Agentic Conversations (formally mlops.community)

Making AI Reliable is the Greatest Challenge of the 2020s // MLOps Podcast #312 with Alon Bochman, CEO of RagMetrics.

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Huge shout-out to RagMetrics for sponsoring this episode!

// Abstract

Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators.

// Bio

Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager. Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player.

// Related Links

Website: ragmetrics.ai

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Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Alon on LinkedIn: /alonbochman

Timestamps:

[00:00] Alon's preferred coffee

[00:15] Takeaways

[00:47] Testing Multi-Agent Systems

[05:55] Tracking ML Experiments

[12:28] AI Eval Redundancy Balance

[17:07] Handcrafted vs LLM Eval Tradeoffs

[28:15] LLM Judging Mechanisms

[36:03] AI and Human Judgment

[38:55] Document Evaluation with LLM

[42:08] Subject Matter Expertise in Co-Pilots

[46:33] LLMs as Judges

[51:40] LLM Evaluation Best Practices

[55:26] LM Judge Evaluation Criteria

[58:15] Visualizing AI Outputs

[1:01:16] Wrap up

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // MLOps Podcast #311 with Devansh Devansh, Head of AI at Stealth AI Startup.

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// Abstract

Open-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure.

// Bio

The best meme-maker in Tech. Writer on AI, Software, and the Tech Industry.

// Related Links

Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/

https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Devansh on LinkedIn: /devansh-devansh-516004168

Timestamps:

[00:00] Devansh's preferred coffee

[01:23] Jailbreaking DeepSeek

[02:24] AI Made Simple

[07:16] Leveraging AI for Data Insights

[10:42] Synthetic Data and LLMs

[19:29] AI Experience Design

[22:20] Synthetic Data Bias Reduction

[26:33] Data Ecosystem Insights

[29:50] Moving Intelligence to Data Layer

[36:37] Minimizing Model Responsibility

[40:04] Workflow vs Generalized Agents

[49:24] AI Second-Order Effects

[55:26] AI Experience vs Efficiency

[1:01:10] Wrap up

GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // MLOps Podcast #310 with Paco Nathan, Principal DevRel Engineer at Senzing & Weidong Yang, CEO of Kineviz.

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// Abstract

Existing BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data.

Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability.

We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data.

// Bio

Paco Nathan

Paco is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is a lead committer for the pytextrank​ and kglab​ open source projects. Formerly: Director of Learning Group at O'Reilly Media, and Director of Community Evangelism at Databricks.

Weidong Yang

Weidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics-based solutions to address complex big data problems. His expertise spans Physics, Computer Science, and Performing Arts, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley, and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production.

// Related Links

Website: https://www.kineviz.com/

Blog: https://medium.com/kineviz

Website: https://derwen.ai/pacohttps://huggingface.co/pacoid

https://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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: /dpbrinkm

Connect with Weidong on LinkedIn: /yangweidong/

Connect with Paco on LinkedIn: /ceteri/

Timestamps:

[00:00] Wei's preferred coffee

[00:26] Takeaways

[00:50] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:06] PII Anonymization Techniques

[09:49] Graph RAG Differentiation Ideas

[19:55] Ontologies vs Embeddings in AI

[30:05] Graph Exploration and Insight

[39:25] Iceberg Data Metaphor

[41:19] Contextual Data Visualization

[42:44] Granularity vs Domain Shifting

[49:51] Visualization Access Control

[53:37] Graph RAG Use Cases

[59:16] IoT and Graphs

[1:01:15] Data Visualization

[1:12:14] Wrap up

AI Data Engineers - Data Engineering after AI // MLOps Podcast #309 with Vikram Chennai, Founder/CEO of Ardent AI.

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// Abstract

A discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data.

// Bio

Second Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers

// Related Links

Website: tryardent.com

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Vikram on LinkedIn: /vikram-chennai/

Timestamps:

[00:00] Vikram's preferred coffee

[00:09] Takeaways

[00:42] Please like, share, leave a review, and subscribe to our MLOps channels! You can give us up to 5 stars on Spotify and leave your reviews!

[01:53] Product User Categories

[02:47] AI Data Engineer Role

[05:40] AI Coding Limits Enterprise

[09:22] Creating Feedback Loops

[14:23] Breaking Down Big Tasks

[19:39] Marketing Data Agent Scope

[28:03] Clear Success Metrics

[32:20] Creating Agent Glossary

[36:43] AI Prompt Toolkits

[38:54] Pricing Strategy Discussion

[43:20] Compute Abstraction and Pipelines

[45:23] Agent Surprises and Logs

[47:12] Wrap up

I am once again asking, "What is MLOps?" // MLOps Podcast #308 with Oleksandr Stasyk, Engineering Manager, ML Platform of Synthesia.

