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    thomashellby

    @thomashellby

    👋 Hi, I’m Thomas.

    💻 I’m a former web developer turned SEO and tech enthusiast, focused on how search, AI, and digital marketing are evolving. I enjoy working with SEO strategies, technical optimization, and content systems that improve online visibility.

    🚀 My interests include AI search, SEO tools, website performance, content marketing, and platforms like Shopify and WordPress. I also explore how AI Search Monitoring and modern search systems are changing the way content is discovered and optimized.

    🔍 I regularly share insights on SEO trends, AI-driven search, and practical growth strategies for websites and online businesses.

    🤝 Always open to connecting with like-minded people who are passionate about SEO, AI, and technology.

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    Website tripleminds.co/

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    Latest posts made by thomashellby

    • Before You Build a Prototype, Can You Answer This One Question: “What Does It Need to Prove?”

      I’ve been thinking about why so many early-stage prototypes end up as expensive demos that don’t teach the team anything. The pattern is usually the same: a founder has a solid idea, hires a team, and jumps straight into screens and features. Three months later there’s a polished product that looks great but doesn’t answer the questions that mattered.

      My take is that most of those failures start before a single line of code, with the brief. Or more accurately, with the lack of one.

      A prototype is a learning tool, not a mini product

      The most useful question before any build is: what do we need this prototype to prove? Maybe it’s that users understand the core value in under a minute, that a booking flow can be completed without help, or that a risky technical integration is feasible. Each of those leads to a very different prototype. A clickable design and a working backend demo are not interchangeable, and treating them as the same thing is where budgets quietly disappear.

      Where I see briefs go wrong

      • Describing the solution without the problem. A paragraph about an “AI-powered ecosystem” tells a team nothing. “Customers can’t compare local providers without calling five of them” tells them exactly what to build.

      • A feature list instead of priorities. If everything is a must-have, nothing is. Splitting features into must-have, should-have, and could-have gives everyone a way to make trade-offs when time or budget gets tight.

      • Never defining “simulated vs. working.” A checkout screen that demonstrates the experience is very different from one that actually processes payments. If this isn’t written down, expect a disagreement at delivery.

      • No acceptance criteria. “Prototype delivered” means different things to different people. Something like “a user completes the main booking journey on mobile without assistance” is something you can actually verify.

      • Ignoring ownership. Who owns the designs, source code, and assets once the work is done? It’s boring, but it’s much easier to settle before work starts than after.

      The unglamorous parts that save money

      • Name the primary user group specifically. “Small restaurant owners managing online orders” beats “anyone online.”
      • Use synthetic data early on unless you truly need real data, which also sidesteps security headaches.
      • Decide who has final say on feedback. A lot of project delays come from unclear approvals, not from development.
      • Define what the prototype will not prove. A clickable demo can show a workflow is understandable. It can’t show people will pay, or that the system handles thousands of users.

      Choosing a partner

      If you’re working with outside help, it’s worth comparing more than price. Look at scope, how feedback and revision rounds are handled, what’s actually delivered, and what happens when requirements change. A cheaper quote that leaves major responsibilities unclear often costs more later. Founders who don’t have in-house product or engineering experience often lean on a prototype development company for startups to help define scope, user flows, and technical priorities before committing to a build. Teams like Triple Minds, which cover development, design, and AI consulting together, are an example of the one-stop model some founders prefer over coordinating separate vendors, though the right fit depends on your project and how much structure you already have.

      Either way, a clear brief makes any partner more effective, because they’re estimating real work instead of guessing.

      Let’s discuss
      I’d like to hear from people who’ve been through this:

      • Have you built a prototype that taught you nothing, and what do you think went wrong?
      • How do you decide what a prototype should not include?
      • Do you prefer a clickable design for early validation, or a working technical build, and what made you choose it?
      • Founders: did writing a brief change how your project went? Developers: what’s the one thing you wish clients put in theirs?

