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

Adil Mania moved from Paris to SF to change that

Now he's building a full media company around one idea: tech doesn't need to be packaged like homework

A live night show. A print magazine. A weekly newsletter. An animated recap

Every major industry learned how to entertain

Now it's tech's turn

🎙️ Adil Mania, Founder, Silicon Mania, on Fondo START pod

01:48 — Moved from Paris to San Francisco two months before the recording

02:47 — Built a startup media company in France, then stopped when it became "boring"

04:00 — Silicon Mania began as a new, "bigger, better, bolder" media idea

05:23 — His philosophy: treat life like an experiment; iterate instead of chasing final wins

11:00 — The recap format started as an iteration on old YouTube-style yearly recaps

11:38 — First recap went from ~2K views to 50K overnight, then kept climbing

12:40 — Weekly recaps validated the format with roughly 250K+ impressions

13:40 — His thesis on media: people don't need automated slop; people need trust

16:14 — Expanding the format into a newsletter with frame-by-frame context

17:00 — Bringing back the monthly format in print through Silicon Mania magazine

18:16 — The night show is his favorite product: live, interactive, and built around the human side of founders

19:54 — His ambition: make tech media mainstream enough that "my mom" can enjoy it

23:03 — The long-term vision: build the "NBC for tech"

Then his 57-year-old mom asked ChatGPT for new running shoes

Nike answer. Bought the shoe. Was super happy. 

Google was never part of that customer journey...

That was the seed of Rankad.ai

He pitched Co-Founder William Gyltman. They went all in

Track and grow brand visibility across ChatGPT, Perplexity, Gemini, and Copilot. 

Enter your domain, 30 seconds later you're in the app. AI agent optimizes your site directly

Liam Karlsson & William Gyltman, Co-Founders, Rankad.ai at The Residency Demo Day 

🎙️ Fondo START pod w/ David J. Phillips (full ep in comments)

00:25 “We’re helping companies earn more money in AI search”
02:34 AI search is moving fast: new models, algorithms, protocols
03:05 A new source of income beyond Google traffic
03:31 AI search is growing fast, but has not outrun Google yet
05:34 Enter your domain and get inside the app in about 30 seconds
05:52 Visibility, competitor, and company data inside one platform
06:07 AI gives optimization tasks based on scan data
06:15 The agent executes tasks on your site
09:41 SEO rankings held, but traffic still disappeared
09:47 Liam’s mom used ChatGPT to buy running shoes
10:00 “That was like the seed of Rankad”

They have a decision problem.

They already have the clicks
The conversions
The impressions
The keyword history

What they do not have is a system that knows what to do with it.

So brands pay agencies $5,000 to $50,000 a month - and still lose money on ads

Matthew Chen built Laurence to change that.

A quantitative system for Amazon advertising.
Built for continuous decision-making under profit constraints.
Using existing ad copy, reinforcement learning, and custom models to run ads on autopilot.

When confidence is high, it acts
When data is sparse, it borrows signal from similar keywords

The result: about 40% better performance for customers.

Amazon is the wedge.
Autonomous performance marketing is the bigger vision.

🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing"

00:23 What Laurence does
02:12 How the company found the wedge
02:47 The customer quote that changed the company
03:15 The flaw in the standard Amazon ad model
05:25 The move from Amazon to the wider internet
06:12 How Laurence uses transformers today

Project managers spend half their time scanning plans page by page for conflicts between disciplines. Plumbing through steel beams. Electrical into HVAC

They still miss most of it. Millions in rework when caught in the field

Milind Sagaram built Articulate to catch these issues before construction starts

AI reads the PDFs. Finds clashes across architectural, structural, and MEP sheets. Generates draft issue reports automatically

The surprise: construction teams aren't resistant. They want it more than anyone expected

🎙️ Milind Sagaram, Co-Founder & CEO, Articulate / Helonic.com on Fondo START pod 

00:18 AI for finding drawing issues before construction starts
02:24 The old workflow: manual plan review
02:45 The consequence: rework and delays
03:00 Small issues, massive downstream cost
03:27 Copilot, not replacement
04:04 Why AI belongs in construction
04:39 What surprised him about selling into the industry
05:59 Who Articulate sells to

Morph trains coding subagents.
Not for humans. For frontier models.

Fast Apply edits at 10,000 tokens/sec.
WarpGrep handles code and log search.

Both keep the main model’s context clean

Because when context gets too large, performance drops.

Now Morph is pushing coding subagents even faster.

One newer model runs at 33,000 tokens/sec: https://docs.morphllm.com/sdk/components/compact

🎙️ Tejas Bhakta, Founder & CEO, Morph

01:30 Fast Apply + WarpGrep
02:26 Context fills up around 100k
02:38 Keep the main model context clean
03:31 “You can’t scale human attention 100x”
07:28 Founders are missing how to value human attention
08:30 New model at 33,000 tokens/sec
10:08 Better, faster, and cheaper than the frontier

He saw the same problem on repeat

Simple legal work took weeks. Pricing was opaque. Lawyers kept starting from scratch instead of using AI

So he built Arcline. 

AI generates the first draft. Elite lawyers from the best firms, and schools (Harvard, Oxford etc.) do the final revision

Up to 80% of the work gone. Same-day turnaround (Try getting that from a traditional law firm) 

50+ venture-backed startups already onboard when this ep was recorded

Most legal AI tries to replace the lawyer. Pamir replaced the busywork

02:09 “Fondo, but for legal” / AI-native legal for startups
03:12 Sold AI to law firms first; then pivoted to end users
04:52 Why Arcline uses experienced lawyers, not junior lawyers
05:43 Faster, higher-quality work — including complex matters
07:00 Big Law was slow; incentives were broken
07:36 Why going direct creates the data loop to improve AI
08:34 AI does the grunt work; lawyers create trust
11:08 What startups actually come to Arcline for
12:26 The long-term vision: legal advice at your fingertips

Not because he was lying. He just didn't have the real ones

That's when the CISO realized they needed Oximy

Ask an enterprise how many AI tools they use. They say 10. The real number is probably 40+

Most of the risk isn't malicious. People just want to get things done faster. So customer lists end up on free tools with no DPA

The first instinct is to block everything. But people bypass restrictions anyway

The real question: how do you say yes to AI without losing control?

That's Oximy. A control layer for enterprise AI adoption. Track usage, manage spend, enforce governance

🎙️ Naman Ambavi, Founder & CEO, Oximy on Fondo START pod

00:19 — What Oximy helps enterprises understand and manage
02:24 — From India to the Bay, and the thesis behind Oximy
03:29 — Track adoption, cost, and risk controls
03:52 — “They say they’re using not more than 10… the number goes roughly over 40”
04:22 — Compliance, retraining risk, and why oversight matters
05:10 — Most misuse starts with harmless intent
06:23 — The gap between Silicon Valley and corporate America
07:35 — How to say yes to AI without losing control
10:25 — Why the pain shows up most clearly at 1,000+ employees
12:59 — Why banning AI is the first instinct — and why it doesn’t last

Show them your product, and the first thing they’ll say is: “the margin is off on this.”

Every hour they spend learning software is an hour they’re not billing.

Raffi Isanians knows that because he lived it.
Kirkland. Gunderson. Years inside private equity and venture work.

That’s why Mage Legal has a simple standard: if a lawyer opens the product with no instructions and can’t figure it out, "we’re failing"

Comprehensive AI coverage across the entire data room:
red and yellow flags, diligence memos, disclosure schedules, redline comparison. 1,500 documents in tens of minutes. 

All async.

05:15 - Puts a TOS into ChatGPT 3.5, comes back 85-90% there

06:20 - Every hour learning a tool is an hour not billed

08:37 - The product worked, nobody wanted it

09:00 - YC partner on the two-year window

09:53 - AI is an F1 engine in a world of bicycles

13:00 - Clients are pushing AI adoption, not the lawyers

15:19 - The goal is zero behavior change

17:00 - 1,500 documents, diligence memos, tens of minutes

18:22 - One good associate now does the work of six

30:56 - Engineers simplify, lawyers complicate

33:00 - 11PM, picture of his daughter, back to work

40:34 - Simple enough to use with zero instructions

Nobody has built the equivalent for the physical world

Previously, Lucas Ngoo co-founded Carousell, scaled it past $1B 

Now at Cortex AI he's collecting the data robotics labs need to train foundation models

Cameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work (Maybe tens of millions, even hundreds of millions of hours)

Not building the robot. Not training the model. Collecting what goes in

2026: scale data in a big way 
2027+: start rolling robots out, 90% teleoperation, 10% autonomy

While humans step in, the system keeps learning

That's the flywheel toward full autonomy

01:38 Cortex as the data layer for general-purpose robotics
02:19 Why text data doesn’t transfer cleanly to the real world
02:51 Recording day-to-day human manipulation work
03:23 “Tens of millions” to “hundreds of millions of hours” of data
06:37 Cortex’s role: “We are really the data piece”
07:28 When humanoids become part of everyday life
08:00 2026 as the year data scales
08:13 90% teleoperation, 10% autonomy
08:37 Data flywheel toward full autonomy
08:55 Synthetic data + real-world data reinforcing each other

Gavin's dad has worked at Marriott for the last nine years. When he showed Gavin the old software, that was the spark

Lance builds AI agents that answer calls, handle back office operations, run sales workflows & more

Started with voice, got inside the hotels, and realized how much more they could automate

