PeerTube Picks is now available on the official Firefox Add-ons page! Firefox users no longer have to worry about losing their data when the browser closes:

FireFox add-on link

The name PeerTube Picks was chosen through collaboration with the PeerTube Lemmy community. This add-on provides video recommendations using a cosine similarity algorithm based on videos you've watched and liked. It aims to predict which videos you're likely to engage with—either by watching for longer periods or hitting the like button.

Updates: The PeerTube Picks icon now appears next to the search bar on any PeerTube page. Clicking it opens a list of recommended videos, ranked by engagement and relevance.

A new Options page allows you to:

-Download or upload your video watch history

-Delete your watch history (recommended occasionally to refresh your recommendations)

I’m open to suggestions and contributions—feel free to share ideas or improvements!

Hot take: Most PeerTube instances shouldn’t just hand out accounts to anyone—and here’s the reality check. Running a PeerTube server isn’t like YouTube. There’s no trillion-dollar corporation footing the bill. Instead, small community admins juggle:

The major points are:

• Storage costs (video files add up fast!)
• Moderation work (spam, trolls, and legal risks)
• Bandwidth limits
• Abuse handling (because yes, people will test boundaries) Yet, a lot of sign-up requests sound like (at least from what I see on my instance):

"I wanna upload videos." "I’m starting a Roblox channel."

Sorry, but that’s not enough. Admins aren’t obligated to give free hosting to strangers. A good admin looks for people who:

• Fit the community’s vibe (e.g., a coding-focused instance won’t host gaming streams).
• Show effort—like sharing a portfolio or explaining why their content adds value.

Example: If you applied with a sample of your work or a clear plan? Hell yes, I’d consider you. But if your pitch is just "I want free hosting," why should the community foot the bill?

TL;DR: PeerTube isn’t a free-for-all. "I just wanna upload stuff" isn’t a good reason. Bring something to the table.

You can also search the Fediverse directly

cross-posted from: https://lemmy.world/post/29207242

PeerTube is a decentralized and federated alternative to YouTube. The goal of PeerTube is not to replace YouTube but to offer a viable alternative using the strength of ActivityPub and P2P protocols.

Being built on ActivityPub means PeerTube is able to be part of a bigger social network, the Fediverse (the Federated Universe). On the other hand, P2P technologies help PeerTube to solve the issue of money, inbound with all streaming platform : With PeerTube, you don't need to have a lot of bandwidth available on your server to host a PeerTube platform because all users (which didn't disable the feature) watching a video on PeerTube will be able to share this same video to other viewers.

If you are curious about PeerTube, we can't recommend you enough to check the official website to learn more about the project. If after that you want to try to use PeerTube as a content creator, you can try to find a platform available there to register or host yourself your own PeerTube platform on your own server.

The development of PeerTube is actually sponsored by Framasoft, a french non-for-profit popular educational organization, a group of friends convinced that an emancipating digital world is possible, convinced that it will arise through actual actions on real world and online with and for you!

If you want to contribute to PeerTube, feel free to:

• Report bugs and give your feedback on Github or on our forums
• Submit your brillant ideas on our Feedback platform
• Help to translate the software, following the contributing guide
• Make a donation to help to pay bills inbound in the development of PeerTube.

If you want to follow the PeerTube project:

• Follow us on Mastodon/BlueSky/Reddit (or here, on Lemmy).
• Subscribe to our newsletter

cross-posted from: https://lemmy.world/post/28964031

VOD – https://video.firesidefedi.live/w/poYgA3RUpyXSAMqViDUgeX

Fedicast – https://audio.firesidefedi.live/@firesidefedi/episodes/chris-were-peertuber-freebooters-and-space-virgins

Welcome Fedi Friends to LUCKY episode 13 of Fireside Fedi! I'm your host ozoned. Fireside Fedi is a show about folks within the Fediverse. If you're seeing this, you are a part of the Fediverse.

With me today is Chris Were “man of the Fediverse”! Chris and I have known each other for idk 10 years, but I'm still very excited to have him on, because I don't know a lot about him! Chris was “born at a very young age and now loves linux and foss”. He's a former successful Youtuber, that give it up because of Google's stances and he's an EXCLUSIVE Peertuber now. On his Peertube he has the show Freebooters, a show about tech with him and a friend Drew, as well as his old YouTube videos. He's also one of three that has a show about The Expanse called “Space Virgins: First Contact”.

