Source URL: https://www.tomtunguz.com/congratulations-robot-youve-been-promoted/
Source: Tomasz Tunguz
Title: Congratulations, Robot. You’ve Been Promoted!
Feedly Summary:
Watching the OpenAI Dev Day videos, I listened as Thibault, engineering lead for Codex, announced “Codex is now a senior engineer.”
AI entered the organization as an intern – uncertain & inexperienced. Over the summer, engineering leaders said treat it like a junior engineer.
Congratulations, Robot. You’ve been promoted – again! From intern to senior engineer in about a year. Quite the trajectory.
Other data points :
92% of technical staff use Codex daily
those staff generate 72% more pull requests (code submissions) than those who don’t use AI
The team shared more. The best design patterns for collaborating with Codex are architect-implementer systems & closed feedback loops.
ARCHITECT-IMPLEMENTER
I wrote about architect-implementer architectures on Monday. The pattern splits work between two separate robots : the first designs the solution, the second executes it.
Ask a robot to write the plan document. You’ll refine your thinking as you review it. The robot manages progress through each step.
The counterintuitive part? The second robot shouldn’t see the first robot’s context. Fresh discerning digital eyes catch more errors.
CLOSED FEEDBACK LOOPS
In the plan, designing the tests / hurdles that a robot must pass to complete the task is critical. The robot runs the tests, fixes the code, runs the tests again, and repeats until passing. These tests can be visual (evaluate screenshots), functional (does the code run), or logical (does the code meet the requirements). Then a third robot reviews for quality & style.
The record at OpenAI is 7 hours of autonomous execution, 150M tokens, and 15K lines of code refactored with this design pattern. Pretty remarkable even for a senior engineer.
Congratulations, Robot. Keep climbing that ladder.
AI Summary and Description: Yes
Summary: The text discusses the rapid advancement of AI capabilities, particularly OpenAI’s Codex, which has evolved from an intern to a senior engineer in less than a year. It highlights the significant productivity benefits evident in the usage statistics of Codex by technical staff and elaborates on effective collaborative patterns to maximize AI performance in software development.
Detailed Description: The content outlines an impressive evolution in AI performance through the lens of OpenAI’s Codex:
– Codex’s Journey: Initially introduced as an intern, Codex has progressed to the role of a senior engineer, indicating significant advancements in AI capabilities and management expectations.
– Usage Statistics:
– 92% of technical staff reportedly use Codex daily.
– Those utilizing Codex generate 72% more pull requests compared to non-users, showcasing its impact on productivity in software development.
– Best Design Patterns for Collaboration:
– **Architect-Implementer System**: This model divides tasks between two roles (or robots), where one designs the solution and the other implements it. This separation is critical for reducing errors by preventing context fatigue; the implementer does not see the context of the architect’s work.
– **Closed Feedback Loops**: This involves a systematic testing and quality assurance process where:
– Robots devise specific tests for the tasks they are working on.
– They self-run tests and iteratively refine code until they meet necessary requirements, ensuring high-quality and functional outcomes.
– Furthermore, a third robot is introduced to review the work for additional quality and style checks.
– Performance Metrics: The document notes an impressive record of Codex’s performance: achieving 7 hours of autonomous execution, navigating 150 million tokens, and refactoring 15,000 lines of code.
This content is particularly relevant for professionals in the fields of AI, software security, and DevSecOps, as it illustrates not only the competitive edge that generative AI can confer in software development environments but also the critical need for structured collaboration and iterative testing to maintain quality in software releases.