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// Abstract

What does it mean to MLOps now? Everyone is trying to make a killing from AI; everyone wants the freshest technology to show off as part of their product. But what impact does that have on the "journey of the model"? Do we still think about how an idea makes its way to production to make money? How can we get better at it? Maybe the answer lies in the ancient "non-AI" past...

// Bio

For the majority of my career, I have been a "full stack" developer with a leaning towards DevOps and platforms. In the last four years or so, I have worked on ML Platforms. I find that applying good software engineering practices is more important than ever in this AI-fueled world.

// Related Links

Blogs: https://medium.com/@sashman90/mlops-the-evolution-of-the-t-shaped-engineer-a4d8a24a4042

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Sash on LinkedIn: /oleksandr-stasyk-5751946b

Timestamps:

[00:00] Sash's preferred coffee

[00:09] Takeaways

[01:21] Vibe Coding Reality Check

[06:27] MLOps and Vibe Coding

[12:53] Data Engineering in GenAI

[14:53] MLOps in MVP Development

[21:13] Platform Engineering Org Models

[27:30] Empathy in Data Engineering

[31:11] Post-DevOps MLOps Evolution

[39:32] AI for Fast Feedback

[46:53] AI Workflow vs Real Work

[50:13] ML Confession Stories

[59:06] Shift Left in Testing

[1:05:49] Wrap up

How Sama is Improving ML Models to Make AVs Safer // MLOps Podcast #307 with Duncan Curtis, SVP of Product and Technology at Sama.

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// Abstract

Between Uber’s partnership with NVIDIA and speculation around the U.S. President Donald Trump enacting policies that allow fully autonomous vehicles, it’s more important than ever to ensure the accuracy of machine learning models. Yet, the public’s confidence in AVs is shaky due to scary accidents caused by gaps in the tech that Sama is looking to fill. As one of the industry’s top leaders, Duncan Curtis, SVP of Product and Technology at Sama, would be delighted to share how we can improve the accuracy, speed, and cost-efficiency of ML algorithms for ​A​Vs. Sama’s machine learning technologies minimize the risk of model failure and lower the total cost of ownership for car manufacturers including Ford, BMW, and GM, as well as four of the five top OEMs and their Tier 1 suppliers. This is especially timely as Tesla is under investigation for crashes due to its Smart Summon feature, and Waymo recently had a passenger trapped in one of its driverless taxis.

// Bio

Duncan Curtis is the SVP of Product at Sama, a leader in de-risking ML models, delivering best-in-class data annotation solutions with our enterprise-strength, experience & expertise, and ethical AI approach. To this leadership role, he brings 4 years of Autonomous Vehicle experience as the Head of Product at Zoox (now part of Amazon) and VP of Product at Aptiv, and 4 years of AI experience as a product manager at Google, where he delighted the +1B daily active users of the Play Store and Play Games.

// Related Links

Website: https://www.sama.com/

Tesla is under investigation: https://www.cnn.com/2025/01/07/business/nhtsa-tesla-smart-summon-probe/index.html

Waymo recently had a passenger trapped: https://www.cbsnews.com/losangeles/news/la-man-nearly-misses-flight-as-self-driving-waymo-taxi-drives-around-parking-lot-in-circles/

https://coruzant.com/profiles/duncan-curtis/

https://builtin.com/articles/remove-bias-from-machine-learning-algorithms

Look At Your ****ing Data :eyes: // Kenny Daniel // MLOps Podcast #292: https://youtu.be/6EMnkAHmoag

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Luca on LinkedIn: /duncan-curtis

Timestamps:

[00:00] Duncan's preferred coffee

[00:08] Takeaways

[01:00] AI Enterprise Focus

[04:18] Human-in-the-loop Efficiency

[08:42] Edge Cases in AI

[14:14] Forward Combat Compatibility Failures

[17:30] Specialized Data Annotation Challenges

[24:44] SAM for Ring Integration

[28:50] Data Bottleneck in AI

[31:29] Data Connector Horror Story

[33:17] Sama AI Data Annotation

[37:20] Cool Business Problems Solved

[40:50] AI ROI Framework

[45:11] Wrap up

Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // MLOps Podcast #306 with Luca Fiaschi, Partner of PyMC Labs.

