AI writes code and you still review it before it ships. AI makes clips now, and someone still has to assemble them into something you'd actually publish. That's the layer cofounders Saksham and Ishan are building at Cardboard.

Two engineers who met in school and taught themselves filmmaking in Bangalore by studying Apple's launch films. Saksham describes Cardboard simply: it's Cursor for video editing.

There's no good VS Code for video, so they're building one. You throw your clips in and give it a goal, and it hands back a first cut in minutes. Brief it the way you'd brief a junior editor.

Video isn't one problem, which is what makes the editor core hard. Screen recordings need different ML pipelines than talking heads or sports footage, so a routing model decides where each query goes.

They don't want to be an AI feature inside the existing editor. They want to build the editor itself.

‍🎙️ Saksham Aggarwal, CEO & Founder, Cardboard on Fondo START 

‍

00:45 The Cursor comparison, and why editing is the product
01:10 Learning filmmaking by studying and recreating Apple's launch films in Bangalore
02:30 The YC launch that reached roughly half a million views
03:15 What WebGPU and multimodal models unlocked
04:40 Why they want to own the whole editor instead of plugging into Final Cut
05:10 AI-generated clips are a new camera—and they still need editing
05:50 Why video is one of AI's hardest multimodal problems
07:00 Briefing Cardboard like a junior editor
07:40 Why they stopped selling launch videos to focus on product

‍

Check out www.usecardboard.com

That was Jayram Palamadai's job at CERN, where he worked as a distributed computing engineer getting collider data out to physicists. At that scale you stop thinking about files and start questioning the protocol underneath them, so he designed a different architecture.

That became Byteport.

It's built on DART, a proprietary transport protocol developed by engineers out of CERN. TCP cuts its congestion window the moment it detects packet loss and then recovers slowly; DART holds near-constant throughput instead, roughly 98 Mbps on a 100 Mbps connection whatever the latency, jitter, or loss. In the field, Byteport has hit speeds up to 1,500× faster than TCP.

It runs wherever critical infrastructure runs: on-premises, air-gapped, hybrid, cloud.

And in those places a failed transfer isn't a file that shows up late. It's a training run stalled at the data-loading step, a satellite pass that comes and goes, a drone flying without the map it was supposed to have.

Byteport runs in all of them. It holds up in intermittent connectivity, high latency, and contested RF. DART runs mission-critical networks spanning land, air, sea, and space.

‍🎙️ Jay Palamadai, Founder, Byteport on Fondo START

‍

00:59  Byteport's faster file transfers
01:13 Moving 30 PB at CERN
01:24 Rethinking internet architecture
01:57 Launch day reflections
02:50 Driving to YC office hours
03:17 Getting into YC
03:45 The YC interview
04:35 What's next for Byteport

‍

Check out byteport.com

What stayed with him was the way the meeting ran. The people across the table were curious rather than performing, and they wanted to understand what he was building.

That's carried into how Pioneer operates now. The team preps hard for every 30-minute founder meeting, several founder-investors join each call, and they'll tell a founder what they like and what they don't, because they've sat on the other side of that table and know how it feels to leave an investor meeting with "nothing".

Then they hand the evaluation back.

Pioneer sends a post-meeting survey asking founders how the conversation went. Many of them say Pioneer Fund understood their business better than anyone else they met while fundraising, despite some of those founders still getting a "no".

Today Pioneer is backed by more than 500 Y Combinator alumni serving as Venture Partners, with 730 portfolio companies and 1,500+ portfolio founders behind it: a network of founders who stay involved long after the investment.

It's still growing. VP spots mostly come through referral. And if you don't have one, reach out to Gaurav

‍

🎙️ Gaurav Maken, Community Partner, Pioneer Fund on Fondo START pod

‍

01:47 — How PG's essays led him to YC
02:04 — Instacart in 2015, then a grocery startup
02:22 — What the 2020 shutdown taught him
02:52 — Starting a mental performance clinic for founders
03:10 — The Demo Day meeting 
03:26 — 500+ venture partners, 1,500+ backed founders
04:50 — The prep behind every 30-minute founder call
05:59 — The survey Pioneer sends after every meeting
06:47 — Expert hours
08:13 — How Jason built a system for investors
09:36 — What it takes to become a Pioneer Fund VP

‍

Check out www.pioneerfund.vc

Then comes everything else: authentication, databases, hosting, monitoring, email, analytics, real-time infrastructure. None of it makes the demo, and every team rebuilds it anyway.

Aram Shatakhtsyan spent nearly a decade scaling a startup and kept hitting the same thing. Too much engineering went into infrastructure that looked nearly identical from one product to the next.

Modelence is a production-ready framework and cloud platform for the era of AI-generated software, where developers spend their time on product logic instead of rebuilding the same foundation.

Generate a full-stack app from a prompt. Auth, database, and production deploys wired in from day one. Inspect, edit, and own the code.

Deploy it to Modelence Cloud with production infrastructure, monitoring, scaling, and a custom domain, without assembling a stack of separate services.

Prototypes are easy. Production isn't.

We filmed this episode with Aram on January 23. Since then, the AI App Builder hit #2 Product of the Day on Product Hunt, and their new Mobile Builder launched in July.

"Build production-ready apps with AI"

‍

🎙️ Aram Shatakhtsyan, Co-Founder & CEO, Modelence on Fondo START pod

‍
00:57 — The scaling challenges that inspired Modelence

01:22 — Why every startup keeps rebuilding the same infrastructure

02:01 — Why TypeScript became the foundation for AI-native development

03:06 — Building the TypeScript equivalent of Ruby on Rails

03:45 — Why infrastructure matters more than another AI app builder

04:18 — The difference between prototyping and production software

05:14 — Why today's frameworks weren't designed for AI coding agents

06:03 — Designing AI systems with guardrails instead of prompts

07:16 — The vision for production-ready AI applications

08:07 — What Aram hopes developers build with Modelence next

‍

Check out modelence.com

The bottleneck isn't serving customers.
It's everything that happens after.

Loans.
Compliance.
Fraud investigations.
Customer operations.

The work keeps growing.
Headcount doesn't.

JPMorgan has tens of thousands of analysts.
A community bank has a handful.

That's the gap Fenrock AI is closing.

Fenrock is building the first AI workspace for the banking back office.

Built alongside U.S. bank CEOs.
Designed for banks.
One workspace for all of it.

AI gathers the information.
Organizes the evidence.
Drafts the analysis.
Prepares the report.

The analyst reviews.
The analyst decides.

No rip-and-replace.
No data migration.
 
Just an AI workspace embedded into existing workflows.

Every action is documented with bullet-proof audit logs.
Built-in regulatory guidance.
Reporting designed for examiners and boards.

An analyst clears about 10 alerts a day.
Fenrock is built for 20x that.

Not by replacing people.
By giving them capacity they've never had.

Banks process more.
Pass their exams.
Improve efficiency ratios.
And keep serving the communities that depend on them.

🎙️ Charu Sharma & Michael Mernagh, Co-Founders, Fenrock AI on Fondo START pod

01:16 — Fenrock AI's vision: AI agents for the banking back office

01:57 — Customer discovery leads from financial crime to banking compliance 

02:33 — Why small and midsize banks became the focus 

03:02 — Charu's early life and the experience that shaped her resilience 

03:52 — Building multiple startups before founding Fenrock 

04:18 — Michael's background building privacy-preserving machine learning at Apple 

05:07 — Finding the right co-founder and choosing a long-term mission 

06:03 — Pivoting the company and using YC to accelerate execution 

07:37 — How YC raised the team's ambition and execution speed 

08:36 — Building the first AI workspace for the banking back office 

09:17 — Helping small banks meet regulatory expectations with AI 

10:24 — Human-in-the-loop workflows, audit trails, and early customer impact

Check out fenrock.ai

But every instrument in the lab ships its own, and someone has to learn all of them.

Researchers don't want to memorize instrument interfaces.

They want experiments to run.
They want answers.
They want data.

That's the problem Infera is solving.

Chloe Sow knows that side of it. Mechanical engineering at Harvard, research at Brigham and Women's, building medical devices and running the experiments by hand.

Her co-founder Troy Cheng spent an entire research summer figuring out how to run his lab's experiments on one machine. That was the summer.

So they built the thing they needed.

Describe an experiment in plain English.
Infera turns it into a validated, instrument-ready run across the equipment your lab already uses.

One system for protocol logic.
Vendor-specific scripts.
Laboratory data.
Inventory.
Institutional knowledge.

Today, running an experiment means going instrument by instrument.
Tomorrow, one place for all of them.

AI agents already design experiments and protocols.

But nothing connects those designs to real laboratory instruments.

One system.
From intent to execution.‍

Infera: "Control lab instruments with natural language"

🎙️ Chloe Sow, Co-Founder, Infera on Fondo START pod

‍

00:45 Claude Code for scientific instruments
01:27 Troy's entire job was one liquid handler
02:39 Every run captures a record. The missing layer between AI and instruments
03:12 Scientists pressing buttons without knowing the machine
04:22 Plain English to validated instrument-ready run. Academic labs and cores first
05:50 Every instrument ships its own closed software
06:02 Meeting at a virtual Caltech admit session
07:19 Applied to YC for feedback. Got in first try
08:35 Staying disciplined instead of chasing the SF event circuit

‍

Check out infera.bio

You donate to a family in a crisis zone. Usually you get a thank-you email. That's where it ends.

Nasrat Khalid couldn't understand why.

We expect complete visibility when we order dinner.
Why not when we're helping another human being?

So he built Aseel. 
A platform for "everything do good"

You buy a food package. 
You see exactly who receives it. 
You can ask for a photo of the delivery.
You can exchange messages with the recipients, if they choose to participate.

Instead of donating into a system and hoping, you know the aid reached the person who needed it.

That's the whole idea. No plane ticket. No project. No abstraction.

"Award-winning US-based platform connecting artisans to global markets and enabling transparent aid"

‍

🎙️ Nasrat Khalid, Founder & CEO, Aseel on Fondo START

‍

00:45  The platform for everything do good
02:25  The first 16 years, as a refugee
02:55  Why he left institutional aid to build Aseel
03:40  Leaving Afghanistan at 16 days old
04:05  No ID means no school, no bank account
04:35  Why identity comes before food
05:15  Seven years inside the World Bank
05:58  What happened to aid in Afghanistan after 2021
07:00  AidOS, for institutions
07:40  The Uber Eats insight
08:20  The $30 package you can trace to a family
09:35  Why he joined the residency
12:30  How to get started

‍

Check out aseelapp.com

The tools we use after we ship haven't caught up.

