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
Back to feedPost
Developed a scalable API automation testing framework using Java, Rest Assured, TestNG, and Maven. The framework follows a modular design with reusable utilities, request/response validation, reporting, and CI/CD integration using GitHub Actions.
Key Features:
REST API automation with Rest Assured
TestNG-based test execution
Maven dependency management
JSON schema and response validation
Reusable utility classes and configuration management
Detailed HTML test reports
GitHub Actions for automated test execution
Easy to extend for new API endpoints
Tech Stack: Java, Rest Assured, TestNG, Maven, GitHub Actions, JSON, Git.
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
Trending
Claude
Claude has entered the design space. How are you using it?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
Brand Design
The best brand designers are on Contra. Scroll to see what's trending in brand design. What are you building?
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?
Related posts
Automating Lead Entry Saves Me Time
I recently worked on a project that involved automating the entry of new leads into our CRM system. The goal was to save time and reduce the likelihood of human error. To achieve this, I used n8n to create a workflow that would automatically create a new contact in HubSpot whenever a new lead was submitted through our form. I also used Google Sheets to store some additional information about each lead. The result is a more efficient and streamlined process for managing new leads.
#n8n #automation #Webhook #HTTPRequest
Follow for more: https://go.phonezait.de/
🚀 Case Study: Production-Grade Autonomous Lead Gen
Basic Zapier flows and fragile prompt chains break as soon as you scale. I recently replaced a client’s brittle setup with a stateful, type-safe Multi-Agent system that autonomously prospects, scores, and drafts outreach for B2B leads.
⚙️ The Stack:
LangGraph + PydanticAI + MCP (Model Context Protocol) + Claude 3.5 Sonnet
⚡ How It Works:
Stateful Orchestration (LangGraph): Manages execution with automatic checkpoints—if an external API fails, the workflow resumes without losing context.
Type-Safe Agents (PydanticAI): Enforces strict data schemas so outputs never hallucinate or break downstream code.
Native Integrations (MCP): Direct, secure read/write access to internal CRMs without messy webhooks.
📈 The ROI:
40+ hours/week saved on manual research and lead scoring.
100% data accuracy (zero JSON parsing errors).
Human-in-the-loop gating for final outreach approval.
💡 Need resilient, enterprise-ready AI infrastructure for your business? Check out my profile to book a Technical Audit & AI Automation Blueprint.
Many companies are building AI applications with LLMs but struggle to understand what is happening behind the scenes. Without proper monitoring and tracing, teams cannot identify slow responses, expensive API calls, failed generations, prompt issues, or areas where AI performance can be improved.
I will integrate professional LLM observability solutions like Langfuse and Helicone to help you monitor, trace, debug, and optimize your AI applications. I will set up complete visibility into your LLM workflows, including prompt tracking, token usage analysis, latency monitoring, cost optimization, evaluation pipelines, and production performance insights.
I use modern AI monitoring and development tools including Langfuse, Helicone, LangSmith, Arize Phoenix, OpenLLMetry, OpenTelemetry, Weights & Biases, PromptLayer, Traceloop, and Grafana to build a complete LLM observability system.
My process includes SDK integration, API configuration, trace setup, prompt management, cost tracking, dashboard creation, evaluation workflows, error monitoring, and performance optimization. Whether you are building AI agents, RAG systems, chatbots, or enterprise LLM applications, I will help you reduce AI costs, improve reliability, and gain full visibility into your AI operations.
