A YC founder turns a manual, low-IQ grind into an AI agent that finds creators, negotiates terms, and scales UGC—sometimes a little too far. What starts as a viral local-model demo becomes Stormy AI’s end-to-end engine for influencer marketing.

Fresh off YC Demo Day, Robert Lukoszko (Stormy AI) breaks down the pivot, the fundraising blitz, and how agencies are replacing hours of scrolling with autonomous outreach. We get into model-proof moats, why micro-creators beat celebrity accounts, and the coming wave of AI-generated influencers.

You’ll learn:

How a YC pivot formed around a founder’s own pain (and real demand)

The playbook: sourcing, outreach, negotiation, and QA with AI agents

Why “every better model makes us stronger” is the right moat test

Micro vs. macro creators: what actually converts in 2025

The next act: AI-native UGC, personalization, and brand-owned AI faces

Tactical Demo Day lessons: energy + social proof = signed checks

Chapters:

00:00 Introduction and Cold Open

00:13 Welcome to Lobster Talks

00:37 Demo Day Insights

03:05 The Journey of Stormy AI

04:39 From Viral Demos to YC Acceptance

07:35 Pivoting to Stormy AI

09:41 Automating Influencer Marketing

18:39 The Future of Influencer Marketing

19:50 AI and UGC: The Future of Influencer Marketing

20:11 The Rise of AI Influencers

20:41 AI-Generated Content vs. Human Content

21:05 The Makeup Industry and AI Influencers

22:26 The Shift to Micro-Influencers

23:23 Emerging Platforms and the Decline of Meta

25:22 The Future of AI in Content Creation

28:58 Preparing for an AI-Driven Future

31:46 Building a Moat in the AI Industry

34:54 The Importance of Vision and Customer Interaction

38:16 Stormy AI: Current and Future Plans

39:20 Conclusion and Call to Action

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

AI won’t live in chat. The future is headless agents doing real work — and calling humans only when it matters. Dexter Horthy, cofounder of HumanLayer, explains how agent-driven software actually ships.

In this fast, tactical deep-dive, we unpack HumanLayer’s origin story (from failed data tools to paid customers in a week), why frameworks lag real production apps, and the workflow that lets AI agents ship in complex codebases. We cover research-plan-implement loops, context engineering, team process, and how “specs become the new code.”

You’ll learn

- How HumanLayer emerged from a SQL “janitor” agent that needed human approvals

- The YC grind as a solo founder and closing first revenue in a week

- Why horizontal AI dev tools are hard — and how top 1% teams actually build

- Context engineering 101: research → plan → implement, and why it beats vibe coding

- How to review plans, not code, to scale quality across a team

Where headless agents win first — and why culture, not models, is the bottleneck.

Chapters

00:00 Introduction and Cold Open

00:21 Welcome to Lobster Talks

00:47 Guest Introduction and Background

01:12 Early Startup Journey

01:35 Building the AI Agent

02:18 Challenges and Pivots

03:52 Solo Founder Experience

05:28 The Importance of Data Tools

11:20 YC Experience and Revenue

13:59 Building for the 1% vs. 99%

23:50 Exploring New Ideas

25:18 Exploring Cloud Code SDK

25:34 Building Experiments with Claude

26:05 Challenges and Learnings

26:49 Insights from AI Engineering Talks

28:22 The Future of Coding with AI

30:22 Context Engineering and Workflow

32:19 Product Development and Customization

38:22 Scaling AI in Teams

48:10 Exciting Future Prospects

50:42 Conclusion and Farewell

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Show Notes & Resources:

• 14min Youtube video on wielding coding agents: https://hlyr.dev/ace

• Blog post version - https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/ace-fca.md

• Sign up for codelayer beta: https://humanlayer.dev/code

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

Founders closing rounds before lunch. Investors making handshake commitments in the hallway. A startup with $9M+ ARR and another with $15M ARR lighting up the room. YC Demo Day isn’t a show; it’s a marketplace where speed and execution decide everything.

In this fast, founder-first debrief, we break down what actually happened at the latest YC Demo Day: the subtle format changes (that matter), why the one-minute pitch is only the opener, and how deals really get done. We cover the batch’s AI/devtools tilt, the contrarian bets in defense and hardware, and why early traction remains the single best predictor at seed. We also unpack portfolio construction, conversion-rate dynamics inside YC, and what support looks like after the cameras stop.

