Marc Baselga’s Post

The concept of MVPs has CHANGED. If you don't understand this, you are living in the dead past. The old "prototype MVP" is dead. AI and a faster GTM shortened patience windows. Buyers scroll past demos and jump to what they can actually use. That changes everything. A rough prototype used to get attention. Not anymore. Now, unless you’re building something truly novel, buyers expect something close to production. Which raises the stakes on ideation and PMF. Because production launches are expensive. A PMF miss can be painfully costly. So stop validating with wishful thinking. Validate with customer signals that mean money or commitment. Three signals that actually move the needle: 1. Generating ICP leads through a landing page before building 2. Interviews that end with a clear yes or pre-commitment. 3. Pre-sales / paid pilots that put money on the table. Think of an MVP like a restaurant soft opening. If guests pay and come back, your menu works. If it’s just friends and compliments, you’re guessing. Note: not every idea needs a billion-dollar build. But every launch needs proof that customers will buy what you ship. PMs need to shift their checklist from “features shipped” to “signals captured.” Founders should strive to get early money and clear yeses over polite feedback. Which validation are you running right now?

If you can take a trip in 3 days, you can ship in 3 days. Just think, in three days, people: - Fly across the country for a wedding - Finish a home project they’ve been putting off for months - Attend a full conference and walk out inspired - Launch a side project Three days is enough to do something real. I’m talking about a finished, ready-to-ship thing that’s well beyond a prototype or plan. That’s the mindset behind the Day 3 Cycle: A complete software loop (Plan, Build, Test, Ship) every 72 hours. It’s a system we’re using that leverages a clear spec, an AI assistant, and a no-fluff workflow. We’ve used it to cut lead time by 30% on legacy services without sacrificing quality. That’s a really big deal. Want the full playbook? For the deep dive into how the Day 3 Cycle works read the full blog: https://lnkd.in/eAQgDv6F What’s the fastest your team has ever gone from idea to shipped release? And what would it take to beat it? #TheMavricMethod #Day3Cycle #SoftwareDevelopment #ProductLeadership

The biggest problem for non-technical people? → Building proof of work. As a non-technical person myself, I’ve felt this gap. Unlike coders or product folks, we don’t have “something” tangible to show, an app, a website, or a tool. For us, proof of work looks different. It’s about building value by selling/marketing something and stacking those experiences. But here’s what’s changed with AI: You don’t need a perfect product anymore. You can launch the scrappiest version possible, Put it out in the market, And if people are showing up for your demo calls— That’s proof. Because if you can sell the scrappiest thing, You’ll sell an average product really well. And if you can sell an average product, You’ll scale when the product is great. Proof of work isn’t about what you build,  It’s about what you prove you can make happen.

Don’t Be a Dummy: Prototypes Aren’t Just Crash Tests 🚗💥 Too many teams treat prototyping like a crash test: strap in a dummy, slam it into the wall, and proudly declare, “Look — it didn’t break!” But just because something survives impact doesn’t mean it’s fit for purpose. A proper prototype isn’t about proving the system won’t fall apart. It’s about proving you’ve actually built what you intended — and whether customers can understand it, use it, and (ideally) love it. Because here’s the reality: robustness in a controlled test is meaningless if real people in the real world look at it and go, “Err… what am I meant to do with this?” That’s why it’s crucial to: 🧩 Prototype early — it’s cheaper to fix when it’s still small 🧩 Involve everyone — not just tech, but ops, marketing, and customer insight 🧩 Put it in real hands — customers will spot what’s “obviously not right” long before a stress test does The difference between “it works” and “it works for customers” is everything. A prototype that survives a crash test but confuses the user? That’s not success — that’s wasted time. 👉 This is exactly what we teach in the one-day Beyond Delivery Toolkit — how to test beyond the tech, involve customers early, and make sure what you’re building really fits the job. 👉 Book a course or get in touch to see how the Toolkit can help your team succeed: kim.lindsay@strategymadesimple.co.uk #StrategyMadeSimple #Toolkit #BeyondDelivery #Prototyping #CustomerExperience

