GitHub user Theospe added a comment to the discussion: Task ideas for the dkNet-AI · Apache Texera Agent Hackathon
A sports-betting pipeline for Valorant kill prop bets on Underdog Fantasy, where Texera does the data work and a machine learning model makes the actual predictions. Texera cleans up raw match data, scrapes daily betting lines, and feeds everything into the model in a repeatable, visual way. The followings are some examples of how this can help: Clean data, ready for the model: Texera handles the messy work — combining stats from multiple sources, weighting recent games more heavily, and packaging it all into the format the model expects. Daily picks with explanations: Texera pulls today's betting lines, runs them through the model, and then an LLM writes a short reason for each suggested bet. The math comes from the model; the LLM only explains it in plain language. Honest performance checks: a backtest workflow replays past games to see if the model actually has an edge, with a strict gate — no real money is placed until the model proves itself across at least 1,000 historical picks. Self-correction over time: as real bets get resolved, Texera updates how confident the model should be in its own predictions, and flags when results start drifting in the wrong direction. Human-in-the-loop reviews: a diagnostics workflow lets an LLM read the model's recent performance and suggest where it's doing well or poorly — like a coach reviewing game tape. The idea is to use Texera as the backbone that turns a tangle of scripts into a clean, transparent pipeline, with the model doing the predicting and AI assistants writing the reasoning — so a human can trust, verify, and improve the system over time. GitHub link: https://github.com/apache/texera/discussions/5059#discussioncomment-16925830 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
