APIs are at the heart of today’s AI and digital transformation, and Kong Insomnia is the essential tool for building scalable, future-proof architectures. Join us for an exploration of how the Kong Gateway and Insomnia can work together to create trustworthy integrations. In this session, you’ll learn how to leverage the Kong Gateway to seamlessly transform your enterprise-grade REST APIs into MCP servers, giving AI agents secure, governed access to your APIs while maintaining the control and visibility you need. We’ll demonstrate how Insomnia serves as your testing and validation environment, allowing you to inspect, test, and verify MCP tools, resources, and prompts before deployment no matter where you build them. From AI-enhanced development workflows to production-ready AI integrations, discover how this combination ensures your connected experiences are not only intelligent, but also reliable and built on a foundation of trust.

Join us to discover how to unlock strategic opportunities at the intersection of APIs, AI, and connectivity.

What you'll learn:

• Trusted API-to-MCP transformations: Convert Kong-managed REST APIs into MCP servers
• Validate and test your MCP servers’ tools, resources and prompts within Insomnia before opening them up for agentic consumption
• Enhance your productivity with Insomnia’s AI features
• Strategic insights into building flexible architectures for the evolving digital landscape.

APIs are at the heart of today’s AI and digital transformation, and Kong Insomnia is the essential tool for building scalable, future-proof architectures. Join us for an exploration of how the Kong Gateway and Insomnia can work together to create trustworthy integrations. In this session, you’ll learn how to leverage the Kong Gateway to seamlessly transform your enterprise-grade REST APIs into MCP servers, giving AI agents secure, governed access to your APIs while maintaining the control and visibility you need. We’ll demonstrate how Insomnia serves as your testing and validation environment, allowing you to inspect, test, and verify MCP tools, resources, and prompts before deployment no matter where you build them. From AI-enhanced development workflows to production-ready AI integrations, discover how this combination ensures your connected experiences are not only intelligent, but also reliable and built on a foundation of trust.

Join us to discover how to unlock strategic opportunities at the intersection of APIs, AI, and connectivity.

What you'll learn:

• Trusted API-to-MCP transformations: Convert Kong-managed REST APIs into MCP servers
• Validate and test your MCP servers’ tools, resources and prompts within Insomnia before opening them up for agentic consumption
• Enhance your productivity with Insomnia’s AI features
• Strategic insights into building flexible architectures for the evolving digital landscape.

APIs are at the heart of today’s AI and digital transformation, and Kong Insomnia is the essential tool for building scalable, future-proof architectures. Join us for an exploration of how the Kong Gateway and Insomnia can work together to create trustworthy integrations. In this session, you’ll learn how to leverage the Kong Gateway to seamlessly transform your enterprise-grade REST APIs into MCP servers, giving AI agents secure, governed access to your APIs while maintaining the control and visibility you need. We’ll demonstrate how Insomnia serves as your testing and validation environment, allowing you to inspect, test, and verify MCP tools, resources, and prompts before deployment no matter where you build them. From AI-enhanced development workflows to production-ready AI integrations, discover how this combination ensures your connected experiences are not only intelligent, but also reliable and built on a foundation of trust.

Join us to discover how to unlock strategic opportunities at the intersection of APIs, AI, and connectivity.

What you'll learn:

• Trusted API-to-MCP transformations: Convert Kong-managed REST APIs into MCP servers
• Validate and test your MCP servers’ tools, resources and prompts within Insomnia before opening them up for agentic consumption
• Enhance your productivity with Insomnia’s AI features
• Strategic insights into building flexible architectures for the evolving digital landscape.

APIs are at the heart of today’s AI and digital transformation, and Kong Insomnia is the essential tool for building scalable, future-proof architectures. Join us for an exploration of how the Kong Gateway and Insomnia can work together to create trustworthy integrations. In this session, you’ll learn how to leverage the Kong Gateway to seamlessly transform your enterprise-grade REST APIs into MCP servers, giving AI agents secure, governed access to your APIs while maintaining the control and visibility you need. We’ll demonstrate how Insomnia serves as your testing and validation environment, allowing you to inspect, test, and verify MCP tools, resources, and prompts before deployment no matter where you build them. From AI-enhanced development workflows to production-ready AI integrations, discover how this combination ensures your connected experiences are not only intelligent, but also reliable and built on a foundation of trust.

