Innovation Prototyping Methods

Before I sold Quest for $1,000,000,000, I wasted millions trusting the wrong thing: My own ideas. Here's the AI validation framework I wish I had when building Quest Nutrition: Most entrepreneurs fail in the same boring way: 1. Have an idea 2. Fall in love with it 3. Build it for months 4. Launch 5. Discover nobody wants it 6. Repeat This is "build and pray" physics. It's suicide. But there's a better way. One that uses AI to kill bad ideas in 72 hours, not 12 months. My 5-step AI validation framework that has saved millions in wasted effort: 1. Problem Verification Your idea isn't special. Period. The only thing that matters is: are people actively suffering from the problem you claim to solve? Feed Perplexity and ChatGPT with Reddit threads, forum posts, and review sites. Let AI extract patterns of pain. No real pain = dead idea. 2. Market Size Analysis Even if the pain is real, is it widespread enough? Let AI analyze Google Trends, search volumes, and TAM data. Create detailed spreadsheets of potential users. Too small = dead idea. Goals make demands. If the goal is to build a substantial business, the market has to be big enough. 3. Competitor Assessment Feed AI your top 5 competitors' websites, pricing pages, and customer reviews. Have it identify gaps and oversaturation. Create a map of what's missing. No clear advantage = dead idea. Build from physics, not analogy. That's the only way to find a real competitive edge. 4. Zero-Cost MVP Design Most founders build full products before validation. That's the most expensive way to learn. With AI, create "fake door" tests instead: • Landing page that looks real • AI-generated mockups • $50 of ads to see if people try to buy No buyers = dead idea. The market doesn't care how hard you worked. It only cares if you solved a real problem. 5. Early Adopter Interviews For ideas that survive steps 1-4, use AI to: • Draft perfect outreach messages • Generate interview questions that reveal buying intent • Analyze interview transcripts for patterns No enthusiasm = dead idea. This is Physics of Progress in action. Test hypotheses. Follow the data. Kill your darlings fast. The hard truth about entrepreneurship is that 90% of ideas SHOULD die. Your job isn't to build - it's to kill bad ideas quickly. Most entrepreneurs think failure is the worst thing that can happen. It's not. The worst thing is wasting years on something nobody wants. Let AI be your reality check. It's ruthlessly honest in a way your friends, your team, and even you can't be. Ideas are worthless. Validation is everything. PS: I’ve trained an entire GPT to track down the root cause of your next revenue plateau - and help you break through it. It’s built based on 100,000s of data points from my group coaching sessions. Grab it for free here: https://buff.ly/nUri82k

Your AI agents might look impressive in demos. But real-world deployment is a completely different game. It’s not about building smarter prompts. It’s about building safe, observable, controllable systems. That’s exactly what this framework highlights. These 8 layers are what turn experimental agents into production-ready AI: Not just tools and models but policies, privacy, monitoring, approvals, audit trails, risk scoring, and incident response. In simple terms: - Policy rules define what your agent is allowed to do. - Data privacy protects sensitive information. - Access control limits which tools and systems the agent can touch. - Model monitoring tracks accuracy, drift, hallucinations, cost, and latency. - Audit logs provide full traceability of every action. - Human approvals step in for sensitive or high-impact decisions. - Risk scoring evaluates actions before execution. - Incident response contains failures fast when things go wrong. This is how teams move from “cool prototype” to “production-grade AI.” If you’re building AI agents for real business workflows, these layers aren’t optional. They’re the foundation. Save this if you’re working on Agentic AI and tell me: which layer do you think teams underestimate the most?

What if the best solutions for your process started with cardboard? When testing new ideas or improvements, jumping straight to high-cost, permanent solutions can be risky—and expensive. That’s where cardboard engineering comes in. Cardboard is one of the simplest, most cost-effective tools for rapid prototyping and testing ideas. It’s lightweight, easy to shape, and lets you visualize, test, and refine your concepts before committing to more expensive materials. Why Cardboard Is Perfect for Prototyping: 1️⃣ Low-Cost Experimentation Testing with cardboard lets you try multiple iterations of a design without worrying about material costs. 2️⃣ Fast Feedback Loops You can build and modify a prototype in minutes, gathering instant feedback from your team or operators. 3️⃣ Hands-On Collaboration Cardboard prototypes allow teams to actively engage with ideas, making it easier to identify issues or opportunities for improvement. 4️⃣ Visual Validation Sometimes, seeing a physical model highlights challenges that wouldn’t be obvious in a drawing or plan. How to Use Cardboard for Lean Improvements: 🔍 Test Workstation Layouts Use cardboard cutouts to mock up layouts and placement of tools, parts, and equipment. Adjust until everything flows smoothly. 📦 Simulate Material Flow Prototype racks, bins, or carts to ensure materials are stored and moved efficiently before building them with more durable materials. 🛠️ Design Fixtures or Jigs Create cardboard versions of fixtures or jigs to test their functionality in the process. Refine the design before investing in the final version. 📐 Test Ergonomics Mock up equipment or workstation designs with cardboard to test ease of use, reach, and operator comfort. Example of Cardboard in Action: A manufacturing team wanted to redesign a workstation to reduce operator motion. Instead of committing to expensive reconfigurations, they used cardboard to prototype the layout. After several iterations, they found the optimal setup, reducing motion by 25% and saving hours of work. Cardboard isn’t just for packaging—it’s a powerful tool for testing and refining your ideas. By prototyping with low-cost materials, you can experiment, learn, and improve quickly without breaking the bank.

