I sit down with Seyon Indran from Concept Ventures, Head of Research at the UK’s largest pre-seed VC fund, to explore how AI and vibe coding are transforming the world of early-stage investing.

We dive into:

• How Seyon is building a founder assessment platform to identify exceptional founders earlier

• Why the rise of AI coding tools like Replit is changing who gets to build (and who gets funded)

• The shift from “gut feel” to data-driven founder evaluation

• What vibe coding means for solo founders, consumer apps, and the future of SaaS

• How VCs themselves are using AI to build internal tools and streamline workflows

If you’re curious about the intersection of venture capital, AI, and product-building, this one’s for you.

🔗 Connect with Seyon Indran:

• LinkedIn: https://www.linkedin.com/in/seyon-indran/

• X/Twitter: https://x.com/SeyonIndran

🎧Listen on Spotify/ iTunes. Watch on Youtube.

⏱️ Episode Highlights:
01:11 Concept Ventures and Investment Philosophy
02:24 Founder Assessment Platform
05:07 Building w/ Replit
06:41 Challenges and Solutions
13:42 Collaboration and Future Plans
18:27 Broader Trends in Vibe Coding
18:54 Impact on VC Investments
26:05 Closing Thoughts and Contact Information

I sit down with Matt Palmer, Head of Developer Relations at Replit, to dive into how AI is transforming the way we build software. Matt shares how we’ve gone from basic autocomplete to fully functional apps, and how Replit’s AI agent is empowering both new and experienced developers to build faster than ever.

We talk about “vibe coding,” using AI as a creative partner, how to ship software safely, and why the developer experience matters more than ever. Matt also breaks down how video content is changing the way we learn to code and shares his thoughts on where AI is making the biggest impact across industries.

Whether you're a seasoned dev, a curious beginner, or just someone who wants to build cool things with AI—this episode is packed with insights, tips, and inspiration.

🔗 Connect with Matt Palmer:

• Twitter/X: https://x.com/mattppal

• YouTube (Replit): https://www.youtube.com/@replit

• YouTube (Personal): https://www.youtube.com/@mattpalmer/videos

• Try Replit: https://replit.com/refer/mannybernabe
  

🎧Listen on Spotify/ iTunes. Watch on Youtube.

⏱️ Episode Highlights:

00:00 Intro
01:23 Replit's Evolution and AI Integration
02:43 The Power of Replit Agent
05:02 Getting Started with Vibe Coding
06:59 Advanced Tips for Vibe Coders
14:56 Empowering Non-Technical Users with AI
18:39 Deep Learning Course and Safe Shipping
24:25 AI's Impact Across Industries
25:45 Exploring AI Automation in Business
26:21 Boosting Developer Productivity with AI
27:26 AI's Impact on Different Experience Levels
28:53 Adoption of AI Tools in Traditional Organizations
30:31 Future Capabilities of AI in Development
35:45 The Role of Video in Developer Advocacy
42:48 Rapid Fire Questions and Insights

In this episode, we dive into NVIDIA's bold push to make Python a first-class citizen in its GPU ecosystem—what guest Charles Frye calls "The Year of CUDA Python." Charles, a developer advocate at Modal, recaps key takeaways from the 2025 NVIDIA GTC conference, spotlighting the growing centrality of Python across CUDA tooling, including the debut of Python-first libraries like cuTile and a fully reworked Python interface for CUTLASS.

We explore why NVIDIA is embracing Python for performance-critical development, how they’re addressing the challenges of Tensor Core programming, and what this all means for AI builders. Charles also breaks down NVIDIA’s hardware strategy shift—favoring scale-up over scale-out—and covers powerful new profiling tools like NSight Systems and Torch Profiler. Plus, we look at distributed inference innovations like Dynamo and how they intersect with platforms like Modal.

Whether you're GPU-curious or deep into LLM infrastructure, this conversation offers insight into how NVIDIA’s ecosystem is evolving—and why Python is at the center of it all.

Connect with Charles Frye

🧠 X (Twitter): @charles_irl
💻 Try Modal: https://modal.com

Listen now on Apple, Spotify, Overcast, and YouTube.

[CHAPTERS]

00:00 Start
01:35 The Year of CUDA Python
01:56 NVIDIA's Software Stack Evolution
03:01 Python's Growing Role in GPU Programming
06:11 CUTLASS and Python Integration
08:30 Tensor Cores and CUDA Complexity
12:02 Scaling Up vs. Scaling Out
18:01 AI Factory Concept
20:42 Hopper GPUs and New Generations
23:44 Memory-Bound Challenges in GPU Scaling
24:49 Performance and Tooling Insights
28:36 GPU Debugging Tools: Torch Profiler and NSight Systems
33:43 Dynamo: Distributed Inference for Language Models
39:37 Introducing the Modal Platform
43:10 How to Connect and Get Started with Modal

Jeff Croft is a two-time founder who has worked at companies like Microsoft and Palantir in business development, go-to-market, and revenue roles.

For the last seven years, he has focused on the emerging AI sector, particularly its industrial applications, and has tried to understand why so many digital transformation projects fall short.

Most recently, he was at Palantir and he offers a unique perspective on what it was like to be at one of the world's most important data companies during the boom in large language models.

You'll appreciate Jeff's point of view on go-to-market strategies and selling AI and machine learning to enterprises, what's different, and what's changed.

Jeff offers a unique perspective not just on the technology front in terms of AI, but also on how to get these technologies into production and what companies must do to truly move the needle on digital transformation efforts.

Listen now on Apple, Spotify, Overcast, and YouTube.

