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Tomer Naydnov
ENעב

§01/Evidence

Fast read. Optional depth.

Work

Four substantial case studies, followed by the earlier engineering work that built the craft. Start with the decision; open the evidence when you want the depth.

§02/Selected work

The work that best shows what was broken, what I owned, how I built and what can be inspected today.

Selected work

Product · Education · Platform

Portal online

Arc

The problem was never where the files lived. It was that authoring, teaching and feedback were separate systems.

The move
Treat the lesson as a living loop: reusable content becomes a classroom release, student work becomes feedback, and feedback changes what gets taught next.
My part
I co-developed Arc across architecture, portal workflows, authentication and authorization, realtime behavior, touch interaction, testing and a broad frontend refactor.

Jan 2026 — now

Co-developer · architecture, product workflows and quality

Product · Platform · Applied AI

Source archived

Applytide

The browser had become the workaround for a process that needed its own operating system.

The move
Make capture almost invisible, then give every application one structured history from discovery to decision.
My part
I independently defined, designed and built Applytide end to end: browser capture, the React interface, FastAPI services, data modelling, document tooling, infrastructure and the bounded AI layer.

Aug 2025 — Apr 2026

Product · design · full-stack engineering

Product

Discontinued · source public

Eventa

A wedding can put strangers in one room while giving them no natural way to meet.

The move
Use the event itself as the trust boundary: a QR opens a short, event-scoped path from arrival to a relevant introduction.
My part
I independently conceived, designed, built and operated Eventa end to end: onboarding, profiles, matching, messaging, privacy and organizer controls.

2026

Product · design · full-stack build · operation

Applied AI · Platform

Completed team project

License Plate Recognition

The hard part was not one model. It was teaching motion, plate recognition and plate analysis to behave like one system.

The move
Treat data gathering, motion gating, plate recognition and plate analysis as one trainable pipeline, then decouple the expensive stages with queues.
My part
On a five-person team, I focused on motion detection and the plate-recognition and plate-analysis models: gathering and preparing data, training, evaluating, fine-tuning and integrating them into the pipeline.

Nov 2024 — Jul 2025

Motion detection · model training & fine-tuning · plate analysis

§03/Earlier engineering work

2022 — 2024 · University

Foundations, at the right weight.

University projects stay visible for technical reviewers without competing with the work that best represents me now.