Open-Source AI-Powered Co-Founder & Developer Talent Matching Platform
OpenHR is a production-ready, AI-powered talent matching platform designed specifically for co-founders and developers seeking startup opportunities. Built with modern web technologies and advanced AI/ML algorithms, OpenHR solves the critical problem of finding compatible startup team members through semantic skill matching, personality compatibility assessment, and intelligent profile enrichment.
Build an open-source talent-matching platform that connects:
- Co-founders seeking technical partners with complementary skills and aligned vision
- Developers looking for startup opportunities with early-stage companies
- Startups seeking founding team members with specific technical and domain expertise
- Semantic skill matching using vector embeddings (sentence transformers)
- Complementary skill gap analysis
- Tech stack alignment scoring
- Multi-factor compatibility (skills, personality, timezone, equity preferences)
- Vision and mission alignment scoring
- Equity split recommendation engine
- Team chemistry prediction models
- Introduction request workflows
- Auto-extract skills from GitHub repos and contributions
- Resume parsing with NLP (spaCy, PyMuPDF)
- LinkedIn integration for experience mapping
- Skill taxonomy normalization
- In-app chat with Supabase Realtime
- Video call scheduling integration
- Context-aware message suggestions
- Collaborative document sharing
- Portfolio verification (GitHub, LinkedIn)
- Skill endorsements from collaborators
- Anti-spam and fake profile detection
- Project success tracking
- Startup stage filtering (idea, MVP, seed, Series A)
- Role type matching (founding engineer, CTO, technical co-founder)
- Compensation model preferences (equity-heavy, salary-focused, hybrid)
- Remote vs. in-person and timezone preferences
Seeking business/product co-founders with complementary skills, shared vision, and startup experience.
Looking for technical co-founders to build their MVP and scale their product.
Developers seeking equity-heavy opportunities at early-stage startups (pre-seed to Series A).
Startups needing founding team members with specific technical expertise and cultural fit.
Frontend:
- React 18+ with TypeScript
- Next.js 14+ (App Router)
- TailwindCSS + Radix UI
- React Query for state management
Backend:
- Node.js + Express.js (REST API)
- Supabase (PostgreSQL, Auth, Realtime, Storage)
- FastAPI (Python) for ML pipelines
AI/ML Layer:
- Sentence Transformers (all-mpnet-base-v2)
- spaCy for NER and text processing
- LangChain/LangGraph for AI agents
- OpenAI GPT-4 / Anthropic Claude for LLM tasks
Infrastructure:
- Vercel (frontend hosting)
- Supabase Cloud (backend + database)
- Railway/Fly.io (Python ML services)
graph TB
A[Frontend - Next.js] -->|REST API| B[Backend - Node.js/Express]
B -->|Database| C[Supabase PostgreSQL]
B -->|Auth| D[Supabase Auth]
B -->|Realtime| E[Supabase Realtime]
A -->|ML Requests| F[AI Agent Layer - FastAPI]
F -->|Vector Search| G[FAISS/Pinecone]
F -->|GitHub API| H[External Integrations]
F -->|LinkedIn API| H
F -->|Resume Parse| I[PyMuPDF + spaCy]
F -->|Skill Match| J[Sentence Transformers]
For detailed architecture, see:
openhr-platform/
├── README.md # You are here
├── LICENSE # MIT License
├── .gitignore # Node/React ignores
├── llm.txt # System prompt for AI agents
│
├── docs/
│ ├── research/ # Market & user research (Days 1-2)
│ │ ├── competitive-analysis.md # Y Combinator, Wellfound, CoFoundersLab
│ │ ├── user-personas.md # 4 target user personas
│ │ ├── pain-point-mapping.md # Critical user pain points
│ │ ├── cofounder-frameworks.md # Team formation research
│ │ ├── skill-matching-algorithms.md # Semantic matching approaches
│ │ ├── skill-taxonomy.md # Skill normalization strategies
│ │ ├── recommendation-engine.md # Collaborative vs content-based filtering
│ │ └── llm-use-cases.md # LLM integration patterns
│ │
│ ├── architecture/ # System design (Day 3)
│ │ ├── system-architecture.md # High-level system design
│ │ ├── database-schema.md # PostgreSQL schema + ERD
│ │ ├── ai-agent-design.md # Agent workflows and tools
│ │ ├── api-specification.md # REST API endpoints
│ │ └── tech-stack-justification.md # Technology choices
│ │
│ ├── platform/ # Feature implementations (Day 4)
│ │ ├── profile-enrichment-pipeline.md # GitHub/resume parsing
│ │ ├── github-integration.md # OAuth2 + skill extraction
│ │ ├── resume-parsing.md # PDF parsing with spaCy NER
│ │ ├── realtime-messaging.md # Supabase Realtime architecture
│ │ └── skill-normalization.md # Taxonomy mapping
│ │
│ ├── open-source/ # Community & monetization (Day 6)
│ │ ├── community-guidelines.md # Contribution workflows
│ │ └── monetization-strategy.md # Freemium + white-label
│ │
│ ├── strategy/ # Growth & partnerships (Day 7)
│ │ ├── seo-content-marketing.md # SEO keywords and content plan
│ │ ├── growth-viral-loops.md # Referral programs
│ │ ├── partnership-opportunities.md # Accelerators, communities
│ │ └── metrics-success-criteria.md # KPIs and analytics
│ │
│ └── deliverables/ # Executive summaries (Day 7)
│ ├── executive-summary.md # High-level overview
│ └── technical-roadmap.md # Phase-based implementation plan
│
├── specifications/ # UI/UX & feature specs (Day 5)
│ ├── onboarding-flow.md # Progressive profile completion
│ ├── match-discovery-ui.md # Swipe/grid interface design
│ ├── messaging-ux.md # Chat interface specifications
│ └── mvp-feature-specs.md # Core MVP features + acceptance criteria
│
├── ui-design/ # Visual design (Day 5)
│ └── wireframes.md # Low-fidelity wireframes (Mermaid)
│
├── datasets/ # Open datasets for training
│ └── open-datasets.md # Curated dataset list with licenses
│
└── agents/ # AI agent implementations (future)
├── profile-enrichment-agent/
├── match-scoring-agent/
├── recommendation-agent/
└── message-suggestion-agent/
This repository is currently in the Research & Planning phase. All implementation specifications are complete and ready for coding agents to begin building.
