Control your X/Twitter feed using a small on-device LLM
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…
In plain words
A Chrome extension and iOS app that filters Twitter posts using semantic matching rather than keywords. Users describe unwanted content in plain language, and an on-device language model removes semantically similar posts from their feed. Because Twitter's algorithm adapts to engagement patterns, consistent filtering gradually reshapes feed recommendations. The tool runs inference on company servers with experimental local processing in development. It handles figurative language imperfectly but outperforms keyword-based muting. No user data is collected.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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.
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