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Full-stack approach for effective AI agents

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内容由Changelog Media提供。所有播客内容(包括剧集、图形和播客描述)均由 Changelog Media 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

There’s a lot of hype about AI agents right now, but developing robust agents isn’t yet a reality in general. Imbue is leading the way towards more robust agents by taking a full-stack approach; from hardware innovations through to user interface. In this episode, Josh, Imbue’s CTO, tell us more about their approach and some of what they have learned along the way.

Join the discussion

Changelog++ members save 2 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

  • Neo4j – Is your code getting dragged down by JOINs and long query times? The problem might be your database…Try simplifying the complex with graphs. Stop asking relational databases to do more than they were made for. Graphs work well for use cases with lots of data connections like supply chain, fraud detection, real-time analytics, and genAI. With Neo4j, you can code in your favorite programming language and against any driver. Plus, it’s easy to integrate into your tech stack.
  • Fly.ioThe home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs.

Featuring:

Show Notes:

Something missing or broken? PRs welcome!

  continue reading

章节

1. Welcome to Practical AI (00:00:00)

2. Josh Albrecht from Imbue (00:00:43)

Chapter image

3. Categorizing agents (00:04:50)

4. Making agents work (00:08:28)

5. Usecases for agents (00:11:47)

6. Full stack approach (00:13:56)

7. Research first (00:18:11)

8. Focusing on environments (00:20:55)

9. Fundamental laws to deep learning (00:24:11)

11. Engineering trust (00:30:02)

12. New interfaces (00:33:11)

13. Reviewable outputs (00:35:07)

14. Day to day coding with an agent (00:36:58)

15. Language context shifting (00:40:39)

16. Things to come (00:43:41)

17. Thanks for joining us! (00:45:50)

18. Outro (00:46:15)

304集单集

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icon分享
 
Manage episode 418393648 series 2385063
内容由Changelog Media提供。所有播客内容(包括剧集、图形和播客描述)均由 Changelog Media 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

There’s a lot of hype about AI agents right now, but developing robust agents isn’t yet a reality in general. Imbue is leading the way towards more robust agents by taking a full-stack approach; from hardware innovations through to user interface. In this episode, Josh, Imbue’s CTO, tell us more about their approach and some of what they have learned along the way.

Join the discussion

Changelog++ members save 2 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

  • Neo4j – Is your code getting dragged down by JOINs and long query times? The problem might be your database…Try simplifying the complex with graphs. Stop asking relational databases to do more than they were made for. Graphs work well for use cases with lots of data connections like supply chain, fraud detection, real-time analytics, and genAI. With Neo4j, you can code in your favorite programming language and against any driver. Plus, it’s easy to integrate into your tech stack.
  • Fly.ioThe home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs.

Featuring:

Show Notes:

Something missing or broken? PRs welcome!

  continue reading

章节

1. Welcome to Practical AI (00:00:00)

2. Josh Albrecht from Imbue (00:00:43)

Chapter image

3. Categorizing agents (00:04:50)

4. Making agents work (00:08:28)

5. Usecases for agents (00:11:47)

6. Full stack approach (00:13:56)

7. Research first (00:18:11)

8. Focusing on environments (00:20:55)

9. Fundamental laws to deep learning (00:24:11)

11. Engineering trust (00:30:02)

12. New interfaces (00:33:11)

13. Reviewable outputs (00:35:07)

14. Day to day coding with an agent (00:36:58)

15. Language context shifting (00:40:39)

16. Things to come (00:43:41)

17. Thanks for joining us! (00:45:50)

18. Outro (00:46:15)

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