In the last episode I told you I'd hit a wall building an AI product in 100 days. This is what happened after I asked for help.

So many of you replied, and a handful of those replies changed how I build. One pointed me to Matt Pocock's skills on GitHub, which exposed an embarrassing habit: session after session I'd been telling Claude "ship it" and assuming the change went live. It didn't.

Others told me I was trying to boil the ocean. So the PLG analyzer now finds one opportunity worth your week instead of three, and a judge agent decides what matters most rather than a formula weighing everything the same. My own rating went from a 4 out of 10 to a 6.

Next up is cost and speed. A free assessment still costs 40 to 60 cents and takes about two minutes, and I want it under 60 seconds.

I close on something that has little to do with the build: using AI to write without ending up sounding 80% like yourself.

IN THIS EPISODE

00:00 Day 39, and why updates are now every two weeks

00:47 Stuck, and what happened when I asked for help

02:01 Matt Pocock's skills, and the 11 I use all the time

02:37 I said ship it, and nothing shipped

03:41 Stop trying to boil the ocean

04:16 Why a formula misses the biggest problem on your site

04:57 From three opportunities to one worth your week

05:46 Why an LLM product needs a judge agent

06:58 Eric's idea: a playbook behind every recommendation

08:14 The next two weeks: cost and speed

09:20 From a 4 out of 10 to a 6

10:08 What I learned

10:42 Sound like yourself when AI writes for you

MENTIONED

Run the free assessment: https://productled.com

Matt Pocock's skills: https://github.com/mattpocock/skills

Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/

What's one skill or tool someone recommended that changed how you build?

Day 27 of 100. There's no win to report in this one.

I'm building an AI product in public, and right now I'd rate it a 4 out of 10. That's my own score, and I'm hard on it, because I advise companies on this stuff for a living.

The product is simple to describe. Give it your website URL, and it should hand back recommendations that blow your socks off. Sales-led company? It should spot what you could be giving away for free. Already product-led? It should look at your pricing page and your signup flow and find the real opportunities. All of it in under 60 seconds, because that's one of my success criteria.

Getting there has been a tour of every AI building tool there is. Lovable got me the first 80% fast and then fought me for every point after that. I ported it to GitHub and opened a codebase I couldn't reason about, so I started over. Claude Code produced good recommendations but took 10 minutes to run. Cursor handled the file structure better, so that's where the real app got built, and we shipped it live on Render. Faster. Still not good enough. And now I get to watch what every free analysis costs us in model calls.

None of that is the actual problem.

The actual problem is codifying expertise. If you've spent years building pattern recognition in your head, getting it out of your head and into a product is the billion dollar question. I thought it was a rubric problem. I started with 30 questions and eventually cut my way down to the 13 that really matter. One of them: on your pricing page, can someone understand what they'll be charged in five seconds or less? That one is easy to write down.

Then the nuance eats you alive. A sales-led company doesn't have a pricing page at all, so what's the recommendation now? Multiply that by every edge case and you start to see the shape of it.

I've built a second app whose only job is to be the brain. I feed it tech websites and it finds the opportunities. It's the closest I've gotten, and I still haven't cracked how to break that thinking down and train the AI on it properly.

Which is why this episode is also an ask.

IN THIS EPISODE

(00:48) What the analyzer does, and why I score it a 4 out of 10

(02:09) Lovable gets you 80% there, then it fights you

(03:09) Cursor, Render, and the cost of every free analysis

(04:16) The real wall: how do you codify what you know?

(04:40) From 30 questions to 13, and the five second pricing test

(05:24) The edge cases that break the rubric

(05:45) The second app I built to be the brain

(06:17) Calling in a favor

(07:09) What we actually plan to monetize

MENTIONED

Lovable, Claude Code, Cursor and Render, the tools behind the three rebuilds

Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/

Have you tried turning your own expertise into a product? Tell me where you got stuck.

The podcast has been quiet for a couple of months. This episode is why.

I'm building a new AI-first product at ProductLed, in public, over 100 days. It launches November 19th. You'll be able to use it while I build it, and rip it apart, and I'm sharing everything as I go, including the revenue.

Before any of that, I wanted to talk about how I'm going to attack it.

I recently finished a full Ironman. It ends with a marathon, and that's after the 3.8km swim and the 180km bike. Training for it changed the way I take on anything large, and I'm running the same structure on this build. I call it full Ironman mode, and this episode walks through all 16 parts of it.

Some of it is obvious. Most of it is not. The part that surprised me most was standards, because when you look at the standards you set for a goal, they should make the goal inevitable. That is the difference between hoping you finish and knowing you will.

Whatever your next 100 days hold, this should be useful.

