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Lies, damn lies, and statistics in social deduction games
2025-08-13 · 17:30
Grace Wilding
– Data Solutions Lead
@ Beever and Struthers
Focusing on the game Mafia, this talk explores some common methods relied upon by players to solve the game. Is the first player to reach 3 votes really mafia? Can you really find mafia off voting patterns alone? And when you apply these methods in a game, how accurate are they in finding a wolf? Inspired by a desire to prove players on their homesite that people place too much faith in these methods, examples of how these methods have been applied in past games will be unpacked, and whether these principles should really be relied upon. After all, it is a social deduction game for a reason – statistics can only get you so far. |
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Pydantic: Too young to learn C, too old to learn Rust, just in time for LLMs
2025-08-13 · 17:30
Pantelis Hadjipantelis
– ML Scientist
When working with Large Language Models (LLMs), how do we ensure a probabilistic blob of text is something our code can actually use? In this talk, we explore how Pydantic emerged at a perfect moment exactly for this task; bridging Python's flexibility with the structured data needs of modern AI applications. We will introduce Pydantic and then demonstrate practical applications of it; from prompt engineering and parsing responses, to example of robust function calling and tool chaining via APIs. |
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