Join us to discover how to unlock strategic opportunities at the intersection of APIs, AI, and connectivity.

What you'll learn:

• Trusted API-to-MCP transformations: Convert Kong-managed REST APIs into MCP servers
• Validate and test your MCP servers’ tools, resources and prompts within Insomnia before opening them up for agentic consumption
• Enhance your productivity with Insomnia’s AI features
• Strategic insights into building flexible architectures for the evolving digital landscape.

APIs are at the heart of today’s AI and digital transformation, and Kong Insomnia is the essential tool for building scalable, future-proof architectures. Join us for an exploration of how the Kong Gateway and Insomnia can work together to create trustworthy integrations. In this session, you’ll learn how to leverage the Kong Gateway to seamlessly transform your enterprise-grade REST APIs into MCP servers, giving AI agents secure, governed access to your APIs while maintaining the control and visibility you need. We’ll demonstrate how Insomnia serves as your testing and validation environment, allowing you to inspect, test, and verify MCP tools, resources, and prompts before deployment no matter where you build them. From AI-enhanced development workflows to production-ready AI integrations, discover how this combination ensures your connected experiences are not only intelligent, but also reliable and built on a foundation of trust.

Join us to discover how to unlock strategic opportunities at the intersection of APIs, AI, and connectivity.

What you'll learn:

• Trusted API-to-MCP transformations: Convert Kong-managed REST APIs into MCP servers
• Validate and test your MCP servers’ tools, resources and prompts within Insomnia before opening them up for agentic consumption
• Enhance your productivity with Insomnia’s AI features
• Strategic insights into building flexible architectures for the evolving digital landscape.

These are the notes of the previous "How to Build a Portfolio That Reflects Your Real Skills" event:

Properties of an ideal portfolio repository:

• Built to prove employable skills and readiness for real work
• Fewer projects, carefully chosen to match job requirements
• Clean, readable, refactored code, and follows best practices
• Detailed READMEs (setup, features, tech stack, decisions, how to deploy, testing strategy, etc)
• Logical, meaningful commits that show development process <- you can follow the git history for important commits/features
• Clear architecture (layers, packages, separation of concerns) <- use best practices
• Unit and integration tests included and explained <-- also talk about them in the README
• Proper validation, exceptions, and edge case handling
• Polished, complete, production-like projects only
• “Can this person work on our codebase?” <-- reviewers will ask this
• Written for recruiters, hiring managers, and senior engineers
• Uses industry-relevant and job-listed technologies <- tech stak should match the CV
• Well-scoped, realistic features similar to real products
• Consistent style, structure, and conventions across projects
• Environment variables, clear setup steps, sample configs
• Minimal, justified dependencies with clear versioning
• Proper logging, and meaningful log messages
• No secrets committed, basic security best practices applied
• Shows awareness of scaling, performance, and future growth <- at least have a "possible improvements" section in the README
• a list of ADRs explains design choices and trade-offs <- should be a part of the documentation

📌 Backend & Frontend Portfolio Project Ideas

These projects are intentionally reusable across tech stacks. Following tutorials and reusing patterns is expected — what matters is:

• understanding the architecture
• explaining trade-offs
• documenting decisions clearly

☕ Junior Java Backend Developer (Spring Boot)

1. Shop Manager Application

A monolithic Spring Boot app designed with microservice-style boundaries. Features

• Secure user registration & login
• Role-based access control using JWT
• REST APIs for:
• Users
• Products
• Inventory
• Orders
• Automatic inventory updates when orders are placed
• CSV upload for bulk product & inventory import
• Clear service boundaries (UserService, OrderService, InventoryService, etc.)

