In this episode of Day Today, Greg Michaelson sits down with Rob Zuber, CTO of CircleCI, for a wide-ranging conversation about software engineering, developer tools, AI coding agents, and the future of building software.

Rob shares his journey from manufacturing engineering and factory-floor data analysis to leading one of the most influential companies in modern software delivery. Along the way, he discusses the evolution of CI/CD, lessons from the dot-com era, and why today's AI boom feels both familiar and fundamentally different.

Topics include:

• The origins and evolution of CircleCI
• Why speed and feedback loops remain the core of software delivery
• How engineering teams are using AI coding agents like Claude Code and Codex
• Managing token costs and AI infrastructure at scale
• The changing role of IDEs, terminals, and developer interfaces
• Agent experience (AX) and designing products for both humans and AI systems
• Fine-tuning models versus large-context workflows
• The future of code reviews, pull requests, and automated validation
• What software engineering organizations will look like as AI-generated code becomes the norm

Rob also explores a provocative question: if AI can write code, can we eventually trust systems enough that humans no longer need to review every line? The discussion offers a thoughtful look at where software development is headed and the challenges engineering teams must solve next.

Whether you're a software engineer, engineering leader, startup founder, or AI enthusiast, this conversation provides valuable insight into the technologies and workflows reshaping the future of software development.

Raheem Bell is a surgical resident at Northwestern and the CEO of Operative Insights, a startup developing an autonomous operating room lighting system designed to help surgeons see better, work more efficiently, and improve patient outcomes. In this episode, Raheem shares how years in the operating room led him to identify a surprisingly common problem: surgeons constantly adjusting lights during procedures. We discuss how computer vision, robotics, and AI can be used to create intelligent lighting that follows the surgical team, eliminates shadows, and adapts to the needs of each operation. Raheem also talks about the challenges of building a medical device startup, navigating FDA approval, raising capital, assembling a multidisciplinary team, and balancing entrepreneurship with the demands of surgical training. It's a fascinating look at how innovation often starts with a simple frustration and grows into a mission to transform an entire industry. 

In this episode of Data Day with Greg Michaelson, Greg sits down with Tony Medrano to explore the rapidly evolving intersection of AI, wearable health data, peptides, and personalized longevity optimization.

Tony shares his journey from founding one of the earliest AI startups in 2016 to helping scale a molecular diagnostics company from zero to $1 billion in revenue during the COVID era, serving organizations like NASA, Google, the NFL, and the NBA. 

The conversation dives deep into:

• What peptides actually are and how they work
• The science behind compounds like BPC-157, GHK-CU, MOTS-c, and tesamorelin
• AI-driven “digital twin” health optimization
• Wearables, HRV tracking, and personalized biometric analysis
• The difference between supplements, peptides, and pharmaceutical drugs
• Longevity strategies for athletes, executives, and people over 40
• Why most longevity advice online is noisy, biased, or incomplete
• The future of AI-powered health coaching and preventative medicine

Tony also explains how Longevity Plan AI combines wearable data, peptide protocols, physician oversight, and AI modeling to create personalized health optimization plans designed to improve recovery, metabolic health, energy, sleep, and performance. 

If you’re interested in biohacking, longevity, AI-driven healthcare, performance optimization, or the future of personalized medicine, this episode is packed with fascinating insights and practical discussion.

In this episode of Data Day with Greg Michaelson, Greg sits down with John Kane to explore the rapidly growing world of sports prediction markets and the AI systems powering next-generation quantitative trading.

John explains how his company, Lazy Edge, applies quantitative finance techniques to live sports trading, using massive real-time data streams, simulation engines, and AI-driven modeling to identify market inefficiencies during games as they unfold.

The conversation dives into:

• How prediction markets like Polymarket and Kalshi actually work
• Why live sports trading resembles quantitative stock trading more than gambling
• Using simulations, AI agents, and “digital twins” to model game outcomes
• Real-time NBA event prediction and market movement analysis
• How bots trade sports markets automatically through APIs
• The future of prediction markets, regulation, and AI-driven trading systems
• Why chaos modeling matters in live game forecasting

John also shares how tools like Zerve help accelerate experimentation and backtesting by allowing rapid iteration on complex predictive models and simulations.

If you’re interested in AI, quantitative finance, trading systems, simulation modeling, or the intersection of sports and machine learning, this episode is a fascinating look at a space that’s evolving incredibly fast.

