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<!-- POST -->
<article class="post">
<a href="/blog/" class="post__back">&larr; All Posts</a>
<span class="post__date">26 May 2026</span>
<h1 class="post__title">Agents That Code Overnight: Our $0.80 Cloud Harness with DeepSeek V4 and Pi</h1>
<p class="post__lead">Every coding agent today — Claude Code, Codex, Cursor, Pi — has the same limitation: it runs in your terminal. You watch it work. You close the laptop, it stops. There's no way to say "build these eight features overnight" and wake up to pull requests.</p>
<div class="post__body">
<p>We built exactly that. A Pi fork for the brain, a Go orchestrator inside our Gitea platform for overnight batch work, and a browser dashboard for daytime. Here's the stack.</p>
<h2>The problem with terminal-only agents</h2>
<p>Claude Code runs on Opus at $15/MTok output. Codex uses GPT-5.5. Running eight agents overnight on either would cost $50-200. That's not sustainable for a four-person studio.</p>
<p>DeepSeek V4 Flash costs $0.28/MTok output. Eight overnight tasks: <strong>about $0.80</strong>. The cost differential changes what's possible — from "I'll use this sparingly" to "run it on everything."</p>
<p>But cost isn't the only issue. Cloud tools are black boxes. You can't add a Gitea API tool, a fal.ai image generator, or a guardrail that blocks destructive commands. With our own harness, you add an extension and it's live. Agents are not a feature to outsource — they're the product.</p>
<h2>Pi — the agent brain</h2>
<p><a href="https://pi.dev" style="color: var(&ndash;c-accent-l);">Pi</a> is an open-source coding agent. MIT license, TypeScript, 51k stars. Four core tools (read, write, edit, bash) and an extension system. We forked it and added four extensions:</p>
<ul>
<li><strong>tinqs-provider</strong> — routes DeepSeek V4 Flash/Pro through our inference proxy</li>
<li><strong>tinqs-tools</strong> — Gitea REST API, fal.ai image generation, vision model access</li>
<li><strong>tinqs-ci</strong> — reads CI pipeline status, 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>Pi's RPC mode is what makes overnight automation possible. It runs headless, accepting JSON on stdin/stdout. The orchestrator spawns it as a subprocess, sends tasks, receives results. No terminal, no editor UI.</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: $0.02-0.10 depending on complexity.</p>
<h2>Go orchestrator — overnight batch work</h2>
<p>Inside our Gitea fork we added <code>modules/agents/</code> — a Go worker pool that spawns Pi processes, tracks task lifecycle, and streams events over SSE to any connected UI. Six 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>The orchestrator lives in the same binary as git — same auth, no extra service to deploy. 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 (≤3)
→ Human reviews PR, merges</code></pre>
<h2>Browser dashboard — daytime UI</h2>
<p>The orchestrator is for overnight batch work. 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(&ndash;c-accent-l);">pi-agent-dashboard</a> into the Pi monorepo. One command:</p>
<pre><code class="language-bash">npm run dashboard:dev</code></pre>
<p>Open <code>localhost:33634</code> and you get live session streaming (watch tool calls and model output in real time), interactive chat, session spawning in any project folder, and per-session cost tracking. The dashboard talks to Pi sessions over WebSocket on port 9999. Inference uses the same Tinqs proxy as the CLI — one API key, one billing account.</p>
<pre><code>Browser (localhost:33634)
↕ WebSocket (port 9999)
Pi sessions (interactive or headless)
↕ OpenAI-compatible API
Tinqs proxy (tinqs.com/api/v1/ai)
↕ DeepSeek V4 Flash / Pro</code></pre>
<h2>The guardrail</h2>
<p>The biggest fear with autonomous agents: hallucination. An agent claiming it read a file without calling <code>read</code>. Three consecutive turns with no tool calls. Running <code>aws ec2 terminate-instances</code> at 3am.</p>
<p>The guardrail extension monitors every turn:</p>
<ul>
<li><strong>Hallucination detection</strong> — claims without tool calls get corrected</li>
<li><strong>No-tool drift</strong> — three turns with zero tool calls triggers a warning</li>
<li><strong>Command blocking</strong> — 29 patterns covering destructive git, AWS teardown, process killing, production API abuse</li>
</ul>
<p>Guardrails at the platform layer, not the prompt layer. Prompts can be ignored. Platform gates cannot.</p>
<h2>What it cost to build</h2>
<p>About 2,000 lines of Go, 900 lines of TypeScript extensions, 52 tests, plus merging the dashboard into the Pi monorepo. No new servers — Pi is a Node subprocess; the dashboard is another Node process on your machine. The orchestrator is a Go module inside our existing Gitea binary — zero additional infrastructure.</p>
<hr>
<p>The harness — inference proxy, guardrails, dashboard, orchestrator API — is in place. Agents code while you sleep for pocket change. And because everything runs on your own infrastructure, you control the models, the tools, and the safety rails.</p>
<p><em>Tinqs Studio is an open platform for game development — git hosting, AI inference, asset generation, and autonomous agents. We're building <a href="https://arikigame.com" style="color: var(&ndash;c-accent-l);">Ariki</a> using the same tools.</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>
</div>
</article>
</body>
</html>
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<span class="blog-card__read">Read &rarr;</span>
</a>
<a href="cloud-harness" class="blog-card">
<span class="blog-card__date">26 May 2026</span>
<h2 class="blog-card__title">Agents That Code Overnight: Our $0.80 Cloud Harness with DeepSeek V4 and Pi</h2>
<p class="blog-card__excerpt">Every coding agent today runs in your terminal. Close the laptop, it stops. We built a cloud harness where agents code overnight for about $0.80 — and you can watch from a browser dashboard.</p>
<span class="blog-card__read">Read &rarr;</span>
</a>
<a href="agent-harness" class="blog-card">
<span class="blog-card__date">25 May 2026</span>
<h2 class="blog-card__title">What an Agent Harness Is and Why Game Dev Needs One</h2>
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---
title: "Agents That Code Overnight: Our $0.80 Cloud Harness with DeepSeek V4 and Pi"
slug: cloud-harness
date: "2026-05-26"
description: "A Pi fork for the brain, a Go orchestrator inside Gitea for overnight batch work, and a browser dashboard for daytime. Agents code while you sleep for about $0.80."
