Today we are dropping another special episode of the Code Story podcast, as part of our series entailed Beyond Bots: the REAL impact of AI on financial services, brought to you by our friends at Ntropy. As a reminder, Ntropy is the most accurate financial data standardization and enrichment API. They can take in any data source, any geography, and understand / enrich a financial transition in milliseconds. Made for developers, for fast, easy implementation. Check out their product at Ntropy.com.
Guest: Ilia Zintchenko, CTO & Co-founder of Ntropy
Questions:
- We talked with Nare about Ntropy and LLM's. But let's dig in more.... what is your LLM stack? How did you choose it, what were the considerations?
- What are the system costs in doing this?
- How do you optimize on reliability - what sort of lever are you pulling to ensure reliability?'
- How are you thinking about predictive vs generative learning?
- You guys have been using small and large LM's since the beginning - why is this significant?
- What data sources have you been using, and are there some that are better than others?'
- Have you had to scrub these data sources in any way to prep them for your LM?
- What is the major benefit that Ntropy is providing by using LLM?
- What would you go back and change if you could?
Links
- Website: https://www.ntropy.com/
- LinkedIn: https://www.linkedin.com/in/iliazintchenko/
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