In this episode, hosts Matt Sharp and Holly Fake sit down with full-stack engineer Ethan Gardner to talk Core Web Vitals, why AI-generated code is quietly getting heavier and slower even as it ships faster, and what it takes to get cited by AI search tools rather than skipped over. Ethan also walks through a live demo using AI-assisted debugging in Chrome DevTools to diagnose a real performance issue in seconds.

Alongside the technical work, Ethan shares how he's paired an LLM with a traditional career coach, one of many AI productivity tools for work now reshaping how people think about growth, to figure out what parts of his work are truly bringing him fulfillment and where he'd been spending energy that wasn't serving him.

Two very different problems with the same lesson: AI can move fast, but it still takes a person to decide what's worth optimizing.

00:00 Introduction
01:06 Using AI as a career coach 
07:15 Core Web Vitals, explained 
10:54 Performance as a proxy for the bottom line 
12:59 Why AI-generated code is getting heavier 
21:26 Testing in CI/CD vs. the real world
28:53 Live demo: AI-assisted debugging 
34:52 Key takeaways

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In this episode, the two engineers join Matt Sharp and Holly Fake to unpack what AI pair programming looks like on their team and how AI agents have changed the practice.

Their conversation covers how their team splits navigator and driver roles, why the hardest part of AI implementation has shifted from writing syntax to getting the architecture right, and what happens when multiple people run the same prompt through Claude Code and get different results. AI lets junior engineers produce far more code than senior engineers can review, and ensembling might be part of the fix.

It's not unique to Flexion, and how this team is adapting could be worth a look for any organization feeling the same pressure.

Subscribe to the Learning Curve for real conversations about AI at work, AI implementation, and what it actually looks like to build with AI inside a technology company.

In this episode, Matt Sharp and Holly Fake sit down with Flexion software engineer Senai Mesfin to walk through the AI invoice automation proof of concept the team built for Hanor, a pork producer operating across six US states. Using AWS Bedrock and machine learning, the team built an AI invoice processing solution from scratch, and by the time the demo was done, the accuracy was sitting at 97%. 

Hanor was impressed, but what they really wanted was someone to rebuild their entire data landscape.

Senai speaks about why that pivot made sense, what it actually takes to get your data foundation into a shape where AI can do anything useful with it, and the personal AI tools he builds outside of work – a side of the story that says a lot about where practical AI is heading.

What began as a three-week brief became a five-month data engineering engagement and lasting client relationship. For organizations planning their first AI project, the Hanor story is a reminder that the data problem almost always comes before the AI solution.

Subscribe to the Learning Curve for real conversations about AI at work, AI implementation, and what it actually looks like to build with AI inside a technology company.

In this episode, Matt Sharp and Holly Fake sit down with Scott Hasse, one of Flexion's founding partners, to talk through how Flexion built its AI policy from the ground up. What triggered it, who was in the room, and what they were trying to get right.

Scott talks honestly about the internal moment that made a policy impossible to ignore, why they deliberately brought skeptics into the process, and how that made the end result something people could actually get behind. He also gets into why having clear AI governance guardrails is what lets teams move faster with AI adoption, not slower, and how the policy continues to hold up in practice today.

The approach Flexion took, from assembling diverse perspectives to defining clear guardrails, is one any company can learn from. If your organization is still figuring out its position on AI, this conversation is a practical place to start.

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Shawn shares how AI shows up throughout a sprint, from summarizing discussions and pulling out themes to supporting retros in tools like EasyRetro and improving meeting workflows with Zoom and Otter.ai. We discuss where AI saves time, where it still feels clunky, and why the fact that AI feels “not weird” anymore might be the clearest sign of its value.

The conversation also digs into small, embedded AI features that make a big impact, the importance of seamless integration for adoption, and how scrum masters can use ai to support team health, navigate difficult conversations, and reduce cognitive load. Plus, we hear a fun example of creative ai use outside of work, including trivia playlists that adapt to the flow of a game.

If you’re a scrum master, agile practitioner, or team lead curious about practical ai tools, large language models, and low-stakes ways to start integrating ai into your workflow, this episode offers grounded, real-world insights you can actually use. Subscribe to Learning Curve for honest conversations about ai, agile teams, and building technology that works for people.

We cover practical AI use cases, what AI agents are genuinely good at, where they still struggle, and what remains weird or surprisingly delightful. Kevin shares how he builds and evaluates AI agents, how different tools perform in different contexts, and how cognitive offloading can change the way teams work. From professional workflows to supercharging tabletop gaming as a dungeon master, this conversation explores real world ai beyond the buzzwords.

If you’re curious about integrating AI into your daily workflow, understanding multi-agent approaches, or learning how teams are using large language models in practice, this episode offers grounded insights you can actually apply. Subscribe to Learning Curve for honest conversations about practical AI, human-centered design, and building technology people actually want to use.

Host Matt Sharp and Holly Fake sit down with innovators working hands-on in regenerative systems, experimentation, and real-world problem solving. This episode moves beyond hype to unpack how applied artificial intelligence, learning through iteration, and human-centered design come together in complex environments like food systems and sustainability.

In this episode, you’ll hear insights on:
- Using AI in real-world applications
- Learning through experimentation and iteration
- Innovation outside traditional technology ecosystems
- Regenerative thinking and sustainable systems
- Why building with technology requires trust, curiosity, and adaptability

If you’re interested in AI, innovation, digital transformation, or how learning really happens in practice, this conversation offers an honest look at what it takes to build, test, and evolve ideas where the stakes are real.

Subscribe to Learning Curve for thoughtful conversations at the intersection of technology, learning, and innovation.