ProductLed Podcast

Day 55, and I'm past the halfway point of building an AI product in 100 days. This one looks back at the first 50 days and lays out the plan for the last 45. The biggest lesson so far: I thought building the app would be the hard part. It isn't. Figuring out what to build, how it fits into the business, and what the next offer should be is still way harder. The PLG analyzer now gets about 8 out of 10 recommendations right. I'm not willing to trade quality for cost or speed, so getting that to 99% comes first. The money side hasn't worked yet. The analyzer was meant to lead to working with a ProductLed operator, but most of the people using it run small companies, and they're hard to serve at that price point. Meanwhile the free product costs about $1,300 a month to run, developer included. So the plan for the next 45 days is one new enterprise client, won through blind calls: analyses of companies I already want to work with. Every one of them also trains the analyzer to give you better recommendations. IN THIS EPISODE 00:00 Day 55, and what I'm building 02:09 Your feedback, from "about as good as ChatGPT" to three scans in a row 03:05 Building the app was never the hard part 04:01 Highlights: shipping, Matt Pocock's skills, new friends 05:20 Lowlights: why 8 out of 10 isn't good enough 06:58 Building an app has no training plan 08:33 The game plan for the last 45 days 09:36 Why the operator offer hasn't worked 10:45 A free product that costs $1,300 a month 11:22 Pay for itself, fit the business, fix quality 13:08 How my advisory work actually runs 14:24 Blind calls: account-based marketing that trains the AI 15:29 One enterprise client and 99% good outputs 16:36 Next: turning internal workflows into products 17:54 What's your take? MENTIONED Run the free PLG analyzer: https://productled.com Matt Pocock's skills: https://github.com/mattpocock/skills Wes Bush on LinkedIn: https://www.linkedin.com/in/wesbush/ What's the biggest challenge you've hit building an AI product?

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 EverythingTibo 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 CountsWhy 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 MarketThe 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 GrowthHow 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 EngineWhy 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 OutcomeTibo 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 StackWhy 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 FeedbackA 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 BuildsTibo 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