IN THIS EPISODE

(04:15) Get crystal clear on your vision

(04:47) Name the core problem you're actually solving

(05:28) Define the end game, and my three success criteria for this build

(07:42) Tap your network for people who have already done it

(08:54) The identity shift, and the line I write every morning

(10:35) Pick the date

(11:39) Why one why is never enough

(12:53) Get a coach or an advisor

(14:37) Create space, and audit what pulls you away

(17:40) Find peers in the trenches

(18:50) Name the price you're willing to pay

(19:54) Resources are accelerants

(20:48) A daily plan you don't have to think about

(22:09) Standards that make the goal inevitable

(23:05) A reward you only get if you finish

(24:57) Write your own rules

MENTIONED

Conquer 100, the documentary about the Iron Cowboy

Mickey Allen, CEO at Foldspace, advising on this build

What's your next 100 days going to be? Let me know.

After spending years building unvalidated products that went nowhere, Tibo Louis-Lucas completely changed how he approached startups. In this episode of the ProductLed Podcast, he shares how those early failures pushed him toward a faster, revenue-first way of building, one that eventually led to the success of Tweet Hunter and Taplio, and now powers a growing portfolio of product-led SaaS businesses.

Tibo breaks down why revenue is the only validation that really matters, how Tweet Hunter stood out in a crowded market by going deep on a single platform, and the unusual distribution playbook that helped it take off. That included giving a major profit share to a creator-partner and building a network of “creative investors” who amplified the product from day one.

The conversation also dives into why selling a company was far less glamorous than it sounds, and why Tibo now prefers building and holding long term. He shares how he thinks about creating an “indie hacker stack” for a specific persona, how AI has changed his day-to-day workflow, and why he now spends less time coding and more time reviewing, iterating, and building systems.

One of the biggest takeaways is his operating style: no calls, fast feedback loops through DMs, and a strong focus on staying close to paying users. For founders building product-led companies, this episode is packed with practical lessons on validation, distribution, focus, and building with speed in the AI era.

Key Highlights:

• 02:21 - Why Two Failed Startups Changed Everything
• Tibo shares the painful lesson of spending years on unvalidated ideas, and how that pushed him to become relentlessly validation-driven.
• 05:38 - Revenue Is the Only Validation That Counts
• Why free users can be misleading, how Tibo evaluates startup ideas today, and what made Tweet Hunter feel different almost immediately.
• 09:47 - How Tweet Hunter Won a Crowded Market
• The strategy behind focusing on one platform deeply, serving creators instead of enterprises, and building something clearly better for a narrower use case.
• 12:11 - The Distribution Deal That Fueled Growth
• How Tibo partnered with influencers using profit share and exit incentives, and why aligning distribution with the product was such a powerful lever.
• 15:25 - The Creative Investors Growth Engine
• Why he gave small ownership stakes to 17 creators, how that amplified launches and updates, and what made the model work.
• 19:31 - Why Selling Wasn’t the Dream Outcome
• Tibo opens up about the pressure of earnouts, platform risk, and why the acquisition experience made him want to build and hold instead.
• 23:46 - Building an Indie Hacker Software Stack
• Why Tibo organizes his portfolio around a specific persona instead of a single vertical, and how he thinks about expanding from five products to more.
• 34:54 - No Calls, More DMs, Better Feedback
• A look at his no-meeting policy, why DM-based customer conversations work so well for him, and how staying close to users improves product decisions.
• 37:18 - How AI Changed the Way He Builds
• Tibo explains how AI emptied his backlog, turned him into a QA-first builder, and created a new challenge: resisting feature creep.

Resources:

• 🚀 Revid AI: https://www.revid.ai/
• 💼 Connect with Tibo Louis-Lucas on LinkedIn: https://www.linkedin.com/in/tibo-the-maker/
• 💼 Connect with Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/
• 💼 Connect with Esben Friis-Jensen on LinkedIn: https://www.linkedin.com/in/esbenfriisjensen/
• 🧠 Sign up for the ProductLed Newsletter: https://www.productled.com/newsletter

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Roeland Delrue, CEO and co-founder of Aikido Security, to unpack how the company reached $40M+ ARR in just three and a half years in one of the most sales-heavy categories in software.

Roeland shares how his team entered cybersecurity without a traditional security background, simply by living the problem themselves. After juggling eight different security tools and watching a security engineer quit from the sheer pain of triaging endless false positives, they decided to build the product they wished existed.

The conversation digs into why Aikido took a radically product-led path in a market dominated by demos, gated trials, and opaque pricing. Roeland explains how transparent pricing, fast time-to-value, and a no-nonsense buying experience helped Aikido win trust with developers and security teams alike.

They also get into the bigger growth story behind the business: why product-led motions scale so well, how compliance trends like SOC 2 create strong tailwinds, and why Aikido chose to build a multi-product platform from day one instead of another point solution.

Toward the end, Roeland shares his view on AI in cybersecurity, where AI pen testing is already replacing human work, and where humans will still matter for a long time. It is a candid look at building a category-defining security company without following the usual playbook.

Key Highlights:

• 01:46 - The Pain That Sparked Aikido

How Roeland and his co-founders went from frustrated security-tool buyers to building their own solution.

• 04:40 - Why Cybersecurity Needed a PLG Rethink

A sharp breakdown of why traditional sales-led security buying feels broken and expensive.

• 10:11 - Trust in Security Without Heavy Sales

How Aikido built trust through product quality, compliance, transparency, and social proof.