Prototyping a system in small numbers on a tight timeline can lead to interesting decisions. For instance, it was quicker and more cost-effective for us to source a copper-core PCB than a sheet-metal part.   In the Framework Laptop SDR, we use two heat pipes to cool the AFEs. The heat pipes are soldered to a 1mm copper sheet that sits on top of the AFEs. Instead of using a copper sheet metal part with a surface finish suitable for low-temperature soldering, we found that, in low quantities, it was much faster to source a copper-core PCB. The surface finish and planarity of the copper-core PCB are also subject to much tighter process control. With a lead time of only two days, and the added benefit of being able to place sensors directly on the heat spreader, it proved to be a much better option than a sheet metal part. The heat pipes are in direct contact with the copper core at critical points, so the thermal performance is equivalent to that of a plain copper sheet.   For higher quantities, the copper sheet metal part is of course a lot cheaper, due to fewer processing steps. However, we might even stick with the copper core PCB solution due to the added benefit of being able to place sensors on the heat spreaders without the need for wiring. #design #hardware #electronics

AI Prototyping Tools Masterclass: If you've been bouncing between v0, Bolt, Replit, and Lovable wondering, "Which one should I actually be using?" You're not alone. They all look impressive. But if you don’t understand what each one actually does best, you're just spinning your wheels. So, let’s break it all down: — ONE - The 4 Major Players (and What They’re Built For) Let me remind you, these aren’t just "tools" anymore. They’re fast-evolving cloud development environments And each one has a clear edge. 1. v0 by Vercel This one’s all about beautiful front-end design - out of the box. Clean UIs, polished interactions, and a $3.25B valuation behind it. Perfect if you’re spinning up a demo for stakeholders... And want something that looks amazing fast. Just don’t expect deep backend stuff without plugging in extras like Supabase. 2. Bolt Built for speed. The CEO told us the whole thing runs in the browser, no VMs & no lag. That's the reason it went from $0 to $40M ARR in just 6 months. If you’re testing ideas fast (think 10-minute prototypes), this is your tool. It’s flexible, but you'll need to connect things like a database yourself. 3. Replit This one goes deep. Founded by Amjad Masad and now valued at $1.16B, Replit gives you full-stack power. Built-in auth, built-in database, built-in deployment. If your prototype needs to function like a real product, this is the play. It’s not as slick as v0 or as lightning-fast as Bolt... But when it comes to handling real logic, Replit is in a league of its own. 4. Lovable Lovable is becoming the most loved "vibe coding" tool. Founded by Antonin Osika, and it hit $17M ARR in just 3 months. Honestly? It’s the easiest tool in the game, especially if you don’t code. Drag, drop, sync with Supabase. That’s it. No setup headaches. No complex environment. Perfect for non-technical PMs or anyone who wants to go... From idea to live prototype without touching a line of code. — TWO - ADJACENT TOOLS But wait, there’s a twist. These tools aren’t where AI prototyping stops. There are adjacent tools you’ll want to layer in depending on your skill level: If you’re just looking to generate quick code or play around with ideas: → ChatGPT and Claude work great. But if you want to build something real (and you can code): → Tools like Cursor, Windsurf, Zed, and GitHub Copilot are insanely powerful. A great flow in my experience so far? Start in Bolt or Lovable → Sync to GitHub → Then build deeper in Cursor. — I broke all this down in my latest newsletter drop: "Ultimate Guide to AI Prototyping Tools (Lovable, Bolt, Replit, v0)" If you want to understand how to actually use these tools and which one fits your workflow best, go here: https://lnkd.in/eRypMZQ8 It’ll save you weeks of trial and error.

