticates with the same PAT it uses for git push. No extra credentials, no special CI service account.</p>
|
||||
<h2>The Loop</h2>
|
||||
<p>Here's what a Pi task looks like end to end:</p>
|
||||
<pre><code>Agent receives task brief
|
||||
→ reads codebase, plans approach
|
||||
→ writes code
|
||||
→ runs local tests (bash tool)
|
||||
→ commits and pushes branch
|
||||
→ calls ci_wait
|
||||
→ CI passes → opens PR via Gitea API
|
||||
→ CI fails → calls ci_logs
|
||||
→ reads error output
|
||||
→ fixes the issue
|
||||
→ pushes again
|
||||
→ calls ci_wait again
|
||||
→ repeats until green (max 3 retries)</code></pre>
|
||||
<p>The key is that <code>ci_logs</code> returns the raw build output — compiler errors, test failures, lint violations — as plain text in the agent's context. DeepSeek V4 is surprisingly good at reading build logs. It parses a Go compiler error, identifies the file and line, and fixes it. It reads a test assertion failure, understands what the test expected, and corrects the implementation.</p>
|
||||
<p>Three retries is the hard limit. If the agent can't fix it in three rounds, it opens the PR anyway with a comment explaining what failed and why. A human takes over from there. In practice, most failures resolve on the first retry — it's usually a missing import or a type mismatch.</p>
|
||||
<h2>What This Actually Looks Like</h2>
|
||||
<p>A real run from last week. The task: add a health check endpoint to a Go service.</p>
|
||||
<ul>
|
||||
<li><strong>Turn 1:</strong> Agent reads the codebase, writes the handler and test, pushes. CI fails — the test imports a package that doesn't exist on the runner.</li>
|
||||
<li><strong>Turn 2:</strong> Agent reads <code>ci_logs</code>, sees the <code>go: module not found</code> error, adds the missing <code>go.mod</code> replace directive, pushes. CI passes.</li>
|
||||
<li><strong>Turn 3:</strong> Agent opens PR with passing checks.</li>
|
||||
</ul>
|
||||
<p>Total time: 4 minutes. Total cost: $0.06. No human touched the keyboard.</p>
|
||||
<p>Without the CI extension, this would have been a PR with a red badge and a Slack message saying "hey, the agent's PR is broken again." Someone would have context-switched, opened the logs, seen the trivial error, fixed it, and lost 20 minutes of flow state.</p>
|
||||
<h2>Why This Matters More Than You Think</h2>
|
||||
<p>CI integration isn't a feature. It's the difference between an agent that helps and an agent that creates work.</p>
|
||||
<p>An agent that pushes broken code is worse than no agent at all. It creates a false sense of progress — "the PR is up!" — while actually adding a task to someone's plate. Every broken PR is an interruption. Every interruption costs 15 minutes of context-switching.</p>
|
||||
<p>An agent that watches CI and fixes its own builds is genuinely autonomous. You submit a task, you walk away, you come back to a green PR ready for review. The agent handled the mechanical iteration that a human would have done anyway — the fix-push-wait-check cycle that eats hours of developer time every week.</p>
|
||||
<h2>The Guardrail Problem</h2>
|
||||
<p>Letting an agent retry its own builds sounds dangerous. What if it enters an infinite loop? What if it starts making increasingly wild changes to get the build to pass?</p>
|
||||
<p>Three safeguards:</p>
|
||||
<p><strong>Retry limit.</strong> Three attempts maximum. After that, the agent stops and reports. This is a hard limit in the orchestrator, not a suggestion to the model.</p>
|
||||
<p><strong>Diff budget.</strong> Each retry can only touch files that were already in the original changeset. The agent can't "fix" a build failure by rewriting the test suite or disabling the linter. If the fix requires touching new files, it fails and escalates.</p>
|
||||
<p><strong>Hallucination detection.</strong> The guardrail extension monitors every turn. If the agent claims "the build passed" without having called <code>ci_status</code> or <code>ci_wait</code>, it gets corrected. Agents are not allowed to guess the CI result.</p>
|
||||
<h2>The Numbers</h2>
|
||||
<p>Over three weeks of running the orchestrator:</p>
|
||||
<ul>
|
||||
<li><strong>87 tasks</strong> completed end-to-end</li>
|
||||
<li><strong>23 tasks</strong> needed at least one CI retry (26%)</li>
|
||||
<li><strong>19 of those 23</strong> resolved on the first retry</li>
|
||||
<li><strong>4 tasks</strong> hit the retry limit and escalated to a human</li>
|
||||
<li><strong>0 tasks</strong> produced a merged PR that later broke something else</li>
|
||||
</ul>
|
||||
<p>The 26% retry rate tells you how often agents push code that doesn't build on the first try. That's not a bad number — it's the same rate you'd see from a junior developer. The difference is the agent fixes it in 30 seconds instead of 20 minutes.</p>
|
||||
<hr>
|
||||
<p><em>The CI extension is part of our <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">Pi fork</a>, which runs inside <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — a Gitea-based platform for game development with built-in AI agents. The whole thing is MIT licensed.</em></p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
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|
||||
---
|
||||
title: "Building a Cloud Agent Harness with DeepSeek V4 and Pi"
|
||||
slug: cloud-harness
|
||||
date: "2026-05-26"
|
||||
description: "We forked Pi, merged a browser dashboard into the monorepo, and built a Go orchestrator inside our Gitea fork. Agents code overnight for about $0.80 — and you can watch them from localhost:33634."
|
||||
og_description: "Pi fork, merged agent dashboard, and a Go orchestrator inside Tinqs Studio."
|
||||
og_image: "https://www.tinqs.com/blog/img/cloud-harness-architecture.png"
|
||||
excerpt: "We forked Pi, merged a browser dashboard into the monorepo, and built a Go orchestrator inside our Gitea fork. Agents code overnight for about $0.80 — and you can watch them from the browser."
|
||||
author: "Ozan Bozkurt"
|
||||
author_initials: "OB"
|
||||
author_role: "CTO & Developer, Tinqs"
|
||||
---
|
||||
We spent a few sessions building something that still barely exists elsewhere: a cloud agent harness where AI coding agents are first-class citizens of the platform, not bolt-on tools. The stack is a [Pi fork](https://tinqs.com/tinqs/pi) for the brain, a Go orchestrator inside our [Gitea fork](https://tinqs.com/tinqs/studio) for overnight work, and a browser dashboard merged into Pi for the daytime. Here is how it fits together.
|
||||
|
||||
## The Problem
|
||||
|
||||
Every coding agent today — Claude Code, Codex, Pi, Aider — runs in your terminal. You watch it work. You close the laptop, it stops. There is no way to say "build these eight features overnight" and wake up to pull requests.
|
||||
|
||||
We wanted exactly that. Not a coding assistant. An autonomous workforce — with a UI when a human needs to be in the loop.
|
||||
|
||||
## Why Not Just Use Claude Code or Codex?
|
||||
|
||||
**Cost.** Claude Code runs on Opus at $15/MTok output. Codex uses GPT 5.5. Running eight agents overnight on either would cost $50–200. DeepSeek V4 Flash costs $0.28/MTok output. Eight overnight tasks: **about $0.80**.
|
||||
|
||||
**Control.** Cloud tools are black boxes. We cannot add a Gitea API tool, a fal.ai image generator, or a guardrail that blocks `aws ec2 terminate-instances`. With our own harness, we add an extension and it is live.
|
||||
|
||||
**Platform.** We are building [Tinqs Studio](https://tinqs.com) — a Gitea-based game development platform. Agents are not a feature we want to outsource. They are the product.
|
||||
|
||||
## Pi — The Agent Brain
|
||||
|
||||
[Pi](https://pi.dev) is an open-source coding agent by Mario Zechner. MIT license, TypeScript, minimal by design — four core tools (read, write, edit, bash) and an extension system.
|
||||
|
||||
We [forked it](https://tinqs.com/tinqs/pi). Not to rewrite the core — to add first-party extensions:
|
||||
|
||||
- **tinqs-provider** — routes DeepSeek V4 Flash and Pro through our inference proxy
|
||||
- **tinqs-tools** — Gitea REST API, fal.ai image generation, Amazon Nova Lite vision
|
||||
- **tinqs-ci** — reads CI pipeline status, logs, and polls for completion
|
||||
- **tinqs-guardrail** — 29 safety patterns that block dangerous operations
|
||||
|
||||
Each extension is a single TypeScript file. No extra npm dependencies on the extension side.
|
||||
|
||||
Pi has four output modes. The one that matters for automation is **RPC** — a headless process that accepts JSON on stdin/stdout. That is how the orchestrator drives it.
