Category: Google
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What is “Help Me Write” in Chrome?
Introduction “Help Me Write” is Google Chrome’s AI-powered writing assistant designed to help users create short-form content directly within their web browser. Launched with Chrome version 121, this feature leverages artificial intelligence to generate text suggestions based on user prompts and the context of the webpage you’re viewing. How Does Help Me Write Work? The…
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Your website is about to start talking. Are you ready for this?
Chrome is about to give all websites a voice through a built-in version of Gemini. Your visitors will have completely private chats with it. No external API calls to Google’s servers and once loaded you can even switch off the internet – it will still work! What will they talk about? The Silent Web is…
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Inside Chrome’s Semantic Engine: A Technical Analysis of History Embeddings
I decoded Chrome’s internal semantic search, found the exact chunking mechanism, embedding logic and am now able to browse, search and cluster my own search history through decoded vector embeddings. This is an in-depth technical analysis of Chrome’s history embeddings system based on Chromium source code and official Google documentation. Google Chrome has implemented a…
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Google’s Query Fan-Out System – A Technical Overview
We have successfully replicated Google’s query fan-out approach following their research papers and this article describes the exact mechanics of automatically generating multiple intelligent variations of search queries using a trained generative neural network model. Unlike traditional systems that rely on pre-defined rules or historical query pairs, this system can actively produce new query variants…
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Analysis of Gemini Embed Task-Based Dimensionality Deltas
When generating vector embeddings for your text using Gemini Embed there are several embedding optimisation modes: For each one you get slightly different embeddings, each optimised for the task at hand. The embeddings for semantic similarity are the most unique from all other types while retrieval query, retrieval document and fact verification embeddings are most…
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Prompt Engineer’s Guide to Gemini Schemas
Prompt Engineer’s Guide to Gemini API GenerateContentResponse Schemas This guide provides a comprehensive and technical deep dive into the GenerateContentResponse schema, which is the primary output structure for the Gemini API’s GenerateContent method. Understanding this schema is crucial for effectively parsing, interpreting, and utilizing the responses generated by the Gemini model. 1. Overview/Summary The GenerateContentResponse…
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Top 10 Most Recent Papers by MUVERA Authors
MUVERA Authors: 1. Laxman Dhulipala (Google Research & UMD) Top 10 Recent Papers (2023-2025) Research Focus Areas 2. Majid Hadian (Google DeepMind) Top 10 Recent Papers (2023-2025) Research Focus Areas 3. Jason Lee (Google Research & UC Berkeley) Top 10 Recent Papers (2023-2025) Research Focus Areas 4. Rajesh Jayaram (Google Research) Top 10 Recent Papers…
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Training Gemma‑3‑1B Embedding Model with LoRA
In our previous post, Training a Query Fan-Out Model, we demonstrated how to generate millions of high-quality query reformulations without human labelling, by navigating the embedding space between a seed query and its target document and then decoding each intermediate vector back into text using a trained query decoder. That decoder’s success critically depends on…
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Training a Query Fan-Out Model
Google discovered how to generate millions of high-quality query reformulations without human input by literally traversing the mathematical space between queries and their target documents. Here’s How it Works This generated 863,307 training examples for a query suggestion model (qsT5) that outperforms all existing baselines. Query Decoder + Latent Space Traversal Step 1: Build a…
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Cosine Similarity or Dot Product?
Google’s embedder uses dot product between normalized vectors which is computationally more efficient but mathematically equivalent to cosine similarity. How Googler’s work and think internally typically aligns with their open source code (Gemini -> Gemma) and Chrome is no exception. It’s why I look there for answers and clarity on Google’s machine learning approaches. After…