Blog/Posts

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.

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.

AI agency isn't about smarter models—it's about the dynamism of the context engineering. From static prompts to RAG to agentic orchestration, each rung on the ladder is an analogue to a human cognitive process. And plan.md might be where the climb stalls.

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.

Specs tell AI agents what to build. But without product context—the why, the who, the validated assumptions—your perfectly executed spec might be perfectly wrong.

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.

Vibecoded codebases don't just have technical debt — they have a different kind of debt that compounds faster because every feature introduces inconsistent patterns the LLM can't see. Here's the anatomy of vibecoding debt and the structural fix.

Traditional customer interviews are slow, biased, and often misleading. AI personas offer a faster, more scalable way to get customer feedback. Here's how they work.

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.

Every product is a stack of assumptions. Learn how to identify, prioritize, and systematically test the assumptions that will make or break your product.

You don't need to build an MVP to validate it. Here's a 48-hour playbook for testing demand, pricing, and positioning—before writing a single line of code.

Most founders guess at pricing and hope for the best. Here's how to price your startup and validate your pricing strategy before launch—avoid leaving money on the table.

A pre-mortem imagines your product has failed and asks why. Here's a complete template to run one in 30 minutes—solo or with your team.

Vibecoding lets you build faster than ever. But speed without validation is just faster failure. Here's how to avoid the most common vibecoding mistakes.

Before you build, imagine it failed. The pre-mortem framework helps you identify risks, test assumptions, and validate product ideas before writing a single line of code.

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.