Jakov Semenski
ChatGPT and Java: A Match Made in Heaven or Hell?
#1about 6 minutes
Understanding ChatGPT and its rapid growth
ChatGPT is an AI model built on GPT-3 that gained one million users in just five days, demonstrating its massive potential and capabilities.
#2about 4 minutes
Generating and refactoring a Java REST controller
A live demonstration shows how to prompt ChatGPT to create a Spring Boot controller, refactor it with Java streams, and extract logic into a separate service.
#3about 6 minutes
Automating unit and integration test creation
ChatGPT can generate unit tests using Mockito and integration tests with WireMock or Spring Cloud Contract based on previously created code.
#4about 8 minutes
Generating project structures, diagrams, and deployment files
The tool can outline a full project directory structure, generate UML class and sequence diagrams using Mermaid syntax, and create deployment files like Helm charts.
#5about 8 minutes
Simulating a running application in a terminal
By instructing ChatGPT to act as a Linux terminal, you can simulate starting a Spring Boot application and interacting with it via cURL commands.
#6about 5 minutes
Using ChatGPT as an interactive learning tool
ChatGPT can explain technical concepts like Java 17 features and design patterns with code examples, and can even adopt an entertaining persona for engagement.
#7about 5 minutes
Preparing for a technical interview with ChatGPT
The AI can act as an interviewer by asking theoretical and coding questions for Java and Spring, then providing feedback on your answers.
#8about 5 minutes
Understanding the limitations and risks of ChatGPT
Key limitations include handling large projects, frequent bugs, monetization plans, privacy concerns, and a lack of up-to-date knowledge.
#9about 1 minute
The right way to leverage AI in development
Use AI for boilerplate code and learning, but avoid blindly copy-pasting solutions or exposing confidential information to maintain code quality and security.
#10about 8 minutes
Q&A on ethics, security, and business models
The Q&A session covers ethical misbehavior, security concerns when using AI-generated code, and potential future business models for the tool.
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Matching moments
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Increasing the value of talk recordings post-event
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Selecting strategic partners and essential event tools
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Organizing a developer conference for 15,000 attendees
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Rapid-fire thoughts on the future of work
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Why HR struggles with technology implementation and adoption
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Why corporate AI adoption lags behind the hype
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Automating formal processes risks losing informal human value
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04:27 MIN
Moving beyond headcount to solve business problems
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+2
