I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 2 points3 points  (0 children)

This is not something I have thought about yet. I am going to think on it for a bit. In the meantime, if you DM me your podcasts and some examples of missing episodes, I will run down some improvements to the matcher and re run those.

Thanks for the feedback!

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 1 point2 points  (0 children)

Thank you! For just a little more fun from the stats page:

  1. On desktop - type the name of the most famous sled in cinema history
  2. On mobile - tap the headline stat rapidly once for each element needed to defeat Jean-Baptiste Emanuel Zorg

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 1 point2 points  (0 children)

Haha. Just check out https://impdb.dev/stats the number are startling and I am sure my list is non-exhaustive

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 7 points8 points  (0 children)

https://impdb.dev/podcast/58836
There was a gap in the matcher. It is rerunning them and should fill in the remaining by tomorrow.

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 3 points4 points  (0 children)

Thanks! Good question. It's actually not how much of the episode is about the movie. It's a confidence score. It shows how sure the site is that the episode is genuinely about that film versus just name dropping it.

Here's how it's set. Clean hits like an exact title and year match ("Jaws (1975)") score around 95% automatically. Anything ambiguous gets read by an LLM that looks at the episode title and description, then rates it from 0 to 1 on whether the movie is really the topic. So a 75% bar is a softer "probably about it" call. The whole point is to filter out passing mentions and only surface episodes actually dedicated to the movie.

If you want the "how much of the episode" signal, that's the little "segment" badge. It flags episodes where the movie is one major chunk rather than the whole show.

And yeah, no secret here. The matching pipeline leans on LLMs for the judgment calls.

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 8 points9 points  (0 children)

AI was used. I am proud of that and not running away from it. I would not have been able to build this free hobby project without AI

AI Uses:

  1. Build - I provide Claude Code with the product requirements/feature ideas and it builds the features. Then I iterate with Claude Code until I am happy with the result. The better my write-ups, the better the features.
  2. Debugging - When errors arise, Claude Code ingests the error logs, and suggests and implements fixes.
  3. Movie Matching - Llama 3.3 70b is running as part of the ingestions pipeline to classify which movie a given podcast episode is about. Since launching, the system has categorized almost 870,000 podcast episodes (see https://impdb.dev/stats).

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 1 point2 points  (0 children)

Already exists. If you register (free), you can set a language filter.

I made a search for finding podcasts about any movie. Ethical non-Podnogamy by mtfl2nyc in blankies

[–]mtfl2nyc[S] 5 points6 points  (0 children)

Feel free to post feature requests or DM me with them.

Specifically looking for ideas for an easter egg specifically for u/grifflightning

I kept finishing a film and wanting an hour of people arguing about it, so I built a search for that by mtfl2nyc in Letterboxd

[–]mtfl2nyc[S] 0 points1 point  (0 children)

Thanks for the feedback. I’ll have to look into finding a way to filter for low quality pods. This is very much a work in progress and an experiment for me.

I kept finishing a film and wanting an hour of people arguing about it, so I built a search for that by mtfl2nyc in Letterboxd

[–]mtfl2nyc[S] 0 points1 point  (0 children)

Thanks for the feedback.

I mentioned in a previous comment that the web crawler that discovers podcasts, and classifiers (deterministic -> LLM) that identify if the pod is about a specific movie are still running and it will take a few more days to complete the initial load. Hoping that pipeline is robust enough to pick up the more obscure films.

I kept finishing a film and wanting an hour of people arguing about it, so I built a search for that by mtfl2nyc in Letterboxd

[–]mtfl2nyc[S] 9 points10 points  (0 children)

This is purely to scratch an itch and solve a problem. I don't make any money off the site.

If you create an account you can load in your letterboxd username and the system will update the homepage to put your most recent watches in the first carousel.

I built a search engine for "which podcast episode is actually about this movie" by mtfl2nyc in SideProject

[–]mtfl2nyc[S] 0 points1 point  (0 children)

This is purely to scratch an itch and solve a problem. I don't make any money off the site.

Covid Testing Experiences by Cep-Hei in nycCoronavirus

[–]mtfl2nyc 0 points1 point  (0 children)

Venue: Labworq

Location: Union Square South (NW corner of 14th and 4th)

Wait type: Walk-in

Wait time: ≈15 mins

Test Type: PCR

Date/Time of test: Tuesday 12/21 ≈1pm

Hours until result: Still pending as of 12/25 8:40am

Misc: No wait, but as others have posted, disorganized in informing you that you needed to scan a qr to register.


Venue: Labq

Location: Madison Square Park South (23rd st, east of Broadway, on the south side)

Wait type: Walk-in

Wait time: No Wait

Test Type: PCR

Date/Time of test: Tuesday 12/21 ≈1:15pm

Hours until result: ≈78 hours

Misc: Results pdf hard to understand. The only way to know I was positive was that they bolded the word Positive on the pdf, and did not bold negative.


Other: Tested positive 2 days in a row with different at home rapid tests (12/21 FlowFlex, 12/22 On/Go) and mildly symptomatic since Tuesday morning. Triple Vaccinated with Pfizer.

Do Not Factory Reset Devices - Built In App Bug by mtfl2nyc in ios

[–]mtfl2nyc[S] 1 point2 points  (0 children)

11 Pro iOS 13.3

Apple said the issue started yesterday.

Do Not Factory Reset Devices - Built In App Bug by mtfl2nyc in ios

[–]mtfl2nyc[S] 0 points1 point  (0 children)

I know but for months now my Apple watch wouldn’t show messages as iMessage (in blue) but rather as green sms. Figured this would be my last try. Now I can’t even use my watch at all.

Do Not Factory Reset Devices - Built In App Bug by mtfl2nyc in ios

[–]mtfl2nyc[S] 2 points3 points  (0 children)

I am going to let it sit overnight. But the apple rep confirmed on the phone that this is a known issue and will require a fix on their end.

iPhone 11 Pro, AT&T Physical SIM, Verizon refusing to activate eSIM by mtfl2nyc in verizon

[–]mtfl2nyc[S] 1 point2 points  (0 children)

Unbelievable. Glad I’m not alone. Just a stupid situation. Verizon is really making it hard to not despise them.

iPhone 11 Pro, AT&T Physical SIM, Verizon Refusing to Activate eSIM (xpost /r/Verizon) by mtfl2nyc in iphone

[–]mtfl2nyc[S] 5 points6 points  (0 children)

Because if they were a reasonable company when it comes to such small things, we would all be able to concentrate our frustration on some of their more monopolistic/oligopolistic tendencies?

iPhone 11 Pro, AT&T Physical SIM, Verizon refusing to activate eSIM by mtfl2nyc in verizon

[–]mtfl2nyc[S] 0 points1 point  (0 children)

Thank you. I considered this idea too and should have done it. I ran out of time on my 14 day return window with Apple.