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Comments

• Dran_Arcana@lemmy.world ⁨2⁩ ⁨days⁩ ago

  First instinct: being an app gives me over-permissive data collection scam vibes. I will not be installing it even though I might otherwise find a website of similar capability useful.

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    That’s a fair concern, and I understand the hesitation.

    This app does not request broad or unrelated permissions. It doesn’t require accounts, contacts, location, or background tracking.

    The reason it exists as a native app is mostly performance and offline UX, not data collection. That said, a lightweight web version is something I’m considering for users who prefer not to install apps.

    Thanks for explaining your perspective — it’s useful feedback.

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    • Dran_Arcana@lemmy.world ⁨2⁩ ⁨days⁩ ago

      No offence: but the problem is an app forces me to trust you; a website does not. I have toghter and easier control over a web request than I do over an app, and even if an app doesn’t have these permissions today, an update or an update after a sale could trivially and silently introduce them.

      A website is obvious if the deal changes-- you put up a login wall to harvest data; I stop using the site. You put trackers and ads into the UI; I block it at the DNS level.

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    • frongt@lemmy.zip ⁨2⁩ ⁨days⁩ ago

      Even your comments stink of chatgpt

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• Auster@thebrainbin.org ⁨2⁩ ⁨days⁩ ago

  I'm not its target audience (not much of movies fan), but considering I made sheets for similar uses for music and games, it makes me think:

  • couldn't it be fully offline? More reassurance of no data collection and it should keep being useful virtually forever, specially if physical media and DRM-free movies and series get traction.
  • instead of an LLM, why not a local database with a tag or like/dislike system, with the system having a bit of randomness for suggestions to have an algorithm?
  • in line with the first point, if an LLM is a must, why not have a local one trained specifically on movies and series?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    Thanks, those are very thoughtful points.

    Offline/local approaches make sense, especially for privacy-conscious users or those using DRM-free/physical media.
    A local database with tags and randomness could definitely handle many cases, and it’s something I’m exploring for future versions.

    Using an LLM is mainly for flexibility and to handle the variety of moods and contexts without requiring the user to manually tag everything.
    A specialized, local model trained only on movies/series is an interesting idea, and something I would consider as a lightweight alternative in future iterations.

    Really appreciate this kind of detailed, constructive feedback.

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• not_me@piefed.social ⁨2⁩ ⁨days⁩ ago

  No thanks,I’m gone

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• db0@lemmy.dbzer0.com ⁨2⁩ ⁨days⁩ ago

  Are you harvesting and/or selling the user data you collect for this app?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    No.

    The app does not sell user data and is not designed around data harvesting.

    It only uses the minimum information required to generate a recommendation (e.g. mood or preference signals entered by the user at that moment). This data is processed for functionality, not for profiling or resale.

    There is no user account system, no cross-app tracking, and no third-party data brokers involved.

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• Retro_unlimited@lemmy.world ⁨2⁩ ⁨days⁩ ago

  AI uses so much power just to give a slop answer, VS having an actual algorithm providing useful info for a fraction of power.

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    That’s a fair point.

    The AI approach was chosen mainly for flexibility — handling various moods and contexts without requiring the user to manually tag or categorize everything.

    A local algorithm with tags and some randomness could definitely achieve a lot of the same functionality more efficiently, and it’s something I’m considering for future iterations, especially for offline or low-power scenarios.

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• Ulrich@feddit.org ⁨2⁩ ⁨days⁩ ago

  No endless lists. Just a fast decision.

  I mean I could click on the first title on Netflix and quit scrolling. But it’s not what I want to watch. That’s why I scroll. You’re suggesting your app knows exactly what I want to watch, every time, based on some very basic input, and I don’t believe that, and I’m not willing to install it to find out.

  The problem with Netflix (and every other platforms) is that they have this massive library of thousands of titles but they just show you the same fucking content over and over again.

  If you had something that simply surfaced different titles, I could see that alone being useful.

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    I understand completely, and that’s a very valid point.

    The app isn’t trying to perfectly predict every choice, and it’s not a magic solution. Even a few quick inputs won’t capture every nuance of a person’s taste.

    What it does aim to do is surface options that the user might not see otherwise, breaking the “same content over and over” pattern you mentioned.

    It’s meant to help reduce endless scrolling and decision fatigue, not replace personal taste or curiosity.

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• db2@lemmy.world ⁨2⁩ ⁨days⁩ ago

  Does it have what plants crave?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    Hopefully the right kind 🙂

    The goal isn’t more content or features, but reducing decision fatigue and getting people to an actual choice faster. If it saves time and removes friction, that’s the value.

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• Zwuzelmaus@feddit.org ⁨2⁩ ⁨days⁩ ago

  suggests what to watch

  From where? From my own collection on my harddisks or from the Big Tech streaming services?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    Recommendations are based on publicly available movie and TV metadata, not on access to personal collections or private libraries. The app does not scan local files or integrate with users’ hard drives.

    As for distribution: Google Play was chosen simply for reach and ease of deployment at this stage. There’s no technical dependency that would prevent a more open distribution model.

    An F-Droid or fully open-source-friendly approach is a reasonable direction, but it requires additional work and tradeoffs that I haven’t tackled yet. That’s a fair point.

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• deltaspawn0040@lemmy.zip ⁨2⁩ ⁨days⁩ ago

  sigh

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• okwhateverdude@lemmy.world ⁨2⁩ ⁨days⁩ ago

  But can you give me a recipe for blueberry muffins? I need to bake some for my sick grandmother. Make sure all measurements are in grams and time units in seconds?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    Fair point — I could give a full blueberry muffin recipe in grams and seconds,
    and yes, I use AI to help draft things faster.

    That said, every answer is reviewed and adjusted by me personally before posting,
    so it’s not blind automation. I just use AI as a tool to speed up mundane tasks,
    not to replace actual thought or judgement.

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• xylogx@lemmy.world ⁨2⁩ ⁨days⁩ ago

  How could this possibly be better than Netflix’s recommendation engine or even just asking chatgpt?

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  • Qaf@lemmy.world ⁨2⁩ ⁨days⁩ ago

    It’s not trying to be “better” in terms of data scale or model sophistication.

    Netflix optimizes for engagement across a large catalog. ChatGPT is general-purpose and requires the user to frame the problem well.

    This app is intentionally narrower: – no browsing – no prompt writing – no rankings or long lists

    It’s optimized for fast decisions, not discovery depth. Different tool, different job.

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• async_amuro@lemmy.zip ⁨2⁩ ⁨days⁩ ago

  If every part of a ship is replaced one by one, is it still the same ship?

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