Muhammad Yaseen@my5757980
4
Events attended
4
Submissions made
Pakistan
1 year of experience
About me
I am an experienced AI Developer with a strong hands-on background in building intelligent systems and AI-powered applications. Currently, I am actively developing AI projects that focus on solving real-world problems using machine learning, deep learning, and generative AI technologies. My work involves end-to-end development — from data collection and model training to deployment and optimization of AI solutions. I have practical experience in: Building and fine-tuning Large Language Models (LLMs) Developing computer vision and NLP-based applications Creating AI agents and automation tools Working with frameworks like LangChain, TensorFlow, PyTorch, and Hugging Face I am passionate about leveraging AI to create scalable, efficient, and impactful products. I continuously experiment with the latest advancements in AI to deliver high-quality solutions. I am very excited to bring my expertise and contribute to innovative AI initiatives.
🤓 Latest Submissions
CompeteIQ — GTM Intelligence AI Agent
CompeteIQ is a full-stack AI-powered GTM Intelligence Agent built with Next.js 15 and powered by Bright Data's web data infrastructure. The platform delivers four core intelligence capabilities for go-to-market teams: 1. Competitor Monitor — Paste any competitor URL and CompeteIQ uses Bright Data's Web Unlocker API to fetch live web content, extract structured data with cheerio, and generate an AI-powered summary using Groq's llama-3.3-70b model. Sales teams instantly understand pricing changes, product updates, and positioning shifts. 2. Market Search — Run natural language market intelligence queries powered by Bright Data's SERP API. Results are surfaced with AI-generated insights, giving GTM teams a real-time pulse on the competitive landscape without manual research. 3. Lead Enrichment — Enter any company name to trigger Bright Data's Web Scraper API, which extracts firmographic data including industry classification, tech stack signals, and company profile information in seconds. 4. Intelligence Report — The flagship feature: a 4-agent SSE-streaming pipeline that generates a comprehensive competitive intelligence report with sections covering Market Position, Technology Stack, Growth Signals, Competitive Landscape, and Strategic Recommendations. The report streams live to the browser using ReadableStream, giving users a real-time agentic experience. Tech Stack: Next.js 15 App Router (full-stack, no separate backend needed), TypeScript, Tailwind CSS, Groq SDK with llama-3.3-70b-versatile, Bright Data Web Unlocker + SERP API + Web Scraper API, cheerio for HTML parsing, deployed on Vercel. CompeteIQ turns hours of manual competitive research into a 30-second automated intelligence workflow — built entirely on Bright Data's live web access infrastructure.
31 May 2026
PolicyForge — AI Agent Security Policy Platform
PolicyForge is an enterprise AI agent security platform that solves a critical gap: security teams cannot write or manage AI agent policies because existing tools require deep YAML expertise. With PolicyForge, any CISO or compliance officer can type a security intent in plain English and have it enforced in seconds. How it works: The user types a natural language policy such as "Block any agent that reads patient SSN or medical records." Gemini 2.0 Flash instantly converts this into a Lobster Trap YAML enforcement rule. The rule is activated and enforced immediately by the Veea Lobster Trap deep prompt inspection proxy — a MIT-licensed tool that sits between AI agents and LLM backends. Key features include a real-time security dashboard showing blocked threats, active policies, and risk scores. An attack simulator lets teams fire 10 real adversarial attacks — prompt injection, PII exfiltration, credential theft, SQL injection, jailbreak attempts — and watch them get blocked live. One-click compliance reports generate HIPAA, SOC2, and finance audit documents directly from the audit trail. PolicyForge directly addresses every Track 1 focus area: guardrails and safety layers, monitoring and observability, access control, audit trails with explainability for regulated industries, and red-teaming frameworks. The tech stack uses Gemini 2.0 Flash for policy generation, Veea Lobster Trap for DPI enforcement, FastAPI for the backend, and Next.js 15 for the frontend — deployed on Railway and Vercel.
19 May 2026
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DocuMind AI — Multi-Modal Document Intelligence
DocuMind AI is a multi-modal, multi-agent document intelligence platform built on AMD MI300X GPU infrastructure via Fireworks AI's OpenAI-compatible inference API. The Problem: Professionals spend hours manually extracting insights from complex documents containing both text and visual content (charts, diagrams, figures). Existing tools handle text OR images, not both together. The Solution: DocuMind AI runs a sequential 4-agent CrewAI pipeline on AMD Instinct MI300X GPUs: 1. Vision Agent — uses Kimi K2.5 to analyze every image, chart, and diagram embedded in the document 2. Reader Agent — uses DeepSeek V3.1 to extract text, identify entities, classify document type, and extract key facts 3. Analyst Agent — synthesizes visual AND textual findings into cross-modal insights 4. Reporter Agent — produces a structured intelligence report: Executive Summary, Key Entities, Categorized Insights, and Actionable Recommendations AMD Technology Used: AMD Instinct MI300X GPUs via Fireworks AI with OpenAI-compatible API — zero-friction integration with CrewAI and Python AI ecosystem. Kimi K2.5 vision-language model + DeepSeek V3.1 open-source frontier text model, both running on AMD hardware. Why This Matters: Covers Track 1 (AI Agents) AND Track 3 (Vision/Multimodal). Real business value for researchers, analysts, and professionals. Live demo on Hugging Face Spaces with zero setup required for judges. MIT open-source with full documentation.
10 May 2026
AgentFlow: AI Agent Economy on Arc
AgentFlow is a real-time economic execution layer for AI agents built on Arc L1 blockchain and Circle infrastructure. Five specialized AI agents — DataAnalyst, ContentWriter, CodeReviewer, Translator, and Orchestrator — autonomously bid, subcontract, and settle USDC micropayments on-chain with no human intervention. Each agent has its own Circle Developer Controlled Wallet pre-funded with 20 USDC. When a task is submitted, the Orchestrator agent routes it to the appropriate specialist, deducts micro-payments ($0.002–$0.008 USDC per task) from agent wallets, and settles on the Arc blockchain in real-time. The live dashboard displays every transaction hash, agent balance, and payment status as it happens. We demonstrated 55 confirmed on-chain transactions totaling $0.31 USDC settled in approximately 30 seconds. Arc's near-zero gas fees make sub-cent machine-to-machine commerce economically viable for the first time — on Ethereum, $1 gas fees would destroy the entire value of these micropayments. Technical stack: FastAPI backend, Next.js 15 frontend, Circle DCW API, Arc EVM L1 testnet. The x402 payment protocol was also wired for HTTP-native payment headers, enabling future pay-per-API-call monetization at internet scale.
26 Apr 2026
