Seeking real-world insights beyond sales pitches, the author turns to the Hacker News community for firsthand experiences with NVIDIA's AI tools and LLMs/multi-modal AI. They are interested in understanding what these technologies are genuinely achieving, their limitations, reliability, and whether challenges are due to resource constraints or fundamental barriers. The author is particularly curious about whether these tools accelerate reaching production compared to traditional methods and seeks honest feedback from those actively working with these technologies.
Key takeaways:
- The author has a traditional engineering background and views AI/LLMs as tools for augmenting tasks rather than reshaping engineering.
- NVIDIA's CES keynote suggests AI can solve problems across modalities and shift engineering focus to data gathering and AI integration.
- The author's consumer experience with AI/LLMs shows they are useful for suggestive tasks and prototyping but unreliable for authoritative answers and production-ready code.
- The author seeks real-world insights on the practical use and limitations of NVIDIA's AI tools and LLMs from those actively working with these technologies.