DEV Community: tworrell

How I'm using ASTs and Gemini to solve the "Codebase Onboarding" problem 🧠

tworrell — Wed, 15 Apr 2026 14:41:03 +0000

Hi everyone! 👋

I’m Tara, a Senior Software Engineer and Consultant. Over the years, I've jumped between a lot of different codebases.

Every time I join a new project, I notice the exact same problem: it takes new engineers 3 to 6 months to truly understand how a complex system is glued together. AI tools like Copilot and Cursor are amazing at making us write code faster, but developers still spend almost 50% of their time just reading and navigating code.

Nobody was solving the comprehension layer. So, I decided to build a tool for it.

I’m super excited to share what I've been working on: AuraCode(Feel free to poke around the live interactive demo there!)

🤔 What does it do?
AuraCode is a neural code agent that transforms messy, undocumented repositories into interactive visual maps and context-aware chat.

Here are the main features I’ve built so far:

Interactive Architecture Canvas 🗺️: Paste a GitHub URL, and it generates a beautiful, interactive D3.js radial tree mapping out component relationships and the system structure.
Contextual Chat + Text-to-Speech 🗣️: You can ask questions like "What breaks if I change this auth utility?" and get answers grounded in your actual code structure. I also hooked the output up to an ElevenLabs TTS engine, so you can have it read the flow to you hands-free while you look at the code.
Smart Onboarding ✅: Say goodbye to outdated setup.md files. AuraCode auto-generates structured onboarding checklists tailored specifically to the architecture of the scanned codebase.
Code Review Summaries 🚀: Point it at a PR, and it will run a trend analysis and spit out a review summary based on the delta.

🛠️ How it works under the hood
Building this required balancing context windows with accurate retrieval. Here is how I approached it:

The AI Architecture: Treating code as "flat text" loses all the important structural relationships (call graphs, dependency chains, etc.). To solve this, AuraCode uses two different approaches depending on the size of the repository:

For small/medium repos: I inject Abstract Syntax Trees (ASTs) directly into the model's context window. This preserves the architectural patterns so the AI actually understands how the code connects.
For massive monorepos: I use what I call Lean RAG. It's a lightweight retrieval layer that selectively surfaces the most structurally relevant nodes before context injection. This keeps accuracy high without blowing out the token limit.

What's next? 🚀
Right now, AuraCode is in pre-launch. Because I am entirely bootstrapped and LLM token costs scale rapidly with usage, I am currently opening up full private-repo access to the first 50 developers on the waitlist.

However, the demo is fully live and open to the public on the site!

I built this because it's exactly the tool I wish I had existed every time I was hired to consult on a massive, legacy codebase.

I would love to hear your thoughts! How do you usually handle ramping up on massive, undocumented codebases?

Let me know if you have any questions about the tech stack, the Lean RAG approach, or the D3 visualizations. I'd love to chat in the comments! 👇

How I'm using ASTs and Gemini to solve the "Codebase Onboarding" problem 🧠

tworrell — Wed, 15 Apr 2026 14:41:03 +0000

Hi everyone! 👋

I’m Tara, a Senior Software Engineer and Consultant. Over the years, I've jumped between a lot of different codebases.

Nobody was solving the comprehension layer. So, I decided to build a tool for it.

I’m super excited to share what I've been working on: AuraCode(Feel free to poke around the live interactive demo there!)

🤔 What does it do?
AuraCode is a neural code agent that transforms messy, undocumented repositories into interactive visual maps and context-aware chat.

Here are the main features I’ve built so far:

Interactive Architecture Canvas 🗺️: Paste a GitHub URL, and it generates a beautiful, interactive D3.js radial tree mapping out component relationships and the system structure.
Contextual Chat + Text-to-Speech 🗣️: You can ask questions like "What breaks if I change this auth utility?" and get answers grounded in your actual code structure. I also hooked the output up to an ElevenLabs TTS engine, so you can have it read the flow to you hands-free while you look at the code.
Smart Onboarding ✅: Say goodbye to outdated setup.md files. AuraCode auto-generates structured onboarding checklists tailored specifically to the architecture of the scanned codebase.
Code Review Summaries 🚀: Point it at a PR, and it will run a trend analysis and spit out a review summary based on the delta.

🛠️ How it works under the hood
Building this required balancing context windows with accurate retrieval. Here is how I approached it:

However, the demo is fully live and open to the public on the site!

I built this because it's exactly the tool I wish I had existed every time I was hired to consult on a massive, legacy codebase.

I would love to hear your thoughts! How do you usually handle ramping up on massive, undocumented codebases?

Let me know if you have any questions about the tech stack, the Lean RAG approach, or the D3 visualizations. I'd love to chat in the comments! 👇

Re-Codenize - Codebase Navigation Assistant

tworrell — Mon, 27 Jan 2025 04:15:30 +0000

This is a submission for the Agent.ai Challenge: Productivity-Pro Agent (See Details)

What I Built

Your AI-Powered Guide to Codebase Mastery — Onboard Faster, Contribute Smarter.

This AI agent is a codebase navigation assistant designed to streamline onboarding for software engineers whether it is for joining a new client or an open source project.

It works by helping software engineers understand and explore a new codebase quickly and efficiently. This AI Agent acts as a guide, providing codebase insights, Smart Search, and File and Function Summaries.

I created this AI Agent because I’ve often faced challenges getting timely meetings with fellow software engineers to provide high-level overviews and explanations of codebases I was assigned to enhance or build upon. I wanted to find a solution to alleviate this bottleneck, and that’s how this idea was born. This concept has been on my mind for over a year, and I had already started developing it independently. However, when I discovered the opportunity to accelerate its development with Agent.ai, I couldn’t pass it up. While there are still many features I’d like to add, this is an incredible starting point.

Demo

Try it Out

Agent.ai Experience

I thoroughly enjoyed bringing my idea to life. While there is a bit of a learning curve when using the Agent.ai interface to build your agent, it’s quite manageable. I’m incredibly excited to see how people use this agent and to hear their feedback on how it’s working for them.

What's Next

I have several additional features I’m excited to add, such as visualization tools and more. I’ll also continue building on this project through the website I’ve been developing, where users will soon be able to access and explore all the features I have planned for this AI agent.

✨Thanks for reading.✨ Happy Coding! 👩🏾‍💻