i like to code. full stack developer with 1 year of internship experience at genesys.
i also graduated from eötvös loránd university (elte), hungary in 2025.
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/_-'' ''-_\software engineer intern
genesys
delivered 100% code coverage in journey management. implemented 5+ novel algorithms and streamlined business logic to enhance performance. resolved 20+ bugs, 7+ angular/vue tickets, and implemented 10+ cypress tests.
qa automation engineer intern
genesys
automated e2e tests with playwright/cypress for angular/vue, resolving 22+ cases to enhance ux. improved ci/cd efficiency via tdd, validating features like login, events, rendering with mock data. ensured daily fedramp compliance, maintaining high security standards.
teaching assistant
eötvös loránd university
mentored 20+ first-year international computer science students in programming fundamentals and university adaptation. supported faculty by leading lab sessions covering foundational coding concepts and academic orientation.
mereader: offline, privacy-preserving, progress-aware ai reading assistant
first author
this paper introduces mereader, a privacy-preserving, offline and progress-aware ai assistant designed to function as a contextual companion for narrative reading on consumer hardware. unlike conventional large language model (llm)-based systems that process documents/books online in an all-texts-at-once type of way, thereby risking narrative spoilers, mereader confines both retrieval and generation to the reader's distinct position in the text. the system integrates a hybrid retrieval architecture, combining vector similarity, bm25, and summary embeddings, to ensure high fidelity to the reader's current context. we evaluate the system across classical english books using both quantitative comprehension metrics and qualitative llm-based independent judging. the results identify a quality-latency frontier, showing that midsized local models (2b-4b) can achieve comprehension parity with larger counterparts (5b+) while maintaining acceptable response quality. importantly, the proposed progress-aware boundary mechanism is shown to prevent future content leakage without compromising retrieval quality and correctness. these findings suggest that a localized, privacy-centric approach can augment, rather than replace, the digital reading experience, offering a good alternative to cloud-dependent services. therein, there remains a lack of amalgamate systems which combine progress-aware, privacy-preserving, and narrative ebook reading with ai support. it is in this very gap that the development of mereader came to be; synthesizing said strands into an offline, book-constrained ai assistant, focused on enhancing comprehension without compromising narrative continuity or user privacy.
privacy-preserving ai reading assistant with progress-aware retrieval