Code is getting cheaper.

Production is getting harder.

That's the gap Sherwood Callaway saw after building with Cursor and Claude Code. AI had transformed how software gets built, but when something broke in production, he was back clicking through observability tools that hadn't kept pace.

Sazabi is built for AI-native engineering teams.

→ Autonomous alerts with root-cause context
→ Conversational debugging instead of endless dashboards
→ AI that learns your codebase, architecture, and past incidents

AI is making it easier than ever to build software.

Sazabi makes it just as easy to understand what's happening in production—helping engineering teams detect issues earlier, identify the root cause faster, and get back to shipping.

Observability is becoming one of the most important layers of the AI software stack.

‍

🎙️ Sherwood Callaway, Founder & CEO, Sazabi on Fondo START pod

‍

01:08 — Building AI-native observability for fast-moving engineering teams
01:45 — Why AI made observability a much bigger problem
02:07 — "Code is basically free"
02:36 — Why architecture and product thinking are the new bottlenecks
03:09 — "Measure twice, cut once" — how Sazabi stays focused
03:35 — History major to Dev Bootcamp: turning down investment banking
05:50 — Joining Brex at employee 70 through hypergrowth to $12B
07:10 — Starting Brex's observability team — the seed of Sazabi
07:48 — What observability actually is (it started with 1960s rocket science)
09:45 — "I will leave Brex today" — the Dalton Caldwell moment
12:16 — Building one of the first production AI agents at 11x
13:33 — The Cursor vs. Datadog contrast that sparked Sazabi

‍

Check out www.sazabi.com

The best product often loses
Not because it's worse
Because no one saw it

Teams ship daily

Marketing got faster too

It just never stopped needing you

Open the recorder.
Fumble the demo.
Redo it fifteen times.

Edit
Design
Post

Your flow is gone.

So next time you don't bother

That's how good products disappear

Cyrus Kelly and co-founder David McDonough were involved in 11 startups.

Every time, they became the marketing team

Never once enjoyed it

So they built tday

Connect your GitHub repo

Or point it at your product

Every release becomes a demo video of the actual feature.

On-brand graphics

Published to your channels

Ship an update and the demo updates itself

Because it's tied to what your product does

Not a one-time recording.

Not three hours in tday instead of four hours in Canva.

Minutes.

Then never thinking about it again (unless you want to)

The problem was never building

It was that no one saw it

Their mission is to make it so the best products do win.

‍

🎙️ Cyrus Kelly, Co-Founder & CEO, Tday on Fondo START pod

‍

00:20 — What tday does: marketing on autopilot for software companies
00:50 — Origin story: three engineers, 11 startups, always stuck doing marketing
01:35 — Why the best product doesn't always win
01:50 — Starting university at 13
02:15 — Building a student club platform with banking at 16
03:05 — The tech stack behind it
03:50 — Applying to YC four times
04:05 — Interviewing with Tom Blomfield
04:55 — How the product changed during the batch
05:35 — Not a better design tool: two minutes instead of hours
06:00 — Why repeated launches matter
06:30 — The idea of a product that records itself
06:50 — Who tday works best for
07:45 — Getting the domain
08:00 — What tday.com actually cost
08:45 — Eight months of waiting, and how the domain shaped the product
10:00 — Sam Altman offering $2 million in tokens
11:15 — Token maxing: agents running in parallel
12:20 — Where to find tday and Cyrus

‍

Check out tday.com

Now it could be AI.

DetectifAI is building the next layer of cybersecurity for the AI era

Not another chatbot

Not another AI wrapper

Foundational models built to detect audio deepfakes

It started with something personal.

Tarini's grandfather lost thousands of dollars to a simple deepfake.

That moment became the reason to build.

Tarini had already been in machine learning since grade six

Nearly a decade studying how AI works

She saw where the technology was headed - and what was missing

Fraud has existed for a very long time

Cybersecurity has existed for a very long time

Deepfake detection is that same discipline extended into the AI world.

Today, DetectifAI serves clients across banking, finance, security, telecoms, and OEMs

An API that integrates into existing enterprise infrastructure

And on mobile, it goes even deeper - into the OEM layer itself.

Which makes the vision tangible.

You answer a call.

Your phone gives you a rolling update on whether that voice is likely AI or a real human

Detection during the call.

Not after the money is gone.

The scale comes from where it's deployed.

A single OEM integration can unlock protection for millions of people.

And deepfake detection is only the first step.

The larger vision is an anti-fraud landscape that helps people navigate scam calls, robocalls, and anything that falls under the purview of fraud.

The next generation of AI companies won't just build intelligence

They'll build trust

🎙️ Tarini Padmanabhuni, CEO, DetectifAI on Fondo START pod

00:57 Founder origin story
01:18 ML background
01:42 Audio deepfake detection
01:53 Enterprise customers
02:15 Growing AI fraud
02:39 AI cybersecurity
03:16 Enterprise integration
03:29 Telecom and OEM strategy
03:46 OEM distribution vision
04:13 Real-time call detection
04:33 Broader anti-fraud vision
04:58 AWS grant journey
05:31 Building in San Francisco
05:52 Residency experience
06:15 Advice for founders

Check out detectif.ai

Until you're personally answering 50+ support tickets a day...

That was Dawson Chen.

He'd scaled Martin, an AI personal assistant, to more than 100,000 users, but growth came with a new problem: 

Refund requests

Subscription cancellations

Login issues

And the same support questions over and over again...

To keep up, the team hired a virtual assistant and relied on tools like Zendesk and Freshdesk. 

But every ticket meant jumping between their inbox, Stripe, their database, and their knowledge base. 

The software wasn't built for how startups actually operate.

So instead of accepting it, they built the tool they wished they'd had.

Then shared it with a few founder friends.

The response was immediate.

That internal tool became Letterbook.

Today, Letterbook is the AI-native alternative to Zendesk, Freshdesk, Zoho Desk, and Intercom - built specifically for startups.

It connects to your inbox, Stripe, database, and knowledge base, then drafts a complete resolution for every support ticket: the reply, the refund, the cancellation, the next action.

For many customers, 70–80% of tickets become a single approval instead of minutes of manual work.

Their Product Hunt launch reached #3 Product of the Day.

Most customer support platforms were built for enterprise support teams.

Letterbook was built for founders.

"The modern AI-native support platform"

🎙️ Dawson Chen, CEO & Founder, Letterbook on Fondo START pod

‍

00:32 — From a 100,000-user AI app to discovering a support bottleneck

01:08 — Turning an internal tool into Letterbook

02:09 — Why AI-native support beats legacy help desks

02:47 — How context-aware AI drafts actions, not just replies

03:23 — Automating 70–80% of repetitive support work

04:20 — Why Letterbook is purpose-built for B2C startups

04:55 — Two YC founders, two co-founder breakups, one new company

06:42 — Building the default customer support platform for startups

08:08 — Lessons from launching to #3 on Product Hunt

‍

Check out www.letterbook.ai

But when she started selling to enterprise logos, it became something that was impossible to ignore.

Now, as Product Lead at Rippling, she channeled her first-hand experience of getting SOC 2 compliant to rethink how all automated compliance frameworks are run. 

It started with a retro. 

Every other SOC 2 vendor works the same way: 

Detect a problem
Alert your team
Fix it...somewhere else

The problem with this checklist is that "somewhere else" requires dozens of additional integrations. Because these vendors are just reporting layers on top of your stack.

Rippling is your stack. 

Which means most of your compliance evidence is collected before you start AND when there's a gap in compliance, Rippling fixes it.

Unencrypted device --> encrypt it.

Wrong app access --> de-provision automatically

Incomplete security training --> gate system access until its done. 

Because compliance really isn't about the report. It's about operating your company securely -- and proving it without a yearly fire drill. 

Emma shares more about how her own founder journey influenced building Rippling's powerful new automated compliance product, available today for SOC 2 Type 1 and 2. 

🎙️ Emma Lawler, Product Lead, Rippling on Fondo START pod

‍

00:57 Introducing Rippling Automated Compliance
02:07 The evolution of compliance software
03:02 AI's role in compliance workflows
03:41 Understanding SOC 2 requirements
04:20 Compliance and enterprise procurement
05:03 Building secure operating practices
05:46 Product development at Rippling
06:28 Building with a lean team
07:15 Lessons from founding startups
08:55 Learning through product pivots
11:01 AI in modern product development
12:41 Planning and research with AI
16:05 Defining product-market fit
17:56 Using first-party data for compliance
21:35 Advice for early-stage founders

‍

Check out www.rippling.com/products/it/automated-compliance

And the only tool most teams have for that is manual A/B tests.

Thousands of dollars per experiment. Weeks to trust one result. One variable at a time. Every test starts from zero.

Stop guessing. Start compounding.

Tranzmit AI builds, tests, and evolves paywalls instead.

Analyze the behavioral data, generate variants, score them against simulation and conversion history, ship the winner with guardrails and auto-rollback.

50x faster than manual A/B.

25M paywall views a month feeding the loop. Every result sharpens the next one.

Four months ago, Agaaz Singhal was pointing the same engine at the opposite end of the funnel.

We recorded this episode in March, when Tranzmit sat on top of the cancel button:

Detected at-risk customers before they cancelled
Intervened with personalized conversations
Identified the root cause of frustration
Filed live Jira tickets for engineering
Recovered customers who were ready to leave

Five lines of code. Three-minute setup.
Not just to stop churn. To understand it.

Read the behavioral data, generate the intervention, measure it, loop.
Same engine. The screen changed.

In the episode Agaaz describes product teams flying blind for weeks, stitching together analytics dashboards while customers walked. Behavioral data, an intervention at the moment that decides revenue, measured and fed back.