Since this browser extension is meant for the peertube community I want run some names by y'all. A few I pick out are: Peer Picks, PeerTube Personalizer, Peer Sight

I'm up for other suggestions..

I'm still a ways out from publishing it outside of GitHub. I think I might go with an icon like posted in this post.

This project is open source and hopefully a basis for other better peertube projects and the general idea for other fediverse projects.

The gooner update is here. Ui now has a toggle switch for NSFW content. NSFW can be on off or the only entries shows. Other than that improvement on the backend where metadata on videos watch will successfully be entered into indexeddb instead of constantly calling for and the same metadata. Fixed the script so that it doesn't freeze the ui processing 5000+ videos

I gonna tighten up the script for chrome version so it's shorter, and fix the code that clocks how long live stream has been viewed. Really just trying to gun it for Firefox conversion, but get held up by errors and inefficiency in the current brave version

Here’s a cleaned-up version of your Lemmy post that keeps your tone but improves clarity, flow, and grammar:

Finally released an alpha build for the PeerTube recommendation algorithm!
Basic UI is complete. If you want to try it out, the link is here:
👉 https://github.com/solidheron/peertube_recomendation_algorythm

New features since the last build:

• Sort by videos that share your time engagement similarity.
• Sort by videos that share your like similarity.
• Display of like similarity cosine values.
• Basic information shown for recommended videos (title, account, and channel names).
• 404 check for generated instance links (so you don’t get stuck clicking into dead videos—you’ll know which instance hosts the video).
• De-ranking for previously seen videos (simply a 0.5x multiplier on time and like similarity).

Features from previous builds:

• Ability to input multiple instance domain names (DNs) and generate playable video links.
• Limit of 5 recommendations per channel to avoid floods (e.g., during testing, The Linux Experiment would dominate otherwise—this limit is more of a failsafe than a feature).

Personal thoughts:
I still think cosine similarity beats chronological algorithms.
This algorithm also synergizes with other algorithms—it's great for finding videos that appear next to or below what you're currently watching.

You can also revisit videos you previously liked to help strengthen your like similarity vectors.

Moving forward: basic design philosophies and current issues

There’s an issue I’m calling the “Linux pipeline.”
Basically, Linux-related videos tend to dominate PeerTube’s well-produced content.
Since the algorithm relies on English words in descriptions, titles, and tags, Linux videos—which sometimes have fewer general keywords—end up being more "orthogonal" to typical user vectors, causing lower ranking.

Another challenge:
It’s really hard to properly combine like cosine similarity and time engagement cosine similarity.
You could add them, but it doesn’t fully make sense:

• High like similarity + high time engagement similarity = you probably like and will watch the video longer.
• But short videos can be liked even if they contribute almost nothing to time engagement (because time engagement is based on percentage watched × video length).

If I combined them, it would basically enter machine learning territory:
You'd have to adjust proportions dynamically based on user behavior.
Since I want this algorithm scoped to one person only (no data sharing yet), that level of ML is out of scope for now.

(Sharing data across devices could come later—Brave browser has sync features, and PeerTube watch history syncing could be possible.)

Summary:
Most of the data structure is settling into place.
Future updates will probably focus on expanding the data structure and making small improvements.

finally some simple UI, you click on browser extension "icon" and get taken to a webpage that will show you all the video that rank by cosine similarity (I just now realized has UUID when it should have shortuuid), linked below is the webpage

https://github.com/solidheron/peertube_recomendation_algorythm/blob/main/example1.PNG

above is just example with my recommendations luckily the links change colors if you've already seen them. of course the video at the top is the video I watched the longest.

other than that I been cleaning up backend stuff and ignoring minor error that pop up. it should more accurately capture watch time on peertube videos and doesn't just say you watch an hour of a video you didn't care about. probably adding a bunch of code that needs to get cleaned up.

my opinion has shifted a bit on this simple algo it seems like the videos I get tend to be random and take require me to find videos independently to get some decent suggestion, also there's a Linux pipeline

