AI and the Future of Software Development
summary
Panel claims developers already use co-pilots and ChatGPT to generate code while teams deploy static and runtime analysis models to flag SQL injection and API auth bugs, improving quality and security. Startups report AI pipelines that estimate carbon dioxide in soil and train models on synthetic data for digital twins. Frontend work ties to vector databases and an architecture of many models (a mixture of experts like Mistral) rather than one monolith, with prompt engineering and agent orchestration still required.
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chapters (9)
- 0:00Welcome
- 6:36AI in development today
- 13:46AI use cases across roles
- 18:26Code quality security automation
- 23:52Future of AI as OS
- 28:03Trust and reliability in AI
- 33:43AI evolution prompt engineering
- 39:21Q&A on LLMs and MoE
- 43:42Closing remarks
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