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Brook Perry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brook Perry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Unpacking METR’s findings: Does AI slow developers down?

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Manage episode 497732302 series 3338504
Brook Perry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brook Perry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this episode of the Engineering Enablement podcast, host Abi Noda is joined by Quentin Anthony, Head of Model Training at Zyphra and a contributor at EleutherAI. Quentin participated in METR’s recent study on AI coding tools, which revealed that developers often slowed down when using AI—despite feeling more productive. He and Abi unpack the unexpected results of the study, which tasks AI tools actually help with, and how engineering teams can adopt them more effectively by focusing on task-level fit and developing better digital hygiene.

Where to find Quentin Anthony:

• LinkedIn: https://www.linkedin.com/in/quentin-anthony/

• X: https://x.com/QuentinAnthon15

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda

In this episode, we cover:

(00:00) Intro

(01:32) A brief overview of Quentin’s background and current work

(02:05) An explanation of METR and the study Quentin participated in

(11:02) Surprising results of the METR study

(12:47) Quentin’s takeaways from the study’s results

(16:30) How developers can avoid bloated code bases through self-reflection

(19:31) Signs that you’re not making progress with a model

(21:25) What is “context rot”?

(23:04) Advice for combating context rot

(25:34) How to make the most of your idle time as a developer

(28:13) Developer hygiene: the case for selectively using AI tools

(33:28) How to interact effectively with new models

(35:28) Why organizations should focus on tasks that AI handles well

(38:01) Where AI fits in the software development lifecycle

(39:40) How to approach testing with models

(40:31) What makes models different

(42:05) Quentin’s thoughts on agents

Referenced:

  continue reading

86 에피소드

Artwork
icon공유
 
Manage episode 497732302 series 3338504
Brook Perry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brook Perry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

In this episode of the Engineering Enablement podcast, host Abi Noda is joined by Quentin Anthony, Head of Model Training at Zyphra and a contributor at EleutherAI. Quentin participated in METR’s recent study on AI coding tools, which revealed that developers often slowed down when using AI—despite feeling more productive. He and Abi unpack the unexpected results of the study, which tasks AI tools actually help with, and how engineering teams can adopt them more effectively by focusing on task-level fit and developing better digital hygiene.

Where to find Quentin Anthony:

• LinkedIn: https://www.linkedin.com/in/quentin-anthony/

• X: https://x.com/QuentinAnthon15

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda

In this episode, we cover:

(00:00) Intro

(01:32) A brief overview of Quentin’s background and current work

(02:05) An explanation of METR and the study Quentin participated in

(11:02) Surprising results of the METR study

(12:47) Quentin’s takeaways from the study’s results

(16:30) How developers can avoid bloated code bases through self-reflection

(19:31) Signs that you’re not making progress with a model

(21:25) What is “context rot”?

(23:04) Advice for combating context rot

(25:34) How to make the most of your idle time as a developer

(28:13) Developer hygiene: the case for selectively using AI tools

(33:28) How to interact effectively with new models

(35:28) Why organizations should focus on tasks that AI handles well

(38:01) Where AI fits in the software development lifecycle

(39:40) How to approach testing with models

(40:31) What makes models different

(42:05) Quentin’s thoughts on agents

Referenced:

  continue reading

86 에피소드

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