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SlopBurn reframes agentic software quality as a depth-first roguelike dungeon crawl. Bugs become monsters, tests become weakpoints, and software quality becomes the main loop instead of an afterthought.
We're evolving from a technical product manager to a research company focused on safe vibecoding. Our mission remains the same: help developers build secure, scalable, and reliable software with AI coding agents — from the first line of code.
A new category of freelance work is exploding: fixing apps that AI built and humans shipped. Full disclosure: I'm a former Upwork employee (2022–2024). All observations below are based on publicly available data. Here's what the numbers say about the vibecoding cleanup economy — and why the hardest 20% is where all the money is.
Whether you just shipped an MVP or are still prompting your first feature, your vibecoded app has security gaps. They're not bugs — they're structural omissions baked into how LLMs generate code. Here's how to find them, fix them, and prevent them at every stage of the software engineering lifecycle.
In 2015, Google warned that ML systems were the 'high-interest credit card of technical debt.' A decade later, vibecoding tech debt makes that metaphor quaint. AI-generated code doesn't carry credit card rates — it carries payday lender rates, with terms designed to look cheap until the first payment is due.
Traditional TDD asks developers to write tests before code. Cutline's Red-Green Refactoring mode flips the script — the constraint graph writes the tests for you, turning every feature into a gauntlet of security, performance, and stability checks that the AI must pass.