CodeTrace

Inspiration

Debugging is frustrating. But debugging a bug you can see—and can’t trace back to the code? That’s next-level pain.

We’ve all been there: The UI glitches. The animation breaks. A button freezes. You re-read your code 17 times. Still nothing.

CodeTrace was born from a simple question:

What if AI could watch your bug happen… and then trace it directly to the line of code responsible?

Instead of developers describing issues in words, we let them show the bug in action. Then we let AI connect the dots.

We didn’t want another chatbot. We wanted a visual debugging detective.

🔥 What It Does

CodeTrace is an AI-powered visual debugging assistant that correlates:

🎥 A screen recording of your bug

📦 Your entire codebase (uploaded as ZIP)

🧠 AI-powered reasoning (Google Gemini 1.5 Flash)

And delivers:

📍 Exact file references

🔢 Line numbers

🛠️ Suggested fixes

📋 Clear explanation of what’s going wrong

🕘 Saved history of past debugging sessions

Instead of “maybe check your state logic,” you get:

The bug originates in components/sidebar.py line 87 where state isn’t being reset after upload.

That’s precision. That’s power.

🛠 How We Built It

We designed CodeTrace as a modular, scalable debugging engine.

🧱 Frontend

Streamlit for rapid UI development

Custom CSS for modern aesthetic

3D rotating hero section using CSS transforms + JS physics

🧠 AI Engine

Google Gemini 1.5 Flash

Multimodal reasoning: video + code + text

Prompt engineering optimized for:

file detection

pattern matching

bug hypothesis ranking

structured debugging output

📦 Code Processing

ZIP extraction + intelligent parsing

Context summarization of large codebases

Clean packaging for AI ingestion

🎥 Video Analysis

Frame interpretation + behavioral analysis

Bug symptom extraction

Visual-state reasoning

🗂️ History Engine

Persistent session tracking

Replayable reports

Developer workflow continuity

We didn’t just build a feature. We built a debugging pipeline.

😵 Challenges We Ran Into

Multimodal Correlation Connecting visual behavior from a video to abstract code logic is not trivial. We had to refine prompts to force structured reasoning.

Context Window Limits Codebases are big. We engineered a smart filtering + summarization strategy to avoid overwhelming the AI.

Precision Over Guessing We tuned output to avoid vague “possible issue” responses and instead push for file-line specificity.

Performance Balance Video + ZIP processing can be heavy. We optimized for responsiveness without sacrificing depth.

UX Simplicity Powerful AI tools can feel intimidating. We made it:

clean

intuitive

one-click actionable

🏆 Accomplishments We’re Proud Of

✅ Built a true multimodal debugging tool (not just code Q&A)

✅ File + line-level fix suggestions

✅ Clean modular architecture

✅ Persistent debugging history

✅ Beautiful 3D hero interface

✅ End-to-end AI reasoning pipeline

But what we’re most proud of?

We made debugging feel less lonely.

🧠 What We Learned

Multimodal AI is powerful—but only with structured prompting.

Good architecture makes AI integration 10x easier.

Developers don’t want theory. They want actionable precision.

UX matters as much as intelligence.

AI isn’t replacing developers—it’s becoming their debugging partner.

And maybe most importantly:

Showing the problem is often more powerful than describing it.

🚀 What’s Next for CodeTrace

We’re just getting started.

🔮 Roadmap

🔄 Real-time screen capture debugging

🔍 GitHub repo link integration (no ZIP required)

🧠 Auto pull-request fix generation

🧪 Test case generation for detected bugs

🤝 Team collaboration mode

☁️ Cloud deployment with scalable backend

🔌 VS Code extension

🧬 Pattern-learning across previous bugs

Long term?

We see CodeTrace becoming:

The first AI-powered visual debugging operating system for developers.

Built With

• 1.5
• 3d
• ai:
• css
• flash
• frontend:
• gemini
• google
• hero:
• javascript
• streamlit
• transforms
• with