Danny Davis took a company public, sold another, and is now building StackYak.

"AI is making things more efficient, but it's still expensive to build a company." Founders say "we only need 500,000." What if you need two million? "You fail before you finish."

We also get into why the company's lawyer is not your lawyer, why you take the small check that gets you to the next round, and why "debt is still cheaper than equity."

Chapters

00:00 People are craving real relationships

00:21 Meet Danny Davis

01:08 Why start a third company?

05:22 AI isn't making things cheaper yet

07:18 The truth about taking a company public

08:59 Selling for less and planning for taxes

11:19 The second exit: Origin Labs

14:24 The company's lawyer is not your lawyer

16:01 Why founders skip legal help

17:20 Don't let ChatGPT write your contracts

19:44 Where AI helps with legal documents

20:25 What he'd do differently: due diligence

21:54 The right investors are your friend

24:21 Investors who do more than write a check

25:54 Get into an accelerator early

27:08 Co-founders versus solo founders

28:20 Take the small check

29:10 Cheaper to start, not cheaper to build

30:30 Debt, grants and specialty investors

32:49 What StackYak is building

33:34 Real conversations, real discovery

Host: R. Hunter Harris

https://www.linkedin.com/in/rhunterharris/

Guest: Danny Davis

https://www.linkedin.com/in/dannydavis12/

https://stackyak.ai/

Newsletter: Don't SaaS Me

https://hunter-software-consulting.beehiiv.com/

By Hunter Software Consulting

https://huntersoftwareconsulting.com/

A company Jeff Patton worked with this year shipped a glossy new release to 2.65 million customers. AI helped them get it out on time. It didn't break. There were no bugs. Nobody liked it.

Jeff, author of User Story Mapping, joins me to talk about dual track development. He says the book needs a second edition: it described a discovery team and a delivery team, but after 10 years of coaching he sees two tracks and one team, with engineers and testers pulled up into discovery.

Jeff asks the whole team, on a scale of 1 to 5, how confident they are that people will see a feature and want to try it. If one says 3 and another says 5, ask what they'd need to learn to be sure. Keep using is the hard one to test: release something usable to a small group, and in Jeff's words, "don't build it at scale."

We also get into release to learn versus release to earn, and what I've seen in client GitHub repos since they adopted AI: a lot more output, but the share of work going toward customer results is flat or down. Jeff on asking AI what customers think: "the AI is not your customer."

His homework: do an opportunity canvas for the feature you're working on right now. If you can't say who it's for, what problem it solves and how you'll measure it, stop building it.

Chapters

00:00 A release customers did not like

00:21 Meet Jeff Patton

04:54 Discovery and delivery together

07:33 Two tracks within one team

09:46 Expose the beliefs behind a feature

12:52 Where does the team's confidence differ?

13:58 A usable release that teaches you something

16:03 Learning about technical risk

17:16 Ship to learn, earn, or burn

23:24 AI output and customer outcomes

27:40 AI opinion and actual customer evidence

33:18 An exercise for the next feature

34:46 Where to learn more from Jeff

Find Jeff: https://jpattonassociates.com/

How much of the feedback on a release actually needs to block it?

David Rasch looks for the checks his team can automate so nobody has to keep thinking about them. If an agent repeatedly makes the same mistake, a deterministic check can catch it before a person spends another review pointing it out. That leaves the person reviewing the work to decide what matters.

We talk about where to spend that judgment: the data model, changes to the interface, and the parts of a system that need careful design. Code may be easier to replace now, but someone still has to understand what the software is supposed to do.

David gives an example of an AI review getting caught up in a race condition for a task that runs about once a month. In that case, he decided the extra complexity wasn't worth it. I share an experiment where repeatedly responding to review feedback left my project unable to do anything at all.

The useful question is what needs fixing before this release, and what can become a separate ticket whose priority you decide later. A patient reviewer can always find more work. You still need a person who can decide when the work is worth doing.

Useful questions from the conversation

Which repeated review comments could become automatic checks?

Which parts of this change need someone to understand the design?

Does this issue need to stop the release, or can you decide its priority separately?

Chapters

00:00 When AI agrees with everything

00:12 Meet David Rasch

02:12 What you still need to understand

05:02 Data foundations before more code

10:00 Building and selling as an engineering education

14:42 Which parts need careful design?

19:12 Automating repeatable review checks

21:20 Code is easier to rework

22:31 When review becomes a loop

25:02 Human judgment and work worth doing

28:55 Where to find David

Host: R. Hunter Harris

https://www.linkedin.com/in/rhunterharris/

Guest: David Rasch

https://www.linkedin.com/in/rasch

Newsletter: Don't SaaS Me

https://hunter-software-consulting.beehiiv.com/

By Hunter Software Consulting

https://huntersoftwareconsulting.com/

What would help your customers more: another round of faster AI output, or a better way to see what you're building?

Josh Colter, the majority owner of Cardboard, has put a lot of thought into that question. He bought the company with a bet on AI and has been combining story maps with detailed instructions so he can see the big picture while the models work through the details.

His comparison comes from the Wright brothers. He says "they thought about control first." A bigger engine is useful once you can steer. Josh applies that idea to building software: make the work visible, understand the customer behavior you're trying to change, and keep releases small enough to inspect.

We talk about the difference between having AI drive and learning to operate it yourself, why a long specification can hide something a human needs to see, and how Josh planned and shipped dark mode in three days after finding years of requests from users of a competing product.

Useful questions from the conversation

Can you see the work clearly enough to tell where it's going?

Can the people building it understand the plan as readily as the AI can?

