Podcast
First Commit
Hosted by Laura Hamilton · EN
First Commit is where founders tell the story from the start: what they saw, what they risked, and what it actually took to get from zero to something real.
13episodes
Episodes
Newest firstAll episodes
Why Math Might Be the Last Language AI Hasn't Conquered | Carina Hong, Axiom
3d ago00:31:55Tap to summarizeMath might be the last programming language AI hasn't conquered.In this episode of First Commit, Eliya Elon and Laura Hamilton sit down with Carina Hong, founder and CEO of Axiom, the company building an AI mathematician — and, fresh off a $200M Series A at a $1.6B valuation led by Menlo Ventures, one of the most-watched bets on "verified AI." Hong leaves a Stanford PhD in March 2025 to chase a specific window: reasoning models maturing, coding capability compounding, and the formal-proof language Lean finally becoming robust enough for industry-scale work. While frontier labs skip formal math as too data-scarce to bother with, she bets the opposite — that math is special, that it transfers the way code does, and that a system which proves its own work is worth building long before anyone is paying attention.Join us as we explore:Formal vs. informal AI: A model that answers in Lean can be verified by simply running the program — Hong explains why that guarantee changes everything you can build on top of it.Where proofs pay off: Chip design, software verification, and cryptography get tractable the moment a plain-English spec becomes something you can actually prove.Research taste as strategy: Hong hires across mathematicians, formal-methods veterans, and young high-agency engineers, and makes the case that taste is the one thing a research org can't get wrong.The founder's obsession: The "I couldn't do anything else" moment, plus her theory on why cheap capital fragments a field instead of cracking it.Follow Notable Capital:Find Us On X: @notablecap Find Us On LinkedIn: @Notable Capital Visit Our Website: https://www.notablecap.com/
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Building AI Agents Enterprises Will Actually Trust | Mayada Gonimah, Thread AI
2w ago00:30:54Tap to summarizeWhat does it take to build AI agents that enterprises will actually trust with their most critical workflows — and what happens when the company building those agents has no security clearance to see them deployed?In this episode of First Commit, Eliya Elon and Laura Hamilton sit down with Mayada Gonimah, co-founder and CTO of Thread AI. Mayada spent her career at the deepest end of enterprise infrastructure — building prime brokerage systems at Goldman Sachs, PCI-compliant payment rails at the New York Times, and software for the Department of Defense at Palantir — without always having the clearance to see her own work run in the field. That constraint forced a discipline that now defines Thread AI: build infrastructure so reliable it functions correctly wherever it's deployed, under whatever conditions, whether you're watching or not. Thread AI builds AI-native orchestration for enterprises running mission-critical, multi-step agentic workflows — think end-to-end invoice processing, insurance claim routing, compliance monitoring — in financial services, healthcare, and public safety.Join us as we explore:The orchestration gap: Why tools like Temporal solved durability but not non-determinism — and why that gap became Thread AI's founding thesis in spring 2023.Selling to enterprises early: How Thread AI closed contracts with some of the largest regulated organizations in the world before product-market fit was fully established — and why multi-cloud and compliance weren't optional.The "applied AI" role: What Thread AI's forward-deployed applied AI engineers actually do, why strong distributed systems fundamentals beat AI hype on the hiring scorecard, and why the last 10% of production always wins.Follow Notable Capital: Find Us On X: @notablecap Find Us On LinkedIn: @Notable Capital Visit Our Website: https://www.notablecap.com/ Subscribe on Spotify: https://open.spotify.com/show/43j3cMkcKIgHuXu1tFHWpz?si=a326d0cd016f47c0 Subscribe on Apple Podcast: https://podcasts.apple.com/us/podcast/notable-perspectives/id1392649094
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On Friction, Conviction, and Knowing When to Join Instead of Found | Dor Fledel, Anthropic
