Tinqs Studio Is an Agent Harness for Game Dev
-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.
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-## What Is an Agent Harness?
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-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..."
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-An agent harness solves this. It wraps around the model and provides:
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-- Identity — who the agent is, what it values, how it should behave
-- Memory — what happened in previous sessions, what was decided, what failed
-- Tools — what the agent can actually do beyond generating text
-- Context — what project this is, who's asking, what infrastructure exists
-- Guardrails — what the agent must never do, what requires human approval
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-Without a harness, you have a chatbot. With one, you have a team member.
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-## Why Game Dev Needs Its Own Harness
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-Generic agent harnesses exist — LangChain, CrewAI, AutoGen. They're built for web apps, data pipelines, and customer support. Game development has different problems:
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-Assets are binary. 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.
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-The pipeline is visual. 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.
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-Scale is physical. 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.
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-The team is small and cross-functional. 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.
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-## How Tinqs Studio Works as a Harness
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-Tinqs Studio is a platform built on a Gitea fork with game-specific features layered on top. But the git platform is just the foundation. The harness is everything around it.
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-### Identity: Soul Files
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-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.
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-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?"
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-### Memory: Markdown Files in Git
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-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.
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-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 git log to see what changed and git revert to fix it.
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-### Tools: The CLI
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-A single Go binary gives every agent access to:
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-- Identity loading — full project context in 100ms
-- Screenshots — capture any window from outside the process
-- Cloud vision — send screenshots to a vision model, get structured descriptions
-- Health checks — verify services, repos, and tools are working
-- Service status — which URLs are live, what's reachable
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-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.
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-### Skills: Teachable Workflows
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-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:
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-- Image Generation — generate game art with fal.ai Flux using a 4-layer prompt pattern
-- Concept Art Pipeline — from design brief through 2D art to 3D model export
-- 3D Model Generation — Tripo Studio text-to-3D and image-to-3D
-- Video Generation — trailer clips with OpenAI Sora 2
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-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.
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-### Git Platform: 3D Preview and LFS
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-The Gitea fork underneath handles the game-specific git problems:
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-- 3D asset preview — rotate GLB/FBX/STL files in the browser during code review
-- LFS-first workflows — auto-tracking for game file extensions, storage dashboards
-- OAuth2 SSO — one login for git, tools, and the game
-- 22 format support — GLB, FBX, OBJ, STL, 3DS, PLY, and more via O3DV
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-### Guardrails: Human-in-the-Loop
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-The harness defines what agents can and cannot do:
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-- Agents can file issues, draft announcements, generate assets, write code
-- Agents cannot merge code, deploy builds, push to public repos, or post to external channels without human approval
-- The public blog repo requires human-approved merge requests — agents can propose changes but a person must review
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-This isn't a limitation — it's a feature. The agent handles volume; the human handles judgement.
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-## The Cold Start Problem
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-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.
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-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.
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-The harness solves this with staged loading:
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-1. CLI identity call (100ms) — loads soul file, company context, machine info, service status
-2. Memory file (instant) — loads cross-session context
-3. Skills (on demand) — loaded only when the task matches a skill name
-4. Repo context (on demand) — read files as needed, not all upfront
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-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.
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-## What Makes This Different from LangChain
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-LangChain, CrewAI, and similar frameworks are code-first. You define agents in Python, chain them with function calls, and deploy them as services. They're powerful for building AI products.
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-Tinqs Studio is file-first. 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.
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-This matters for game teams because:
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-- Non-engineers can contribute. The designer writes a skill for concept art. The PM writes a skill for sprint planning. No Python required.
-- Everything is auditable. git log shows who changed what, when, and why. Memory changes are commits. Skill updates are diffs.
-- It works with any AI tool. The same soul files and skills work in Cursor, Claude Code, or any tool that reads markdown. You're not locked into one framework.
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-## The Stack
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-| Layer | What | How |
-|——-|——|—–|
-| Identity | Soul files, company context | Markdown in git, loaded via CLI |
-| Memory | Cross-session context | Markdown in git, updated by agents |
-| Skills | Teachable workflows | Markdown playbooks, loaded on demand |
-| Tools | CLI, screenshots, vision | Go binary, one install per machine |
-| Git | 3D preview, LFS, SSO | Gitea fork with game-specific features |
-| Creative | Image gen, 3D models, video | fal.ai, Tripo, Sora 2 via skills |
-| Guardrails | Human approval gates | Branch protection, MR requirements |
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-## Getting Started
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-If you want to build your own agent harness for game dev:
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-1. Start with a soul file. Write 50 words about your project's identity, values, and scope. Put it in your repo root as SOUL.md.