How I Map Strategy Without a Budget When there’s no formal strategy, I use this 3-step framework: 🧩 Ask: “What’s the actual problem we’re solving?” 🧪 Test: Pilot one idea with one team 📈 Show: Measure what moved and share the result I’ve used this model across projects where time, money, and clarity were all missing. 🎯 It builds credibility—fast. #LearningAndDevelopment #AIinLearning #ChameleonHubAI #LxDesign #PurposeDrivenBusiness

Before you invest in features, you need proof they’ll actually work. That’s why you need a Proof of Concept. PoCs are where ideas stop being “nice on paper” and start proving they can work. There are plenty of challenges on the way from PoC to a full feature. Time is always the biggest one: you can’t explore every solution endlessly. But that’s also why PoCs are powerful. Using minimal code, you can quickly test: ⚒️ If the feature works as intended 💸 What it might cost to run 🏗️ Whether the architecture can withstand high traffic As a dev, I love working on PoCs. I get to switch into R&D mode, read up, test different approaches, and break a few things. I’m really excited about my next PoC: an AI functionality I’ve only read about until now. It’s a chance to try something I’ve been curious about for a long time, and even better, I’ll be working with a teammate, so we can bounce ideas and share knowledge along the way. What’s your opinion on PoCs? Have you worked on some recently?

The best ideas don’t always show up in meetings. Sometimes they spark when your mind finally has space to wander. But once inspiration strikes, the real question is: does it have legs? With Validic, you don’t need to spend months chasing SDKs, fixing OAuth issues, or deciphering undocumented APIs. You already have the tools to test your idea - fast. ✅ A human-readable data model ✅ A live sandbox to explore and validate ✅ A marketplace with over 600 connected devices From “what if?” to prototype, Validic helps you move quickly and confidently without extra contracts, vendors, or rebuilding your stack. Read more in our latest blog by our very own Selma Pittman to learn how to turn sparks into real solutions: https://lnkd.in/eQTxGgzP)

Here’s why I keep coming back to it: 1. Predictable reasoning > flashy tricks. o3 follows structure and constraints without fighting me. My prompts land, my outputs match spec. 2. Speed-to-quality sweet spot. It’s fast enough for production work, yet thoughtful enough for strategy and perfect for shipping daily. 3. Tool-use discipline. When I wire actions (DB, webhooks, schedulers), o3 stays on-rails and doesn’t wander. 4. Long-thread stamina. Multi-step plans, revisions, all stay coherent across iterations. 5. Tone control. From executive briefs to human, local voice ,o3 mirrors the vibe without sounding robotic. 6. Lower hallucination pressure. It’s not perfect (none are), but o3’s “don’t make stuff up” instinct is stronger in my workflows. 7. Cost/latency balance. For 80% of real work, o3 hits the ROI target. Where it wins for me right now: • Designing agent flows and prompts for sales/ops assistants • Product copy + landing pages (FR/EN) with tight brand voice • Architecture notes → checklists → deploy-ready tasks • Quick code refactors and API scaffolds without overthinking Yes, I’ll switch to vision models for image/UX work, and to heavyweight reasoners for deep research. But for daily shipping, o3 stays my default. Curious: if you tried o3 recently, what did it help you ship faster?

Lost in product ambiguity, knowing "roughly what you want" but unsure where to start?   Our AI Expedited Product Discovery engagement is designed to eliminate this pain point. We provide clarity before code, delivering clear, validated product requirements, design concepts, and a clickable prototype.   Catch scope, data, or workflow issues before build starts. Get crystal clear on your next product before you invest heavily in engineering.

"Most MVPs fail because they try to solve 10 problems poorly instead of 1 problem brilliantly." Everyone misunderstands MVP. Minimum doesn't mean broken. Viable doesn't mean barely functional. I use the SLC framework instead: - Simple: Easy to understand and use - Loveable: Users actually want to use it - Complete: Solves one problem end-to-end Example: Slack's MVP wasn't "messaging with bugs." It was complete team communication that was simple and genuinely delightful to use. Your Cursor-built prototype might tick the "minimum" box but it does it really tick the "loveable" box? If users tolerate your product instead of choosing it, you've built a demo not an MVP. 👉 The difference matters more than ever. With AI tools making it trivial to build software, the bar for "good enough" has actually risen. 👈 #MVP #ProductStrategy