Jun 1200:51:33Tap to summarizeWhat does it actually take to validate a startup idea — and why should you make it harder on yourself?In this episode of First Commit, Eliya Elon sits down with Dor Fledel, co-founder and CEO of Spera Security. Built in just under 18 months, Spera — an identity security posture management company — was acquired by Okta for a reported ~$100M. Dor then led Okta's SecureAI, PAM, and ISPM product lines before recently joining Anthropic to help enterprises adopt AI securely. A Talpiot program alumnus and Unit 8200 veteran, Dor brings an intelligence-trained rigor to how he thinks about building, validating, and committing.Join us as we explore:Friction as a Feature: Why deliberately making it harder for customers, co-founders, and employees to say yes is actually the sharpest signal you have about whether your idea is real.Meeting People Where They Are: How Dor learned to stop pitching identity security to people who were already convinced — and start positioning for the world they've already heard ten times.The Personal Stack: Why the hardest co-founder conversation often has nothing to do with equity or roles — and everything to do with whether someone's partner will move across an ocean in 18 months.Follow Notable Capital: Find Us On X: @notablecap Find Us On LinkedIn: @Notable Capital Visit Our Website: https://www.notablecap.com/
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The Real Bottleneck in AI Agents Isn't the Tech | Regina Lin, ThirdLayer
Jun 300:40:31Tap to summarizeWhat if your browser knew your entire workday — and could quietly handle the parts that drain your focus?In this episode of First Commit, Eliya Elon and Laura Hamilton sit down with Regina Lin, co-founder and CEO of ThirdLayer, the company building Dex — an AI workspace that lives inside Chrome and acts as a true knowledge worker for the browser. Regina started building Dex as a college side project with her co-founder Kevin while juggling a math degree and years of competitive piano performance — originally just trying to get GPT-4o to order DoorDash at midnight. The insight that it could replace entire categories of manual, browser-bound work crystallized when they snuck into a Boston AI conference with fake lanyards and ran into someone managing hundreds of UiPath bots. What Dex became is a hybrid agent that pulls context directly from the browser while executing actions through precision tool calling — not screen scraping, not MCP — built for the knowledge workers who spend every minute of their day living in tabs.Join us as we explore:ICP Discovery: How Regina landed on go-to-market professionals as the core user — people whose entire workday is implicit, scattered across tabs, with no to-do list because their to-dos are the tabs.Hybrid Agent Architecture: Why Dex combines browser context with custom tool calling for maximum accuracy — and why the "pure browser agent" framing misses what actually makes it fast.The Education Problem: Why giving people agents isn't enough — and how human creativity, not technology, turns out to be the real bottleneck to adoption.Lessons from Piano: How eight-hour practice days, public masterclass critiques, and improvising a concerto ending she'd forgotten trained Regina for the pressure of building in real time.Follow Notable Capital: Find Us On X: @notablecap Find Us On LinkedIn: @Notable Capital Visit Our Website: https://www.notablecap.com/ Watch on YouTube: https://www.youtube.com/@notablecapital
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E67: Monitoring the Probabilistic Stack with Alexis Gauba (Raindrop)
Feb 2600:34:56Tap to summarizeThis week, we’re joined by Alexis Gauba, Co-Founder of Raindrop, an AI native observability platform built for agents in production.Alexis breaks down why operating agents is fundamentally different from monitoring traditional software. As systems shift from deterministic code to probabilistic behavior, dashboards alone are not enough. Teams need to detect unknown issues, track signals like forgetting, and understand long agent trajectories across millions of AI events.We discuss why agent observability has become essential over the past year, what makes agent infrastructure distinct from prior platform shifts, and when internal tooling stops scaling. Alexis also explains Raindrop’s approach to production monitoring, combining explicit signals with automated detection to help teams not just find issues, but fix them.Episode chapters:2:05 - Founding Raindrop3:54 - Building with Close Friends5:44 - What Raindrop Actually Does7:45 - The Reliability Challenge of Agents9:55 - Monitoring Agents at Scale14:45 - Experiencing the Pain Firsthand18:00 - The Rise of Agent Infrastructure22:17 - Internal AI Use Cases24:07 - Hiring for Initiative and Ownership28:30 - The Power of Multitasking32:06 - Quick Fire Round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata’s AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.