-2. Write one skill. Pick the workflow you repeat most — concept art generation, bug triage, build verification — and write the steps as markdown.
-3. Build a CLI identity command. Even a shell script that prints "project name, repos, services" gives your agent a warm start.
-4. Put everything in git. Not a database, not a SaaS tool. Git. You already have it.
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-The rest — 3D preview, LFS management, OAuth SSO, creative pipelines — you can add as you need it. Or use Tinqs Studio, where we've already built it.
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-—
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-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.
What an Agent Harness Is and Why Game Dev Needs One
+Open Claude or ChatGPT right now and ask it to review your last PR. It'll say "I don't have access to your repository." Ask it to take a screenshot of your game. It'll say "I can't interact with your operating system." Ask it what you were working on yesterday. It'll say "I don't have memory of previous conversations."
A raw AI model is a brain without hands, eyes, or memory. An agent harness is the layer that gives it all three — plus identity, tools, and guardrails. And game development needs one that understands binary assets, visual pipelines, and spatial systems.
+What a harness provides
+Every agent harness, regardless of domain, needs five things:
+Identity. Who the agent is, what it values, how it should behave. Not "you are a helpful assistant" — that's generic and unmoored. A soul file that says "you're working on Ariki, a survival colony sim. The team is four people. Never push to main without review. Prefer existing conventions." Identity creates consistency across sessions.
+Memory. What happened last session. What decisions were made. What failed and why. Without memory, every conversation is a cold start — "let me explain the project..." Memory stored as markdown in git means it's version-controlled, diffable, and human-readable. When something goes wrong, you git log instead of debugging a vector database.
Tools. What the agent can actually do beyond generating text. A CLI that takes screenshots, checks service health, and loads project context. API wrappers for git, CI, image generation. Without tools, the agent is a very articulate oracle that can't touch anything.
+Context. Which project this is. Who's asking. What machine they're on. What services are reachable. A single CLI call — tstudio identity — returns all of this in 100ms. No re-reading the README. No "what repo are we in?"
Guardrails. What the agent must never do. No merging to main without review. No pushing to public repos without approval. No running destructive commands. The harness enforces these at the platform layer, not in the prompt. Prompts can be ignored. Platform gates cannot.
+Why generic harnesses fail for game dev
+LangChain, CrewAI, and AutoGen are built for web apps. They assume text-in, text-out. Game development is different in ways that break those assumptions:
+Assets are binary. A web PR is a text diff. A game PR is a 150MB GLB file with textures, rigging, and animations. You can't review it without seeing it. Our harness renders 3D models in the browser during code review — rotate, zoom, check materials. The artist pushes, the lead inspects, no downloads required.
+The pipeline is visual. Concept art → 3D model → rigged character → in-engine asset. Each step uses different tools. The harness needs to orchestrate image generators, 3D modellers, auto-riggers, and game engines as a single workflow — not as five separate API calls the human has to stitch together.
+Scale is physical. A web app's complexity is in business logic. A game's complexity is in geometry — 12km worlds, 155 vegetation types, 2,000 crowd instances. The agent needs to understand spatial systems, GPU memory budgets, and frame timing. "Add more RAM" isn't an answer when you have 8GB of VRAM.
+The team is small and cross-functional. Four people. No dedicated DevOps, no dedicated artist, no dedicated PM. The harness fills all those gaps, not just one.
+The toolchain that makes it work
+Our harness runs on Tinqs Studio, built on a Gitea fork with game-specific features. The key pieces:
+The CLI — a single Go binary. One command (tstudio identity) gives the agent full project context in 100ms. Screenshots, cloud vision, health checks — all subcommands of the same binary.
The soul file — a markdown document in the repo root. The agent reads it on session start. It defines values, scope, and behavioural rules. The same soul file works in Cursor, Claude Code, or any tool that reads markdown.
+Skills — markdown playbooks for specific workflows. Image generation, concept art pipeline, 3D model creation, video generation. Each skill is a procedure the agent follows. Write once, use forever.
+3D preview — click a .glb file in a PR and rotate the model in your browser. 22 formats supported. This alone transformed our review process — nobody approves a binary diff blind anymore.
