Ahmad Azeem@ahmadazeem67

The Problem Logistics and freight insurance fraud costs enterprises billions annually. Currently, human adjusters must manually read a driver's transcript (e.g., "the cargo was completely destroyed") and cross-reference it against photographic evidence to approve or reject a claim. This process is slow, expensive, and highly prone to error. Our Solution: SafeHands AI SafeHands AI completely automates this process using a distributed multi-agent system built on the Band network. We ingeniously divided the cognitive load across three specialized, independent remote agents that collaborate over Band WebSockets to make high-stakes financial decisions: 1. The Intake Agent (Powered by Featherless Llama 3.1 8B): Listens to the driver's unstructured voice dictation, parses the messy input, and extracts structured JSON containing the claimed cargo type and claimed damage severity. 2. The Vision Agent (Powered by Featherless Qwen2.5-VL 72B): Acts as the "eyes" of the operation. It analyzes the uploaded cargo image, detects the physical cargo type, and independently estimates the actual damage percentage using multi-modal visual reasoning. 3. The Compliance Agent (Powered by AI/ML API Llama 3.3 70B): The central decision-maker. It receives the context from both the Intake and Vision agents via Band and cross-references them to catch discrepancies. If a driver claims 100% damage but the Vision agent detects only 30% damage, the Compliance Agent instantly flags the discrepancy and REJECTS the claim, logging the decision to an immutable ledger. If the evidence matches, the claim is APPROVED. Why it fits the Hackathon SafeHands AI was built specifically for Track 3: Regulated & High-Stakes Workflows. Band is not just a wrapper in our project; it is the absolute backbone coordination layer allowing our independent Python agent processes to discover each other, divide work, and seamlessly share context across different LLM provider frameworks.

In the rapidly evolving landscape of oncology, researchers and investors are often hindered by fragmented data. Triple-Negative Breast Cancer (TNBC) research is currently siloed across disparate sources—including PubMed, ClinicalTrials.gov, patent databases, and news outlets—making manual monitoring slow, expensive, and error-prone. TNBC Insight AI solves this by aggregating, analyzing, and delivering real-time intelligence through a single, unified dashboard. Built on a robust, enterprise-grade architecture using React and a Python/Flask API, our platform leverages OpenAI’s NLP to distill complex publications and trial reports into concise, actionable briefs. The system automates the entire intelligence workflow, allowing users to track drug pipelines, monitor trial enrollment, identify funding signals, and visualize patent landscapes in real-time. By automating these tasks, TNBC Insight AI enables teams to move at the speed of science, ensuring that critical competitive moves and emerging research trends are never missed.

DevDoc AI is an advanced developer tool designed to streamline and automate the documentation process, improve code comprehension, and optimize workflows for developers. Built primarily with TypeScript and Python, the project leverages AI technologies to generate, maintain, and enhance project documentation with minimal manual effort. DevDoc AI assists in understanding complex codebases by providing summaries, explanations, and code insights, making onboarding and collaboration easier. With CSS, it offers a user-friendly interface. Looking ahead, DevDoc AI aims to become an extension that can be integrated directly into popular IDEs like VS Code. This future enhancement will provide real-time documentation generation and intelligent code assistance right within the developer’s workflow. By focusing on automation and productivity, DevDoc AI helps developers save time, maintain higher code quality, and accelerate project delivery across diverse development environments.

📅 June 12–19, 2026 • June 12: Kick-off Stream • June 12–19: Online Build Phase • June 19: Project Submissions Ends 🌍 Fully online hackathon. Join and build from anywhere in the world. 🤝 Build practical enterprise workflows where AI agents communicate, coordinate, exchange context, and complete work together. 🧠 Powered by Band and Codeband, built for collaborative AI systems and agent orchestration. 🏆 Prize Pool: $10,000+ 🧑‍💻 Apply now to build the next generation of enterprise multi-agent systems.

🌍 Hybrid Hackathon | 💻 Online Build: May 25–30,2026 | 📍 SF Onsite Finale: May 30–31,2026 We’ve unlocked the web for your AI. Now build what wasn’t possible before. Build AI agents with live web data, then join the SF onsite finale at Bright Data’s new Web Data Loft. 🚀 The onsite experience will take place during the opening of Bright Data’s new SF space, The Web Data Loft. 📍 On-site Venue (May 30–31): The Web Data Loft 625 2nd St, San Francisco, CA 🏆 $18,300 cash prize + Bright Data AI Startup Program opportunities 🍕 Free food, drinks, networking, workshops, mentorship, and live demos 🎁 Onsite attendees are automatically entered into a live giveaway for 4 × $100 Amazon gift cards. 🧑‍💻 Apply now to build AI agents powered by real-time web access.

Build solutions that improve how software is built. Work with an AI that understands your codebase, helping you reduce repetitive work and build with real context. ⏳ Build your project in 48 hours. 🚀 Get hands-on with IBM Bob and explore how AI fits into real development workflows before it becomes standard. 💡 Build tools and workflows that developers would actually use. ⭐ Learn directly from experts throughout the event. 🤝 Join a team or build solo. 💰 Prize pool: $10,000 🧑🏻‍💻 Register before the Kick-Off Stream and start building with Bob.