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How Specialized Models Drive Developer Productivity | Tabnine’s Brandon Jung
Manage episode 441549973 series 2844204
Content provided by LinearB. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by LinearB or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
What are the limitations of general large language models, and when should you evaluate more specialized models for your team’s most important use case?
This week, Conor Bronsdon sits down with Brandon Jung, Vice President of Ecosystem at Tabnine, to explore the difference between specialized models and LLMs. Brandon highlights how specialized models outperform LLMs when it comes to specific coding tasks, and how developers can leverage tailored solutions to improve developer productivity and code quality. The conversation covers the importance of data transparency, data origination, cost implications, and regulatory considerations such as the EU's AI Act.
Whether you're a developer looking to boost your productivity or an engineering leader evaluating solutions for your team, this episode offers important context on the next wave of AI solutions
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- Brandon Jung on LinkedIn
- Brandon Jung (@brandoncjung) / X
- Tabnine (@tabnine) / X
- Tabnine AI code assistant | Private, personalized, protected
- Managing Bot-Generated PRs & Reducing Team Workload by 6%
Support the show:
- Subscribe to our Substack
- Leave us a review
- Subscribe on YouTube
- Follow us on Twitter or LinkedIn
Offers:
223 episodes
Share
Manage episode 441549973 series 2844204
Content provided by LinearB. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by LinearB or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
What are the limitations of general large language models, and when should you evaluate more specialized models for your team’s most important use case?
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Whether you're a developer looking to boost your productivity or an engineering leader evaluating solutions for your team, this episode offers important context on the next wave of AI solutions
Topics:
- 00:31 Specialized models vs. LLMs
- 01:56 The problems with LLMs and data integrity
- 12:34 Why AGI is further away than we think
- 16:11 Evaluating the right models for your engineering team
- 23:42 Is AI code secure?
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Links:
- Brandon Jung on LinkedIn
- Brandon Jung (@brandoncjung) / X
- Tabnine (@tabnine) / X
- Tabnine AI code assistant | Private, personalized, protected
- Managing Bot-Generated PRs & Reducing Team Workload by 6%
Support the show:
- Subscribe to our Substack
- Leave us a review
- Subscribe on YouTube
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Similar to Dev Interrupted
On The Bike Shed, hosts Joël Quenneville and Stephanie Minn discuss development experiences and challenges at thoughtbot with Ruby, Rails, JavaScript, and whatever else is drawing their attention, admiration, or ire this week.
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continue reading
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continue reading
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continue reading
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