Guardrails — agents can file issues, draft announcements, generate assets, and write code. They cannot merge, deploy, or push to public repos without human approval. Branch protection rules enforced at the git platform layer.
+The cold-start problem, solved
+Every AI agent session starts blank. Most teams solve this with long system prompts — but when your context is 200 markdown files, 15 skills, and 3 years of project history, you can't paste all of that.
+The harness uses staged loading:
+1. CLI identity call (100ms) — soul file, company context, machine info, service status
+2. Memory file (instant) — cross-session context from the docs repo
+3. Skills (on demand) — loaded only when the task matches a skill name
+4. Repo context (on demand) — files read as needed, not all upfront
+Agent goes from cold to fully contextual in under a second. No "let me explain the project." No re-reading onboarding docs. Just start working.
+The bet
+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.
+We're betting that specialised harnesses beat generic ones. A harness built for game dev — with 3D preview, LFS management, and creative pipelines — will outperform a general-purpose agent framework on game dev tasks. Not because the AI is smarter, but because it has the right hands, eyes, and memory for the job.
++
Tinqs Studio is an agent harness for game development — git hosting, AI agents, creative pipelines. Open for teams. We're building Ariki with the same tools.
How a Small Game Studio Runs on AI Agents
-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.
+How a 4-Person Studio Runs on AI Agents
+Last week one of our agents caught a stale bug at 3am — a vegetation culling issue that had been open for six days. It nudged the team chat, drafted a fix summary, and by morning the issue was resolved. Nobody lost sleep. Nobody context-switched. The agent just handled it.
The Problem Every Small Studio Knows
-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.
-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.
-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.
-The Architecture: Agents with Identity
-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 soul file that defines its values and operating principles, and a memory file that persists context across sessions.
-The key insight: all knowledge lives in markdown files in one repo. 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.
-What the agent actually does
+This is what happens when you stop treating AI as a chatbot and start treating it as a team member with a persistent identity, a memory, and a set of skills it can actually execute.
+The problem with "just use ChatGPT"
+Every small studio hits the same wall: four people, forty roles. Nobody has time to keep documentation current. Bugs pile up. The backlog rots. Someone asks "what did we decide about the inventory system?" and three different answers come back.
+The usual fix is more tools — Notion, Trello, Linear, Slack integrations. But tools are passive. They sit there waiting for humans to update them. In a team where the lead developer is also the CTO, that human is already stretched thin.
+We tried something different. Instead of adding more tools for humans to maintain, we gave AI agents persistent identities, connected them to our entire knowledge base, and let them do the maintenance.
+Soul files: giving agents a personality that sticks
+The core idea is embarrassingly simple. Every agent gets a soul file — a markdown document that defines who it is, what it values, and how it should behave:
+-
+
- Values — "never break the build," "always verify before acting," "prefer existing patterns over novelty" +
- Knowledge scope — what repos exist, who's on the team, what the game is about +
- Behavioural rules — when to act autonomously, when to ask, what requires explicit human approval +
This isn't theatre. It's the difference between an agent that asks "what project is this?" every session and one that says "I see the vegetation grid was updated yesterday — want me to check the cache eviction?"
+The soul file loads in 100ms when the agent starts. No cold starts. No re-explaining.
+Memory: markdown in git, not a vector database
+Agent memory lives as plain markdown files in our docs repo. No vector databases, no proprietary stores, no SaaS dashboards. The agent writes to its memory file during work, commits it, and reads it on the next session.
+This is deliberately low-tech. Markdown in git gives you version history, diffs, branching, and human readability for free. When memory goes wrong — and it will — you git log to see what changed and git revert to fix it. Try debugging a corrupted vector embedding at 11pm.
Skills: teachable playbooks, not prompt engineering
+Agents don't just have instructions. They have skills — markdown playbooks that teach specific workflows. When someone says "generate concept art for a character," the agent reads skills/image-generation.md and follows the procedure. No prompt engineering per session. No "let me try a different prompt."
We've open-sourced several skills:
+-
+
- Image Generation with fal.ai — 4-layer prompt pattern that actually produces usable game art +
- Concept Art Pipeline — full 2D concept → 3D model workflow +
- 3D Model Generation — Tripo Studio text-to-3D +
- Video Generation — trailer clips with Sora 2 +
Each skill took about 30 minutes to write. After six months, our agents have 15+ skills covering art generation, competitive research, video production, and project management. Skills compound — every playbook you write makes every future session more capable.