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E66: From Alerts to Action with Anish Agarwal (Traversal)
Feb 1200:36:35Tap to summarizeThis week, we’re joined by Anish Agarwal, CEO of Traversal, an AI-native site reliability platform helping teams detect, diagnose, and remediate incidents before they spiral into prolonged downtime.Anish shares how Traversal is tackling the full lifecycle of reliability work: identifying what caused an incident, determining which signals actually mattered during alert triage, and helping teams plan how their infrastructure should evolve over time. As modern systems grow more complex, spanning microservices, serverless, and multi-cloud environments, the surface area for failure continues to expand, especially as AI-generated code accelerates change faster than humans can reasonably keep up.We talk about why observability has produced some of the largest outcomes in software, why the traditional dashboard-first model is breaking down, and why the next generation of tooling needs agents that can search, reason over, and act on observability data, not just display it. Anish also breaks down where “self-healing systems” are real today, where expectations need to be reset, and why many AI-SRE products risk building faster horses instead of rethinking the experience entirely.Episode chapters:1:50 — Anish’s background and early building2:30 — The origins of SRE and why it emerged8:40 — Research-driven thinking in product and engineering10:07 — Building for large enterprise environments13:00 — AI-driven code and the growing infrastructure surface area16:11 — Preparing infrastructure for an AI-native future20:10 — Fear, trust, and operating critical systems23:05 — From detection to automated action24:20 — How the SRE role is changing26:05 — Hiring and building the right team29:08 — Raising capital from Sequoia and KP32:38 — Reflections and lessons learned35:50 — Quick-fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata’s AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.
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E65: Infrastructure for AI-First Teams with Ivan Burazin (Daytona)
Jan 1400:32:36Tap to summarizeThis week, we’re joined by Ivan Burazin, co-founder of Daytona - a company rethinking developer environments for an AI-native world.We talk about how Daytona creates real value for developers, why the most advanced agent companies are emerging bottom up, and the idea that agents should be treated as first class users with reliable access to compute. Ivan also shares some of Daytona’s most demanding use cases including AI scientists in chemistry and pharma running agents inside massive sandboxes with hundreds of CPUs.We also cover what reliability looks like when customers define success as whether the system works at all, how Daytona maintains sub 90 millisecond latency while spinning up millions of environments per day, and how they support always on usage with no fixed schedule. Finally, we dig into go to market lessons from putting engineers on the front lines early to why Daytona prioritizes in person engagement and intentional events as the most authentic way to build trust with developers.Episode chapters:1:43 – Market timing and why now3:18 – Building for developers and dev tools4:55 – Global developer communities7:55 – Computers as infrastructure for agents10:32 – Product-led growth12:17 – Enterprise use cases and adoption16:25 – Managing cost, performance, and latency19:20 – Hiring for resiliency at scale20:40 – Internal AI use cases at Daytona24:25 – Creating a bottom-up go-to-market motion27:42 – Hiring and scaling developer relations29:55 – Partnerships and ecosystem strategy31:00 – Quick-fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata’s AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.
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E64: Reinventing How Teams Find Talent with Ishan Gupta (Juicebox)
Dec 300:29:23Tap to summarizeThis week, we’re joined by Ishan Gupta, co-founder of Juicebox — a company redefining how recruiting works in an AI-native world.Ishan shares how they pivoted from earlier ideas to focus on the highest value part of hiring: identifying the right people and getting them into process. With Juicebox, you describe what you want in natural language and the platform searches more than 800 million professional profiles, surfaces the best matches, and engages candidates through recruiting agents.Their breakout feature, Autopilot, uses LLMs to semantically evaluate and stack rank candidates based on nuanced criteria, which quickly drove organic growth and strong PMF. We talk about why their data makes the product so sticky, how they see recruiting evolving over the next five years, what led them to raise their Series A, and the culture they are building around moving fast and being intellectually honest.Ishan also shares what they are building next with their memory layer, which will make it clear what the AI is learning over time and how it improves future searches.Episode chapters:1:44 - Competitive programming4:35 - Choosing the company name5:55 - YC and the pivot8:00 - How Juicebox differs from traditional recruiting9:51 - The killer feature14:30 - What makes the product sticky17:15 - Is recruiting a zero sum game19:10 - The macro view of hiring22:05 - Raising a later Series A24:10 - Product expansion26:45 - Quick fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata’s AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.
