Mikita Martynaū’s Post

Stop falling for the “MVP in 72 hours” hype. I’ve seen too many founders burn money and energy chasing it— waiting for a magic MVP… getting something half-baked… paying again to fix it… and sometimes never launching at all. If speed alone could win customers, everyone would be doing it. What actually works? UFP - Ultimate Focused Product: - Solves a real pain - Fits naturally into your customer’s life If you really want something in 72 hours, use AI tools like Lovable and build it yourself. But if you want a product people actually love, give it the time it deserves.

Yesterday’s masterclass by Julien Steel at Wintercircus Ghent on scaling product–market fit was a great reminder of timeless product core principles: 1. Get the fundamentals right AI allows us to move faster and become more efficient as product teams, engineers, and business developers. But if we’re going in the wrong direction, AI just gets us there faster. Getting the fundamentals right and solving a real user need is non-negotiable. 2. Anchor on a value metric Julian framed this like an onion: your value metric is the center, and everything else layers around it to support it. Side note: metrics will never be perfect, but they’re still essential for aligning the team and making decisions. 3. Decisiveness > hesitation Indecision can be more damaging than a wrong call. Staying in the gray zone creates confusion for developers, clients, and the whole team. A clear yes/no on an initiative, even if imperfect, is better than not making a decision. In a world moving faster than ever, clarity and fundamentals are what keep us on track. #productmanagement #productmarketfit #scaling

📢 Earlier today, I gave a talk at Liberty Mutual's inaugural Future of Product conference, where we heard some awesome stories at the intersection of Product Management and User Experience. I shared how I built a GenAI chatbot that transforms customer and competitive research into a conversational "product memory." The best part? I built this with zero programming experience - just used AI to guide me through the process step by step. I had a prototype up and running in a couple hours and am already talking about how to scale it. My key message isn't about just one tool. It's about shifting from using AI to building with AI. When we start creating solutions for our specific challenges, that's when AI becomes truly transformative. I'm proud to work at an organization that encourages this kind of experimentation and gives us the platform to share bold ideas. The future of product is bright! I'll leave you with the same prompt I gave my audience. Regardless of your role, it's time to shift from AI user to AI builder. So what will YOU build?

I found my first AI product launch post from 24 months ago. 3 signups. 1 customer. $29 MRR. I was convinced I'd "cracked" the AI SaaS game. 😅 Fast forward to today: Multiple AI products launched (including VidMe), automated systems generating consistent revenue, and workflows that actually scale. The difference? I stopped building features and started solving real problems. Here's what I learned building AI products that actually work: → Ship fast, even if it's embarrassing → Talk to users before writing a single line of code → Automate everything you do more than twice → One profitable product beats ten "cool" ideas → AI tools are only as good as the problems they solve The hardest part isn't the AI integration or the automation setup. It's accepting that your first version will probably suck. But here's the thing - every indie maker with a successful AI product started with a terrible first launch. The ones who made it just kept shipping. What's your biggest lesson from launching something that flopped? Drop it below 👇

Really enjoyed this piece on AI-powered product development by Naseema Perveen today. AI tools have turned the dial on the ability to 'fail fast' up to 11. But without doing the upfront (and often unglamorous) work to align on real business problems, it’s easy to chase the shiny new tech and miss the mark. My favorite thought: "But here’s the uncomfortable truth: most of those prototypes don’t survive. They’re fast, yes — but fragile, unscalable, and often irrelevant to what the business actually needs. That’s why the winners aren’t the teams who build fastest — it’s the teams who learn fastest." https://lnkd.in/eceRzhjr

⚠️ You can build an AI product in 48 hours now. That’s exactly why so many founders are screwing it up. With tools like Vibe Coding, shipping has never been easier. You can go from idea to live product in days - not months. But here’s the shift: ❌ The question isn’t can you build it. ✅ The question is: should you? I’m speaking with multiple founders right now who are skipping the customer development part entirely. They’re charging ahead, building features - without first checking if anyone actually wants them. No interviews. No validation. Just vibes and velocity. 🚀 Tools evolve. But principles don’t. Lean Startup thinking is more important than ever: Validate the problem before you write a single line of code. Talk to customers before you commit to features. Use prototypes as a learning tool, not a vanity milestone. The upside? You can get to something tangible faster than ever. Which means you can get to truth faster too. 💡 Lesson: The real advantage isn’t speed to product. It’s speed to validation. 📸 Pictured: Me with the author in London, 2012. 🕵️♂️ Bonus points if you can guess which book launch it was. Drop it in the comments 👇

