• Google Ads Mystery Shopping Rating — What?

    Have any of you heard of the Google Ads mystery shopping rating? Well, it may be used by Google for the overall store rating, which is calculated from customer reviews and a «Mystery Shopping Rating,» according to a Google Ads rep.

  • Google Ads Triple Serving Ads On Same Search Results Page

    Last December, Google confirmed they are testing double serving ads on the search results. Double serving ads is showing the same ad from the same advertiser more than once on the search results page. It was something against the Google Ads policy. Well, now Google is triple serving ads on the same page!

  • Google Tests AI Overviews In Germany, Switzerland, Italy & More

    While we’ve seen AI Overviews in the United States for a while now, and while Google has expanded them to 100+ countries — they are still not officially launched in many European countries like Germany, Switzerland, Italy and others. Well, now we are hearing that Google is testing AI Overviews in those countries.

  • Fast, Broken, and Stuck? Your Content Team Needs Processes To Break

    Let’s hear it for the unsung heroes of marketing — the people who develop the processes and standards. Without them, you’ll struggle to spot your content superstars. Here’s why they’re a crucial foil to innovators who buck their systems.

  • Defog AI Open Sources Introspect: MIT-Licensed Deep-Research for Your Internal Data

    Modern enterprises face a myriad of challenges when it comes to internal data research. Data today is scattered across various sources—spreadsheets, databases, PDFs, and even online platforms—making it difficult to extract coherent insights. Many organizations struggle with disjointed systems where structured SQL queries and unstructured documents do not easily speak the same language. This fragmentation…

  • Accelerating AI: How Distilled Reasoners Scale Inference Compute for Faster, Smarter LLMs

    Improving how large language models (LLMs) handle complex reasoning tasks while keeping computational costs low is a challenge. Generating multiple reasoning steps and selecting the best answer increases accuracy, but this process demands a lot of memory and computing power. Dealing with long reasoning chains or huge batches is computationally expensive and slows down models,…

  • Acute Effects of Aerobic Exercise on Risky Decision Making and Reward Processing in Young Adults

    Acute exercise is suggested to elicit benefits for cool executive function, but the sensitivity of its hot components, such as risky decision making, to exercise remains unclear. However, improvements in risky decision making are relevant due to its predictive value for engagement in unhealthy behaviors in young adults in particular. We investigated the acute effects…

  • A multi-analyte blood test for acute spinal cord injury

    BACKGROUNDRapid diagnosis to facilitate urgent intervention is critical for treatment of acute spinal cord injury (SCI). We hypothesized that a multi-analyte blood biomarker would support point-of-care SCI diagnosis, correlate with injury severity, and predict long-term neurologic outcomes.METHODSDroplet digital PCR (ddPCR) assays were designed to amplify differentially hypomethylated genomic loci in spinal cord tissue. An optimized…

  • Building a Collaborative AI Workflow: Multi-Agent Summarization with CrewAI, crewai-tools, and Hugging Face Transformers

    CrewAI is an open-source framework for orchestrating autonomous AI agents in a team. It allows you to create an AI “crew” where each agent has a specific role and goal and works together to accomplish complex tasks. In a CrewAI system, multiple agents can collaborate, share information, and coordinate their actions toward a common objective.…

  • NeoBERT: Modernizing Encoder Models for Enhanced Language Understanding

    Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. However, while decoder-based large language models (LLMs) like GPT and LLaMA have evolved rapidly—incorporating architectural innovations, larger datasets, and extended context windows—encoders have stagnated. Despite their critical role in embedding-dependent…