With coding agents one engineer acts like five

Big contracts need custom solutions. Now they can deliver

🎙️ Gavin Brennen, Co-Founder, Lance (YC W26) on Fondo START pod

01:02 The reality of hotel software
01:53 The insight that sparked the company
02:01 A better way to handle guest requests
02:46 Why Lance had to build desktop agents
03:08 The centralized dashboard layer
04:00 Why hotel problems are highly custom
04:32 One engineer can now act like five
05:51 “Go all in”

Nikhil Reddy saw the shift early: as trust matters more, partnerships become a real revenue channel

Problem is most partnership teams are still running on spreadsheets. They don't know where to focus. Attribution across emails, events, and co-marketing is a mess

Arzule uses CRM data, market signals, and ecosystem signals to help partnership teams discover and prioritize the partnerships that actually drive revenue

Two people building it. Already working with companies generating over $400M in ARR

Applied late to YC's fall batch, never even got a reply. Applied again for winter, got in, and their group partner told them to pivot the next day. That became Arzule

🎙️ Nikhil Reddy, Co-Founder & CEO, Arzule on the Fondo START pod

01:09 What Arzule does and who it's for
02:24 Direct sales is dying. Trust is taking over
02:43 Pivoting mid-batch from multi-agent coordination to partnerships
03:12 Why partnership teams are finally getting their moment
03:54 The data layer: CRM signals, ecosystem signals, company-specific inputs
04:09 Why attributing revenue to partnerships is so hard
05:24 Affiliate links, rev-share, bounties, and contracts in one platform
06:03 What onboarding looks like for larger customers
07:02 Start analyzing everything early, even if it feels relationship-driven
07:48 Tracking signals like new partnership hires and market expansion
08:28 Revenue attribution as the core of predictable partnerships
09:26 Applied late, got no reply, applied again, pivoted the day after getting in
10:02 Move fast and pick something you want to do for 10 years

With Pillar it takes one sentence

JJ Maxwell built an open source copilot you build into your app. Users talk to it in natural language and it drives the app for them

The problem: products can do a lot but users don't always know what's there. So they ask support. Or they churn

Before Pillar, JJ built a creator ad marketplace with about 40,000 creators. Then spent two years on another product through YC W24. About $30M on the platform, real users, decent growth. 

Pivoted anyway. 

As soon as he lost belief it was gonna work, he ripped the bandaid off

Web MCP is already rolling out. Companies trying to stop agents from taking actions are fighting a losing battle

🎙️ JJ Maxwell, Founder & CEO of Pillar (trypillar.com) on the Fondo START pod

01:56 What Pillar is: a copilot that’s easy to build into your app
02:19 Why users ask support or churn when product complexity hides value
03:05 “30 clicks” in Zendesk becomes a sentence
03:39 Why AI can do this now: models are better at reasoning and chaining actions
04:19 What implementation looks like: wrap existing frontend code and tool calls
05:25 Why teams can often get Pillar working in about a day
06:09 The Double journey, real traction, and the decision to pivot
11:00 Web MCP and why agent-ready software is coming
12:23 Why companies may not be able to stop agents from taking actions forever

Investors demanded it.
Accelerators screened for it.

The narrative became so entrenched that founders started pairing up out of obligation, not alignment.

Julian Weisser, founder of SOLO and ODF, has a name for this phenomenon: co-founders of convenience

And he's proving they're not just unnecessary-they're often the reason companies fail.

This week, Julian released 'The State of Solo Founding' report: "Today, solo founding is considered odd. Soon it will be the default. This report features exclusive Carta data alongside commentary from solo founders who have raised over $250M and the investors who backed them."

The comprehensive tracks solo founder rates across thousands of startups, the headline finding is historic: for the first time, over one-third of new startups are solo-founded (That's 36% in 2025, up from under 25% in 2019)

👉 Download the report here: https://solofounders.com/report

👉 Apply to the Solo Founders Program today. A three-month, in-person residency for 6 ambitious solo founders. Next cohort starts Jan. 23, 2026: solofounders.com/program

(00:44) Why the report matters now
(02:17) The data: 24% → 36%, first year over 1-in-3
(04:32) 3 forces: AI, visible wins, collapsing narrative
(06:00) Investor POV
(07:30) Org design insight: cutting the middle layer
(09:47) Download report: http://solofounders.com/report
(11:01) ODF26: half the cohort flipped to solo
(12:33) Inside SOLO
(14:30) Why coworking doesn't work for startups
(16:37) Outcomes: $1M ARR in 2 months & more
(17:19) Follow @solofounding & @joinodf

Where to find Julian Weisser:‍
X: https://x.com/julianweisser
LinkedIn: https://www.linkedin.com/in/julianweisser
Website: https://weisser.io‍

Where to find SOLO: ‍
X: https://x.com/solofounding
LinkedIn: https://www.linkedin.com/company/solo-founders
Website: https://solofounders.com

Where to find ODF:‍
X: https://x.com/joinodf
LinkedIn: https://www.linkedin.com/company/solo-founders
Website: https://joinodf.com‍

Newsletters: ‍
Texts with Founders: https://textswithfounders.com
Multitudes: https://multitudes.weisser.io‍

Where to find David Phillips:
X: https://x.com/davj
LinkedIn: linkedin.com/in/davjphillips‍

Brought to you by:‍ Fondo — All-in-one accounting for startups @ fondo.com

Numeral was one of his early bets. He sent Numeral's CEO Sam an email. By the end of the day, he was working there as Head of Growth. Now he's helping scale the YC-backed sales tax platform serving over 2,000 customers.

Nate's seen what happens when founders don't think about sales tax. "They actually found out that they owed about a half million dollars in sales tax… the buyer subtracted that off what would have gone to the founders."

When Nate was a founder, he'd log into Stripe and that sales tax dashboard was lit up like a Christmas tree. Numeral does free nexus studies and monitoring — takes five minutes to set up, plugs into Stripe and Rippling, then runs itself.

They launched their SaaS product recently, and their whole concept is set it and forget it. They actually love it when customers don't log in. Some of Nate's new learnings? "When I was a founder, I was always pretty good at marketing. I was just marketing not-so-great products, which made marketing a lot harder." At Numeral, with the right product at the right time, everything clicked.

And he knows: great products make marketing easy. Timing makes it effortless.

‍

Key Topics Covered

• [00:10] $500K sales tax bill found during due diligence
• [01:10] What is nexus and how it triggers
• [01:51] From founder to angel to fund manager to operator
• [04:02] 10+ years building, angel investments, vc fund
• [05:27] Numeral: e-commerce to SaaS journey
• [08:12] States that tax SaaS + global VAT complexity
• [08:40] "Set it and forget it"
• [10:37] Free nexus study + monitoring in 5 minutes
• [11:24] What's working in growth 
• [13:28] Great products make marketing easy, timing makes it effortless
• [14:25] Nate's investing rule per batch

Follow Nate Matherson:
X: @NateMatherson
LinkedIn: linkedin.com/in/natematherson

‍

Follow Numeral:
X: @numeral
YouTube: youtube.com/@numeraltax
Website: numeral.com
LinkedIn: linkedin.com/company/numeralhq

‍

‍FollowDavid Phillips:

‍X: https://x.com/davj

‍LinkedIn: https://www.linkedin.com/in/davjphillips

‍

Brought to you by: Fondo — All-in-one accounting for startups: fondo.com

After music school, a hedge fund tour, and a B2B SaaS sprint, he launched MOTS—short, sharp episodes designed to be both of the moment and built to last a quarter. 

His north star isn’t “go viral.” It’s “be clear.”

The insight is disarmingly pragmatic: structure is not the enemy of creativity—it’s the amplifier. Lists compress cognition. 

A beginning–build–end gives every clip a runway and a landing. 

When a five-replies-deep roast on X unexpectedly detonated, MOTS podcast already had the scaffolding to catch the surge. That’s the signature move: follow a consistent weekly cadence, then publish “emergency episodes” when the culture pops. 

The result is a feed that feels alive without feeling random.

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Key Topics Covered

• Career path: SF Conservatory → hedge fund → B2B SaaS → MOTS + Atlas Media Labs
• Jazz formulas = content formulas: two-five-one progressions, building blocks, beginning-middle-end
• Why lists work: digestibility, structure, pattern-matching
• "Neither timely nor timeless": weekly SF tech culture + emergency current-thing episodes
• Emergency episode #1: Brian Chesky's bench press 
• The viral ratio: Meta Ray-Bans clip → five-tweets-deep → Theo's reply → Seth's "roast" GIF
• "16 hours of screen time": the competitive advantage
• Distribution strategy across Twitter, YouTube, Apple, Spotify

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Timestamps:

00:20) "neither timely nor timeless"

(00:51) Keeping the music alive

(02:10) SF Music → hedge fund → SaaS → pod

(03:20) Jazz improvisation: a content strategy

(03:42) Building blocks & two-five-one progressions

(04:35) How to make content easily digestible

(04:53) The @theo ratio backstory

(06:46) @sethsetse viral quote-tweet

(07:17) Emergency pods vs. weekly episodes

(07:22) Emergency Pod #1: @bchesky's bench press

(09:42) Where to find @mots_pod
‍

Where to find Jayden Clark:

X: @creatine_cycle
LinkedIn: linkedin.com/in/jayden-clark-75991a1aa

‍

Where to find MOTS Podcast:

X: @mots_pod
YouTube: @motspod
LinkedIn: linkedin.com/company/mots-pod

Where to find Atlas Media Labs:

Website: atlasmedialabs.com

‍

Where to find David Phillips:

‍X: https://x.com/davj

‍LinkedIn: https://www.linkedin.com/in/davjphillips

‍

Brought to you by: Fondo — All-in-one accounting for startups: fondo.com

Their catalyst was realizing founders were building in public on X but deals were happening on LinkedIn. After meeting advisor Gustaf, they pivoted distribution strategy to focus on where B2B founders actually live and transact. Sky calls this "content-market fit"—a state where your content hits your target customer every single time, creating scalable, repeatable inbound motion. They were fully booked from their first week post-YC launch, landing Series B customers. One founder messaged urgently, jumped on a 15-minute call, and paid on Stripe immediately. They recruited over Halloween weekend instead of partying. They hosted a yacht party with $10 billion in collective GDP (320 capacity, 750+ on waitlist). Neo's philosophy: "The product is just amplifying what we already are. Just be authentic." Sky's vision references Westworld: "Your agents will interact with each other instead of humans."

