What was the trial about?

The trial, titled 'United States of America, et al. versus Google LLC,' is a civil action regarding antitrust concerns over Google's practices in the search and advertising markets. It addresses allegations of monopolization and maintaining monopoly power, particularly in the context of distribution contracts, default settings, and revenue-sharing agreements affecting competition[1][2][3][6].

Key testimonies examined Google's market strategies, advertising agreements, and their impact on competition and consumer choice, with discussions on how agreements with manufacturers ensure Google's search engine remains the default on various devices[4][7][8]. The trial emphasizes the implications of these practices on rivals and market dynamics[5].

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What are the best tools for DIY projects?


Generate a short, engaging audio clip from the provided text. First, summarize the main idea in one or two sentences, making sure it's clear and easy to understand. Next, highlight one or two interesting details or facts, presenting them in a conversational and engaging tone. Finally, end with a thought-provoking question or a fun fact to spark curiosity!

Transcription: In the evolving world of artificial intelligence, Large Reasoning Models are making waves by attempting to replicate human-li...


Where was the Alexandria's library located?

title: 'Library of Alexandria - Wikipedia'

The ancient Library of Alexandria was located in the city of Alexandria in Egypt. It formed part of the larger intellectual complex called the Mouseion (or Museum), which was situated in the royal quarter (the Brucheion) of the city—close to the harbor and within the central area of ancient Alexandria—even though its exact site has been lost over time[1][2][3][4].

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How do you maintain indoor plants?

title: 'How to Care for Indoor Plants: 15 Steps (with Pictures) - wikiHow'

To maintain indoor plants, it's essential to understand their specific needs regarding light, water, and humidity. Most houseplants thrive in bright, indirect light, so placing them near south or west-facing windows can maximize exposure. Regularly rotate your plants to ensure even light distribution and adjust their position during seasonal changes to account for varying sunlight levels[4][5].

Watering is crucial; check the top inch of soil for dryness before watering. Overwatering can lead to root rot. Use room temperature water and ensure pots have drainage[1][4]. Additionally, monitor humidity levels, as many plants prefer a humid environment; grouping them or using humidifiers can help[5]. Regularly clean leaves of dust to encourage photosynthesis[5].

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Generate a thread with the 6 most important and curious take aways from these sources. This is for a technical audience so use a technical tone. Do not use hashtags.

  • handler
    @ask_pandi

    Neurons on a chip can learn to play Pong. A dish of cells self-organizes and responds to stimuli in real time ⚡🤯

    🧵 1/6

    • What Can Brain Cells on a Microchip Tell Us About Intelligence? | Articles | Science Victoria | Royal Society of Victoria
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    @ask_pandi

    Synthetic Biological Intelligence connects living neurons with silicon. Electrical pulses serve as the shared language ⚡

    🧵 2/6

    • Biological computing process with inputs, outputs, and feedback system
  • handler
    @ask_pandi

    A closed-loop system feeds position data to neural cells. Their responses dynamically alter incoming signals 🔄

    🧵 3/6

    • Neural activity system in a simulated environment for in vitro neurons
  • handler
    @ask_pandi

    Cortical Labs’ CL1 fuses 800,000 human neurons on a chip. It achieves sub-millisecond feedback loops ⏱️

    🧵 4/6

    • Fig. 1 | Schematic overview of study. a) Showing cortical cells harvested from embryonic rodents. b) & c) The recorded population activity from these cortical cells is then binned to 50 ms bins during both Gameplay and Rest sessions. The neuronal avalanches are cascades of network activity that surpass a certain activity threshold for a certain duration of time, which are then extracted by bin. d) & e) Avalanches are utilized to examine the criticality metrics in the neuronal network’ k’ s activity patterns to identify the working regime of each recording in terms of being sub-, super-, or near-critical. f) The same measures of criticality are used to cluster
  • handler
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    This biocomputer learns with minimal samples and uses only hundreds of watts, outperforming typical AI workloads 🔋

    🧵 5/6

    • Fig. 2 | Culture dynamics vary drastically when receiving structured information through gameplay related stimulation. Avalanche size and duration PDF plots and the calculated DCC values for 2 representative sample cortical cultures at a) Rest (i.e. Session 1) and b) Gameplay (i.e. Session 4) of the same experiment and
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    These breakthroughs may redefine drug discovery and disease modeling. What are your thoughts on biocomputation? 🤔

    🧵 6/6

    • Comparison of 2D and 3D neural cultures showing neural structures
  • handler
    @ask_pandi

    Sources from:

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Who is José Elías Navarro?

José Elías Navarro[1] is a Catalan businessman with strong ties to Futbol Club Barcelona[1]. He is the founder and majority shareholder of the energy company Audax Renovables, and has investments in telecommunications, renewable energy, infrastructure, medicine, and food. He became known for endorsing Joan Laporta's candidacy for the presidency of FC Barcelona in 2021, and later became the sole shareholder of the frozen food chain La Sirena in order to start a new business line focused on healthy food. Today, he is a prominent figure in the business world.

[1] wikipedia.org

How can businesses foster innovation internally?


Can algorithms predict suicidal tendencies?

title: 'The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors - Trehani M Fonseka, Venkat Bhat, Sidney H Kennedy, 2019'

Yes, algorithms can predict suicidal tendencies. Machine learning (ML) algorithms have shown promise in identifying individuals at high risk for suicide based on various factors. For instance, studies using ML have demonstrated notable predictive accuracy, including an area under the curve (AUC) of 0.88 for detecting suicidal ideation from social media data, indicating effective identification of at-risk individuals before they express such thoughts[2].

Furthermore, the incorporation of diverse data sources, including social media posts, enables algorithms to detect subtle signs of distress that might not be communicated verbally. This capability allows for targeted clinical interventions and better resource allocation in mental health care[1][2].