I do have a software engineering problem where view time is only input to the algorithm, like, dislike, and finished status of a video is available. I have decided on going with cosine similarity for likes and dislikes and adding it to the time engagement. if you like a video all the tokens of that video get a +1 in length and dislike makes all the tokens -1 in length. I thought it was a good solution because it doesn't rely on converting a like to time. I wouldn't know how to deal with like being a multiplier for time engagement vector and would dislike be negative or something or just zero. generally adding a like cosine vector to a time engagement vector generally means both time and likes normalized (sorta) both can contribute to a video recommendation.

seems like cosine recommendation will need processing

So I’ve completed the cosine similarity function, which means the script is now recommending videos in a raw way. Below is just a ranking of videos that match my watch history (all three are most likely videos I’ve already watched):

2: {shortUUID: "saKY2TWfwNYgPUQFkE4xsi", similarity: 0.4955} 3: {shortUUID: "kk7x8GAs7gNvkzaPs6EPiU", similarity: 0.4099} 4: {shortUUID: "uXeAyVfX1WEzqSPsDxtH3p", similarity: 0.2829}

Getting to this point made me realize: there’s no such thing as a simple algorithm—just simple ways to collect data. The code currently has issues with collecting data properly, so that’s something that needs fixing. Hopefully, once the data collection in this script is improved, it can be reused for future Fediverse algorithms.

There are countless ways to process the data. Cosine similarity is a simple concept and easy to implement in code, but it has a flaw: content you’ve already watched tends to rank higher than anything new. So a basic "pick the highest cosine similarity" approach probably isn’t ideal. It either needs logic to remove already-watched videos, or to bias toward videos lower down in the ranking. But filtering out watched videos isn’t perfect either—people do like to rewatch things.

The algorithm currently just looks at how much time you spent watching unique segments of a video, then assigns a value in seconds to all the words in the title, description, and tags, and sums that over all videos.

The algorithm is actually okay—subjectively, it’s better than just sorting by date. I picked a few videos at random from the top 300 ranked by cosine similarity , and there was content interesting enough to watch for more than 30 seconds, and some that was just too weird for me. Here are a few examples:

• https://dalek.zone/w/7zifNjSwiafLnsoVVWDcHE
• https://peertube.1312.media/w/92B9U3DQ7cD4iGLS8pFrCX (Japanese Spider-Man video—I'm not into it)
• https://peertube.wtf/w/gmmi53JorcBQLEZaoTaSQE
• https://peertube.wtf/w/5eeLAVm5ZQBMK1PjTimpMv

Some of these links are across different instances because no single PeerTube instance has all the videos. I loaded metadata for over 6,000 videos across five instances during testing.

The question is: should the algorithm be scoped to a single instance (only looking at content on the user’s home instance), or should it recommend from any instance and take you there?

funny thing to note is that there might be a linux pipeline in this algo

Background and Rationale

YouTube's Play Button awards are physical trophies given to creators when they reach significant subscriber milestones, such as 100,000 (Silver), 1 million (Gold), and 10 million (Diamond) subscribers.

These awards have become an iconic part of YouTube's creator culture, serving as both recognition and motivation for content creators.

PeerTube, as a decentralized, open-source alternative to YouTube, currently does not have an equivalent system for recognizing creator achievements at a network-wide level.

Given PeerTube’s federated structure—where each instance is independently managed—implementing a similar recognition system would need to be thoughtfully adapted to fit its unique, decentralized model.

Proposal: Federated Creator Milestone Badges

• Digital Badges: Instead of physical trophies, PeerTube could introduce digital milestone badges that appear on creators’ channel pages and video overlays. These badges could be awarded for reaching certain numbers of subscribers, video views, or other engagement metrics.
• Instance-Level and Network-Level Recognition: Since PeerTube is federated, badges could be awarded both at the instance level (e.g., 1,000 subscribers on a specific instance) and, optionally, at the federated network level (e.g., 10,000 followers across all federated instances).
• Customizable by Instance Admins: Instance administrators could define their own milestone thresholds and badge designs, aligning with PeerTube’s ethos of customization and community control.
• Open-Source Badge Plugin: The badge system could be implemented as an official/Fan-made PeerTube plugin, allowing easy adoption and further customization by the community.
• Optional Physical Awards: For larger, community-driven instances, there could be an option for admins or community groups to crowdfund and send physical awards to creators, if desired.

