Veritas

Hi everyone, I'm having trouble finding a Lemmy instance that works well for me. The main instance I use is down, and most others are too slow. I'm wondering if there's a way to choose an instance based on latency and the least blocked users. I found two relevant issues on the awesome-lemmy-instances GitHub page: issue #12 about choosing an instance based on latency and issue #17 about choosing an instance with the least blocked users. However, I'm not sure how to implement these into the main script to generate a readme with a few recommended instances. Does anyone have any advice or tips on how to choose a Lemmy instance based on these criteria? Thanks in advance for your help!

I'm particularly looking for youtubers that are very active and more entertaining than focused on theory. But I would be happy to hear any of your recommendations.

A highly sedentary adult aged 18-64 should aim to gradually increase their physical activity levels to meet the recommended guidelines. According to the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), adults in this age range should:

1. Do at least 150-300 minutes of moderate-intensity aerobic physical activity (e.g., brisk walking) or at least 75-150 minutes of vigorous-intensity aerobic physical activity (e.g., jogging or running) per week, or an equivalent combination of both[1][3].
2. Engage in muscle-strengthening activities at moderate or greater intensity that involve all major muscle groups on 2 or more days a week[1][3].
3. Limit the amount of time spent being sedentary and replace sedentary time with physical activity of any intensity, including light intensity, for health benefits[3].

It's important to note that some physical activity is better than none, and adults who sit less and engage in any amount of moderate-to-vigorous intensity physical activity will gain health benefits[2]. As a highly sedentary adult, you should start with small, manageable increments of physical activity and gradually increase the duration, frequency, and intensity as your fitness level improves. It's also a good idea to consult with a healthcare professional before starting a new exercise program, especially if you have any medical conditions or concerns[5].

Citations:

[1] https://www.cdc.gov/physicalactivity/basics/age-chart.html

[2] https://www.cdc.gov/physicalactivity/basics/adults/index.htm

[3] https://www.who.int/news-room/fact-sheets/detail/physical-activity

[4] https://www.health.gov.au/topics/physical-activity-and-exercise/physical-activity-and-exercise-guidelines-for-all-australians/for-adults-18-to-64-years

[5] https://www.nhs.uk/live-well/exercise/exercise-guidelines/physical-activity-guidelines-for-adults-aged-19-to-64/

[6] https://www.acsm.org/education-resources/trending-topics-resources/physical-activity-guidelines

[7] https://www.ncbi.nlm.nih.gov/books/NBK305058/

[8] https://www.ncbi.nlm.nih.gov/books/NBK566046/

[9] https://health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf

I have had reservations about balancing scores across communities based on the number of users, as it may dilute the uniqueness and individual themes of each instance. However, upon further consideration, I have noticed that smaller instances are underrepresented on the main feed. This has prompted me to reconsider the approach and explore the possibility of balancing instances based on monthly active users instead of communities. By doing so, we can ensure a more diverse representation of instances and avoid a situation where only big instances dominate the feed. I am uncertain about the best approach, and I would appreciate hearing your opinions on this matter.

Please consider leaving a thumbs up on the GitHub issue.

I'm reaching out to the community to gather your thoughts and suggestions on how we can enhance Lemmy's search functionality, as discussed in Issue #846. Currently, the search options (new or top of a specific time) do not consistently deliver relevant or useful results, which creates difficulties for users trying to find posts based on specific keywords. While search engines like Google employ factors such as backlinks, freshness, keyword mentions, user experience, and topical authority, we need to strike a balance between improving search results and maintaining low complexity.

Please consider leaving a thumbs up on the GitHub issue.

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To find videos with above average popularity in your YouTube subscriptions, you can follow these steps:

1. Retrieve a list of your YouTube subscriptions using the YouTube API or a third-party tool.
2. For each subscribed channel, obtain the view count or engagement metrics of their recent videos.
3. Calculate the average view count or engagement metrics across all videos for each channel.
4. Compare the view count or engagement metrics of each video to the average for its channel.
5. Select the videos that have above-average view count or engagement metrics as the ones with above average popularity in your subscriptions.

Here's an example code snippet in Python using the YouTube Data API to retrieve the list of subscriptions and calculate the average view count for each channel:

import googleapiclient.discovery

# Create a YouTube API client
youtube = googleapiclient.discovery.build('youtube', 'v3', developerKey='YOUR_API_KEY')

# Retrieve the list of subscriptions
subscriptions = youtube.subscriptions().list(
    part='snippet',
    mine=True
).execute()

# Iterate through each subscribed channel
for subscription in subscriptions['items']:
    channelId = subscription['snippet']['resourceId']['channelId']

    # Get the channel's uploads playlist
    playlist = youtube.channels().list(
        part='contentDetails',
        id=channelId
    ).execute()

    uploadsPlaylistId = playlist['items'][0]['contentDetails']['relatedPlaylists']['uploads']

    # Get the videos in the uploads playlist
    videos = youtube.playlistItems().list(
        part='contentDetails',
        playlistId=uploadsPlaylistId,
        maxResults=10 # Adjust the number of videos to fetch as needed
    ).execute()

    totalViewCount = 0
    numVideos = 0

    # Calculate the total view count for the channel's videos
    for video in videos['items']:
        videoId = video['contentDetails']['videoId']

        videoStats = youtube.videos().list(
            part='statistics',
            id=videoId
        ).execute()

        viewCount = int(videoStats['items'][0]['statistics']['viewCount'])
        totalViewCount += viewCount
        numVideos += 1

    # Calculate the average view count for the channel
    if numVideos > 0:
        averageViewCount = totalViewCount / numVideos
    else:
        averageViewCount = 0

    print(f"Channel: {subscription['snippet']['title']}")
    print(f"Average View Count: {averageViewCount}\n")

You can modify this code to include other engagement metrics like likes, comments, or shares, and use them to determine the videos with above average popularity in your subscriptions.