Same loop, pointed at the front door. Running 24/7. "Building self-improving software infrastructure"

🎙️ Agaaz Singhal, Founder, Tranzmit AI on Fondo START pod

00:26 Building AI product teams to reduce churn on consumer platforms
00:42 AI-native cancel buttons that understand why customers leave
01:11 Five lines of code. Three-minute implementation.
01:25 Predicting churn before users hit cancel
02:05 Recovering up to 28% of customers at the point of cancellation
02:35 Why product teams spend weeks trying to understand churn
03:20 Scaling a consumer platform to 700K monthly active users
03:35 The insight that led to building Tranzmit AI
03:50 Building in San Francisco at Founders Inc

Check out tranzmitai.com

They open a browser, look at a screenshot, and try to figure out what to click

That's like ripping the LIDAR off a Waymo and handing it a camera

Tarun & Alex kept running into this problem. 

They were building an AI receptionist for dentists, and at conference after conference people asked the same thing: do you integrate with X or Y platform?

Seven other people were doing the same thing. None of them could integrate. Alex and Tarun could, because they knew how to reverse engineer network requests

That's when they stopped digging for gold and started selling shovels

Zatanna turns legacy software into APIs by working at the network layer. No browsers. No screenshots. Just requests

10x faster on the workflows they benchmarked. Near-zero browser costs. Reliability goes from 70% to nearly 100%.

Computers shouldn't navigate the web like humans do. Humans use screens. Computers use requests

"No browser bots to babysit"

‍

🎙️ Tarun Vedula & Alex Blackwell, Co-founders, Zatanna on Fondo START

‍

00:57 From jury research to API infrastructure

02:04 The "sell the shovels" realization

03:18 Why browser agents break at scale

04:42 Turning legacy systems into APIs

05:47 Why repetitive workflows shouldn't use browsers

06:37 Teaching AI agents to navigate like computers

08:34 Founder-market fit and knowing when to pivot

10:20 From sneaker bots to reverse engineering anti-bots

12:23 The future of AI vs anti-bot warfare

13:37 Why companies are still stuck at 70% reliability

Check out www.zatanna.ai to learn more. 

Now they can generate fifty.

But more content didn't solve marketing.

It exposed a new bottleneck.

Most of it is slop.

AI solved the quantity problem.

It created a quality problem.

Kyle Wong and co-founder Colin Tseung built InstaAgent to solve the second one.

A consumer brand hands InstaAgent a single marketing brief. From there, the team handles the strategy, the creative, the distribution, and the analytics.

The result isn't 50 generic ads.

It's personalized content built for hundreds of distinct audiences - each with its own messaging, creative, and targeting.

Think about a global CPG company.

One hundred SKUs.

Ten countries.

That's a thousand creative variations before you even start optimizing performance.

The hard part isn't generating content anymore.

It's knowing what to create.

Anyone can pay $25 a month for an AI model.

Very few know how to turn that model into marketing that actually performs.

That's why InstaAgent isn't positioning itself as another self-serve AI tool.

It's an AI-native marketing partner - combining human judgment with AI execution to produce creative that brands can actually scale.

The market is responding.

From $0 to $1M ARR in just 10 months.

Enterprise customers including Nestlé and P&G.

Enterprise sales cycles closing in less than a month.

The first wave of AI made content abundant.

The next wave will reward the companies that know exactly who that content is for.

🎙️ Kyle Wong, Co-Founder & CEO, InstaAgent on Fondo START pod

00:15 — What InstaAgent actually does: scaling one campaign across hundreds of personas
00:40 — The full loop from a single brief: strategy, creation, distribution, analytics
00:55 — Why the bet is on TikTok and Meta, where AI video is moving fastest
01:20 — The old way: five creatives per campaign, slow and expensive
01:39 — How AI multiplied output to 50 — and why most of it misses
02:06 — Moving upmarket from SMBs to enterprise names like Nestlé and P&G
02:30 — The CPG math that makes manual creative impossible: 100 SKUs, 10 countries
03:20 — Replacing the Canva-and-Photoshop resizing grind
03:44 — Why an agency model beats self-serve SaaS for AI creative
04:45 — On token-maxing and the tokens offer from Sam Altman at YC
05:47 — From eight years at Goldman to a game that hit 1M users in a month
07:20 — How a hot sauce brand used synthetic personas to broaden its audience

‍

Check out instaagent.com

Koby Conrad believes AI changes that equation.

His own story starts much smaller.
High 24/7 from 19 to 24.
He got sober.
Taught himself to code.
Built a side project called Sunflower.

Years later, that side project became a VC-backed company with a much bigger ambition.

Today, Sunflower has:
→ 500,000+ users and counting
→ Grown from 200 to 100,000 monthly active users in under 6 months
→ Reached a $1M run rate in about 10 months
→ Tracked 109 years of sobriety every single day
→ Expanded to users in almost every country, across 5 languages

The product combines consumer software with clinical care.

A consumer app featuring:

An AI sponsor for addiction recovery
Visual sobriety progression tracking
A social network built for recovery
CBT journaling and educational content across major addiction types

Alongside a tele-therapy clinic:

- Talk therapy in California and Texas, expanding nationwide with MAT
- World-class doctors building the most effective protocols for addiction

The mission began with one billion sober days.
The team realized they'd reach that goal sooner than expected.
So they raised the target.
One trillion.

Koby's growth philosophy mirrors the one behind his book, "Channels of Growth":
Don't choose between product and distribution.
Build both.
Because at real scale, only a handful of growth channels matter. Find the one that fits your business—and optimize it relentlessly.

His biggest prediction isn't about sobriety.
It's about work.

At Sunflower, they call it 'Operation Ender's Game'

Map every task.
Automate what AI can do today.
Design for what AI will do tomorrow.
The only role left? fleet operator.

🎙️ Koby Conrad, CEO & Founder, Sunflower Sober on Fondo START pod

Chapters:

00:49 – Koby joins the show
01:10 – The pitch: a sobriety platform built for the age of ASI
01:35 – Sunflower's origin as a learn-to-code project
01:54 – Koby's own story: high 24/7 from 19 to 24
02:31 – From bootstrapped side project to VC-backed company
02:46 – Zero to $1M run rate in 10 months
03:00 – Tracking 109 years of sobriety every day
03:10 – Why the mission grew from one billion to one trillion days
03:52 – The growth framework: product and distribution, never one or the other
05:03 – The only channels that scale: virality, paid, organic audience
06:01 – Doing YC with his brother, from selling eggs to SF Compute
08:00 – Operation Ender's Game and the fleet operator future

So he automated his own.

His team used to forward deploy the old way — go into customers' offices, sit next to their teams, watch how the work actually got done, then build the automations by hand.

Then they asked the obvious question: why are humans doing all of this?

Now Hessian's AI agents construct a digital twin of your business, map your back-office workflows, and take the work over end-to-end — plugging into whatever you already use.

Slack. HubSpot. Salesforce.

No new dashboard. No workflow migration. No figure-it-out-yourself AI tooling.

The thesis behind it is blunt: the future of enterprise AI isn't selling more SaaS.

Businesses want to adopt AI. They don't know how. Another tool doesn't solve that problem — it adds to it.

So the companies that win won't sell software.

They'll sell outcomes.

🎙️ Bao Nguyen, Co-Founder & CEO, Hessian on Fondo START pod

01:28 — Introducing Hessian's vision for forward deployed AI agents that automate business operations.
01:58 — Why conversations with customers revealed that AI adoption is still a major challenge for mid-market companies.
02:34 — How Hessian evolved from a workflow orchestration platform into an AI-first automation company.
03:15 — The insight that led the team to replace traditional forward deployed engineering with AI agents.
03:57 — Why Hessian integrates into existing workflows instead of requiring companies to adopt another SaaS product.
04:28 — Bao's perspective on why enterprise AI is shifting toward outcome-based implementations rather than software licenses.
05:02 — Early customer results, including significant time savings and eliminating the need for a dedicated AI engineer.
05:34 — Meeting his co-founders at university and getting accepted into Y Combinator on their first application.
06:22 — The difference between building startups in London versus San Francisco, and why speed matters.
07:20 — The biggest lesson from Y Combinator: validating ideas with customers before investing heavily in product development.
08:05 — Thoughts on OpenAI's token offer and the changing economics of AI startups.
08:48 — Who Hessian is built for today and how the company approaches AI-powered back-office automation across different stages of growth.

‍

Check out hessian.sh

They need a better system.

Jessica Hubley spent years in Big Law, served as General Counsel at a venture-backed startup, and built her own practice serving startup founders.

She kept seeing the same problem.

Not that founders lacked intelligence.

They lacked exposure to the legal playbook.

They didn't know what kind of lawyer they needed.
Or what needed to happen next.

So she built Story.

"Story.law is your expert General Counsel - fear no legal!"

Aegis transforms your mess wherever it lives today - bringing AI and human legal experts together

Cap table.
Data room.
Pro forma.
Document intelligence.

Built on top-tier lawyers' logic.

Not software that asks founders to think like lawyers.

Software that brings lawyer logic directly into the workflow.

Because founders shouldn't be paying thousands of dollars for lawyers to operate software.

"A cap table is math. Software is better at math than lawyers."

The goal isn't more legal spend.

It's spending less on lawyers - and getting an equal or better result.

Organized before diligence.

Prepared before fundraising.

Deal-ready before the deal.

The future of startup legal isn't replacing lawyers.

It's making expert legal judgment available exactly when founders need it.

‍

🎙️ Jessica Hubley, Founder & CEO, Story LLP on Fondo START pod

00:00 — Jessica Hubley, founder & CEO of Story, joins the show

00:11 — What Story.Law is building: automated in-house counsel that also manages human lawyers

00:44 — Why founders come to Story - and why second-time founders get it immediately

01:30 — The bigger vision: custom contracts drafted and signed in under 30 seconds

02:04 — From Stanford Law to Big Law to hitting the glass ceiling

02:47 — GC at a health tech startup, then a nonprofit teaching trafficking survivors to code

03:06 — The insight: a YC/Yale founder and a trafficking survivor had the exact same legal problem

03:33 — The Bookface post that got responses for three years straight

04:45 — The absurdity of paying lawyers thousands a month to operate cap table software

06:35 — Founder legal 101: the three mistakes almost everyone makes at the start

10:56 — The #1 reason YC companies die (it's not product-market fit or funding)

14:45 — When you shouldn't spend money on lawyers yet

‍
Check out story.law

Zero idea which one finished.

That was the problem.

