Stop asking your AI to "just review the code." The shift from "model-driven" to "tool-driven" control flow - putting the process in charge, not the agent - dramatically improves reliability, reduces technical debt, and scrubs thousands of issues. Beyond "vibe coding," welcome engineered AI systems.
Building a full, respectable marketing site - copy, media, and SEO - took only four hours with AI tools. I leveraged Replit's product knowledge for the core build, then fine-tuned images & video with chatGPT & Pika. Box checked, site live, and a huge "Good job!" to my AI helpers!
Pure vibe coding breaks down quickly. We introduce a model-driven scaffold: a lightweight, AI-driven review process that gives the LLM structure, focus, and staying power without taking control away. It's a significant improvement over naive prompting and key to self-scaffolding a code auditor.
We've found the 'Vibe Coding Limit' for AI. Stunning productivity hits a wall where human engineering must step in to prevent 'Spaghetti Code.' AI agents miss best practices (SSOT, KISS, DRY) & struggle with context, showing human review is critical for code quality.
Our multi-model AI code audit (Claude Opus 5.5 & GPT-5.2) improved things, but a human engineer still found basic, overlooked issues. The Spaghetti Code Limit (SCL) still lingers on the horizon, proving AI is a powerful tool, but not a replacement for a human expert.
Vibe coding is fast, but it hits the 'Spaghetti Code Limit.' To build a real product, PRDs (Product Requirements Documents) and best practices are still essential for scaling beyond the brink. Don't let the magic run out.
Six months of "weekend AI warrioring" led to the launch of our MVP, Burrow. We're now focused on hardening the product and validating with real user input. The journey proves AI is impressive, but it must include proper product development process.
Level up your code quality with a 3-LLM review process! Learn the 6-step workflow using ChatGPT, Claude Opus (via Replit), and Gemini to tap into the collective intelligence of current state-of-the-art AI. A tiny team's secret weapon for better code, faster.
Treat your codebase like your house: give it a deep clean with a single-prompt, AI-driven refactoring! I use the most advanced model (Opus 4.5) to iteratively refactor, clean, consolidate, simplify, and harden my code, maintaining a solid foundation. It's well worth the cost for the best quality.
Keeping your agents' knowledge base pristine isn't just about tidiness - it’s about speed and preventing confusion. I use a clear file structure (like an agents/ folder) and a specific cleanup prompt to maintain a concise single source of truth, ultimately making it easier to keep the code clean.
A significant benefit of AI assistive agents is documenting things you learn as you develop, preventing "context drift." They turn troubleshooting into permanent, accessible best practices. Stop losing context; start preserving history's lessons alongside your code.
What if your development project had an AI product team? I used expertise-specific "agents" to enforce best practices and add rigor to my project on Replit, creating guardrails tailored to my needs. These are currently powerful reference libraries, anticipating real-time monitoring in the future.