Office Hours with Tom Tunguz & Steven Goldfeder

02/27/24 • 42 min

Office Hours with Tomasz Tunguz

00:06 Introduction

02:10 Arbitrum Statistics

02:47 Building Your Developer Community

09:08 Arbitrum One v. Arbitrum Nova

14:55 L3 & Customization

19:41 Arbitrum Orbit & Chain Clusters

24:39 Future Customizations for Developers

28:30 Accepting Developer Languages: To reduce barriers to entry

30:11 Accepting Developer Languages: To access legacy code

32:38 Accepting Developer Languages: To reduce fees

33:48 Convergence of Web2 and Web3

37:55 Community-Source-Software

42:03 Summary

00:06 Introduction

02:10 Arbitrum Statistics

02:47 Building Your Developer Community

09:08 Arbitrum One v. Arbitrum Nova

14:55 L3 & Customization

19:41 Arbitrum Orbit & Chain Clusters

24:39 Future Customizations for Developers

28:30 Accepting Developer Languages: To reduce barriers to entry

30:11 Accepting Developer Languages: To access legacy code

32:38 Accepting Developer Languages: To reduce fees

33:48 Convergence of Web2 and Web3

37:55 Community-Source-Software

42:03 Summary

Previous Episode

Office Hours with Tom Tunguz & Philip Zelitchenko

0:00 Office Hours with Philip Zelitchenko

01:47 Q: How did you decide to structure your team like software engg?

05:07 Q: Determining the value of data 09:17 Structuring data teams: data PMs

10:47 Q: Same data team responsible for internal v. external PRDs?

11:14 Structuring data teams: data engineering 12:02 Q: What is a data product?

12:47 Structuring data teams: data analysts 13:22 Structuring data teams: data governance

13:37 Structuring data teams: data platform

14:18 Q: What distinguishes a DPRD from a PRD?

17:33 Q: Role of the DPRD?

20:05 Q: DPRD v. TEP?

20:55 Demystifying data governance

23:22 Data alert management - internal team and customers

26:20 Q: Motivating ownership of data assets?

28:51 Defining value of a data asset

30:29 Measuring data usage

31:37 Q: Can tools today handle the stochastic nature of data?

33:28 Building a data team within the enterprise

35:42 Q: How to test data products prior to release?

40:00 Q: How do you use observability to manage diversity of alerts?

41:51 Summary

Next Episode

Office Hours with Tom Tunguz & Colin Zima

Takeaways from this discussion include:

There is a pendulum between governance and self-serve, and that swing is narrowing with developments like Omni
There's a dynamic with centralization and decentralization hybrid execution at the edge which allows super-interactive user experiences.
As these experiences improve, the number of people who benefit and can access data in meaningful ways only increases.

00:06 Introduction

01:27 Being Chief Analytics Officer

04:08 Evolution of BI

08:23 Data Organization Structure

10:53 Data Permissioning Philosophy

14:12 Hybrid Execution

17:36 BI Application Architecture

19:36 Mitigating Buyer Fatigue

21:20 BI & AI

25:59 Semantic Layer

27:12 Audience Question: Should AI Suggest Analyses?

28:43 Embedded Analytics