Markus Walker
ChatGPT: Create a Presentation!
#1about 2 minutes
From image recognition to modern generative AI
AI has evolved from extracting information, like in image recognition, to creating new content, which is a fundamental shift for technology.
#2about 3 minutes
Understanding ChatGPT's capabilities and limitations
ChatGPT is a large language model that predicts text but cannot perform actual computations, making it a powerful yet fallible tool for developers.
#3about 2 minutes
Analyzing, refactoring, and converting code with AI
ChatGPT can quickly analyze obfuscated code, refactor it for readability, and even convert it from JavaScript to TypeScript.
#4about 4 minutes
Explaining and generating complex regular expressions
The model can deconstruct and explain existing regular expressions and generate new ones for tasks like validating phone numbers, though with varying accuracy.
#5about 6 minutes
Generating algorithms and solving complex logic problems
While ChatGPT can generate standard algorithms like quicksort, it often makes mistakes with implementation details like memory complexity and struggles with novel logic problems.
#6about 2 minutes
Using a test-driven development approach with AI
Providing a unit test as a prompt can guide the AI to generate the correct function implementation, demonstrating a test-driven development workflow.
#7about 5 minutes
Creative prompting for simulation and prompt refinement
Advanced prompting techniques can make the AI act as a prompt engineer to refine your requests or even simulate complex systems like a SQL database server.
#8about 2 minutes
Exploring popular AI tool integrations for developers
AI capabilities are integrated into developer tools like GitHub Copilot for IDE assistance, AIaC for generating Terraform code, and even a new version of Clippy.
#9about 2 minutes
Key takeaways for using AI in software development
To use AI effectively, be specific with prompts, always validate the output for correctness and security, and treat it as a powerful but fallible tool.
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Matching moments
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Why corporate AI adoption lags behind the hype
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Moving beyond headcount to solve business problems
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Incentivizing automation with a 'keep what you kill' policy
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Rapid-fire thoughts on the future of work
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