-
CAPS: A Content Attribution Payment Scheme for the AI Era
The Problem: A Broken Content Ecosystem We’re watching the collapse of the web’s economic model in real-time, and everyone knows it. AI assistants have fundamentally changed how people consume information. Why wade through ten articles when Claude, ChatGPT, or Gemini can synthesize an answer in seconds? Why maintain 100 browser tabs for research when AI…
-
AI Search Citation Mining
This is the raw data dump from our citation mining pipeline demo on social media. Entered Entities ✅ AEO (10 prompts) ✅ AI Marketing (10 prompts) ✅ AI Optimization (10 prompts) ✅ AI SEO (10 prompts) ✅ AIO (10 prompts) ✅ Answer Engine Optimization (10 prompts) Mining Parameters Available Prompts: 60GPT-5 Citations: 141Gemini Citations: 400Total…
-
Using GPT-5 Structured Output Markers to Detect AI-Generated Content Online
When you populate your website with language model–generated text, you inherit a subtle but real risk: AI-specific artifacts may leak into the published content. These markers aren’t always obvious to human readers, but they can be highly visible to search engines, researchers, and competitors. One such artifact is the structured output marker that GPT-5 (and…
-
TimesFM-ICF
In-Context Fine-Tuning for Time-Series: The Next Evolution Beyond Prophet and Traditional Forecasting How Google’s TimesFM-ICF achieves fine-tuned model performance without training – and why this changes everything for production forecasting systems If you’re reading this, you’ve likely wrestled with time-series forecasting in production. Perhaps you’ve implemented Facebook Prophet for its interpretable seasonality decomposition, experimented with…
-
Chrome Screen AI Protos
├───aocr│ └───google_ocr│ └───engine│ └───page_layout_mutators│ group_rpn_text_detection_mutator_runtime_options.proto│├───aphotos│ └───vision│ └───visionkit│ ├───drishti│ │ hexagon_delegate_calculator.proto│ ││ ├───engines│ │ └───proto│ │ audio_classifications.proto│ ││ ├───pipeline│ │ ├───drishti│ │ │ └───calculators│ │ │ tflite_task_object_detector_calculator.proto│ │ ││ │ └───proto│ │ face_cascade_options.proto│ │ hand_tracking_result.proto│ ││ └───text│ └───proto│ text_orientation_tracker.proto│├───chrome│ └───accessibility│ └───machine_intelligence│ └───chrome_screen_ai│ chrome_screen_ai.proto│├───frameworks│ └───client│ └───data│ data_annotation.proto│├───google│ ├───api│ │ inclusion.proto│ │ visibility.proto│ ││ ├───internal│ │ └───visionkit│ │…
-
RexBERT
RexBERT is a domain-specialized language model trained on massive volumes of e-commerce text (product titles, descriptions, attributes, reviews, FAQs). Unlike general-purpose transformers, it is optimized to understand the quirks of product data and the way consumers phrase queries. For a technical SEO professional, this means better alignment between how search engines interpret product content and…
-
Annotated Page Content (APC)
1. Introduction What is APC? Annotated Page Content (APC) is a structured and actionable representation of a webpage’s content and layout. Its primary function is to enable a deep understanding of page structure, content, and interactive elements by downstream clients, who can receive the information as a protobuf tree. Core Principles APC is designed with…
-
Deconstructing DomDistiller: How Chrome’s Reader Mode Algorithm Impacts Technical SEO
Chrome’s “Reader Mode” and its underlying engine, DomDistiller, provide a transparent look into the principles of machine readability. It’s a valuable, real-world model of how a sophisticated Google technology parses, evaluates, and isolates main content from boilerplate. Understanding its mechanics is not about optimizing for a browser feature; it’s about reverse-engineering a proxy for how…
-
LLM is a Presentation Layer in AI Search
Classic IR: crawl, index, retrieve, rank remain with search engines. There is a persistent myth that large language models (LLMs) have fundamentally replaced search. In truth, LLMs do not crawl the web, do not maintain indexes, and do not enforce ranking algorithms at internet scale. They operate as presentation and reasoning layers on top of…
-
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.
