IAC has announced the retirement of Ask.com, the successor to the iconic mid-90s search engine Ask Jeeves, after 30 years of operation. The service officially closed on May 1, 2026, as part of a strategic shift by its parent company. Known for pioneering natural language queries that allowed users to ask questions like they were speaking to a person, the platform was an early precursor to modern AI chatbots.
- Ask.com officially shut down on May 1, 2026.
- The site pioneered natural language processing in search technology.
- IAC is shifting its corporate focus away from the search business.
- Ask.com officially shut down on May 1, 2026.
- The site pioneered natural language processing in search technology.
- IAC is shifting its corporate focus away from the search business.
This article introduces the Dr. Rajat Mukherjee Memorial Scholarship, established to honor the memory of a visionary technologist, inventor, and creative spirit. The scholarship aims to empower future generations by supporting students who demonstrate excellence in academics, sports, and the arts.
Key details include:
- Tribute to Dr. Rajat Mukherjee's legacy in computer science, innovation, and mentorship.
- Partnership with St. Joseph’s Boys’ High School – Old Boys’ Association.
- Target audience: Financially underprivileged students entering Std. X ICSE.
- Selection criteria based on consistent performance from Std. VII to IX.
- Evaluation weightage: 50% Academics, 25% Sports, and 25% Arts.
Key details include:
- Tribute to Dr. Rajat Mukherjee's legacy in computer science, innovation, and mentorship.
- Partnership with St. Joseph’s Boys’ High School – Old Boys’ Association.
- Target audience: Financially underprivileged students entering Std. X ICSE.
- Selection criteria based on consistent performance from Std. VII to IX.
- Evaluation weightage: 50% Academics, 25% Sports, and 25% Arts.
Google's recent Pixel Drop introduces a groundbreaking, albeit unusual, screen automation feature for Gemini. Unlike previous assistants limited by strict APIs, Gemini uses visual reasoning to interact with third-party applications directly. By reading on-screen elements like menus and text fields, the AI can perform complex tasks such as ordering food or booking rides within a secure sandbox. While this offers significant benefits for multitasking and accessibility, it also raises critical questions regarding privacy, the stability of automation when app UIs change, and the potential disruption of the ad-supported economy. Currently, this beta feature is limited to high-end devices like the Pixel 10 and Galaxy S26 series in select regions.
App Finder is an independent search engine that indexes the Google Play Store, offering advanced filtering options to locate niche apps that are often buried by the Play Store's algorithm. It allows users to filter by permissions, keywords, features, ratings, update dates, and more, providing a more precise search experience.
MCP UI extends the Model Context Protocol to enable AI agents to return fully interactive UI components, solving the challenge of delivering commerce experiences that require visual and interactive elements like product selectors and cart flows. It uses an intent-based messaging system to maintain agent control.
A list of patents assigned to Quixey, Inc. including inventions related to application search, deep linking, application functionality access, and user intent identification.
A deep dive into the unseen systems behind Google Search – uncovering live experiments, entity-based infrastructure, AI agents, and more. The article details findings from a research project into Google's internal workings, including a list of nearly 1,200 experiments, the importance of entities and the Knowledge Graph, the development of AI agents, and how Google profiles users.
Here's a summary of the most interesting facts from the Search Engine Land article, presented as bullet points:
* **Extensive Experimentation:** Google is running approximately 1,200 experiments within its search system, with over 800 currently active as of June 2025. This highlights a continuous, iterative approach to search improvement.
* **Key Systems Still Active:** Systems previously revealed in leaks (Mustang, Twiddlers, QRewrite, Tangram, QUS) remain central to Google Search.
* **AI Agent Focus:** Google is developing a "constellation" of over 90 specialized AI agents (e.g., MedExplainer, Travel Agent) rather than a single all-purpose assistant, all under the "Project Magi" umbrella.
* **Knowledge Graph as Central Nervous System:** The Knowledge Graph isn’t just a side panel feature; it’s the core infrastructure powering many Google services, with a focus on data verification using a layered namespace hierarchy (kc, ss, hw).
* **Ghost Entities:** Google utilizes "ghost entities" – temporary, unanchored entities – to react quickly to emerging events and trends.
* **User Embedding (Nephesh):** Google creates mathematical embeddings representing user preferences and behaviors across all its products, influencing personalization.
* **Real-time Query Scoring:** Google employs a complex real-time scoring system for query terms, factoring in various elements like term placement and entity recognition.
* **Specialized Embeddings:** Beyond the main Knowledge Graph, Google uses specialized embeddings for verticals (shopping, travel) and temporal data.
* **AI Mode UI Changes:** Google is experimenting with integrating AI Mode into various UI elements, including the search bar and "I'm Feeling Lucky" button.
* **Focus on Entity Validation:** The article emphasizes the importance of brands establishing themselves as validated entities within Google’s Knowledge Graph for improved visibility.
Guide
Here's a summary of the most interesting facts from the Search Engine Land article, presented as bullet points:
* **Extensive Experimentation:** Google is running approximately 1,200 experiments within its search system, with over 800 currently active as of June 2025. This highlights a continuous, iterative approach to search improvement.
* **Key Systems Still Active:** Systems previously revealed in leaks (Mustang, Twiddlers, QRewrite, Tangram, QUS) remain central to Google Search.
* **AI Agent Focus:** Google is developing a "constellation" of over 90 specialized AI agents (e.g., MedExplainer, Travel Agent) rather than a single all-purpose assistant, all under the "Project Magi" umbrella.
* **Knowledge Graph as Central Nervous System:** The Knowledge Graph isn’t just a side panel feature; it’s the core infrastructure powering many Google services, with a focus on data verification using a layered namespace hierarchy (kc, ss, hw).
* **Ghost Entities:** Google utilizes "ghost entities" – temporary, unanchored entities – to react quickly to emerging events and trends.
* **User Embedding (Nephesh):** Google creates mathematical embeddings representing user preferences and behaviors across all its products, influencing personalization.
* **Real-time Query Scoring:** Google employs a complex real-time scoring system for query terms, factoring in various elements like term placement and entity recognition.
* **Specialized Embeddings:** Beyond the main Knowledge Graph, Google uses specialized embeddings for verticals (shopping, travel) and temporal data.
* **AI Mode UI Changes:** Google is experimenting with integrating AI Mode into various UI elements, including the search bar and "I'm Feeling Lucky" button.
* **Focus on Entity Validation:** The article emphasizes the importance of brands establishing themselves as validated entities within Google’s Knowledge Graph for improved visibility.
Guide
Katanemo Labs introduces Arch-Router, a 1.5B parameter model that intelligently maps user queries to the most suitable LLM, achieving 93% accuracy without the need for costly retraining. It uses a preference-aligned routing framework based on a Domain-Action Taxonomy, allowing for flexible adaptation to evolving models and use cases.
This paper introduces Arch-Router, a preference-aligned routing framework for large language models (LLMs). It addresses limitations in existing routing approaches by focusing on matching queries to user-defined preferences (domain and action types) rather than solely relying on benchmark performance. The framework includes a 1.5B parameter model, Arch-Router, and a data creation pipeline. Experiments demonstrate state-of-the-art results in matching queries with human preferences and improved adaptability.
This paper proposes a preference-aligned routing framework for LLMs that guides model selection by matching queries to user-defined domains or action types. It introduces Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions, outperforming proprietary models in subjective evaluation criteria.
