We return from a break to discuss the effects of an avalanche of app making due to Claude and Codex, including “Camp,” an experiment by Nabeel in native multiplayer AI-assisted group work beyond shareable outputs. We cover: In order to be a founder leading AI transformation do you need to lead by example? Conductor’s viral “prompt feature requests” workflow, reality of one-shot apps versus iterative prompting, how teams may use less open-source, Gemini's comparative strengths, and what does it mean when the engineering pod optimal size has moved from six to two. We end by discussing Granola’s MCP and why data moats are fragile, favoring best interfaces and customer-centric access.
00:00 Divergent Paths: Two types of Engineering post Claude 4.5
00:00 Introduction: The New Reality of Coding
00:21 Building Camp: Multiplayer Knowledge Work
03:12 Open Source in the Age of Models
08:56 The Recommendation Problem: From Average to Expert
15:29 Why Gemini Works for Personalization
18:32 Submit a Prompt: Conductor's Product Innovation
21:50 Two types of Engineering: Automatic vs Iterative
32:08 Rethinking Team Structure: From Six to Two
35:01 Can you AI transform without living it yourself?
40:12 Data Moats and the MCP Shift
44:05 Making Context Ubiquitous
- (00:00) - The two types of engineering post Claude 4.5
- (00:00) - Introduction: The New Reality of Coding
- (00:21) - Building Camp: Multiplayer Knowledge Work
- (03:13) - Open Source in the Age of Models
- (08:57) - The Recommendation Problem: From Average to Expert
- (15:30) - Why Gemini Works for Personalization
- (18:33) - Submit a Prompt: Conductor's Product Innovation
- (21:51) - Two types of Engineering: Automatic vs Iterative
- (32:09) - Rethinking Team Structure: From Six to Two
- (35:02) - Can you AI transform without living it yourself?
- (40:13) - Data Moats and the MCP Shift
- (44:06) - Making Context Ubiquitous
