# How to Build a 24/7 AI Software Factory With GPT-6 Astra > Source: > Published: 2026-09-13 00:00:00+00:00 # How to Build a 24/7 AI Software Factory With GPT-6 Astra A step-by-step guide to deploying a self-hosted autonomous coding harness that turns GitHub issues into validated pull requests using GPT-6 Astra. ## What is an AI software factory? An AI software factory is a fully autonomous coding harness that runs on a remote server around the clock, accepting a product requirement document or a GitHub issue as input and producing validated, shippable code as output. Instead of a developer opening a chat window and prompting a coding assistant turn by turn, the factory runs independently: it reads the task, writes the code, tests it, and submits a pull request without a human sitting in the loop for every step. The concept has been circulating in AI coding circles for most of this year, but it only recently became practical enough to actually run, largely because of gains in model reliability from systems like GPT-6 Astra. ## TL;DR - **GPT-6 Astra** is OpenAI’s newest model and, according to hands-on testing described in the source material, it requires noticeably less hand-holding than prior models like Opus, making fewer wrong assumptions about developer intent. - The **software factory** is an open-source harness: a PRD or GitHub issue goes in, and validated code comes out, with the goal of running the whole loop unattended on a remote machine. - Setup is largely automated: you point a coding agent (Claude Code or Codex) at the factory’s repo URL and prompt it to install itself, and it interviews you for context before deploying. - The harness works both on a **brand-new codebase** started from a PRD and on an**existing repository** you want to bolt automation onto. - Deployment targets any **Ubuntu Linux VPS** ; the walkthrough used Hostinger’s KVM instance and its MCP plugin so the coding agent could manage the server directly over SSH. - Two manual steps can’t be automated away: **GitHub device authentication** and**Codex/ChatGPT device authentication** , both done by running commands the agent supplies once it hits that point. - The factory is not presented as fully reliable yet. It’s described as “close enough” to be worth running and experimenting with, not as a hands-off replacement for engineering judgment. ### Everyone else built a construction worker. We built the contractor. One file at a time. UI, API, database, deploy. ## How does GPT-6 Astra change what’s possible here? The push toward autonomous software factories has existed for a while, but earlier in the year the underlying models and harnesses weren’t consistent enough to make the idea reliable. Autonomous pipelines amplify a model’s flaws just as much as its strengths: if a model misreads intent or drifts on a long task, an unattended harness will happily ship that mistake without anyone catching it. The claim from hands-on testing is that GPT-6 Astra needs less clarification than predecessors like Opus. It makes fewer “weird assumptions,” meaning a developer has to spend less time re-explaining context or intent mid-task. For a chat-based assistant, that’s a nice quality-of-life improvement. For an autonomous harness meant to run without supervision, it’s closer to a requirement. A model that guesses wrong on ambiguous instructions and then keeps building on that wrong guess for hours is far more damaging in a factory setting than in an interactive chat. Astra is described as benchmark-competitive with other frontier models released around the same time, but outperforming in practical head-to-head testing across a range of coding tasks. It’s worth noting these are subjective impressions from real usage, not a formal benchmark suite, and the source material is explicit that this is not AGI, just a meaningful step up. ## What do you actually need to set this up? The core requirements are modest: - A coding agent capable of running shell commands and managing files: Claude Code and Codex are both explicitly supported and tested. - A remote Ubuntu Linux instance. The walkthrough used a Hostinger KVM VPS, but the setup process is designed to be flexible: if you tell the agent you’re on a different provider or already have a server, it adapts. - A GitHub repository, either a fresh one built from a PRD or an existing codebase you want the factory layered onto. - Access credentials for GitHub and for your coding agent’s underlying account (OpenAI for Codex), since authentication has to be done manually for security reasons. The whole thing starts with a single action: paste the factory’s repo URL into your coding agent and tell it to set things up. From there the agent interviews you, asking simple questions to build the core context files the factory needs, like which repo to target, whether it’s a new build or an existing app, and which coding agent you want running the automated loop. ## How does the deployment process actually work? Once the interview establishes context, the agent moves into infrastructure setup. For a Hostinger-hosted instance, this means installing a plugin (an MCP server) that lets the coding agent talk to your hosting account directly, listing instances, reading IP addresses, and eventually configuring the server over SSH. ## Seven tools to build an app. Or just Remy. Editor, preview, AI agents, deploy — all in one tab. Nothing to install. Notably, the plugin can’t create a new VPS instance on its own. That step is left to the human deliberately, so a coding agent can’t spin up cloud resources without oversight. Once you’ve created the instance yourself and handed over its ID and public IP address, the agent takes over: installing tooling, configuring SSH access, setting up the firewall, and installing whatever applications the factory needs to run. The process stalls at exactly two points where the agent hands control back to you: 1. **GitHub authentication.** You SSH into the instance yourself, run the login command, visit a URL, and enter a device code. 2. **Codex/OpenAI authentication.** Same pattern: a device authorization flow where you visit a URL and enter a code tied to your ChatGPT account. For the OpenAI side, there’s a setting that needs to be enabled ahead of time in the ChatGPT dashboard: under Settings, Security and Login, “Enable device code authorization for Codex” needs to be checked, or the remote authentication flow won’t work cleanly. After both logins succeed, you can run a simple test command against the remote instance to confirm the model responding is in fact GPT-6 Astra, and the harness is live. ## Is running your own software factory worth it right now? The honest answer from the source material is: worth experimenting with, not worth betting production reliability on yet. The framing is explicit that we’re “not close enough to AGI” for a software factory to be 100% reliable across arbitrary codebases and tasks. Autonomous harnesses still make mistakes, still need validation steps, and still benefit from human review before code gets merged. What’s changed is the cost-benefit calculation. Earlier in the year, the reliability gap made these harnesses more of a novelty. With more capable models reducing the rate of misinterpreted intent and wrong assumptions, the factory model starts to look like a genuinely useful way to offload well-scoped tasks, especially repetitive ones like bug fixes, small feature additions, or maintenance work defined clearly in a GitHub issue. The practical vision described is a future where individuals and companies keep some version of a software factory running continuously in the cloud, ready to accept a GitHub issue at any hour and return a validated pull request, with humans reviewing and merging rather than writing every line themselves. ## Frequently Asked Questions ### What is GPT-6 Astra? It’s a large language model from OpenAI positioned as a significant capability jump over prior models, tested extensively for coding tasks. It’s compared favorably against other frontier models released around the same time, particularly for how much less clarification it needs during multi-step coding work. ### Do I need Hostinger specifically to run a software factory? No. The walkthrough used a Hostinger KVM VPS as a concrete example, including its MCP plugin for easier remote management, but the setup process is designed to work with any Ubuntu Linux instance on any provider. You just tell the coding agent which environment you’re using and it adjusts. ### Can I use this on an existing codebase, or do I need to start from scratch? Both are supported. You can apply the software factory harness to an existing working application, or start fresh from just a product requirement document if you’re building something new. ### Which coding agents work with this setup? - ✕a coding agent - ✕no-code - ✕vibe coding - ✕a faster Cursor The one that tells the coding agents what to build. Claude Code and Codex have both been tested directly with this harness. Other coding agents may work but aren’t confirmed in the same way. ### Is the software factory fully autonomous with no human involvement? Almost, but not entirely. Two steps require manual action: authenticating GitHub and authenticating your coding agent’s account (Codex/OpenAI) on the remote machine, both for security reasons. Everything else, from server configuration to code generation to validation, is designed to run through the agent itself.