Around November, Austin Wang and his co-founder Lawrence Chen stopped opening their IDE.

They weren't writing code by hand anymore

Everything ran through Claude Code and Codex in the terminal

10 panes. 10 agents. 10 copies of the repo.

And two problems appeared almost immediately.

They couldn't tell what each agent was working on.

They couldn't tell when an agent had finished.

So they built cmux.com:

Native macOS Ghostty-based terminal with vertical tabs, notifications, and built-in browser

Each workspace shows the task, branch, ports, and pull request - so you always know what every agent is doing.

When an agent finishes, it moves to the top with a blue notification ring.

Like iMessage...

But for coding agents.

Who's adopting it first?

Staff and senior engineers.

It makes sense.

They're already used to breaking large problems into smaller ones and delegating the work.

The difference now?

Instead of junior engineers, they're orchestrating dozens of coding agents in parallel.

Even Austin and his co-founder don't work the same way.

One prefers worktrees.

The other doesn't.

That's why cmux ships primitives, not prescriptions.

Developers build the workflow that fits them.

Because nobody has figured out the "right" way to work with coding agents yet.

And as those agents write more of the code...

The bottleneck won't be software.

It will be human attention.

 Austin Wang, Co-Founder & CEO, Manaflow on Fondo START pod

00:15 — Introducing cmux and the YC journey

00:00:50 — Pivoting from enterprise AI to dev tools

00:01:25 — Leaving the IDE for the terminal

00:02:00 — The problem that led to Arc-inspired workspaces 

00:02:45 — Notifications for coding agents

00:03:25 — Why developers are moving to terminal-first workflows 

00:04:30 — Why senior engineers adopt cmux first 

00:05:50 — Primitives over prescribed workflows

00:07:00 — The future of autonomous code review 

00:08:40 — Why human attention becomes the bottleneck

00:09:20 — Growing to 14.5K GitHub stars

00:11:15 — Building cmux for speed and performance

Check out cmux.com

OpenClaw is brilliant but can be brutal to set up. Days of configs. 

Agents messaging random contacts on WhatsApp...

Tensol: one click. Five minutes. Each AI employee runs 24/7 in its own isolated VM with full company context

Connect Slack, GitHub, Sentry, HubSpot, Gmail. Every action logged. Enterprise-grade security built in

When we recorded this ep: ~100 AI employees running. 300K+ Twitter views

Not chatbots. Agents with their own computer for engineering, sales, SDR

🎙️ Oliviero Pinotti, Co-Founder, Tensol (YC W26) on Fondo START

01:33 what Tensol does and how it works
02:07 300K Twitter views and what launch day looked like
02:25 ~100 AI employees deployed within 24 hours
02:44 Why it took off
03:42 The problem Oliviero saw early: Mac Minis, firewalls, configs
03:57 Tensol's answer: one-click deploy and isolated sandboxing
04:37 How Tensol handles enterprise security and prompt injection
05:46 Five minutes vs four days to get up and running
06:59 Most common integrations: Notion, HubSpot, GitHub, email
09:43 Getting into YC after multiple attempts
10:43 The full-circle pivot back to the original vision
14:02 Pricing, trial access, and getting started

They don't.

They have a context problem.

Every company runs on things that were never written down - they just live in people's heads.

That's the insight that led Max Arnold and his co-founders Leon Iwanowitsch & David Korn to start Ontora (YC P26).

Because AI agents can only be as useful as the context they're given.

And most organizations have never captured how work actually gets done.

Who owns what

Why decisions were made

Where critical knowledge lives

The result?

Teams spend more time searching than building.

More time aligning than executing.

More time asking who knows the answer than actually acting on it.

Ontora's AI agents interview employees across an organization and build a context layer that grows as the company grows.

What consultants have traditionally spent months uncovering through interviews, Ontora helps companies discover on an ongoing basis.

Because the future isn't just AI

It's AI that understands your company

🎙️ Maximilian Arnold, Co-Founder, Ontora (YC P26) on Fondo START

‍

0:00 — What Ontora builds: agents that interview employees to map how work gets done
1:32— Why most companies have nothing documented, and why AI needs that missing context
2:48 — The consulting tool that turned into the real problem worth solving
3:14 — Meeting the co-founders at CODE, and the pipeline from Berlin to Stanford
4:19 — How project-based learning turns students into founders
5:21 — Getting into YC on the first application, and skipping the German incorporation flip
6:37 — Going Delaware from day one, and interviewing with Tom Blomfield
7:21 — Helpful over friendly, and the real lesson of living with your co-founders
8:05 — Who Ontora is actually for, and where to follow the journey

‍

Check out at ontora.com

The people who reset the passwords. Clear the MFA requests. Provision the access. Push the tickets through the systems so the rest of the company can work.

Seb Poole wants to give that work to agents.

Not a better way to track the request. A workforce that completes it.

He's seen the problem from the inside.

His last company, Frankie Health, was acquired by Unmind - a leading workplace mental health provider whose customers include Disney and Uber.

At every company he built, the same drag: ServiceNow and Jira tracked the work, but a human still had to do it.

So he and his co-founder Aleks Tomovski built Modern (YC P26). Seb scaled Frankie Health into Unmind; Aleks built developer platforms at Wix and Canva, including Wix's first low-code platform.

Modern is secure agents that understand your business, learn the right workflows, and resolve tickets end to end across the IT function - inside the tools your team already uses, from Slack and Teams to email, voice, WhatsApp, and Telegram.

The result they're seeing today: 80%+ automation across IT team requests.

Not another ticketing system.

The workforce that resolves the ticket.

🎙️ Seb Poole, Co-Founder & CEO, Modern on Fondo START pod

‍

Check out getmodern.ai

00:30 — Building the future of ServiceNow
00:50 — Why most systems track work but never execute it
01:00 — Modern as system of record and the work itself
01:35 — The operational pain that sparked the idea
01:55 — Standing up a shadow workforce for service desks
02:10 — Grounding agents in the context of an organization
02:35 — How agents can 10x service teams
02:50 — Scaling Frankie into Unmind across 150 countries
03:15 — Why he started over instead of resting on an exit
03:40 — Building a company in the post-LLM era
05:00 — Getting into YC on the third application
06:30 — Why the best IT teams are the real problem solvers

But closing that gap isn't about buying more AI. It's about a problem most people get wrong.

You can test code by running it.
You can't test an investment by buying it.

Software engineering and investing aren't the same problem

In software, we've moved from autocomplete to agents to long-running agent swarms - each step climbing another level of abstraction

In investing, human judgment still has to exist at the object level

So the challenge was never deploying agents

The challenge is giving them the context to be useful

Every source of knowledge

Internal and external

Structured and unstructured

Connected to the people making decisions.

That's what Ian M.J. McInnis and his co-founder Ben Finch built at WithAI (YC P26)

Not another AI tool

A customized environment where agents can work the way investors actually do.

Ian knows the problem firsthand. Before co-founding WithAI, he worked at the center of AI and investing at Bridgewater, studying how AI would reshape markets and knowledge work.

His conclusion after trying the alternative:

"The DIY approach ends in pain"

The future isn't simply managing swarms of agents

It's knowing what to delegate

What to automate

And where human judgment remains essential

That's why WithAI focuses on amplifying investors rather than replacing them

Research faster

Monitor more companies

Surface information that would otherwise be missed

"AI for asset managers, built with you": Security, infrastructure, and white-glove service for harnessing the latest agents

🎙️ Ian McInnis, CEO & Co-Founder, WithAI on Fondo START pod

‍

00:20 Why AI adoption in investing requires more than simply deploying agents

00:35 Building a customized AI operating environment for asset managers

01:12 Making hedge fund-grade AI capabilities accessible to smaller firms

01:55 From Princeton mathematics to a career in investing

02:35 Becoming one of Bridgewater's internal AI specialists

03:25 The origin story of WithAI and partnering with a longtime collaborator

04:35 Identifying the missing tools investors would need in an AI-driven future

05:05 Lessons from an initial YC rejection and what changed

06:15 Why AI agents are creating immediate value for knowledge workers

07:20 The fundamental difference between software engineering and investing

08:35 How to balance AI automation with human judgment

09:50 Where investors can safely delegate work to AI agents

11:00 Measuring AI-driven investment outcomes and client value creation

12:15 The ideal asset manager profile for WithAI today

13:10 Opportunities and excesses emerging in AI infrastructure investing

14:00 Thoughts on SpaceX, xAI, and technology valuations

15:00 The future of AI adoption across institutional investing

Check out www.withai.co

Maanav Agrawal kept seeing the same pattern.

Some of the coolest products weren't getting the attention they deserved

Meanwhile, companies that consistently shared what they were building were generating inbound

He'd lived it himself
He saw it in other industries like real estate
He saw it across his YC batch and among founders generally

The problem wasn't always the product.

It was making sure people knew the product existed.

So he and Co-Founder Jason Zhan built Memoir (YC P26)

A marketer with the soul of an engineer. 

Memoir connects to your product source of truth and understands what changed, why it matters, who should hear about it, and how to tell the story.

A new feature ships
A PR worth talking about gets merged
A meaningful update goes live

Memoir turns it into:

→ Social posts 
→ Blogs 
→ Changelogs 
→ Demo videos 
→ Customer updates 
→ Launch content

All in your company's voice.

They built it for themselves: Engineers who love building

But, like most founders, don't always have time to build in public.

Now they don't have to.

The work speaks for itself - automatically

Memoir turns product velocity into market velocity

🎙️ Maanav Agrawal, CEO & Co-Founder, Memoir on Fondo START 

00:53 "Shipping is not the bottleneck, attention is."

01:19 How Memoir connects directly to GitHub and monitors product changes

01:38 Deciding which product updates are important enough to share publicly

02:03 Keeping founders in control with approval before publishing

02:40 Why some of the best products never get the attention they deserve

03:20  The challenge facing startups without dedicated marketing teams

03:49 Why engineering teams move faster than marketing teams can keep up

05:17 The original YC idea: developer infrastructure and latency optimization

05:58 The realization that marketing was the biggest bottleneck in real estate

06:18 The insight behind Memoir: unify engineering velocity and marketing velocity

‍

Check out trymemoir.ai

‍

The entire year

In Q1 2026 alone: more than four million

At the time of this recording (Apr 2026) tenant demand reached 7.5 million square feet

The highest ever recorded.