Potential Benefits

• Boosts creator motivation and engagement, fostering a sense of achievement and community recognition.
• Encourages creators to grow their audience and contribute more content.
• Promotes healthy competition and collaboration between instances.
• Reinforces PeerTube’s commitment to supporting creators in a decentralized, privacy-respecting way.

Implementation Considerations

• Ensure privacy: Participation in the badge system should be opt-in, respecting creators who prefer anonymity.
• Prevent abuse: Badges should be resistant to manipulation (e.g., bot-generated subscribers).
• Federation logic: For network-level badges, develop a protocol for aggregating metrics across instances while maintaining decentralization.

Conclusion

Introducing a federated, customizable creator milestone badge system would bring the motivational benefits of YouTube's Play Button to PeerTube, while respecting its decentralized, open-source ethos and giving communities the flexibility to define their own standards for recognition.

so I spent last several days making collecting watch time on both videos and livestreams more robust and work across multiple peertube instances, im sure it still has gaps in the structure so that jenk data can get in.

if you want to try it heres the link https://github.com/solidheron/peertube_recomendation_algorythm/ btw its a browser extension

so now I got two parts left that I know of first being creating the user_recomendation_vector and the function that gets recommendation based on that vector. I settle on cosine similarity vector since its easy to implement and can be run in browser with only data collected by the user device, and doesnt requires sharing outside of peertube api. user_recomendation_vector should have two part AOLR: (algorithm of last resort) which will be the words in the title, tags, and description tokenized with an float value and recomended_standard: which will be based on what category either programs or people decide a video belongs to along with an associated float value to make it a vector.

I do have issues with deciding if engagement is important, if short video should have multiplier if they're completed, how much is a like worth, how important is it to get an end of the video.

I should add that I have made complimentary video_description_vector thats store in browser all vector dimentions are 1.

cross-posted from: https://lemmy.world/post/28252147

Fedicast / Podcast: https://audio.firesidefedi.live/@firesidefedi/episodes/dansup-pixelfed-loops-sup-etc

working on peertube recommendation algorithm has made me realize that we should be collecting our own data.

my program that monitors peertube watching habit and stores it locally and should track YouTube watching habit just so that data can be used in a recommendation algorithm. it would be a good idea for fediverse if there were programs that monitors and stores the data locally for the fediverse and corporate counterpart.

the reason why I would track youtube data was to get the titles of the video

I made a browser extension that track the video you watch on a peertube instance linked here https://github.com/solidheron/peertube_recomendation_algorythm

it works decently well but it still has some glitches and a bug that causes minor information loss that will need to be patched out. so this is in alpha and needs to be worked on. I do have another script that gets the data of a hundreds of peertube videos and the closed captions. so got some basic components of an algorithm started. next ill probably figure out how to categorize videos based on peertube api and closed captions.

It's weird knowing someone will improve on this at some point

bellow is the data structure of the browser extension. I did watch videos to get that data and all of this will be stored locally. I recommend looking at it with https://jsonformatter.org/json-viewer . leave a comment/suggestion, I'm up for any submissions.

