Anatoli Abahumna

Anatoli Abahumna

I build practical software across web, iOS, cloud, operations tooling, and computer vision.

View work Research
Layered product surfaces for web, iOS, operations, agriculture, and computer vision systems
Lead system

YeneDesk

A personal productivity workspace built across web, API, iOS, local data, cloud beta, and operations tooling.

The work spans a React/Vite web app, Node/Express API, SQLite local mode, Supabase Auth and Postgres RLS cloud mode, SwiftUI iOS, BYOK AI integrations, Telegram capture, and an internal ops dashboard.

Product surface
Tasks, notes, finance, meals, Telegram capture, and AI-assisted review.
Platform work
Local-first desktop web, cloud beta, API layer, and SwiftUI iOS companion.
  • React / Vite
  • Express API
  • SQLite
  • Supabase RLS
  • SwiftUI iOS
  • Ops dashboard
Today Cloud beta
Inbox capture AI draft triage
Weekly review Tasks, notes, finance
BYOK integrations Encrypted per user
Desk Chat
Web iOS Telegram API SQLite Supabase
Selected systems

Product work with working edges.

Each project is framed around what was actually built: product surface, data model, deployment path, and verification evidence.

SwiftUI product system

YeneFitness

Native SwiftUI fitness app with SwiftData persistence, AI coach, workout import, nutrition extraction from meal photos, progress tracking, supplements, backup/restore, and XCTest/XCUITest coverage.

Mobile app
Workout, progress, supplement, and backup flows.
Meal photo work
Nutrition extraction lives inside the fitness product rather than a separate product.
  • SwiftUI
  • SwiftData
  • OpenAI
  • XCTest
YeneFitness coach screen
YeneFitness progress screen

Public site + internal tooling

Anatoli Seeds + Operations Desk

Procurement-focused public website and internal RFQ, proforma, order, dispatch, reservation, quality-record, supply-planning, and operations-log tooling for seed supply workflows.

Commercial workflow
RFQ intake, proforma handling, order state, and dispatch review.
Operations surface
Reservations, quality records, supply planning, and activity logs.
  • Astro
  • Vite
  • Express
  • SQLite
Seed cleaning and warehouse image from the Anatoli Seeds website assets
Operations Desk RFQ intake
ProformaReady
ReservationHeld
DispatchReview

Applied computer vision

Smart Medicine Box Vision System

FastAPI, OpenCV, and YOLO11 blister-pack pill counting work with dataset tooling, evaluation notes, a macOS photo normalizer, and visual debugging outputs.

Pipeline
Image normalization, candidate detection, classification, and API response shaping.
Debugging
Overlay and gallery outputs to inspect failures instead of hiding them in logs.
  • FastAPI
  • OpenCV
  • YOLO11
  • pytest
Blister-pack debug gallery showing computer vision pipeline outputs
Research system

Smart Medicine Box Vision System

Blister-pack pill counting with an explainable OpenCV baseline, a YOLO11 detector path, a local FastAPI service, dataset tooling, and visual debug artifacts.

01 Image normalization

Prepare one blister-pack image for consistent downstream checks.

02 Cavity detection

Locate candidate regions with classical computer vision and model-based paths.

03 Classification

Return present, missing, low-confidence regions, timings, and warnings.

04 Debug outputs

Produce overlays and galleries that make failures inspectable.

API response present / missing / low confidence timings and warnings
  • Classical CV baseline
  • YOLO detector path
  • Dataset and labeling tooling
  • Visual debug gallery