Key Topics Covered:

· What Imagine AI is: a chat-first AI clone with high-fidelity persona creation, subject matter expert interviews, and content engineering to hit content-market fit

· From X to LinkedIn: pivoting distribution to where B2B deals actually happen; Gustaf's advice on market selection

· Sky's origin arc: Chengdu → LA → Bay Area → UCSD student body president → $150M state funding advocacy → Break the Outbreak nonprofit

· Neo's journey: UCSD → Berkeley dropout → "Skygodkingdom" beach encounter → haircut in the woods → building startups pre-Imagine AI

· Content-market fit framework: when your content hits your customer every single time—scalable, repeatable motion with high-intent top-of-funnel inbound

· Week-one hypergrowth: fully booked post-YC launch, Series B customers, 15-minute Stripe close during conference, recruiting over Halloween

· Authenticity over algorithm: amplification not fabrication; the product shapes around you, not the other way around

· Building clones that replicate voice, context, backstory, heuristics, and cognition

· The $10B GDPyacht party: 320 founders, 3 DJs, 750 waitlist—building community as cultural moment

· The 'Westworld' thesis: AI agents interacting on your behalf

· Building in public as 2025 narrative: why founders do great work but nobody knows; solving discovery through personal brand at scale

· Design philosophy: one infinite content motion thread vs. scattered posts; AI handles artifacts, humans make strategic decisions

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Chapters:
‍
01:21 - The origin story: "Are you Skygodkingdom?"
02:00 - Neo cuts Sky's hair in the woods
02:36 - Sky's journey: Youngest student body president at UCSD
04:20 - Securing $150M in state funding for student housing
06:28 - The nonprofit during COVID
06:40 - How Imagine AI started: solving their own problem
07:15 - Launching on YC and getting booked solid
08:00 - Using their own product for personal branding
09:08 - What is "content-market fit"?
10:08 - The future: AI clones
11:09 - The $10 billion GDP yacht party in SF
12:11 - Where to find Sky, Neo, and Imagine AI

Where to find Sky Yang:

‍LinkedIn: https://www.linkedin.com/in/skyyang

‍X: https://x.com/skygodkingdom

‍

Where to find Neo Lee:

‍LinkedIn: https://www.linkedin.com/in/neo-lky

‍X: https://x.com/neo_lky

‍

Where to find Imagine AI:

‍Website: https://www.imagineai.me

‍X: https://x.com/imagineagi‍

LinkedIn: https://www.linkedin.com/company/ai-imagine

‍

Where to find David Phillips:

‍X: https://x.com/davj

‍LinkedIn: https://www.linkedin.com/in/davjphillips

‍

Brought to you by:Fondo — All-in-one accounting for startups: fondo.com

Rebecca Medina had the network. She had decades of Big Tech experience. She had the credibility. But when she needed project management help on a client engagement as an independent consultant, none of it mattered.

"Even with my network of project managers, I couldn't find the right person fast enough," Rebecca recalls. "And it created a big problem for the company because we weren't able to scale as quickly as we wanted."

That pain point became Talent Cheetah.

Five years later, Rebecca and her co-founder Jeff Phillips have built an AI-powered talent marketplace connecting pre-vetted project managers with SMBs. They've scaled to 300 PMs across 34 US states. They've even partnered with the Project Management Institute. But the metric that matters most: the Bureau of Labor Statistics says it takes 90 days to hire a technical project manager. Talent Cheetah does it in minutes—with same-day hiring possible.

In this episode, Medina and Phillips break down the recruitment model that turns recruiting on its head: transparent pricing that exposes hidden markups, lower take rates than traditional agencies, direct PM-to-company negotiation, and real-time hiring through AI matching.

Their core unlocks: many traditional staffing firms charge companies a significantly higher rate than what PMs actually earn—often without disclosing the difference to either side; cultural fit matters just as much as credentials (project management exists on a broad spectrum — the skills needed vary widely across industries, company sizes, and stages of growth.); and past execution remains the strongest predictor of future performance. Their 25-point vetting process includes one pivotal test: candidates must be able to produce legitimate professional references—if you can't find even one after years in the field, you're not ready for the platform.
‍

In this conversation, they reveal just how much AI is automating routine PM artifacts (like meeting notes, risk logs, and timelines) while increasing the premium on leadership and communication; how their intentional U.S.-based strategy competes on quality and transparency in an industry racing to the bottom on cost; and how Talent Cheetah is opening doors for underrepresented groups in project management; why fractional engagements (such as part-time PM support for short durations) are suddenly viable when traditional agencies can't deliver them well.

Key Topics Covered

The pain point origin: Rebecca's consulting crisis when her network couldn't deliver PM talent fast enough

The 90-day problem: Bureau of Labor Statistics average vs. Talent Cheetah's minutes-to-same-day matching

Exposing the hidden markup: traditional agencies bill $x/hour, pay PMs $x/hour, keep $x secret from both parties

No posting fees: free to post unlimited jobs (vs. ZipRecruiter/Indeed/LinkedIn pay-per-post), no sign-up fees for PMs

The 25-point vetting process: professional references, credential validation, and candidates who wait years

The reference test: some applicants can't find anyone to vouch for them after 12-24 months

Four-year minimum: experience requirement (not just title) focused on herding cats and managing projects
US-based strategy: competing on quality, transparency, and credential familiarity instead of global price competition

PMP vs. experience: why certification proves framework knowledge but not execution capability

Direct negotiation: PMs and companies set rates transparently, eliminating hidden recruiter markups

AI-powered matching: real-time algorithm surfaces top 3 PMs, with 297 more to browse

Cultural fit dynamics: startup PMs vs. Big Tech PMs require different personalities
Expanding beyond PMs: network architects, developers, product managers using same vetting framework

PMI partnership: hiring bonanzas and visibility programs in San Francisco

White glove service: helping first-time contractors negotiate rates and structure engagements

AI's impact on PMing: automating artifacts while amplifying leadership and communication needs

Fractional engagements: 10-hour/week arrangements that traditional agencies can't serve
Transparent pricing model: complete visibility vs. hidden markups, lower take rates than Robert Half/Adecco/Tech Systems

Chapters:

(01:55) Origin story: Talent Cheetah

(03:16) What makes Talent Cheetah different: Speed as the #1 differentiator, same-day hiring possible

(04:05) US-based strategy: competing on quality and credential familiarity

(06:08) Supporting underrepresented groups: veterans (logistics → PM transitions) and women in tech

(07:33) Serving both sides: job search help for PMs, FAANG-quality talent for clients

(08:43) White glove service: flexible involvement based on needs, negotiation help included

(09:16) How it works: 30-second account creation, under-5-minute posting, real-time AI matching

(10:15) Platform scale: 300 PMs across 34 US states, discipline-specific but industry-agnostic

(11:04) The 25-point vetting process: four-year minimum, references, credentials, interviews

(14:09) PMP certification vs. hands-on experience: gold standard plus practical execution

(16:02) Exposing the hidden markup: how traditional agencies work

(17:08) AI's impact on PM work: automating artifacts, amplifying leadership and communication

(20:40) Expanding beyond PMs: network architects, developers, product managers

(23:02) PMI partnership: 'hiring bonanzas' and visibility programs in SF

(25:12) Ideal clients

(26:47) Transparent pricing model: no posting fees for companies, no sign-up fees for PMs

(27:36) Getting started: talentcheetah.com, instant talent matching

(30:48) Internal messaging and AI matching: top 3 matches with direct communication

(32:00) Where to find them on LinkedIn, YouTube, talentcheetah.com

Where to Find

Rebecca Medina:
LinkedIn: https://www.linkedin.com/in/rebeccarm
Website: https://www.talentcheetah.com

Jeff Phillips:
LinkedIn: https://www.linkedin.com/in/jeffreyjphillipspmp
Website: https://www.talentcheetah.com

Talent Cheetah:
X: https://x.com/talentcheetah
LinkedIn: ht...

As an angel investor with more than 150 portfolio companies including Levels, Astroforge, and MagicSchool, he's seen patterns in what actually predicts startup success versus what investors claim they're looking for. He writes the Texts with Founders newsletter sharing bite-sized practical wisdom for entrepreneurs and publishes Multitudes, a newsletter exploring founder psychology and startup strategy.

In this episode, Weisser breaks down the denominator delusion: solo-founded companies were more likely to succeed than co-founded ones, but nobody talked about it because when you look at the total number of successful companies, co-founded businesses eclipse solo successes—while hiding how many unsuccessful co-founded companies exist in the denominator. 