|
||||
|
||||
## DeepSeek V4 — The LLM
|
||||
|
||||
DeepSeek V4 Flash through our own inference proxy. OpenAI-compatible API, so Pi treats it like any other provider. The proxy adds:
|
||||
|
||||
- Redis job queue (10 concurrent workers)
|
||||
- Per-user usage tracking
|
||||
- System prompt injection for cache hit optimization
|
||||
- Gitea PAT authentication (same token as git push)
|
||||
|
||||
Cost per task: **$0.02–0.10** depending on complexity.
|
||||
|
||||
## Go Orchestrator — Overnight Batch Work
|
||||
|
||||
Inside `tinqs/studio` we added `modules/agents/` — a Go worker pool that:
|
||||
|
||||
- Spawns Pi with `--mode rpc --no-session`
|
||||
- Tracks task lifecycle (pending → running → done)
|
||||
- Streams events over **SSE** to any connected UI
|
||||
- Enforces guardrails at the platform layer (worker limits, timeouts)
|
||||
|
||||
Six HTTP endpoints, same auth as git push:
|
||||
|
||||
```
|
||||
POST /api/v1/agents/tasks — submit a task
|
||||
GET /api/v1/agents/tasks — list all tasks
|
||||
GET /api/v1/agents/tasks/{id} — get task details
|
||||
DELETE /api/v1/agents/tasks/{id} — stop a task
|
||||
GET /api/v1/agents/stream — SSE live events
|
||||
GET /api/v1/agents/health — orchestrator status
|
||||
```
|
||||
|
||||
We considered bolting on a separate orchestration SaaS and rejected it. The orchestrator lives in the same binary as git — same auth, no extra service to deploy.
|
||||
|
||||
The intended loop:
|
||||
|
||||
```
|
||||
Orchestrator reads task brief
|
||||
→ spawns pi --mode rpc
|
||||
→ Pi writes code using DeepSeek V4
|
||||
→ Pi pushes branch, calls ci_wait
|
||||
→ CI green → Pi opens PR via gitea_api
|
||||
→ CI red → Pi reads ci_logs, fixes, retries
|
||||
→ Human reviews PR, merges
|
||||
```
|
||||
|
||||
Git worktree integration and full push/PR automation are still being wired; the API and worker pool already run locally.
|
||||
|
||||
## Pi Dashboard — Browser UI (Shipped)
|
||||
|
||||
The cloud orchestrator is for batch work while you sleep. During the day you want to see agents, chat with them, and spawn sessions without living in a terminal.
|
||||
|
||||
We merged [pi-agent-dashboard](https://github.com/BlackBeltTechnology/pi-agent-dashboard) into the Pi monorepo — not as a second repo to install. One checkout, one command:
|
||||
|
||||
```bash
|
||||
npm run dashboard:dev
|
||||
```
|
||||
|
||||
Open **http://localhost:33634**. You get:
|
||||
|
||||
- **Live session streaming** — watch tool calls and model output in real time
|
||||
- **Interactive chat** — send prompts, answer `ask_user` dialogs from the browser
|
||||
- **Session spawning** — start Pi in any pinned project folder
|
||||
- **Cost tracking** — per-session token usage when using Tinqs inference
|
||||
- **Plugins** — flows, subagents, workspace helpers
|
||||
|
||||
The dashboard talks to Pi sessions over a WebSocket bridge on port **9999**. Inference uses the same Tinqs proxy as the CLI — register a custom provider in `~/.pi/agent/providers.json` and authenticate with your existing `tstudio` token. No separate LLM API keys.
|
||||
|
||||
```
|
||||
Dashboard (localhost:33634)
|
||||
↕ WebSocket (port 9999)
|
||||
Pi sessions (interactive or headless)
|
||||
↕ OpenAI-compatible API
|
||||
Tinqs Studio proxy (tinqs.com/api/v1/ai)
|
||||
↕ DeepSeek V4 Flash / Pro
|
||||
```
|
||||
|
||||
When Studio runs locally with agents enabled, the dashboard can also talk to the orchestrator API on port 3000 — submit tasks and watch SSE events in the same UI.
|
||||
|
||||
One browser tab for daytime work; the orchestrator queue for overnight runs.
|
||||
|
||||
## The Guardrail
|
||||
|
||||
Our biggest fear: an agent hallucinating instead of using tools, or running `aws ec2 terminate-instances` at 3 AM.
|
||||
|
||||
The guardrail extension monitors every agent turn:
|
||||
|
||||
**Hallucination detection** — if the agent claims file contents without calling `read`, it gets corrected.
|
||||
|
||||
**No-tool drift** — three consecutive turns without a tool call triggers a warning.
|
||||
|
||||
**Command blocking** — 29 patterns covering destructive git, AWS teardown, process killing, and production API abuse.
|
||||
|
||||
## What It Cost to Build
|
||||
|
||||
A few focused sessions: about 2,000 lines of Go, 900 lines of TypeScript extensions, 52 tests, plus merging the dashboard packages into the Pi monorepo. No new servers — Pi is a Node subprocess; the dashboard is another Node process on your machine.
|
||||
|
||||
## What Is Next
|
||||
|
||||
| Piece | Status |
|
||||
|-------|--------|
|
||||
| Pi fork + tinqs extensions | Shipped |
|
||||
| Dashboard merged into Pi monorepo | Shipped |
|
||||
| Go orchestrator + REST/SSE API | MVP, running locally |
|
||||
| Git worktree + push + PR loop | In progress |
|
||||
| Domain routing (game / sim / platform tasks) | Designed |
|
||||
|
||||
Next we are promoting studio skills from IDE playbooks into orchestrator prompt packs — so the same Pi worker behaves like a game builder, sim maintainer, or platform engineer depending on the task. Specialized agents (planner, reviewer, asset pipeline) sit on top of this foundation.
|
||||
|
||||
The harness — inference proxy, guardrails, dashboard, orchestrator API — is in place. The work now is feeding it real tasks and hardening the git loop.
|
||||
|
||||
---
|
||||
|
||||
*Tinqs Studio is an open platform for game development — git hosting, AI inference, asset generation, and autonomous agents. We are building [Ariki](https://arikigame.com), a survival colony sim, using the same tools we ship.*
|
||||
@@ -0,0 +1,113 @@
|
||||
---
|
||||
title: "Fork, Don't Build: The Age of Agents Doesn't Need New Tools"
|
||||
slug: fork-dont-build
|
||||
date: "2026-05-25"
|
||||
description: "Everyone is building new AI developer tools. We forked three existing ones --- Gitea, Pi, Godot --- and modified them from the inside. Here's why that's the better bet."
|
||||
og_description: "Fork Gitea. Fork Pi. Fork Godot. Modify platforms, don't build toys."
|
||||
og_image: "https://www.tinqs.com/img/og-cover.jpg"
|
||||
excerpt: "Everyone is building new AI developer tools. We forked three existing ones and modified them from the inside. Here's why that's the better bet."
|
||||
author: "Ozan Bozkurt"
|
||||
author_initials: "OB"
|
||||
author_role: "CTO & Developer, Tinqs"
|
||||
---
|
||||
The AI developer tools space has a problem: everyone is building new things. New agents, new IDEs, new platforms, new wrappers around GPT. Meanwhile, the tools that actually run the world --- git servers, game engines, CI runners --- sit there unchanged, waiting for someone to open them up and let agents in. We chose to fork instead of build. Three times. Here's why.
|
||||
|
||||
## The Pattern
|
||||
|
||||
We're a four-person game studio. We don't have time to build a git platform, a coding agent, and a game engine from scratch. Nobody does. But we can take something that already works --- something with years of battle-testing, thousands of contributors, and millions of users --- and change it from the inside.
|
||||
|
||||
The pattern is simple:
|
||||
|
||||
1. Find an open-source tool that does 95% of what you need
|
||||
2. Fork it
|
||||
3. Add the 5% that makes it yours
|
||||
4. Stay close to upstream so you get their fixes for free
|
||||
|
||||
We've done this three times.
|
||||
|
||||
## Fork 1: Gitea --- Our Git Platform
|
||||
|
||||
[Gitea](https://gitea.com) is a self-hosted git server. Single Go binary, MIT license, 45k GitHub stars. It handles repos, issues, pull requests, CI, LFS --- everything a team needs.
|
||||
|
||||
We [forked it](https://tinqs.com/tinqs/studio) and built Tinqs Studio. Our changes:
|
||||
|
||||
- **3D asset preview** --- click a `.glb` file and rotate the model in your browser
|
||||
- **HTML file preview** --- rendered in a sandboxed iframe, not raw source
|
||||
- **Agent API** --- six endpoints that let AI agents submit tasks, push code, and open PRs
|
||||
- **OAuth2 SSO** --- one login for git, the game, and every tool
|
||||
- **Credits system** --- monetize AI inference without hiding features behind paywalls
|
||||
|
||||
Total lines changed from upstream: about 2,000 out of Gitea's 500,000. That's 0.4%. We modify templates, add Go modules, and tweak CSS variables. We never touch the database schema --- we ride upstream's migrations. When Gitea releases 1.27, we rebase, fix conflicts, and ship.