This is the proudest moment in Quilter's history: our AI just designed an 843-component Linux computer - and it booted on the first try. It was the culmination of a year-long effort that pushed our team and our technology to their limits. VentureBeat captured that journey in a way that finally does justice to what went into this milestone. Link to the article and the full announcement video in the comments. Project Speedrun was our hardest test yet. Two boards, 5,141 pins, high-speed DDR4, eMMC, PCIe, CSI/DSI, GigE — the kind of system normally quoted at 400–450 hours of manual layout. Quilter reduced that to 38.5 hours, with the rest done autonomously. That's a 10x acceleration. This is the moment when physics-driven AI stops being a demo and becomes something you can build real hardware with. The article also shares something publicly for the first time: Tony Fadell has been an investor, advisor, and deep product partner to Quilter. I want to take a moment to thank Tony. VentureBeat mentions our “dozen-page emails” discussing product and company strategy, and that’s real. Tony goes deep on everything from UX to design philosophy to enterprise workflows in electronics. He was uniquely helpful in working through a subtle but critical question: How do you automate PCB design without removing control from the engineer? Project Speedrun is the outcome of that thinking. The computer booting on the first try wasn’t luck — it was the direct result of building a workflow that balanced automation with intentional human input. I’m grateful for the collaboration, the challenge, and the belief. Most hardware ideas don’t fail because of imagination. They fail because teams can’t iterate fast enough. With Quilter, that bottleneck shrinks from months to days. This unlocks an entirely new pace of invention in PCB design. The next generation of hardware companies will be built on cycles measured in days, not quarters — and I can’t wait to see what they create.

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.

If you’ve been building AI agents recently, you know the deployment phase is often where things get messy. Managing versions, tracking changes, and moving from a notebook to a live service is currently a major pain point for many teams. I’ve been digging into the new Agent Deployment strategy on Databricks (using Databricks Apps), and it brings some much-needed software engineering rigor to the process. Here is why this approach is actually useful: ✅️Git-Based Versioning: You can finally treat your agent code like actual software. Push to Git to manage versions, rather than relying on notebook checkpoints or obscure model registry tags. Awesome, right!? ✅️Local Development: Coolest one! You aren't forced to code in the browser. You can build in your local IDE (VS Code, Cursor, etc.) and sync directly to the workspace. ✅️Full Server Control: Since it runs on Databricks Apps, you have full control over the underlying Python/FastAPI server. This makes custom middleware, routing, and heavy customization much straightforward. ✅️Production Ready: It integrates natively with MLflow for tracing and evaluation, so you don't have to wire up a separate observability stack (an important one) from scratch. It basically moves agent development away from "experimental scripts" and into a standardized deployment workflow. If you are tired of fragile deployments, this is worth a read. https://lnkd.in/efFKfzkU

Wow. I just built 3 mini-apps for PMs in under 10 minutes: an empathy mapper, a journey analyzer, and a competitive analysis tool with Opal (Google Labs). No PRD. No Figma. No tickets. Just an idea → an experience. Instead of debating documents, I’m now sharing working mini-apps with my team ask them "react to this, let’s refine it” I used Opal to prototype the vibe with an: -Empathy Mapper -User Journey Analyzer -Competitive Landscape Tool Each one took minutes. Each one was immediately shareable. Each one changed the conversation. Use Opal when: -You want to validate an idea before writing a PRD -You need a quick tool for a workshop or meeting -You want to make research or concepts visible -You want to better empathize about your user Think of Opal as your 10-minute lab. If it takes longer than that, move it to a full prototype — that’s where other AI prototyping tools come in. Tips for PMs adopting this workflow -Start tiny. Your first Opal app should take under ten minutes. That constraint keeps you focused on intent, not polish. -Think in verbs, not nouns. Prompts like “summarize feedback” or “visualize trends” produce far better prototypes than static descriptions. -Collaborate live. Invite designers, engineers, and stakeholders into the session. Watching the prototype evolve creates alignment faster than any meeting. -Reflect. After every prototype, note what worked. Each build sharpens your prompting instincts and your product intuition. 🔗 Guides + masterclass in the comments 👇

🚀 AI is revolutionizing prototyping—making it faster, smarter, and more sustainable. From my early work with Sony’s AIBO to advising AI startups today, I’ve seen firsthand how AI-generated prototypes are transforming industries. What once took months of manual iteration is now done in days with AI-driven design, 3D printing optimizations, and digital twins. 🌍 The impact? Lower costs, reduced waste, and smarter material usage. Companies like Airbus, BMW, and Adidas are already leveraging AI to cut material waste by up to 50% and reduce costs by over 70%. Startups can now test and refine products virtually before manufacturing a single physical model. This is not just about efficiency—it’s about sustainable innovation. AI is reshaping how we build, test, and bring ideas to life. Those who embrace it now will gain a massive competitive edge. Read my latest article on the rise of AI-generated prototypes and how they are changing the game 👇 #AI #Innovation #Sustainability #Prototyping #3DPrinting #DigitalTransformation #AIStartups #FutureOfTech