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Fix-It-Flow

Most e-waste starts with a repair knowledge gap. Fix-It-Flow gives people a hands-free repair guide that sees the item, asks a few questions, and pushes toward repair before replacement.

RoleFull-stack: multimodal AI flow, voice UX, and persistence
When2026 · RocketHacks
RecognitionRocketHacks 2026 · 2x Winner
The problem

A broken lamp, coffee maker, or small appliance often gets thrown away because the owner does not know where to start. The goal was a sustainability-first assistant that feels like a patient technician, not a generic chatbot.

Fix-It-Flow runs two connected modes: inspection to identify the item and problem, then repair to walk the user through tailored steps with hands-free voice controls.

How it works

Inspection flow

The user describes the problem while pointing the camera at the item. Gemini Vision analyzes live frames and turns visual evidence into structured context.

Sustainable reasoning

Featherless.AI running Llama 3.1 reasons over the visual context and conversation, asks up to three clarifying questions, then recommends repair, part replacement, repurpose, donation, or recycling.

Repair walkthrough

After diagnosis, the app generates a short step-by-step repair sequence tailored to the item and broken part.

Voice-first UX

ElevenLabs reads each step aloud, while Web Speech API commands like next, repeat, help, and end session let the user keep both hands on the repair.

Session state

AWS DynamoDB stores conversation history and repair progress so the flow can survive refreshes and continue from the right point.

Key decisions

Sequential vision-to-reasoning pipeline

Running the model calls in parallel looked faster, but it risked giving the reasoning model stale visual context. The final pipeline runs Gemini first, then passes fresh observations into Featherless.

Sustainability priority in the prompt

The model had to be steered away from default replacement advice. Recommendations are ordered around repair, replacing only the broken part, repurposing, donating, and recycling.

Voice as the primary interface

Repair is a hands-busy workflow. Speech commands and TTS made the app usable while someone is holding tools, checking parts, or looking at the device.

Outcome
Won the RocketHacks 2026 Sustainability Track.
Won the Featherless.AI sponsor track for best use of Featherless.AI inference.
Built an end-to-end loop: speak → camera frame → Gemini observation → Featherless recommendation → spoken repair guidance.

Built with

Next.js 14TypeScriptGemini VisionFeatherless.AIElevenLabsAWS DynamoDBWeb Speech API
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