OpenAI has accumulated nearly a million square feet in Mission Bay

Coinbase, Nvidia, and the Warriors all landed at Mission Rock (It's effectively full - premium space there is almost impossible to get)

Reuben Torenberg has brokered SF startup office deals through every major cycle since 2016

The 3% vacancy boom and record rents

The 38% vacancy COVID collapse

And the AI-driven resurgence happening now

We're probably still early in this cycle

The AI wave showed you can do more with less

And that's changing how founders think about space

🎙️ Reuben Torenberg, Senior Vice President, CBRE on Fondo START

00:36 Why brokering compounds the longer you do it

01:08 SF's pre-COVID boom, then the jump to 38% vacancy

02:18 How AI restarted SF office demand

02:42 Where we are in the cycle, and why it isn't the top

03:25 The leasing numbers that signal a real shift

04:34 Why startups are leasing differently now

05:34 How distressed buildings changed hands at a discount

06:11 How landlords regained leverage

07:38 What landlords actually check before they'll sign you

09:48 Why plug-and-play subleases win with startups

12:18 Advice for founders hunting their first office

13:01 Treating the office as a recruiting tool, not a status symbol

learn more at cbre.com

Leo Kankkunen is building the gear that pulls it out

Tankless diving technology that generates breathable oxygen directly from water. 

It sounds impossible at first. Then you hear the physics...

Then you realize the implications stretch far past diving

Water is hydrogen and oxygen. Apply a DC current, swing away the electrons binding them, and you get the purest form of breathable gas.

The idea started with a simple frustration

Leo is an electrical engineer and a diver. Every dive exposed the same limits: Bulky tanks, Logistics, Safety risks, Cost, and Operational complexity.

Instead of accepting those constraints, he asked a question: Is there any way we could breathe like a fish?

That sent him down a rabbit hole into electrolysis

If water already contains oxygen, why haul tanks at all? 

Why not generate breathable oxygen exactly where and when it's needed?

Enter DAIVIN! (YC W26) - Tankless dive gear that generates oxygen directly from the water around you. No tanks to haul, fill, or run out of.

But diving is just the wedge

Anywhere breathing depends on a tank, the tank sets the limit. 

Crisis zones. High altitude rescue. Anywhere time is human lives. 

Generate the oxygen on site, and there's no supply chain to depend on.

🎙️ Leo Kankkunen, CEO & Founder, Daivin on Fondo START pod

00:18 What Daivin is building and why oxygen tanks are the real problem
00:43 One glass of water holds roughly 20 hours of breathing
01:05 The science of splitting water into oxygen and hydrogen with a DC current
01:38 The wearable system that draws in surrounding water and produces oxygen
02:10 Leo's path from electrical engineer and diver to founder
02:48 Where the tech stands today: commercial close, consumer further out
03:15 Why removing oxygen logistics matters in crisis response and rescue
03:38 The Netflix versus Blockbuster framing for oxygen infrastructure
04:05 Why water beats gas tanks for breathing beyond Earth
05:20 From a Slush side event in Finland to a YC interview within weeks
06:25 Building in San Francisco versus Finland and compressing months into weeks
07:15 Leo's advice for founders: build what genuinely interests you

Check out daivin.tech

Verbiflow was built to change that

It's Outbound Infrastructure for growth teams

Outbound used to be a stack

One tool for email.

Another for LinkedIn.

Something else for cold calls.

Verbiflow is the system that runs your outbound, all in one place

Run sequences through email, LinkedIn, and cold calls

Handles sending

Manages replies

Runs follow-ups

Keeps it all in sync

Because the real bottleneck isn't finding leads

It's getting in front of the right people before momentum disappears

And in a world flooded with AI-generated outreach, they're betting on: better conversations, not more messages

From prospect to pipeline

From outreach to conversation

From disconnected tools to one outbound system

🎙️ Kash Ambarani & Rishi Mahadevan, Co-Founders, Verbiflow on Fondo START pod

‍

00:57 Platforms for email, LinkedIn, and cold calling are completely separate

02:08 We built an MCP that gets you live in 10 minutes

03:25 Why AI "personalization" in email is making outbound worse

04:18 Cold calling is the most underrated channel right now

05:16 AI coaching and real-time objection handling on calls

06:03 A targeted list is way better than trying to fill a giant list

07:35 Try to get there in person and actually talk to them

08:10 How they mapped every Series A-C startup in SF by scraping T&Cs

‍

Check out verbiflow.com

They launch campaigns. Spend real money. Watch the dashboards. Then hope.

Months later, a report arrives explaining what happened. 

The campaign is over. 
The budget is gone.
The market has already moved.

That's how brand tracking has worked for years.

Henk and Harry are repeat founders with exits behind them - two decades building brand trackers for the world's largest brands on one side, a startup built and sold plus a market research career on the other. They'd watched the same problem from the inside for years.

Tracking was expensive. Slow. Built around snapshots. 

Not how brands actually grow.

So they built Timelaps: An AI-native brand tracker that continuously surveys thousands of real consumers and shows whether your marketing is moving the metrics that matter. 

Brand awareness. 
Consideration. 
Preference. 
Category ownership. 
Brand growth.

Research-grade tracking. 
Updated continuously. 
Running in days instead of months. 
At a fifth of the cost of traditional tracking.

From "What happened?" to "What's happening right now?"

‍

🎙️  Henk Pretorius & Harry Zhang, Co-Founders, Timelaps on Fondo START pod

‍

00:17 Building AI-native brand intelligence for modern brands

00:45 Why brand tracking remains one of marketing's biggest pain points

01:53 How two founders met at ODF and began validating ideas together

02:42 Combining market research expertise with startup experience

03:24 Landing customers from a prototype before building the full product

03:46 Winning Product Hunt with an early demo

04:35 Why the original customer problem never changed

05:30 The limitations of traditional brand tracking and brand audits

06:03 Moving from delayed reports to continuous insights

06:40 The metrics that matter most in brand intelligence

07:35 Making enterprise-grade brand tracking accessible to challenger brands

11:43 Why San Francisco accelerates founder learning and execution

12:47 The "Olympic Games of building companies" mindset

‍

Check out www.timelaps.io

Everything started sounding the same.

Same hooks.
Same cadence.
Same “we’re not doing X — we’re doing Y.”

Jameson Zaballos thinks that’s exactly why human taste matters more now.

Napa is the team behind those viral startup memes you’ve probably already seen.

But underneath the memes is a bigger thesis:
The future of founder marketing belongs to people who still sound human.

Not polished.
Not optimized.

Human.

AI can generate infinite content.
But it still averages the internet.

The founders winning attention right now are the ones adding taste back into the loop.
Write like you talk.

Post when nobody else posts.

Make your ICP feel seen.

Usenapa.com

🎙️ Jameson Zaballos, Co-Founder & CEO, Napa on Fondo START

01:07 Why founders can't cut through the noise
02:12 "If you outsource the opinion to Claude, you level the playing field"
02:45 How AI writing quietly cheapens your brand
04:15 Why human unpredictability is still the moat
05:58 The overlooked advantage of posting on weekends
08:42 Why memes work when they reflect real customer pain
09:55 The return of long-form storytelling

Millions of traders outside the US watch Tesla move. Watch Nvidia earnings.
Watch SpaceX rumors explode timelines.

But when it's time to act? The door closes.

Mochatrade is rebuilding that layer.
A perpetual futures platform for US equities. Built for global traders first.

Trade TSLA, NVDA, AAPL, and 50+ US stocks

Up to 50x leverage
24/7 markets
No US brokerage account required

Deposit in local currency. 
Trade in minutes. 
Stay self-custodial the entire time.

Because the future of trading won't look like legacy brokerage apps.

It'll be:
Borderless
Always-on
Crypto-native infrastructure underneath
Simple UX on top

India already drives massive derivatives (60%) volume. But global traders still can't easily access leveraged US equities.

That contradiction became the company.

"We have experienced the pain point ourselves" 

Parth and Chetan grew up in India watching this door stay closed.

So they built the platform they wished existed.

Not another broker. An access layer for the next generation of retail traders.

🎙️ Parth Maheshwari & Chetan Manda, Co-Founders, Mochatrade on Fondo START pod

‍

00:57 Why global traders still can't access leveraged US stocks

02:21 Turning INR deposits into seamless US stock trading

03:27 Why India dominates 60% of global derivatives activity

04:06 Trading pre-IPO companies like SpaceX through perps

05:10 What perpetual futures actually are and why they're simpler

06:27 Why perps fix the retail options problem

07:45 From AI agents to financial infrastructure

08:52 Leaving AI to rebuild global market access

09:30 How three IIT friends and former roommates found the idea

‍

Check out www.mochatrade.com

Every software company for years has made the same pitch to manufacturers: clean APIs, migrate your stack, rip out your ERP.

It never works. 

The ERP is the company brain. Years of data. 

You can't rip it out any more than you can rip out someone's memory.

Nikolas Keller & Co-Founder Lukas Postulka figured out the obvious thing that everyone missed

Stop trying to replace the software. 

Give an AI employee a login instead.

Walter (YC P26) signs into SAP, Microsoft Dynamics 365, and Oracle the way a new hire would. 

Ready to go from day 1.

Works out of Teams and email like the rest of the team. 

Reads orders. Enters them. Places supplier POs. Catches pricing errors before they ship...

Not another dashboard. Not another integration project. Not another rip and replace.

A purchase order that used to take 15 minutes now takes a few seconds.

The insight came from a $109M manufacturer who had a full-time employee doing nothing but typing purchase orders into an ERP

Not because the work was valuable. Because the software demanded it.

With Walter, your software stays. The manual work disappears.

🎙️ Nikolas Keller, CEO & Co-Founder of Walter (YC P26) on Fondo START

02:02 Growing up across Beijing, Zurich, Munich, and Singapore
02:53 Walking into French restaurants asking for a job
04:10 Why he walked away from the Michelin-star path
04:48 Choosing startups because uncertainty was the point
05:25 Flying to San Francisco with a duffel bag
06:18 Getting rejected, then forcing his way into a startup
07:06 Learning software engineering and customer discovery
08:00 Meeting future co-founder Lukas Pistoor
09:00 Lukas' bias toward action and unconventional journey
10:16 Discovering the manufacturing workflow problem
10:48 Selling Walter before the product existed
11:26 Why AI employees work where ERP replacements fail

visit www.walter.one to learn more

That's why more companies are investing in dinners, customer events, founder meetups, and community gatherings.