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// Abstract

Traditional product development cycles require extensive consumer research and market testing, resulting in lengthy development timelines and significant resource investment. We've transformed this process by building a distributed multi-agent system that enables parallel quantitative evaluation of hundreds of product concepts. Our system combines three key components: an Agentic innovation lab generating high-quality product concepts, synthetic consumer panels using fine-tuned foundational models validated against historical data, and an evaluation framework that correlates with real-world testing outcomes. We can talk about how this architecture enables rapid concept discovery and digital experimentation, delivering insights into product success probability before development begins. Through case studies and technical deep-dives, you'll learn how we built an AI-powered innovation lab that compresses months of product development and testing into minutes - without sacrificing the accuracy of insights.

// Bio

With over 15 years of leadership experience in AI, data science, and analytics, Luca has driven transformative growth in technology-first businesses. As Chief Data & AI Officer at Mistplay, he led the company’s revenue growth through AI-powered personalization and data-driven pricing. Prior to that, he held executive roles at global industry leaders such as HelloFresh ($8B), Stitch Fix ($1.2B), and Rocket Internet ($1B). Luca's core competencies include machine learning, artificial intelligence, data mining, data engineering, and computer vision, which he has applied to various domains such as marketing, logistics, personalization, product, experimentation, and pricing. He is currently a partner at PyMC Labs, a leading data science consultancy, providing insights and guidance on applications of Bayesian and Causal Inference techniques and Generative AI to Fortune 500 companies. Luca holds a PhD in AI and Computer Vision from Heidelberg University and has more than 450 citations for his research work.

// Related Links

Website: https://www.pymc-labs.com/

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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)]

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MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Luca on LinkedIn: /lfiaschi

Timestamps:

[00:00] Luca's preferred coffee

[00:12] Takeaways

[00:39] HelloFresh vs Stitch Fix

[02:49] ML in Fresh Produce

[06:10] Bayesian Theory and Applications

[11:35] GenAI and ML Synergy

[16:08] Ensuring Data Quality

[18:20] LLM Augmentation in SQL

[20:47] Agent Eval Challenges

[25:35] LLMs for User Feedback

[30:45] Rage Clicks and LLMs

[38:32] Bridging ML and Business

[44:32] Detective Analysts and Use Cases

[47:30] PDF Automation ROI Challenges

[53:25] Deep Research for Products

[1:00:17] Wrap up

Real-Time Forecasting Faceoff: Time Series vs. DNNs // MLOps Podcast #305 with Josh Xi, Data Scientist at Lyft.

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// Abstract

In real-time forecasting (e.g., geohash level demand and supply forecast for an entire region), time series-based forecasting methods are widely adopted due to their simplicity and ease of training. This discussion explores how Lyft uses time series forecasting to respond to real-time market dynamics, covering practical tips and tricks for implementing these methods, an in-depth look at their adaptability for online re-training, and discussions on their interpretability and user intervention capabilities. By examining these topics, listeners will understand how time series forecasting can outperform DNNs and how to effectively use time series forecasting for dynamic market conditions and decision-making applications.

// Bio

Josh is a data scientist from the Marketplace team at Lyft, working on forecasting and modeling of marketplace signals that power products like pricing and driver incentives. Josh got his PHD in Operations Research in 2013, with minors in Statistics and Economics. Prior to joining Lyft, he worked as a research scientist in the Operations Research Lab at General Motors, focusing on optimization, simulation, and forecasting modeling related to vehicle manufacturing, supply chain, and car-sharing systems.

// Related Links

Website: https://www.lyft.com/

Real-Time Spatial Temporal Forecasting @ Lyft blog: https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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: /dpbrinkm

Connect with Josh on LinkedIn: /joshxiaominxi

Timestamps:

[00:00] Josh's preferred coffee

[00:23] Takeaways

[01:10] AI Process Deep Dive

[05:01] Event Data in Predictions

[11:01] External Data Challenges

[15:25] Time Series Foundational Models

[18:20] DNN Model Support Tradeoffs

[21:57] AR vs DNN for Timeseries

[26:49] Model Retraining Strategies

[31:30] Model Selection vs Averaging

[38:20] Model Testing Strategies

[43:00] Spatial Data Correlation Issues

[49:21] Root-Cause vs Business as Usual

[52:22] Wrap up

We're All Finetuning Incorrectly // MLOps Podcast #304 with Tanmay Chopra, Founder & CEO of Emissary.

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Get the newsletter: https://go.mlops.community/YTNewsletter

// Abstract

Finetuning is dead. Finetuning is only for style. We've all heard these claims. But the truth is, we feel this way because all we've been doing is extended pretraining. I'm excited to chat about what real finetuning looks like - modifying output heads, loss functions, and model layers, and its implications on quality and latency. Happy to dive deeper into how DeepSeek leveraged this real version of finetuning through GRPO and how this is nothing more than a rediscovery of our old finetuning ways. I'm sure we'll naturally also dive into when developing and deploying your specialized models makes sense and the challenges you face when doing so.

