Faiqa Rafiq@Faiqa

Trustless AI Agent is a demonstration of agentic commerce, showing how autonomous AI systems can transparently price tasks and settle payments in USDC on the Arc blockchain using Circle’s developer infrastructure. The project addresses a common problem in AI services: pricing trust. Users often do not know how much a task should cost, while developers struggle to implement fair, usage-based billing. This challenge becomes more important in agentic systems, where AI agents act autonomously and interact with financial infrastructure. In this system, a user submits a task through a simple frontend. The backend calls Gemini to analyze the input and classify its complexity as LOW, MEDIUM, or HIGH. Based on this classification, the AI suggests whether the task should be free or paid and proposes a USDC amount. To keep the system trustless, the AI never directly controls payments. The backend enforces deterministic guardrails using a fixed pricing table. Even if the AI output is incorrect or attempts to overcharge, the backend corrects it before any transaction occurs, ensuring predictable and auditable behavior. If payment is required, the backend executes a USDC transfer using Circle Developer-Controlled Wallets, settling the transaction on Arc Testnet. Wallet balances can be queried to verify that funds were transferred correctly, demonstrating real onchain settlement. Circle Product Feedback This project uses Arc Testnet, USDC, and Circle Developer-Controlled Wallets. Circle Wallets enable secure, programmable wallet management suitable for autonomous agents, while USDC provides stable pricing for pay-per-task models. Wallet creation, balance checks, and transfers worked well. The main challenge was limited Arc-specific end-to-end examples. More agent-focused reference implementations would improve the developer experience.

The Problem: Moral Failure and Stagnation (Focus: Relatable) Open-source healthcare innovation is crippled by subjective recognition, slow development, and the moral failure of systems prioritizing profit over patient care. Developers lack objective metrics for contribution quality, leading to burnout and talent exclusion based on static credentials, not real-world impact (e.g., the 900x faster MCP Server). The Solution: QUBIC's Meritocratic Architecture (Focus: Mechanics) QUBIC is the solution. It is a hybrid, autonomous governance protocol built for the Nostromo Launchpad. Off-Chain AI Analysis: Our AI Judge (LLM-Powered) scores every contribution (code quality, documentation, clinical utility, security compliance) against a public, weighted rubric. On-Chain Integrity: A secure Oracle posts the composite score to the Qubic C++ Smart Contract DAO. Trustless Rewards: The Smart Contract registers the contributor with a Platinum, Gold, or Silver tier and automatically distributes UC tokens and weighted voting power. The Qubic Advantage (Focus: Technical Edge) QUBIC leverages Qubic's high-performance C++ architecture for fast, deterministic execution, essential for mission-critical healthcare governance. This ensures that the DAO remains a Meritocratic Democracy—where governance power is based on proven, life-saving contributions, not token wealth. QUBIC is the trust layer that funds and sustains the Ubuntu Patient Care Rebellion.

Code Compass – AI-Powered Open-Source Contribution Helper Code Compass makes open-source contribution easier by helping developers understand complex codebases, visualize dependencies, and find actionable contribution opportunities. Core Problem New developers struggle with large, unfamiliar repositories and lack clear starting points. Code Compass bridges this gap with AI-driven insights and interactive visualization. Key Features ✅ AI-Powered Analysis – GPT-5-based repository summarization, code explanations, and intelligent Q&A. ✅ Visual Knowledge Graphs – Interactive dependency maps using NetworkX + PyVis for files, classes, and functions. ✅ Smart Contribution Guidance – Detects TODOs, missing tests, and suggests beginner-friendly GitHub issues. ✅ Multi-Language Support – Handles Python, JavaScript, TypeScript, HTML, CSS, and config files. How It Works Repo Ingestion – Clone any GitHub repo via API. Graph Mapping – Build a knowledge graph of imports, function calls, and file relationships. AI Insight Layer – GPT-5 answers natural language questions, explains architecture, and recommends contributions. Interactive Exploration – Filter graphs, view file summaries, and discover improvement areas. Tech Stack Frontend: Streamlit, PyVis Backend: FastAPI, Python AI & NLP: GPT-5 (via AIML), Google Generative AI embeddings, FAISS for semantic search Visualization: NetworkX, Plotly Integration: GitHub API (PyGithub) Why It’s Innovative Unlike basic code search tools, Code Compass combines graph visualization, AI-driven explanations, and predictive contribution detection. It not only answers “what” and “where” but also explains “why” and “how”, empowering newcomers to contribute confidently. One-Line Summary: Code Compass transforms codebases into interactive, explainable maps, making open-source projects accessible to everyone.

