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  • 🤖 Anthropic’s “Clio” Shows How People Use AI Models Every Day

🤖 Anthropic’s “Clio” Shows How People Use AI Models Every Day

PLUS: Seventh Day of “12 Days of OpenAI” Event, Microsoft Launches “Phi-4”: Small and Powerful

Welcome back AI enthusiasts!

In today’s Daily Report:

  • 📊Anthropic’s “Clio” Shows How People Use AI Models Every Day

  • 🎄Seventh Day of “12 Days of OpenAI” Event

  • 🚀Microsoft Launches “Phi-4”: Small and Powerful

  • 🛠Trending Tools

  • 💰Funding Frontlines

  • 💼Who’s Hiring?

Read Time: 3 minutes

🗞RECENT NEWS

ANTHROPIC

📊Anthropic’s “Clio” Shows How People Use AI Models Every Day

Image Source: Anthropic/“Clio: Privacy-Preserving Insights into Real-World AI Use”/Screenshot

Anthropic released Claude Insights and Observations, or “Clio,” a tool that reveals patterns in how people use AI models throughout their day.

Key Details:
  • What do people actually use AI models for? Despite the rapid growth of AI adoption, we don’t actually know how people rely on AI models. “Clio” is Anthropic’s attempt to answer this question.

  • “Clio” analyzes millions of conversations on Claude and groups them into topics or themes such as “Coding,” “Copywriting,” or “Finance.”

  • Here’s a concise breakdown of how “Clio” works:

    1. Extracting Facets: Extracts multiple attributes from conversations such as topics discussed, language used, and number of exchanges.

    2. Semantic Clustering: Similar conversations are automatically grouped together by topic or theme.

    3. Cluster Description: Each group is given a title with a brief summary.

    4. Building Hierarchies: The groups are organized into a multi-level hierarchy to explore patterns across different dimensions.

  • These four steps are powered entirely by Claude, not by human analysts. Claude prioritizes a “privacy-first design” by omitting private details from conversations.

Why It’s Important:
  • After analyzing over a million conversations, “Clio” discovered that Web and Mobile App Development dominated most conversations, representing 10.4% of interactions.

  • Usage patterns varied significantly by region and language, with a higher prevalence of social issues and economic frameworks discussed in non-English conversations.

🩺 PULSE CHECK

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OPENAI

🎄Seventh Day of “12 Days of OpenAI” Event

Image Source: OpenAI/YouTube/“Projects-12 Days of OpenAI: Day 7”/Screenshot

OpenAI showcased a new Projects feature within ChatGPT during the seventh day of the “12 Days of OpenAI” event.

Key Details:
  • The “12 Days of OpenAI” event involves 12 livestreams across 12 days of “a bunch of new things, big and small.”

  • Projects transforms ChatGPT into a dynamic, task-oriented workspace by allowing you to:

    1. Upload files relevant to your tasks.

    2. Organize conversations into smart folders.

    3. Use Search (i.e., get timely responses with links to relevant sources) and Canvas (i.e., write content and solve coding tasks) within the context of your tasks.

  • Projects is built to create a tailored experience where every aspect of ChatGPT is optimized to streamline your tasks and manage your projects.

  • Any files, instructions, or conversations you upload enables Projects to adjust ChatGPT to fit your needs better and promote context-aware workflows.

Why It’s Important:
  • Developers can leverage Projects to compartmentalize different parts of their codebase (e.g., frontend, backend, and debugging) in separate smart folders.

  • This mirrors how developers structure their Repositories, which store and manage a project’s source code, but with the added benefit of conversational context through ChatGPT.

MICROSOFT

🚀Microsoft Launches “Phi-4”: Small and Powerful

Image Source: Canva’s AI Image Generators/Magic Media

Microsoft launched “Phi-4,” a Small Language Model (SLM) that can easily be fine-tuned to meet the specific needs of companies with limited computing resources.

“Phi-4” was only trained on 14 Billion Parameters. In comparison, OpenAI’s GPT-4o (“o” for “omni”) was trained on roughly 1 Trillion Parameters. Despite this size difference, “Phi-4” outperforms these LLMs at math problems and complex reasoning tasks.

SLMs are designed for applications that can run locally on laptops or smartphones. They’re also perfect for performing tasks that don’t require extensive reasoning. SLMs also require fewer computational resources (e.g., processing power, cooling systems, and memory), making them faster to train and deploy.

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💰FUNDING FRONTLINES

  • ANYbotics secures a $60M Funding Round to build AI-based robotic inspection solutions.

  • Vapi raises a $20M Series A to create an advanced developer platform for deploying voice AI agents.

  • Liquid AI lands a $250M Series A to scale general-purpose AI systems with Liquid Neural Networks (LNNs).

💼WHO’S HIRING?

  • Zoox (Foster City, CA): Developer Platforms Intern, Summer 2025

  • Scale AI (New York, NY): Research Intern, Post-Training, Summer 2025

  • Activision (Los Angeles, CA): Computer Graphics Intern, Summer 2025

  • NVIDIA (Santa Clara, CA): Enterprise Marketing Campaigns Intern, Summer 2025

  • ServiceNow (Santa Clara, CA): Associate DevOps Intern, Machine Learning (ML), Summer 2025

🤖PROMPT OF THE DAY

PERFORMANCE

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📒FINAL NOTE

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