Build Your Second Brain One Piece At A Time

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 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

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 future of Pieces?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

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.

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Links

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

Podcast Episode

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0 Sponsored By:Starburst: 

This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics.

Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)Dagster: 

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