Engineering Focus

• Clean architecture (controllers, services, repositories)
• Global exception handling
• Database migrations (Flyway/Liquibase)
• Unit & integration testing
• Clear README explaining architecture decisions

2. Parallel Data Processing Engine

Backend service for processing large datasets efficiently. Features

• Upload large CSV/log files
• Split data into chunks
• Process chunks in parallel using:
• ExecutorService
• CompletableFuture
• Aggregate and return results

Demonstrates

• Java concurrency
• Thread pools & async execution
• Performance optimization

3. Distributed Task Queue System

Simple async job processing system. Features

• One service submits tasks
• Another service processes them asynchronously
• Uses Kafka or RabbitMQ
• Tasks: report generation, data transformation

Demonstrates

• Message-driven architecture
• Async workflows
• Eventual consistency

4. Rate Limiting & Load Control Service

Standalone service that protects APIs from abuse. Features

• Token bucket or sliding window algorithms
• Redis-backed counters
• Per-user or per-IP limits

Demonstrates

• Algorithmic thinking
• Distributed state
• API protection patterns

5. Search & Indexing Backend

Document or record search service. Features

• In-memory inverted index
• Text search, filters, ranking
• Optional Elasticsearch integration

Demonstrates

• Data structures
• Read-optimized design
• Trade-offs between custom vs external tools

6. Distributed Configuration & Feature Flag Service

Centralized config service for other apps. Features

• Key-value configuration store
• Feature flags
• Caching & refresh mechanisms

Demonstrates

• Caching strategies
• Consistency vs availability trade-offs
• System design for shared services

🐹 Mid-Level Go Backend Developer (Non-Kubernetes)

1. High-Throughput Event Processing Pipeline

Multi-stage concurrent pipeline. Features

• HTTP/gRPC ingestion
• Validation & transformation stages
• Goroutines & channels
• Worker pools, batching, backpressure
• Graceful shutdown

2. Distributed Job Scheduler & Worker System

Async job execution platform. Features

• Job scheduling & delayed execution
• Retries & idempotency
• Job states (pending, running, failed, completed)
• Message queue or gRPC-based workers

3. In-Memory Caching Service

Redis-like cache written from scratch. Features

• TTL support
• Eviction strategies (LRU/LFU)
• Concurrent-safe access
• Optional disk persistence

4. Rate Limiting & Traffic Shaping Gateway

Reverse-proxy-style rate limiter. Features

• Token bucket / leaky bucket
• Circuit breakers
• Redis-backed distributed limits

5. Log Aggregation & Query Engine

Incrementally built system: Step-by-step

1. REST API + Postgres (store logs, query logs)
2. Optimize for massive concurrency
3. Replace DB with in-memory data structures
4. Add streaming endpoints using channels & batching

🐍 Mid-Level Python Backend Developer

1. Asynchronous Task Processing System

Async job execution platform. Features

• Async API submission
• Worker pool (asyncio or Celery-like)
• Retries & failure handling
• Job status tracking
• Idempotency

2. Event-Driven Data Pipeline

Streaming data processing service. Features

• Event ingestion
• Validation & transformation
• Batching & backpressure handling
• Output to storage or downstream services

3. Distributed Rate Limiting Service

API protection service. Steps

• Step 1: Use an existing rate-limiting library
• Step 2: Implement token bucket / sliding window yourself

4. Search & Indexing Backend

Search system for logs or documents. Features

• Custom indexing or Elasticsearch
• Filtering & time-based queries
• Read-heavy optimization

5. Configuration & Feature Flag Service

Shared configuration backend. Steps

• Step 1: Use a caching library
• Step 2: Implement your own cache (explain in README)

🟦 Mid-Level TypeScript Backend Developer

1. Asynchronous Job Processing System

Queue-based task execution. Features

• BullMQ / RabbitMQ / Redis
• Retries & scheduling
• Status tracking

2. Real-Time Chat / Notification Service

WebSocket-based system. Features

• Presence tracking
• Message persistence
• Real-time updates

3. Rate Limiting & API Gateway

API gateway with protections. Features

• Token bucket / sliding window
• Response caching
• Request logging

4. Search & Filtering Engine

Search backend for products, logs, or articles. Features

• In-memory index or Elasticsearch
• Pagination & sorting

5. Feature Flag & Configuration Service

Centralized config management. Features

• Versioning
• Rollout strategies
• Caching

🟨 Mid-Level Node.js Backend Developer

1. Async Task Queue System

Background job processor. Features

• Bull / Redis / RabbitMQ
• Retries & scheduling
• Status APIs

2. Real-Time Chat / Notification Service

Socket-based system. Features

• Rooms
• Presence tracking
• Message persistence

3. Rate Limiting & API Gateway

Traffic control service. Features

• Per-user/API-key limits
• Logging
• Optional caching

4. Search & Indexing Backend

Indexing & querying service.

5. Feature Flag / Configuration Service

Shared backend for app configs.

