1. Dev publishes app with Google Admob integration to the Play Store.

2. Buys Google Ads to drive traffic to the app.

3. Google Admob bans his account for invalid traffic.

https://www.reddit.com/r/admob/comments/1vzg3fu/i_paid_googl...

I wanted to once again share my app that takes YouTube videos and turns them into little language lessons: https://lingolingo.app/

Recently I've been getting back into learning French using Duolingo, and I had this idea of matching videos from the app's collection to Duolingo lessons.

The matched videos are either instructional videos about the unit's grammar ("Learn to use ou and et") or cover the same vocabulary/setting ("Order at a café").

Example flow for Duolingo lesson 1, unit 1:

1. Finish the Duolingo lesson ("Order at a café")

2. Pick one of the candidate videos matched to that unit: https://lingolingo.app/french-course-companion/section/1/uni...

3. Paste the code in the app

4. Get exercises while watching the video, tailored to that Duolingo unit.

In the app there's a toggle to automatically jump between exercises, if you just want to watch the relevant parts. Or watch the whole video for full immersion and comprehensible input, even if you won't necessarily understand everything that's being said. It's all about training your ear and encountering the newly learned concepts in the wild.

Example guidebook: https://www.duolingo.com/guidebook/fr/1

Comments URL: https://news.ycombinator.com/item?id=49146615

Points: 1

# Comments: 1

If you're dealing with uid in -> uid out, where you're hoping to get the same uid out, intuitively the entropy would be greatly reduced anyways. Then the question becomes, are words conducive to keeping input->output consistent, given the way LLMs work (e.g. attention mechanism)? I could see it go either way, that's why I'm supporting the idea of running your experiment.

> Where UUIDs cost ~23 tokens and get hallucinated by LLMs, id-agent produces memorable word-based IDs at ~14 tokens with equivalent collision resistance.

Backfilling it further is definitely in the cards, I just want to stabilize the methodology first.

If a comment just mentions Opus without being more specific and in the absence of relevant context clues, it gets mapped to Opus Latest. So it's saying more about the model family than a specific version. Tbh I'll probably remove all "-latest" data points going forward, as I mentioned in another comment.

Searching for it on HN shows very few results, that's why it's not showing up in the analysis yet. But it might in the future, once it gains traction.

I'll keep an eye on it, thanks for bringing it up!

https://news.ycombinator.com/item?id=47911464

And it's probably a good idea to create a list of model release dates, so older comments can't accidentally map to models that weren't released yet.

I thought I'd keep these as a rating for model families rather than specific models. But tbh it's probably better to remove them, too confusing.

The context would be really nice to have, but reading the comments myself, it often just isn't very clear what exactly users are building or which programming language they are using.

I think analyzing more comments is promising. If you get enough data, you can generalize across use cases and get more meaningful ratings. The obvious lever is including more posts, although it might hit diminishing returns. I'll play around with it.

For the context, I want to try giving Gemini a "scratch pad", where it can note down strengths and weaknesses per model that it finds in the comments. Something like "some users say that model x is good for writing tests". Then on each run, I let it update the scratch pad and publish the results as more of a qualitative analysis.

For the wording, I'd like to keep a certain amount of click bait, sorry ;)

Edit: Done