Breaking news

Google Unveils Deep Research: Revolutionizing AI-Enabled Research With NotebookLM

Google is set to redefine digital research with its latest upgrade to NotebookLM. The new Deep Research tool integrates seamlessly into the AI note-taking assistant, automating the process of collecting and synthesizing online information to create detailed, source-grounded reports.

How Deep Research Works

Deep Research acts like a dedicated research assistant. Users can initiate a search in the source panel and choose “Web” as their source, then select between a comprehensive briefing using Deep Research or opt for a quicker overview with Fast Research. The tool creates a research plan from your inquiry, browses relevant websites, and compiles findings into a structured report that can be directly added to your notebook.

Enhancing Workflow With Expanded File-Type Support

Beyond its research capabilities, NotebookLM now supports additional file types. Users can upload Google Sheets, Drive files via URLs, PDFs from Google Drive, and Microsoft Word documents. This update facilitates the generation of summaries from diverse data sources and enables effortless management of multiple Drive files.

Continued Investment In AI-Driven Tools

Since its launch in late 2023, Google has steadily expanded NotebookLM’s functionality. Innovations such as Video Overviews and Audio Overviews have transformed raw multimedia content and dense documents into digestible visual and auditory formats. With recent releases of dedicated apps for Android and iOS, Google is making these powerful research tools accessible across multiple platforms.

Looking Ahead

The Deep Research tool and expanded file support are expected to be available to all users within the week. This development underscores Google’s commitment to enhancing productivity and efficiency in research, empowering professionals to build in-depth, organized knowledge bases without interrupting their workflow.

CSE Reports March Market Shares As Argus Tops With 30.83%

Overview

Cyprus Stock Exchange (CSE) reported €31.50 million in share transactions for March 2026, including €11.24 million in pre-agreed trades. Data also cover the first quarter, with total transactions reaching €86.06 million across January to March.

Detailed Market Analysis

CSE provides market share calculations both including and excluding pre-agreed transactions. March figures incorporate these trades, while separate data sets highlight activity without them. Such differentiation reflects varying trading dynamics and offers a clearer view of market structure. Bond values are excluded from percentage calculations.

Quarterly Performance Metrics

Figures for the January–March period show how market shares shift depending on the calculation methodology. Year-to-date data provide a broader perspective on member activity across the exchange. Inclusion or exclusion of pre-agreed transactions affects comparative positioning. These metrics are used to assess overall performance trends.

Key Participant Performance

Argus Stockbrokers Ltd recorded a 30.83% market share in March, with transactions totaling €9.71 million, placing it first for the month. CISCO Ltd held a 24.54% share in March and ranked first for the quarter with 26.19%. Mega Equity Financial Services Ltd followed with 18.31% in March and 24.08% across the quarter. Additional participants included Eurobank EFG Equities with 8.04% and Atlantic Securities Ltd with 7.46%, contributing to overall market activity.

Aggregate Trading Volumes

Pre-agreed transactions accounted for €11.24 million of March’s total turnover. Overall trading value reached €86.06 million for the first quarter. These figures reflect both negotiated and regular market activity, providing a fuller picture of trading volumes.

Conclusion

CSE data outline the distribution of market shares and transaction volumes across members. Distinctions between pre-agreed and regular trades highlight differences in activity patterns. Reported figures provide a basis for evaluating market structure and participant performance.

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