Sift

Sift through the noise. Score your feed with EmbeddingGemma, right in the browser.

Sift runs EmbeddingGemma-300M (q4) directly in your browser to score feed items against your interests and fade low-relevance posts. All inference happens locally — no data ever leaves your machine. SUPPORTED SITES - Hacker News - Reddit - X (Twitter) HOW IT WORKS 1. Pick scoring categories — 25 built-in across tech, world, and lifestyle 2. Browse normally — Sift embeds every title and scores it against your categories 3. Low-relevance items fade, high-relevance items stay vivid 4. Category pills show which topics match each item FEATURES - WebGPU acceleration with WASM fallback - Score inspector — click "?" to see why an item scored the way it did - Per-site toggles and sensitivity slider - Auto-detected category pills in the popup and feed - Light/dark mode (follows system) TASTE PROFILE After labeling 10+ items with thumbs up/down, Sift builds a contrastive taste profile showing your top interests ranked by affinity, with an interactive radar chart. TRAINING LOOP - Label items with thumbs up/down as you browse - Curate labels in the Label Manager — edit, flip polarity, reassign categories - Export as CSV training triplets - Fine-tune the model with the included Python pipeline or free Colab notebook - Load your fine-tuned model back into the extension PRIVACY-FIRST - No backend server, no analytics, no telemetry - All inference runs locally in your browser - Labels and settings stay in local storage - Model weights are the only network download (from HuggingFace Hub) Open source (Apache-2.0): <https://github.com/shreyaskarnik/Sift>