// Bio

Tanmay is a machine learning engineer at Neeva, where he's currently engaged in reimagining the search experience through AI - wrangling with LLMs and building cold-start recommendation systems. Previously, Tanmay worked on TikTok's Global Trust&Safety Algorithms team - spearheading the development of AI technologies to counter violent extremism and graphic violence on the platform across 160+ countries. Tanmay has a bachelor's and master's in Computer Science from Columbia University, with a specialization in machine learning.

Tanmay is deeply passionate about communicating science and technology to those outside its realm. He's previously written about LLMs for TechCrunch, held workshops across India on the art of science communication for high school and college students, and is the author of Black Holes, Big Bang and a Load of Salt - a labor of love that elucidated the oft-overlooked contributions of Indian scientists to modern science and helped everyday people understand some of the most complex scientific developments of the past century without breaking into a sweat!

// Related Links

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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: /dpbrinkm

Connect with Tanmay on LinkedIn: /tanmayc98

Timestamps:

[00:00] Tanmay's preferred coffee

[00:17] Takeaways

[00:55] LLM Potential vs Reality

[06:41] Prompting and Workflow Challenges

[13:39] LLM Fine-Tuning vs Prompt Engineering

[16:53] Foundational Models vs ML

[23:32] Vertical vs Horizontal Workflows

[28:25] AI CoE Concerns

[32:09] 500 Examples vs API

[36:38] LLM as DAG Node

[39:26] Success with AI Tools

[43:45] AI for regional ads

[48:13] AI Systems and Infrastructure

[51:13] Prompt Experimentation and Evaluation

[56:38] Python vs IML Tools

[59:32] Wrap up

From Shiny to Strategic: The Maturation of AI Across Industries // MLOps Podcast #303 with David Cox, VP of Data Science; Assistant Director of Research at RethinkFirst; Institute of Applied Behavioral Science.

Join the Community: https://go.mlops.community/YTJoinIn

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// Abstract

Shiny new objects are made available to artificial intelligence(AI) practitioners daily. For many who are not AI practitioners, the release of ChatGPT in 2022 was their first contact with modern AI technology. This led to a flurry of funding and excitement around how AI might improve their bottom line. Two years on, the novelty of AI has worn off for many companies but remains a strategic initiative. This strategic nuance has led to two patterns that suggest a maturation of the AI conversation across industries. First, conversations seem to be pivoting from "Are we doing [the shiny new thing]" to serious analysis of the ROI from things built. This reframe places less emphasis on simply adopting new technologies for the sake of doing so and more emphasis on the optimal stack to maximize return relative to cost. Second, conversations are shifting to emphasize market differentiation. That is, anyone can build products that wrap around LLMs. In competitive markets, creating products and functionality that all your competitors can also build is a poor business strategy (unless having a particular thing is industry standard). Creating a competitive advantage requires companies to think strategically about their unique data assets and what they can build that their competitors cannot.

// Bio

Dr. David Cox can formally lay claim to being a bioethicist (master's degree), a board-certified behavior analyst at the doctoral level, a behavioral economist (post-doc training), and a full-stack data scientist (post-doc training). He has worked in behavioral health for nearly 20 years as a clinician, academic researcher, scholar, technologist, and all-around behavior science junky. He currently works as the Assistant Director of Research for the Institute of Applied Behavioral Science at Endicott College and the VP of Data Science at RethinkFirst. David also likes to write, having published 60+ peer-reviewed articles, book chapters, and a few books. When he's not doing research or building tools at the intersection of artificial intelligence and behavioral health, he enjoys spending time with his wife and two beagles in and around Jacksonville, FL.

// Related Links

~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

Join 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: /dpbrinkm

Connect with David on LinkedIn: /coxdavidj

Timestamps:

[00:00] David's preferred coffee

[00:08] Takeaways

[02:11] AI Beyond LLMs

[06:05] Notifications and Screen Time

[08:35] Exercise and Brain Chemistry

[10:30] Data Privacy and Improvement

[12:14] God Mode Habit Tracker

[13:58] Breaking Habits for Reading

[20:15] Evaluating Lasting Change

[24:23] Phone Free Bedtime Ritual

[29:00] Identity and Habit Formation

[32:05] Voice Memo AI Future

[33:32] Unsupervised Learning in Education

[37:08] Data-Driven Classroom Insights

[40:33] Wrap up