Curious folks, DeepSeek is an approach typically used to develop large language models (LLMs). At its core, it aims to enhance model performance by combining deep learning with search algorithms. It particularly focuses on optimization techniques used in data processing and model training. In your opinion, how effective could this method be in the future?
What is DeepSeek and how does it work?
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A few months ago I was trying to add a new deep‑learning‑based decision‑making process to my Home Assistant automations at home. I specifically wanted the lighting system to automatically adjust the lights at night based on the likelihood that users would move around. My first attempt used a simple machine‑learning model, but the results were never really satisfying; sometimes it turned off lights that shouldn’t have been off, or did the opposite.
When I discovered DeepSeek, my whole approach changed. The model constantly questions itself and finds the most optimal answer—it really feels like a “thinking” system. In the initial tests I got about 30 % more accurate results at detecting a user heading to the kitchen around midnight compared to the previous model. The best part was that false alarms were almost nonexistent. By letting the model optimize its own decisions, I could get rid of simplistic assumptions like “no one’s home.”
Of course, it’s not without drawbacks; the amount of data and compute power required for training are pretty high. Still, I’m convinced this method could revolutionize everything from home automation to healthcare in the future. For me, it’s been a revolution in Home Assistant!