DEV Community: Hussain Nazary

The Experiment

Hussain Nazary — Fri, 05 Dec 2025 17:59:51 +0000

I Built a 100+ File Android App Using Only Vibe Coding with Kiro

What happens when you try to build a complex Android app entirely through conversation with an AI? I decided to find out using Kiro, an AI-powered IDE.

The goal: Build a production-quality personal assistant app with voice commands, finance tracking, location-based reminders, and home screen widgets — without writing boilerplate manually.

What I Built

Smart Calendar — an AI-powered Android assistant that combines:

🎤 Voice-first chat interface with natural language understanding
🔊 Custom "Kiro" wake word detection
💰 Finance tracking with AI-powered SMS parsing
📍 Location-based reminders using geofencing
✅ Tasks, alarms, and recurring reminders
📱 Three home screen widgets
💬 Floating chat bubble overlay

The result: 100+ Kotlin files, 8 database tables, Jetpack Compose UI, and features I'd normally spend weeks implementing.

How Kiro Changed My Approach

1. Spec-Driven Development

Instead of jumping into code, I described features in plain English. Kiro's spec system breaks this into:

Requirements — what the feature should do
Design — how it should work technically
Tasks — step-by-step implementation checklist

This forced me to think through features before coding, catching design issues early.

2. Vibe Coding

Traditional development: Google → Stack Overflow → copy → adapt → debug → repeat.

Vibe coding with Kiro: "I want wake word detection that listens for 'Kiro' and triggers voice input."

Kiro generated the AudioRecord implementation, threshold detection, and SpeechRecognizer handoff. I reviewed, tweaked, and moved on.

3. Incremental Complexity

I started with a basic reminder app. Each session added a layer:

Session 1: Basic reminders
Session 5: Voice input
Session 10: Location geofencing
Session 15: Finance tracking with SMS parsing
Session 20: Home screen widgets

The app grew organically through conversation.

Code Example: Wake Word Detection

Here's what Kiro generated for the wake word service:

class WakeWordService : Service() {
    private var audioRecord: AudioRecord? = null
    private val sampleRate = 16000
    private val bufferSize = AudioRecord.getMinBufferSize(
        sampleRate,
        AudioFormat.CHANNEL_IN_MONO,
        AudioFormat.ENCODING_PCM_16BIT
    )

    private fun startListening() {
        audioRecord = AudioRecord(
            MediaRecorder.AudioSource.MIC,
            sampleRate,
            AudioFormat.CHANNEL_IN_MONO,
            AudioFormat.ENCODING_PCM_16BIT,
            bufferSize
        )

        // Audio level detection triggers SpeechRecognizer
        // which checks for wake word variants:
        // "Kiro", "Kyro", "Kero", "Cairo", "Kira"
    }
}

I described what I wanted. Kiro handled the Android audio APIs I'd otherwise spend hours researching.

What Worked Well

✅ Rapid prototyping — Features that would take days took hours

✅ Consistent architecture — Kiro maintained patterns across 100+ files

✅ Learning accelerator — I learned Android APIs through generated code

✅ Documentation built-in — Specs serve as living documentation

What Required Iteration

⚠️ Complex state management — Sometimes needed manual refinement

⚠️ Edge cases — AI handles happy paths well; edge cases need guidance

⚠️ Platform quirks — Android-specific gotchas (like exact alarm permissions) required explicit prompting

The Numbers

Metric	Value
Kotlin files	100+
Database tables	8
Lines of code	~15,000
Development approach	90% vibe coding, 10% specs
Time saved	Estimated 60-70%

Try It Yourself

The full source is available on GitHub. Clone it, explore the .kiro/specs/ folder to see how features were planned, and try extending it with your own vibe-coded features.

Final Thoughts

Kiro didn't replace my development skills — it amplified them. I still made architectural decisions, reviewed code, and debugged issues. But the tedious parts? The boilerplate, the API lookups, the repetitive patterns? Those became conversations.

If you're curious about AI-assisted development, give vibe coding a try. Start small, iterate, and see how complex you can go.

Built for the Kiroween Hackathon — Frankenstein Category

"Speak it. Track it. Never forget it."

How I mad and Enhanced GGUF Loader With Kiro

Hussain Nazary — Mon, 15 Sep 2025 11:14:35 +0000

Building local AI tools has never been easier thanks to Kiro, the AI-powered coding assistant. I used Kiro to develop GGUF Loader, a privacy-first desktop app that runs open-source GPT models locally — no cloud, no setup, no data leaks.

🛠️ How Kiro Helped

Working with Kiro felt like gaining 1000 years of software development experience in just a few weeks. With Kiro, I was able to:

Design modular architecture for backends and addons
Generate UI components and Python scripts instantly
Optimize performance for large models
Automate documentation and spec creation
Streamline testing and validation

🌟 Key Features of GGUF Loader

Zero-setup installer for Windows & Linux
Floating Smart Assistant: summarize, translate, or comment on text globally
Addon system: install, activate, and manage addons easily
Privacy-first: all processing happens locally
Cross-platform ready: macOS support coming soon

🔮 What’s Next

GPU auto-detection and acceleration
Community addon marketplace
Advanced AI workflows like RAG pipelines
Voice command integration

Using Kiro, I could speed up development, reduce errors, and make GGUF Loader more powerful and accessible.

