vibecoding
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.
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.
Vibecoding collapsed the build cycle but expanded the product management gap. AI can now handle the analytical heavy-lifting of product management — risk analysis, assumption testing, constraint tracking — so PMs can focus on what only humans can do: judgment.
AI coding agents are excellent at building what you ask for. They're terrible at making it fast, secure, accessible, and observable — because non-functional requirements are exactly the kind of cross-cutting, implicit constraint that LLMs handle worst.
You're burning 3–5x more tokens than necessary on every feature because your AI coding agent keeps forgetting your architecture. Here's the math on LLM token burn—and the structural fix.
You fix the auth. It breaks the database. You fix the database. It breaks the error handling. This is the vibecoding whack-a-mole problem — and it's why most AI-assisted prototypes never reach production.
LLMs generate code that works. They also generate code that's insecure — not because they're incompetent, but because their training data is full of insecure patterns. Here are the seven vulnerabilities that show up in almost every vibecoded codebase.
Vibecoding gets you to a working prototype fast. But production-ready software needs more. Here's how to productionalize your vibecode and ship AI features that actually work.
Cursor rules are the first line of defense against AI-generated chaos. Here's how to write rules that actually work, the patterns that scale, and why static rules eventually hit a ceiling.
Vibecoding makes building fast. But speed without direction is just failing faster. Most vibecoded products fail because of what comes before the code. Here's how to check.
AI coding agents can build anything you describe. But without product context—the why, the who, the constraints—they'll build the wrong thing perfectly. Here's the missing layer.