og_description: "Pi fork + Go orchestrator + browser dashboard. Agents code overnight for $0.80."
og_image: "https://www.tinqs.com/blog/img/cloud-harness-architecture.png"
excerpt: "Every coding agent today runs in your terminal. Close the laptop, it stops. We built a cloud harness where agents code overnight for about $0.80 — and you can watch from a browser dashboard."
author: "Ozan Bozkurt"
author_initials: "OB"
author_role: "CTO & Developer, Tinqs"
---
Every coding agent today — Claude Code, Codex, Cursor, Pi — has the same limitation: it runs in your terminal. You watch it work. You close the laptop, it stops. There's no way to say "build these eight features overnight" and wake up to pull requests.
We built exactly that. A Pi fork for the brain, a Go orchestrator inside our Gitea platform for overnight batch work, and a browser dashboard for daytime. Here's the stack.
## The problem with terminal-only agents
Claude Code runs on Opus at $15/MTok output. Codex uses GPT-5.5. Running eight agents overnight on either would cost $50-200. That's not sustainable for a four-person studio.
DeepSeek V4 Flash costs $0.28/MTok output. Eight overnight tasks: **about $0.80**. The cost differential changes what's possible — from "I'll use this sparingly" to "run it on everything."
But cost isn't the only issue. Cloud tools are black boxes. You can't add a Gitea API tool, a fal.ai image generator, or a guardrail that blocks destructive commands. With our own harness, you add an extension and it's live. Agents are not a feature to outsource — they're the product.
## Pi — the agent brain
[Pi](https://pi.dev) is an open-source coding agent. MIT license, TypeScript, 51k stars. Four core tools (read, write, edit, bash) and an extension system. We forked it and added four extensions:
- **tinqs-provider** — routes DeepSeek V4 Flash/Pro through our inference proxy
- **tinqs-tools** — Gitea REST API, fal.ai image generation, vision model access
- **tinqs-ci** — reads CI pipeline status, 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.
Pi's RPC mode is what makes overnight automation possible. It runs headless, accepting JSON on stdin/stdout. The orchestrator spawns it as a subprocess, sends tasks, receives results. No terminal, no editor UI.
## 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 our Gitea fork we added `modules/agents/` — a Go worker pool that spawns Pi processes, tracks task lifecycle, and streams events over SSE to any connected UI. Six 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
```
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 (≤3)
→ Human reviews PR, merges
```
## Browser dashboard — daytime UI
The orchestrator is for overnight batch work. 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. One command:
```bash
npm run dashboard:dev
```
Open `localhost:33634` and you get live session streaming (watch tool calls and model output in real time), interactive chat, session spawning in any project folder, and per-session cost tracking. The dashboard talks to Pi sessions over WebSocket on port 9999. Inference uses the same Tinqs proxy as the CLI — one API key, one billing account.
```
Browser (localhost:33634)
↕ WebSocket (port 9999)
Pi sessions (interactive or headless)
↕ OpenAI-compatible API
Tinqs proxy (tinqs.com/api/v1/ai)
↕ DeepSeek V4 Flash / Pro
```
## The guardrail
The biggest fear with autonomous agents: hallucination. An agent claiming it read a file without calling `read`. Three consecutive turns with no tool calls. Running `aws ec2 terminate-instances` at 3am.
The guardrail extension monitors every turn:
- **Hallucination detection** — claims without tool calls get corrected
- **No-tool drift** — three turns with zero tool calls triggers a warning
- **Command blocking** — 29 patterns covering destructive git, AWS teardown, process killing, production API abuse
Guardrails at the platform layer, not the prompt layer. Prompts can be ignored. Platform gates cannot.
## What it cost to build
About 2,000 lines of Go, 900 lines of TypeScript extensions, 52 tests, plus merging the dashboard into the Pi monorepo. No new servers — Pi is a Node subprocess; the dashboard is another Node process on your machine. The orchestrator is a Go module inside our existing Gitea binary — zero additional infrastructure.
---
The harness — inference proxy, guardrails, dashboard, orchestrator API — is in place. Agents code while you sleep for pocket change. And because everything runs on your own infrastructure, you control the models, the tools, and the safety rails.
*Tinqs Studio is an open platform for game development — git hosting, AI inference, asset generation, and autonomous agents. We're building [Ariki](https://arikigame.com) using the same tools.*