In this episode of Data Day with Greg Michaelson, Greg sits down with Gaurav Jain to explore one of the hardest problems in modern developer tooling: understanding developer intent. They dive into how technical buyers actually evaluate products, why traditional B2B funnels break down when selling to engineers, and how reo.dev tracks developer behavior across GitHub, docs, package managers, MCPs, and AI agents to help companies understand when users are truly ready to engage. The conversation also explores the rise of agent-driven discovery, the decline of documentation traffic, MCPs as the future interface layer for AI systems, and what happens when developers stop browsing websites and start delegating research to AI. 

Tom Coyle’s career path sounds fictional until you realize it’s all true. West Point graduate, Army officer, CIA operations specialist, AI strategist, startup founder, and now health tech entrepreneur focused on improving cancer outcomes through nutrition and data.

In this episode, Tom shares stories from his time tracking enemy mortar patterns in military simulations, helping intelligence teams think differently about finding Osama bin Laden, and eventually joining DataRobot during the rise of enterprise AI. He explains how pattern recognition, predictive analytics, and human behavior shaped his career long before modern AI tools existed.

We also dive deep into his current company, Science Cella, an AI-driven nutrition platform designed to help cancer patients manage symptoms, improve treatment outcomes, and give dieticians scalable tools for personalized care. Tom talks candidly about surviving cancer himself, the realities of healthcare innovation, startup fundraising, clinical trials, and why he believes “food as medicine” deserves serious scientific validation.

This conversation covers military technology, intelligence work, AI, startups, healthcare, nutrition, entrepreneurship, and the lessons learned from building products in high-stakes environments.

In this episode of Data Day, I sit down with Bhaskar Sunkara, co-founder and former CTO of AppDynamics, to unpack the evolution from application performance monitoring to AI-driven business analytics.

We go deep on how AppDynamics redefined monitoring by focusing on business transactions instead of infrastructure metrics, why self-serve was a game-changing GTM move, and the behind-the-scenes story of their near-IPO turned Cisco acquisition.

Then we shift to what comes next. Bhaskar shares how his new company is rethinking analytics with always-on agents that detect, explain, and act on business signals before you even ask the question. We dig into what actually makes AI systems useful in production, where coding agents are already replacing engineering work, and why the real bottleneck has shifted from writing code to designing the right system.

If you’re building in AI, data, or modern SaaS, this is a clear look at where things are going and what actually matters.

I sat down with Todd Persen from Hydraulics to unpack observability, log data, and what actually happens behind the scenes when your apps, streams, and infrastructure are running at scale. We get into how systems are monitored, why logs are exploding in size and complexity, and why LLMs struggle once the data gets truly massive. The conversation takes a turn into the future, where agentic traffic may outnumber humans, and what it means to tell good bots from bad ones. We also get into real-world use cases from CDNs, streaming, and even what happens when AI starts doing your grocery shopping for you.

In this episode of Day to Day, Greg reconnects with Darius Sabas to unpack his journey from DataRobot to leading analytics teams across European marketplaces. Darius shares lessons from scaling data organizations, building modern data stacks with tools like Snowflake and Mixpanel, and navigating the transition from basic reporting to predictive modeling and AI-driven products.

The conversation dives into the realities behind marketing mix models, the challenges of balancing data rigor with business politics, and why marketplaces are a goldmine for data science use cases. They also explore the growing role of LLMs in both work and personal life, from agentic workflows to the future of personalized AI, data privacy, and trust in an AI-generated world.

Saad Ansari has had one of the more unusual paths in AI.

He shares what it was like serving as an advisor inside the Saudi Ministry of Hajj and Umrah, helping guide digital transformation for one of the largest recurring events in the world. We talk about how AI intersects with government systems, why execution looks different across countries, and what it takes to modernize legacy infrastructure.

Then we shift to his new company, Partake.ai, where he’s focused on reinforcement learning that optimizes for human outcomes rather than engagement metrics.

We cover:

• Why today’s AI systems optimize for attention
• What “human-aligned” AI might actually mean
• Pokémon Go as the most successful fitness app ever
• Whether AI can nudge people toward healthier behavior
• Edge inference and privacy tradeoffs
• And whether companies can build something that is both profitable and good

It’s a thoughtful conversation about AI, incentives, and what kind of systems we actually want to live with.