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.

This AI-powered web application recommends movies similar to the one you like using content-based filtering with TF-IDF vectorization and cosine similarity. It’s built with Streamlit to provide an interactive and user-friendly experience, fetching movie posters dynamically via the OMDb API. ✨ Key Features: 🎯 Get top 5 similar movies instantly 🧩 AI-driven content similarity engine 🖼️ Real-time poster fetching from OMDb API ⚡ Clean, fast, and responsive Streamlit UI 🧰 Tech Stack: Python | Streamlit | Pandas | NumPy | Scikit-learn | Requests API The dataset (movies.csv) serves as the foundation of the Movie Recommendation System. It contains essential metadata about movies such as titles, genres, overviews, keywords, and other descriptive features that help the model understand each movie’s characteristics. This structured data enables the system to learn patterns and similarities between different movies, allowing it to recommend films with related content, themes, or genres to the user. The preprocessing phase (preprocess.py) plays a crucial role in transforming raw movie data into a clean, usable format. It removes duplicates, handles missing values, and performs text normalization to ensure consistent input for the model. The movie metadata is then vectorized using TF-IDF (Term Frequency–Inverse Document Frequency) to convert textual information into numerical feature vectors. This enables the use of cosine similarity to measure how closely related two movies are based on their content, forming the core of the recommendation engine.

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.

SwarmAid is an AI-powered disaster response platform designed to demonstrate how multiple specialized AI agents can collaborate to improve crisis management. When disasters strike, information is often fragmented and response times are critical. SwarmAid brings together four agents – a Data Analyst that interprets satellite and hazard feeds, a Medic Coordinator that analyzes social signals to triage urgent medical needs, a Logistics Manager that plans safe and efficient delivery routes, and a Critic that validates and improves plans. By integrating real-world APIs such as NASA EONET, Twitter/X, and OpenRouteService with advanced AI models, SwarmAid simulates a coordinated, intelligent response system that empowers first responders, NGOs, and governments to save lives faster and more effectively.

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!

🕒 6 days to explore the full potential of GPT-5 💡 Build next-gen AI apps 🤝 Compete solo or form a dream team 🚀 Connect with global innovators 🧑🏻‍💻 Sign up before the Kick-Off Stream to secure your spot!