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E63: Building the Agent Marketplace Layer with Henry and Ani (Smithery)
Nov 400:34:28Tap to summarizeThis week’s guests are Henry and Ani, co-founders of Smithery. Smithery is building the marketplace and runtime for MCP, making it easier for developers to distribute, discover, and monetize tools in the emerging agent ecosystem. Smithery provides a platform to connect your repo, integrate with CI/CD, and handle hosting for servers seamlessly. MCP shines when agents become more general, but it only scales if demand grows for more connections between tools and systems. Drawing from their experiences at Browserbase, Whatnot, and prior AI startups, Ani and Henry saw the need for standardization across the agent stack and set out to build the distribution and trust layer for MCP. We cover what early traction they’re seeing from developers, how they’re approaching payments and identity to reduce configuration friction, and what the broader shift toward open, composable AI infrastructure means for the future of agents. Episode chapters: 1:51 — Getting into AI 5:30 — The Origin of Smithery 7:40 — Building the Supply Side 9:10 — Why Smithery Is the Easiest Way to Discover MCPs 13:30 — Agent Authentication and Identity 17:08 — The Most Popular MCPs 19:50 — MCP and the Principle of Separation of Concerns 22:50 — How AI Is Being Used Today 25:57 — Defining Success as Founders 31:45 — Quire Fire RoundThis episode is brought to you by Grata, the world's leading deal sourcing platform. Our AI-powered search, investment-grade data, and intuitive workflows give you the edge needed to find and win deals in your industry. Visit grata.com to schedule a demo today.Fresh out of Y Combinator’s Summer batch, Overlap is an AI-driven app that uses LLMs to curate the best moments from podcast episodes. Imagine having a smart assistant who reads through every podcast transcript, finds the best parts or parts most relevant to your search, and strings them together to form a new curated stream of content - that is what Overlap does. Podcasts are an exponentially growing source of unique information. Make use of it! Check out Overlap 2.0 on the App Store today.
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E62: Reinventing Consumer AI Through Generative Video with Dean Leitersdorf (Decart)
Oct 1600:42:12Tap to summarizeThis week’s guest is Dean Leitersdorf, CEO of Decart. Decart is a vertically integrated AI research lab building some of the world’s most advanced audio and video models, including Mirage, a real-time generative video system where users can transform themselves or their environments instantly.We dive into Dean’s path from finishing his PhD in computer science at 21 to founding Decart with the ambition of creating a true “before-and-after” company in AI. We cover how Decart evolved from GPU optimization to leading-edge video models, why Mirage has attracted massive adoption in advertising and ecommerce, and what it means to build entirely new consumer experiences on the internet. We also discuss the broader enterprise interest in real-time video, how Decart balances continuous consumer iteration with lab-first research, and why Dean believes this is one of the few moments in history where founders can truly build trillion-dollar businesses.Episode Chapters: 2:10 — From Israel to Palo Alto4:22 — What Dean is running toward6:15 — Building for the future of AI9:40 — How people use Decart14:05 — Building a model from scratch19:08 — Who adopts these models first24:05 — Decart’s focus areas27:06 — Attracting world-class talent30:00 — Choosing to integrate or partner34:04 — Thinking about consumer impact39:38 — Quick fire roundThis episode is brought to you by Grata, the world's leading deal sourcing platform. Our AI-powered search, investment-grade data, and intuitive workflows give you the edge needed to find and win deals in your industry. Visit grata.com to schedule a demo today.Fresh out of Y Combinator’s Summer batch, Overlap is an AI-driven app that uses LLMs to curate the best moments from podcast episodes. Imagine having a smart assistant who reads through every podcast transcript, finds the best parts or parts most relevant to your search, and strings them together to form a new curated stream of content - that is what Overlap does. Podcasts are an exponentially growing source of unique information. Make use of it! Check out Overlap 2.0 on the App Store today.
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