01:26 Journey to Snowflake

02:32 Snowflake and AI

06:43 Choosing your model

07:44 Snowflake & OS

09:43 Innovations to reduce training data size

10:59 From large to small models

13:14 Snowflake and agentic systems

15:50 AI & data security

17:17 Access control layer

18:14 Embedded applications

19:55 Data sharing

21:37 Snowflake training & inference

23:12 Data reshaping

24:40 Structured versus unstructured model inputs

25:24 Models providing the mean v. exceptions

27:19 Vector databases

30:33 Summary

01:43 Tabular Acquisition by Databricks

05:57 BI's Third Form

09:12 Future of BI

12:50 Data Quality in the World of LLMs

18:48 Building Resilient Data Pipelines & ETL

21:01 Evolving Role of BI Analysts

23:18 Data and Decision-Making

28:05 Conclusion

01:55 Big Data is Dead

06:41 Ease of Use

08:54 Hybrid Architecture

10:30 Audience Question: LLMs for Onboarding?

12:55 Hybrid Architecture Enables New Software Design

16:58 DuckDB & ETL

19:24 Duck Puns

22:07 Duck Community

24:32 Summary

0:00 Office Hours - Evan Cheng

01:28 From Meta to Mysten

04:54 Why Develop a New Language, Move

11:15 Developer Response to Move

13:21 Zero Knowledge (ZK) Proof of Login

19:00 Kiosk

24:34 On Chain Storage Limitations

29:01 DAGs & Web3

30:01 Mysten Labs Ecosystem & SUI

34:03 SUI Token Launch

38:30 Closing

Takeaways from this discussion include:

1. There is a pendulum between governance and self-serve, and that swing is narrowing with developments like Omni
2. There's a dynamic with centralization and decentralization hybrid execution at the edge which allows super-interactive user experiences. 
3. 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

32:48 Will AI Automate BI Users Away?

35:00 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

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

00:11 Introduction 

02:54 What is Outbound Fury (OBF)? 

03:34 Inspiration for OBF 

05:03 OBF Tactics 

07:44 Determining the Line 

11:22 The Challenger Sale 

14:11 Personas & OBF 

16:05 Product-Led Growth (PLG), ABM & Outbound Fury 

19:35 Setting Up Your Team for Success 

21:54 Managing Internal Stakeholders 

25:28 Measuring Success 

27:16 Brand & OBF Campaigns 

29:25 Pricing in Marketing Considerations 

31:47 Analyst Community (e.g. Gartner) & OBF

33:43 Company Scale & OBF 

36:23 Conclusions 

Materials Mentioned in Today's Session: 

-- Raj Sarkar's Post: https://rajsarkar.substack.com/p/mark... 

-- Marc Benioff, Behind the Cloud https://www.amazon.com/Behind-Cloud-S... 

-- Matthew Dixon, The Challenger Sale https://www.amazon.com/The-Challenger...

00:00 Introduction to Tom and Oliver

03:07 Overview of PLG at Dropbox

05:30 Overview of PLG at Asana

06:07 How to succeed in PLG end user acquisition phase

09:15 Tactics for Generating Awareness

10:17 Customer Expansion Phase

13:15  When Tension Arises Between PLG & Enterprise Security Needs

15:24 Security is an All Consuming Roadmap, not a Feature

18:35 How the Organization Shifts during the Transition from PLG to SLG

20:48 How Pricing Changes from PLG to SLG

26:22 The PLG Trap

28:03 Avoiding the PLG Trap

31:55 Value-Based Selling: Generalizable or Vertical/Use-Case Specific?

35:35 Atlassian v. Asana's Approaches

37:11 Advice for New Startups Pursuing PLG

38:22 Navigating from SLG to PLG

42:19 Resources for Founders

43:06 PLG, SLG & AI