+What the agents actually do, every day
+During the day (interactive, inside the IDE):
- Triages and grooms the issue backlog -
- Keeps documentation in sync with the game state -
- Processes bug reports from testers and creates structured issues -
- Drafts team announcements, reviews PRs, manages cross-repo coordination -
- Generates concept art, trailer frames, and UI assets using integrated API skills -
- Conducts competitive research — analysing Steam pages, player reviews, pricing strategies +
- Keeps documentation in sync with what's actually in the game +
- Processes tester bug reports and creates structured issues +
- Generates concept art, trailer frames, UI icons on demand +
- Conducts competitive research — Steam pages, player reviews, pricing
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.
-Background Automation
-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.
-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.
-What it handles
+The team talks to the agent through voice. The IDE microphone transcribes, and the agent interprets intent from messy voice-to-text. "There's a tree floating two meters above the terrain on the west beach" becomes a filed issue with a screenshot, a vision-model description, and coordinates.
+At night (background daemon, $15/day):
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- Chat monitoring — polls team chat, responds to commands, acknowledges messages -
- Bug intake — when a tester reports a bug in chat, creates a structured issue automatically -
- Stale issue detection — flags issues that haven't been touched, nudges the team -
- Daily summaries — posts a morning digest of what happened overnight -
- Self-learning — creates its own skill files when it discovers better approaches -
The two agents coordinate through the docs repo itself. One writes, the other reads. No API calls between them, no message queue. Just git.
-The Skill System
-Agents don't just have instructions — they have skills. 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.
-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.
-We've open-sourced several of our skills in this repo:
--
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- Image Generation with fal.ai -
- Concept Art Pipeline -
- 3D Model Generation with Tripo -
- Video Generation with Sora 2 -
Soul Files: Why Identity Matters
-Giving the agent a persistent identity isn't theatre. It creates consistency across sessions. The soul file defines:
--
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- Values — what the agent prioritises (e.g., "never break the build," "always verify before acting") -
- Knowledge scope — what repos, services, and team members exist -
- Behavioural rules — how to handle ambiguity, when to ask vs act, what requires human approval -
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.
-What We've Learned
-Plain text is the universal API. 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.
-Cheap models for routine, expensive models for thinking. 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.
-The human stays in the loop for decisions. 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.
-Voice input changes everything. 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.
-Skills compound. 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.
-The Numbers
--
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- Team size: 4 humans + AI agents -
- Background agent cost: ~$15/day (~$450/month) -
- Knowledge files: 200+ markdown documents -
- Skills: 15+ agent skill files and growing -
- Infrastructure: Multiple machines, self-hosted git, zero DevOps engineers +
- Polls team chat every 15 minutes, responds to commands +
- When a tester reports a bug in chat, creates a structured issue automatically +
- Flags stale issues that haven't been touched +
- Posts a morning digest of what happened overnight +
- Creates its own skill files when it discovers better approaches
What we learned
+Plain text is the universal API. Every tool, every agent, every human can read a markdown file. We store everything — design docs, meeting notes, agent memory, team contacts — as .md in one repo. It sounds too simple, but it eliminates an entire class of integration problems.
Cheap models for routine, expensive models for thinking. Most of what an agent does is pattern matching — "does this look like a bug report?" You don't need DeepSeek Pro for that. Save the premium tokens for decisions that actually require reasoning. Our background daemon costs $15/day with a three-tier model strategy.
+Voice changes everything. When you can describe a bug while looking at the screen, and the agent transcribes, interprets, and files it — that collapses the distance between noticing a problem and tracking it. Keyboard-free bug reporting is a superpower.
+Skills compound exponentially. One skill saves 15 minutes per session. Fifteen skills save hours per day across the whole team. The investment curve is absurdly favourable — 30 minutes of writing per skill, compounding returns forever.
+We're four people. With agents doing the mechanical work, we operate like forty. Not because the AI is magic — because we gave it identity, memory, and the right playbooks, and then got out of its way.
-
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.
-We're building all of this as part of Tinqs Studio — 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.
+We're building Ariki, a survival colony sim, using the same agent workflow described here. Everything runs on Tinqs Studio — a game dev platform with built-in AI agents, git hosting, and creative pipelines.
