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WeAreDevelopers LIVE • Apr 18, 2023

ChatGPT and Java: A Match Made in Heaven or Hell?

Jakov Semenski

Jaco Siminski warns ChatGPT either makes you 10x faster or generates buggy Java code at record speed. Discover how to master this AI copilot without breaking your architecture.

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#1 about 4 min

Introduction to ChatGPT and its rapid growth

Large language models like GPT-3 offer unprecedented generative capabilities but require massive parameters and compute costs.

#2 about 4 min

Fabricating code contributions and synthetic performance metrics

Creating plausible software contributions and technical terminology using rapid prompt engineering for non-technical leadership.

#3 about 5 min

Generating a Spring Boot controller and masking service

Iterative prompting extracts business logic from a basic Java controller into a dedicated masking service leveraging streams.

#4 about 6 min

Writing automated tests with Mockito and WireMock

Generative AI assists in creating unit tests with mocked dependencies and integration stubs using cloud contract frameworks.

#5 about 5 min

Visualizing system architecture with Mermaid UML diagrams

Developers can generate class and sequence charts by instructing the AI to output valid Mermaid diagram syntax.

#6 about 4 min

Generating Kubernetes Helm charts and deployment scripts

Prompting attempts yield mixed results when requesting infrastructure as code tools like AWS CloudFormation and Kubernetes descriptors.

#7 about 9 min

Simulating a Linux terminal and running Spring Boot

Instructing the chatbot to act as a Linux terminal allows developers to compile code and query databases virtually.

#8 about 5 min

Learning Java features and design patterns interactively

Programmers can explore modern programming languages and architecture principles through customized dialogue and practical messaging examples.

#9 about 6 min

Simulating a senior Java developer technical interview

Practicing algorithmic and framework-specific scenarios builds confidence when the model is guided to withhold direct answers.

#10 about 5 min

Identifying technical limitations and AI logical inconsistencies

Uncritical reliance on AI exposes bugs, hallucinated logic, outdated framework knowledge, and serious enterprise privacy risks.

#11 about 2 min

Balancing AI productivity gains with developer responsibility

Treating AI tools as conversational coworkers requires vigilant manual verification to avoid blind copy-pasting defects.

#12 about 9 min

Audience Q&A on privacy, monetization, and accuracy

Practitioners address questions regarding prompt safety updates, enterprise data handling, and expected subscription pricing for API access.

Matching moments

10:17 min

Discussion on AI hallucinations and practical developer workflows

Akmal Chaudhri Akmal Chaudhri · LIVE

4:32 min

Audience Q&A on tooling choices and AI application prototyping

MIlan Todorović MIlan Todorović · WWC Europe 2026

3:58 min

Launching a ChatGPT driver's license for HR professionals

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

2:37 min

Impact of current generative AI tools on developer velocity

Daniel Tao +3 · WWC 2024

11:41 min

Generative artificial intelligence and programming fundamentals

Chris Heilmann +2 · LIVE

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

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