⚛️ Mid-Level Frontend Developer (React / Next.js)

1. Dynamic Analytics Dashboard

Interactive data visualization app. Features

• Charts & tables
• Filters & live updates
• React Query / Redux / Zustand
• Responsive layouts

2. E-Commerce Store

Full shopping experience. Features

• Product listings
• Search, filters, sorting
• Cart & checkout
• SSR/SSG with Next.js

3. Real-Time Chat / Collaboration App

Live multi-user UI. Features

• WebSockets or Firebase
• Presence indicators
• Real-time updates

4. CMS / Blogging Platform

SEO-focused content app. Features

• SSR for SEO
• Markdown or API-based content
• Admin editing interface

5. Personalized Analytics / Recommendation UI

Data-heavy frontend. Features

• Filtering & lazy loading
• Large dataset handling
• User-specific insights

6. AI Chatbot App — “My House Plant Advisor”

LLM-powered assistant with production-quality UX. Core Features

• Chat interface with real-time updates
• Input normalization & validation
• Offensive content filtering
• Unsupported query detection
• Rate limiting (per user)
• Caching recent queries
• Conversation history per session
• Graceful fallbacks & error handling

Advanced Features

• Prompt tuning (beginner vs expert users)
• Structured advice formatting (cards, bullets)
• Local LLM support
• Analytics dashboard (popular questions)
• Voice input/output (speech-to-text, TTS)

✅ Final Advice

You do NOT need to build everything. Instead, pick 1–2 strong projects per role and focus on depth:

• Explain the architecture clearly
• Document trade-offs (why you chose X over Y)
• Show incremental improvements
• Prove you understand why, not just how

📌 Portfolio Quality Signals (Very Important)

• Have a large, organic commit history → A single or very few commits is a strong indicator of copy-paste work.
• Prefer 3–5 complex projects over 20 simple ones → Many tiny projects often signal shallow understanding.

🎯 Why This Helps in Interviews

Working on serious projects gives you:

• Real hands-on practice
• Concrete anecdotes (stories you can tell in interviews)
• A safe way to learn technologies you don’t fully know yet
• Better focus and long-term learning discipline
• A portfolio that can be ported to another tech stack later (Java → Go, Node → Python, etc.)

🎥 Demo & Documentation Best Practices

• Create a 2–3 minute demo / walkthrough video
• Show the app running
• Explain what problem it solves
• Highlight one or two technical decisions
• At the top of every README:
• Add a plain-English paragraph explaining what the project does
• Assume the reader is a complete beginner

🤝 Open Source & Personal Projects (Interview Signal)

Always mention that you have contributed to Open Source or built personal projects.

• Shows team spirit
• Shows you can read, understand, and navigate an existing codebase
• Signals that you can onboard into a real-world repository
• Makes you sound like an engineer, not just a tutorial follower

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

Please register here

Platform engineering teams often face a persistent challenge: fast, reliable access to high-quality test data. Production data is restricted, masking is slow, and manual dataset creation becomes a bottleneck across development, testing, and AI workflows. Agentic AI now makes it possible to automate this entire process and deliver synthetic data on demand in the exact formats your teams need.

Join synthetic data expert Mark Brocato as he demonstrates how Tonic Fabricate removes data friction by generating realistic, structured synthetic test data automatically. See how platform teams can use this capability to support developers and AI practitioners with quick, compliant, and self-service access to data, exported directly into supported databases or delivered as JSON, PDFs, DOCX, EML, and more.What you'll learn during the webinar:

• How to select the right approach to synthetic data: Compare rule-based synthesis, de-identification, and Agentic AI to determine which method best supports your platform and your teams.
• How to generate highly realistic, nuanced data: Learn how Agentic AI creates complex test data for development and testing, significantly reducing the need for deep domain expertise.
• Best practices and practical platform use cases: See how automated synthetic data fits into platform engineering responsibilities, including CI/CD pipelines, API mocking, developer self-service, test automation, and AI model development.