Where to find Jeff Croft:

• LinkedIn: https://www.linkedin.com/in/jeffreythomascroft/
  

In this episode, we cover:

02:35 Introduction to Gen AI in Enterprises
03:17 The Initial Rush and Pullback of Gen AI Adoption
03:43 Palantir's Unique Approach to Gen AI
05:18 Exploring the Practical Applications of Gen AI
07:29 The Challenge of Implementing Gen AI in Legacy Organizations
12:47 Tackling Boring, Expensive Problems with Gen AI
20:35 The Evolution of AI Adoption Strategies
22:27 The Impact of Bootcamps and Rapid Prototyping
27:00 Navigating the Strategy and Execution of AI Initiatives
31:30 The Future of AI Strategy and Consultancy
33:27 The Power of Embedded Teams in Business Strategy
34:13 The Evolution of Advisory Shops and Vendor Management
34:42 Challenges and Surprises in Deploying AI Solutions
35:15 The Skepticism and Transformation Dilemma in Companies
37:08 The Digital Era's Acceleration and Its Impact on Business
39:18 The Advantage of Early Adopters in Technological Shifts
43:01 Exploring the Enterprise Gen AI Landscape
45:56 The Unique Challenges and Opportunities for Startups in Gen AI
49:33 The Return to Relationship Selling in a Digital Age
54:41 Navigating New Ventures and the Importance of Relationships

Referenced:

• Big Bet Leadership by Rossman and McCaffrey: https://www.amazon.com/Big-Bet-Leadership-Transformation-Hyper-Digital/dp/1957588225

Adam King, enterprise leader at Medtronic, joins me LIVE to talk about finding practical AI applications for medical devices. Adam and I will chat about the process of finding AI use cases, the unique challenges of AI in MedTech and how/why business leaders can learn more about AI.

Listen now on Apple, Spotify, Overcast, and YouTube.

Where to find Adam King:

• LinkedIn: https://www.linkedin.com/in/adam-king-34265717/

In this episode, we cover:

• Ambulatory surgery centers—known as ASCs—are modern healthcare facilities focused on providing same-day surgical care, including diagnostic and preventive procedures. (Ambulatory Surgery Center Association)

• Use Cases

  • AI to route appropriate care for cardiovascular-related ASCs.

  • AI to optimize for total cost of care by leveraging ASCs and other lower cost options. Route patients to the best healthcare service.

  • Using AI to review imaging to find new insights to recommend interventions early enough to make a difference.

• You’ll need more to more efficiency optimize case management as you leverage lower cost options like ACSs.

• This data that is already being generated and captured. Challenges exist with data silos, combining structured and unstructured data.

• Digital twins (”simulation”) may be used to make better decisions, right interventions, and recommendations. Potential use for pharmacological (drugs) and anticipating outcomes from treatments and recommendations (i.e. diet changes, nutrition, etc.).

• Wearables (whoop, Oura Ring, Apple Watch) present a great opportunity. Great way to complete a picture of a patient. Challenges existing in integrating into healthcare systems seamlessly and safety.

• Key challenge in MedTech AI is measuring the effectiveness of treatment. Wearables might be helpful here.

• Key question is can we identify the right intervention at the right time. I.e. You have a very limited window to alert on a stroke.

• Consider how we are going to manage precision versus accuracy. Getting the right balance is key.

• Brittle models are another limitation of healthcare. Will need to model post production.

• For enterprises leaders looking to get started with AI, here are some options:

  • Massive Open Online Course (MOOC’s)

  • Executive Grad Programs (i.e., MIT executive class)

  • Internal company programs (Medtronic’s “AI Considerations for Management”)

  • See links above

• Regulatory is a major challenge. AI is new to industry. It’s REALLY new to the FDA.

• Annotation (or generating labels) for data, in particular imaging can be expensive and time consuming. Easy to under appreciate this part of the AI work flow.

• AI is about augmenting existing medical professionals. Needs to be framed as such. Key question is how much do they trust AI recommendations? Measuring trust will be important to new tools.

• Put the AI recommendation in the context of what is generating the prediction. “Show your work.” This will help build trust with the end user.

• The guiding principle of AI in MedTech, “What is the ultimate impact we want to have on the patient?”

Referenced:

AI in MedTech courses mentioned:

• Harvard AI in Healthcare (Harvard Medical School)

• Technology and Innovation Acceleration Program (MIT xPro)

• AI For Everyone (coursera)

• AI in Healthcare Specialization (coursera)

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Brandon Cosley joins me live on The Manny Bernabe Show to talk about the impact of AI on healthcare. Brandon is the Director of AI for a major health care insurance group. He is also an educator and speaker.

Listen now on Apple, Spotify, Overcast, and YouTube.

Where to find Brandon Cosley:

• LinkedIn: https://www.linkedin.com/in/brandoncosley/

In this episode, we cover:

00:01:30 — Promising AI healthcare use cases : “Health Map”
00:06:00 — Merging data sources, expert knowledge, user experience
10:00:00 — AI overpromised and under delivered in Healthcare
12:30:00 — Selecting the right utilization for AI platforms
00:14:45 — Explainable AI ⭐
00:18:37 — Intelligent Automation: AI powered medical notes, Rx auditing, etc. 🔥
00:22:37 — IoT based insurance policies
00:26:07 — Genetic variability
00:28:12 — Barriers to innovation in healthcare
00:30:00 — Web3, decentralization ⭐
00:33:16 — Data traceability in healthcare
00:36:20 — Palantir for healthcare
00:38:05 — Bias in Healthcare data
00:43:32 — AI governance in healthcare ⭐
00:47:34 — How innovation leaders and managers can get started
00:53:37 — Intelligent automation: technology + adoption ⭐

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