AI Stories

Our guest today is Ritchie Vink, CEO & Founder of Polars: an open source data manipulation library known for being extremely fast. As of today, polars has over 32k stars on github.  In our conversation, Ritchie first explains how Polar which started as a side project evolved to what it is today. We then discuss the differences between Polars and Pandas, why Polars is fast and optimised and dig into Polars cloud: a platform which manages the compute infrastructure, allowing users to focus solely on writing queries.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel [https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA]. 👉 From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3 [https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3] Link to Polars: https://pola.rs/ [https://www.linkup.so/] Follow Ritchie on LinkedIn: https://www.linkedin.com/in/ritchievink/ [https://www.linkedin.com/in/loubna-ben-allal-238690152/] Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ [https://www.linkedin.com/in/leiserneil/]   --- (00:00) Intro (02:23) How Ritchie got into Data & AI (08:00) How Polars started (12:02) Query Optimization (14:38) Polars vs. Pandas (22:16) Why Pandas is still popular (27:52) Polars Cloud (34:45) Polars’ Challenges & Future Roadmap (40:57) Career Advice

Our guest is Philippe Mizrahi, CEO of Linkup: a french startup building the world's best search agents.  In our conversation, Philippe first shares how he got into search by building an internal dataset search tool at Lyft. We then dive into Linkup where Phil explains how linkup started, how it evolved and how they managed to build the best search agents achieving state-of-the-art results on OpenAI SimpleQA dataset.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel [https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA]. 👉 From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3 [https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3] Link to Linkup: https://www.linkup.so/ [https://www.linkup.so/] Follow Philippe on LinkedIn: https://www.linkedin.com/in/philippe-mizrahi/ [https://www.linkedin.com/in/loubna-ben-allal-238690152/] Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ [https://www.linkedin.com/in/leiserneil/]   --- (00:00) Intro (03:13) How Philippe Got into AI  (06:03) Building Lyft’s Internal Search Tool (11:40) How Linkup Started (16:10) How to use Linkup (21:55) Search Agents (31:58) Competing with OpenAI Operator (36:47) Use Cases of Linkup (42:28) Phil's Challenges as a CEO (51:06) Career Advice

Our guest is Samuel Colvin, Co-Founder and CEO of pydantic: a data validation library with millions of downloads per month.  In our conversation, we first discuss Pydantic and their observability platform: logfire. We then dive into agents where Samuel shares his vision on how to build production ready agents and what makes PydanticAI different than other frameworks.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel [https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA]. 👉 From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3 [https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3] 👉 8-hour GenAI Primer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3 [https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3] Follow Samuel on LinkedIn: https://www.linkedin.com/in/samuel-colvin/ [https://www.linkedin.com/in/loubna-ben-allal-238690152/] Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ [https://www.linkedin.com/in/leiserneil/]   --- (00:00) Intro (02:10) How Samuel Got into AI (04:06) What is Pydantic? (06:44) Observability with Logfire (13:28) PydanticAI (17:33) Agent Frameworks (24:40) PydanticAI vs Other Agent Frameworks (32:06) Strengths and Weaknesses of Agents (36:43) Future Challenges with AI Agents (46:45) Career Advice

👉 From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3 [https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3] 👉 8-hour GenAI Primer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3 [https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3] Our guest today is Max Buckley, Senior Software Engineer at Google.  From business analyst intern to Senior LLM engineer at Google! Max has been at Google for 11+ years and what a great career he’s had! Max probably knows Google better than anyone else.  In our conversation, we go through Max's career with over 11 years of experience working at Google. We start with his first internship as a business analyst before he transitioned to software and then data engineering roles. In 2022, Max transitioned again and started building AI projects for Google including an Anti Money Laundering algorithm and internal RAG systems. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel [https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA]. Follow Max on LinkedIn: https://www.linkedin.com/in/maxbuckley/ [https://www.linkedin.com/in/loubna-ben-allal-238690152/] Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ [https://www.linkedin.com/in/leiserneil/]   --- (00:00) Intro (05:37) Internship to Full-Time Job at Google (07:20) Career Evolution Across 10+ Teams (10:30) Learning Through Side Projects (18:22) Joining Google Cloud AI (25:58) First AI Project (31:14) AML AI Project Breakdown (37:34) Shifting to LLMs (42:33) Retrieval & Contextual Chunking (47:28) Limitations of RAG (50:44) Google’s Evolution Over 10 Years (54:46) Career Advice

👉 From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3 [https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3] 👉 8-hour GenAI Primer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3 [https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3] Our guest today is Wojtek Kuberski, Co-Founder and CTO at NannyML. In our conversation, we first discuss Wojtek's experience working as a freelancer. We then talk about NannyML: the platform for post deployment Data Science. We dive deep into model monitoring and discuss the key causes of model failure including covariate shift, concept drift and bad data quality. Wojtek also explains how NannyML's algorithms can estimate model performance without access to labels. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel [https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA]. https://academy.towardsai.net/courses/8-hour-genai-primer?ref=63e5e3 Check out NannyML: https://www.nannyml.com/ [https://www.nannyml.com/] Follow Wojtek on LinkedIn: https://www.linkedin.com/in/wojtek-kuberski/ [https://www.linkedin.com/in/loubna-ben-allal-238690152/] Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ [https://www.linkedin.com/in/leiserneil/]   --- (00:00) Intro (02:36) How Wojtek got into AI (04:48) Early Projects & Learnings in Freelance (13:40) Building NannyML (16:20) Model Monitoring (18:32) Covariate Shift vs Concept Drift (24:57) Technical Insights into Model Monitoring (27:56) NannyML’s Platform & Workflow  (35:50) Retraining Model & Model Failures (41:39) Future of NannyML  (47:09) Career Advice