WE BUILD STORES - NEWSLETTER ================================================== Pattern Recognition Fluency: Why 'Build Me a HubSpot Lite' Works Better Than 50 Requirements Week 44, 2025 Tuesday 28 October 2025 Where 26 years of Experience Delivers in One Hour What Twenty Six Hours of Not Knowing Cannot IN THIS ISSUE: • Why 'HubSpot Lite' Beats Detailed Specifications • The Don Draper Home Page That Actually Works • Pattern Recognition vs Prompt Engineering • Content Studio Lite in One Sentence • Why The Flourish Unlocks The Pattern ----------------------------------------------- I went back through hundreds of Git commits last week to analyse what I was asking Claude Code six months ago vs what I ask now. The evolution surprised me when I saw it. Six months ago: Detailed technical specifications. Today: Recognisable patterns with verbal flourish. _Here's what I discovered:_ You're not writing better specifications. You're speaking in patterns the AI's training data recognises. _Junior Developer:_ "Build a contact form with validation and email handling" _Senior Developer:_ "Create a contact form component with name, email, message fields, client-side validation, server-side sanitisation, honeypot spam protection, success/error states, accessible ARIA labels, mobile-responsive layout, and integration with our email service" _Me:_ "Add a HubSpot Lite lead pipeline" The third one delivers contact management, activity timeline, deal stages, email integration, task management, conversion reporting, lead scoring, and pipeline velocity metrics. _From four words._ The AI has seen HubSpot implemented thousands of times in its training data. It knows exactly what that pattern looks like. I don't need to specify features. I need to trigger the pattern. _This isn't better prompt engineering. It's pattern recognition fluency._ -------------------------------------------------- In This Issue ================================================== _Why 'HubSpot Lite' Beats Detailed Specifications_ — Pattern recognition delivers more comprehensive solutions than functional requirements _The Don Draper Home Page That Actually Works_ — Cultural references trigger design patterns the AI recognises instantly _Pattern Recognition vs Prompt Engineering_ — The profound difference nobody talks about _Content Studio Lite in One Sentence_ — Social media management without writing the specification _Why The Flourish Unlocks The Pattern_ — Describing the feeling matters more than listing the features -------------------------------------------------- The HubSpot Lite Discovery ================================================== Last month I needed lead tracking for the platform. _What I could have said:_ "Build a lead management system with contact database, custom fields, activity logging, deal stages, kanban board, email integration, task management, reminders, conversion tracking, funnel reporting, lead scoring, and pipeline analytics." _What I actually said:_ "Build a HubSpot Lite lead pipeline." _What Claude Code delivered:_ • Complete contact management with custom fields • Deal stages with drag-and-drop kanban interface • Activity timeline tracking emails, calls, meetings • Task management with automated reminders • Email integration following HubSpot patterns • Conversion funnel with stage-to-stage metrics • Lead scoring framework based on engagement • Pipeline velocity and health monitoring The AI recognised the HubSpot pattern from thousands of implementations in its training data. It didn't need me to specify each feature. _The pattern reference unlocked the complete implementation._ Four words triggered what would have taken 200 words of specifications - and delivered a more comprehensive result. _This is pattern recognition fluency._ -------------------------------------------------- The Don Draper Home Page ================================================== The same principle applies to design language. Two weeks ago, working on positioning for a manufacturing client. _What I said:_ "Give the home page Don Draper product positioning - make it feel like the confident choice for people who know quality when they see it." _What Claude Code understood:_ • Minimalist design with confident white space • Bold, declarative headlines (not questions) • Product-first imagery showing craft and detail • Testimonials from recognisable brands • Subtle social proof (years established, clients served) • CTAs that assume the sale ("Start Your Project" not "Learn More") • Sophisticated colour palette avoiding bright urgency • Copy that sells benefits through confident statements The AI has processed Mad Men discussions, advertising case studies, classic positioning frameworks. _"Don Draper positioning" triggered an entire