Categories: Image

Made a local AI pipeline that yells at drivers peeing on my house

Last week I built a local pipeline where a state machine + LLM watches my security cam and yells at Amazon drivers peeing on my house.

State machine is the magic: it flips the system from passive (just watching) to active (video/audio ingest + ~1s TTS out) only when a trigger hits. Keeps things deterministic and way more reliable than letting the LLM run solo.

LLM handles the fuzzy stuff (vision + reasoning) while the state machine handles control flow. Together it’s solid. Could just as easily be swapped to spot trespassing, log deliveries, or recognize gestures.

TL;DR: gave my camera a brain and a mouth + a state machines to keep it focused. Repo in comments to see how it’s wired up.

submitted by /u/Weary-Wing-6806
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Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM

Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.

June 17, 2026

In "AI/ML Research"