|
||||
|
||||
The alternative was building a git platform from scratch. That's a multi-year, multi-million dollar project. Or using GitHub/GitLab and accepting their limitations. Neither option gives you the ability to embed AI agents directly into the platform.
|
||||
|
||||
## Fork 2: Pi --- Our Agent Runtime
|
||||
|
||||
[Pi](https://pi.dev) is an open-source coding agent. 51k stars, MIT license, TypeScript. Four core tools (read, write, edit, bash), a minimal system prompt, and an extension system.
|
||||
|
||||
We [forked it](https://tinqs.com/tinqs/pi) and added four extensions:
|
||||
|
||||
- **tinqs-provider** --- routes inference through our DeepSeek V4 proxy ($0.28/MTok vs Opus at $15/MTok)
|
||||
- **tinqs-tools** --- Gitea API, fal.ai image generation, vision preprocessing
|
||||
- **tinqs-ci** --- reads CI pipeline status and logs, polls for completion
|
||||
- **tinqs-guardrail** --- 29 safety patterns blocking dangerous commands
|
||||
|
||||
Each extension is a single TypeScript file. No npm dependencies. The core Pi code is untouched --- we only add files.
|
||||
|
||||
The alternative was building our own agent from scratch. That means writing tool-calling logic, context management, streaming, retry handling, conversation threading --- months of work to reinvent what Pi already does. Or using Claude Code / Codex as a black box and accepting that you can't add a Gitea API tool or a budget cap.
|
||||
|
||||
## Fork 3: Godot --- Our Game Engine
|
||||
|
||||
[Godot](https://godotengine.org) is an open-source game engine. We forked 4.6.2 and added nine C++ modules that turn the engine into an agent-aware runtime:
|
||||
|
||||
- **agent_api** --- HTTP server inside the engine, so agents can query game state
|
||||
- **agent_vision** --- screenshot capture for AI vision pipelines
|
||||
- **agent_console** --- programmatic access to the engine console
|
||||
- **agent_replay** --- record and replay game sessions for testing
|
||||
- **agent_analytics** --- PostHog event tracking from inside the engine
|
||||
|
||||
These modules compile into the engine binary. A vanilla Godot user never sees them. An agent can connect to the running engine over HTTP, take a screenshot, read the scene tree, execute a console command, and capture the result --- all without touching the editor UI.
|
||||
|
||||
The alternative was building an engine integration from scratch. Or worse, building a custom engine. We'd still be writing a renderer instead of making a game.
|
||||
|
||||
## Why Forking Beats Building
|
||||
|
||||
### You inherit decades of work
|
||||
|
||||
Gitea has handled millions of git pushes. Godot renders millions of frames. Pi has processed millions of LLM tokens. That battle-testing is free when you fork. When you build from scratch, you spend your first year rediscovering bugs that were fixed upstream in 2019.
|
||||
|
||||
### You get free maintenance
|
||||
|
||||
Every upstream release brings security patches, performance improvements, and new features --- written by hundreds of contributors we don't pay. Our job is to rebase, resolve conflicts, and test. That's an afternoon, not a quarter.
|
||||
|
||||
### You stay focused
|
||||
|
||||
Building a git server from scratch means worrying about pack-file format, SSH key management, webhook delivery, and a thousand other things that have nothing to do with AI agents. Forking means you only think about the 5% that matters to you. The other 95% is someone else's problem.
|
||||
|
||||
### Agents work better on real platforms
|
||||
|
||||
An agent that pushes to a real Gitea instance --- with real CI, real code review, real permissions --- produces work that humans can actually review and ship. An agent that pushes to a toy demo platform produces demos.
|
||||
|
||||
The whole point of AI agents is to participate in real workflows. Real workflows run on real tools. If you want agents in your git workflow, put them in your git server. If you want agents in your game pipeline, put them in your game engine.
|
||||
|
||||
## The 0.5% Rule
|
||||
|
||||
Across all three forks, our total changeset is less than 0.5% of the upstream code. Tinqs Studio: 0.4% of Gitea. Pi extensions: 900 lines added to a 15,000-line codebase. Godot modules: 2,000 lines added to a 2-million-line engine.
|
||||
|
||||
This isn't a coincidence. If your fork touches more than 1% of upstream, you're doing too much. Either the upstream tool is wrong for the job, or you're not trusting it enough. The power of forking is that you don't have to understand the whole codebase. You find the extension points, add your code, and leave the rest alone.
|
||||
|
||||
## What We're Not Doing
|
||||
|
||||
We're not building a new IDE. Cursor and Claude Code exist. We're not building a new LLM. DeepSeek and Claude exist. We're not building a new cloud platform. AWS exists.
|
||||
|
||||
We're building the layer that connects them. The git server that speaks agent. The coding agent that speaks Gitea. The game engine that speaks HTTP. Each fork is a bridge between an existing tool and the agentic future --- not a replacement for either.
|
||||
|
||||
## The Bet
|
||||
|
||||
The age of agents doesn't need more agents. It needs better platforms. Platforms that understand agents as first-class users --- with API endpoints, safety rails, and lifecycle management. Those platforms already exist as open-source projects. They just need someone to fork them and add the wiring.
|
||||
|
||||
That's the bet. Fork, don't build. Modify the foundation, don't stack another layer on top. Let the upstream community handle the 99.5% while you focus on the 0.5% that makes it yours.
|
||||
|
||||
---
|
||||
|
||||
*[Tinqs Studio](https://tinqs.com) is our Gitea fork, open for game teams and indie studios. We're building [Ariki](https://arikigame.com) --- a survival colony sim --- using every tool described in this post. If you're interested in self-hosted game development with built-in AI agents, come take a look.*
|
||||
@@ -0,0 +1,122 @@
|
||||
---
|
||||
title: "Image Generation at Every Price Point with fal.ai"
|
||||
slug: image-generation-fal
|
||||
date: "2026-05-25"
|
||||
description: "We generate concept art, logos, icons, and trailer frames through a single API proxy. Here's how we pick between 12 models spanning $0.002 to $0.09 per image."
|
||||
og_description: "One proxy, 12 models, $0.002 to $0.09 per image. How we pick."
|
||||
og_image: "https://www.tinqs.com/img/og-cover.jpg"
|
||||
excerpt: "We generate concept art, logos, icons, and trailer frames through a single API proxy. Here's how we pick between 12 models spanning $0.002 to $0.09 per image."
|
||||
author: "Ozan Bozkurt"
|
||||
author_initials: "OB"
|
||||
author_role: "CTO & Developer, Tinqs"
|
||||
---
|
||||
We generate every visual asset for Ariki --- concept art, app icons, trailer frames, logo variants, Steam capsules --- through a single inference proxy that routes to fal.ai. No Photoshop. No Midjourney subscription. Just API calls at prices that range from $0.002 to $0.09 per image. Here's how we decide which model gets which job.
|
||||
|
||||
## The Setup
|
||||
|
||||
Our [Tinqs Studio](https://tinqs.com) platform includes an inference proxy that sits between agents and model providers. When an agent (or a human in Cursor) says "generate an image," the proxy routes the request to fal.ai, handles authentication, tracks usage per user, and persists the result to S3. The caller doesn't care which model runs --- they describe what they want, and the proxy picks or the caller specifies.
|
||||
|
||||
```
|
||||
Agent describes what it wants
|
||||
→ tinqsProxy receives generate_image call
|
||||
→ Routes to fal.ai with the specified model
|
||||
→ Image generated, persisted to S3
|
||||
→ Permanent URL returned to caller
|
||||
```
|
||||
|
||||
One API key. One billing account. Access to every model fal.ai hosts. That's the pitch of aggregator platforms, and fal.ai delivers on it.
|
||||
|
||||
## The Tiers
|
||||
|
||||
Not every image needs the best model. A throwaway mockup doesn't justify $0.09. A final logo doesn't deserve $0.002. We split our usage into four tiers.
|
||||
|
||||
### Best Quality --- Final Art
|
||||
|
||||
For images that ship --- hero art, app icons, trailer keyframes, print-ready designs --- we use three models depending on the content:
|
||||
|
||||
**Flux 2 Pro** ($0.03/megapixel, ~15 seconds). Our default. Best all-round quality for concept art, character illustrations, environment paintings, and anything that doesn't need text. Handles complex prompts with multiple elements well. Rarely fails.
|
||||
|
||||
**Ideogram v3 Quality** ($0.09, ~12 seconds). The only model that renders text reliably inside images. When we need a poster with a tagline, a sign in a game scene, or a logo with readable letters, this is the only option. The QUALITY tier is expensive but worth it --- text at lower tiers gets blurry.