You’ll learn:

- The real Demo Day mechanics: tranches, chat apps, long breaks for dealmaking

- Why some hot rounds are already full and what to do about it

- How to win YC deals: first-meeting decisions, 24–48h timelines, and prep work

- The $9M+ ARR investment we made—and why traction beats narrative

- Why we’re now backing deeptech/hardware (missiles vs drones, autonomous excavators)

- Portfolio strategy: aiming for the fund returner, not spray-and-pray

Chapters:

00:00 Introduction and Demo Day Overview

00:15 Behind the Scenes of Demo Day

01:03 Changes and Improvements at YC Demo Day

03:11 Investor Insights and Strategies

04:23 Engaging with YC Startups

06:35 The Importance of Early Traction

09:49 Demo Day Pitch Dynamics

13:42 Fundraising Conversations and Strategies

18:07 Lobster Capital's Investment Approach

21:54 Portfolio Construction and Future Prospects

27:02 AI and Dev Tools: A Crowded Space

27:39 Challenges in Identifying Winners

28:48 Customer Acquisition: The Key to Success

29:57 The Importance of Traction

33:32 Investing in Deep Tech and Hardware

39:24 Evaluating Flywheel's Potential

45:46 Supporting Startups Post-Demo Day

50:09 Looking Ahead: The Never-Ending Cycle

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

A surfer duct-tapes a phone to his board… and ends up saving enterprise couriers 32 seconds per stop. The last meter of delivery — not the last mile — is where the money is.

In this Lobster Talks episode, Ingo Boegemann, co-founder/CEO of truemetrics, breaks down how sensor fusion + mission intelligence turn messy building entrances, courtyards, and wrong pins into precise, repeatable delivery actions. We go deep on Europe vs. US GTM, GDPR constraints (and why the US may unlock even more value), landing whales like GLS, and the unscalable POC that unlocked scale.

You’ll learn

• Why “a generic geocode is just the starting point” — and how to map entrances that actually work
• The POC → pilot → rollout playbook (and why Truemetrics charges for POCs)
• How to integrate via SDK without slowing ops — and show value before engineering lifts a finger
• Europe vs. US: privacy ceilings, data linking, and why boots-on-the-ground still wins enterprise sales
• The real driver bottleneck: pressure, compliance, and turning best drivers’ tacit knowledge into software
• The long game: building a data moat for autonomous last-meter delivery

Chapters

00:00 Introduction to Last Meter Delivery

01:07 Meet Ingo Boegemann: The Journey to Truemetrics

02:15 From Surfboards to Sensor Fusion

05:04 Challenges and Realizations in Delivery Solutions

06:47 European vs. US Market Dynamics

11:42 The Path to Scaling in North America

19:02 Innovative Solutions for Delivery Logistics

21:37 How Truemetrics Technology Works

26:36 Magnetic Field Intensity and Machine Learning Models

27:29 Challenges in Courier Data Integration

29:52 Sales Process and Proof of Concept

32:08 Logistics Industry Vulnerabilities

35:42 Future of Autonomous Deliveries

38:30 Data-Driven Delivery Solutions

48:31 Closing Remarks and Contact Information

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

What if you could simulate human reactions — and know exactly how your customers, investors, or audience will respond before you act? James He is building precisely that with Artificial Societies.

In this episode, we dive deep with James, YC W25 founder of Artificial Societies — a wildly ambitious startup that simulates entire groups of humans using AI personas to predict how messages spread, markets react, and products succeed (or fail). From simulating 1,000 VCs to get into YC… to replacing $20,000 market research surveys, this conversation is a masterclass in founder execution, behavioral science, and AI-first GTM strategy.

What you’ll learn:

• How Artificial Societies works (and how founders are really using it today)

• Why traditional A/B testing and market research are broken

• Product-led growth lessons from 15,000+ activated users

• The surprising YC tricks James used to get into the batch

• Practical ways AI can upgrade your messaging, GTM, and PMF search

• The future of simulating entire economies (and why we’re not there yet)

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

Chapters:

00:00 Simulating Investors: The Early Days

00:27 Introducing Artificial Societies

00:48 Journey to YC and Beyond

01:06 How Artificial Societies Works

03:02 The Science Behind Social Influence

05:27 Technical Insights and Applications

17:28 James' Personal Journey

22:29 Building and Growing the Startup

24:50 Using Internal Tools for Optimization

27:38 The Evolution of A/B Testing with AI

29:41 Challenges in Market Research

33:14 AI's Role in Market Research

34:40 Balancing Customer Feedback and Product Vision

42:55 Future Predictions and AI Capabilities

47:48 Closing Remarks and Final Thoughts

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@UCahCIZ9KfmeK4rLosGNYpdg

My other YouTube Channel: https://www.youtube.com/@GJarrosson

YC just dropped its latest Requests for Startups — and they’re not just ideas, they’re roadmaps to the future. From AI-native enterprise software to 10-person $100B companies, these signals reveal where the smartest founders (and investors) should be looking.