What customer request are you trying to meet, and how will you check the result?

Chapters

00:00 Control before thrust

00:45 Show intro

00:53 Meet Josh Colter

01:26 Buying Cardboard and betting on AI

02:46 Chauffeurs and airmen

08:47 Why a bigger engine needs control

11:05 Make the work visible

15:20 Story maps, specifications and human understanding

20:32 Planning and shipping dark mode

25:26 Checking what you've actually built

26:35 Where to find Josh and Cardboard

In this episode, Brandon Valentine, a CPA and the founder of Valentine Advisory Group, explains why a founder who can't speak to their unit economics can't raise capital: lenders and investors want to understand how the company makes money and how they get their capital back. He breaks down what goes into the numbers (cost of goods sold, operating expenses and a price the market will pay), why pricing starts with market research and the problem you solve for the customer, and how a three statement financial model turns "I want money because my idea is very good" into a specific ask backed by numbers. Hunter adds why bad unit economics get worse with growth: lose money on one widget and you lose more on a thousand. They also cover finance tools versus finance expertise, how to vet a CPA, and how AI is changing accounting work, including why it still depends on good data going in. Founders preparing to raise will learn which numbers to get right before the pitch.

Chapters

00:00 Asking for capital with numbers, not belief

00:37 Introducing Brandon Valentine and Valentine Advisory Group

01:11 Where founders should start: unit economics

03:40 Why you can't raise without your unit economics

04:53 Labor, operating expenses and early foundations

06:55 Bad unit economics lose more as you scale

07:40 Pricing, freemium and market research

10:16 Pricing to the problem you solve

12:50 The three statement model and the capital ask

15:03 Finance tools versus finance expertise

16:33 How to find and vet a CPA

18:07 How AI is changing accounting and finance

23:02 Where AI goes wrong: bad data in, bad data out

24:23 Where to find Brandon

Host: R. Hunter Harris

https://www.linkedin.com/in/rhunterharris/

Guest: Brandon Valentine

Brandon is a CPA from Atlanta and the founder of Valentine Advisory Group. He helps people buy and sell businesses in the lower middle market and helps founders and small business owners build their accounting and finance foundations.

https://www.linkedin.com/in/brandon-valentine-cpa

https://valentineadvisorygroup.com/

By Hunter Software Consulting

https://huntersoftwareconsulting.com/

In this episode, Kevin Callahan shares what startup fundraising actually required at Hopscotch: $7.5 million across pre-seed and seed, conversations with 150 to 200 VCs per round, and roughly 1,000 hours of work. He and Hunter also discuss leaving big tech, fundraising through the 2023 downturn, the financial and personal strain of startup life, and Kevin's case for targeted investor outreach over mass automated messages. Founders preparing to raise will get a realistic account of the work behind the funding announcement.

Chapters

00:00 Fundraising persistence

00:33 Introducing Kevin Callahan and Hopscotch

01:41 What working at Twitter was really like

03:55 Global platform responsibility and China

09:12 Why Twitter became culturally influential

13:04 Leaving big tech to start a company

16:22 Fundraising during the 2023 downturn

19:26 The personal cost of startup life

21:07 Fundraising outcomes and founder reality

25:43 Cold outreach and investor introductions

30:50 Hopscotch and closing

Host: R. Hunter Harris

https://www.linkedin.com/in/rhunterharris/

Guest: Kevin Callahan

https://www.linkedin.com/in/kevgcallahan/

https://hopscotchlabs.ai/

By Hunter Software Consulting

https://huntersoftwareconsulting.com/

In this episode, R. Hunter Harris explains how product decisions ripple through an organization, why building and maintaining multiple product surfaces multiplies development and QA work, and how platform-specific demands can fracture a supposedly narrow ICP. He shows why better AI and process improvements cannot erase the underlying scaling math, then makes the business case for serving one target segment completely before expanding. Perfect for B2B SaaS founders deciding which platforms, product promises, and customer needs to prioritize for profitable growth.

Chapters
00:00 The cost of sales you cannot support
01:06 Challenges of building multiple product surfaces
05:38 How product divergence multiplies development and QA work
09:39 Narrowing down to one ICP before expanding

Host: R. Hunter Harris
https://www.linkedin.com/in/rhunterharris/

By Hunter Software Consulting
https://huntersoftwareconsulting.com/

In this solo episode, Hunter Harris digs into the difference between a hobby and a business. Hobbies are internally directed. Businesses answer to customers. If you're building a side hustle without customer conversations, customer discovery, and working unit economics, you may have a project - but you don't have much of a business.

Hunter explains why successful businesses often become boring, why your time is usually the most expensive input, how multiple side hustles dilute focus, and why monetizing a hobby can kill the joy that made it worthwhile. He closes with Ship to Learn: a way to preserve experimentation while staying focused on customers and building something people will actually pay for.

Chapters

00:00 Why side hustles fail without customers

00:23 Build and Break Through intro

00:30 Business or hobby?

00:57 External direction vs. self-direction

01:06 Why successful businesses become boring

04:35 What hobbies are actually for

05:59 The messy middle: monetized hobbies

06:24 Unit economics and the true cost of your time

09:06 A business requires customer conversations

09:33 Why multiple side hustles dilute focus

10:54 When monetizing a hobby kills the joy

12:05 Customers may not want what you built

12:33 The danger of becoming a market of one

14:15 Ship to Learn: exploration with customer focus

15:35 The through line: focus on the customer

16:27 Closing

Host: R. Hunter Harris

https://www.linkedin.com/in/rhunterharris/

By Hunter Software Consulting

https://huntersoftwareconsulting.com/