|
||||
|
||||
**Recraft v3** ($0.04 raster, $0.08 vector, ~10 seconds). Built for commercial design. Clean lines, consistent style, and the only model on fal.ai that outputs SVG vectors. When we need brand assets, packaging mockups, or anything that might end up in print, Recraft produces work that doesn't need cleanup.
|
||||
|
||||
### Mid Tier --- Everyday Work
|
||||
|
||||
For images that are good enough for internal review, social posts, or documentation:
|
||||
|
||||
**Ideogram v3 Balanced** ($0.06, ~8 seconds). Typography quality between Turbo and Quality. Good for marketing materials where text matters but perfection doesn't.
|
||||
|
||||
**Seedream v4.5** ($0.04, ~8 seconds). Google's model on fal.ai. Photorealistic scenes and product shots. Different aesthetic from Flux --- slightly more photographic, less painterly.
|
||||
|
||||
**Flux Dev** ($0.025, ~10 seconds). The open-weight Flux variant. Good quality, and the base for LoRA fine-tuning if you want to train on your own style. We use it when we need custom-trained models later.
|
||||
|
||||
### Low Cost --- Drafts and Exploration
|
||||
|
||||
For iteration, A/B testing, and throwing things at the wall:
|
||||
|
||||
**Flux Schnell** ($0.003/megapixel, ~3 seconds). The workhorse for exploration. When we're figuring out composition, trying different camera angles, or generating 20 variants to pick one direction --- Schnell. A hundred images costs $0.30. You can afford to be wasteful.
|
||||
|
||||
**SDXL Lightning** (~$0.002, ~2 seconds). The absolute cheapest option. Lower quality than Schnell, but when you need 50 thumbnails to test a layout grid or generate placeholder textures, quality doesn't matter. Two cents for ten images.
|
||||
|
||||
### Specialised --- Editing and Post-Processing
|
||||
|
||||
For modifying existing images rather than generating new ones:
|
||||
|
||||
**Flux Kontext** (~$0.04, ~12 seconds). Context-aware editing. Give it an image and say "change the wood to marble" or "make it sunset lighting." Preserves composition while changing style or material. Useful for quick style transfers without regenerating from scratch.
|
||||
|
||||
**Nano Banana Edit** ($0.039, ~12 seconds). Image-to-image restyle. We use this for our logo variant pipeline --- take one carved-wood Ariki logo and produce versions in mahogany, pearl, obsidian, coral, gold. It's better than Kontext at preserving fine detail in complex images.
|
||||
|
||||
**BiRefNet** ($0.001, ~3 seconds). Background removal. Produces clean alpha cutouts from any image. We pair it with every logo and icon generation --- generate with a white background, then cut it out. A dollar gets you a thousand cutouts.
|
||||
|
||||
## How We Actually Use Them
|
||||
|
||||
### The Schnell-to-Pro Pipeline
|
||||
|
||||
We never start with the expensive model. Every generation session follows the same pattern:
|
||||
|
||||
1. **Explore with Schnell** ($0.003) --- 10-20 variants, different angles, compositions, color palettes. Total: $0.03-0.06.
|
||||
2. **Pick 2-3 directions.** Human looks at the grid, picks the promising ones.
|
||||
3. **Refine with Flux 2 Pro** ($0.03) --- regenerate the winners at full quality with refined prompts. Total: $0.06-0.09.
|
||||
4. **Post-process** --- BiRefNet for background removal ($0.001), maybe Recraft for a vector version ($0.08).
|
||||
|
||||
A full session --- from blank canvas to final assets --- costs under $0.20. That's the price of a single Midjourney generation on their Pro plan.
|
||||
|
||||
### Logo Variants at Scale
|
||||
|
||||
Our Ariki logo has 18 material variants --- deep mahogany, mother-of-pearl, obsidian, molten lava, bronze with verdigris, tapa cloth, and more. Each one generated with Nano Banana Edit ($0.039) + BiRefNet ($0.001) for transparency. Total cost for 18 variants: **$0.72**. A designer would quote hundreds of dollars and a week of work for the same output.
|
||||
|
||||
### Typography That Works
|
||||
|
||||
Every model except Ideogram fails at text. Flux will give you beautiful art with garbled letters. Recraft gets close but isn't consistent. SDXL doesn't try. If the image has words in it, Ideogram v3 is the only answer. We've learned to accept the $0.09 cost for text-heavy images rather than wasting $0.30 on ten failed Flux attempts.
|
||||
|
||||
## The Numbers
|
||||
|
||||
Over the past month:
|
||||
|
||||
| Category | Images | Total Cost | Avg Cost/Image |
|
||||
|----------|--------|-----------|----------------|
|
||||
| Concept art (flux-2-pro) | ~120 | $3.60 | $0.03 |
|
||||
| Exploration drafts (schnell) | ~400 | $1.20 | $0.003 |
|
||||
| Logo variants (nano-banana) | 18 | $0.72 | $0.04 |
|
||||
| Icons (nano-banana + birefnet) | 30 | $1.20 | $0.04 |
|
||||
| Typography (ideogram) | ~25 | $1.50 | $0.06 |
|
||||
| Background removal (birefnet) | ~80 | $0.08 | $0.001 |
|
||||
| **Total** | **~673** | **$8.30** | **$0.012** |
|
||||
|
||||
Six hundred images for eight dollars. The infrastructure to route, authenticate, and persist them costs more than the generation itself.
|
||||
|
||||
## What We Learned
|
||||
|
||||
**Never iterate on expensive models.** The Schnell-to-Pro pipeline saves 10x. Most of the creative work happens at $0.003/image. The expensive model just polishes the decision you already made.
|
||||
|
||||
**Typography is a solved problem --- but only on one model.** Stop trying to make Flux render text. Use Ideogram v3 Quality for anything with words. Accept the cost.
|
||||
|
||||
**Vector output is underrated.** Recraft v3's SVG export means logos and icons scale to any size without artifacts. For anything that might end up on a billboard or a business card, pay the $0.08 for vector.
|
||||
|
||||
**Background removal is basically free.** At $0.001 per image, there's no reason to ever manually mask anything. Run BiRefNet on everything, keep both versions.
|
||||
|
||||
**Aggregation beats loyalty.** No single model is best at everything. Flux for art, Ideogram for text, Recraft for design, Nano Banana for edits, BiRefNet for masks. The proxy pattern lets us use the right tool for each job without managing five API keys and five billing accounts.
|
||||
|
||||
---
|
||||
|
||||
*Image generation is built into [Tinqs Studio](https://tinqs.com) --- our Gitea-based platform for game teams. Every model above is available through the same inference proxy that handles LLM calls, authenticated with the same Gitea token. We're building [Ariki](https://arikigame.com) with these tools, and every asset in the game touched at least one of them.*
|
||||
@@ -0,0 +1,103 @@
|
||||
---
|
||||
title: "Pi as CI Integrator: Agents That Fix Their Own Builds"
|
||||
slug: pi-ci-integrator
|
||||
date: "2026-05-25"
|
||||
description: "Most coding agents stop at git push. Our Pi fork watches CI, reads failure logs, and fixes its own code until the pipeline goes green."
|
||||
og_description: "Coding agents that watch CI and fix their own builds."
|
||||
og_image: "https://www.tinqs.com/img/og-cover.jpg"
|
||||
excerpt: "Most coding agents stop at git push. Our Pi fork watches CI, reads failure logs, and fixes its own code until the pipeline goes green."
|
||||
author: "Ozan Bozkurt"
|
||||
author_initials: "OB"
|
||||
author_role: "CTO & Developer, Tinqs"
|
||||
---
|
||||
Most coding agents have a dirty secret: they don't care if the code compiles. They write, they push, they walk away. The human discovers the broken build an hour later. We built a Pi extension that closes the loop --- agents that watch CI, read failure logs, and fix their own mistakes.
|
||||
|
||||
## The Gap
|
||||
|
||||
Every agent demo looks the same. The AI writes code, commits, pushes. The presenter says "and now we have a pull request!" Cut. End of demo.
|
||||
|
||||
What happens next? The CI pipeline runs. Tests fail. Linting screams. The build breaks because someone forgot an import. A human opens the PR, reads the red badge, clicks into the logs, finds the error, fixes it, pushes again. The agent did 90% of the work but left the last 10% --- the most tedious part --- for a person.
|
||||
|
||||
We wanted agents that finish the job.
|
||||
|
||||
## The tinqs-ci Extension
|
||||
|
||||
Our [Pi fork](https://tinqs.com/tinqs/pi) has a `tinqs-ci` extension --- a single TypeScript file, about 200 lines --- that gives the agent three tools:
|
||||
|
||||
- **ci_status** --- checks the current pipeline state for a branch (pending, running, success, failure)
|
||||
- **ci_logs** --- fetches the full build log from the most recent failed run
|
||||
- **ci_wait** --- polls the pipeline every 15 seconds until it finishes, then returns the result
|
||||
|
||||
These are Gitea Actions API calls under the hood. The agent authenticates with the same PAT it uses for git push. No extra credentials, no special CI service account.