Contrarian take: The faster horse problem is real, and it's killing your product. Customers know exactly where things hurt. They'll tell you their AI system is unreliable, their dashboard is overwhelming, their workflows are broken. They're never wrong about the problem. But when they start pitching solutions? That's where things get dangerous. YC talks about this all the time, and I've seen it play out firsthand. For us, customers ask for more tests when what they actually need is smarter test curation. They'll want better filters when the real issue is fundamentally rethinking prioritization. They're essentially asking for a faster horse when what would actually solve their problem is inventing the car. This isn't their fault - it's not their job to imagine solutions that don't exist yet. That's yours and ours. At Openlayer, we're learning to listen selectively. When feedback comes in, we absorb every detail about the problem but proactively try to ignore the proposed fix. The temptation is to just build what they're asking for. It feels collaborative, customer-focused, safe. But that's how you end up with feature bloat instead of breakthrough moments. Take their problems seriously. But never outsource your product vision to feature requests. The faster horse gets you incremental gains. The car changes everything.

Everyone talks about product-market fit. But ElevenLabs hit it like a freight train. In 24 months, they went from $0 to $200M ARR. Let me break down how. → They launched a beta that actually worked. Not a half-baked MVP. Their voice AI blew people away from day one. → They focused on one use case. Text-to-speech that sounds human. Not a dozen AI tools packed into a Frankenstein UI. → They ignored vanity. No hype. No YC badge. No Twitter threads. Just a link that did one job: convert. → They rode the distribution flywheel. Users shared voice samples. Which brought in more users. Which brought in more data. Which made the voices better. → They charged early. No endless free tiers. People paid because it worked. Simple as that. The lesson? Find something people need. Ship it fast. And make sure it’s 10x better than the alternative. Bonus: Their original pre-seed deck is making the rounds. 10 slides. Zero fluff. Straight to the point, just like their product. If you’re building in AI, study ElevenLabs. They didn’t grow by luck. They grew by making something great and shipping fast.

At Reforge’s first AI Product Summit yesterday, one theme kept coming up: “The full-stack builder is becoming the new norm.” Not PMs vs designers vs engineers — just people who specialize in the problem, not the skillset. Not shipping specs — shipping prototypes. The companies going all in (like Shopify, Zapier, SuperMe, LinkedIn, and Laurel) are already operating with this new mindset and are scaling faster because of it. Huge shoutout to Brian Balfour and the Reforge team for bringing together 70+ product leaders to dig into how AI is reshaping product development.

After 4 weeks of building, we just shipped something that might finally solve the early-stage founder's biggest headache: Making clear product decisions while everything changes 10 times a day. 𝗠𝗲𝗲𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗔𝗱𝘃𝗶𝘀𝗼𝗿, an AI agent that knows your entire product. Every document, decision, and piece of your product graph. Ask it 'Who's our ICP?' and it shows you its thinking, references your actual data, and gives you the kind of analysis you'd expect from a real product leader. It's like having a chief product officer available 24/7 and one that actually remembers every decision you made 3 months ago. Thanks to: Han Kim, Angela Gao, Josh Sirota, Sahil Dhull, Daisy Dolan, Taylor Offer, Jeffrey Erickson, Sam Shank, Andrew Lee, Alex H. Lee, Kevin Holmes and the rest of Brief's early users, customers, and advisors! And Guillermo Rauch and, recently, Daniel Erickson for Vercel's AI SDK. It's pretty incredible that a solo dev can ship a tool-calling agent in hours. If you're tired of making gut decisions and ready to have a Chief Product Officer that can operate at AI velocity, link 👇