Senior Flutter Developer · Go · LLM integrations

Anton Kozyrev

Flutter developer with 4.5+ years of commercial mobile development. For the last three years I've been the sole mobile developer behind three products: architecture, stack, API contracts, releases. I build my own products end to end — Go backend, web, LLM integration, deployment.

  • Kaliningrad, Russia · Remote
  • Open to projects and full-time offers
  • 4.5+ yrs Flutter
  • 10 apps in the stores
  • 3 products solo
agent · session
  1. task: add a monthly budget guard to the LLM client
  2. plan: check worst-case cost before each call → fail fast → test
  3. ~ internal/llm/client.go
  4. + internal/llm/budget.go
  5. + internal/llm/budget_test.go
  6. 3 files to change. Approve?
  7. go test ./... — ok
  8. diff reviewed · secrets scan clean

01What I can help with

Flutter features and releases

New screens and flows from Figma to the App Store and Google Play: state, API integration, push notifications, crash reporting.

Taking over Flutter code

Picking up someone else's codebase, refactoring legacy code without stopping feature work, covering key logic with tests.

Go backend and MVP API

REST API in Go with PostgreSQL and CI on GitHub Actions — enough to launch a product and validate the idea.

LLM features with guardrails

Budget limits, fact-checking and fallbacks: the model does not invent data and does not burn the budget.

Part-time project, long-term contract or a full-time role — tell me about the task, and I'll come back with a clear plan and estimate. Get in touch →

02Selected work

Flutter · 4.5+ years

Commercial mobile apps

Platforms for working with external contractors: onboarding, documents, deals, payouts and tax receipts. The code is under NDA, so this is what I did rather than the code itself.

  • 10 mobile apps in the stores — from greenfield launches to taking over code from other teams
  • Migrated a native Android (Java) app to Flutter at full feature parity and shipped it on iOS and Android; now maintaining version 3.x
  • Feature-first Clean Architecture, Riverpod for state, API contracts co-designed with the backend team, real-time chat on Socket.IO, FCM push, Crashlytics, Remote Config
  • Key logic covered with unit and integration tests; legacy refactoring without stopping feature work

Flutter · Dart · Riverpod · GoRouter · Dio · Socket.IO · Firebase · Drift/SQLite

Go · LLM · static sites

sitefarm — data-driven site pipeline

A Go pipeline that turns open data into a content site: collect data (NOAA, PRISM) → generate text with an LLM → fact-check with a second model → build a static site.

  • Pilot of ~2,100 pages; generation costs about 0.5 RUB (under one US cent) per page
  • Numbers come from the data, never from the model; a second model fact-checks every text
  • 42 of 50 pages (84%) passed the automated fact check in the pilot run; a rejected page gets one automatic retry, and if it fails again it goes to a manual review queue and is never published
  • Budget guard: the model is not called if the worst case would exceed the monthly limit
  • Models compared on the real task — a reasoning model spent the whole token limit on reasoning and returned no answer, so it was rejected

Go · Yandex AI Studio (gpt-oss-120b) · Hugo · Cloudflare

Code: sitefarm-showcase →

Go · PostgreSQL · PWA · LLM

Terroir — personal wine assistant

An assistant with memory: cellar, taste profile, wine matched to your own recipes. Retrieval-grounded answers: the code picks the data, the model only explains it.

  • Answers are validated: the model cannot reference a bottle that is not in the cellar
  • Monthly token budget and a fallback when the LLM fails
  • 90+ tests, CI on GitHub Actions

Go · PostgreSQL/SQLite · PWA · OpenAI-compatible LLM client

Code: terroir-monorepo →

B2B SaaS · in development

TapReply — replies to customer reviews

AI replies to reviews from several platforms for venues, in local languages. A Go core with platform adapters: a new review platform is a new adapter, not a rewrite.

  • Go core + platform adapters
  • Flutter / PWA client
  • PostgreSQL

Go · Flutter · PWA · PostgreSQL

tapreply.net →

03Apps I built

Built in parallel with my main product role — I'm used to running several projects at once: separate repos, clear scopes, every app released to the stores.

Zhabby — screenshot 1
Zhabby — screenshot 2

Zhabby

2025 · Flutter

Short-video social network that rewards activity: personal feed, video upload, points for views, likes and comments, rewards exchange.

Second developer on the team: built every screen from Figma designs, connected API requests and state management in several sections. Published on the App Store and Google Play.

Mealhub — screenshot 1
Mealhub — screenshot 2

Mealhub

2022–2023 · Flutter

Restaurant search on a map, table booking and food ordering.

Second developer: responsive UI from Figma, Google Maps API, state management, REST API integration, bug fixing and feature work.

GreenFeed — screenshot 1
GreenFeed — screenshot 2

GreenFeed

2022–2023 · Flutter

Feed product catalog and animal diet calculator for farmers.

Second developer: UI from Figma, state management, REST API integration, bug fixing and feature work.

Jobline

2023–2024 · Flutter

Platform for working with external contractors: contractor onboarding, documents and receipts, payouts with tax (NPD) withholding.

Built the mobile app from scratch.

Q-Working

2023–2024 · Flutter

Service that finds contractors, handles deals and pays them.

Built the worker mobile app from scratch: onboarding, profile and services, orders and payouts.

04How I work with AI agents

Agents take on the volume of work; responsibility for the result stays with me.

Agent toolchain
  • Veaidaily driver
  • Cursorused
  • GitHub Copilotused
  • Claude Codesame workflow
  • OpenAI Codexsame workflow
  1. 01

    Agent in the IDE, plan first

    My daily driver is Veai (AI coding agent for JetBrains IDEs), in IntelliJ. It reads the project, edits code, runs tests and commands. Plan and decomposition come first, code second; the agent keeps project memory, my own skills and rules. I have also worked with Cursor and GitHub Copilot; Claude Code and OpenAI Codex follow the same agentic workflow — plan → diff → tests — so switching between them is quick.

  2. 02

    Parallel agents, review before merge

    One session: 6 agents in parallel, each in its own repo (Astro/Hugo sites, PWA clients, Go backend), ~2 hours of staged work with acceptance criteria. Then I review: tests, builds, commit authorship, secret scan. Within one repo, parallel agents get separate git worktrees. Nothing lands in main without this review.

  3. 03

    LLMs inside the product, with guardrails

    In sitefarm texts are generated through Yandex AI Studio and checked by a second model; in Terroir code picks the data and every answer is validated against the database. Both have hard monthly budgets.

05Stack

Each skill points to where it is used — not a tag cloud.

Mobile
Flutter, Dart, Riverpod, GoRouter, Dio, Socket.IO, Firebase (FCM, Crashlytics, Remote Config), Drift/SQLiteCommercial apps · TapReply
Backend
Go, REST API, PostgreSQL, SQLite, CI/CD (GitHub Actions)terroir-monorepo · sitefarm-showcase
LLM
OpenAI-compatible APIs, Yandex AI Studio, second-model fact-checking, budget limits, fallbackssitefarm · Terroir
Web & infra
Astro, Hugo, PWA, Cloudflare (Pages, DNS), Linux VPSsitefarm · this site
Architecture & quality
Clean Architecture, feature-first, unit and integration tests, legacy codeCommercial apps · Terroir tests

06Contact

Tell me about your task — a project, a long-term contract or a full-time role. Before moving into development I spent 5 years running projects and led a department of 6.

hello@antonkozyrev.dev