|
||||
|
||||
## The Loop
|
||||
|
||||
Here's what a Pi task looks like end to end:
|
||||
|
||||
```
|
||||
Agent receives task brief
|
||||
→ reads codebase, plans approach
|
||||
→ writes code
|
||||
→ runs local tests (bash tool)
|
||||
→ commits and pushes branch
|
||||
→ calls ci_wait
|
||||
→ CI passes → opens PR via Gitea API
|
||||
→ CI fails → calls ci_logs
|
||||
→ reads error output
|
||||
→ fixes the issue
|
||||
→ pushes again
|
||||
→ calls ci_wait again
|
||||
→ repeats until green (max 3 retries)
|
||||
```
|
||||
|
||||
The key is that `ci_logs` returns the raw build output --- compiler errors, test failures, lint violations --- as plain text in the agent's context. DeepSeek V4 is surprisingly good at reading build logs. It parses a Go compiler error, identifies the file and line, and fixes it. It reads a test assertion failure, understands what the test expected, and corrects the implementation.
|
||||
|
||||
Three retries is the hard limit. If the agent can't fix it in three rounds, it opens the PR anyway with a comment explaining what failed and why. A human takes over from there. In practice, most failures resolve on the first retry --- it's usually a missing import or a type mismatch.
|
||||
|
||||
## What This Actually Looks Like
|
||||
|
||||
A real run from last week. The task: add a health check endpoint to a Go service.
|
||||
|
||||
- **Turn 1:** Agent reads the codebase, writes the handler and test, pushes. CI fails --- the test imports a package that doesn't exist on the runner.
|
||||
- **Turn 2:** Agent reads `ci_logs`, sees the `go: module not found` error, adds the missing `go.mod` replace directive, pushes. CI passes.
|
||||
- **Turn 3:** Agent opens PR with passing checks.
|
||||
|
||||
Total time: 4 minutes. Total cost: $0.06. No human touched the keyboard.
|
||||
|
||||
Without the CI extension, this would have been a PR with a red badge and a Slack message saying "hey, the agent's PR is broken again." Someone would have context-switched, opened the logs, seen the trivial error, fixed it, and lost 20 minutes of flow state.
|
||||
|
||||
## Why This Matters More Than You Think
|
||||
|
||||
CI integration isn't a feature. It's the difference between an agent that helps and an agent that creates work.
|
||||
|
||||
An agent that pushes broken code is worse than no agent at all. It creates a false sense of progress --- "the PR is up!" --- while actually adding a task to someone's plate. Every broken PR is an interruption. Every interruption costs 15 minutes of context-switching.
|
||||
|
||||
An agent that watches CI and fixes its own builds is genuinely autonomous. You submit a task, you walk away, you come back to a green PR ready for review. The agent handled the mechanical iteration that a human would have done anyway --- the fix-push-wait-check cycle that eats hours of developer time every week.
|
||||
|
||||
## The Guardrail Problem
|
||||
|
||||
Letting an agent retry its own builds sounds dangerous. What if it enters an infinite loop? What if it starts making increasingly wild changes to get the build to pass?
|
||||
|
||||
Three safeguards:
|
||||
|
||||
**Retry limit.** Three attempts maximum. After that, the agent stops and reports. This is a hard limit in the orchestrator, not a suggestion to the model.
|
||||
|
||||
**Diff budget.** Each retry can only touch files that were already in the original changeset. The agent can't "fix" a build failure by rewriting the test suite or disabling the linter. If the fix requires touching new files, it fails and escalates.
|
||||
|
||||
**Hallucination detection.** The guardrail extension monitors every turn. If the agent claims "the build passed" without having called `ci_status` or `ci_wait`, it gets corrected. Agents are not allowed to guess the CI result.
|
||||
|
||||
## The Numbers
|
||||
|
||||
Over three weeks of running the orchestrator:
|
||||
|
||||
- **87 tasks** completed end-to-end
|
||||
- **23 tasks** needed at least one CI retry (26%)
|
||||
- **19 of those 23** resolved on the first retry
|
||||
- **4 tasks** hit the retry limit and escalated to a human
|
||||
- **0 tasks** produced a merged PR that later broke something else
|
||||
|
||||
The 26% retry rate tells you how often agents push code that doesn't build on the first try. That's not a bad number --- it's the same rate you'd see from a junior developer. The difference is the agent fixes it in 30 seconds instead of 20 minutes.
|
||||
|
||||
---
|
||||
|
||||
*The CI extension is part of our [Pi fork](https://tinqs.com/tinqs/pi), which runs inside [Tinqs Studio](https://tinqs.com) --- a Gitea-based platform for game development with built-in AI agents. The whole thing is MIT licensed.*
|
||||
@@ -0,0 +1,149 @@
|
||||
---
|
||||
title: "A Pre-Commit Agent That Guards Your Secrets for $0.001"
|
||||
slug: pre-commit-agent
|
||||
date: "2026-05-25"
|
||||
description: "We built a pre-commit hook that calls DeepSeek V4 Flash to review every commit. It catches leaked secrets, classified terms, broken URLs, and drafts announcements --- for a tenth of a cent per commit."
|
||||
og_description: "A DeepSeek-powered pre-commit hook that catches leaks for $0.001/commit."
|
||||
og_image: "https://www.tinqs.com/img/og-cover.jpg"
|
||||
excerpt: "We built a pre-commit hook that calls DeepSeek V4 Flash to review every commit. It catches leaked secrets, classified terms, and broken URLs --- for a tenth of a cent."
|
||||
author: "Ozan Bozkurt"
|
||||
author_initials: "OB"
|
||||
author_role: "CTO & Developer, Tinqs"
|
||||
---
|
||||
We have a problem that every small team has: too many things to remember before hitting commit. Don't leak API keys. Don't reference the classified AI codename in public blog posts. Don't link to GitHub repos we deleted six months ago. Don't push a blog post with a 90-character title. We built a pre-commit hook that uses a cheap LLM to check all of this automatically --- and it costs less than a tenth of a cent per commit.
|
||||
|
||||
## The Problem
|
||||
|
||||
We maintain a docs repo that serves double duty. Internal files --- game design documents, architecture notes, agent configuration --- sit alongside a public blog and website. The internal side references classified codenames, machine hostnames, and internal URLs. The public side must never contain any of that.
|
||||
|
||||
We also deleted all our GitHub repos in April 2026 and moved everything to our own Gitea platform. But old links keep creeping back in --- someone copies a URL from an old document, a blog post references the wrong remote. These are invisible bugs. The blog looks fine, the build passes, and three weeks later someone notices a dead link.
|
||||
|
||||
A checklist in the README doesn't work. Humans skip checklists. Code review catches some issues but not all --- reviewers focus on logic, not whether a URL points to a deleted GitHub org. We needed something automatic, fast, and cheap enough to run on every single commit.
|
||||
|
||||
## Two Layers: Regex + Agent
|
||||
|
||||
The hook has two layers. The first is instant and free. The second is smart and nearly free.
|
||||
|
||||
### Layer 1: Local Blocklist (0ms, $0.00)
|
||||
|
||||
A text file of regex patterns, each tagged with a scope and a message:
|
||||
|
||||
```
|
||||
public|\bCosmos\b|Classified codename — use "advanced colonist AI"
|
||||
all|github\.com/(tinqs-ltd|tinqs)/|GitHub repos deleted — use tinqs.com
|
||||
all|sk-[a-zA-Z0-9]{20,}|Possible API key leaked
|
||||
all|AKIA[A-Z0-9]{16}|AWS access key leaked
|
||||
public|admin\.arikigame\.com|Internal admin URL in public content
|
||||
```
|
||||
|
||||
The scope field controls where the pattern is enforced. `all` means every file. `public` means only files under `website/` --- our public-facing content. This is critical. We *want* classified codenames in our internal architecture docs. We just don't want them in blog posts.
|
||||
|
||||
The blocklist runs grep against the staged diff. No network call, no API, no latency. If it finds a match, the commit is blocked immediately with a file path and explanation. This catches 80% of issues before the LLM ever wakes up.
