Just a few high level points about how this works. It works in multiple stages:

  1. I apply a domain and keyword filter to the feed
  2. The content of the remaining articles are run twice daily through a Modern Bert-based classifier fine-tuned to detect AI-related content (~8000 training examples)[0]. 

It also filters out Github repos that contain AI authorship. My backend scans:

  - Commit messages for agent attribution
  - The contributor graph for agents
  - Repo files for instructions/configs

RSS feed here: https://hcker.news/feeds/atom?period=day&ai=exclude

HN Frontpage minus AI: https://hcker.news/?view=frontpage&ai=exclude

You may also like Small Web HN: https://hcker.news/?view=frontpage&smallweb=include

0: The original workflow for this was a little different. For the better part of the past year, I had an AI agent detect AI-related stories and raise ~20 stories to me to make a judgement call on. After a while, a workflow like this just doesn't make sense when small models can do it equally as well. The training data is based on the machine and my labeling.