His core unlocks: two-thirds of startups die from co-founder disputes before reaching product-market fit or running out of money, being solo is far better than 99% of potential co-founders, and authorship (the desire to express yourself and put your vision into the world) matters more than contortionism (twisting your company to match what investors want to see). 

The flippening already happened in ODF 26—over half chose solo. In this conversation, he breaks down why MagicSchool's Adil Khan (a former high school principal with no startup experience) succeeded solo, how "co-founders of convenience" kill companies before they reach potential, what makes the Solo Founders residency feel like having "five co-founders while building your own company," and why mimicking trends accrues value to memes instead of founders.

Key Topics Covered:

• The denominator delusion: why solo success rates are higher but invisible in the narrative.
• Two-thirds die early: co-founder disputes kill startups before product-market fit or funding issues.
• Co-founders of convenience: rushing into partnerships because investors demand it.
• Invalid constraints: questioning beliefs (like needing school/work co-founders) that prevent great companies.
• ODF's evolution: expanding who can start companies and who they can start them with.
• The flippening moment: over 50% of ODF 26 chose solo after the program.
• Authorship vs. contortionism: building authentically vs. pattern-matching for investors.
• Solo Founders residency: six to seven founders per cohort, living/working together for three months.

Chapters:

• (00:11) The denominator delusion: why solo-founded companies are more likely to succeed
• (02:53) How ODF expanded the co-founder search beyond school and work connections
• (07:05) The flippening: when solo becomes the default instead of the exception
• (09:27) Why two-thirds of startups die from irreconcilable co-founder disputes, not lack of product-market fit
• (10:02) Best practices for co-founding: avoiding assumptions and pre-mortems
• (14:03) How early founders decide to go solo (most don't even consider it initially)
• (17:42) ODF 26 results: over half chose to build solo
• (18:48) Founder characteristics across boom cycles: more mimicry and trend-chasing than ever
• (20:21) Mimicry vs. authorship
• (22:33) The growth narrative trap: why $100B outcome fixation from massive funds limits great companies
• (25:09) The Solo Founders residency: three months, "five co-founders"
• (30:13) The space: own rooms, common areas, office on ground floor, 6am-3am usage
• (33:27) Who applies: half bootstrapped and sold companies
• (36:46) Authorship as the defining trait

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Where to find Julian Weisser:

‍
X: https://x.com/julianweisser
LinkedIn: https://www.linkedin.com/in/julianweisser
Website: https://weisser.io

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Where to find SOLO:
‍

X: https://x.com/solofounding
LinkedIn: https://www.linkedin.com/company/solo-founders
Website: https://solofounders.com

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Where to find ODF:
‍

X: https://x.com/joinodf
LinkedIn: https://www.linkedin.com/company/'odf'
Website: https://joinodf.com

‍

Newsletters: 

‍
Texts with Founders: https://textswithfounders.com
Newsletter (Multitudes): https://multitudes.weisser.io

‍

Where to find David Phillips:
X: https://x.com/davj
LinkedIn: linkedin.com/in/davjphillips

‍

Brought to you by:
‍

Fondo — All-in-one accounting for startups: fondo.com

Naliath's catalyst was advice from a founder mentor: "If you want to work on startups when you graduate, don't even apply to Apple and Google. If you have no plan B, plan A has to work." His core insight: most people's win condition depends on the other person saying yes. He reframed it so yes and no are both wins—the win condition is in his control just by asking. That philosophy runs through Friday's design: it doesn't put email on full autopilot (which "induces anxiety"), it gets users 99% of the way. Friday started as a hackathon project, evolved into a mobile text assistant, then became a Chrome extension after realizing Gmail integration was faster than building feature parity. The average person spends two hours per day in email; Friday users get through 30 emails in 60 seconds.

‍

Key Topics Covered:

- Rejection challenge: daily "no" requirement, mindset shift from fear to relief

- Win condition reframe: "Yes and no are both wins. The win condition is in my control just by asking."

- Cold approaches: Sam Altman golf cart ambush, Garry Tan LinkedIn add during Stanford talk

- Friday evolution: hackathon project → mobile assistant → Gmail Chrome extension

- Anti-autopilot philosophy: "That induces anxiety. It gets you to 99%—you stay in control."

- Predicted action buttons: archive, reply, unsubscribe—all one-keystroke approvals

- Voice matching: Friday drafts replies that sound like you, including dash preference

- 10-minute inbox zero: personal onboarding using auto-archive rules for old emails

- Chat feature: "Look him up online, find his email in my inbox, draft an intro."

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Chapters:

(00:33) The rejection challenge that rewired his confidence
(02:08) Sam Altman signed his laptop

(03:35) Changing you win-condition to be in your control
(04:25) Asking for things that are "hard to get"

(05:20) Meeting Silicon Valley Legends

(06:05) "Is this a Psyop?" - how a cold LinkedIn ask to Garry Tan led to YC

(07:03) Dropping out of Stanford: "If you have no plan B, plan A has to work."

(09:23) Friday DEMO: how enter-enter-enter clears 30 emails in 60 seconds

(13:45) The inbox zero system: snooze what matters, archive the rest, empty daily

(15:13) Why Friday stops at 99%: "Full autopilot induces anxiety—you need control."

(17:36) Chat-powered bulk actions: "Look him up online, find his email, draft an intro."

(19:21) Make every day feel like Friday

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Where to find Allen Naliath:

X: https://x.com/AllenNaliath
LinkedIn: https://www.linkedin.com/in/allennaliath

Where to find Friday:

‍Company X: https://x.com/fridaymail
Company Website: https://www.friday.so
Company LinkedIn: https://www.linkedin.com/company/fridaymail

Where to find David Phillips:

X: https://x.com/davj
LinkedIn: linkedin.com/in/davjphillips

Brought to you by:
Fondo — All-in-one accounting for startups: fondo.com

Amos's catalyst was living both media eras: nine months shepherding a single Wired story about Square moving into a new office versus today's "algorithms plus authenticity" environment. Her core unlocks: lead with the what (then earn the why), tie every pitch to a macro trend your audience already cares about, and default to exclusives over embargoes until you're big enough to run a press gauntlet. New media isn't a replacement for traditional outlets; the best founders run both lanes—because audiences follow people first, products second. In this conversation, she breaks down how to pick the right channel, prep for tough interviews, avoid blacklist behaviors, and time transparency (share the "personal hell" after you've won, to teach—not spiral).

‍

Key Topics Covered:

- What "news" actually is: a hook plus a macro trend your customer already thinks about.

- Founder brand vs. company brand: why audiences follow people first (and how to use it).

- Exclusive > embargo (early): how editors green-light stories and why timing matters.

- Practical media ops: avoid Friday pitches, follow up once, don't text or Signal reporters.

- Content that converts: entertaining, educational, or perspective—never just ads.

- Cinematic launches: when video helps, when it's sizzle; why distribution still wins.

- New media shift: reporters → Substack/podcasts; find where your audience actually is.

- The To-Do List Summit: teaching founder-led marketing when agencies aren't the answer.

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Chapters:

• (01:58) Old media → new media
• (05:26) Why founder-on-camera works—and when it doesn't.
• (08:59) Playing the LinkedIn game, Substack, and sustaining the channels you'll keep.
• (11:10) "Personal hell" as narrative fuel—share it after the win.
• (21:58) Defining a real news hook; anchoring to macro trends (IRL + wellness example).
• (25:48) Exclusive vs. embargo: how reporters decide what to cover.
• (26:53) Pitch etiquette that keeps you off blacklists (days, follow-ups, warm intros).
• (32:32) Founder brand > company brand (early) and the three content modes.
• (36:33) The To-Do List Summit: workshops over thought leadership; hands-on playbooks.

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Where to find Lindsay Amos:
X: https://x.com/lindsayaamos
LinkedIn: https://www.linkedin.com/in/lindsayamos, https://www.linkedin.com/company/amoscomms
Website: https://www.amoscomms.com
Substack: https://lindsayamos.substack.com
To-Do List Summit: https://x.com/todolistsummit

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Where to find David Phillips:
X: https://x.com/davj
LinkedIn: linkedin.com/in/davjphillips

Brought to you by:
Fondo — All-in-one accounting for startups: fondo.com

Holberg’s catalyst was seeing financial confusion across backgrounds—even among peers with professional-class parents. Early Spring had universal interest but low willingness to pay; the unlock was changing the buyer (HR) and making a firm pricing decision: “Pricing isn’t science—it’s a decision.” In this conversation, he discusses building Spring, the B2B pivot, lessons from pricing and sales, and his views on city governance, housing supply, business climate, and tech-literate leadership. This episode presents his perspective and experiences as a founder and candidate.

Key Topics Covered:

• What Spring was: outcomes-oriented financial wellness delivered as a workplace benefit.
• D2C → B2B: universal desire vs. $20/mo friction; employers fund, employees benefit.
• Pricing lessons: fewer options, clearer value, faster decisions.
• Builder arc: bootstrapping (2015–2018), $1M seed, top-rated product, 2023 acquisition; stayed through early 2025.
• Sales scrappiness: writing a book to establish credibility with HR leaders.
• Entering politics: motivations, background across economic circumstances, and emphasis on tech literacy.
• Chicago context (as framed by the guest): population and business trends; collaboration vs. adversarial postures.
• Governance mechanics: mayor/city council dynamics; CPS school board changes; housing supply constraints.
• Campaign posture: outsider experience and how he frames his narrative as a candidate.

Chapters:

(00:36) Spring’s origin — addressing financial education gaps observed across income levels.

(01:43) Early arc — glow-stick hustle; first-gen college; sleeping in the car; AmeriCorps; Google; leaving to build.