Our Blog Just Got a Visual Upgrade — Here's How We Did It
-Until yesterday, the Tinqs blog looked... fine. Readable. Semantic. It had the brand amber accent, proper typography, and all the SEO metadata in the right places. But it didn't have much personality. The code blocks were unstyled. The headings sat flat. And the design said "competent" more than "intentional".
+How We Restyled Our Blog with Two Template Files and Zero Dependencies
+Our blog looked fine. Readable, semantic, proper typography. But it didn't have much personality. Code blocks were unstyled. Headings sat flat. The design said "competent" more than "intentional."
Then we looked at our own internal team guide — the onboarding doc we keep at docs/team/dev-basics-env-secrets-git.html. It had gradient titles that clip to transparent. Dark, crisp code panels. Callout boxes with coloured left borders. Pill-shaped date labels. A restrained four-colour palette that felt cohesive without screaming.
We wanted the blog to feel like it came from the same shop. So we restyled it.
-The design source
-Our team guide is a single self-contained HTML file with a dark theme — background #0d1117, panels #161b22, ink #e6edf3. It uses a four-accent palette:
-
-
- Green
#22c55e— for.envand environment topics
- - Blue
#38bdf8— secondary links, kickers, syntax table headers
- - Purple
#a855f7— git topics, hover states
- - Amber
#f59e0b— warnings, emphasis, callouts
-
The title is the star: an h1 filled with a linear-gradient across all four colours, clipped to the text via -webkit-background-clip: text. Code blocks live in dark #0a0e14 panels with #2a3340 borders. Blockquotes become amber-tinted callouts. The whole thing radiates a "well-maintained developer doc" energy without feeling like a Bootstrap template.
We wanted that energy on the public-facing blog.
-The build system (and why it mattered)
-The blog is generated by a zero-dependency Node script — build.js — that converts markdown posts into HTML. The pipeline is:
Then we looked at our internal team guide — a self-contained HTML doc with gradient titles that clip to transparent, dark code panels, and callout boxes with coloured borders. It radiated a "well-maintained developer doc" energy. We wanted the blog to feel like it came from the same shop.
+Two template files, one build step, zero external dependencies. Here's what we changed.
+The build system (why it mattered)
+The blog is generated by build.js — a zero-dependency Node script that converts markdown to HTML:
posts/*.md + _template.html / _index_template.html → *.htmlThis means we never touch a generated .html file by hand. Every visual change flows through the two templates. build.js then stamps out all 11 posts plus the index in under a second.
The site-wide CSS — navigation, footer, base typography, the brand amber 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. 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. 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. More importantly, nobody on the team could see 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. We evaluated GitLab, Forgejo, Gogs, and Gitea. The decision came down to: 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. Tinqs Studio is our fork. It tracks upstream Gitea on the The headline feature. When you open a PR that contains a 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. Vanilla Gitea treats LFS as an afterthought. You configure Tinqs Studio adds auto-LFS tracking on repository creation. Game file extensions ( Tinqs Studio integrates project management — issues, sprints, time tracking — and OAuth2 SSO. One login for git, the game tools, and the team dashboard. Staying close to upstream is critical. We don't want to maintain a fork that diverges forever: We deliberately limit what we touch. We modify templates, locale strings, CSS variables, and a handful of Go packages. We never touch the database models — schema is owned by upstream, and we ride their migrations. This keeps rebasing manageable. Self-hosting git is surprisingly easy. 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. LFS changes everything for game repos. 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. Forking is maintenance, not rebellion. 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. 3D preview is a game changer. 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." Tinqs Studio 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.–c-accent: #c9935a — lives in ../style.css, which is served by Git Studio from outside the blog repo. We deliberately did not touch that file. Instead, we injected a self-contained
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- Why We Forked Gitea and Built Tinqs Studio
- The Problem with GitHub for Game Dev
-Why Self-Host, and Why Gitea
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-What We Built: Tinqs Studio
-main branch and keeps all customisations on a separate branch. We rebase onto upstream releases periodically, fix conflicts, and deploy.3D Asset Preview
-.glb, .gltf, or .fbx 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.LFS-First Workflows
-.gitattributes 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..fbx, .glb, .png, .wav, .ogg, .tscn, .tres) are tracked by default. An API endpoint exposes LFS storage stats per repo. The goal: LFS should be invisible. It should just work.Platform Integration
-The Branching Strategy
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-main tracks upstream go-gitea/gitea. We never commit to it directly.What We Learned
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