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# Buy You Before — FitLab: AI-Powered Spatial Commerce Buy You Before is an AI-powered furniture shopping platform that solves a common e-commerce problem: determining whether furniture will actually fit a customer's room before purchasing it. Customers can describe their requirements in natural language, browse suitable products, upload photographs of their rooms, and visualize selected furniture in an interactive 3D simulation. **Our core innovation is FitLab**, an AI-driven failure-diagnosis and repair system. Rather than simply rejecting an unsuitable product, FitLab analyzes simulation failures, proposes corrective actions such as rotating furniture or selecting an alternative, and executes new simulations to test those actions. An independent geometry engine verifies every result, ensuring that AI-generated suggestions are never mistaken for proven fit. For AMD ACT III, we aim to integrate an open AI model served through vLLM on AMD GPU infrastructure using ROCm. Evolus AI will orchestrate agent workflows and connect the model to our existing simulation services through MCP tools. We also plan to develop a reproducible benchmark comparing conventional geometry-based recommendations with AI-assisted repairs. Metrics will include verified repair success, false-fit claims, simulation attempts, and latency. These experiments will help us evaluate improvements and potentially fine-tune the model using recorded failure-and-repair examples. **Technology stack:** Next.js, React, TypeScript, Tailwind CSS, Three.js, Python, FastAPI, PostgreSQL, Dapr, Redis, OpenCV, Shapely, vLLM, AMD ROCm, Evolus AI, and MCP. Our mission is to transform furniture shopping from guesswork into evidence-backed spatial decision-making, demonstrating AI that does not merely recommend solutions but tests its decisions in an executable environment and proves whether they work.
18 Oct 2026

Tablekeeper is an autonomous software engineering project built for the Dark Factory Hackathon by WeAreDevelopers and BAND. Under strict dark factory rules, zero lines of application code were written by humans. Our team acted solely as factory builders, defining generic mandates and orchestrating three autonomous AI agent seats inside BAND Desktop: Coordinator, Developer, and Reviewer. The Coordinator parsed the official track specification into structured work items, managed room handoffs, and maintained strict stage isolation. The Developer autonomously implemented the complete Stage 1 REST JSON API service, resolving complex domain requirements such as concurrency locking to prevent double-booking, safe idempotent write replays, atomic reservation transactions, state export and import, and IANA timezone daylight-saving transitions. The Reviewer independently inspected commits and verified test runs. The completed backend was verified in an isolated container environment against the official hackathon test harness, achieving a clean pass across all 120 test cases. The project repository includes generic mandate files passing the swap test, the complete room.json collaboration log, and container deployment files.
6 Oct 2026