We built a Chrome extension and iOS app that filters Twitter's feed using Qwen3.5-4B for contextual matching. You describe what you don't want in plain language—it removes posts that match semantically, not by keyword.

What surprised us was that because Twitter's ranking algorithm adapts based on what you engage with, consistent filtering starts reshaping the recommendations over time. You're implicitly signaling preferences to the algorithm. For some of us it "healed" our feed.

Currently running inference from our own servers with an experimental on-device option, and we're working on fully on-device execution to remove that dependency. Latency is acceptable on most hardware but not great on older machines. No data collection; everything except the model call runs locally.

It doesn't work perfectly (figurative language trips it up) but it's meaningfully better than muting keywords and we use it ourselves every day.

Also promising how local / open models can now start giving us more control over the algorithmic agents in our lives, because capability density is improving.