At Zugvögel Festival 2026, our floor became a church - and what is a church without a confessional? So I built one: a wooden confessional box where festival visitors confess their sins to an AI priest, who teases the juicy details out of them, grants absolution, and then sends them onwards to the Holy Toilet - where their sins, anonymized and retold in an angel’s voice, are played back to whoever is enthroned there at the moment.
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In this (german-language) episode of Code for Thought, Carina Haupt (DLR) and I talk with host Peter Schmid about large language models in Research Software Engineering: where tools like Copilot genuinely help (routine tasks, tests, small scripts), and where real-world projects expose their limits (builds, requirements, missing project context). I share my current work on matching papers to repositories via embeddings at the project level rather than just functions - so models carry meaningful context across an entire codebase.
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During my discontinued PhD at DLR, I developed and trained a graph neural network to combine code embeddings into repository-level representations and align them with scientific publications. Preliminary evaluation showed a promising improvement over pooling baselines, as a first step towards finding related implementations and their underlying assumptions across research fields.
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