DevBug-AI is an AI-powered system that automates bug classification and developer recommendation for software teams. As applications scale, bug triage becomes slow and error-prone due to unclear reports, inconsistent categorization, and manual assignment. DevBug-AI removes this bottleneck by intelligently analyzing bug reports and assigning them to the most suitable developers. Using natural language processing and machine learning, the system classifies bugs from their title and description and recommends the top three developers based on historical bug data, domain, and tech stack. Confidence scores are provided to support reliable decision-making. The solution is built as a Streamlit web app with a unified ML pipeline. It uses TF-IDF and sentence embeddings for text understanding, Scikit-learn for bug classification, and LightGBM for developer recommendation. The system is trained on 50,000+ real bug reports and gracefully handles unseen technologies by mapping them to an “Other” category. DevBug-AI reduces triage time, improves assignment accuracy, and enables data-driven bug management at scale. Future work includes integrations with Jira and GitHub, workload-aware recommendations, and LLM-based bug summarization—making it a practical, production-oriented AI solution for modern engineering teams.

AgentPay-AI is a proof-of-concept platform that demonstrates how Generative AI services can be monetized using pay-per-use, token-based billing—similar to real-world AI APIs. Built with Streamlit and Google Gemini, the system simulates a USDC-style wallet that estimates token usage, deducts balance per request, and only executes AI tasks when sufficient funds are available. This project addresses a major gap in AI demos: cost transparency and usage accountability. AgentPay-AI showcases how AI-as-a-Service (AIaaS), agent marketplaces, and crypto-enabled AI platforms can implement realistic billing logic. Key Highlights: Token-based cost estimation Simulated USDC wallet per session Controlled AI execution based on balance Google Gemini / PaLM integration Simple, intuitive UI Designed as a hackathon and portfolio project for GenAI, SaaS, and Web3 applications.

An AI-powered interactive web application built with Streamlit that predicts whether a candidate will get placed in a job (or admitted) based on academic performance and other features. The model simplifies decision-making for students, HR teams, and academic advisors by providing data-driven placement predictions. The user inputs academic and background features, including: SSC percentage HSC percentage Degree percentage MBA percentage Work experience Specialization Gender And more Inputs are one-hot encoded for categorical features. A Logistic Regression model (trained offline) is loaded using Pickle. The model outputs a binary prediction: “Placed” or “Not Placed”. The result is displayed on the Streamlit app in a clear, user-friendly format.

AskTheWeb: AI-Powered Website Question Answering System AskTheWeb (also known as WebMind AI) is an advanced Question-Answering application designed to tackle information overload by transforming static websites into interactive, conversational experiences. Built using Streamlit, the application leverages the speed and intelligence of Google Gemini 2.0 Flash to understand and synthesize web content in real-time. The core functionality relies on a robust RAG (Retrieval-Augmented Generation) pipeline designed for accuracy and persistence. When a user inputs a URL, the system employs Requests and BeautifulSoup to scrape and clean the HTML data, acting as an efficient ETL transformer to remove messy code and script tags. This cleaned text is converted into high-dimensional vector embeddings using Google’s GenAI SDK and stored in ChromaDB, which acts as the application's "Long-Term Memory". This architecture allows users—such as students, researchers, and analysts—to ask complex natural language questions and receive instant answers that are grounded specifically in the context of the provided URL. Unlike standard chatbots, AskTheWeb ensures that answers are relevant to the specific source material provided. We also addressed significant technical challenges during development, specifically ensuring compatibility with cloud environments. We implemented a custom solution using pysqlite3-binary to patch SQLite version incompatibilities on Streamlit Cloud, ensuring the vector database runs smoothly in production. The result is a scalable, modular tool that makes researching the web faster and more intuitive.

AgentFlow: AI for Productivity A smart to-do list that prioritizes tasks into 4 quadrants: Urgent & Important | Important, Not Urgent | Urgent, Not Important | Not Urgent, Not Important AI agents provide instant resources & tools for each task. Users can add custom agents or discover new ones from lablab.ai How It Works Tech Stack: Frontend: Next.js + TailwindCSS AI: Gemini 2.5 Flash Deployment: Vercel Process: User creates a task. AgentFlow assigns it to the right priority box. AI recommends best agents/resources (e.g., blog writer, code assistant). Task can be tracked, updated, or deleted once done.