This project is a fully open-source AI application that automates the process of reviewing code changes in GitHub pull requests. It uses the power of BLACKBOX.AI’s Coding Agent and leverages Groq’s high-speed inference capabilities to run LLaMA models by Meta for analyzing code diffs and generating insightful, natural-language review comments. Built to enhance developer productivity, the tool seamlessly integrates with GitHub, scans PRs for changes, and offers intelligent suggestions including bug detection, optimization tips, and documentation prompts all within seconds. It features a clean, user-friendly interface that supports both GitHub-connected workflows and manual code review for standalone snippets. By combining ultra-low latency inference (via Groq) with sophisticated language modeling (via LLaMA), this tool aims to reduce review time, increase code quality, and make collaborative development more efficient for teams of all sizes.

Access to affordable healthcare remains a critical challenge, especially for financially disadvantaged patients. Administrative inefficiencies, lack of transparency in treatment costs, and unequal allocation of donated funds exacerbate the problem. Existing systems often fail to prioritize those in dire need, leaving many patients without timely access to necessary treatments. There is an urgent need for a system that leverages AI to ensure fairness, transparency, and efficiency in healthcare administration and donation allocation. CareAI aims to tackle the critical issue of access to affordable healthcare, especially for financially disadvantaged patients, by addressing the inefficiencies in healthcare administration and donation allocation. The solution combines the power of Artificial Intelligence (AI) with data-driven insights to ensure that treatment costs are transparent, funds are allocated fairly, and the most urgent cases are prioritized.

SuperHuman AI: Automating web-based tasks with AI-driven intelligence for improved efficiency Objective: SuperHuman AI is designed to automate complex activities over web browsers based on user-defined objectives. By leveraging advanced AI models for vision and text intelligence, the project focuses on streamlining tasks such as job applications, form-filling, and data entry. It reduces manual effort and human error, enabling faster execution and better user productivity. Key Features: Visual Intelligence: Powered by Llama 3.2 11B Multimodal Vision Instruct Model, capable of processing visual data to automate complex tasks that require an understanding of on-screen elements. Automation through Selenium: Uses Python Selenium for navigating and interacting with web pages, understanding DOM elements, and mimicking user interactions. Advanced RAG Pipeline: Integrates Vectara and Chroma DB for advanced retrieval-augmented generation (RAG) to provide real-time, accurate answers and suggestions during automation. AI Agent with CrewAI: Built using CrewAI for orchestrating complex tasks in sequence, with the ability to break down objectives into manageable steps and execute them efficiently. AI Agent Steps: Initiates the browser and navigates to the target website. Utilizes Selenium to analyze and understand the site's DOM elements. Visual intelligence processes visual data for more advanced activities (e.g., interpreting dynamic content, etc.). Gathers the user’s overall objective and breaks it into implementable steps. Executes each step while adapting to the dynamic web environment. Provides analytics and feedback to the user at the end of the task. Core Technologies: AI Agent: CrewAI LangGraph AI Tools: Llama 3.2 11B Multimodal Vision Instruct Model Llama 3.2 3B Lightweight Text Model Advanced RAG Pipeline Use-cases: LinkedIn Job Automation Google Forms Filler KYC Processing Data Entry Automation

The weather AI app is a multifaceted platform designed to offer more than just standard weather updates. It integrates real-time weather data for any location, fetching details like temperature, humidity, wind speed, and weather descriptions using the OpenWeatherMap API. Additionally, the app retrieves an image of the location from the Unsplash API, providing a visual representation of the place for a richer user experience. One of the unique aspects of the app is its ability to generate personalized suggestions based on the current weather. For instance, depending on the temperature and weather conditions (rain, snow, clear skies), the app suggests suitable clothing, activities, and even whether it's a good day for a picnic. These recommendations are generated using Hugging Face’s GPT-2 model, adding a layer of AI-driven decision-making to enhance user interaction. To further elevate the experience, the app converts these recommendations into audio using Hugging Face's FastSpeech2 model. This feature allows users to listen to the recommendations instead of reading them, making the app accessible and interactive for various users. Overall, the app combines real-time weather data, city imagery, smart suggestions, and voice-enabled features to deliver an engaging and informative weather solution.