|
||||
|
||||
### Layer 2: DeepSeek V4 Flash Review (~4s, $0.001)
|
||||
|
||||
If the commit touches public-facing files (`website/`, blog posts), the hook sends the staged diff to DeepSeek V4 Flash through our inference proxy. The system prompt tells the model exactly what to check:
|
||||
|
||||
- **Leaked secrets** --- API keys, tokens, credentials that the regex might have missed
|
||||
- **Classified terms** --- codenames that aren't in the blocklist yet
|
||||
- **Internal URLs** --- references to internal services that shouldn't be public
|
||||
- **Blog quality** --- title length, meta description, slug consistency, missing fields
|
||||
- **Broken links** --- malformed URLs, obvious typos
|
||||
- **Announcements** --- if it's a new blog post, draft a one-line summary
|
||||
|
||||
The model responds with structured JSON: errors (block the commit) or warnings (inform but allow). If the API is unreachable or times out, the commit proceeds --- the hook never blocks work for infrastructure reasons.
|
||||
|
||||
## Why Not Pi?
|
||||
|
||||
Our [Pi fork](https://tinqs.com/tinqs/pi) is a full coding agent with tool calling, file I/O, and context management. It's what we use for overnight autonomous coding. But for pre-commit review, it's overkill.
|
||||
|
||||
A pre-commit hook needs to finish in under 5 seconds. Pi takes 2--3 seconds just to start the Node.js process and load extensions. The review itself is a single LLM call with a system prompt and a diff --- no tools needed, no file reads, no iteration. A direct curl to DeepSeek is faster and simpler.
|
||||
|
||||
That said, the hook is designed as a stepping stone. The blocklist patterns, the review prompt, and the classification logic are all reusable. When we build a Pi-based CI reviewer that runs on pull requests --- with tool access to read the full file, check links live, and verify image URLs --- it will use the same prompt and the same patterns. The pre-commit hook is the fast, cheap first pass. Pi is the thorough second pass.
|
||||
|
||||
## The Architecture
|
||||
|
||||
```
|
||||
git commit
|
||||
↓
|
||||
.githooks/pre-commit (bash)
|
||||
↓
|
||||
Phase 0: Collect staged diff + classify files
|
||||
↓
|
||||
Phase 1: Regex blocklist scan (instant, free)
|
||||
→ Match found → BLOCK (exit 1)
|
||||
→ Clean → continue
|
||||
↓
|
||||
Phase 2: Public files changed?
|
||||
→ No → exit 0 (skip AI, no cost)
|
||||
→ Yes → send diff to DeepSeek V4 Flash
|
||||
↓
|
||||
Phase 3: Parse JSON response
|
||||
→ Errors → BLOCK (exit 1)
|
||||
→ Warnings → print, exit 0
|
||||
→ Announcement → print draft
|
||||
→ API failure → warn, exit 0
|
||||
```
|
||||
|
||||
The hook lives in `.githooks/` inside the repo --- committed, version-controlled, shared by the whole team. A setup script configures `git config core.hooksPath` to point there. The LFS pre-push hook sits in the same directory.
|
||||
|
||||
## What It Costs
|
||||
|
||||
The system prompt is ~500 tokens. An average diff is 2,000--4,000 tokens. The response is ~200 tokens. At DeepSeek V4 Flash rates:
|
||||
|
||||
| | Tokens | Cost |
|
||||
|--|--------|------|
|
||||
| Input (prompt + diff) | ~4,000 | $0.00056 |
|
||||
| Output (JSON response) | ~200 | $0.00006 |
|
||||
| **Per commit** | | **$0.00062** |
|
||||
|
||||
Call it a tenth of a cent. Twenty commits a day across the team: **$0.012/day**. About **$0.40/month**.
|
||||
|
||||
Commits that only touch internal files (architecture docs, agent config, game design) skip the AI review entirely. Zero cost. The hook only calls DeepSeek when public-facing content changes.
|
||||
|
||||
## What It Catches
|
||||
|
||||
In the first week:
|
||||
|
||||
- **2 classified codename leaks** in draft blog posts --- caught by the blocklist before the AI even ran
|
||||
- **1 GitHub URL** that crept back in from a copy-paste --- caught by the blocklist
|
||||
- **3 blog SEO warnings** --- titles over 60 characters, missing og_description --- caught by the AI review
|
||||
- **1 announcement draft** generated automatically when a new blog post was committed
|
||||
|
||||
Zero false positives on the blocklist (the patterns are specific enough). Two false positives from the AI (flagged an internal URL in a code example that was clearly illustrative, not a real link). We added a note to the prompt to ignore URLs inside fenced code blocks.
|
||||
|
||||
## Setup
|
||||
|
||||
One command per machine:
|
||||
|
||||
```bash
|
||||
bash scripts/setup-hooks.sh
|
||||
```
|
||||
|
||||
Or on Windows:
|
||||
|
||||
```powershell
|
||||
.\scripts\setup-hooks.ps1
|
||||
```
|
||||
|
||||
Set your inference token:
|
||||
|
||||
```bash
|
||||
export TINQS_HOOK_TOKEN=<your-gitea-pat>
|
||||
```
|
||||
|
||||
That's it. Every `git commit` now runs the two-layer review. Bypass with `git commit --no-verify` when you need to (emergencies, known false positives).
|
||||
|
||||
## The Pattern: Guard Rails at the Edge
|
||||
|
||||
This is the same pattern we apply everywhere: put the guard rail where the action happens. Don't rely on a human checklist. Don't wait for code review. Don't hope someone remembers.
|
||||
|
||||
The pre-commit hook is $0.001 worth of prevention. A leaked API key in a public blog post is hours of rotation, revocation, and audit. A classified codename in a public post is a confidentiality breach. A dead GitHub link is a broken user experience that nobody notices for weeks.
|
||||
|
||||
The tools exist. DeepSeek V4 Flash is cheap enough to call on every commit. The hook is 150 lines of bash. The blocklist is a text file. The total infrastructure cost is zero --- it runs on the developer's machine, calls an API we already pay for, and adds 4 seconds to the commit flow.
|
||||
|
||||
The age of agents doesn't just mean agents that write code. It means agents that watch the code you write.
|
||||
|
||||
---
|
||||
|
||||
*The pre-commit hook is part of [Tinqs Studio](https://tinqs.com), our open platform for game development. The inference proxy, the blocklist patterns, and the review prompt are all open and reusable. We're building [Ariki](https://arikigame.com) with these tools --- every commit in the game repo runs through the same guard.*
|
||||
@@ -0,0 +1,222 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>A Pre-Commit Agent That Guards Your Secrets for $0.001 — Tinqs Blog</title>
|
||||
<meta name="description" content="We built a pre-commit hook that calls DeepSeek V4 Flash to review every commit. It catches leaked secrets, classified terms, broken URLs, and drafts announcements --- for a tenth of a cent per commit.">
|
||||
<meta name="robots" content="index, follow">
|
||||
<link rel="canonical" href="https://www.tinqs.com/blog/pre-commit-agent">
|
||||
|
||||
<meta property="og:type" content="article">
|
||||
<meta property="og:url" content="https://www.tinqs.com/blog/pre-commit-agent">
|
||||
<meta property="og:title" content="A Pre-Commit Agent That Guards Your Secrets for $0.001">
|
||||
<meta property="og:description" content="A DeepSeek-powered pre-commit hook that catches leaks for $0.001/commit.">
|
||||
<meta property="og:image" content="https://www.tinqs.com/img/og-cover.jpg">
|
||||
|
||||
<meta name="twitter:card" content="summary_large_image">
|
||||
<meta name="twitter:title" content="A Pre-Commit Agent That Guards Your Secrets for $0.001">
|
||||
<meta name="twitter:description" content="A DeepSeek-powered pre-commit hook that catches leaks for $0.001/commit.">
|
||||
<meta name="twitter:image" content="https://www.tinqs.com/img/og-cover.jpg">
|
||||
|
||||
<script type="application/ld+json">
|
||||
{
|
||||
"@context": "https://schema.org",
|
||||
"@type": "BlogPosting",
|
||||
"headline": "A Pre-Commit Agent That Guards Your Secrets for $0.001",
|
||||
"datePublished": "2026-05-25",
|
||||
"author": {
|
||||
"@type": "Person",
|
||||
"name": "Ozan Bozkurt"
|
||||
},
|
||||
"publisher": {
|
||||
"@type": "Organization",
|
||||
"name": "Tinqs Limited",
|
||||
"url": "https://www.tinqs.com"
|
||||
},
|
||||
"description": "We built a pre-commit hook that calls DeepSeek V4 Flash to review every commit. It catches leaked secrets, classified terms, broken URLs, and drafts announcements --- for a tenth of a cent per commit."
