Uploaded March 2026 | Updated September 2026, 2 weeks ago
๐ Connect with Seth DeLand on LinkedIn: linkedin.com/in/seth-deland
In this episode, we sit down with Seth DeLand, Product Manager for Generative AI at MathWorks, to explore how agentic AI is transforming engineering workflows in 2026 and beyond.
We discuss the evolution from classical machine learning (gradient descent, optimization, deep learning) to large language models and agentic AI systems that can call tools, write code, refactor models, and assist across the full engineering lifecycle.
Seth shares how engineers can move from manual implementation toward higher-level problem definition, requirements engineering, architecture design, and AI-assisted validation. We also explore MATLAB Copilot, Simulink Copilot (beta), and the Model Context Protocol (MCP), and how these tools integrate generative AI directly into real engineering workflows.
Tune in!
ONLINE PRESENCE
๐ My website - jousefmurad.com
๐ My weekly science newsletter - jousef.substack.com
๐ธ Instagram - instagram.com/jousefmrd
๐ฆ Twitter - https://x.com/Jousefm2
CONTACT:
โโโโโโโโ
If you need help or have any questions or want to collaborate feel free to reach out to me via email: support@jousefmurad.com
#agenticai
#engineering
#matlab
Disclaimer: Some of these links are affiliate links that make me earn a small commission when you make a purchase at no additional cost.
๐ Connect with Seth DeLand on LinkedIn: linkedin.com/in/seth-deland
In this episode, we sit down with Seth DeLand, Product Manager for Generative AI at MathWorks, to explore how agentic AI is transforming engineering workflows in 2026 and beyond.
We discuss the evolution from classical machine learning (gradient descent, optimization, deep learning) to large language models and agentic AI systems that can call tools, write code, refactor models, and assist across the full engineering lifecycle.
Seth shares how engineers can move from manual implementation toward higher-level problem definition, requirements engineering, architecture design, and AI-assisted validation. We also explore MATLAB Copilot, Simulink Copilot (beta), and the Model Context Protocol (MCP), and how these tools integrate generative AI directly into real engineering workflows.
Tune in!
ONLINE PRESENCE
๐ My website - jousefmurad.com
๐ My weekly science newsletter - jousef.substack.com
๐ธ Instagram - instagram.com/jousefmrd
๐ฆ Twitter - https://x.com/Jousefm2
CONTACT:
โโโโโโโโ
If you need help or have any questions or want to collaborate feel free to reach out to me via email: support@jousefmurad.com
#agenticai
#engineering
#matlab
Disclaimer: Some of these links are affiliate links that make me earn a small commission when you make a purchase at no additional cost.







![My First App Using Lovable - Mass Spring Damper Simulator ๐ฅ
Try it here: https://msd.jousefmurad.com/
Itโs an interactive 1-DOF system with base excitation. Tweak parameters and watch the animation update in real time. Built for engineers, students, and anyone who likes turning math into motion.
What you can control:
- Mass m [kg]
- Spring k [N/m]
- Damping c [Nยทs/m]
- Amplitude A [m]
- Frequency f [Hz]
- Rest length L0 [m]
- Block geometry h [m], a [m]
- Simulation time tF [s] and frame rate [fps]
Default setup Iโm using: m=1740, k=57200, c=5500, A=0.19, f=1.44, L0=0.70, h=0.49, a=0.80, tF=15, fps=60
Why Iโm excited:
- Instant visual feedback while you tune parameters
- Clean UI with Play and Reset for quick tests
- Great for intuition building and quick demos
Try it out here: https://msd.jousefmurad.com/
#engineering #lovable My First App Using Lovable - Mass Spring Damper Simulator ๐ฅ](https://i.ytimg.com/vi/qK66_yRsXr8/mqdefault.jpg)


