faris fauzi@farisbinmdfauzi40642

Menatap is an AI-powered travel planning app that turns a destination and a handful of preferences, like budget, interests, and pace, into a complete, ready-to-use itinerary generated in seconds. Each plan is broken down day by day, with timed stops, estimated costs, short descriptions of why each stop is worth visiting, and an interactive map showing the full route at a glance. Beyond solo trips, Menatap also supports group planning: an organizer can create a shared trip room and invite friends with a single link, no account required, and each member sets their own preferences, which the AI blends together when proposing stops. Instead of coordinating a trip through endless group chats, everyone can vote on proposed stops and leave quick notes, so the group actually converges on a plan rather than just talking about one. The result is a faster, more personalized, and more collaborative way to plan travel, built for the lablab.ai NativeBuilder Hackathon.

Video Caption Agent is an AI powered tool that watches video clips and generates captions in four distinct styles: formal, sarcastic, humorous_tech, and humorous_non_tech. Built for the AMD Developer Hackathon: Act II Track 2, this agent uses Fireworks AI's minimax-m3 model to understand video content through native video input. The pipeline follows a two-stage approach: 1. Video Understanding: The model watches the video directly and produces a detailed factual description of the setting, subjects, actions, and mood. 2. Style Generation: The description is rewritten into each requested caption style in a single API call, with intelligent key normalization to handle variations in model output. Key optimizations include: - Single-pass generation requiring only 2 API calls per video - Automatic handling of common misspellings without retries - Graceful failure handling that continues processing remaining tasks The agent processes videos in a Docker container, reading tasks from /input/tasks.json and writing results to /output/results.json, making it easily deployable and scalable. Features: - Supports four caption styles: formal, sarcastic, tech-humour, non-tech humour - Handles videos up to 2 minutes in length - Native video understanding without frame extraction - Fast processing with optimized API calls - Robust error handling and fallback mechanisms The project demonstrates efficient use of multimodal AI models for video understanding and creative text generation, all packaged in a production-ready container.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.