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rewrite: refresh all blog posts for public audience

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Merged overlapping posts:
- forking-gitea + fork-dont-build → one post about the fork philosophy
- fal-image-generation + image-generation-fal → one post about AI art pipeline

Rewrote all posts with external/public voice:
- Stronger hooks, concrete examples, punchier language
- agentic-workflow: restructured around soul files + skills + numbers
- agent-harness: clearer framing of 'what an agent harness is'
- cloud-harness: tighter narrative about overnight agents
- godot-optimisation: same depth, sharper opening
- pre-commit-agent: clearer architecture, cost breakdown
- studio-cli: reframed around identity/cold-start problem
- blog-visual-upgrade: tightened the restyle story

10 posts total (9 markdown + 1 hand-authored HTML)
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---
title: "One Binary to Rule Them All: Building a Studio CLI"
title: "One Binary to Rule Them All: Our Studio CLI"
slug: studio-cli
date: "2026-05-18"
description: "A single Go binary that handles machine identity, screenshots, cloud vision, and health checks. The glue that makes AI agents useful across a multi-machine game studio."
og_description: "A single Go binary for machine identity, screenshots, cloud vision, and AI agent coordination."
description: "A single Go binary that gives AI agents full context about your machine, project, and services in 100ms. Screenshots, cloud vision, health checks — one install, every machine."
og_description: "One Go binary for machine identity, screenshots, cloud vision, and AI agent context."
og_image: "https://www.tinqs.com/img/og-cover.jpg"
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."
excerpt: "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 — in 100ms."
author: "Ozan Bozkurt"
author_initials: "OB"
author_role: "CTO & Developer, Tinqs"
---
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.
Every AI agent session starts the same way: cold. The agent doesn't know what project this is, who's asking, what tools are available, or what happened yesterday. You spend the first five minutes re-explaining context.
## Why Build a CLI
Our CLI solves this in 100ms. One command — `tstudio identity` — and the agent knows everything. The binary is 15MB, has zero runtime dependencies, and runs on every machine in the studio.
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?
## The identity command (100ms)
We tried shell scripts. A `setup.sh` for Mac, a `setup.ps1` for Windows, a `check-services.sh` for health checks, a `screenshot.py` that never worked on Windows. They drifted. They broke. Nobody updated them.
When an agent starts, the first thing it calls is `tstudio identity`. The output:
So we built one Go binary that does everything.
- **Soul file** — the agent's persistent identity, values, operating principles
- **Company context** — team members, roles, what the company does
- **Machine context** — hostname, OS, which repos are cloned, what services are running
- **Ecosystem** — other repos and their purpose
- **Service status** — which URLs are live and reachable
## The Identity System
This data lives in markdown files in the docs repo. Any machine on the network can read it. The agent goes from blank to fully contextual in under a second.
The most important command is `identity`. 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:
This started as a convenience tool for humans. It became the single most important function in our stack. Every agent session — Cursor, Claude Code, Pi — starts with `tstudio identity`. Without it, every conversation begins with "let me explain the project." With it, the agent already knows.
- **The soul file** --- the agent's persistent identity, values, and operating principles
- **Company context** --- team members, roles, what the company does
- **Machine context** --- hostname, OS, which repos are cloned, what services are running
- **Ecosystem** --- other repos and their purpose
- **Service status** --- which URLs are live and reachable
## Screenshots and cloud vision
This solves a fundamental problem with AI agents: **cold starts.** 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.
The CLI can capture any window from outside the process. No in-game overlay, no rendering pipeline integration. OS-level capture — GDI+ on Windows, screencapture on Mac.
The data lives in markdown files in the docs repo --- the source of truth. Any machine on the network can read it.
A `photo` command sends the screenshot to a cloud vision model. The agent says "take a photo of the game" and gets back: "The player character is standing near a half-built hut. Three palm trees to the left. The terrain has a visible seam between two biomes."
## Screenshots and Vision
This is how you file bugs without typing. Look at the game, tell the agent what's wrong. It takes a screenshot, describes what it sees, and creates an issue with both the description and the image attached. Keyboard-free bug reporting.
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).
## Health checks
A `photo` 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."
`tstudio doctor` runs a comprehensive check:
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.
## Health Checks
A `doctor` command runs a comprehensive health check:
- Is the git platform reachable? Can we authenticate?
- Is the git platform reachable and authenticated?
- Is the game server running?
- Are all expected repos cloned and on the right branch?
- Are required tools installed and at the right version?
- Are required tools installed at the right version?
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.
Output is a green/yellow/red table. Essential for unattended agent sessions — the agent verifies its environment before starting work. No "the build failed because port 3000 was already taken" at 3am.
## Why Go
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.
Go compiles to a single static binary. 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.
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.
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. The binary is 15MB, starts in under 100ms, has zero runtime dependencies.
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.
## What we learned
## What We Learned
**The CLI is the API for AI agents.** What started as a human convenience tool became the primary interface for agents. Every session starts with `tstudio identity`. The agent's "hands and eyes" — screenshots, vision, health checks — are subcommands of the same binary.
**The CLI is the API for AI agents.** When we started, this was a convenience tool for humans. It became the primary interface for AI agents. The `identity` command was originally "nice to have" --- now it's the single most important function in our stack. Every agent session starts with it.
**One binary beats ten scripts.** Scripts rot. They have different shells, different PATH assumptions, different error handling. A compiled binary either works or it doesn't. It ships with dependencies baked in. It doesn't care if your Python is 3.9 or 3.12.
**One binary beats ten scripts.** 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.
**Cloud vision is underrated for game dev.** Sending a screenshot to a vision model 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 communicate when the AI is looking at the same screen.
**Cloud vision is underrated for game dev.** 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.
**Agent cold starts are the real problem.** 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.
**Agent cold starts are the real problem.** Without the identity system, every session starts with the agent asking "what project is this?" With it, the agent knows everything in 100ms. That's the difference between an AI assistant and an AI team member.
---
The CLI is part of [Tinqs Studio](https://tinqs.com) --- 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.
*The CLI is part of [Tinqs Studio](https://tinqs.com). Every time we find ourselves about to write 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.*