🩺 Problem Statement Medical students often struggle to bridge the gap between theory and practice. While textbooks provide knowledge, students rarely get the chance to practice clinical reasoning, patient interviews, and decision-making in a safe, low-stakes environment. Traditional case-based learning is often static, repetitive, and lacks interactive feedback, leaving students unprepared for real-world patient encounters. 💡 Our Solution: DocQuest DocQuest is an interactive AI-powered simulation platform where learners can: Interview virtual patients powered by GPT-5. Practice diagnostic reasoning by proposing tests, forming differential diagnoses, and planning management. Receive instant AI-driven feedback with scores, learning points, and red-flag highlights. It’s like a safe “practice ward” where students can make mistakes, learn, and improve — without any risk to real patients. 🚀 Key Features Case Library: Browse cases across multiple specialties (Cardiology, Neurology, Respiratory, Endocrinology, etc.). AI Patient Interview: Ask questions and interact dynamically with a simulated patient. Solve Cases: Enter diagnosis, key tests, and management plans. Feedback & Scoring: AI evaluator provides structured feedback (Diagnosis 4/4, Tests 3/3, Plan 3/3). Progress Tracking: Track cases completed, average score, and improvement over time. Today’s Challenge: Daily highlighted case for gamified learning. 🔮 Future Additions Leaderboard & Badges: Gamify the experience with rewards and peer competition. Multiplayer Mode: Allow group discussions on a case with collaborative solving. Custom Case Builder: Let educators create and upload their own cases. API Integration: Connect with medical knowledge APIs (e.g., UpToDate, PubMed) for deeper references. 👉 Our vision is to empower medical students and young doctors worldwide with a safe, engaging, and effective tool to sharpen their diagnostic reasoning skills.

The Friendly Medicine Reminder App is a smart and compassionate solution designed to help elderly people maintain their medication schedules without relying on constant caregiver supervision. Built with simplicity in mind, the app sends timely reminders, ensuring users never miss a dose. In case of skipped medication or health concerns, it automatically routes an emergency call through Bland.ai and schedules an appointment with the appropriate doctor by checking the patient’s condition. Hospitals can also use the app in a multi-patient dashboard mode to monitor medication adherence, improving patient outcomes and reputation. With features like secure environment configuration, emergency automation, and planned support for wearables and voice interfaces, this project bridges the gap between healthcare and daily life management for elderly users. Our vision is to empower independent living while reducing pressure on families and medical staff. We are seeking credits and technical support to continue building and scaling this impact-driven solution.

Join our $1,000,000+ startup challenge series powered by Surge ⏱️ 8 days to turn your idea, or existing product, into an investable demo. 📅 February 6 - 15, 2026 • Feb 6 - 14 (Online Phase) - Collaborate and build online with developers and AI innovators from around the world. All projects must be submitted by the end of the online phase on February 14th. • Feb 14 (On‑site Build Day) - Selected participants will be invited to an exclusive in‑person session to refine their projects and connect directly with mentors. • Feb 15 (On‑site Demos & Awards) - Live pitching sessions to a panel of judges and ecosystem partners, followed by the official winner announcement. 🌟 Get feedback from startup mentors and technical experts. 📍 On-site Venue (Feb 14–15): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📲 Real-time on-site updates (SF) For real-time announcements and information during the on-site portion (Feb 14–15), join the LabLab SF Chapter WhatsApp group: 👉 Join the WhatsApp group 🤝 Join solo or with a team. New founders and existing startups are welcome. 🏆 $1,000,000+ in credits, token prizes, perks and funding opportunity. 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Apply now to build, validate, and pitch with purpose.

Join us for an exclusive hackathon leading up to the /function1 AI Conference & Exhibition in Dubai (May 2, 2025) - a conference with one pivotal notion: to create a #1 platform for startups and founders of the AI field; to give them the spotlight they often lack, and represent the countless possibilities of Artificial Intelligence. 📅 April 25 – May 1, 2025 – Join us online and collaborate with global AI enthusiasts. 🌟 Expert mentors will guide you every step of the way. 🤝 Work alone or form a team to build something extraordinary. 🧑🏻‍💻 Sign up before the Kick-Off Stream to secure your spot!

Join the next edition of our flagship AI hackathon — now featuring Google, AppliedAI, and Qdrant as official partners, with over $45,500 in prizes and exclusive partner challenges. After an incredible launch earlier this year, we’re back with the next edition of our AI hackathon series leading up to the /function1 AI Conference & Exhibition in Dubai. Tap into the power of AI to innovate, build, and take your AI skills to the next level in this high-impact hybrid experience. 📅November 14–19, 2025 • Nov 14–18: Collaborate and build online with AI enthusiasts from around the world. All projects must be submitted by end of day on November 18 • Nov 18 (Dubai): An exclusive on-site build day at Festival Arena Dubai for selected participants. • Nov 19 (Dubai): Live on-stage pitching & winners announcement at the /function1 Conference. 🌟 Get support from expert mentors throughout. 👥 Go solo or team up. 📍 Please note: On-site participation is by invitation only. Travel and accommodation will not be covered for selected participants. Further details will be shared with those accepted. 🧑🏻‍💻 Secure your spot now—sign up before the Kick-Off Stream!

Join the Internet of Agents Hackathon to build real-world, rentable agents using Coral Protocol and Solana-powered payments. 📅 September 14–21, 2025 Sept 14–21: Build online with global innovators. Submit projects by the deadline. Sept 20 (NYC): On-site build day at Solana Skyline (invitation only). Sept 21 (NYC): Live pitches, networking, and winner announcements. 🧠 Mentorship from experts and Coral specialists throughout. 🤝 Participate solo or in teams. 📍 Travel and accommodation not covered. 🚀 Sign up now to secure your spot before the Kick-Off Stream!