It’s just too much noise.

Keeping up with AI News is tough. I spend way too much time skimming past titles that look techy but are totally irrelevant to what I’m actually wanting. Keyword filters are brittle because they miss the nuance.

I wanted a way to filter news based on “vibes”, not just regex strings.

So, I’ve been playing around with the EmbeddingGemma Tuning Lab, a new Hugging Face Space that provides a tool for fine-tuning Google’s embeddinggemma-300m model to understand your specific personal taste.

The Vibe Check

The coolest part about this project is that it doesn’t rely on a massive LLM prompting strategy. It uses EmbeddingGemma, a lightweight 300M parameter model. Because it’s an embedding model, it turns text into vectors. Check out my blog post if you want to learn more about how the model works and how it was trained.

The core idea is actually pretty funny but effective. The system relies on a "Semantic Similarity" score against a hard-coded anchor phrase: MY_FAVORITE_NEWS.

By default, the model doesn’t know what that means. But by fine-tuning it, you warp the model’s understanding of the universe so that articles you actually like are mathematically closer to that magic phrase, and the ones you hate are pushed away.

The "EmbeddingGemma Tuning Lab": 3 Ways to Run It

The EmbeddingGemma Tuning Lab isn’t just a training script; it contains three different apps depending on how you like to experiment:

The Trainer (Gradio): This is where the magic happens. You load up the Gradio app, it pulls the current top 10 Hacker News stories, and you just check the boxes next to the ones you like. Click "Fine-Tune", and under the hood, it uses MultipleNegativesRankingLoss to update the model. You can literally watch the semantic search results shift in real-time. 1.

The Terminal Viewer (CLI): This one is for the true terminal junkies. It’s an interactive CLI app that lets you scroll through the live feed. It color-codes the stories based on the model’s score - green for "good vibes," red for skips. 1.

The Web Viewer (Flask): Once you’re happy with the model, there’s a lightweight Flask app included. You can deploy this as a standalone "Mood Reader" on a local server just to have your personalized feed running in the background.

Try It Out

If you want to stop doomscrolling and start vibe-checking your news, check out the space or grab the code. It handles the data fetching, the training loop, and the visualization for you.

• Check out the Space: EmbeddingGemma Tuning Lab
• See the Code: The repo includes everything you need to export your dataset and download your fine-tuned model as a ZIP.

Happy tuning!