[{"title":"The Blues Brothers (1980) - Dalek Zone","url":"https://dalek.zone/w/56ozwq8eomr2eNymWiMcLK","currentTime":131.072663,"duration":8209.360000000015,"percentWatched":1.46,"liked":false,"disliked":false,"last_update":"2025-04-14T02:46:37.218Z","session":{"segments":[{"start":5.200358,"end":45.190443,"seg_duration":39.990085,"timestamp":"2025-04-14T02:44:31.217Z"},{"start":48.475819,"end":128.483879,"seg_duration":80.00806,"timestamp":"2025-04-14T02:46:37.218Z"}]},"sessionStart":48.475819,"finished":false},{"title":"R.E.P.O. - ...do you also hear boss music? - Dalek Zone","url":"https://dalek.zone/w/aQmz8pyKM6fLKZtYCzNGf7","currentTime":265.658139,"duration":419.099996,"percentWatched":26.64,"liked":false,"disliked":false,"last_update":"2025-04-14T02:52:39.226Z","session":{"segments":[{"start":0,"end":57.671082,"seg_duration":57.671082,"timestamp":"2025-04-14T02:47:49.217Z"},{"start":83.927695,"end":87.916426,"seg_duration":3.9887310000000014,"timestamp":"2025-04-14T02:49:45.218Z"},{"start":215.192046,"end":265.193005,"seg_duration":50.00095900000002,"timestamp":"2025-04-14T02:52:39.226Z"}]},"sessionStart":215.192046,"finished":false},{"title":"Tesla Vandals & Uncontacted Tribes: 5CAST with Andrew Callaghan (#2) w/ Joel Lava & Anticar Activist - Dalek Zone","url":"https://dalek.zone/w/8etzRokpLKKCKVyaffpscn","currentTime":324.723803,"duration":6737.166660999999,"percentWatched":2.17,"liked":false,"disliked":false,"last_update":"2025-04-14T04:02:17.237Z","session":{"segments":[{"start":1.210278,"end":147.218129,"seg_duration":146.00785100000002,"timestamp":"2025-04-14T04:00:17.237Z"}]},"sessionStart":1.210278,"finished":false},{"title":"CHICKEN JOCKEY - Dalek Zone","url":"https://dalek.zone/w/9RLSpB9X74kNipex9gCBQf","currentTime":18.999999,"duration":18.999999,"percentWatched":91.49,"liked":false,"disliked":false,"last_update":"2025-04-14T04:03:15.239Z","session":{"segments":[{"start":0,"end":17.383907,"seg_duration":17.383907,"timestamp":"2025-04-14T04:03:15.239Z"}]},"sessionStart":0,"finished":true},{"title":"Reddit is garbage and their stock proves it - Dalek Zone","url":"https://dalek.zone/w/vpjkuvD1Phm4GvGtDkKWJx","currentTime":371.271227,"duration":376.566666,"percentWatched":2.66,"liked":false,"disliked":false,"last_update":"2025-04-14T04:05:17.239Z","session":{"segments":[{"start":359.374113,"end":369.374488,"seg_duration":10.000374999999963,"timestamp":"2025-04-14T04:05:17.239Z"}]},"sessionStart":359.374113,"finished":true},{"title":"Trump SAVES the Economy (from himself) - TechNewsDay - Dalek Zone","url":"https://dalek.zone/w/wDdgqKZtaErZPf2StfYxqb","currentTime":22.353169,"duration":2697.799999,"percentWatched":0,"liked":false,"disliked":false,"last_update":"2025-04-14T04:05:53.238Z","session":{"segments":[]},"sessionStart":null,"finished":false},{"title":""Fully A.I." Lawyer Shuts Down During Court & Weezer Shooting?! - News Dump - Dalek Zone","url":"https://dalek.zone/w/uTw4ktcZ14rLeGb4ZGjP4m","currentTime":232.988303,"duration":3143.500004,"percentWatched":5.85,"liked":false,"disliked":false,"last_update":"2025-04-14T04:09:17.238Z","session":{"segments":[{"start":2.144733,"end":12.144229,"seg_duration":9.999495999999999,"timestamp":"2025-04-14T04:06:21.238Z"},{"start":58.472636,"end":232.472428,"seg_duration":173.999792,"timestamp":"2025-04-14T04:09:17.238Z"}]},"sessionStart":58.472636,"finished":false},{"title":"Mantras - Mentalism (Full Album 2025) - Dalek Zone","url":"https://dalek.zone/w/gwcHVcg2m2A2LYoFaciUQw","currentTime":27.119449,"duration":2655,"percentWatched":0.83,"liked":false,"disliked":false,"last_update":"2025-04-14T04:17:10.613Z","session":{"segments":[{"start":4.210964,"end":26.199443,"seg_duration":21.988478999999998,"timestamp":"2025-04-14T04:17:10.613Z"}]},"sessionStart":4.210964,"finished":false}]

Little update on making a peertube recommendation algorithm. I made a script that collects video json and closed caption from instances. Later on I'll figure out a way to organize the data into video description vector. (Data manipulation will be ever improving process but data collecting should be stagnant and only change with activityhub and peertube updates)

My question is if there's a program that monitors or gets a users video watch time and interaction data and stores it locally. I just need something to record my video interactions of peertube videos such as like or dislike a video.

writing down my thoughts for added elements called Video_description_vector and Video_description_vector_history. Video_description_vector is an element that an instance gives to describe what categories a video does and does not belong to and Video_description_vector_history is record of user submissions of what categories they say a video belongs to so that way categories can be removed.

