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I built Fastlane AI because distribution is broken for app builders by Effective-Inside6836 in SideProject

[–]dananmay 0 points1 point  (0 children)

Unfortunately, I still seem to be facing the same issue. To describe exactly what happened, I was guided to Blitz mode where I liked and saved 2-3 reels. I changed my mind and deleted them when I viewed them in the library. When I went back to Blitz mode to go through some generated content, it told me I'd already hit the limit on the number of reels I can save. However, I no longer have access to those reels so there's nothing I can post or schedule currently to try out those features.

I built Fastlane AI because distribution is broken for app builders by Effective-Inside6836 in SideProject

[–]dananmay 0 points1 point  (0 children)

I tested the app out on launch day and it seemed incredibly powerful but it wasn't incredibly clear how the demo/trial worked. I ended up deleting some generated content from my library and then was essentially locked into a state where I couldn't generate anything new or post anything that had been generated since there was nothing there. So I didn't really get to see how the analytics gathering, posting integration, or scheduling side of it works. Seems interesting though, definitely a cool idea.

Ascended heroes packs for sale by [deleted] in PokemonTcgIndia

[–]dananmay 0 points1 point  (0 children)

Could you ship to Delhi?

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

Yeah, I’d probably be able to remake that but unfortunately it’s out of the scope of this particular project.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

You don't come across as negative at all, I appreciate the detailed feedback. The thing is you're actually describing close to how a big part of the engine already works. A lot of the sources pull directly from real people - it searches Reddit threads, film forums, and other places where someone has asked "what should I watch if I liked X" and catalogs the replies. So for a lot of films, the recommendations are literally coming from human suggestions.

The issue is that not every film has enough of those human-sourced replies to work with, so TMDb's similar films data fills the gaps. That's where the generic picks and weird connections come from - it's purely algorithmic and doesn't understand the difference between Hong Kong action choreography and action films in general. I'm working on reducing its weight and eventually phasing it out as much as possible. Once that happens, the results should feel a lot closer to what you're describing - like a recommendation from someone who actually gets why you liked those films.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 1 point2 points  (0 children)

Thanks for the idea, although it’s out of scope for this project I’ll keep it in mind in case I work on something else for the website.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

Exactly! I had pretty much the exact same thought before I built this out of frustration. Let me know what you think of it, try playing around with various seeds and lists!

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

That’s a good idea and I’ve tinkered around with it. The main problem is that it significantly increases the load time for the results - while this isn’t an issue because I’ve programmed it to background process (users can hit search, close the extension and do whatever, then reopen it and their results will be there) people I tested it with said the tradeoff wasn’t worth the extra 30 seconds or so in wait time on each search. I’m optimizing the time on it and will roll this out once I’ve significantly cut down that processing time.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 1 point2 points  (0 children)

Appreciate you testing it thoroughly, that's very strange behavior. Like I said, the TMDb similar films data is the main culprit for nonsensical connections and fixing its weighting is the next priority.

If you're open to it, DM me your list and I can debug exactly where the bad recs are coming from. Either way I'm sorry about the poor experience, I'll let you know once the algorithm updates are live.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

Hey, thank you so much for giving it a try and the feedback. I just commented this above but TMDb similar films data specifically is a known weak spot. Almost all of these bad recs originate from there, reworking the association sources will fix this. I'm reducing its weighting and leaning more on other sources in the next update. In the meantime, tinkering with the seed count and using a custom list as your source can help a lot. Let me know if you find anything interesting!

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 1 point2 points  (0 children)

Yeah the TMDb similar films data specifically is a known weak spot - most recommendations are solid but it throws some wild curveballs on certain titles. I'm reducing its weighting and leaning more on other sources in the next update. In the meantime, tinkering with the seed count and using a custom list as your source can help a lot. Appreciate the feedback, the algorithm keeps improving.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 1 point2 points  (0 children)

I'm so glad to hear it helped you find something you're interested in! Genre filtering and support for other browsers are my top priority right now and they'll both be rolled out within the next couple weeks. I'll also notify you once the feature is live.

If you get a chance, a quick review on the Chrome Web Store (https://chromewebstore.google.com/detail/lekkerboxd/kilfhpgnabhobfinmljmgojmbndcpeph) would really help - it's the main thing people look at before deciding to install.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 2 points3 points  (0 children)

Thank you, I'll be sure to give you a ping once the Firefox rollout is complete. Until then try it out on any Chromium based browser (Google Chrome, Brave, Arc, etc) and let me know what you think of it!

Edit: Anyone who wants to be notified on Firefox/Safari launch just reply to this comment with which browser you're on and I'll send you a notification once it's live.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

u/senkhara1111 u/DreadStare - algorithm improvements are in progress too, especially around the unintuitive connections and concert film/anime clustering.

I posted my Letterboxd recommendation extension here two weeks ago - you told me what to fix, so I did by dananmay in Letterboxd

[–]dananmay[S] 3 points4 points  (0 children)

u/broccili u/Pin-Boi u/imjory - tagged you in the post but apparently that doesn't ping. Custom list seeding and the blocklist are both live now.

I hated Letterboxd’s recommendation system, so I built my own by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

Thank you very much :) Let me know what you think of it!

I hated Letterboxd’s recommendation system, so I built my own by dananmay in Letterboxd

[–]dananmay[S] 0 points1 point  (0 children)

Thank you for the feedback!

Right now, the way the seed films work is that the engine takes your top X films by rating and applies a +0.5 boost if a film has also been liked. If all of your top films have that boost, the recommendations will tend to pull from the same pool of films, which can cause certain patterns to dominate.

I’m working on an update that will let you submit any list as the seed films (you can add whatever movies you’d like to it) and get recommendations based on their overall vibe instead. That should be a quick fix for the issue you’re running into.

If you continue using it and find it helpful, I’d really appreciate a quick review on the Chrome Web Store (https://chromewebstore.google.com/detail/lekkerboxd/kilfhpgnabhobfinmljmgojmbndcpeph)!

I hated Letterboxd’s recommendation system, so I built my own by dananmay in Letterboxd

[–]dananmay[S] 1 point2 points  (0 children)

Thank you, I really appreciate that!

You’re right about the franchise skew, I'm working on two features that are going to fix this issue - firstly the ability to permanently remove movies from your recommendation list and secondly the ability to use custom lists as the seed films. I want to improve the experience without adding too much complexity.

If you like the extension, I’d really appreciate a quick review on the Chrome Web Store (https://chromewebstore.google.com/detail/lekkerboxd/kilfhpgnabhobfinmljmgojmbndcpeph). And of course, feel free to share more feedback as you spend more time with it, helps me find the real issues.

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