But here's what happens after the event:

You sourced the attendees

You sent the invites

You hosted the event

Then everyone goes home

Now you're digging through Slack messages, WhatsApp chats, notes apps, spreadsheets, and email threads trying to remember:

Who showed up.

Who you talked to.

What was discussed.

Who deserves a follow-up.

Most of that context never makes it back into the business.

The event created value.

The workflow lost it.

Pavoot is an AI event manager built for companies that run customer events

It helps teams find the right attendees and draft personalized invitations

It shows who's in the room and why they're relevant

It lets teammates capture notes around each attendee and share context with each other in real time

And afterward, those conversations can flow back into the CRM instead of disappearing into someone's phone.

The event was never the hard part.

Keeping what it created is.

Because relationships don't create value when they're made

They create value when they're remembered

🎙️ Gohar Tamrazyan, CEO & Co-Founder Pavoot (YC P26) on Fondo START

00:22 Ana's journey from Brazil to AI research at ETH Zürich

01:06 Gohar's chess background and Swiss national team experience

01:40 Building a media management tool and discovering a larger problem

02:45 Why companies are investing more heavily in in-person events

03:14 The real goal behind customer events: relationships and outcomes

04:05 Using AI to source attendees and build the right room

04:21 Event recommendations based on attendee interests

04:38 The challenge of remembering conversations after events

05:00 Why teams still rely on Slack, WhatsApp, notes, and voice recordings

05:30 Capturing attendee context and team notes in one place

06:17 Launching Pavoot's Luma integration

08:22 Which companies benefit most from AI-powered event management

learn more at pavoot.com

Laskie was sourcing engineers on Twitter while everyone else lived on LinkedIn.

Along the way Chris started tweeting his own takes on how Twitter should fix recruiting.

Not as a pitch - just because he had strong opinions about what was broken.

Elon liked one. Then followed him. For the next nine months Chris kept the takes sharp on purpose, knowing exactly who was watching.

Then the DM came. A phone number and a note to call that Saturday. His wife was sure he was getting catfished. 

He called anyway, and Elon picked up on the first ring.

Six minutes later it was an invite to dinner at the Tesla Fremont factory that week.

Deal closed in 45 days - Elon's first acquisition at Twitter

Then Twitter became X, X got acquired by xAI, xAI got acquired by SpaceX.

Chris came out the other side holding SpaceX shares

None of it happens if he builds quietly. The ideas did the outreach for him.

🎙️ Chris Bakke, Founder with exits to @X @Indeed @Zillow to on Fondo START pod

02:08 Building Laskie as a recruiting startup in the COVID remote-work wave
03:24 The thesis: reverse-arbitrage engineering talent outside the US
04:41 Why being in an exponentially growing space matters more than anything
05:17 The Twitter takes that got Elon to follow, then DM
06:33 What it's like getting a DM from the richest man in the world
07:06 The Saturday phone call and the Tesla Fremont dinner
08:29 Why you never take the acquirer's first number
09:14 Using optionality as leverage
10:48 Selling from a point of weakness as the hiring market collapsed
11:22 Two years at X and xAI, and a deal around $50M
12:05 The acquisition chain: shell company to Twitter to X to xAI
13:19 SpaceX deal, and the shares he kept

Visit www.chrisbakke.com to learn more

It connected to your call recordings, pulled out the data, and filled in your CRM. customers told him it was a nice add-on. he could have explained why they were wrong. he chose to find out why they were right.

So he looked closer, and what the most interested customers actually wanted was underneath it.

They didn't just want cleaner data. they wanted to use it to improve how their teams sold, to make their median reps perform like their top ones.

The data was the input. The training was the point.

He then worked with hundreds of enablement managers to learn what the best teams do differently, and built Prepse around the answer: reps run simulations of real buyer conversations before they have them for real.

The real product was hiding one question deeper than the one he set out to answer.

The useful question about ai in sales isn't what it can replace. It's what it can't. It can't decide how your company should handle a pricing objection, or what a good discovery call sounds like in your market.

That judgment lives in a handful of your best people, and it's the scarce thing.

He built Prepse on that premise.

Instead of automating the seller, it takes the judgment your best people already have and turns it into something every rep can practice against. Your playbook, your objections, your definition of good, run as simulations a rep can repeat until the real call feels like the fourth take.

The leaders set the bar. The software makes sure everyone can reach it.

‍

🎙️ Teddy Li, Co-Founder, Prepse on Fondo START pod

‍

00:00 Teddy introduces Prepse and what an AI enablement manager actually does
01:00 The original product: pulling call-recording data into the CRM
01:25 Why customers treated it as an add-on, and what they actually wanted instead
01:45 Studying hundreds of enablement managers to model best-in-class teams
02:05 Snagging the six-letter domain for ten bucks, and what Prepse stands for
02:40 His previous company, Nofin, and going through YC
03:05 Why a founder's skills carry over non-linearly between startups
04:00 The two kinds of teams Prepse sells to
04:30 The real goal: turning the median rep into a top rep
05:35 His onboarding philosophy, getting users to the magic immediately
06:30 What he tells founders about cutting onboarding friction
07:00 Revealing product depth one layer at a time, like an onion

‍

Check out prepse.com

Not because AI won't get smarter

Because good therapy isn't just information
It's trust
It's compassion

It's the human element.

Most therapists spend only about half their time doing therapy

The other half?

Running the business

Notes. Paperwork. Marketing. Insurance claims.

Klarify automates the work around the session so therapists can focus on the work only they can do

Today, more than 7,000 therapists use the platform

The result is clear: they spend less time running a business, more time doing the work only they can do

Moody Abdul, Co-Founder & CEO of Klarify, on Fondo START Podcast

01:53 Therapy changed my life
02:22 A therapist asked to use my previous product
02:31 Therapists spend much of their time on admin
03:22 Surpassing 7,300 therapists on the platform
03:46 Why AI won't replace therapists
05:07 The real value hidden inside therapy notes
05:28 Turning session insights into therapist marketing
06:09 Building tools for independent therapists
07:00 Fighting insurance claim denials with AI
07:28 Why therapist SEO and client discovery matter
08:17 The therapist Klarify serves today
09:32 From solo therapists to 60-person clinics
10:00 Solving therapist-client matching

Learn more at www.klarify.ca

Customers use it. New hires use it. Engineers use it.

And when documentation falls out of date, the whole system starts working against itself

People stop trusting what they're reading

Teams lose context

And nobody can fix it, because the problem is everywhere at once

The longer it goes unfixed, the harder it is to untangle

That's the opportunity Manicule saw

They're building an AI-native DevRel company for developer tools - owning documentation, technical content, GEO, and distribution across social channels.

The premise isn't that AI should replace expertise. It's that expertise should scale.

Their AI agents audit and test at scale. Their humans own the architecture, the writing, and the creative direction.

Every review improves the system

Every project creates more context. Every iteration raises the bar

What started as a highly manual business - helping developer companies create better technical content - has become a scalable AI-native operation

Today, Manicule works with some of the fastest-growing developer tool companies - including Supermemory, Greptile, and Reducto - has scaled fast over the last few months, and has more demand than it can take on.

Not because they publish more content. Because they help developer companies create content developers actually trust.

🎙️ Naman Bansal & Shreyans Jain Co-founders of Manicule (YC P26) on Fondo START

00:57 What Manicule is building
03:43 The Supermemory customer that changed everything
04:47 Building startups together in high school
05:55 Early pivots, recruiting agencies, and first revenue
06:18 Applying to YC as teenagers
07:24 Lessons from being the first engineer at Supermemory
08:49 How DevRel became a growth engine
09:54 Why long-form content beats viral marketing
10:53 Growing Manicule 6× and crossing $60K MRR
12:17 Why developer documentation is often broken
13:19 How AI changes go-to-market without replacing quality
14:42 The biggest misconception about GEO
16:00 What founders misunderstand about getting into YC
17:42 The hackathon that led to YC
19:05 Turning a manual agency into an AI-native company
21:33 Building toward $5M ARR

learn more at manicule.dev

Most observability tools just dump alerts. Duplicates, no context, and you still fix it yourself

Nicolò Magnante  and his cofounder Arseniy Shishaev - who spent years building part of Datadog's metrics product - think that's backwards

So they built Superlog. Observability that's meant never to be opened.

A wizard scans your repo, installs proper OpenTelemetry, and runs daily to keep up as you ship

When something breaks, it groups the errors into one incident, investigates with full context - logs, traces, recent deploys, past Slack threads - and drops a single mergeable PR in Slack.

Merge it
Ignore it
Or open it in Claude Code and tweak it

Vendor-neutral, so you keep every log, trace, and metric - even if you leave

Not another notification. A fix.

🎙️  Nicolò Magnante, CEO & Co-Founder, Superlog "Observability that fixes your bugs"

02:00 Why existing observability tools create friction
03:00 The YC pivot that led to Superlog
05:22 Why observability must evolve beyond monitoring
05:40 Installing observability with a single prompt
06:14 Why startups are the ideal first customers
10:01 Why the future is self-healing software

Learn more at superlog.sh

They can't order you a pizza because they don't have a phone number

Think about how much of the real world still runs on phone numbers

SMS verification

Delivery coordination

Account creation

Calling a business...

Without one, an agent is stuck behind glass

Manav Modi & cofounder Meet Modi built AgentPhone to fix that

Give your agent a phone number and it can suddenly act

He told his agent to order DoorDash

It created an account, talked to the delivery driver, made the payment

Pizza showed up at his door. 

