Beyond Connections Labs - Matchmaker
Beyond Connections Labs - Matchmaker is a web app that uses generative AI to intelligently match professionals within the Beyond Connections community. Rather than manually sifting through hundreds of member profiles or relying on biased personal introductions, members can instantly see their top 10-20 most relevant connections based on sophisticated matching algorithms - complete with contact information and context, ready to reach out.
Project Highlights
The Challenge
I participated in the Beyond Connections program earlier in 2025 and loved both the program and the people I met. However, I quickly identified a critical limitation: with a vast network of 500+ members, finding the most meaningful and helpful connections for my specific professional situation and goals was incredibly challenging.
Before This Tool
Members had only two realistic options for discovering valuable connections:
- Option 1: Personal Introductions - Wait for someone to say, "Hey, you remind me of XYZ, let me introduce you." While valuable, these suffer from selection bias, recency bias, and limited scope—constrained by who the introducer knows and remembers.
- Option 2: Manual Profile Review - Brute-force your way through 500+ member profiles in Circle, one by one. This takes enormous time, creates decision fatigue, and frankly, most people won't do it. I know I didn't.
Neither option satisfied my needs, so I knew I wanted to build something better.
The Make-or-Break Challenge
Building Trust Through Algorithm Quality - The matching algorithm needed to be accurate enough that users would trust it. Alpha testers needed to see both familiar good matches (validation) and new relevant people (discovery) to build confidence in reaching out. If the matches felt random or low-quality, nobody would use it.
The Approach
Six Strategic Phases
I approached this project methodically, building from foundation to full product:
- Alignment & Strategy - Got buy-in from leadership and defined the high-level strategy. Transcribed stakeholder conversations and used AI to synthesize them into formal project proposals, budgets, and contracts.
- Core Infrastructure - Built authentication flows and data ingestion pipelines to work with real user profiles and member data.
- Rule-Based Matching Engine - Started with a deterministic system that was cheaper to test. This proved both the concept and its limitations—rule-based matching alone was inadequate for nuanced professional relationships.
- LLM Matching Engine - Implemented AI-powered matching that proved far more compelling and useful than pure rules. The hybrid approach (rules + LLM) delivered the best results.
- Marketing & Onboarding - Built the "front-door" marketing page and end-to-end user onboarding flows to ensure seamless experience.
- Iterative Refinement - Conducted video-based alpha testing, transcribed feedback, and fed it directly back into Claude Code to generate documentation and prioritized issues—creating an extremely tight development loop.
100% AI-Native Development Lifecycle
This project represents a fully AI-native product development workflow. I used AI for literally every aspect:
- Discovery & Planning: Transcribed stakeholder conversations, drafted proposals/budgets/contracts, generated project plans and task breakdowns, used SpecKit constitution-driven development
- Development: Built the entire application using Claude Code, leveraged Claude sub-agents for complex tasks, implemented edge functions and API queuing
- Quality & Iteration: Performed code reviews and debugging with AI, transcribed and summarized alpha testing sessions, synthesized user feedback into actionable issues
- Communication: Generated stakeholder updates from meeting notes
This workflow—from brainstorming to debugging to stakeholder communication—was entirely AI-mediated. None of this was possible a year ago, and even six months ago it wasn't achievable at this level.
Building in 30-Minute Sprints
The key constraint was building this after work and family time in short bursts of effort. I needed to stay organized and focused, which is where my AI workflow with SpecKit and Claude Code really shined. By chunking work into small, achievable pieces with clear specifications, I could maintain momentum across fragmented work sessions.
Technology Stack
- Framework: Next.js App Router + TypeScript
- Styling: Tailwind CSS, shadcn/ui, Magic UI
- Backend: Supabase (database & auth)
- Hosting: Vercel
- AI Development: Claude Code (primary), Monologue (voice dictation), SpecKit (specification-driven development)
Learning & Outcomes
What I Learned
This project was where I really leveled up from side projects to production systems:
- ✓SpecKit Constitution-Driven Development - Learned the full SpecKit paradigm and how specification-based development creates clearer, more maintainable codebases
- ✓Edge Function Architecture - Learned how to properly stack edge functions, implement API queuing for both LinkedIn profile data pulls and LLM calls
- ✓LLM Cost Management - First-hand experience with production LLM costs taught me optimization strategies, model selection trade-offs, and when to use cheaper vs. more powerful models
- ✓Production Rigor - First live-coded project with public users required a completely different level of quality, error handling, security, and UX polish
- ✓Conversational Development Workflow - Refined my Claude Code + Monologue workflow for efficient context-switching and building while holding a baby using voice dictation
- ✓Claude Sub-Agents - Used Claude sub-agents extensively to parallelize work and handle complex, multi-step tasks autonomously
Challenges Overcome
Time Constraints & Fragmented Work - Building production software in short chunks after hours and on weekends requires diligent organization and careful context switching. This constraint actually forced better practices—clear specifications, modular development, and efficient AI-assisted tooling.
API Queuing Complexity - As someone without a traditional software engineering background, tackling API queuing, rate limiting, and asynchronous job management was entirely new territory. I had to learn these patterns from scratch while simultaneously implementing them in production.
Proudest Achievement
I'm most proud of the user reaction when they first see their matches.
The amount of positive enthusiasm for what is ultimately a simple tool solving a pressing problem is extremely rewarding. People instantly get it: they log in, see their top matches, and immediately understand both the value and how to use it.
That instant "aha moment" is the product of nailing both problem-solution fit and user experience design. Alpha testers consistently said, "I see people I know are good matches, plus people I've never met who look really interesting. I should reach out to them." That trust-building (validation + discovery) is exactly what I was aiming for.
What I'd Do Differently
Looking back with fresh eyes, I would organize the data pipeline and edge function triggers more cleanly from the start. I would adopt TDD practices much earlier in the project rather than retrofitting tests later. And I would test different LLM models and their cost profiles earlier in development. This would have saved money during alpha testing and helped optimize the matching algorithm's cost-effectiveness from day one.