• 15:24 - What Drove Aikido’s Fast Growth

Why self-serve foundations, fast setup, and faster time-to-value helped the company scale quickly.

• 18:06 - Compliance and AI Fueling Demand

How SOC 2, ISO requirements, open source risk, and AI-driven software growth are expanding the market.

• 20:15 - Building a Security Platform Day One

Why Aikido bet on an all-in-one platform instead of a narrow point solution, and how they keep quality high.

• 27:08 - Brownfield vs Greenfield Growth

Roeland explains why Aikido started by replacing existing tools and is now moving into faster AI-driven markets.

• 34:16 - A Practical View of AI in Security

Why Roeland believes the future is hybrid, with deterministic scanners and AI working side by side.

• 36:31 - Can AI Replace Human Pen Testing?

Where AI pen testing already works today, where it still falls short, and what adoption barriers remain.

Resources:

• 🚀 Aikido Security: https://www.aikido.dev/
• 💼 Connect with Roeland Delrue on LinkedIn: https://www.linkedin.com/in/roelanddelrue/
• 💼 Connect with Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/
• 💼 Connect with Esben Friis-Jensen on LinkedIn: https://www.linkedin.com/in/esbenfriisjensen/
• 🧠 Sign up for the ProductLed Newsletter: https://www.productled.com/newsletter

Most founders hear stories about lean SaaS companies and assume they are the exception.

Jeremy Clarke lived one.

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeremy Clarke, founder of WebMerge and now the builder behind Quin, to unpack what it really took to grow WebMerge into a multi-million dollar business with an incredibly small team.

Jeremy shares how WebMerge started as a simple PDF generation tool, why integrations became the growth engine that unlocked scale, and how a strategic hire helped expand distribution without bloating the company. He also gets honest about what has changed in today’s AI market: thinner margins, tougher distribution, less generous free plans, and far more noise.

The conversation also dives into the founder mindset behind building highly effective companies. Jeremy explains why staying close to support made WebMerge stronger, why he delayed hiring for as long as possible, and what drove his decision to eventually sell. From there, he opens up about building Quin, what it means to compete in a crowded AI category, and why word of mouth and customer trust still matter more than ever.

If you want to build a meaningful software business without defaulting to a big team or venture funding, this episode is packed with practical insight.

Key Highlights:

• 00:43 - From WebMerge to Quin
• Jeremy shares what he’s focused on today, why Quin is a much harder business to build than WebMerge, and how AI margins change the game.
• 07:36 - The WebMerge growth playbook
• How WebMerge evolved from a simple PDF tool into an integration-driven platform, and why partnerships became a major distribution engine.
• 12:40 - How WebMerge really got off the ground
• The early days of the business, the first customer outreach, and how a slow trickle of traction compounded into millions in revenue over time.
• 16:36 - Why Jeremy bootstrapped from day one
• Jeremy talks through his decision to stay self-funded, avoid outside control, and build on his own terms.
• 19:14 - How to reach $5M with almost no team
• A candid discussion on why Jeremy delayed hiring, what work he kept for himself, and when he finally saw the need for a strategic hire.
• 21:51 - Why founders should stay close to support
• Jeremy and Esben discuss support as a product advantage, how it tightens the customer feedback loop, and why speed matters so much.
• 25:28 - Why he sold a highly profitable business
• Jeremy shares the reasoning behind selling WebMerge, including risk, lifestyle, hiring pressure, and the chance to join a larger story.
• 47:18 - What still wins in a crowded AI market
• Jeremy explains what has changed in his new playbook, what has not changed, and why customer trust and word of mouth still matter most.

Resources:

• 🚀 Quin: AI assistant for busywork and follow-through:
• 💼 Connect with Jeremy Clarke on LinkedIn
• 💼 Connect with Wes Bush on LinkedIn
• 💼 Connect with Esben Friis-Jensen on LinkedIn
• 🧠 Sign up for the ProductLed Newsletter

When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis.

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since.

Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool.

They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos.

Key Highlights:

• 00:00 - The 72-Hour Crisis That Changed Everything

Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency.

• 01:40 - Why Big Tech Wanted the Windsurf Team

A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding.

• 04:10 - The Future of Coding Is Multi-Agent

Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift.

• 08:54 - How Free Became Their Growth Wedge

From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf’s early PLG motion and how it created awareness and pipeline.

• 13:10 - The Hard Truth About AI Pricing

A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit.

• 16:13 - Why Enterprise AI Sales Are Top-Down

Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts.

• 20:51 - What It Takes to Drive Real AI Adoption

Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team.

• 24:40 - Jeff’s AI Workflows as CEO

Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards.

• 32:32 - Jeff’s Advice for Every Product Founder

Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from.

Resources:

• 🚀 Windsurf
• 💼 Connect with Jeff Wang on LinkedIn
• 💼 Connect with Wes Bush on LinkedIn
• 💼 Connect with Esben Friis-Jensen on LinkedIn
• 🧠 Sign up for the ProductLed Newsletter