Y Combinator is a customer. After An engineer tried Twilio and got buried in compliance, AgentPhone had him live in a day

Every person will soon have hundreds, maybe thousands of agents working for them

The hard part isn't making them smart

It's giving them the tools to touch the real world 

‍

🎙️ Manav Modi, CEO & Co-Founder, AgentPhone on Fondo START pod

‍

00:57 Why AI agents need phone numbers to interact with the real world

01:24 The tools that move agents closer to human-level autonomy

01:50 The emerging infrastructure stack powering AI agents

02:18 Why payments may become a critical layer for autonomous agents

02:42 An AI agent ordering DoorDash end-to-end without human involvement

03:08 What happened when 100+ developers built on AgentPhone

03:26 Processing 4,000+ calls and 5,000+ messages in a single day

03:43 Unexpected real-world use cases discovered during the hackathon

04:24 How phone numbers unlock authentication and action

05:37 The vision of every person having hundreds or thousands of agents

06:16 Building identity and authentication systems for AI agents

07:31 The startup pivot that led to AgentPhone and ultimately YC

‍

Check out agentphone.ai

Then they automate it.

It’s an AI-native consulting firm where agents learn how a business actually runs - process by process - then automate the most manual work directly inside the company’s existing systems

Not in quarters. In days.

Samuel kept seeing the same pattern every time a consulting firm walked into a large organization:

Great strategy
Beautiful slides
Minimal implementation

The recommendations were smart. The operational change rarely came.

He also saw a second problem: Consulting fees were disconnected from outcomes. Clients paid upfront whether the recommendations worked or not.

The incentives were broken by design

So he and his co-founder Javier built the thing consulting always promised but rarely delivered:

Implementation.

The pitch to clients is simple:

Grow your company with the headcount you already have.

🎙️ Samuel Mirpuri, Co-Founder, flowscope on Fondo START pod

00:57 Why traditional consulting stopped at recommendations instead of implementation
02:33 The disconnect between consulting fees and measurable outcomes
03:18 How Flowscope maps entire enterprise departments in two weeks
04:24 Why AI should augment human judgment instead of replacing people
05:30 From aeronautical engineering classmates to AI-native founders
06:02 Sam Altman’s $2M OpenAI token offer to YC startups
07:00 Why AI tokens may become a new startup currency layer
07:29 Customer, Customers, Customers

‍

Check out www.flowscope.com

The other 1% is where the friction hides

The bugs.
The failed onboarding flows.
The conversion leaks.

No team has time to watch them all.

That's the problem Michael Egan and his co-founder kept hitting at their last company. They had analytics. They had session replays. They had ideas to improve conversion.

The small fixes always lost to bigger priorities..

So they pivoted and built CodeCanary: An AI product engineer that connects to GitHub, PostHog, and Slack. It watches your session replays, finds the UX friction, and opens pull requests with the fix

Always on. Always shipping.

Not dashboards
Not alerts

Actual fixes

‍

🎙️ Michael Egan, CEO & Co-Founder, CodeCanary on Fondo START pod

‍

00:57 AI agents connected to your product analytics and codebase

01:20 "You wake up to pull requests the next morning"

03:41 Pivoting from enterprise mapping software

04:42 Why nobody actually watches their session replays

05:02 Surfacing the 1% of user behavior that matters

05:47 Autonomous A/B testing tied directly to revenue

06:37 Why this solves problems engineers never had time for

07:30 "With every new user, your product gets a little better"

08:30 How to know when it's time to pivot

‍

Check out www.codecanary.ai

‍

FinalDose thinks the real opportunity is one layer deeper: DNA

Jeff Liu and his team are building a programmable drug platform that uses genetic mutations to identify and selectively destroy cancer cells

Cells without those mutations are not targeted

Instead of creating a completely new drug for every cancer type, the platform is designed as a reusable system with different genetic instructions

One chassis.
Different disease targets.

The broader bet: DNA is more deterministic than proteins, which could unlock therapeutic approaches traditional drugs struggle to reach

Search.
Destroy.

"A programmable cell elimination platform."

🎙️ Jeff Liu, Cofounder & CEO of FinalDose on START

00:11 Why proteins may be the wrong layer for cancer treatment
01:14 Using DNA mutations as programmable kill signatures
03:20 Building a reusable therapeutic platform instead of one-off drugs
05:00 Why hard biotech problems require interdisciplinary teams
06:00 Getting into YC after a 4 a.m. interview in Japan
08:00 Sam Altman’s $2M AI token offer — and why FinalDose passed
10:08 How AI-native biotech is accelerating programmable medicine
12:00 Why the future of medicine may look more like engineering
13:05 The regulatory bottleneck for programmable therapeutics

Learn more at finaldose.ai

It’ll be about bringing things back down.

After four years building satellites at Astranis, Payton Case realized something: as launch costs collapse, the bottleneck flips.

There’s still no infrastructure for manufacturing products in space and returning them safely to Earth.

So Dispatch is building it.

The thesis is simple: gravity is an invisible constraint on manufacturing.

Remove gravity and semiconductor defects drop. Pharmaceutical crystals become more stable. Biological structures that collapse on Earth can solidify in microgravity.

To prove the concept, the team built a full-scale heat shield, drove into the Mojave Desert, and blasted it with a rocket engine at 14× expected re-entry force.

It survived 6× what they needed.

The long-term vision:
Factories in orbit. Permanent industrial infrastructure in space.

🎙️ Payton Case, Co-Founder & CEO of Dispatch, on Fondo START pod

00:12 What Dispatch is building: reusable re-entry vehicles for orbital manufacturing
01:16 The next big space race... will involve bringing things back down to Earth
02:05 Expanding from re-entry vehicles to industrial space stations
02:33 Mojave Desert heat shield testing
03:48 Gravity is this invisible constraint on manufacturing
04:06 Pharmaceutical crystal growth in microgravity
04:53 Semiconductor manufacturing with lower defect rates
06:39 Understanding the modern space infrastructure stack
08:01 3D printing organs in microgravity
09:37 The coolest thing to be done in space has not been thought of yet

Check out dispatch.space

‍

AutoFAB is a robotic desktop factory that prints, assembles, quality-checks, and packages hardware autonomously

It’s already being used to manufacture more of its own robotic arms

The insight behind it is simple: Prototyping is easy

Production is the completely different ballgame - and that’s where hardware startups die

Factories. QA. Packaging. Scaling production.

That’s the bottleneck AutoFAB wants to eliminate

The long-term vision: AutoFABs making more AutoFABs

🎙️ Ansel Dias, Founder & CEO, AutoFAB on Fondo START pod 

00:18 AutoFAB is a robotic desktop factory
00:33 It prints things, then assembles them
00:41 It does quality checks and even packs shipping boxes
01:03 Putting hardware into production is extremely difficult
02:01 AutoFAB to make more AutoFABs
02:14 Growth will become geometric
02:37 Right now it's making more of its own robotic arms

Check out autofab.net to learn more

It'll be knowing which ones are right.

Right now AI writes code, solves problems, produces proofs. But there's no way to guarantee any of it is correct. Pedro Nobre is building that guarantee.

Cajal sits at the intersection of formal verification and AI. They use Lean, a language that lets you formalize a statement and derive a proof that's either correct or incorrect. 

Binary. No ambiguity.

The hard part: the space of possible proofs is combinatorially large. Humans somehow navigate it with strange inductive biases. Machines couldn't keep up.

Then reinforcement learning changed what's possible. AI can now iterate against an infinite source of reward: mathematical correctness itself.

The thesis: create a superintelligent mathematician, and you solve most problems.

They're already working with frontier AI labs. Starting in quantum computing and finance. Software verification and cryptography next.

🎙️ Pedro Nobre, Co-Founder, Cajal on Fondo START Pod

‍

01:37 Formal verification explained - verifying whether software or mathematics is correct

02:24 We need to make sure what AI outputs is correct

03:07 Why mathematical proof search is combinatorially difficult

03:42 How reinforcement learning is changing theorem proving

04:11 Why AI is suddenly solving harder math problems

04:28 We already have access to a superhuman mathematician

04:46 The future of checking whether all mathematics is actually correct

05:42 Quantum information theory and applied verification research

06:36 Smart contracts, specifications, and provably correct systems

07:11 If you create a super intelligent mathematician, then you solve most problems.

‍

Check out caj.al

Lucent spots it 

Drops an alert in Slack

Someone tags a coding agent in the thread 

It ships a PR

Fixed before anyone knew it was broken

This is the workflow Lucent users are actually running today

Lucent watches your session replays like a human analyst: friction points, bugs, failed upgrades...

Patterns no human has time to sit through

Connects to PostHog. Quick set up. 

Julius, Mastra, BrowserUse and more already on it

Every session replay you're collecting and not watching is data you're wasting

Lucent can help

🎙️ Alisa Rae, Founder, Lucent on Fondo START pod 

00:44 Building an AI that watches your session replays 

1:10 Leaving Australia's Tall Poppy Syndrome for SF 

2:41 Getting rejected by YC as a solo founder 

3:32 Why she decided to keep building alone 

4:36 Raising a $1.3M pre-seed round 

6:43 Pivoting Lucent into AI training data 

7:29 Returning to the original vision 

9:27 Why this was impossible before modern AI 

11:21 AI agents generating PRs from Lucent alerts 

13:28 get a co-founder for YC?

Check out www.lucenthq.com to learn more

Everyone optimized for speed

Prompt in, app out, as fast as possible

But the bottleneck isn't just speed
It's context

Give coding agents better context and everything improves.

Lower cost
Faster execution
Better output

Woz is built for that shift.

A Claude Code plugin that makes coding agents faster, cheaper, and higher performing

Up to 55% cheaper
Up to 40% faster
Higher benchmark scores than Claude Code alone

And the timing matters:

The age of tokenmaxxing is ending.
The age of more measurable ROI is beginning.

🎙️ Ben Collins, CEO & Co-Founder, Woz on Fondo START pod

00:57 Context is the biggest bottleneck
02:11 Why prompt-to-output breaks at enterprise scale
03:28 Apple cracking down on AI-generated apps
05:02 Why local AI models are becoming viable
06:37 The rumored Cursor and xAI acquisition
07:01 Coding agents and the zero-margin problem
09:48 The end of token maxing
12:32 Why AI infrastructure is shifting toward ROI

Check out wozcode.com to learn more.

Not your E&O policy
That assumes a human made the error

Not your cyber policy
That assumes an attacker breached the system

Autonomous AI agents are explicitly excluded from both

Ines Boutemadja discovered this while building AI agents for enterprises

Then procurement started asking for proof the AI was actually covered

Existing policies couldn’t provide it.

