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Absolutely love the idea! Although I'll wait for a while for reviews, congrats on the launch!


A quite complete and not so much focus on meta Geoguessor's guide can be found here if someone interested: https://somerandomstuff1.wordpress.com/2019/02/08/geoguessr-.... This single document is fantastic!


IMO, this is the best course to start with. It has everything you need from theory to pratical code. Very high quality stuff.



Great! I've just watch Loving Vincent and this show up.


For those that don’t know, “loving Vincent “,it’s a movie done with paintings. It’s kind of spectacular and amazing. I remember and older movie called “Vincent and Theo”.

https://en.m.wikipedia.org/wiki/Vincent_&_Theo

Theo being Van Gogh brother who would send him money and who corresponded with letters very frequently.

The Rodin museum in Paris had a couple of VanGoghs which I was instantly drawn to. They fly a little under the radar as they’re not sculptures.

https://en.m.wikipedia.org/wiki/Musée_Rodin


This will be very useful! I missed a lot of interesting stories in HN just because the title seems unrelated to me.


Thanks, I feel your pain! The dehyped title is mostly an improvement.


Their ASR model is Conformer trained on 1.1M hours, so the result should be better than Whisper. From their pricing page, with ~ length of a meeting, input size 15000 tokens (60 minutes audio file), output size 2000 tokens (1500 words), LeMUR default, the price estimate is $0.353, which is I think a fairly good price. This tool can save a lot of time for a secretary, even replace them. But I think sending your meeting data is still quite risky.


Comparison by competitor but it’s believable IMO. Basically about the same performance as whisper:

- https://deepgram.com/learn/nova-speech-to-text-whisper-api

Not surprising though as at this level all these options are starting to be leveled by inconsistencies in manual groundtruth. Conformer alone also isn’t the most powerful architecture out there for speech. This is also slower than, say running a large k2 zipformer via onnx on cpu.

Also if you have a small shop at this point you can do all of this yourself with whisper large v2 on a single 16gb gpu via some tweaking of https://github.com/guillaumekln/faster-whisper and an OSS LLM.

Interesting stuff but I think margins in this space are getting ready to simply vanish.


Deepgram will correlate the text in your transcription with the timestamp where that was uttered. This is really really impressive and useful.


Learning needs effort, and that's pretty clear, but not many folks truly understand this. Particularly when it comes to reading, some individuals only focus on the quantity of books they consume rather than the insights they can gain from them. Personally, I prefer using my Kindle to read, where I highlight noteworthy points and then transcribe those highlights into Notion. I'm not sure if writting down the highlights is better than speaking them out, but I find it to be quite effective.

The concept of a "learning box" sounds interesting, and it would be great if there were an extension for it.


Lost at Rule 24. Really shouldn't ignore the 1 minutes feed notice ^^ Fun game btw.


Lost there too doing the opposite... I tried to get ahead of 3 per minute by creating a nice stockpile. Dead from overfeeding.


Cool! It seems to work well with slow songs, the transition is very smooth


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