Speaker: Mark Brocato - Head of Engineering for Fabricate @ Tonic.ai

Mark Brocato is a software developer and entrepreneur best known as the founder of Mockaroo, one of the world’s leading synthetic data generators, launched in 2014. The idea for Mockaroo came while Mark was watching QA engineers struggle to test complex life science workflows at a startup called BioFortis, inspiring him to make realistic test data easier for everyone. With over two decades in software development, he’s built tools for developers at Sencha, Layer0, and beyond. In 2024, Mark launched Fabricate, the AI-powered synthetic data platform that was acquired by Tonic.ai in 2025, where Mark continues to lead its development. A Ruby, JavaScript, and Rust developer, he divides his time between Sparta, New Jersey, and Tallinn, Estonia.

API testing includes so many things in it - functionality, data, performance, security. We'd like to know as much as we can about our APIs, but we've got so little time. Can AI help? You bet. It can help in planning, case suggestions, preparations for testing, documenting the tests and help with integration with our favorite tools. Things that took hours now take seconds. API Testing is changing. We want to take advantage of AI's power, and make sure that our testing is not only productive, but effective. I'll show you how.

In the fast-paced realm of travel experiences, GetYourGuide encountered the challenge of maintaining consistent, high-quality content across its global marketplace. Manual content creation by suppliers often resulted in inconsistencies and errors, negatively impacting conversion rates. To address this, we leveraged large language models (LLMs) to automate content generation, ensuring uniformity and accuracy. This talk will explore our innovative approach, including the development of fine-tuned models for generating key text sections and the use of Function Calling GPT API for structured data. A pivotal aspect of our solution was the creation of an LLM evaluator to detect and correct hallucinations, thereby improving factual accuracy. Through A/B testing, we demonstrated that AI-driven content led to fewer defects and increased bookings. Attendees will gain insights into training data refinement, prompt engineering, and deploying AI at scale, offering valuable lessons for automating content creation across industries.

Hello Testers, With great pleasure, we announce the upcoming Webinar series, “Agentic AI for Testers: The Next Big Leap in QA Automation" by Vignesh Srinivasa Raghavan.

When is it happening? 26th August 2025 \| Tuesday \| 9:00 PM IST What will Vignesh speak about: In this free 1-hour session, you’ll discover what Agentic AI really is, how it differs from traditional GenAI tools like ChatGPT, and why it’s set to transform the way QA teams work. We’ll explore real QA pain points—like repetitive test case writing, bug triage, and reporting—and show how agents can automate them end-to-end. Through a live demo, you’ll see the striking difference between doing a task in ChatGPT versus having an agent perform it automatically, giving you a clear picture of how Agentic AI can save time, boost accuracy, and change your daily QA workflows.

About Vignesh Srinivasa Raghavan: Vignesh is a Senior Automation Engineer with significant expertise in Web automation, Mobile automation, API automation, and Performance testing. He has been involved in Software Automation Testing for over 9+ years, demonstrating a keen interest in learning new technologies and tools that facilitate automation. Due to his passion for automation testing, he has gained extensive exposure to several tools and languages. He’s also a passionate Educator having taught over 2000+ students over the past 5 years.

About The Test Tribe: The Test Tribe is the World’s Largest Software Testing Community turned EdTech Startup. Started in 2018 with a mission to give Testing Craft the glory it deserves while we co-create Smarter, prouder, and confident Testers. We take pride in solving upskilling and growth for global Testing professionals through our unique offerings like Expert Courses, Membership, Cohorts, Offline Mixers, online Community spaces, and a lot of global Events. Our offerings enable Software Testers globally to collaborate, learn, and grow together. With around 400+ Software Testing Events like Conferences, Hackathons, Meetups, Webinars, etc., and with other Community initiatives, we have reached a global footprint of over 120K+ Testers from 130+ Countries. We intend to provide life-altering growth to every single Testing professional on the planet through community and technology. Join thousands of other Testers in the community. Discord Community

Hello Testers, With great pleasure, we announce the upcoming Webinar series, “Agentic AI for Testers: The Next Big Leap in QA Automation" by Vignesh Srinivasa Raghavan.

When is it happening? 26th August 2025 \| Tuesday \| 9:00 PM IST What will Vignesh speak about: In this free 1-hour session, you’ll discover what Agentic AI really is, how it differs from traditional GenAI tools like ChatGPT, and why it’s set to transform the way QA teams work. We’ll explore real QA pain points—like repetitive test case writing, bug triage, and reporting—and show how agents can automate them end-to-end. Through a live demo, you’ll see the striking difference between doing a task in ChatGPT versus having an agent perform it automatically, giving you a clear picture of how Agentic AI can save time, boost accuracy, and change your daily QA workflows.

