🏄‍♂️ Vibe-Coding: Fast Starts, Slow Finishes, and the Path Forward

I’m a technical PM who’s all about shipping products that solve real problems for customers quickly. AI code generation tools like Replit, Cursor, Windsurf, and Lovable promise rapid prototyping, seamless API integration, and quick demos to win over stakeholders and customers. If you understand how to build software, these tools give you unprecedented leverage to ship more, faster.

What is Vibe Coding?

Vibe coding—coding on instinct assisted by instant AI execution and feedback—is thrilling when it works. Now anyone can build full-stack apps in one prompt, and I’ve shipped dozens myself. But without a clear plan and deep technical chops, you’re debugging more than building. These tools struggle in many places which we’ll explore below—one sloppy or mistimed prompt, and you’re stuck in a debugging death spiral. 

The Promise vs. Reality

Ease of Use

• Promise: Anyone can build and ship features, fast.
• Reality: You still need a clear plan, architecture, and working understanding of software—like structuring a REST API or managing state—or you’re untangling AI’s messes instead of delivering value.

Functionality

• Promise: Rapid prototyping and seamless API integration.
• Reality: Models use stale API docs (due to their training run cutoffs), generating code that hits deprecated endpoints or misreads parameters. Even with updated docs, they often create conflicting logic, breaking integrations. 

Production Readiness

• Promise: Skip boilerplate gruntwork, focus on high-level strategy.
• Reality: Flaky code—fine for demos, but it crashes under load, fails  audits, and tanks performance. For complex workflows, it flops, sparking customer complaints and diluting focus.

Scaling and Iteration

• Promise: Works well with codebases of varying complexity and maturity.
• Reality: AI disregards your architecture—overwriting files, ignoring naming conventions, and turning clean code into a maintenance nightmare, delaying sprints and frustrating engineers.

Self-Healing & Adaptation

• Promise: AI evolves alongside the latest coding models and best practices.
• Reality: Code-gen tools lean on outdated models, tripping over basics like dependencies. They can’t fix their own errors, leaving you to mop up.

The Beginning of Infinity

Deutsch nailed it: even a machine containing all knowledge is only as powerful as the questions asked of it. These tools are a leap forward, but they hinge on your prompts. Ask for a database schema without relationships, and it’s chaos. As PMs, we need tools that scale from prototype to production—because we ship products, not demos. I love a quick rip on a feature-set as much as the next PM, but for enterprise-grade needs, these tools just aren’t ready to enable non-technical teams to ship production ready scalable products. The promises are enticing, but the pitfalls have been very real in my experience.

The Need for a Better Way

I built a full-stack web app in two hours—video downloads, Whisper transcriptions, OpenAI embeddings, aggregate summaries, and a chat interface. It was remarkable, but scaling it took two weeks of grinding. That experience, among others, exposed the gaps. We’ve seen the beginning of what is possible with AI, which is frankly incredible, but now we need tools that bridge the gap between prototyping and production. If you’re anything like me,  posted up on the bleeding edge of tech on a quest to make your lever ever larger, you need tools built for real-world with: 

• Seamless integration with existing codebases.
• Mastery of design patterns for complex workflows.
• Real-time API updates to avoid integration hell.
• A context window that remembers your project—like not re-explaining your schema.
• Self-healing automation to fix errors fast.
• The best models available.

How We Solved This

AI tools promise speed but falter in production. That’s why we built vnow\.dev for ourselves at Intrinsic, to build more, faster, and reliably. Unlike traditional AI code generators, vnow\.dev is built for technical PMs, engineers, and non-technical entrepreneurs alike looking to build AI-powered applications—at scale. It:

• Syncs with your codebase, preserving architecture.
• Generates optimized, scalable code that won’t crash under load.
• Keeps APIs current, dodging integration headaches.
• Automates error fixes, cutting debug time.

Final Thought

Bottom line: AI coding tools promise speed but crumble in production—leaving PMs scrambling. vnow\.dev blends rapid prototyping with reliability. Visit vnow\.dev today to ship AI-powered products faster and smarter.