Impact of LLMs on the Tech Stack and Product Development // Anand Das // #188
Episode description
MLOps podcast #188 with Anand Das, Co-founder and CTO of Bito, Impact of LLMs on the Tech Stack and Product Development.
// Abstract
Anand and his team have developed a fascinating Chrome extension called "explain code" that has garnered significant attention in the tech community. They have expanded their extension to other platforms like Visual Studio Code and Chat Brains, creating a personal assistant for code generation, explanation, and test case writing.
// Bio
Anand Das is the co-founder and CTO of Bito. Previously, he served as the CTO at Eyeota, which was acquired by Dun & Bradstreet for $165M in 2021. Anand also co-founded and served as the CTO of PubMatic in 2006, a company that went public on NASDAQ in 2020 (NASDAQ: PUBM). Anand has also held various engineering roles at Panta Systems, a high-performance computing startup led by the CTO of Veritas, as well as at Veritas and Symantec, where he worked on a variety of storage and backup products. Anand holds seven patents in systems software, storage software, advertising, and application software.
// MLOps Jobs board
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: https://bito.ai/
--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Anand on LinkedIn: https://www.linkedin.com/in/ananddas/
Timestamps:
[00:00] Anand's preferred coffee
[00:15] Takeaways
[02:49] Please like, share, and subscribe to our MLOps channels!
[03:08] Anand's tech background
[10:06] Fun at Optimization Level
[12:59] Trying all APIs
[17:55] Model's evaluation decision tree
[22:51] Weights and Biases Ad
[25:04] AI Stack that understands the code
[28:27] Tools for the Guard Rails
[33:23] Seeking solutions before presenting to LLM
[38:46] Prompt-Driven Development Insights
[40:16] Prompting best practices
[42:51] Unneeded complexities
[45:45] Cost-benefit analysis of buying GPUs
[49:13] ML Build vs Buy
[51:26] Best practices for debugging code assistant
[54:58] Wrap up