Vibe Coding Isn't Dead. You Just Need More Than Vibes.

Matt Glein — Sat, 11 Apr 2026 01:03:33 +0000

I spent 15 years perfecting my coding skills, only to watch someone build my latest project idea in an afternoon using ChatGPT and Cursor. The demo looked identical to what I had sketched out. Same layout, same features, even similar color choices. It stung, but it also clarified something important about where we're headed.

The Vibe Coding Reality Check

Let me be direct: AI has fundamentally changed how fast you can go from idea to working prototype. What used to take weeks of careful architecture and implementation now happens in hours. The barrier to entry for building functional software has collapsed.

I've tested this personally. Using Cursor and Claude, I built a complete invoice tracking system in two days that would have taken me two weeks just a year ago. The AI handled the boilerplate, suggested optimal patterns, and even caught edge cases I might have missed. The code quality was surprisingly good.

But here's where it gets interesting. When I showed the prototype to potential users, their feedback wasn't about the code elegance or technical implementation. They wanted better onboarding, clearer pricing tiers, and integration with tools they already used. The technical execution was table stakes.

The Commodity Trap: When Everyone Builds the Same Thing
The democratization of coding has created an unexpected problem. When everyone can build the same basic application using the same AI assistants trained on similar codebases, products start looking remarkably similar.

Open Product Hunt any day and count the AI powered productivity apps with nearly identical feature sets. The same authentication flows, the same dashboard layouts, the same monetization approaches. It's not because developers lack creativity. It's because AI optimization tends toward convergent solutions.

This commodity trap is real and growing. When the technical implementation becomes trivial, the only way to differentiate is through everything surrounding the code. The market dynamics shift from "can you build it?" to "should you build it, and how will people actually use it?"

The developers who recognize this shift early will adapt. Those who don't will find themselves competing on implementation speed in a race they can't win against improving AI tools.

Where Technical Depth Still Wins

Despite AI's capabilities, certain technical challenges remain stubbornly difficult. Understanding these areas helps you focus your continued technical growth where it actually matters.

Performance optimization still requires deep system knowledge. AI can suggest general improvements, but optimizing a React app from 3 second load times to sub 500ms requires understanding browser internals, bundle analysis, and performance profiling that goes beyond pattern matching.

Cost optimization becomes critical as applications scale. Knowing how to structure database queries, when to implement caching layers, and how to optimize LLM API calls can mean the difference between profitable unit economics and burning money. AI might suggest using Redis, but it won't redesign your data architecture to minimize API calls.

LLM efficiency itself presents new technical challenges. Prompt engineering, context window optimization, and knowing when to fine tune versus using retrieval augmented generation requires understanding both the business constraints and the underlying model architectures.

Integration complexity multiplies in real business environments. While AI excels at connecting clean APIs, dealing with legacy systems, handling authentication across multiple services, and managing data consistency across distributed systems still demands systems thinking.

The New Competitive Battleground

The real competition has shifted to business skills that AI cannot yet replicate. These skills determine whether your technically sound application actually succeeds in the market.

Design intuition separates functional interfaces from compelling user experiences. This goes beyond choosing colors or fonts. It's understanding user mental models, reducing cognitive load, and creating interfaces that feel obvious in retrospect. AI can implement your design decisions, but it can't make the fundamental choices about what users need to accomplish their goals.

Conversion optimization requires understanding human psychology and business metrics. You need to know which metrics actually predict retention, how to structure pricing experiments, and where users typically drop off in your funnel. The technical implementation of A/B tests is straightforward. Knowing what to test requires domain expertise.

SEO strategy demands understanding both technical implementation and content strategy. AI can help with technical SEO basics, but knowing which keywords to target, how to structure content for search intent, and how to build topical authority requires market understanding that extends far beyond code.

UX research skills become crucial when everyone can build, but few know what to build. Learning to conduct user interviews, interpret behavioral data, and translate user needs into product requirements becomes a differentiating capability.

Product positioning determines whether your application finds an audience. Understanding market segments, competitive positioning, and value proposition articulation matters more than the underlying tech stack.

Your Survival Playbook

Adapting to this landscape doesn't mean abandoning your technical foundation. Instead, it means expanding your skill set strategically while maintaining your technical edge in areas that matter.

Start by picking one business skill that complements your technical background. If you're backend focused, dive deep into database optimization and cost management. If you're frontend oriented, develop design intuition and conversion optimization skills. Build expertise that connects your technical knowledge to business outcomes.

Stay technically sharp in areas where AI still struggles. Focus on performance optimization, system design, and integration complexity. These skills become more valuable as the baseline technical bar rises.

Develop a product mindset alongside your engineering mindset. Learn to evaluate features based on user value rather than technical elegance. Understand how your technical decisions impact business metrics like customer acquisition cost and lifetime value.

Practice explaining technical concepts to non technical stakeholders. Your ability to translate between technical constraints and business requirements becomes increasingly valuable as AI handles more of the pure implementation work.

The game has changed, but it hasn't ended. Developers who adapt to focus on business impact while maintaining technical excellence will find themselves more valuable than ever. Those who cling to pure coding as their only differentiator may discover that vibes alone aren't enough to sustain a career.

The future belongs to developers who can bridge the technical and business worlds, using AI as a tool to execute faster while focusing their human skills on the problems that actually matter.

Building an autonomous travel agent: the journey begins

Matt Glein — Wed, 08 Apr 2026 22:21:04 +0000

Hey everyone!

I’ve been working on something called Pack and figured I’d share it here to get some honest feedback.

Travel has always felt way more manual than it should be to me. You’re bouncing between sites, comparing flights, checking your calendar, re-entering the same info… and then if anything goes wrong you’re suddenly dealing with it yourself at the worst possible time.

So the idea behind Pack is pretty simple — you just tell it what you need, like “book my trip next weekend” or “I need to get to Chicago for a wedding,” and it actually handles the planning and booking for you.

When you sign up, it pulls everything together so you can see your full travel history in one place, and from there it starts learning how you travel and making better decisions over time.

One thing I’ve found interesting while building it is how messy group travel is. Right now we’re trying to make it so you can plan a trip and send it to someone else, and it rebuilds it for them based on their preferences and schedule

Still early, but we’ve got a site up and are iterating pretty quickly:
👉 https://trypackai.com

Would really love any thoughts on the product and current website

Also just curious how other people are thinking about building real products with AI right now vs just demos

And yeah, part of this idea came from trying to rebook a flight in a Vegas nightclub at 2am which was… a low point

Appreciate any feedback, I'm looking forward to check out what others are working on too

Forem: Matt Glein

Vibe Coding Isn't Dead. You Just Need More Than Vibes.

Building an autonomous travel agent: the journey begins