design and messaging pattern._ I could have written a 500-word design brief. Or I could reference a pattern the AI has seen analysed thousands of times. _The flourish did the work._ -------------------------------------------------- Pattern Recognition vs Prompt Engineering ================================================== _The dangerous myth:_ "If the AI doesn't understand, add more detail." _The reality:_ More detail on unfamiliar patterns creates MORE problems, not fewer. You're forcing the AI away from proven implementations into experimental territory where every specification introduces new uncertainty. _Example:_ _Prompt Engineering:_ "Create a social media management interface with post scheduling, content calendar, draft management, image upload, multi-platform posting, analytics dashboard, and engagement tracking." _Pattern Fluency:_ "Build a Content Studio Lite for social media management." The second one triggers scheduling UI patterns, calendar interfaces, multi-platform posting logic, analytics dashboards, and engagement metrics - because the AI has seen Content Studio implementations hundreds of times. _The difference:_ Prompt engineering tries to be clearer. Pattern fluency tries to be more recognisable. Prompt engineering adds detail. Pattern fluency adds context. Prompt engineering explains what you want. Pattern fluency triggers what the AI has seen working. Better prompt: Less detail referencing familiar patterns. Worse prompt: More detail forcing unfamiliar approaches. -------------------------------------------------- Why The Flourish Matters ================================================== Which raises the question: why does "Lite" matter? Why not just say "HubSpot"? Because the "Lite" in "HubSpot Lite" isn't decoration. It's signal. It tells the AI: • Core features, not enterprise complexity • Proven patterns, not custom innovation • Fast implementation, not months of development • Familiar UX, not learning curve The "Don Draper" in "Don Draper positioning" isn't fluff. It's context. It triggers: • Confident minimalism over busy urgency • Product quality over price competition • Established authority over startup energy • Sophisticated restraint over aggressive conversion _The flourish is how you tell the AI which variant of the pattern you want._ "Build me a HubSpot" could mean enterprise sales pipeline with forecasting, territories, and complex workflows. "Build me a HubSpot Lite" means essential CRM with proven UX patterns and rapid implementation. _That one word changed the entire implementation approach._ -------------------------------------------------- The Velocity Multiplier ================================================== So how does this translate to actual website development? Here's how I build production websites in 4 hours while agencies quote 4 weeks. _Not better technical skills. Pattern recognition fluency with boring technology._ When building escudero-auto.com (see Week 42's £49/month case study), I didn't write component specifications. I said: "Build the service showcase section - think Apple product pages but for industrial 3D printing. Six service cards, each highlighting technical capability with confident minimalism." _What Claude Code delivered:_ • Astro component with proven layout patterns • Tailwind CSS matching Apple's restrained design language • Responsive grid (1/2/3 columns) from training data • Hover effects following established UX patterns • Mobile-first approach (seen 10,000+ times) • Accessible markup (ARIA patterns well-documented) • Performance-optimised (Astro static generation) One sentence triggered complete implementation. _Because "Apple product pages" is a pattern the AI has seen analysed extensively in Astro/Tailwind contexts._ _Traditional agency approach:_ • Design mockups and client revisions (1 week) • Write detailed component specifications (4 hours) • Developer implements from scratch (8-12 hours) • Debug responsive layout issues (4 hours) • Accessibility fixes (2 hours) • Performance optimisation (3 hours) _Pattern fluency with Astro/Tailwind:_ • "Apple product page style service cards" (10 seconds) • AI applies proven Astro/Tailwind patterns (2 minutes) • Review and content refinement (20 minutes) _This is where the 40x velocity comes from._ Astro and Tailwind do the heavy lifting. Pattern fluency just triggers the right implementation instantly. -------------------------------------------------- The Critical Caveat ================================================== Before you rush off to try "HubSpot Lite" with your experimental framework... This only works with the boring tech stack. Boring, proven technology unlocks AI development velocity. Pattern recognition is the reason why. When you say "HubSpot Lite" using Django and PostgreSQL - the AI has seen that implementation thousands of times. Proven patterns everywhere. When you say "HubSpot Lite" using a bleeding-edge framework released six months ago - the AI is guessing. It understands the concept, but it hasn't seen the proven implementation patterns. _Pattern fluency requires patterns the AI recognises._ That's why I build with: • Astro (static sites - pattern-proven since 2021) • Django (backend - 15+ years of training data) • Tailwind CSS (styling - seen everywhere) • PostgreSQL (databases - decades of implementations) The AI has processed millions of repositories using these technologies. When I reference a pattern, it applies proven implementations instantly. _Try the same pattern fluency with experimental frameworks?_ You get conceptual understanding but implementation uncertainty. _What about WordPress?_ The AI has seen millions of WordPress sites. But "HubSpot Lite" in WordPress means: Which plugin? Which theme conflicts? Which version compatibility? Which hosting limitations? Pattern recognition breaks because implementation varies wildly between installations. With Django/PostgreSQL or Astro/Tailwind - one proven pattern works everywhere. With WordPress - every site is different. This is where 26 years of experience matters. I'm not discovering new patterns. _I'm recognising proven patterns the AI has seen thousands of times, then triggering them efficiently with boring, pattern-CONSISTENT technology._ The AI amplifies pattern recognition. But only when the patterns exist in its training data AND implement consistently. -------------------------------------------------- What This Actually Means ================================================== Prompt engineering is real. But it's not about writing better specifications. _It's about recognising which patterns from the AI's training data solve your specific problem, then triggering those patterns with minimal but precise verbal cues._ "HubSpot Lite" works because HubSpot is well-documented, widely implemented, and thoroughly analysed in the AI's training data. "Don Draper positioning" works because Mad Men's advertising philosophy has been discussed and deconstructed extensively. "Content Studio Lite" works because content management patterns are proven and prevalent. _The flourish isn't poetry. It's applied pattern recognition._ -------------------------------------------------- Try This Tomorrow ================================================== Next time you're about to write detailed specifications for Claude Code, stop. Ask yourself: "What proven pattern does this resemble?" _Building a contact form?_ → "Create a HubSpot-style contact form" _Creating a pricing page?_ → "Build an Apple-style pricing page with confident minimalism" _Adding user authentication?_ → "Implement Stripe-style authentication patterns" _Building a dashboard?_ → "Create a Notion-style dashboard interface" _Adding payment processing?_ → "Build a Stripe-style checkout flow" One sentence referencing a pattern the AI knows > 10 sentences of specifications. _You'll know it worked when Claude Code delivers MORE than you asked for._ That's not over-engineering. That's the AI recognising the pattern and applying proven implementations you didn't have to specify. -------------------------------------------------- _P.S. - Next Week:_ The tech stack decisions that looked boring six months ago but enabled 40x velocity today. Why I chose Astro over Next.js, Django over microservices, and PostgreSQL over MongoDB. The unglamorous decisions that actually mattered. _P.P.S. - Pattern Recognition:_ Want to see how this works in practice? Reply with "PATTERNS" and I'll send you the framework for identifying which recognised patterns solve your specific business problems. Not prompt templates. Pattern recognition methodology. -------------------------------------------------- _Tony Cooper_ We Build Stores - Where Pattern Recognition Delivers What Detailed Specifications Cannot tony.cooper@webuildstores.co.uk 07963 242210 -------------------------------------------------- _This Week:_ Pattern recognition fluency - why "HubSpot Lite" delivers more than 50 functional requirements CONTINUE READING THIS WEEK'S NEWSLETTER Get the full insights, client examples, and strategic frameworks that could transform your business approach. Read online: https://webuildstores.co.uk/newsletter/2025/week-44 ================================================== Tony Cooper Founder We Build Stores tony.cooper@webuildstores.co.uk 01952 407599 You're receiving this because you've engaged with We Build Stores content or requested our insights. Website: https://webuildstores.co.uk We Build Stores Ltd, Registered in England & Wales