<![CDATA[AI, Data, and Memory]]>a3b0df07-7274-545e-9781-9959646e078bMon, 30 Mar 2026 17:56:01 +0000Captivate.fmChris Latimerhttps://artwork.captivate.fm/bbadb3b9-92e8-4b3d-9e1c-ce40d88f5a98/AI-Data-and-Memory-Podcast-Cover.jpgAI, Data, and MemoryChris LatimerChris LatimerAI, Data, and Memory is a podcast for founders, engineers, and technical leaders building AI-powered products. Hosted by Chris Latimer, the show features relaxed conversations with people who are actively designing, building, and operating AI systems. The focus is on real experience, what’s worked, what hasn’t, and how thinking changes once systems move beyond demos and into production. These are not polished interviews or sales conversations. Each episode is a practical, peer-to-peer discussion with builders who are close to the work and willing to talk honestly about it. The goal is simple: share grounded perspectives from people building real systems, and make sense of how AI, data, and memory actually come together over time.https://tbd-podcast-vectorize.captivate.fmfalseepisodicnopodcast“How Alexander Hamilton Is Bridging AI Agents from Demo to Production”“How Alexander Hamilton Is Bridging AI Agents from Demo to Production”Episode Summary:

In this episode of AI Data and Memory, host Chris Latimer sits down with Alexander Hamilton to explore what it really takes to move AI agents from demos into production.

Alexander shares why autonomy must be earned through human oversight, not assumed—and how many agent systems fail when scaling beyond controlled environments. He also breaks down the shift from raw data-heavy approaches to structured, tool-driven systems that enable more reliable outcomes.

The conversation highlights how AI is reshaping product development, collapsing roles across engineering, design, and product into more unified, AI-powered workflows.

Key Takeaways:

  • Autonomy must be earned with oversight and testing
  • Production agents require reliability, not just capability
  • Structured tools beat raw data overload
  • AI is merging product, design, and engineering roles
  • Speed of building is no longer the bottleneck

Connect with Alexander Hamilton:


If this episode changed how you think about AI agents, share it with your team—and follow AI Data Memory Podcast for more insights from builders shaping the future 🚀

]]>Episode Summary:

In this episode of AI Data and Memory, host Chris Latimer sits down with Alexander Hamilton to explore what it really takes to move AI agents from demos into production.

Alexander shares why autonomy must be earned through human oversight, not assumed—and how many agent systems fail when scaling beyond controlled environments. He also breaks down the shift from raw data-heavy approaches to structured, tool-driven systems that enable more reliable outcomes.

The conversation highlights how AI is reshaping product development, collapsing roles across engineering, design, and product into more unified, AI-powered workflows.

Key Takeaways:

  • Autonomy must be earned with oversight and testing
  • Production agents require reliability, not just capability
  • Structured tools beat raw data overload
  • AI is merging product, design, and engineering roles
  • Speed of building is no longer the bottleneck

Connect with Alexander Hamilton:


If this episode changed how you think about AI agents, share it with your team—and follow AI Data Memory Podcast for more insights from builders shaping the future 🚀

]]>73497dad-5507-495c-b193-8b3605aa1789Wed, 25 Mar 2026 09:00:00 -040023:58falsefull“How Travis Frisinger Is Building Self-Evolving AI Systems”“How Travis Frisinger Is Building Self-Evolving AI Systems”Episode Summary:

In this episode of AI Data and Memory Podcast, host Chris Latimer sits down with Travis Frisinger, Head of Agentic AI at Eighth Light.

Travis shares how he built Hydroflow—a self-evolving AI system that can generate, test, and merge its own code. The conversation explores the rise of agentic AI, the role of memory and feedback loops, and why human-in-the-loop validation still matters.

They also dive into how AI is collapsing traditional team structures, making the “team of one” a real possibility, and reshaping the build vs. buy decision for modern software.

Key Takeaways:

  • Agentic AI is moving from assistance to autonomy
  • Self-evolving systems are already emerging
  • Human oversight remains critical
  • AI enables the “team of one”
  • Memory systems drive continuous improvement
  • Build vs. buy is being redefined

Connect with Travis Frisinger:


Listen Now & Subscribe:

Apple Podcasts, Spotify, Amazon Music, or wherever you get your podcasts.

If this episode changed how you think about AI and development, share it with your team—and follow AI Data Memory Podcast for more insights from builders shaping the future 🚀

]]>Episode Summary:

In this episode of AI Data and Memory Podcast, host Chris Latimer sits down with Travis Frisinger, Head of Agentic AI at Eighth Light.

Travis shares how he built Hydroflow—a self-evolving AI system that can generate, test, and merge its own code. The conversation explores the rise of agentic AI, the role of memory and feedback loops, and why human-in-the-loop validation still matters.

They also dive into how AI is collapsing traditional team structures, making the “team of one” a real possibility, and reshaping the build vs. buy decision for modern software.

Key Takeaways:

  • Agentic AI is moving from assistance to autonomy
  • Self-evolving systems are already emerging
  • Human oversight remains critical
  • AI enables the “team of one”
  • Memory systems drive continuous improvement
  • Build vs. buy is being redefined

Connect with Travis Frisinger:


Listen Now & Subscribe:

Apple Podcasts, Spotify, Amazon Music, or wherever you get your podcasts.

If this episode changed how you think about AI and development, share it with your team—and follow AI Data Memory Podcast for more insights from builders shaping the future 🚀

]]>31772a10-60f4-4208-9265-34ee33ff85d3Tue, 24 Mar 2026 09:00:00 -040016:56falsefull