Software is going through its biggest shift since the internet itself — the next generation of products won't be built by large teams writing boilerplate, they'll be built by engineers who can fuse AI, systems design, and product instinct into one skillset. That's the bet I'm making.
I build full-stack products where the backend is an AI system, not a feature bolted onto one. That means treating LLM calls like any other unreliable network dependency — timeouts, retries, circuit breaking, structured logging, graceful degradation — while still shipping a UI people actually want to use.
scrape → LLM analysis → resilience layer → score → stream → ship
Right now: agentic data pipelines on serverless infra, production reliability for LLM-backed APIs, and the unglamorous plumbing that keeps AI features from falling over under real traffic.
Focus areas:
Multi-Agent Architectures Agentic Workflows RAG Systems Knowledge Graphs LLM Applications AI Infrastructure System Design Distributed Systems Backend Architecture Developer Platforms Platform Strategy Product Engineering
A developer platform combining AI-native workflows, market intelligence, and builder infrastructure. Live. Deployed. Hardened against real failure, not a demo.
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Agentic pipeline: scrapes target subreddits → LLM-driven analysis → ranked SaaS opportunity scoring. Built to survive serverless constraints and flaky upstream APIs.
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Full market-intelligence integration: buyer intent, competitor analysis, opportunity scoring, category browsing, credit management.
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Stack: Next.js 14 (App Router) TypeScript Tailwind CSS Prisma Aiven MySQL InsForge AI G2 API V2 EmailJS
Platform pillars: AI-native workflows · intelligence layer (startup discovery, competitive analysis, trend detection) · builder infrastructure (developer communities, project discovery) · platform engineering (scalable, cloud-native architecture)
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🤖 Multi-Agent AI Assistant Production-grade AI orchestration powered by LangGraph — agents that plan, delegate, and execute as a coordinated system rather than a single prompt-response loop. |
🔍 AI Knowledge Systems Retrieval-first architectures for intelligent information discovery — RAG pipelines built for accuracy over vector-search vibes. |
I validate API behavior directly against the live service — curl, raw request/response inspection — before trusting an SDK's abstraction or assuming the docs are right. The hard bugs in AI-integrated systems live in the gap between what a provider claims and what it does under load, rate limits, or cold starts.
## 🛠️ Tech Stack
Core stack used in production (Next.js · TypeScript · Prisma · Aiven MySQL · InsForge AI) marked in the DevCastle section above — the full list below spans what I build with day-to-day across full-stack and AI-native engineering.
Frontend
Backend & APIs
Data & Storage
AI / Agentic Engineering
RAG pipelines Multi-agent orchestration Agent memory & state Tool/function calling Prompt engineering Embeddings & semantic search LLM evaluation & guardrails Streaming inference (SSE)
Infra, DevOps & Observability
Serverless architecture Structured logging Retry & circuit-breaker patterns Rate limiting Cold-start optimization Log aggregation (Log Drain)
Tooling & Workflow
Integrations shipped in production
InsForge AI G2 API V2 EmailJS Vercel Log Drain OAuth providers
| Principle | What it means in practice |
|---|---|
| LLM calls are unreliable I/O | Timeouts, retries, fallbacks aren't polish — they're what separates a demo from a product |
| Observability before scale | If a prod failure can't be traced request → root cause, it isn't done |
| Verify, don't assume | Test against the real API/DB/service before believing a fix works |
Open to conversations on agentic system design, production AI reliability, and full-stack architecture.
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