Building production software requires predictable systems, not magical assumptions. AI models and external APIs are inherently non-deterministic, high-latency network dependencies. I architect full-stack applications around one core rule: the software stack must remain deterministic, type-safe, and resilient—regardless of service volatility.
Client Input ──> [Type Boundary] ──> [State & Caching] ──> [Resilience Gateway] ──> [LLM / Data Service] ──> [Streaming UI]
- Type Safety & Data Contracts: Full end-to-end type safety from database models to UI components. All incoming inputs, API responses, and LLM outputs are validated at runtime with strict Zod/Pydantic schemas before reaching core application logic.
- Backend Resilience & Scale: High-throughput Node.js/TypeScript and Python services engineered with multi-tier caching (In-Memory ➔ Redis), sliding-window rate limiters, retries with exponential backoff + jitter, circuit breakers, and connection pooling.
- Applied AI & LLM Systems: Productionizing LLM features using stateful multi-agent orchestrations (LangGraph), hybrid search RAG (dense + sparse vector indexing), structured JSON output enforcement, and semantic caching to reduce latency and token cost.
- Real-Time Streaming UI: Building non-blocking user interfaces powered by Server-Sent Events (SSE) and WebSockets to render token streams in real-time while bypassing serverless gateway execution limits.
- System Observability: End-to-end telemetry with structured JSON logs tied to request correlation IDs, ensuring edge-case failures, API throttles, or LLM drops are isolated and debugged instantly.
request → input validation (Zod) → state machine → resilience gateway → distributed DB / LLM stream → UI render
Core Focus: High-throughput Node.js/TypeScript & Python backends, MERN/Next.js architectures, serverless platform engineering, production RAG & multi-agent systems, and real-time streaming interfaces.
A production-grade platform for modern developers combining community infrastructure, market intelligence, launch arenas, and career engines. Live. Deployed. Hardened against real failure.
🔍 View Complete Feature Suite & System Breakdown
- Decodo Proxy-Backed Scraper — Cheerio + Decodo proxy pipeline extracts top weekly threads and nested comments from targeted subreddits.
- LLM Opportunity Scorer — OpenAI/Insforge LLM processes scraped data to generate scored (0–10) SaaS opportunities with demand breakdown, existing competitors, monetization models, and go-to-market strategies.
- Non-Blocking SSE Streaming — Server-Sent Events stream incremental progress to dodge serverless connection limits (Vercel 504 gateway timeouts).
- Dual-Layer Caching — In-memory LRU Map → Prisma MySQL (12h TTL) delivering near-zero latency for repeated queries.
- Upstash Distributed Rate Limiting — Sliding-window IP limiter enforcing API endpoint protection with standard
X-RateLimit-*headers.
- Startup Discovery Engine — Live graph integration searching Crunchbase organizations for funding rounds, investor networks, and valuation metrics inline.
- Server Actions SSR — Instant, server-rendered data fetching using Next.js 14 Server Actions with built-in caching.
- Rate-Limited Client & Mocking — In-memory request throttling with a full offline mock layer for local testing and CI/CD pipelines.
- Sub-Dev Communities — Granular member controls, moderation queues, topic spaces, and unique community identities.
- Threaded Feed & EditorJS 2.0 — Fast feed with nested comments, syntax-highlighted code blocks, link previews, and image embeds.
- LaunchPad Marketplace — Dedicated arena for project launches featuring image carousels, status tracking, and upvote-weighted ranking algorithms.
- Engagement Engine — Follow graph, notification fan-out, bookmarks, and real-time activity metrics.
- Pro-Grade Job Board — MySQL full-text search indexing with multi-dimensional filtering (roles, compensation, location, tech stack).
- GitHub Curation Showcase — Direct repository curation and showcasing on developer profiles.
- AI Creator Studio — Long-form EditorJS workspace powered by Google Gemini for drafting, summarizing, and technical documentation.
- Stripe Monetization — Subscription gating, recurring billing, and creator payout management via Stripe Webhooks.
MySQL (Prisma)
├── User, Account, Session → Auth & Security Layer
├── Community, Subscription → Social & Follow Graph
├── Post, Comment, Vote → Threaded Feed Engine
├── LaunchProject, LaunchVote → LaunchPad Marketplace
├── Job, Company, Application → Career & Talent Engine
├── Essay, Category → AI Creator Studio
├── RedditAnalysis → Market Intelligence Cache
└── StripeSubscription, Customer → Payments & Subscriptions
Tech Stack: Next.js 14 (App Router) TypeScript 5 React 18 Tailwind CSS Prisma ORM Aiven MySQL Upstash Redis Decodo Proxy + Cheerio Google Gemini InsForge AI G2 API V2 UploadThing NextAuth.js Stripe API PostHog Vitest / Bun Test
An intelligent travel planning system coordinating specialized agents via LangGraph. GitHub Repo
- LangGraph Multi-Agent Orchestration — Graph-based agent state management where specialized agents plan, execute, and pass state deterministically.
- Domain-Specialized Sub-Agents — Dedicated agent nodes for flight search, hotel discovery, custom itinerary synthesis, and final travel plan aggregation.
- Tool Calling & External Search — Dynamically fetches travel intelligence, pricing, and availability through structured tool interfaces.
- Streamlit Interactive UI — Clean, responsive web frontend for real-time plan generation, interactive edits, and structured recommendations.
Stack: Python LangGraph LangChain Streamlit OpenAI / Claude API Tavily / Search APIs
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🌐 Production Full-Stack Platforms Scalable web applications built on Next.js, React, Node.js/Express, and Python. Engineered with strict type boundaries, optimized database indexing (SQL/NoSQL), automated CI/CD pipelines, and zero-trust authentication. |
🤖 Multi-Agent Orchestration & RAG Production LangGraph & Python multi-agent orchestration engines. High-accuracy Retrieval-Augmented Generation (RAG) pipelines utilizing dense vector indexing, hybrid search, and semantic caching over raw vector similarity. |
Core Languages & Runtimes
Frontend & Client State
Backend Architecture & Middleware
Data Infrastructure & ORMs
Agentic AI & LLM Systems
Stateful Agent Memory Multi-Agent Planning Tool/Function Execution Dense Semantic Search Hybrid RAG Structured Output Control
DevOps, Reliability & Cloud
| Pillar | Engineering Execution |
|---|---|
| Defensive I/O Execution | Assume all external I/O will eventually fail, time out, or rate limit. Enforce strict timeouts, retries with jitter, and circuit breakers. |
| Strict Type Boundaries | Compile-time validation with TypeScript paired with runtime schema enforcement via Zod at every API boundary. |
| Full Traceability | Structured JSON logging tied to request correlation IDs. If an incident cannot be traced to root-cause in seconds, the telemetry is incomplete. |
| Verification over SDK Assumptions | Inspect raw HTTP payloads (curl, packet dumps) directly against the wire before trusting third-party abstractions or client SDKs. |
Open to technical leadership discussions, distributed systems design, high-performance web architecture, and production AI engineering.
📊 GitHub Engineering Metrics