Category: Uncategorised
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Gemini App Tools – A Technical Overview
At its core, Gemini operates as an orchestration layer managing a foundational large language model (LLM). Its primary function is to deconstruct a user prompt into a directed acyclic graph (DAG) of executable tasks. These tasks are then delegated to a suite of specialized tools accessed via synchronous API calls.
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AI Overviews = Dialogflow Agent?
Joshua Squires shared one of the most interesting AI Overview leaks and for some reason it was mostly ignored by the SEO industry. I’d like to draw your attention to it today because it provides two key details framing AI Overviews as an implementation of Google’s Dialogflow agentic framework which is backed up with an…
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Fan-Out Query Search Volume Prediction Using Deep Learning
While traditional keyword research tools provide valuable data, they often fall short in discovering truly novel or long-tail search query variations that a business might not yet rank for, or even be aware of. This is where our query fan-out model comes in. Using advanced language models to generate a vast array of related search…
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Understanding and Control
The two pillars of AI optimization are model understanding and control with well-established analogues in the machine learning industry called mechanistic interpretability and model steering. SEO Machine Learning Understanding Mechanistic Interpretability Control Model Steering Mechanistic Interpretability A subfield of AI interpretability that aims to understand neural networks at the level of individual components (neurons, attention…
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Multi-Step Research Agent
This post is the output from the implementation of Google’s query fan-out in an agentic framework inspired by Google’s Gemini Agent repo. Query: What services does DEJAN AI offer? The following is raw copy/paste from the agent’s output: Research Progress Initial Search Strategy: The search strategy aims to identify the range of services offered by…
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Why deep learning works.
Here’s a powerful excerpt from “Deep Learning with Python” by François Chollet”: The nature of generalisation in deep learning has rather little to do with the deep learning models themselves and much to do with the structure of the information in the real world. The input to an MNIST classifier (before preprocessing) is a 28 × 28 array of…
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How Gemini Selects Results
In its own words. Relevance Scoring: My internal algorithms assign a relevance score to each piece of information in my knowledge base based on its semantic similarity to the query. Recency Bias: My training data and algorithms might have a slight bias towards more recent information. Diversity and User Intent: In some cases, I might…