About Vignesh Srinivasa Raghavan: Vignesh is a Senior Automation Engineer with significant expertise in Web automation, Mobile automation, API automation, and Performance testing. He has been involved in Software Automation Testing for over 9+ years, demonstrating a keen interest in learning new technologies and tools that facilitate automation. Due to his passion for automation testing, he has gained extensive exposure to several tools and languages. He’s also a passionate Educator having taught over 2000+ students over the past 5 years.

About The Test Tribe: The Test Tribe is the World’s Largest Software Testing Community turned EdTech Startup. Started in 2018 with a mission to give Testing Craft the glory it deserves while we co-create Smarter, prouder, and confident Testers. We take pride in solving upskilling and growth for global Testing professionals through our unique offerings like Expert Courses, Membership, Cohorts, Offline Mixers, online Community spaces, and a lot of global Events. Our offerings enable Software Testers globally to collaborate, learn, and grow together. With around 400+ Software Testing Events like Conferences, Hackathons, Meetups, Webinars, etc., and with other Community initiatives, we have reached a global footprint of over 120K+ Testers from 130+ Countries. We intend to provide life-altering growth to every single Testing professional on the planet through community and technology. Join thousands of other Testers in the community. Discord Community

Welcome to the Model Context Protocol (MCP) Workshop!

This comprehensive hands-on workshop combines two cutting-edge technologies to revolutionize AI application development: 🔗 Model Context Protocol (MCP): An open standard for seamless AI-tool integration 🛠️ AI Toolkit for Visual Studio Code (AITK): Microsoft's powerful AI development extension

By the end of this workshop, you'll master the art of building intelligent applications that bridge AI models with real-world tools and services. From automated testing to custom API integrations, you'll gain practical skills to solve complex business challenges.

Pre-requisites: Check out the prerequisites here

Welcome to the Model Context Protocol (MCP) Workshop!

This comprehensive hands-on workshop combines two cutting-edge technologies to revolutionize AI application development: 🔗 Model Context Protocol (MCP): An open standard for seamless AI-tool integration 🛠️ AI Toolkit for Visual Studio Code (AITK): Microsoft's powerful AI development extension

By the end of this workshop, you'll master the art of building intelligent applications that bridge AI models with real-world tools and services. From automated testing to custom API integrations, you'll gain practical skills to solve complex business challenges.

Pre-requisites: Check out the prerequisites here

Welcome to the Model Context Protocol (MCP) Workshop!

This comprehensive hands-on workshop combines two cutting-edge technologies to revolutionize AI application development: 🔗 Model Context Protocol (MCP): An open standard for seamless AI-tool integration 🛠️ AI Toolkit for Visual Studio Code (AITK): Microsoft's powerful AI development extension

By the end of this workshop, you'll master the art of building intelligent applications that bridge AI models with real-world tools and services. From automated testing to custom API integrations, you'll gain practical skills to solve complex business challenges.

Pre-requisites: Check out the prerequisites here

In today’s fast-evolving tech landscape, automation is no longer just about writing test scripts. It’s about building a strategy that scales across services, teams, and time. In this session, Ronak Ray breaks down how modern QA teams can move from patchwork testing to future-ready automation architectures. Learn how to design modular, API-first frameworks, reduce test flakiness, align coverage with risk—and most importantly, how to harness AI to generate, prioritize, and self-heal tests. This is the new blueprint for automation: faster, smarter, and scalable.

Each LLM has unique strengths and weaknesses, and there is no one-size-fits-all solution. Companies strive to balance cost reduction with maximizing the value of their use cases by considering various factors such as latency, multi-modality, API costs, user need, and prompt complexity. Model routing helps in optimizing performance and cost along with enhanced scalability and user satisfaction. Overview of cost-effective models training using AI gateway logs, user feedback, prompt, and model features to design an intelligent model-routing AI agent. Covers different strategies for model routing, deployment in Mosaic AI, re-training, and evaluation through A/B testing and end-to-end Databricks workflows. Additionally, it will delve into the details of training data collection, feature engineering, prompt formatting, custom loss functions, architectural modifications, addressing cold-start problems, query embedding generation and clustering through VectorDB, and RL policy-based exploration.