(04:21) “Credibility book” — unconventional sales asset for HR conversations.

(06:14) The pivot — strong demand, low D2C conversion; employer-paid model.

(08:43) Building years — 2015 start, 2018 $1M seed, solo grind → top-rated 3 years, 25k+ users; 2023 acquisition; through early 2025.

(12:39) Pricing "aha" — choosing and owning a price to accelerate qualified deals.

(14:37) Why enter politics — empathy across the income spectrum; need for tech-aware governance.

(20:02) Entering the arena — outreach, mentorship, and announcing candidacy.

(24:23) Status quo (guest’s view) — resident/business trends; collaboration with builders.

(27:22) How Chicago governance works — mayor vs. council; CPS board; housing supply.

(30:55) Voter expectations — vision, ideas, results.

(32:32) Closing themes — affordability, fiscal considerations, and civic participation.

Where to find the Joe Holberg:
X: @holbergj
LinkedIn: linkedin.com/in/joeholberg
Website: joeforchicago.com

Where to find David Phillips:
X: @davj
LinkedIn: linkedin.com/in/davjphillips

Disclosure / Non-Endorsement Note:

The views expressed by the guest are their own and do not reflect the views of David J. Phillips, Fondo or the Startup Growth Podcast. Appearance on the podcast does not constitute an endorsement of any candidate, campaign, or policy proposal. This episode is provided for informational purposes only.

Jay's catalyst was realizing he didn't want to just talk weekends—he wanted to build. He and his co-founders first tackled computer-use evals for labs. Inside that work, the language shifted: labs asking for "evals" really needed environments—places where you design rewards, iterate, and actually improve model behavior. Today, Jay frames Hud as the "Next.js of RL environments": opinionated lifecycle, backend-agnostic training, and infra that returns signal fast. Early on, use a foundation model; post-PMF, train your own with SFT/RL—that's where environments matter. Looking ahead, he sees post-training speciation: domain-tuned models for finance, accounting, creative tooling, and more—because teams will own more of their stack again.

Key Topics Covered:

· What Hud is: tools to set up your agent for RL, define tasks, shape rewards, and plug into RFT/other RL backends.
· From evals to environments: why scores measure but rewards improve—and how iteration loops change outcomes.
· Where it fits: use foundation models early; post-PMF train your own for cost leverage + last-mile gains.
· Design + infra: a new category needs opinionated UX and fast results; why lab researchers use Hud for computer-use evals.
· Market timing: the "DeepSeek moment" pulled RL from hobbyists into enterprise interest in 2025.
· Pre-train vs post-train: scale vs accuracy + domain depth—and why post-training is the real edge.
· Future of work: enterprises will own more of the stack; model speciation by domain.
· Reality check: agents ace toy DBs, struggle in production; modeling real environments is the unlock.
· YC W25 arc: vision matched the original app more than mid-batch; enterprise demand is catching up now.
· Finance stack aside: keep ops boring; focus cycles on shipping product (Fondo shoutout in-episode).

Chapters:

(00:15) Cold open — "We give you all the tools to set up your agent for RL."
(00:59) Intro — Jay Ram, Hud, and the origin story
(01:41) What Hud does — build RL environments; backend-agnostic (OpenAI RFT, Thinking Machines, etc.)
(02:12) Where environments fit — early: foundation models; post-PMF: train for cost + accuracy
(02:50) From quant to builder — leaving Wall Street to make things
(03:30) The prank-calling app — #1 on App Store; ≈500k calls; why the customers weren't it
(04:40) Evals → environments — labs' "eval" asks were really RL environments with rewards
(05:40) Evals vs RL — scores vs rewarded steps; how updates happen
(07:14) Hard parts — opinionated design + infra speed for researchers and teams
(08:08) Before Hud — no toolkit/standards; emerging gymnasium-style efforts vs Hud's opinionated path
(09:25) YC W25 — applying, partners (Aaron & Matt), why YC felt like "actual college"
(11:05) Vision vs timing — market caught up; enterprises now exploring environments
(12:20) Trend — teams rolling their own models post-PMF (SFT/RL)
(13:01) Today's fragmented stack — hosting, inference, data; Hud's role in the loop
(13:48) The "DeepSeek moment" — hobbyist RL → enterprise interest in 2025
(15:57) Future of agents — own the stack, post-training speciation
(18:26) Why end-to-end is hard — production data systems need real environments
(19:29) Forward-deployed labs — domain hires and environments; how Hud plugs into RFT
(20:15) Rapid wrap — it's early; the stack is shifting fast

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Where to find Jay Ram:
X: @jayendra_ram
LinkedIn: www.linkedin.com/in/jay-ram-29003b198/

Where to find Hud:
X: @hud_evals
Website: hud.ai

Where to find David Phillips:
X: @davj
LinkedIn: linkedin.com/in/davjphillips

‍

Brought to you by:‍

Fondo — All-in-one accounting for startups: fondo.com

Kevin’s catalyst was realizing the products he loved—Google, Wikipedia—were built by real people. That sent him to YC, then Stripe for world-class reps, then into the internet’s finance classroom: Reddit. He posted every win and loss, learned in public, and distilled trading into rules like “If it’s good enough to screenshot, it’s good enough to sell.” After building After Hour to socialize trading, he’s now productizing that edge with Alpha AI: a proactive, personable copilot designed to build money confidence for the next million millionaires.

Key Topics Covered:

 • What Alpha AI is: a chat-first AI money friend with market context + your portfolio, proactive “what just happened” nudges, and customizable character.
 • From WSB to product: turning public receipts (35K→10M) into a system—floors, catalysts, concentration, disciplined exits.
 • Earnings humility: why reports are a coin flip; behavior, sizing, and timing are the real edges.
 • Founder arc: Stanford → YC pivot muscle → Stripe discipline → Google scale → After Hour → Alpha AI.
 • Culture shift: finance as entertainment/sport; people don’t need courses—they need context at the right moment.
 • Design over dashboards: one infinite chat thread > scattered tools; AI handles background work, humans make decisions.
 • Missed GME, learned anyway: thesis right, timing wrong—how to keep momentum without hero trades.
 • Distribution & trust: followable identities, real screenshots, timely alerts—how credibility compounds.
 • Building in 2025: attention-maxxing, shipping fast, leaning into new formats (e.g., Sora experiments).
 • Finance stack mindset: keep ops boring—Fondo for the back office, Brex for cash/cards—so you can ship product.

Chapters

 (00:00) Cold open — “I wanted a cool dream”: realizing real people build the internet
 (00:59) Intro — Kevin Xu, Alpha AI, and the origin story
 (01:28) Stanford → YC S13 double-interview; pivot from Alpha Labs to Fan Hero
 (06:11) Stripe (#~300) → Google/YouTube: seriousness vs. internet-native play
 (09:00) WallStreetBets culture: memes, transparency, learning in public
 (12:26) The 401(k) stake: missed HR toggle → $35K starting gun
 (14:53) Early pandemic plays: APT, CODX; the floor + catalyst lens
 (17:33) Chasing pops: cruises, Chewy-era stories, and disciplined exits
 (20:11) The GME almost: all-in October, out in December; lessons on timing
 (23:33) Million-dollar swing days; detachment and the screenshot rule
 (25:10) Big 5 finale → $10M peak; why earnings are coin flips
 (27:15) After Hour: social finance, trust via receipts, real-time notifications
 (30:50) Alpha AI: proactive context, AI friends as the interface
 (32:52) Beyond investing: building money confidence; simple company finance stack

Where to find Kevin Xu:

LinkedIn: https://www.linkedin.com/in/imkevinxu
X: https://x.com/kevinxu
Instagram: https://www.instagram.com/founderkevin

Where to find Alpha AI:

Website: https://alpha.so
X: https://x.com/alpha_ai
Instagram: https://www.instagram.com/chatwithalpha

Where to find David Phillips:
‍

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: fondo.com

Daivik and co-founder Avi Konduru submitted their YC application at 7:59 PM, one minute before the deadline. After multiple prior rejections, they got an interview, then a follow-up call, then acceptance. They started YC with a crypto payment idea, pivoted five weeks before demo day to global payroll—a problem they'd worked on two years earlier—and shipped contractor payroll within a week. They've since raised funding and are scaling.

‍

Key Topics Covered:

• What Shor is: global payroll/EOR rebuilt for startups; automation handles ops, AI teammates deliver docs/actions in Slack/WhatsApp.

• From clever to sellable: pivoted inside YC from crypto/fiat rails to payroll where they had access and clear pain.

• Cost math that breaks: why legacy EORs charging ~$7k/yr on a $20k salary fail SMB/unit economics—and how Shor attacks the middle.

• Ship speed as strategy: prior fintech muscle let them launch contractor payroll in one week (KYC/KYB, payouts, tax flows).

• Design →  dashboards: move work to the user (chat interfaces), keep humans making decisions, let AI do the background jobs.

• Distribution as a moat: serve the massive long tail priced out by incumbents; win on affordability + responsiveness.

• YC pragmatism: plain-English interviews beat pitch theater; momentum over mockups.

• Execution after Demo Day: demand first, fundraising next, delivery always—scaling compliance/country coverage without losing speed.

• Founder operating cadence: daily inches over hype cycles; embrace “pivot hell,” but pick battles you can actually win with customers.

• Finance stack mindset: reliability and support matter most when back-office tools fail—opt for vendors who show up.

