I build production systems for AI automation, data pipelines, and analytics.
Backend architecture · LLM orchestration · Scalable data infrastructure.
Explore selected systems and case studies below.
Selected Projects
Real systems, shipped and deployed.

Divergence Detector (Advanced)
Event-driven market scanner processing 500+ ticks/s

Regime-Switching Scalper
Production 24/7 multi-asset scalper with runtime market regime detection
Code Snippets
Small excerpts from real systems — the kind of details that make products reliable.
Stable content hashing + near-duplicate detection
A layered dedupe strategy: stable hash → pg_trgm similarity → pure-Python fallback.
1# normalize + stable hash (accounts for platform & angle)2def normalize_content(content: str) -> str:3 normalized = content.lower()4 normalized = re.sub(r"\s+", " ", normalized)5 normalized = re.sub(r"[^\w\s]", "", normalized)6 return normalized.strip()789def compute_content_hash(content: str, platform: str, angle: str) -> str:10 normalized = normalize_content(content)11 hash_input = f"{normalized}|{platform.lower()}|{angle.lower()}"12 return hashlib.sha256(hash_input.encode("utf-8")).hexdigest()131415# orchestrator: exact-hash → trigram → fallback16async def check_duplicate(session, campaign_id, content, platform, angle, threshold=0.7):17 content_hash = compute_content_hash(content, platform, angle)1819 exact = await check_exact_duplicate(session, campaign_id, content_hash)20 if exact:21 return {"is_duplicate": True, "type": "exact", "existing_post_id": exact.id}2223 near = await check_near_duplicate_trgm(session, campaign_id, content, threshold)24 if not near:25 near = await check_near_duplicate_fallback(session, campaign_id, content, threshold)2627 return {"is_duplicate": bool(near), "type": "near" if near else None, "content_hash": content_hash}
JSON-LD graph injector
Keeps schema composable while emitting one canonical @context (and @graph when needed).
1type JsonLdProps = {2 data: Record<string, unknown> | Array<Record<string, unknown>>;3};45export function JsonLd({ data }: JsonLdProps) {6 const payload = Array.isArray(data) ? data : [data];7 const graphNodes = payload.map((node) => {8 const { "@context": _ctx, ...rest } = node as Record<string, unknown>;9 return rest;10 });1112 const json =13 graphNodes.length === 114 ? { "@context": "https://schema.org", ...graphNodes[0] }15 : { "@context": "https://schema.org", "@graph": graphNodes };1617 return (18 <script19 type="application/ld+json"20 dangerouslySetInnerHTML={{ __html: JSON.stringify(json) }}21 />22 );23}
Stack I actually use.
These are the tools in the projects above — Python, FastAPI, LangGraph, React/Next.js, PostgreSQL, Redis, Docker, and the surrounding ecosystem.
AI & LLM
10 skillsLLM orchestration, RAG pipelines, and applied ML.
Backend & Data
9 skillsAPIs, async pipelines, and data persistence.
Frontend
8 skillsProduction UIs and interactive dashboards.
Infra & Deployment
7 skillsEnd-to-end deployment and cloud infrastructure.
Low-Code & Automation
5 skillsRapid delivery with robust integrations.
Quant & Data Systems
6 skillsMarket data pipelines and strategy tooling.
How I Work
I handle architecture, backend, and delivery myself — end to end, Dockerized, and deployed. For larger scopes, I bring in people I've worked with before. You deal with one person throughout.
Frequently Asked Questions
Everything I ship is real, Dockerized, and deployed end-to-end. Common questions about how I work and what to expect.
Let's Build Something Great
I'm always interested in working on exciting projects and collaborations.