Building AI That Doesn't Suck

Most AI applications today feel like tech demos. They showcase impressive capabilities but fail at the most basic requirement: making users want to come back tomorrow.

The Problem with AI Products Today

After building NbAIl (our HackHazards 2025 winning AI assistant) and experimenting with various AI tools, I've noticed a consistent pattern: most AI products prioritize technical achievement over user experience.

What Makes AI Products Suck

1. Over-engineering: Too many features, not enough focus
2. Poor UX: Complex interfaces that require a manual
3. Unreliable outputs: Great 80% of the time, unusable 20% of the time
4. No clear use case: Cool tech looking for a problem

Lessons from Building NbAIl

When we built NbAIl, we focused on three core principles:

1. Solve One Problem Really Well

Instead of building a general-purpose AI assistant, we focused on desktop automation with voice control. This narrow focus allowed us to nail the user experience.

2. Make It Feel Natural

We integrated Three.js for visual feedback and Groq for ultra-fast responses. The goal wasn't just to process commands—it was to make the interaction feel conversational and human.

3. Handle Failure Gracefully

AI will fail. Accept it. Build systems that degrade gracefully and give users clear feedback when things go wrong.

The Secret: Start with the User, Not the Model

Here's the controversial take: your AI model doesn't matter if your product sucks.

Users don't care about:

• Your model's parameter count
• Which LLM you're using
• Your fine-tuning approach

They care about:

• Can it solve my problem?
• Is it fast?
• Does it work reliably?

Building AI That Doesn't Suck: A Framework

Phase 1: Validate the Use Case

Before writing any code, answer these questions:

• What specific problem are you solving?
• Why can't existing tools solve it?
• Will users pay for this solution?

Phase 2: Design the Experience First

Sketch the user journey before choosing your AI stack. The AI should be invisible—users should just feel like things work.

Phase 3: Start Simple

Build with the simplest AI that could work. GPT-4 API calls? Fine. Rule-based systems? Even better if they work.

Phase 4: Iterate Based on Usage

Deploy early. Watch how people actually use it. Most users won't use your product how you imagined.

Case Study: NutriSnap

When building NutriSnap (our AI nutrition tracking app), we could have gone wild with custom models. Instead:

1. Started with OpenAI's Vision API
2. Built a simple image → nutritional breakdown flow
3. Added Indian food support (the actual problem)
4. Deployed and gathered feedback

Result? Users loved it because it solved their specific problem (Indian food tracking) better than competitors.

The Mumbai Perspective

Building from Mumbai, India, gives a unique lens on AI products. We see global tools that completely ignore local contexts. This taught me:

Great AI products are context-aware. They understand user needs beyond just the technical problem.

Conclusion: Make It Useful, Then Make It Smart

The best AI products follow this hierarchy:

1. Useful: Solves a real problem
2. Usable: Easy to understand and use
3. Reliable: Works consistently
4. Fast: Responds quickly
5. Smart: Uses AI to be better than alternatives

Most builders start at step 5. Start at step 1.

Your Challenge

If you're building with AI:

1. Talk to 10 potential users before writing code
2. Build the dumbest version that could work
3. Deploy it to real users within 2 weeks
4. Make one improvement based on feedback

Building AI that doesn't suck isn't about having the best model. It's about having the best understanding of your users.

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Want to discuss AI product development? Reach out—I'm always interested in talking with builders solving real problems.