|
||||
}
|
||||
</script>
|
||||
|
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|
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400;1,500;1,600;1,700&display=swap" rel="stylesheet">
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<body>
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<!-- NAV -->
|
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<nav class="nav nav--scrolled" id="nav">
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<a href="/" class="nav__logo" aria-label="Tinqs home">
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<span class="nav__wordmark">TINQS</span>
|
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</a>
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<div class="nav__links">
|
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<a href="/#game" class="nav__link">Games</a>
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<a href="/#tech" class="nav__link">Technology</a>
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<a href="/#about" class="nav__link">About</a>
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<a href="/blog/" class="nav__link" style="color: var(--c-accent-l);">Blog</a>
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<!-- MOBILE MENU -->
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<div class="mobile-menu" id="mobileMenu">
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<a href="/#game" class="mobile-menu__link">Games</a>
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<!-- POST -->
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<article class="post">
|
||||
<a href="/blog/" class="post__back">← All Posts</a>
|
||||
<span class="post__date">25 May 2026</span>
|
||||
<h1 class="post__title">A Pre-Commit Agent That Guards Your Secrets for $0.001</h1>
|
||||
<p class="post__lead">We have a problem that every small team has: too many things to remember before hitting commit. Don't leak API keys. Don't reference the classified AI codename in public blog posts. Don't link to GitHub repos we deleted six months ago. Don't push a blog post with a 90-character title. We built a pre-commit hook that uses a cheap LLM to check all of this automatically — and it costs less than a tenth of a cent per commit.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>The Problem</h2>
|
||||
<p>We maintain a docs repo that serves double duty. Internal files — game design documents, architecture notes, agent configuration — sit alongside a public blog and website. The internal side references classified codenames, machine hostnames, and internal URLs. The public side must never contain any of that.</p>
|
||||
<p>We also deleted all our GitHub repos in April 2026 and moved everything to our own Gitea platform. But old links keep creeping back in — someone copies a URL from an old document, a blog post references the wrong remote. These are invisible bugs. The blog looks fine, the build passes, and three weeks later someone notices a dead link.</p>
|
||||
<p>A checklist in the README doesn't work. Humans skip checklists. Code review catches some issues but not all — reviewers focus on logic, not whether a URL points to a deleted GitHub org. We needed something automatic, fast, and cheap enough to run on every single commit.</p>
|
||||
<h2>Two Layers: Regex + Agent</h2>
|
||||
<p>The hook has two layers. The first is instant and free. The second is smart and nearly free.</p>
|
||||
<h3>Layer 1: Local Blocklist (0ms, $0.00)</h3>
|
||||
<p>A text file of regex patterns, each tagged with a scope and a message:</p>
|
||||
<pre><code>public|\bCosmos\b|Classified codename — use "advanced colonist AI"
|
||||
all|github\.com/(tinqs-ltd|tinqs)/|GitHub repos deleted — use tinqs.com
|
||||
all|sk-[a-zA-Z0-9]{20,}|Possible API key leaked
|
||||
all|AKIA[A-Z0-9]{16}|AWS access key leaked
|
||||
public|admin\.arikigame\.com|Internal admin URL in public content</code></pre>
|
||||
<p>The scope field controls where the pattern is enforced. <code>all</code> means every file. <code>public</code> means only files under <code>website/</code> — our public-facing content. This is critical. We <em>want</em> classified codenames in our internal architecture docs. We just don't want them in blog posts.</p>
|
||||
<p>The blocklist runs grep against the staged diff. No network call, no API, no latency. If it finds a match, the commit is blocked immediately with a file path and explanation. This catches 80% of issues before the LLM ever wakes up.</p>
|
||||
<h3>Layer 2: DeepSeek V4 Flash Review (~4s, $0.001)</h3>
|
||||
<p>If the commit touches public-facing files (<code>website/</code>, blog posts), the hook sends the staged diff to DeepSeek V4 Flash through our inference proxy. The system prompt tells the model exactly what to check:</p>
|
||||
<ul>
|
||||
<li><strong>Leaked secrets</strong> — API keys, tokens, credentials that the regex might have missed</li>
|
||||
<li><strong>Classified terms</strong> — codenames that aren't in the blocklist yet</li>
|
||||
<li><strong>Internal URLs</strong> — references to internal services that shouldn't be public</li>
|
||||
<li><strong>Blog quality</strong> — title length, meta description, slug consistency, missing fields</li>
|
||||
<li><strong>Broken links</strong> — malformed URLs, obvious typos</li>
|
||||
<li><strong>Announcements</strong> — if it's a new blog post, draft a one-line summary</li>
|
||||
</ul>
|
||||
<p>The model responds with structured JSON: errors (block the commit) or warnings (inform but allow). If the API is unreachable or times out, the commit proceeds — the hook never blocks work for infrastructure reasons.</p>
|
||||
<h2>Why Not Pi?</h2>
|
||||
<p>Our <a href="https://tinqs.com/tinqs/pi" style="color: var(–c-accent-l);">Pi fork</a> is a full coding agent with tool calling, file I/O, and context management. It's what we use for overnight autonomous coding. But for pre-commit review, it's overkill.</p>
|
||||
<p>A pre-commit hook needs to finish in under 5 seconds. Pi takes 2–3 seconds just to start the Node.js process and load extensions. The review itself is a single LLM call with a system prompt and a diff — no tools needed, no file reads, no iteration. A direct curl to DeepSeek is faster and simpler.</p>
|
||||
<p>That said, the hook is designed as a stepping stone. The blocklist patterns, the review prompt, and the classification logic are all reusable. When we build a Pi-based CI reviewer that runs on pull requests — with tool access to read the full file, check links live, and verify image URLs — it will use the same prompt and the same patterns. The pre-commit hook is the fast, cheap first pass. Pi is the thorough second pass.</p>
|
||||
<h2>The Architecture</h2>
|
||||
<pre><code>git commit
|
||||
↓
|
||||
.githooks/pre-commit (bash)
|
||||
↓
|
||||
Phase 0: Collect staged diff + classify files
|
||||
↓
|
||||
Phase 1: Regex blocklist scan (instant, free)
|
||||
→ Match found → BLOCK (exit 1)
|
||||
→ Clean → continue
|
||||
↓
|
||||
Phase 2: Public files changed?
|
||||
→ No → exit 0 (skip AI, no cost)
|
||||
→ Yes → send diff to DeepSeek V4 Flash
|
||||
↓
|
||||
Phase 3: Parse JSON response
|
||||
→ Errors → BLOCK (exit 1)
|
||||
→ Warnings → print, exit 0
|
||||
→ Announcement → print draft
|
||||
→ API failure → warn, exit 0</code></pre>
|
||||
<p>The hook lives in <code>.githooks/</code> inside the repo — committed, version-controlled, shared by the whole team. A setup script configures <code>git config core.hooksPath</code> to point there. The LFS pre-push hook sits in the same directory.</p>
|
||||
<h2>What It Costs</h2>
|
||||
<p>The system prompt is ~500 tokens. An average diff is 2,000–4,000 tokens. The response is ~200 tokens. At DeepSeek V4 Flash rates:</p>
|
||||
<p>| | Tokens | Cost |</p>
|
||||
<p>|–|——–|——|</p>
|
||||
<p>| Input (prompt + diff) | ~4,000 | $0.00056 |</p>
|
||||
<p>| Output (JSON response) | ~200 | $0.00006 |</p>
|
||||
<p>| <strong>Per commit</strong> | | <strong>$0.00062</strong> |</p>
|
||||
<p>Call it a tenth of a cent. Twenty commits a day across the team: <strong>$0.012/day</strong>. About <strong>$0.40/month</strong>.</p>
|
||||
<p>Commits that only touch internal files (architecture docs, agent config, game design) skip the AI review entirely. Zero cost. The hook only calls DeepSeek when public-facing content changes.</p>
|
||||
<h2>What It Catches</h2>
|
||||
<p>In the first week:</p>
|
||||
<ul>
|
||||
<li><strong>2 classified codename leaks</strong> in draft blog posts — caught by the blocklist before the AI even ran</li>
|
||||
<li><strong>1 GitHub URL</strong> that crept back in from a copy-paste — caught by the blocklist</li>
|
||||
<li><strong>3 blog SEO warnings</strong> — titles over 60 characters, missing og_description — caught by the AI review</li>
|
||||
<li><strong>1 announcement draft</strong> generated automatically when a new blog post was committed</li>
|
||||
</ul>
|
||||
<p>Zero false positives on the blocklist (the patterns are specific enough). Two false positives from the AI (flagged an internal URL in a code example that was clearly illustrative, not a real link). We added a note to the prompt to ignore URLs inside fenced code blocks.</p>
|
||||
<h2>Setup</h2>
|
||||
<p>One command per machine:</p>
|
||||
<pre><code class="language-bash">bash scripts/setup-hooks.sh</code></pre>
|
||||
<p>Or on Windows:</p>
|
||||
<pre><code class="language-powershell">.\scripts\setup-hooks.ps1</code></pre>
|
||||
<p>Set your inference token:</p>
|
||||