‍

Chapters

(00:00) Cold open — the 7:59 PM YC submission
(00:37) Intro — Davik & what Shor is (affordable global payroll)
(02:51) Waterloo → founder mindset and process discipline
(06:05) YC journey and batch dynamics
(08:26) First leap without an idea + early GTM lessons
(11:56) Marketplaces are hard — takeaways that shaped Shor
(14:56) The last-day YC rush & the crypto/fiat idea
(24:49) Pivot hell inside YC → choosing global payroll
(27:28) Shipping contractor payroll in one week + why now (AI/stablecoins)
(29:33) Fundraising wrapped; AI teammates over dashboards; what’s next

‍

Where to find Daivik Goel:

‍
Multilink: https://bento.me/daivik
LinkedIn: https://www.linkedin.com/in/daivikg
X: https://x.com/DaivikGoel
Instagram: https://instagram.com/daivikgoel
YouTube: https://m.youtube.com/channel/UCzkRfrCXIrW1v60Wyasgq7Q
Substack: https://daivikgoel.substack.com
TikTok: https://tiktok.com/@daivikgoel

‍

Where to find Shor:

‍
Website: https://tryshor.com
X: https://x.com/shor_pay
LinkedIn: https://www.linkedin.com/company/shorpay
Instagram: https://www.instagram.com/shor.pay/
YouTube: https://www.youtube.com/watch?v=OF1m1H0arYY

‍

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, Cody outlines a practical path from data sprawl to decisions: skip steep BI learning curves and ticket queues; connect sources and ask in plain English for charts and basic analyses. He also talks about how creative volume now functions as targeting—ship lots of concepts, let algorithms find buyers—and positions Graphed as the way to see what’s working without waiting on a data team. For founders and marketers, it’s a clear primer on turning raw rows into faster feedback loops.

‍

Key Topics Covered:

• What Graphed is: an AI agent for marketing analytics that connects sources, manages the warehouse, and lets you chat to generate charts and basic analyses. 
• From tickets to answers: why BI queues and tool learning curves slow teams—and how a chat interface shortens time-to-insight.
• Scale as a requirement: handling large datasets (e.g., ~25M rows of ads data) and avoiding rate limits via a managed warehousing layer.
• Roadmap preview: proactive weekly Slack briefs that summarize what changed and why (future functionality).
• Creative = targeting: in 2025 paid acquisition, high-volume creative acts as the audience filter while algorithms find buyers.
• Stacking S-curves: double down on the working channel, then layer the next before growth plateaus.
• Arbitrage windows: underpriced media (e.g., creator CPMs ≈ $2; low-cost local streaming TV CPMs) and why illiquid channels create edge.
• Unit economics discipline: CAC/ARPU/LTV/payback thinking—losing on month one can be rational if LTV justifies it.
• Validation before build: use ads and landing pages to test demand—even before a product exists.
• Founder ops stack: practical setup (e.g., Stripe Atlas, Mercury, Carta, Fondo) to keep focus on product and sales.

Chapters

(00:00) Introduction to Graphed.com
(02:12) Cody's Journey at Rupa Health
(05:36) Growth Strategies and Metrics
(11:19) Paid Advertising Insights
(15:10) Exploring Programmatic TV Advertising
(18:57) The Vision Behind Graphed.com
(21:57) Building a Financial Stack for Startups

Where to find Cody Schneider:

LinkedIn: https://www.linkedin.com/in/codyxschneider

X: https://x.com/codyschneiderxx 

Where to find Graphed:

X: https://x.com/graphed
Website: https://www.graphed.com

‍

Where to find David Phillips:
‍

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, Craig shares velocity advice like: ship before perfect (feedback > stealth), build pro-human AI (human-in-the-loop), and treat fundraising like sales (expect 19 no’s, optimize investor–founder fit, when it’s right—TTFM). He outlines the back-office stack that keeps your startup in good shape and his board philosophy: offer perspective, not prescriptions. If you’re building enterprise AI—or just want to move in weeks, not quarters—this one’s for you.

Key Topics Covered:

• Ogentic AI focuses on enterprise productivity and automation.
• Building a strong back office is crucial for startups.
• Fundraising is a numbers game; persistence is key.
• Feedback is essential for product development.
• AI will replace some jobs but also create new ones.
• Having a technical co-founder can accelerate growth.
• Navigating the fundraising landscape requires understanding investor fit.
• MassChallenge supports entrepreneurs in solving global challenges.
• The future of work will involve augmenting human capabilities with AI.
• Startups should find their niche in the AI market.

Chapters

(00:00) The Rise of Ogentic AI

(13:37) Building a Strong Back Office

(17:02) Navigating Fundraising Challenges

(19:14) The Role of MassChallenge

(23:12) AI and the Future of Work

(27:40) Fundraising in the AI Era

Where to find Craig J. Lewis:

Linkedin: https://www.linkedin.com/in/mrfutureofwork

X: https://x.com/CraigJamalLewis 

Instagram: https://www.instagram.com/craigjlewis

Where to find Ogentic AI:

 
Website: https://ogenticai.com
LinkedIn: https://www.linkedin.com/company/ogenticai
X: https://x.com/ogenticai
Instagram: https://www.instagram.com/ogenticai

‍

Where to find David Phillips:
‍

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, Grace outlines a practical approach to community-building: curate for outcomes, not optics (every seat should benefit from every other seat). Her angel filter doubles as her invite list. Online → IRL is the sequence: earn trust digitally, concentrate it offline.  For founders aiming to stand out without burning cash, this is a clear primer on turning audience into deal flow.

‍

Key Topics Covered:

• Building community has to happen organically.
• Engaging with entrepreneurs can lead to unexpected opportunities.
• Sales and storytelling are crucial skills for success in VC.
• Offering value to others is key to building relationships.
• The people you meet at events can significantly impact your journey.
• Planning events requires meticulous attention to logistics.
• Creating a curated experience enhances networking opportunities.
• AI is transforming the media landscape and how we build companies.
• Networking is essential for both founders and investors.
• Continuous learning and adaptation are vital in the fast-paced tech world.

Chapters

(00:00) Building Community: The Organic Approach
(02:50) Journey into Venture Capital: From Real Estate to VC
(05:44) Insights from Interviews: Lessons Learned in VC
(08:53) Angel Investing: Key Considerations
(11:51) Creating Value: The Importance of Community
(15:02) Event Planning: From Small Gatherings to Large Conferences
(17:59) The Smart AI Summit: Curating Experiences
(20:54) Future of Media: Building with AI
(23:48) Final Thoughts and Online Presence

Where to find Grace Gong & Smart Venture Media:

Linktree: https://linktr.ee/gracegong115

‍

Where to find David Phillips:
‍

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, Collin gives founders some great advice: you’re running two businesses (product for customers, equity for investors). Fund math in concentrated portfolios means ~2 of ~20 bets must carry returns; with dilution to ~10% at exit, winners need multi-billion-dollar potential. Sequence your proof: Pre-seed = prove value; Seed = prove people pay (repeatably); Series A = scale what’s already repeatable. Don’t scale misses (the Steph Curry test). And match capital to your vehicle - venture is rocket fuel: perfect for rockets, destructive for "pickup trucks".

Key Topics Covered:

• Running a startup involves selling to customers and investors.
• Different VCs have varying expectations based on fund size and strategy.
• Founders should tailor their pitches to the specific needs of investors.
• Understanding investor dynamics can improve fundraising success.
• Successful founders diverge from conventional thinking in their industries.
• Ambition and hustle are key traits for founders.
• Expectations change significantly after receiving funding.
• Consistency and repeatability are crucial for scaling a startup.
• Community engagement can foster innovation and collaboration.
• The back office is essential but often seen as a distraction.

Chapters

(00:00) Introduction to Colin Wallace and His Journey
(02:14) The Shift in Growth Expectations for Startups
(05:03) Understanding Investor-Fit and Fundraising Dynamics
(11:12) The Importance of Founder Attributes
(17:15) Navigating the VC Landscape and Expectations
(21:03) Post-Funding Realities for Founders
(22:40) Understanding Seed Capital and Series A Expectations
(25:19) The Evolution of Funding: Series B and C
(29:05) Coaching the Next Generation of Founders
(32:09) Building the Back Office: The Unsung Hero
(35:42) Community Building and Inclusive Events

Where to find Collin Wallace:

‍

Linkedin: https://www.linkedin.com/in/collin-wallace/ 

X: https://x.com/pithyprof 

Website: https://lobby.vc/people/collin-wallace/ 

‍

Where to find Lobby Capital:
Linkedin: https://www.linkedin.com/company/lobby-capital/
X: https://x.com/lobby_vc
Website: https://lobby.vc/

‍

‍

Where to find David Phillips:

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, we unpack the mechanics and timing that make—or break—QSBS outcomes. We cover the core tests (acquiring shares before $50M in assets, five-year hold, qualified C-corp status), state-level differences (New York recognizes QSBS; California does not), and why early planning can start both the QSBS and long-term capital gains clocks while avoiding later surprises. 

Chesser talks about trust stacking—gifting shares into multiple family trusts so each may pursue its own QSBS exclusion—and notes practical guardrails and expert advice for dong it right. Beyond the tax planning, Chesser shares go-to-market lessons from Carta and Dynasty: using the network effect (e.g., certificates signed), creating urgency with must-do workflows (like 409A), iterating growth levers monthly, hiring decisively, and using social + creator partnerships instead of traditional cold outbound. The result is clear: tactical advice for founders on when to exercise, when to gift, how to document, and how to avoid the common QSBS pitfalls discussed in the conversation.