An IBM website chatbot is an advanced AI-powered virtual assistant that plays a pivotal role in enhancing the user experience on IBM’s digital platforms. Designed with cutting-edge technology, the chatbot is integrated into IBM’s website to provide real-time support, guidance, and information to users, making their interaction with the site more efficient and user-friendly. This chatbot is not just a simple query-response system; it is a comprehensive tool that leverages sophisticated AI models, such as the IBM Granite 13B, to deliver a wide range of functionalities tailored to meet the needs of various users. ### Purpose and Functionality The primary purpose of the IBM website chatbot is to assist users in navigating the website, finding relevant information, and resolving issues without the need for human intervention. This is particularly important for large, complex websites like IBM’s, where users might find it challenging to locate specific information or troubleshoot issues on their own. The chatbot serves as a virtual guide, leading users to the right resources, answering frequently asked questions (FAQs), and providing step-by-step assistance for more complicated queries. The chatbot's functionality extends beyond just answering questions. It is designed to handle a variety of tasks that enhance the user experience, such as: - *Website Navigation Assistance*: The chatbot helps users find the information they need by guiding them through different sections of the website. Whether a user is looking for specific product details, support documentation, or company information, the chatbot can quickly direct them to the right page. - *Customer Support*: For users seeking support with IBM products or services, the chatbot offers troubleshooting tips and solutions. It can address common issues directly or escalate more complex problems to human support agents when necessary.

Learn how modern AI agents interact with the live web through hands-on workshops, real infrastructure, and a community builder weekend in San Francisco. 📅 May 25–31, 2026 • May 25 – 30: Online Build Phase • May 30: Hybrid Build Day (Online & Onsite) • May 31: Demos & Awards (Online & Onsite) 🌍 Hybrid event. Join online from anywhere or build onsite in San Francisco. 🚀 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 🏆 $5,000 cash prize + Bright Data AI Startup Program opportunities 🍕 Free food, drinks, networking, workshops, mentorship, and live demos 🧑‍💻 Apply now to build AI agents powered by real-time web access.

Grab your ideas, spin up compute, and start building what’s next! Start with $100 in AMD Developer Cloud credits and begin building right away. Get access to hands-on tutorials, workshops, and curated learning resources to help you build faster and go further. To unlock credits and join the hackathon, sign up for the AMD AI Developer Program. 🤝 Go solo or team up — ship something real. 💰 $21,500+ total prize pool & 1 × AMD Radeon™ AI PRO R9700 GPU up for grabs. 📍 On-site Venue (May 9-10): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📍On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.

In this hybrid hackathon powered by Arc, developers will build the next generation of agentic commerce systems using Arc with USDC. Explore a world where AI agents and web services can pay for APIs, data, compute, and content in dynamic, usage-based ways, while Arc provides stablecoin-native settlement with predictable USDC fees and deterministic, sub-second finality. This hackathon is supported by Google & Google DeepMind, with a dedicated challenge and $40,000 in Google Cloud Platform (GCP) credits awarded for the best use of Gemini models and Google AI Studio. 📅 January 9 – 24, 2026 • Jan 9 – 23 (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 January 23rd. • Jan 23 (On‑site Build Day – San Francisco, CA) – Selected participants will be invited to an exclusive in‑person session to refine their projects and connect directly with mentors. • Jan 24 (On‑site Demos & Awards – San Francisco, CA) – Live pitching sessions to a panel of judges and ecosystem partners, followed by the official winner announcement. Get guidance from Arc and Circle experts through on-site support. 📍 On-site Venue (Jan 23–24): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 🤝 Go solo or team up, it's your flow. 🏆Compete for $50,000 in USDC & GCP Credits. 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑🏻‍💻 Secure your spot now - sign up before the Kick-Off Stream!

⏱️ You’ve got 48 hours - create, build, and launch! 🌟 Get direct access to Qubic, mentors, and ecosystem support. 💰 Compete for your share of $44,550 in prizes across two tracks. 🤝 Work solo or team up to design the next wave of blockchain innovation. 🧑‍💻 Sign up and start building on one of the world’s fastest decentralized networks..