Front-end developer. React, Next.js, Astro. Pixel-perfect to the design, fast in Core Web Vitals, 30+ shipped.
TypeScript, React, Next.js, Astro, Tailwind CSS, GSAP, Figma, Core Web Vitals
One front-end, 22 months, two home rebuilds.
A PPC agency's site and free-tools library, run solo for ~22 months: the library grew from 19 to ~78 tools.
Figma to deploy, then every redesign since.
A design agency's site, built solo from Figma to deploy and kept up for ~2.5 years, SEO included.
Via Sollas · Metamorfosi · Freelance
3+ yrs · remote
Full-Stack Developer
10/2023 – Present · Contract · Remote
Next.js · React · TypeScript · Astro · Node.js · Strapi · PostgreSQL · Firebase · Stripe · Cloudflare Workers
Front-End Developer · client: Bold, UK
06/2024 – 05/2026 · Agency · Remote
HTML · PostCSS · JavaScript · Figma
Front-End Developer
03/2023 – 10/2023 · Freelance · Remote
JavaScript · HTML · CSS · WordPress
Kyiv, Ukraine
Hi, I'm Vitaliy, or just Italik. I'm a full-stack developer from Kyiv who's been shipping for the web for 3+ years, taking products from a Figma file all the way to production.
I care about the details on the front end: layouts that match Figma to the pixel, animation that feels right, and pages that load fast and rank. For two years I shipped 1–2 marketing sites a week for an agency, and I still maintain sites I first built in 2024. When a product needs AI, I can build that too.
I enjoy owning a feature from idea to something that actually ships. I work remotely from Kyiv, alongside designers and, where there are any, the client's own developers, and I'm happy to own the whole stack or slot into an existing team.
Next, I want to own the front end of a product or a site that matters to a team. Full-time, contract or part-time all work for me.
Personal projects & agents
LiveWeb appMCP
An AI finance app where every number comes from SQL: ~97% categorisation accuracy at ~80% lower cost.
A multi-user finance app that syncs Monobank and CSV statements, categorises spending and answers questions about your money. Each user's data lives in its own Durable Object. A small judgment model and Claude share categorisation in a measured cascade. The same numbers are exposed to Claude over an MCP server with built-in OAuth 2.1.
TypeScript · React · RTK Query · Cloudflare Workers · Durable Objects (SQLite) · D1 · Hono · Anthropic API · TypeSafe Jev
LiveMCPOpen source
Recolours a Lottie animation into any palette or theme, by hand, by script or by an agent that checks its result by rendering it.
A browser editor, a CLI, an MCP server and a live editor–agent bridge that find every colour in a Lottie file, remap it and render the result to check it. All four are thin shells over one core package, published to npm, so a hand edit, a script and an agent come out identical.
TypeScript · Next.js · React · Zustand · lottie-web · Tailwind CSS · shadcn/ui · MCP SDK · Anthropic SDK · GitHub Actions
v2 in progressBotRAG
A group-chat AI bot where code decides when the model runs: an estimated $3-6 a month instead of $75-150.
A serverless Telegram bot for a friends' group chat. It logs messages, summarises on demand and answers as a persona with semantic memory. An LLM intent classifier routes requests, and cheap deterministic gates decide whether a model is called at all.
TypeScript · Cloudflare Workers · grammY · D1 · xAI Grok · Workers AI · Vectorize · R2
LiveToolAutomation
A job-search tool where code makes every decision and the model only reads new text: cost down from ~$15 to an estimated $1-2 a month.
A personal tool that collects vacancies and company catalogues, filters them with plain code and calls a cheap model only on new text. The score is deterministic. I pick every company and send every letter myself; the tool only drafts a first paragraph, and code checks it before I see it.
TypeScript · Hono · Drizzle ORM · Cloudflare Workers · D1 · Anthropic API · Workers AI · React · Mantine · Zod
LiveAgentLLM
The AI chat on this site. It answers only from my real content, for under a cent a question.
A streaming chat that knows my projects, experience and how I work. The whole knowledge base is built from this site's content at build time and sits in a cached system prompt, with rate limits, a daily budget and a golden-question eval set.
Next.js · TypeScript · Anthropic API · Upstash Redis · Zod
Live demo
It knows my projects, experience and how I work. Ask it anything a recruiter would.
Site content and my notes are loaded into one cached prompt. The model answers only from them and cites the files it used.
Italik.aiExample answer
It keeps the model away from arithmetic. Every number comes from one canonical SQL layer over D1, and each user's data is isolated in its own Durable Object. The LLM advisor only receives those query results as grounded context and explains them; it never computes totals or writes to the data itself.
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