The Vibe Coding Hangover Cure
It's time to production-ise the magic trick.
The honeymoon with AI code generation is over. Now we have to live with what we've built, and it's not always pretty.
Your AI-Generated Code Isn't Good Enough
A Reddit thread reveals the three prompts that move you from prototype to production.
"Vibe coding" gets you a first draft, fast. But that speed creates a nasty hangover of security flaws, performance issues, and unreadable code that someone, probably you, has to fix later. The initial magic of AI generation often ignores the boring, essential principles of good engineering.
The fix is to stop treating the AI like a magical black box and start treating it like a junior developer. A Reddit discussion highlights three specific prompts: demand robust security and input validation, push for optimal performance and complexity analysis, and force it to refactor for readability and maintainability. This isn't just about asking for code; it's about enforcing standards.
This marks a shift from passive generation to active architectural guidance. By embedding engineering discipline directly into your prompts, you mitigate technical debt from the start. This is how we move from making cool demos to building software that lasts.
The New AI Workforce
As AI gets better at following orders, we're giving it more ambitious jobs.
Graphite Chat: Your AI PR wingman who actually reads the docs.
An AI co-pilot for your pull requests that has full codebase context. It doesn't just suggest changes, it can generate tests and commit fixes for you.
Tasker Builder: From prompt to product to pipeline.
Build an app from a prompt, then deploy AI agents to handle market research and customer outreach. It’s an attempt at an entire startup-in-a-box, moving beyond just code.
Jotform Instagram Agent: Your 24/7 social media assistant.
An AI agent that manages your Instagram DMs and comments with a custom brand voice. It handles customer service and lead capture so you don't have to.
Taming the AI Stack
With all this new AI power comes new complexity, new costs, and new plumbing to build.
Tokyo: The financial advisor for your AI stack.
Finally, a way to track AI costs on a per-customer basis. Now you can see exactly which client is secretly burning through your entire GPU budget.
TaskWand: Describe your workflow, and poof, it's real.
An AI that builds complex automation workflows from a simple text description. It's AI for building the AI plumbing, saving hours of manual drag-and-drop.
Quick hits
AIBI Pocket: Your new bestie fits on your shirt and chats with ChatGPT.
A tiny, wearable AI pet that acts as a physical assistant and companion, moving beyond the screen.
Creem 1.0: Making global partner payouts less painful.
Solves the massive headache of splitting SaaS revenue with partners across borders, currencies, and even stablecoins.
FlowStack: A bouncer for your brain's multitasking habit.
A productivity app that forces you to tackle one task at a time, bringing sequential focus back to a chaotic workflow.
My takeaway
The magic trick of AI generation is over; now the real work of integration and management begins.
We're rapidly moving from just creating things with AI to managing the technical debt, operational costs, and workflow complexity it creates. The tools emerging now reflect this necessary shift from novelty to utility. We're building the guardrails for a world saturated with AI-generated stuff.
This second wave is about building sustainable systems, not just dazzling demos. It’s about making AI a reliable, scalable, and profitable part of the stack. What part of your workflow is still in the 'magic trick' phase, and what needs a dose of this new reality?
What's the biggest "vibe coding hangover" you've had to clean up recently?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().