Mastering the AI Shift: Adaptive Strategies for Software Evolution
summary
Panel reports real-world use of Co-pilot and ChatGPT for coding, email triage and market research, with seniors gaining efficiency while junior PR-quality falls and attention drops; teams run Llama, Gemini and Replicate (Hugging Face) and host models on Cloudflare, and IBM-built tooling switches between models. Concrete problems: model hallucinations causing SQL injection risks, prompt‑injection/prop‑injection across DB queries, legal ownership of LLM output unresolved, and scaling a generator over a 1,000-class repo still fails; a notable Llama on-device demo (Samsung watch) shows offline inference is possible.
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chapters (22)
- 0:00AI’s impact on software development
- 2:12AI reshaped daily habits
- 4:46AI tools for productivity
- 6:53Ethical AI use responsibilities
- 8:59AI for junior vs senior productivity
- 11:25AI’s influence on languages
- 13:31AI for market analysis
- 15:59Legal challenges with AI code
- 18:07AI’s hidden risks
- 20:15Bias in AI models
- 23:49Getting started with AI tools
- 27:00AI limitations in projects
- 28:47Security threats from AI systems
- 30:10AI business use cases
- 33:35AI models’ carbon footprint
- 37:28Career strategies in AI era
- 39:14Disruptive AI beyond coding
- 41:42UIs and APIs in AI era
- 42:47Mastering AI prompting skills
- 44:54Ethical AI and critical thinking
- 46:01Staying adaptable in tech
- 47:26AI as innovation tool
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