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SHOWCASE

Selected projects, in detail.

Here are selected projects presented in more detail: their context, technical choices and the parts of the implementation that mattered.

01

Edge AI · Android

OfflineLingo

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QUESTION

How can emergency communication be supported when the network is unavailable and transcription uncertainty must not be hidden?

SYSTEM

An Android speech-to-text translation application running locally, designed for cross-border response scenarios.

CONTRIBUTION

Designed an end-to-end Android flow: voice capture, local transcription, translation and an interface that makes uncertainty understandable.

IMPLEMENTATION

  • whisper.cpp for on-device speech recognition.
  • llama.cpp with a quantized Qwen 2.5 model for local translation.
  • No Wi-Fi, mobile data or cloud API required to run.

RESULT

A standalone field-translation prototype for situations where a potential error must be noticed and verified.

DESIGN CHOICES

  • On-device transcription and translation, with no dependency on a cloud service.
  • Word-level and overall confidence indicators to make uncertainty visible.
  • High-contrast interface, one-record control, fast language switching and an emergency-vocabulary glossary.
KotlinAndroidSpeech-to-textOn-device AI
02

Multi-agent systems

Good News Agent

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QUESTION

How can a personalised newsletter be assembled without reducing editorial selection to a simple LLM query and summary?

SYSTEM

A news-intelligence platform orchestrating search, source assessment, verification and newsletter composition.

CONTRIBUTION

Designed the agentic workflow and its safeguards, from document research through newsletter assembly for subscriber groups.

IMPLEMENTATION

  • A LangGraph workflow separating planning, collection, qualification, verification and writing.
  • FastAPI API and React interface around a SQLite database.
  • Deduplication rules and audit trails linking each selection to the sources consulted.

RESULT

A system that makes selection steps explicit instead of conflating text generation with editorial judgment.

DESIGN CHOICES

  • Planning research angles and parallel collection by sub-topic with LangGraph.
  • Explicit scoring for relevance, freshness, credibility, journalistic signal and constructive valence.
  • Fact checking, source deduplication, subscriber-group management and email delivery.
PythonFastAPILangGraphReactSQLite
03

Private AI · Android

Gemmory

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QUESTION

What does a memory assistant look like when notes, answers and the model must remain on the phone?

SYSTEM

A local-first AI memory vault that turns raw notes into connected knowledge that can be queried with Gemma 4 on device.

CONTRIBUTION

Integrated Gemma 4 E2B with LiteRT-LM and built the memory, conversation and relationship-exploration features on Android.

IMPLEMENTATION

  • Incremental token streaming, multi-turn conversations and cancellable generation.
  • Interactive memory graph and backlinks for exploring relationships between notes.
  • Model size and SHA-256 verification, with local Room persistence.

RESULT

An application without a backend or cloud inference: notes, conversations and generated answers remain on the device.

DESIGN CHOICES

  • Local inference with LiteRT-LM, without a backend, account, cloud sync or fallback API.
  • Model installation checked by size and SHA-256, with the model kept off the main thread.
  • Conversations, backlinks and the knowledge graph persisted in Room and stored in the app’s private space.
KotlinAndroidGemma 4LiteRT-LMRoom
04

Robotics · Full stack

Robot Manager UI

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QUESTION

How can a LeRobot environment be made practical for daily use, from hardware detection to episode recording?

SYSTEM

A web interface and API for LeRobot SO-101 / SO-100 arms, structured around real setup and experiment steps.

CONTRIBUTION

Designed a single operator flow, from hardware detection to building and reviewing robotic datasets.

IMPLEMENTATION

  • React/TypeScript frontend and FastAPI API, containerised with Docker.
  • Port, camera and calibration management before teleoperation.
  • Trajectory recording, replay and dataset administration in the same tool.

RESULT

An interface that brings together manipulations usually scattered across robotics scripts and commands.

DESIGN CHOICES

  • Guided management of USB ports and camera feeds before robot control.
  • Calibration, low-latency teleoperation and real-time visualisation in a tabbed interface.
  • A single pipeline to record, replay trajectories and administer datasets.
TypeScriptReactFastAPIDockerLeRobot
05

Medical AI

Ovarian Cancer Segmentation

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QUESTION

How can tumour and metastasis regions be handled separately while accounting for a competition-specific medical metric?

SYSTEM

A segmentation pipeline for the PINKCC challenge, developed in a two-person team around training, evaluation and ensemble voting.

CONTRIBUTION

Developed U-Net segmentation models, validation pipelines and an ensemble strategy tailored to the two region types.

IMPLEMENTATION

  • One model specialised for each target region: tumour or metastasis.
  • Data preparation, augmentation, cross-validation, metrics and prediction visualisation.
  • Majority and weighted voting evaluated to combine model outputs.

RESULT

PINKCC Challenge finalist: 12th team out of 42, with a 0.5082 score on the Dilated Dice + F2 metric.

DESIGN CHOICES

  • A specialised model per region type, then combination through majority voting.
  • Preparation, augmentation, cross-validation, evaluation and prediction-visualisation pipelines.
  • Final score of 0.5082 on a metric combining Dilated Dice and F2.
PythonTensorFlowEnsemble learningMedical imaging

ADDITIONAL PROJECTS

Local Chat

Local interface for multiple LLMs with Ollama, RAG and LangChain tools.

RAG Chatting UI

Local document chat using LangChain, FAISS, Ollama and Streamlit.

Credit Card OCR

OpenCV, Tesseract and CNN pipeline for card-data extraction.

Ship Image Classification

Top 3/117 CNN in an internal Kaggle competition.