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Greg Kamradt: Benchmarking Intelligence | ARC Prize
What makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical.// BioGreg has mentored thousands of developers and founders, empowering them to build AI-centric applications.By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers.Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools.He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company.// Related LinksWebsite: https://gregkamradt.com/YouTube channel: https://www.youtube.com/@DataIndependent~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Greg on LinkedIn: /gregkamradt/Timestamps:[00:00] Human-Easy, AI-Hard[05:25] When the Model Shocks Everyone[06:39] “Let’s Circle Back on That Benchmark…”[09:50] Want Better AI? Pay the Compute Bill[14:10] Can We Define Intelligence by How Fast You Learn?[16:42] Still Waiting on That Algorithmic Breakthrough[20:00] LangChain Was Just the Beginning[24:23] Start With Humans, End With AGI[29:01] What If Reality’s Just... What It Seems?[32:21] AI Needs Fewer Vibes, More Predictions[36:02] Defining Intelligence (No Pressure)[36:41] AI Building AI? Yep, We're Going There[40:13] Open Source vs. Prize Money Drama[43:05] Architecting the ARC Challenge[46:38] Agent 57 and the Atari Gauntlet
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48:30
Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325
Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractI’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.// BioDeepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.// Related LinksWebsite: https://www.snowleopard.ai/AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Deepti on LinkedIn: /thedeepti/Timestamps:[00:00] Deepti's preferred coffee[00:49] MLflow vs Kubeflow Debate[04:58] GenAI Data Integration Challenges[09:02] GenAI Sidecar Spicy Takes[14:07] Troubleshooting LLM Hallucinations[19:03] AI Overengineering and Hype[25:06] Self-Serve Analytics Governance[33:29] Dashboards vs Data Quality[37:06] Agent Database Context Control[43:00] LLM as Orchestrator[47:34] Tool Call Ownership Clarification[51:45] MCP Server Challenges[56:52] Wrap up
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57:13
The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324
The Creator of FastAPI’s Next Chapter // MLOps Podcast #324 with Sebastián Ramírez, Developer at FastAPI Labs.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractThe creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs.// BioSebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open source tools.He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem.// Related LinksWebsite: https://tiangolo.com/FastAPI: https://fastapi.tiangolo.com/FastAPI Cloud: https://fastapicloud.com/FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Tiangolo on LinkedIn: /tiangoloTimestamps:[00:00] Sebastián's preferred coffee[00:15] Takeaways[01:43] Why Pydantic is Awesome[06:47] ML Background and FastAPI[10:44] NASA FastAPI Emojis[15:21] FastAPI Cloud Journey[26:07] FastAPI Cloud Open-Source Balance[31:45] Basecamp Design Philosophy[35:30] AI Abstraction Strategies[42:56] Engineering vs Developer Experience[51:40] Dogfooding and Docs Strategy[59:44] Code Simplicity and Trust[1:04:26] Scaling Without Losing Vision[1:08:20] FastAPI Cloud Signup[1:09:23] Wrap up
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1:09:37
Everything Hard About Building AI Agents Today
Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance.The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success.Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing.Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve.// BioShreya ShankarPhD student in data management for machine learning.Willem PienaarWillem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories. Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high scale ML systems for the Southeast Asian decacorn.// Related Linkshttps://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_pagehttps://cleric.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Shreya on LinkedIn: /shrshnkConnect with Willem on LinkedIn: /willempienaarTimestamps:[00:00] Trust Issues in AI Data[04:49] Cloud Clarity Meets Retrieval[09:37] Why Fast AI Is Hard[11:10] Fixing AI Communication Gaps[14:53] Smarter Feedback for Prompts[19:23] Creativity Through Data Exploration[23:46] Helping Engineers Solve Faster[26:03] The Three Gaps in AI[28:08] Alerts Without the Noise[33:22] Custom vs General AI[34:14] Sharpening Agent Skills[40:01] Catching Repeat Failures[43:38] Rise of Self-Healing Software[44:12] The Chaos of Monitoring AI
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47:02
Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318
Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractPrithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.// BioRaj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.// Related LinksWebsite: https://www.databricks.com/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Raj on LinkedIn: /rajammanabroluTimestamps:[00:00] Raj's preferred coffee[00:36] Takeaways[01:02] Tao Naming Decision[04:19] No Labels Machine Learning[08:09] Tao and TAO breakdown[13:20] Reward Model Fine-Tuning[18:15] Training vs Inference Compute[22:32] Retraining and Model Drift[29:06] Prompt Tuning vs Fine-Tuning[34:32] Small Model Optimization Strategies[37:10] Small Model Potential[43:08] Fine-tuning Model Differences[46:02] Mistral Model Freedom[53:46] Wrap up
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54:01