Episode
Build Your Second Brain One Piece At A Time
- Podcast
- AI Engineering Podcast
- Published
- Jul 28, 2024
- Duration seconds
- 2907
- Processing state
failed- Canonical source
- https://www.aiengineeringpodcast.com/episodepage/32
Actions
POST https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/build-your-second-brain-one-piece-at-a-time/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/ai-engineering-podcast/build-your-second-brain-one-piece-at-a-time.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Summary Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers Interview Introduction How did you get involved in machine learning? Can you describe what Pieces is and the story behind it? The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives? model selections architecture of Pieces application local vs. hybrid vs. online models model update/delivery process data preparation/serving for models in context of Pieces app application of AI to developer workflows types of workflows that people are building with pieces What are the most interesting, innovative, or unexpected ways that you have seen Pieces used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces? When is Pieces the wrong choice? What do you have planned for the fut…