In this episode of Lobster Talks, Laurie and I break down Y Combinator’s newest RFS — from retraining workers for the AI economy to video generation as a computing primitive. We debate the real opportunities, the traps, and how these trends could reshape the startup ecosystem.

What you’ll learn:

- Why YC’s RFS is one of the best ways to predict upcoming demo day winners

- The massive opportunity in retraining workers for the AI economy

- Why video generation is shifting from an output to a primitive

- How AI-native startups could disrupt giants like Salesforce

- Whether 10-person $100B companies are actually possible

- The risks, politics, and second-order effects of LLMs in government

Chapters:

00:00 Introduction: The Value of Customer Money

00:26 Y Combinator's Request for Startups

01:14 AI and Worker Retraining

02:30 The Future of Semiconductors and Deep Tech

04:34 AR, VR, and AI in Training

09:02 Video Generation: The Next Frontier

16:12 The First 10-Person $100 Billion Company

22:31 The Future of Small Teams and Fundraising

24:03 Challenges and Opportunities in Multi-Agent Systems

28:12 AI Native Enterprise Software

34:39 Using AI to Revolutionize Government Consulting

41:24 Conclusion and Upcoming Events

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

What if you built a tool so powerful, it became the #1 repo on GitHub—twice?

That’s exactly what Daniel Farrell did with Onlook, an open-source AI-powered visual editor for code. From crashing Chrome extensions to crashing Hacker News, Daniel takes us through how he and his co-founder built momentum, community, and virality from nothing.

In this fast, founder-first conversation, we cover:

• How Onlook went from prototype to #1 GitHub repo

• Why Hacker News beat Product Hunt for traction

• The real benefits and tradeoffs of going open source

• How open source GTM can win enterprise customers

• What most people misunderstand about AI and design• Why taste, story, and community still matter more than code

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

00:00 The Most Trending Repo in the World

00:16 Meet Daniel Farrell: Co-Founder of Onlook

00:59 The Vision Behind Onlook

01:57 Building the Prototype and Early Challenges

04:55 Finding the Perfect Co-Founder

11:57 The Open Source Journey

14:49 The Hacker News Effect

16:47 Global Impact and Community Building

19:40 The Importance of Open Source

24:31 Challenges of Maintaining a Community

25:00 Open Source Success Stories

25:21 The Super Base Playbook

26:33 Advantages of Open Source for Enterprises

33:42 Impact of AI on Design and Marketing Teams

42:25 Future Bets and Bold Predictions

48:00 Where to Find Onlook and Final Thoughts

Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson

What if launching a tech startup was as easy as filling out a form?

Ben Collins, co-founder of Woz, joins Gabriel to unpack how his AI platform helps anyone — even non-technical founders — build full-stack software businesses. From betting against vertical SaaS to designing an AI cofounder you can trust, this conversation is a tactical deep dive into where startup creation is headed.

🔍 In this episode:

Why Woz pivoted away from a holding company model

What most AI-first code tools get totally wrong

How mobile-first enables higher quality standards

A unique approach to betting on future AI capability curves

The real reason most AI startups are failing users

📌 Watch now and subscribe for more smart, fast, founder-first interviews.

⏱ Chapters:

00:00 Introduction to Scaling and Bottlenecks

00:18 Introducing Ben Collins and Woz

01:04 The Vision Behind Woz

03:43 The Founding Story

06:06 The Shift to a Platform Approach

08:53 Navigating the Competitive Landscape

12:39 Philosophical and Strategic Insights

17:02 The Mobile-First Strategy

25:34 The Journey to Product-Market Fit

27:15 Navigating Rapid Technological Changes

32:21 The Impact of Y Combinator

35:00 AI's Role in Scaling Service Businesses

36:42 Challenges for Non-Technical Founders

39:13 Bold Predictions and Future Bets

45:02 Exciting Announcements and Closing Thoughts

🎧 Listen on the go:

Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP

Apple: https://apple.co/4cZ8RMm

My other YouTube Channel: https://www.youtube.com/@GJarrosson