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This project was developed as a containerized solution for the AMD Developer Hackathon: ACT II, specifically targeting the Track 2 Video Captioning challenge. The primary objective was to build a robust, scalable, and highly lightweight backend application that acts as the core routing layer for interacting with the Fireworks AI API. Built using Python and FastAPI, the architecture is designed to handle automated video captioning requests and route them to generate summaries across four distinct styles: formal, sarcastic, humorous-tech, and humorous-non-tech. Due to the strict hackathon requirement for all submissions to be fully cloud-ready and containerized, this application is packaged using an ultra-efficient Alpine Linux Docker image. This ensures that the environment footprint remains incredibly small (under a few megabytes), allowing for lightning-fast deployment onto AMD Developer Cloud infrastructure without the heavy overhead typically associated with local machine learning environments. While the initial architecture explored heavy computer vision pipelines and local model routing, the final submission prioritizes a streamlined, API-first approach to maximize deployment speed and perfectly align with the constraints of the Fireworks AI API track. The backend is fully Dockerized, pre-configured with the necessary REST endpoints, and ready to be wired directly to the remote Fireworks models to generate and return the stylized captions seamlessly.
13 Jul 2026

BuildLane Horizon is a fully decoupled, multi-agent intelligence platform engineered to map competitive market transitions in real-time. Modern enterprise web data is locked behind aggressive anti-bot firewalls, and the data that is accessible is highly unstructured. BuildLane Horizon solves this by autonomously bypassing scraper blocks, extracting raw DOM matrices, and utilizing AI to synthesize that unstructured data into a persistent, relational cognitive memory graph. How it Works & Core Features Targeted Extraction: The engine utilizes asynchronous Python streams to safely pull raw HTML layouts from enterprise domains without triggering bot protections. Context Synthesis: Instead of standard regex scraping, we use AI to parse the complex DOM tree, identifying high-level strategic shifts like changes in monetization structures, evolving value propositions, and targeted engineering hiring trends. Graph Serialization: The system ingests the AI output and maps it into a vector/graph database, creating persistent relational chains (e.g., mapping a competitor's pivot toward Enterprise AI). The Architecture (Tech Stack) Extraction Engine: Bright Data Web Unlocker Cognitive Parser: Google Gemini 3.5 Flash Memory Graph Layer: Cognee Vector & Graph Memory Backend Framework: Python 3.11 + FastAPI Frontend Dashboard: React + Vite + Tailwind CSS v4 By strictly separating the extraction backend from the high-contrast React UI, BuildLane Horizon delivers a deterministic, real-time stream of actionable market intelligence rather than just raw, unformatted data.
31 May 2026