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Mohit
Mohit

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AURA AI: Context-Aware Workspace Assistant Built for Project Teammates

#devchallenge #weekendchallenge #hf26challenge #opensource

Modern developer teams lose hours each week sifting through scattered documentation, message histories, and project trackers just to get on the same page. When building under tight deadlines, teammates often spend more time answering "Where is that API schema?" or "What did we decide on the schema last week?" than actually writing code.

To solve this, we created AURA AI—a context-aware workspace assistant built to keep project teams aligned, productive, and focused.

What is AURA AI?

AURA AI is an intelligent workspace collaborator that bridges project context with active development. Instead of acting as an isolated chatbot, it ingests your team's project notes, architecture decisions, and codebase details to provide contextual assistance, summarize meetings, and generate implementation blueprints in real time.

System Architecture

We designed AURA AI for low-latency streaming and modularity:

  • Frontend: Next.js (App Router) paired with Tailwind CSS for a responsive, modern UI.
  • Backend & Real-time State: Supabase for relational data storage, user authentication, and real-time updates.
  • LLM Engine: Powered by the Gemini API for fast, high-context retrieval and response synthesis.
  • Deployment: Continuous integration and deployment via cloud-native pipelines for high availability. 
+-----------------------------------------------------------+
|                     Next.js Frontend                      |
|            (Interactive Workspace & Chat UI)              |
+-----------------------------+-----------------------------+
                              |
                     REST / WebSocket API
                              |
+-----------------------------v-----------------------------+
|                     Backend Services                      |
|        +-----------------------------------------+        |
|        |                Supabase                 |        |
|        |  (Auth, PostgreSQL, Real-time Channels) |        |
|        +--------------------+--------------------+        |
|                             |                             |
|                             v                             |
|        +-----------------------------------------+        |
|        |               Gemini API                |        |
|        |     (Workspace Context & Reasoning)     |        |
|        +-----------------------------------------+        |
+-----------------------------------------------------------+
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Key Features

  1. Context-Driven Answers: Ask questions directly about your repository's features, APIs, and project scope without context drift.
  2. Real-time Team Collaboration: Instant synchronization across team members so everyone works with identical project references.
  3. Structured Blueprints: Automatically translate high-level feature discussions into actionable implementation checklists and schema designs.
  4. Clean Developer Experience: Minimalist interface designed to reduce cognitive overhead during active coding sprints.

What We Learned Building It

  • Context Filtering Matters: Sending an entire database dump to an LLM creates latency and noise. Selecting clean, structured metadata yields significantly sharper responses.
  • Real-time Sync Simplifies UX: Using Supabase real-time channels enabled instant message propagation without custom WebSocket boilerplate.
  • Speed Drives Adoption: By leveraging fast inference pipelines, developer interactions feel snappy rather than disruptive.

What's Next?

  • Direct integrations with GitHub pull request workflows.
  • Automated changelog generation based on workspace activity.
  • Offline caching and local embedding indexing.

If you are participating in open source sprints or hackathons this month, give your teammates a shared brain! Feel free to drop feedback or questions in the comments below.

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