‍

Key topics covered

- QSBS allows startup shareholders to sell up to $15 million tax-free.
- Most startups qualify for QSBS, but there are specific criteria.
- Holding shares for at least five years is crucial for QSBS eligibility.
- The new rules under the big beautiful bill change QSBS eligibility timelines.
- Dynasty helps founders maximize QSBS benefits through trust stacking.
- Early exercise of stock options can prevent alternative minimum tax issues.
- Filing an 83B election is essential for QSBS qualification.
- Social media is a powerful tool for startup growth and marketing.
- Building partnerships with influencers can enhance visibility and credibility.
- The cost of setting up trusts for QSBS is significantly lower with Dynasty.

‍

In This Episode, We Cover

(00:00) Introduction to QSBS and Its Importance

(06:35) Understanding QSBS Eligibility and Benefits

(13:08) The Role of Dynasty in Maximizing QSBS Benefits

(16:29) Alessandro's Journey and the Birth of Dynasty

(18:36) Growth Strategies and Lessons from Carta

(27:14) Leveraging Social Media for Growth

‍

Where to Find Alessandro Chesser:

‍

LinkedIn: https://www.linkedin.com/in/alessandro-chesser-84763748

X: https://x.com/SandroChess

‍

Where to Find Dynasty:

‍

Website: https://www.getdynasty.com

LinkedIn: https://linkedin.com/company/getdynasty

X: https://x.com/getdynasty_com

‍

Where to Find David Phillips:

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips

Brought to you by:

Fondo — All-in-one accounting for startups: https://fondo.com

In this episode, we trace the evolution of Web3 gaming from its origins in the CryptoKitties community to today's institutional adoption cycle. The conversation explores how manual onboarding and white-glove user acquisition laid the foundation for viral growth. Jeff shares the pivotal moments that shaped Axie's trajectory: tokenizing experience points, creating the "play-to-earn" model that democratized crypto mining, and the strategic decision to build their own blockchain when existing infrastructure couldn't scale. We also examine the current state of crypto gaming, the shift from retail mania to Wall Street adoption, and why the next wave of innovation might create entirely new cultural mediums rather than just new ways to make money.

Key topics covered:

• The CryptoKitties Mafia: How a December 2017 viral game spawned the founders of Axie, OpenSea, and the modern NFT ecosystem
• Manual onboarding at scale: From personally gifting Axies to Binance angels to hitting 2 million users
• The biological insight: Why CryptoKitties failed (no death = exponential breeding) and how ecosystem balance became Axie's core principle
• 300 users was "#1": How being the largest crypto game with just 300 players became a marriage proposal line - and a growth trajectory
• Tokenizing the game economy: The moment players asked to buy experience points and accidentally invented play-to-earn
• "You can't build your startup on another startup": Why Loom Network's failure forced Sky Mavis to create Ronin blockchain
• The Ronin Effect: Deploying at 30,000 users, scaling to 2 million in six months - and the infrastructure playbook now powering 70+ games
• The Uniswap wealth effect: How every Axie player unexpectedly received $4,000, creating a growth catalyst nobody predicted
• Why gaming onboards better than DeFi: More people game than trade - and nostalgia beats complexity when introducing scary new technology
• From Binance to NYSE: This cycle's institutional meta and why crypto gaming hasn't figured out Wall Street yet
• The new Renaissance: How fractional reserve banking created the actual Renaissance, and why crypto's lasting impact will be cultural, not financial
• Loyalty programs vs. helicopter money: Evolving from infinite money glitches to targeted behavioral incentives
• 70+ economic experiments: From AI-powered tanuki battles to on-chain "Runescape" - why only one or two need to work
• The cypherpunk optimism: Why crypto offers a more definite, grounded vision for the future than AI or robotics

Where to find...

Jeff 'Jiho' Zirlin:

• Instagram: https://www.instagram.com/axieinfinity
• X: https://x.com/Jihoz_Axie
• LinkedIn: https://www.linkedin.com/in/jeffzirlin
  
  

Skymavis:

• Website: https://skymavis.com
• Instagram: https://www.instagram.com/axieinfinity
• X: https://x.com/skymavishq
• LinkedIn: https://www.linkedin.com/company/skymavis

Axie 

• Website: https://axieinfinity.com/
• Instagram: https://www.instagram.com/axieinfinity
• X: https://x.com/AxieInfinity
• LinkedIn: https://www.linkedin.com/company/axieinfinity

Ronin

• Website: https://roninchain.com
• Instagram: https://www.instagram.com/axieinfinity
• X: https://x.com/ronin_network 
• LinkedIn: https://x.com/ronin_network

David Phillips:

• Website: www.fondo.com
• Instagram: https://www.instagram.com/axieinfinity
• X: https://x.com/davj
• LinkedIn: https://www.linkedin.com/in/davjphillips

In This Episode, We Cover

(00:00) From 300 to thousands of Users: The Binance Effect

(17:41) Community-Driven Growth: The Role of Guilds

(18:38) Experimentation as a Growth Strategy

(19:52) Challenges and Advantages in Crypto Growth

(20:03) Learning Through Gaming: Onboarding to Crypto

(21:27) The Uniswap Airdrop: A Catalyst for Growth

(22:18) Onboarding and Scaling in Crypto Gaming

(23:17) The Ronin Network: A Solution for Scalability

(24:40) The Evolution of Ronin and Its Community

(25:10) Expanding the Ronin Ecosystem: New Games and Innovations

(27:02) Economic Experiments in Crypto Gaming

(28:56) The Cultural Renaissance of Crypto

(30:14) Future Innovations in Web3 Gaming

(31:36) Optimism for the Future of Crypto

Brought to you by:

Fondo — All-in-one accounting for startups: https://tryfondo.com

In this episode, we explore the fundamental shift happening in B2B sales as traditional cold outbound becomes less effective and companies invest in higher-effort tactics to stand out. The conversation covers the evolution from Letterdrop's origins as an SEO tool to its current focus on conversation intelligence, driven by market changes from ChatGPT's emergence. Parthi shares insights about the three essential components of effective outbound messaging, why customer conversations represent untapped content goldmines, and his firsthand experience being demoed by an AI sales agent. We also examine his predictions about AGI's timeline and the philosophical question facing all founders: do you build for today's market or tomorrow's technological reality?

Key topics covered:

• Why cold email reply rates dropped 40% in 2024 and the shift away from "spam your TAM" tactics
• The reality that only 2-3% of your market wants to purchase at any given time
• Three components of effective outbound: solid observation, poking the P0 problem, and value-first offers
• Letterdrop's strategic pivot from SEO tools to conversation intelligence as ChatGPT emerged
• How customer and prospect conversations contain unique content that competitors can't replicate
• The founder journey from Google product manager through multiple micro-SaaS startups to YC
• Real experience with an AI AE conducting demos better than human salespeople
• Why "marinating" in a single problem space beats jumping between different markets
• The philosophical choice between building Cursor (for today) versus Anthropic (for the future)
• AGI timeline predictions and whether UBI will arrive before widespread job displacement

Where to find Parthi Loganathan:

Linkedin: https://www.linkedin.com/in/parthiloganathan/

X: https://x.com/parthi_logan

Where to find Letterdrop:

Website: https://letterdrop.com/

Linkedin: https://www.linkedin.com/company/letterdrop/

X: https://x.com/letterdropco

Podcast: https://open.spotify.com/show/43bSCi3FcFaJ28H7qEK59X?si=2f6afe15cea342ea

Where to Find David Phillips:

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips/

In This Episode, We Cover

(00:00) Introduction to LetterDrop and Its Mission
(02:52) The Evolution of Outbound Sales Strategies
(06:11) Crafting Effective Outbound Messages
(09:09) Parthi's Journey as a Founder
(12:02) Leveraging Social Conversations for Sales
(14:59) Creating Content from Customer Conversations
(17:55) Back Office Operations for Startups
(20:49) The Role of AI in Sales
(23:38) The Future of Work: AGI and UBI
(26:46) Closing Thoughts and Future Questions

Brought to you by:

Fondo — All-in-one accounting for startups: https://tryfondo.com

JP and his cofounders brings a unique blend of technical expertise and entrepreneurial drive to the healthcare technology space, having previously worked across diverse fields from real estate automation to web development. 

Since co-founding CareSwift, he's helped scale the platform to serve over 2,000 EMTs in New York City alone, generating more than 90,000 automated reports. Beyond product development, JP leads go-to-market strategy and is currently pursuing EMT certification himself to deepen his understanding of the industry's challenges.

In this episode, we explore the journey from scrappy prototype to venture-backed startup and the critical lessons learned along the way. 

