Role
Founder & Principal Engineer
Timeline
2024 - Present
Key Technologies
Project Overview
Designed and built a full-scale AI-powered EdTech platform from concept to production. The platform features an AI Coach with multi-tier memory systems, intelligent content generation, and a complete community platform—serving 1,000+ product managers with measurable learning outcomes.
The platform integrates 35+ AI models (Gemini, GPT-4, Claude, DeepSeek, Grok), implements advanced LLM patterns like Chain of Verification and Reflexion, and includes automated content pipelines for blog generation, social media, and personalized coaching.
Key Challenges
- Building AI systems that maintain context across conversations for personalized coaching.
- Integrating multiple LLM providers with fallback chains, cost optimization, and quality benchmarking.
- Creating an intelligent content pipeline that generates PM-specific curriculum at scale.
- Building a complete community platform with real-time features, replacing third-party solutions.
Platform Architecture
- 35+ LLM integrations
- Agent memory system
- Multi-model arena
- Content generation
- 433 lessons deployed
- Quiz engine
- Certificates
- Progress tracking
- 1,000+ members
- Real-time chat
- Events & spaces
- Gamification
- 25+ job sources
- Social media AI
- Email automation
- Resume optimizer
AI Systems Built
Three-Tier Agent Memory System
Built a production-grade memory system enabling the AI Coach to maintain context across sessions, remember user goals, and provide personalized guidance over time.
- Working Memory: Current conversation context
- Short-Term: Recent interactions (7-30 days)
- Long-Term: Permanent episodic & semantic memory
- pgvector: Semantic retrieval with 768d embeddings
Technical Implementation
- Entity extraction (PM-specific NER)
- Importance scoring for retention
- Memory compression & summarization
- Checkpoint/restore sessions
- 770+ lines production code
Results & Impact
35+ AI models integrated
Multi-provider architecture with automatic fallbacks and cost optimization.
433 lessons deployed
100% curriculum coverage with AI-generated and curated content.
1,000+ community members
Active community with real-time features and mentorship programs.
25+ job data sources
Aggregated and enriched PM job listings with AI-powered matching.
60-70% reduction in AI errors
Chain of Verification pattern dramatically improved output reliability.
$99/mo SaaS product
Recruiter portal monetization with Stripe integration.
Technology Stack
Frontend & API
- Next.js 15 / React 19
- TypeScript
- Tailwind CSS / shadcn/ui
- Vercel deployment
- Server Actions & API routes
Backend & Data
- Neon PostgreSQL
- pgvector for embeddings
- Ably for real-time
- Stripe for payments
- Vercel Blob for storage
AI & ML
- OpenAI / Anthropic / Gemini
- OpenRouter multi-model
- Replicate for video/image
- Custom LLM patterns
- Agent memory architecture
Key Learnings
AI Pattern Selection Matters
Different tasks require different LLM patterns. Chain of Verification dramatically improved blog generation reliability, while Reflexion caught issues in submission reviews that single-pass generation missed. The key is matching pattern to problem complexity.
Memory Enables Personalization
Building a proper memory system transformed the AI Coach from a chatbot into a genuine coaching experience. Users report that the coach "remembers" their goals and provides contextually relevant guidance—this is entirely due to the episodic/semantic memory architecture.