Essential Reading (in order):
- System Architecture – Understand the overall system design
- Database Schema – Review the PostgreSQL data model
- AI Agent Design – Learn the AI agent workflows
- API Specification – REST API endpoints and contracts
- Tech Stack Justification – Why React, Node.js, Supabase, etc.
- MVP Feature Specs – Core features to build first
- Skill Matching Algorithms – Semantic matching implementation
Market & User Research:
- Competitive Analysis – Y Combinator, Wellfound, CoFoundersLab
- User Personas – 4 detailed target personas
- Pain Point Mapping – Critical user challenges
AI/ML Strategy:
- LLM Use Cases – Profile summarization, match explanations
- Recommendation Engine – Collaborative vs content-based filtering
- Skill Taxonomy – Normalization strategies
Note: Implementation has not started yet. This section will be populated once coding begins.
Planned Setup Steps:
# Clone the repository
git clone https://github.com/ArjunFrancis/openhr-platform.git
cd openhr-platform
# Install frontend dependencies
cd frontend
npm install
# Install backend dependencies
cd ../backend
npm install
# Install ML service dependencies
cd ../ml-service
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your Supabase, OpenAI, GitHub API keys
# Run development servers
npm run dev # Frontend on localhost:3000
npm run server # Backend on localhost:4000
python main.py # ML service on localhost:8000Current Phase: Research & Planning (Documentation Only)
Contributions Welcome: Documentation improvements, research validation, architecture feedback
We welcome contributions from the community! Since we're in the research phase, the best ways to contribute right now are:
- Review Documentation – Provide feedback on specs and architecture
- Research Validation – Verify our competitive analysis and user personas
- Dataset Curation – Suggest open datasets for skill matching and resume parsing
- Community Building – Share OpenHR with startup communities and developer networks
Once implementation begins:
- Follow the Community Guidelines (coming soon)
- Check GitHub Issues for "good first issue" labels
- Submit PRs with clear descriptions and tests
- ✅ Day 1: Market Intelligence & User Persona Research (COMPLETE)
- ✅ Day 2: Skill Matching Algorithms & AI/ML Strategy (COMPLETE)
- ✅ Day 3: System Architecture & Database Design (COMPLETE)
- ✅ Day 4: Profile Enrichment & Real-Time Messaging (COMPLETE)
- 🔄 Day 5: UI/UX Design & Feature Specifications (IN PROGRESS)
- ⏳ Day 6: Trust, Privacy & Open-Source Strategy (PENDING)
- ⏳ Day 7: Growth Strategy & Final Assembly (PENDING)
Overall Completion: ~60% (15/25 deliverables complete)
This project is licensed under the MIT License – see the LICENSE file for details.
Open-Source Forever. OpenHR will always remain free and open-source for individual developers and co-founders. Commercial white-label licensing and enterprise support may be offered in the future.
We believe every founder deserves to find their ideal co-founder, and every developer deserves to find their dream startup. OpenHR aims to be the open-source standard for talent matching in the startup ecosystem.
Our Goals:
- 🎯 10,000+ successful co-founder matches in Year 1
- 🌍 100,000+ developers and founders on the platform by Year 2
- 🤝 Partner with top accelerators (Y Combinator, Techstars, 500 Startups)
- 🏆 Become the #1 open-source HR platform for startups
- GitHub Issues: Report bugs or request features
- Discussions: Join community discussions
- Project Owner: @ArjunFrancis
Inspired by the open-source community and the challenges faced by founders worldwide. Special thanks to:
- Y Combinator Co-Founder Matching research
- Wellfound (AngelList) for talent matching insights
- Sentence Transformers team for semantic search models
- Supabase for the incredible open-source backend platform
Built with ❤️ by the OpenHR Community