Check it out on GitHub: https://github.com/gguf-loader/gguf-loader

Kiro #LocalAI #DesktopAI #OpenSource #Hackathon

How Kiro Changed the Way I Build Software

Hussain Nazary — Sun, 14 Sep 2025 05:38:37 +0000

🚀 How Kiro Changed the Way I Build Software

I recently worked with Kiro, the AI-powered development partner, and honestly — it completely redefined how I approach coding.

Instead of spending days writing boilerplate and setting up architecture, I just started by describing my idea in plain language. From there, Kiro did the heavy lifting.

💡 From Idea → Spec

I explained what I wanted the app to do.

Kiro turned that into a clear specification with:

📐 Architecture design
🔍 Feature breakdown
⚠️ Edge case handling

⚙️ From Spec → Code

Kiro generated production-grade code:

🧩 Modular architecture
🔄 Reusable components
🛡️ Error handling & validation
🧪 Unit + integration tests

📚 Beyond Just Code

Kiro didn’t stop at code. It also:

✨ Suggested performance optimizations
🧪 Created a test suite
📝 Wrote documentation & guides

🔮 Why It Matters

With #Kiro, I now focus more on what to build instead of repetitive tasks.

It feels less like using an autocomplete tool, and more like working with a senior engineer who understands my goals.

✅ My Takeaway

Kiro isn’t just about speed — it’s about better workflows:

🧠 Brainstorm ideas in natural language
🏗️ Architect robust systems with specs
💻 Build production-ready apps faster
🔍 Refine with tests & docs

🔥 This is the future of development: idea → spec → production-ready code.

And I’m all in with #Kiro.

🚀 Introducing Local AI Zone — Discover & Run AI Models Locally

Hussain Nazary — Sun, 14 Sep 2025 05:21:46 +0000

I’m excited to share my new open-source project: Local AI Zone
🎉

What it is:
A professional, premium web application for discovering and browsing GGUF machine learning models. The platform provides:

🔍 Model Discovery: Explore 5,000+ quantized AI models

⚡ Advanced Search & Filtering: Real-time fuzzy matching, filter by quantization, type, license

📊 Engagement Metrics: Likes, downloads, popularity

🎨 Premium UI/UX: Dark mode, responsive design, smooth animations

🔗 Direct Download Links: Grab models instantly

Under the Hood:

Built with a modular JavaScript architecture

Automated data fetching + enrichment from Hugging Face

Performance optimized for fast browsing

Deployed on GitHub Pages

How I Built It with #Kiro 🤖
This project was developed with assistance from Kiro, my AI-powered dev partner:

🏗️ Helped design the modular architecture

✍️ Generated core components & styling

🔧 Automated Python scripts for model fetching

✅ Created test suites and documentation

Kiro saved me weeks of manual work and let me focus on refining the product.

👉 Try it out: https://local-ai-zone.github.io

👉 GitHub: GitHub Repo: https://github.com/local-ai-zone/local-ai-zone.github.io
Would love feedback from the community, especially around:
More filtering options
Model ranking metrics
Any UI/UX improvements
⚡ Tags: #AI #OpenSource #WebDev #MachineLearning #Kiro

Meet GGUF Loader: Run Local LLMs with Zero Hassle

Hussain Nazary — Wed, 10 Sep 2025 15:32:15 +0000

Have you ever wanted to run an AI model like LLaMA or Mistral locally, but got stuck in command-line hell?

That’s exactly the problem I wanted to solve when building GGUF Loader.

🎯 What is GGUF Loader?

GGUF Loader is a cross-platform desktop app (Windows, Linux, macOS) that makes running local GGUF models as simple as drag-and-drop.

✨ Key Features:

🖥️ No terminal commands — just a clean GUI.
⚡ Works out of the box with popular models (LLaMA, Mistral, Gemma).
🔌 Plugin-ready design so you can extend it.
📊 Hardware dashboard to track CPU/GPU usage.
🔒 100% local — no cloud, no data leaks.

🧩 GGUF Loader turns any laptop into a secure, multilingual AI workstation.

🛠️ Why I Built It

I’ve seen too many people give up on local AI because the setup is painful.

Developers want tools, not roadblocks.

My goals were simple:

✅ Make it easy enough for non-technical users.
✅ Keep it powerful enough for developers.
✅ Ensure it’s privacy-first and runs on low-resource hardware.

🌍 Who’s It For?

👩‍💻 Indie devs building AI-powered apps.
🎓 Students and hobbyists exploring local LLMs.
🏢 Small businesses where cloud AI isn’t practical or safe.

🚧 What’s Next

🔮 Upcoming features include:

📦 More model presets (Qwen, Mixtral, etc.)
🛍️ Plugin marketplace for custom AI workflows.
📱 Exploring a lightweight mobile companion app.

🔗 Try It Out

👉 GitHub: GGUF Loader

👉 Website: ggufloader.github.io

If you give it a try, I’d love your feedback.

Let’s make local AI accessible to everyone 💡