      Real examples welcome, especially the messy ones. Those tend to be the most useful.

      posted in Artificial Intelligence
      thomashellby
      thomashellby
    • đź’ˇ What's the Biggest Mistake First-Time Founders Make in AI SaaS App Development?

      I've been reading a lot about startups lately, and one pattern keeps showing up.

      Many first-time founders believe that success comes from building the most advanced AI product possible. They spend months adding features, integrating multiple AI models, and polishing every screen before anyone has even used the app.

      But is that really the right approach?

      From what I've seen, the biggest challenge in AI SaaS app development isn't building the software—it's building something people actually want.

      Imagine spending six months developing an AI tool with dozens of features, only to discover that users only care about one of them. That's time, money, and effort that could have been saved by talking to potential customers much earlier.

      I think many founders underestimate the value of starting small.

      Instead of creating a platform that tries to solve ten problems, why not build one that solves a single problem exceptionally well? A focused product is easier to develop, easier to explain, and much easier for customers to understand.

      Another common mistake is chasing trends instead of solving problems.

      Every time a new AI model is released, there's excitement around building the next big application. While new technology is exciting, users don't buy products because they use the latest AI model. They buy products because those products save time, reduce costs, eliminate frustration, or help them achieve better results.

      That's an important distinction.

      Technology changes quickly. Customer problems usually don't.

      I've also noticed that many founders spend most of their budget on development while leaving very little for marketing, customer support, and product improvement. Even an excellent application won't grow if nobody knows it exists.

      And marketing itself is evolving. Traditional SEO is still essential, but many SEO professionals are now expanding their strategies to include AI search. Even manual SEO is now using techniques like AI search monitoring to understand how brands appear in AI-generated answers, identify visibility gaps, and discover opportunities that don't always show up in conventional search reports. It's becoming another useful data point rather than a replacement for SEO. 📊

      Successful SaaS companies don't stop working after launch. In many ways, launch day is just the beginning. That's when real feedback starts coming in, bugs are discovered, and users begin suggesting improvements that shape future versions of the product.

      One thing I admire about successful founders is that they're willing to change direction when the market tells them to. They don't become emotionally attached to every feature they've built. Instead, they listen, adapt, and continue improving.

      To me, that's what successful SaaS app development is really about.

      It's not creating the most complex platform.

      It's understanding your users, validating your ideas early, improving continuously, and solving real-world problems better than anyone else.

      The AI is simply the technology that makes those solutions possible. 🤖

      đź’¬ What do you think is the biggest mistake first-time AI SaaS founders make? And do you think AI visibility tools like AI Search Monitoring will become a standard part of every startup's marketing strategy over the next few years?

      posted in Artificial Intelligence
      thomashellby
      thomashellby
    • AI Search Monitoring – Is This the Next Step in SEO?

      Hello everyone,

      I would like to start a discussion around a concept that is starting to gain attention in the SEO and digital marketing space: AI Search Monitoring.

      With the rise of AI-powered search systems and AI-generated answers, search is no longer limited to traditional search engine rankings. Users are increasingly getting direct responses from AI tools that summarize information and recommend sources.

      What is AI Search Monitoring?

      AI Search Monitoring refers to tracking how a brand, website, or piece of content appears within AI-generated responses across different AI search systems and assistants.

      Simple example:

      If a user asks an AI:
      “Best ERPNext implementation companies”

      The goal is to understand whether your business appears in that response, is referenced indirectly, or is completely missing, even if it ranks well in traditional search engines.

      Why it matters:
      SEO is expanding beyond traditional search engines like Google
      AI systems are influencing how users discover information and businesses
      Visibility is no longer only defined by rankings
      Content may need to be optimized for both search engines and AI-generated responses
      Discussion:

      Do you believe AI Search Monitoring will become a standard part of SEO strategy in the coming years, or is it still too early to treat it as an important metric?

      I would be interested to hear different perspectives and real-world experiences.

      posted in Artificial Intelligence
      thomashellby
      thomashellby