<pre><code class="language-bash">export TINQS_HOOK_TOKEN=<your-gitea-pat></code></pre>
|
||||
<p>That's it. Every <code>git commit</code> now runs the two-layer review. Bypass with <code>git commit –no-verify</code> when you need to (emergencies, known false positives).</p>
|
||||
<h2>The Pattern: Guard Rails at the Edge</h2>
|
||||
<p>This is the same pattern we apply everywhere: put the guard rail where the action happens. Don't rely on a human checklist. Don't wait for code review. Don't hope someone remembers.</p>
|
||||
<p>The pre-commit hook is $0.001 worth of prevention. A leaked API key in a public blog post is hours of rotation, revocation, and audit. A classified codename in a public post is a confidentiality breach. A dead GitHub link is a broken user experience that nobody notices for weeks.</p>
|
||||
<p>The tools exist. DeepSeek V4 Flash is cheap enough to call on every commit. The hook is 150 lines of bash. The blocklist is a text file. The total infrastructure cost is zero — it runs on the developer's machine, calls an API we already pay for, and adds 4 seconds to the commit flow.</p>
|
||||
<p>The age of agents doesn't just mean agents that write code. It means agents that watch the code you write.</p>
|
||||
<hr>
|
||||
<p><em>The pre-commit hook is part of <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a>, our open platform for game development. The inference proxy, the blocklist patterns, and the review prompt are all open and reusable. We're building <a href="https://arikigame.com" style="color: var(–c-accent-l);">Ariki</a> with these tools — every commit in the game repo runs through the same guard.</em></p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
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<span class="post__date">18 May 2026</span>
|
||||
<h1 class="post__title">One Binary to Rule Them All: Building a Studio CLI</h1>
|
||||
<p class="post__lead">Every machine in our studio runs the same Go binary. It knows who you are, what machine you're on, and what services are reachable. It takes screenshots, sends them to cloud vision, and runs health checks. This is the glue that makes AI agents actually useful in a multi-machine game studio.</p>
|
||||
|
||||
<div class="post__body">
|
||||
<h2>Why Build a CLI</h2>
|
||||
<p>When you have multiple machines across several people, two operating systems, and AI agents that need context about the environment they're running in, the glue becomes the hardest part. Which machine is this? What services are reachable? Is the game running? Can I take a screenshot of what the developer is looking at?</p>
|
||||
<p>We tried shell scripts. A <code>setup.sh</code> for Mac, a <code>setup.ps1</code> for Windows, a <code>check-services.sh</code> for health checks, a <code>screenshot.py</code> that never worked on Windows. They drifted. They broke. Nobody updated them.</p>
|
||||
<p>So we built one Go binary that does everything.</p>
|
||||
<h2>The Identity System</h2>
|
||||
<p>The most important command is <code>identity</code>. When an AI agent starts a new session — Cursor, Claude Code, any tool — the first thing it does is call this command. The output tells the agent:</p>
|
||||
<ul>
|
||||
<li><strong>The soul file</strong> — the agent's persistent identity, values, and operating principles</li>
|
||||
<li><strong>Company context</strong> — team members, roles, what the company does</li>
|
||||
<li><strong>Machine context</strong> — hostname, OS, which repos are cloned, what services are running</li>
|
||||
<li><strong>Ecosystem</strong> — other repos and their purpose</li>
|
||||
<li><strong>Service status</strong> — which URLs are live and reachable</li>
|
||||
</ul>
|
||||
<p>This solves a fundamental problem with AI agents: <strong>cold starts.</strong> Every new chat window, every new agent tab, every new session is a blank slate. The agent doesn't know what project this is, who's asking, or what infrastructure exists. One CLI call gives it full context.</p>
|
||||
<p>The data lives in markdown files in the docs repo — the source of truth. Any machine on the network can read it.</p>
|
||||
<h2>Screenshots and Vision</h2>
|
||||
<p>The CLI can capture any window from outside the process. No in-game overlay, no rendering pipeline integration. It uses the OS-level window capture API — works on Windows (via GDI+) and Mac (via screencapture).</p>
|
||||
<p>A <code>photo</code> command does the same thing but sends the screenshot to a cloud vision model for analysis. The agent says "take a photo of the game" and gets back a structured description: "The player character is standing near a half-built hut. There are 3 palm trees to the left. The terrain has a visible seam between two biomes."</p>
|
||||
<p>This is how you file bugs without typing. Look at the game, tell the agent what's wrong, and the agent takes a screenshot, describes what it sees, and creates an issue with both the description and the image attached.</p>
|
||||
<h2>Health Checks</h2>
|
||||
<p>A <code>doctor</code> command runs a comprehensive health check:</p>
|
||||
<ul>
|
||||
<li>Is the git platform reachable? Can we authenticate?</li>
|
||||
<li>Is the game server running?</li>
|
||||
<li>Are all expected repos cloned and on the right branch?</li>
|
||||
<li>Are required tools installed and at the right version?</li>
|
||||
</ul>
|
||||
<p>The output is a green/yellow/red table. If something's wrong, the agent knows immediately and can diagnose or escalate. This is essential for unattended agent sessions — the agent can verify its environment before starting work.</p>
|
||||
<h2>Why Go</h2>
|
||||
<p>Go compiles to a single static binary with no runtime dependencies. No Python virtualenvs, no Node.js version managers, no DLL hell on Windows. The same binary runs on a gaming PC, a designer's MacBook, and a CI runner in AWS.</p>
|
||||
<p>Cross-compilation is trivial. We build Windows, Mac (arm64 + amd64), and Linux binaries from a single CI workflow. Push a tag, CI builds all three, uploads to S3, done.</p>
|
||||
<p>The binary is 15MB. It starts in under 100ms. It has zero runtime dependencies. For a tool that AI agents call on every session start, speed matters.</p>
|
||||
<h2>What We Learned</h2>
|
||||
<p><strong>The CLI is the API for AI agents.</strong> When we started, this was a convenience tool for humans. It became the primary interface for AI agents. The <code>identity</code> command was originally "nice to have" — now it's the single most important function in our stack. Every agent session starts with it.</p>
|
||||
<p><strong>One binary beats ten scripts.</strong> Scripts rot. They have different shells, different PATH assumptions, different error handling. A compiled binary either works or it doesn't. It ships with its dependencies baked in. It doesn't care if your Python is 3.9 or 3.12.</p>
|
||||
<p><strong>Cloud vision is underrated for game dev.</strong> Sending a screenshot to a vision model and getting back a structured description sounds gimmicky. In practice, it's the fastest way to document visual bugs. "The tree is floating 2m above the terrain" is much faster to write when the AI is looking at the same screen you are.</p>
|
||||
<p><strong>Agent cold starts are the real problem.</strong> Without the identity system, every new session starts with the agent asking "what project is this?" and the human re-explaining context. With it, the agent knows everything in 100ms. That's the difference between an AI assistant and an AI team member.</p>
|
||||
<hr>
|
||||
<p>The CLI is part of <a href="https://tinqs.com" style="color: var(–c-accent-l);">Tinqs Studio</a> — our game development platform that brings git hosting, AI agent tools, and team workflows together. Every time we find ourselves writing a script that needs to work on multiple machines, we add a subcommand instead. One binary that makes the studio work, whether the operator is human or AI.</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="post__author">
|
||||
<div class="post__author-avatar">OB</div>
|
||||
<div class="post__author-info">
|
||||
<span class="post__author-name">Ozan Bozkurt</span><br>
|
||||
CTO & Developer, Tinqs
|
||||
</div>
|
||||
</div>
|
||||
</article>
|
||||
|
||||
<!-- FOOTER -->
|
||||
<footer class="footer">
|
||||
<div class="footer__inner">
|
||||
<span class="footer__wordmark">TINQS</span>
|
||||
<div class="footer__links">
|
||||
<a href="/#game">Games</a>
|
||||
<a href="/#tech">Technology</a>
|
||||
<a href="/#about">About</a>
|
||||
<a href="/blog/">Blog</a>
|
||||
<a href="mailto:hello@tinqs.com">hello@tinqs.com</a>
|
||||
<a href="/press">Press Kit</a>
|
||||
</div>
|
||||
<p class="footer__copy">Tinqs Limited — London, est. 2020</p>
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script>
|
||||
const burger = document.getElementById('navBurger');
|
||||
const mobileMenu = document.getElementById('mobileMenu');
|
||||
burger.addEventListener('click', () => {
|
||||
const open = mobileMenu.classList.toggle('mobile-menu--open');
|
||||
burger.classList.toggle('nav__burger--open', open);
|
||||
document.body.style.overflow = open ? 'hidden' : '';
|
||||
});
|
||||
mobileMenu.querySelectorAll('a').forEach(link => {
|
||||
link.addEventListener('click', () => {
|
||||
mobileMenu.classList.remove('mobile-menu--open');
|
||||
burger.classList.remove('nav__burger--open');
|
||||
document.body.style.overflow = '';
|
||||
});
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
After
|
Reference in New Issue
Block a user