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Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development
Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world?
Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them.
Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.”
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned:
Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues
Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo)
Language agent tree search, a framework for LLM planning, acting and reasoning
The Bitter Lesson, Rich Sutton’s essay on scaling in search and learning
Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs
Transcript: https://www.sequoiacap.com/podcast/training-data-factory/
00:00 Introduction
01:36 Personal backgrounds
10:54 The compound lever
12:41 What is Factory?
16:29 Cognitive architectures
21:13 800 engineers at OpenAI are working on my margins
24:00 Jeff Dean doesn't understand your code base
25:40 Individual dev productivity vs system-wide optimization
30:04 Results: Factory in action
32:54 Learnings along the way
35:36 Fully autonomous Jeff Deans
37:56 Beacons of the upcoming age
40:04 How far are we?
43:02 Competition
45:32 Lightning round
49:34 Bonus round: Factory's SWE-bench results
View all episodes
By Sequoia Capital
4.5
2626 ratings
Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world?
Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them.
Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.”
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned:
Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues
Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo)
Language agent tree search, a framework for LLM planning, acting and reasoning
The Bitter Lesson, Rich Sutton’s essay on scaling in search and learning
Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs
Transcript: https://www.sequoiacap.com/podcast/training-data-factory/
00:00 Introduction
01:36 Personal backgrounds
10:54 The compound lever
12:41 What is Factory?
16:29 Cognitive architectures
21:13 800 engineers at OpenAI are working on my margins
24:00 Jeff Dean doesn't understand your code base
25:40 Individual dev productivity vs system-wide optimization
30:04 Results: Factory in action
32:54 Learnings along the way
35:36 Fully autonomous Jeff Deans
37:56 Beacons of the upcoming age
40:04 How far are we?
43:02 Competition
45:32 Lightning round
49:34 Bonus round: Factory's SWE-bench results
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