So she built Klaimee: the first purpose-build E&O coverage for AI agents

Built for companies deploying AI in healthcare, fintech, and voice & more

Her bet on the future: AI liability is inevitable

‍

🎙️ Ines Boutemadja, CEO & Co-Founder, Klaimee on Fondo START pod 

‍

00:57 "Klaimee is the insurance for your AI agents" 

02:11 Building therapeutic agents and insurance claims agents 

02:48 Existing policies "completely excluded" agentic AI 

03:31 Enterprise procurement blocking deals without coverage 

05:02 Why cyber and E&O fail autonomous AI risk 

06:01 AI liability coverage for agentic system providers 

10:20 First Algerian woman founder in YC 

11:15 "Don't wait for permission" 

14:20 "AI liability is inevitable" 

16:18 Why stealth mode is overrated

Learn more at www.klaimee.ai

‍

QA still hasn't caught up

Aakash saw it firsthand at Windsurf during the AI coding explosion

Then realized:

QA is probably a bigger bottleneck than coding itself

So he built Canary

An AI QA engineer that reads your source code, maps changes to real user flows, spins up remote browsers, and drops video recordings directly into the PR

15 minutes instead of days

Now the workflows are getting even crazier

One customer already runs a fully autonomous loop:

Linear ticket → Cursor background agent → PR → Canary QA → Merge

The engineer just reviews the outputs before approving the change

Code generation is becoming autonomous

QA has to become autonomous too

🎙️ Aakash Mahalingam, Co-founder & CEO, Canary on Fondo START pod 

‍

00:15 What Canary does 

0:57 Spinning up remote browsers to test 

1:42 User flow validation vs file reviews 

2:29 How this changes developer workflows 

3:12 Lessons from Windsurf and Cognition 

3:43 QA is a bigger bottleneck than coding itself

4:08 Pivoting inside YC from regression to feature testing 

5:02 Applying to YC five times 

6:11 Background agents opening PRs autonomously 

6:37 "They didn't write a single line of code to merge that"

‍

Check out runcanary.ai to learn more. 

Not because outbound stopped working

Because the data got smarter than the emails

Kathryn Wu and her co-founder built Openmart as an SMB intelligence layer:
Verified owners
Decision-maker contacts
Google reviews
Website quality
Location intelligence & more

Now they've launched "OpenClaw for Sales"

A conversational outbound workspace built on top of that data

No spreadsheet cleanup
No disconnected enrichment tools
No giant table views

Just ask the database what you need

DoorDash uses Openmart for regional planning
Whatnot uses it to identify high-quality sellers
Clay customers use the data directly inside outbound workflows

Try it out at openmart.com

🎙️ Kathryn Wu, Co-Founder of Openmart on Fondo START pod

‍

01:13 Openmart’s SMB intelligence database and Openclaw for Sales

02:16 The shift from giant lead lists to AI qualification and scoring

03:43 Why SMB data differs from LinkedIn-centric sales platforms

05:57 How modern GTM teams use multi-channel outbound workflows

06:39 The highest-value qualification signals in SMB sales

08:25 Openclaw for Sales and conversational outbound workflows

09:02 How DoorDash uses Openmart for regional planning

11:28 Deduplication and the hidden pain of outbound infrastructure

12:16 “The future is qualification and scoring”

14:08 Why proprietary data is the moat behind AI outbound

Learn more at openmart.com

Then it failed clinical trials in the US

No one could figure out why

The answer was hiding in the gut microbiome
Anto Bio’s model helped identify it

99% of the genes in your body aren't yours 
They belong to your microbiome
Over two-thirds of drugs are heavily affected by how your gut processes them

But the microbiome data was too noisy. Too messy. Petabytes of it just sitting there

Anto Bio's mission: make the gut microbiome computable for the first time

They're building foundation models that help pharma design drugs that work for more people

🎙️ Arvid Gollwitzer, Co-Founder, Anto Bio on Fondo START pod

1:18 What Anto Bio is building and why it's novel

2:10 Why this didn't exist before

3:05 99% of your genes aren't yours

4:00 Why pharma ignoring the microbiome is a huge problem

5:04 The microbiome is modifiable

6:30 Modeling microbiome differences across populations

7:00 Drug approved in China, failed in the US, and how Anto found out why

8:02 How many drugs are affected by microbiome metabolism

10:00 GLP-1s and the gut microbiome connection

12:42 The biggest unsolved variable in drug development

Learn more at www.anto.bio

They need less friction

But training plans assume your life stays static

You travel
It rains 
You have 20 mins between meetings

People don't quit because they lack discipline

They quit because adapting the plan is harder than the workout itself

HYBRD brain can help

An adaptive AI agent that adjusts your training to whatever life throws at you

Built by ultramarathoners, Ironman finishers, and a founding engineer who won a 100-mile cycling race

🎙️ Matthew Ruiters, CTO & Co-Founder, HYBRD on Fondo START pod

02:01 Connecting every wearable into one training system

02:42 Using sports science to measure total training load

03:07 How former Whoop teammates started building HYBRD

03:50 Why “serious athlete” is really a mindset

04:49 The idea behind HYBRD Brain

05:18 Adapting workouts around travel, weather, and hotel gyms

06:13 The consistency insight from 895,000 athletes

10:44 Why athletes wanted adaptation, not just tracking

11:35 Why friction stops founders from staying healthy

14:03 How AI tools are changing software engineering

learn more at www.hybrd.com

The moment you stop talking, it starts talking

But humans don’t work that way

In real conversations, pauses don’t always mean interruption. Sometimes they mean: keep going

Current AI misses this completely.

Amit Yadav spent years building real-time AI systems at Meta Reality Labs for Ray-Ban smart glasses

Now he’s building Fern: a sales copilot that assists teams live during meetings.

Not after the call.
During it.

The hard problem isn’t retrieval anymore.

It’s judgment.

Because the future of AI won’t be defined by smarter answers alone.

It’ll be defined by systems that understand how humans actually communicate.

🎙️ Amit Yadav, CEO & Co-Founder of Fern, on Fondo START pod

‍

01:24 “Imagine your sales rep hears a prospect question and answers appear instantly.”

01:55 The hardest AI problem: knowing the right time to help

02:09 “Our in-house model beats human judgment, ChatGPT, and Gemini.”

02:46 Building realtime AI systems for Ray-Ban Meta smart glasses

03:57 Fern’s desktop copilot listens and assists during meetings

04:34 The industry shift from prompted AI → proactive AI assistance

05:28 Replacing Slack searches, docs, and tabs during live calls

07:10 “The most exciting thing is AI helping you without asking for it.”

11:09 Why silence in human conversation doesn’t always mean interruption

14:15 The need for a realtime social-awareness layer between humans and LLMs

‍
Check out getfern.ai

It's actually the whole game

Ruben Harris knows this better than anyone

He built a 300-person call center across 3countries to reach 1M people a month 

...and saw exactly where the ceiling was

The problem isn't effort. It's physics. 

A working-class student fills out a form at 10 PM. Nobody's staffed at 10 PM

So 90% of leads go cold
Not because they weren't interested
Because nobody got back to them fast enough

Outrival's AI agents respond in under a minute

They call, text, email, and follow up

One client made $10M in new revenue from leads they already had

The best outbound AI isn't replacing humans
It's doing the work humans never had time to do

🎙️ Ruben Harris, Co-founder & CEO, Outrival on Fondo START pod
‍

01:12 Why Outrival focuses on outbound AI
03:18 Building a 300-person call center
05:27 Why speed-to-lead changes everything
08:52 Aged leads as hidden revenue
09:57 100,000 calls in an hour
14:11 Turning one school’s leads into $10M revenue
18:43 Enterprise sales advice for AI founders
24:48 Pricing digital workers like employees
27:56 From cello to Wall Street to Silicon Valley
50:14 Building startups in the AI era

Check out www.outrival.com

"Turn every interaction into a long-term relationship"

AI solved that 

Cursor, Claude Code, Replit...

Building is nearly instant now
So what's slow?

Knowing if it actually works.

When AI writes your feature, someone still has to answer: did it break everything else?

That's the new bottleneck. Not creation. Validation
Jack Brown is building Lark to solve exactly this. 

An AI QA engineer that writes tests, keeps them updated, and continuously validates

There's the building, and then there's the thing that lives on after the building

Lark is focused on that second half
‍
🎙️ Jack Brown, Cofounder, Lark on Fondo START pod

00:15 Building an AI QA engineer
01:56 AI dramatically increased development velocity
02:15 The bottleneck becomes verification
02:25 Cursor writes the code. Lark writes the tests.
03:24 Acceptance criteria → automated validation
04:15 Non-technical founders building software with AI
05:35  Lark agents autonomously defining tests
06:31 Everything is just validation
07:56 Fully agentic software creation
08:53 Why testing becomes more critical in AI-generated codebases

Visit getlark.ai to learn more

Now it surfaces consultancies advising on it.

And LLMs have gotten worse at this too. As they’ve tried to cut costs and focus on the information 99.99% of users want, long-tail edge-case data has become much harder to find.

Natalie Aresta-Katz was at a big law firm. One of their biggest clients wanted to track new and proposed laws in 45 countries that didn’t publish in English.

They hired lawyers in every single country. Tried probably 15 tools on the market. Ran manual searches.

Nothing worked well.

Now she's building the tool she wished she had.

One workflow went from up to 13 paralegals and around 120 hours a month to one user and about an hour and a half.

‍

🎙️ Natalie Aresta-Katz, CEO & Founder of Regbase, on Fondo START pod (full ep in comments)

‍

00:20 “Regbase was really born out of a place of necessity.”

01:32 Tracking new and proposed laws across 45 countries

02:00 Why existing tools failed

02:43 The “Wizard of Oz” backroom behind regulatory databases

03:26 Why long-tail legal data is getting harder to find

04:23 Dropping into YC after Andrew said, “an MBA isn’t really school”

05:37 Finding unspent state-level funding for school safety upgrades

07:05 From 120 hours to about 1.5 hours

07:38 How Regbase uses LLMs, specialty search, and link-following

08:33 Why legal work may shift from hourly billing to output

09:38 Searching laws in languages teams don’t speak

10:27 Why Regbase unlocks more opportunity for law firms

11:00 Who should book a demo: government vendors, corporate lawyers, AI law trackers, employment lawyers, and teams tracking high-volume regulatory change

visit regbase.com to learn more