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API Integration & Workflow Automation
A Python-based approach to connecting applications, APIs, databases, and business processes into reliable automated workflows.
The workflow can coordinate:
Input / Event → API Request → Authentication → Data Transformation → Workflow Logic → API Response → System Update → Automated Action
Core capabilities:
• REST API integration
• JSON data handling and transformation
• API-based workflow automation
• Webhooks and event-driven workflows
• Database integration
• Multi-step process orchestration
• Automated system-to-system data flows
Built with a focus on reliable integrations, structured data flow, maintainable automation, and practical business workflows.
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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!
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
🛠️ Show & Tell: AI Voice Receptionist — Retell AI + n8n + Google Sheets + Retool
Hey Contra community 👋
Just wrapped a full AI voice receptionist build for a home services client and wanted to share what went into it.
What it does:
Answers inbound calls 24/7 using Retell AI
Runs 3 real-time checks via n8n: service area · slot availability · customer lookup
Books the appointment live on the call — no double-booking
Logs to Google Sheets and fires a confirmation email automatically
Full Retool command center for the client to manage everything
The tricky part:
Retell AI has a toggle called "Payload: args only" — when enabled, it strips the parent wrapper from the webhook payload. Took a solid debug session to figure out why n8n kept returning empty data. Once I caught it and rebuilt the payload mapping, everything worked end to end.
Stack: Retell AI · n8n · Google Sheets · Retool MCP
Happy to answer questions on any part of the build — webhooks, n8n sub-workflow structure, Retool dashboard setup, whatever.
If you're a builder who gets overflow work in voice AI or home services automation, let's connect 🤝
Nice systems thinking in this build—the handoff between voice intake, availability checks, booking, and confirmation is clearly mapped to a real user outcome. The Retool command center also makes the automation legible instead of hiding it behind the scenes.