Build Your Second Brain One Piece At A Time
Data Engineering Podcast
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Exploring the Evolution of Agents and Efficiency of Always-On Language Models
Exploring the rise of autonomous agents and the efficiency of large language models like always-on tools, while tackling the challenges of integrating machine learning comprehensively and scaling up compute capabilities.
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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
Parting Question
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 Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers
- 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 future of Pieces?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- Pieces
- NPU == Neural Processing Unit
- Tensor Chip
- LoRA == Low Rank Adaptation
- Generative Adversarial Networks
- Mistral
- Emacs
- Vim
- NeoVim
- Dart
- Flutter
- Typescript
- Lua
- Retrieval Augmented Generation
- ONNX
- LSTM == Long Short-Term Memory
- LLama 2
- GitHub Copilot
- Tabnine
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