Key topics covered:

• How a ChatGPT prototype evolved into a venture-backed healthcare AI platform
• The hidden costs of poor EMT documentation: $1,800 per error and 11% industry revenue loss
• Why 25% of New York's EMTs organically adopted CareSwift without marketing
• Critical incorporation mistakes that can delay funding and how to avoid them
• The strategic decision to expand from narrative reports to full documentation workflow
• Why domain expertise matters when building AI for specialized industries
• Navigating regulatory compliance and the founder stack for healthcare startups
• The reality of Y Combinator: 996 work culture and rapid iteration cycles
• From 15-20 minute reports to 2-minute automated workflows saving hours per shift
• Why sometimes a bug in Apple Mail can redirect your entire startup journey

Where to find John Paul Mussalli 

- Linkedin: https://www.linkedin.com/in/jpmussalli/
- X: https://x.com/jpm1126

Where to find CareSwift:

- Website: https://careswift.ai/

- Linkedin: https://www.linkedin.com/company/careswift/

Where to Find David Phillips:

- X: https://x.com/davj

- LinkedIn: https://www.linkedin.com/in/davjphillips/

In This Episode, We Cover

(00:00) Introduction to CareSwift and Its Founders

(02:54) The Birth of CareSwift: Addressing EMT Challenges

(06:09) Impact of CareSwift on EMT Efficiency

(09:01) Navigating the Startup Journey: Lessons Learned

(09:35) Navigating Startup Structures and Legalities

(12:14) The Journey Through Y Combinator

(15:06) Daily Life as a Founder in Y Combinator

(17:13) Building a Founder Stack: Tools and Resources

(18:57) Future Plans

Brought to you by:

Fondo — All-in-one accounting for startups: https://tryfondo.com

In this episode, we explore the dramatic transformation of San Francisco's commercial real estate market and the evolving dynamics between landlords, tenants, and the broader tech community. The conversation delves into current market trends in both office and retail spaces, examines how AI companies are reshaping demand patterns, and discusses the critical importance of community building in the tech industry through initiatives like SF Hoops. We also dive deep into pricing strategies, emerging market opportunities, and provide a comprehensive outlook for businesses seeking space in San Francisco.

Topics covered:

• Why SF's commercial real estate recovery is finally here after 5 years of decline
• How AI companies are driving massive demand and changing the market dynamics
• Where to find the best deals: neighborhood analysis and sweet spot sizing (10-20K sq ft)
• Why rents are rising and landlords are getting more confident by the day
• Buildings selling at 80% discounts and what it means for new opportunities
• The return-to-office mandate trend and its impact on space demand
• Lower SoMa as the last frontier for deeply discounted office space
• Retail space conversion opportunities in Union Square
• Why you should secure space now vs. waiting for better deals
• Pricing breakdown: what 10, 25, and 50-person companies should budget
• How to navigate the search process and when to use a broker
• & Much more

Where to Find Reuben Torenberg:

CBRE: https://www.cbre.com

X: https://x.com/RTorenberg021

LinkedIn: https://www.linkedin.com/in/reuben-torenberg-b985b646

Where to Find SF Hoops:
https://sfhoopsleague.com

https://x.com/SFHoopsleague

Where to Find David Phillips:

X: https://x.com/davj

LinkedIn: https://www.linkedin.com/in/davjphillips/

In This Episode, We Cover

(00:00) Current Trends in San Francisco Commercial Real Estate

(02:53) Navigating the Market: Opportunities and Challenges

(05:54) The Shift in Office Space Demand

(08:43) Retail Space and Its Transformation

(11:55) Landlord Strategies and Market Dynamics

(14:52) The Rise of SF Hoops: Networking Through Sports

(17:59) Future Outlook: What to Expect in the Coming Months

Brought to you by:

Fondo — All-in-one accounting for startups: https://tryfondo.com

In this episode, Stephen shares the full story behind Capabuild: how it started, what he got wrong early on, and the key insights that helped turn it into a real business.

One of the biggest takeaways? The power of watching customers work in their real environment — instead of relying on what they say they need.

We dive into how observing contractors in the field led to unexpected product decisions, how Capabuild evolved its pricing model after early pushback, and what it takes to build trust in a traditional, change-resistant industry.

Stephen also shares his thoughts on building for overlooked markets, supporting local service businesses, and why long-term traction comes from delivering real operational value — not chasing trends or vanity metrics.

If you’re building software for non-obvious industries or trying to unlock early traction, this episode is packed with practical, hard-earned wisdom.

Check out Capabuild:

• https://www.capabuild.app/
• https://apps.apple.com/us/app/capabuild/id1642228115
• https://play.google.com/store/apps/details?id=com.capabuild.app&pcampaignid=web_share

This episode is brought to you by:

• Fondo — Automate your accounting and unlock up to $500k from the IRS: tryfondo.com
  
  

Where to find Stephen Llevano

• X: https://x.com/StephenLlevano
• LinkedIn: https://www.linkedin.com/in/stephen-llevano/

Where to Find David Phillips

• X: https://x.com/davj
• LinkedIn: https://www.linkedin.com/in/davjphillips/

Takeaways

1. Connecting directly with customers as a founder is crucial for product success
2. Observing customers in their actual work environment reveals true needs beyond feedback
3. Pricing strategies must evolve based on real customer insights and market dynamics
4. Building a startup requires more time and emotional investment than initially expected
5. The insurance industry is shifting, creating new opportunities for adaptive contractors
6. Personal relationships often drive initial customer acquisition and business development
7. Deep market understanding is essential for navigating industry complexities
8. Operational efficiency directly impacts a contractor's ability to serve clients effectively
9. Supporting local service businesses creates stronger community economic foundations
10. Every entrepreneurial experience offers valuable learning opportunities worth embracing

Chapters

• (00:01) The Entrepreneur's Journey
• (03:03) Identifying Market Opportunities
• (05:55) Building the First Version of Capabild
• (08:51) Customer Acquisition and Pricing Strategies
• (11:57) The Evolution of Capabuild
• (20:56) Operational Challenges and Solutions
• (23:51) Future of the Industry and Capabild's Mission

In this conversation, Jake breaks down the recent changes in tax legislation that every startup founder needs to know about, particularly the game-changing provisions in the One Big Beautiful Bill and how startups can leverage R&D tax credits to get substantial cash back on their development investments.

We explore the challenges that Section 174 has created for startups and dive into practical strategies for navigating these changes, including when amending tax returns makes sense and how to leverage bonus depreciation and Section 179 deductions. Jake also explains the powerful long-term benefits of Qualified Small Business Stock (QSBS) for founder wealth optimization.

Key topics covered:

• How the One Big Beautiful Bill creates new tax optimization opportunities for startups
• Maximizing R&D tax credits for substantial cash returns on development investments
• Navigating Section 174's impact on R&D expense deductions and cash flow management
• Strategic use of amended returns to recover from unexpected tax positions
• Leveraging bonus depreciation and Section 179 for immediate equipment deduction benefits
• Understanding QSBS benefits and the five-year holding period requirements
• The importance of proactive tax planning partnerships between startups and advisors
• Staying ahead of evolving tax legislation to capture emerging opportunities
• And much more

Brought to you by:

• Fondo — Automate your accounting and unlock up to $500k from the IRS: https://tryfondo.com
  
  

Where to find Jake Wedig

• LinkedIn: https://www.linkedin.com/in/jacob-wedig
  
  

Where to Find David Phillips

• X: https://x.com/davj
• LinkedIn: https://www.linkedin.com/in/davjphillips/
  
  

In This Episode, We Cover

• (01:42) Understanding the New Tax Bill
• (03:31) R&D Tax Credits and Their Importance
• (07:51) Impact of Section 174 on Startups
• (13:05) Amending Returns and Cash Flow Considerations
• (16:38) The Role of Tax Advisors for Startups
• (30:55) Bonus Depreciation and Section 179
• (35:23) Qualified Small Business Stock (QSBS) Benefits

In this conversation, Nathan shares hard-earned lessons from building and exiting companies, explains why most founders don’t understand the true cost of raising VC, and offers a compelling case for why debt and secondaries can be a smarter option. We also explore:

• How he bootstrapped Heyo to $2M ARR before raising VC
• The $6.5M exit offer he had to turn down (and regrets)
• What most founders misunderstand about venture capital
• How GetLatka became the #1 ranked SaaS benchmarking database
• Why the future belongs to tiny teams with huge revenue
• The three AI trends shaping SaaS company formation
• How FounderPath prices startup equity daily—instantly enabling secondaries
• Why hooks and attention matter more than ever
• And much more

Brought to you by:

• Fondo — Automate your accounting and unlock up to $500k from the IRS: https://TryFondo.com

Where to Find Nathan Latka

• X: https://x.com/NathanLatka
• Instagram: https://www.instagram.com/nathanlatka/
• LinkedIn: https://www.linkedin.com/in/nathanlatka/
• Podcast: Latka Podcast
• SaaS Database: https://getlatka.com
• Book: How to Be a Capitalist Without Any Capital

Where to Find David Phillips

• X: https://x.com/davj
• LinkedIn: https://www.linkedin.com/in/davjphillips/

In This Episode, We Cover

• (00:00) Intro to Nathan and FounderPath
• (01:30) The story behind Heyo and bootstrapping to $2M ARR
• (03:50) Raising venture and losing optionality
• (05:30) The $6.5M offer and why his board said no
• (07:10) Lessons from a slow death and how to move on
• (08:15) Why Nathan started the Latka podcast
• (09:30) Building GetLatka to 3,000+ daily organic clicks
• (11:00) The power of hooks and attention in modern SaaS
• (12:45) Inside FounderPath: non-dilutive capital for SaaS
• (14:30) How venture debt differs from traditional VC
• (16:15) Why secondaries are healthy, not harmful
• (18:00) How FounderPath prices startup equity daily
• (20:10) Big trends: tiny teams, chat-based dashboards, attention > tech

Referenced

• GetLatka – https://getlatka.com
• FounderPath – https://founderpath.com
• Heyo (archived) – https://en.wikipedia.org/wiki/Heyo
• How to Be a Capitalist Without Any Capital (Book) – https://www.amazon.com/How-Be-Capitalist-Without-Capital/dp/0525534440
• ZoomInfo – https://www.zoominfo.com
• Techstars – https://www.techstars.com
• G2 Crowd – https://www.g2.com