My Projects

imaginate

AISAASTypeScript

A vibe coding web app that takes a prompt and generates full web applications, with live-streamed agent thoughts and sandboxed execution across multiple AI providers.

  • Vercel AI SDK
  • Inngest
  • E2B Sandboxes
  • OpenAI / Anthropic / Gemini
  • PostgreSQL
  • Prisma
  • tRPC
  • Next.js

insight-flow

AISAASTypeScript

A YouTube video transcription and RAG chat platform with vector embeddings, pgvector similarity search, and multi-turn conversational AI.

  • pgvector
  • Gemini Embeddings
  • LangChain
  • RAG
  • Inngest
  • Prisma
  • Next.js

autonix

AISAASTypeScript

An n8n/Zapier-like workflow automation platform with a visual node editor, topological execution engine, and AI provider integrations.

  • React Flow
  • Topological Sort Engine
  • Vercel AI SDK
  • Inngest
  • OpenAI / Anthropic / Gemini
  • PostgreSQL
  • Next.js

A C++23 LLM inference engine built from scratch, featuring arena allocation, mdspan-backed tensors, and cache-line aligned memory.

  • C++23
  • Arena Allocator
  • std::mdspan Tensors
  • Llama 3 / Mistral
  • CMake
  • GoogleTest

dcl-store

Distributed SystemsGo

A distributed append-only commit log in Go with Raft consensus, Serf cluster discovery, memory-mapped indices, and leader-aware gRPC load balancing.

  • Go
  • HashiCorp Raft
  • HashiCorp Serf
  • gRPC / Protobuf
  • Memory-Mapped I/O
  • Casbin ACL

Built a Python transportation-network optimization project using NetworkX and A* pathfinding to simulate traffic across weighted road graphs and identify the highest-value unconnected roads to add.

  • NetworkX
  • A* Pathfinding
  • Traffic Simulation
  • Greedy Optimization

Built a Python network-flow optimization project using NetworkX to simulate flow assignment across directed graphs with edge-capacity and flow-conservation constraints, benchmarked against Edmonds-Karp maximum flow.

  • NetworkX
  • Simulated Annealing
  • Edmonds-Karp Max Flow
  • Boltzmann Criterion

Built a Python grid-world policy optimization project using NetworkX to model a reward-based environment with terminal states, obstacles, and probabilistic action noise to learn optimal navigation policies.

  • NetworkX
  • Value Iteration
  • Stochastic Transitions
  • Policy Optimization