Volodymyr Shynkarov.
Chicago · Full-stack systems · Applied AI

I turn messy operational work into reliable AI-assisted systems.

I design and build internal tools that connect live data, automation, and human review.

Sole developerarchitecture and delivery 104+ testsdeterministic core Up to 1 hour savedper research cycle
View featured case study GitHub profile
Keyword Path Explorer discovery results with a niche hypothesis and ranked App Store keywords Private product · Case study
Featured work

Evidence over claims.

Selected systems documented through their workflows, engineering decisions, limitations, and real product screens.

Keyword Path Explorer generated ASO positioning package with name options and ranked keywords
Working private tool

Keyword Path Explorer

ASO research that joins live App Store data, queued workflows, novelty memory, and LLM analysis into reviewable positioning packages.

Next.jsPostgresLLM workflows
Read the case study →
LoadHaste dashboard with fleet vehicles, matched freight, route details, and broker communication options
Operations automation

LoadHaste

A freight search workflow combining fleet-specific criteria, demand-driven container workers, matched loads, and broker communication.

Next.jsNode.jsDockerPostgres
Read the case study → Open live product ↗
Battle 2048 realtime match with health and damaged board state
Realtime product

Battle 2048

A competitive multiplayer puzzle with synchronized combat, serialized room actions, adaptive opponents, and persistent statistics.

Next.jsSocket.IOSupabaseFly.io
Read the case study → Open live product ↗
Reveal Sequence preview showing the final words of a text stream emerging from transparency
Open-source React package

Reveal Sequence

Append-aware reveal sequencing for streamed text and newly inserted keyed UI, with bounded animation work and a clean settled DOM.

ReactTypeScriptWeb Animations API
Open package ↗ Open playground ↗
Capabilities

From workflow to maintainable system.

01

Workflow automation

Queues, background work, integrations, and explicit failure handling.

02

Applied AI

LLM stages grounded in live data, deterministic controls, and human review.

03

Full-stack delivery

APIs, data models, interfaces, testing, deployment, and documentation.

Public engineering

Code you can inspect.

App Store Scraper + MCP ↗

A maintained public fork with a TypeScript MCP server, resilient HTTP behavior, research tools, schemas, and tests.

TypeScriptMCPVitest

Shipri ↗

A privacy-first peer-to-peer file transfer project with WebRTC, E2EE design, signaling contracts, tests, and self-hosted deployment.

WebRTCNode.jsDocker
Approach

Build the useful part first.

I start with the real workflow, the available data, and the smallest system that can be tested. AI is useful when it has clear inputs, boundaries, and a reviewable result.

Most of my current product work is private. I publish focused evidence without exposing source code, credentials, customer data, or proprietary details.

TaleScroll · Narrative product in development.

Contact

Interested in practical AI and reliable product systems?

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