Video_description_vector has sub elements that are format standards, this is done so that so that future potentially better format can be entered into Video_description_vector . I'm working on my recommended standard, so far I have isTrue array that lists what categories the video belongs to, while the isFalse element lists what categories it does not belong to—subjectively. done that way so that isn't a sea of element like "cartoon":True or "action":False . I do know that I need sub-element for music metrics

Below is a made-up example. It’s about the Cleveland Browns, an American football team, doing a fundraiser for charity. Categories like “football” or “sports” are excluded because the Browns are not actually playing football or engaging in sports in the video. { "Video_description_vector": { "recommended_standard": { "isTrue": ["Browns", "Charity", "Cleveland", "ALS", "fundraiser"], "isFalse": ["Sports", "football", "Cincinnati"] }, "future_standard": { "doesnt": { "subarray": { "example": "no" }, "exist": ["Sports", "football", "Cincinnati"] } } }, "Video_description_vector_history": [ { "name": "vidchase", "host": "videovortex.tv", "submitted_date": "11/15/2020", "uuid": "this and everything above can be removed if inside a video.json", "recommended_standard": { "isTrue": ["Browns", "Charity", "Cleveland"], "isFalse": ["Sports", "football", "Cincinnati"] } }, { "name": "composite", "host": "combined.instance", "submitted_date": "4/15/2024", "uuid": "this and everything above can be removed if inside a video.json", "recommended_standard": { "isTrue": ["fundraiser", "Charity", "ALS"] } }, { "name": "Troll", "host": "wrong.info", "submitted_date": "6/9/0420", "uuid": "example", "recommended_standard": { "isTrue": ["sack", "ballz"] } }, { "name": "GoodFaith_but_wrong", "host": "other.instance", "submitted_date": "1/14/2023", "uuid": "example", "recommended_standard": { "isTrue": ["Browns", "NFL", "football"] } } ] }

So I started by doing research and by research I mean watching two videos on YouTube about basic recommendation algorithms.

I did watch a 30 minute video on Netflix software engineer talking about using machine learning and complex matrix and these bandit style machine learning algorithms to recommend TV shows/movies really the base conclusion is that there's a 50% improve over doing all these complex things over their baseline measurement. Baseline will mean traditional pre neral network based algorithms.

The way I interpret it is that basics take you a long way and all the basics are is just organizing any peertube video into a vector and people watching into a vector as well. The idea would be that which videos are more similar to each other would be good recommendations if a watcher watch one of those videos, or if they didn't like it don't recommend any videos similar to that. Once these videos get vectorized then the watcher's vector can be updated in a basic way more watch time mean its more of what they want and a like would give it a boost, or comment could boost multiplier.

I'd say that the watcher's vector can be stored locally while videos vector is public. It will be a while to figure out a function/algorithm to adapt to watcher. Does the watcher taste change, do they multiple things , should the algorithm adapt fast or slow as new videos come in, novelty/consistency. I don't expect this problem to be solved anytime soon , but the recommendation algorithm will simply evolve and split as to have their own unique benefits and drawbacks.

To start foundation is to start a standard for video vector. Video can be quantified and qualified. There's only a few measurable quantities like video length and existing views. Qualitative attribute of videos like "is it a cooking tutorial, "is it a sports commentary ", or "is it a Livestream VOD" are going to require that the vector be stored in a format that can adapt to the expanding number of dimensions the quality a peertube video can have. Next issue is measure qualities to an actual number is something sports related or sports adjacent would a 1 mean yes or would a 0 mean neutral/agnostic or no.

The last simplist issue would be communicated the algorithm that updates the watcher's vector since that can be done via updates from peertube server or GitHub

I hate how if you search trans on peertube (at least on .wtf) it’s straight up porn, like I’m tryna find relatable content, not wank off (update, so porn is against peertube.wtf’s rules so I went through and reported them)

"The Fediverse has its own full-fledged YouTube alternative that possesses some unique superpowers thanks to ActivityPub. In the previous article in this series I covered what it’s like to use PeerTube from the point of view of a casual user:

Today’s post will discuss: how to use the software as a video creator – and its advantages over video hosting platforms by Big Tech companies."