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Enterprise Solution: Engineered an AI-driven API Governance Platform for BIAT to eliminate manual architectural review bottlenecks.
Smart Auditing Pipeline: Combined Spectral static linting with Qwen 2.5 + PGVector semantic search (85% similarity gate) to block duplicate APIs.
End-to-End Automation: Automated WSO2 API Manager lifecycle publishing alongside real-time AI YAML remediation for developers.
Tech Stack: FastAPI, React, Qwen 2.5, PGVector, WSO2 API Manager, Spectral Linter, Docker, & Monitoring Tools.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Experimented a bit today with Krea and image generation for a case study I’m putting together around AI EarPods connected to OpenAI.
The focus has been on creating fashion-forward product imagery and art directing a world that feels specific to the identity, rather than just generating nice-looking AI images.
The trickiest part has been product consistency. Especially getting the EarPods to actually sit snug in the ear. If you’ve worked through this process, you probably know the struggle 😅
Simply telling AI to “make it fit more snug or in the ear” doesn’t always work. It loves to reinterpret the product every time.
Still experimenting, but getting closer. If anyone has found a good workflow for keeping products consistent across AI-generated shoots, I’d love to hear it!
TOP!
Today, I want to show you the music player that inspired me to build SukhSangeet — a Personal Audio Workspace & Playlist Engine.
This project started as an idea and eventually evolved into SukhSangeet, with a stronger focus on playlists, music discovery, and a better listening experience.
MyJobs is an AI-powered job application automation platform for LinkedIn
🎯 Core Mission
Automate the entire LinkedIn job application lifecycle—allowing users to apply to dozens of relevant jobs per week with minimal effort. Target: 75% time reduction in job searching.
🏗️ Architecture Highlights
Frontend: React 18 + TypeScript + Vite (chat, dashboard, automation controls)
Backend: FastAPI + LangGraph agent orchestration + MCP servers (Jobs, Resume, Interview, Analytics)
AI/ML: Multi-LLM (Ollama local + Anthropic Claude), RAG pipeline with Qdrant vector store
Data: PostgreSQL (relational) + Qdrant (vector embeddings)
Automation: Playwright for LinkedIn interactions, VAPI for voice screening calls
💡 Key Features
Chat Interface — Natural language commands (/apply, /status, /search)
Resume Management — Upload, AI-optimize for ATS, tailor per job
LinkedIn Automation — Auto-fill applications, send follow-ups, track status
Job Profiles — Create multiple search profiles (keywords, locations, levels)
Applications Dashboard — Kanban board + table view of all applications
Interview Prep — VAPI voice agent for HR screening calls
Analytics — Insights on response rates, time-to-interview, etc.
📊 Data Flow
User message → WebSocket → LangGraph Agent → RAG retrieves resume/job context → LLM (Ollama/Claude) → MCP tools execute → Response streams to user
