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# Software Factories - Lights Out or Lights On? | S2E8
- URL: https://varops.com/osnt-software-factories-lights-out-or-lights-on/
- Published: 2026-08-05T09:05:00.000Z
- Updated: 2026-08-05T09:05:00.000Z
- Description: Lights-out AI software factories produce slop; Ran’s lights-on loop keeps humans at plan and PR — and refuses to automate that gate.
- Author: Ran Aroussi
- Tags: Old School / New Tech

Software factories — automated systems that plan, write, test, and ship code beyond a single laptop agent — aren’t a “whether” anymore. They’re a “how.” Ran’s take is rooted in running a real human software shop ([Automaze](https://automaze.io/?ref=varops.com)) *and* an internal AI factory (**Cloop**): you need extreme constraints to trust machines, and you still need humans in the right places.

## The spark

Dex Horthy ([Human Layer](https://www.humanlayer.dev/?ref=varops.com)) wrote about a lights-off factory experiment — always-on, minimal human interaction — and got a lot of slop. Ran’s joking reply: *you’re holding it wrong.* Not because lights-off is evil, but because removing humans from the *wrong* seats is the failure mode.

Uncle Bob’s extreme-constraint story rhymes: when unit tests, QA, mutation testing, coverage, linters, and performance gates surround the agent hard enough, maybe you stop reading every line. Trust comes from the harness, not vibes.

## Workstation → work environment

Next projects outgrow one machine. Agents should run when you’re offline. Steering from a phone, dedicated “local-cloud” boxes (from the previous episode’s setup) — the station becomes a virtual environment. Principle holds wherever the metal lives.

## How Cloop runs (lights on)

**Setup.** Index the whole project (multi-repo aware), build a code knowledge graph / AST map, ask until it can stand up an instance.

**Intake.** Tickets from humans, clients, agents — or **Ladybug** (Sentry-like production catch → verify real bug vs third-party outage → issue). Or a mini-PRD dumped from Claude Desktop brainstorming.

**Plan.** Factory drafts a PRD against that graph. Scope matters (button color vs army-of-agents feature). A second-opinion agent (GPT + Fable rotation) reviews code + PRD for gaps, first principles, reuse. **Human reviews** artifacts/diagrams — iterate or approve.

**Implement.** Break into parallel tracks / Kanban sub-issues (Linear-like). [Factory](https://factory.ai/?ref=varops.com) Droid as the coding harness; model routing by task (e.g. GLM for frontend) and cost.

**Prove.** Unit / QA / e2e. Then a simplification pass (strongest models: efficiency, profiling — not “make it pretty”). Then code + security review (GPT/Claude; Fable for security — with workarounds so it doesn’t refuse as if you’re hacking the NSA).

**Loop.** Fail → implement → test → simplify → review, up to \~five times, then **ping the human** (“I’m stuck”). Pass → human gets a **PR** (plus temp deploy for visual once-over). Merge triggers doc/wiki/graph updates *after* merge — and a parallel loop rebases main hotfixes into the working branch so the PR doesn’t rot.

## Why lights-off fails

Without humans at plan and PR, agents optimize for green tests: mocks, TODOs, false positives, or endless retries and a huge token bill with no off-ramp. Temporary deploy + human eyes on the PR is the anti-slop gate.

Ran’s claim: lights-out removed the **wrong** human. Keep humans for (1) what needs doing, (2) whether the plan matches, (3) whether it actually got done. Don’t bother them mid-loop while the machine still thinks it can finish — but don’t merge unattended either.

## Old school, new tech

We’ve always done loops: write, test, rewind. Machines sped them up; the methodology shouldn’t pretend otherwise. Fancy “loop engineering” language doesn’t replace judgment seats. Human time is expensive; machine time isn’t — so stage humans where proof of work (videos, evidence via Automaze’s proof skill) actually matters.

As models get stronger, *where* the human sits may shift. The factory shape — constraints + humans at plan and merge — is what Ran would bet on sticking.