arthurdelerue

You can try our tool for example: https://KWatch.io

It monitors keywords mentioned on X, Linkedin, Facebook, Youtube, Quora, Reddit, and YC.

More details about social monitoring on Facebook here: https://kwatch.io/monitoring-keywords-facebook-kwatch

If you need to get instant reply notifications and you do not want to refresh manually, you can use KWatch.io: that's a platform that I developed that monitors HN.

https://kwatch.io/monitoring-keywords-hacker-news-kwatch

You can give it the URL of a specific post or comment and it will notify you when new comments are made in the thread.

I hope it is useful.

We use Whisper Large on NLP Cloud (https://nlpcloud.com/home/playground/asr). It works very well and it's simple to set up in my opinion. If you have a batch to process you could simply subscribe to their pay-as-you-go plan for a couple of weeks/months maybe?

That looks like a useful tool. Identifying actual competitors is not as easy as it sounds, especially when one wants to start a new business. As far as I'm concerned I launched several SaaS businesses and everytime I've only been able to have a clear view of the competition after at least 6 months...

And then once entrepreneurs find their competitors on your platform, they can track them on social media thanks to my platform: https://kwatch.io :)

Good luck with the launch!

I'm not sure why you say that current video transcriptions are bad. I use Whisper on NLP Cloud for video transcription (https://docs.nlpcloud.com/#automatic-speech-recognition) and it works very well.

As far as I understand, video transcription is a no-brainer as long as you install ffmpeg.

For my SaaS I use https://kwatch.io which is equivalent to F5bot. The free plan is less generous than F5Bot, but on the paid plans you can monitor Twitter, Reddit, Quora... These days getting organic traffic from Google seems to be harder and harder, so such platforms are good customer acquisition alternative solutions in my opinion.

NLP Cloud is a good example of a smaller provider that proposes a good Whisper implementation (no downtimes and relatively cheap): https://nlpcloud.com/nlp-automatic-speech-recognition-speech...

Social media like Reddit, Hacker News, Twitter, etc. contain tons of genuine discussions that you might want to analyze automatically with sentiment analysis. For example you might want to monitor what people say about you, your product, your competitors, etc.

This is an article I just wrote that shows how to implement such a sentiment analysis pipeline using the following steps:

- Implement social media listening

- Integrate the data in your system with an API webhook processed in Python/FastAPI

- Analyze the sentiment thanks to generative AI models like GPT-4, LLaMA 3, ChatDolphin, etc.

I hope you will find it useful. If you have some comments about how to improve this pipeline I would love to hear them!

I agree, except about this statement: "it wouldn’t be too difficult to port most models to Jax"

--> We tried such ports at https://kwatch.io (the company I work for), and it appeared to be much harder than expected (at least for us). I don't think so many people are capable of porting an LLM based on PyTorch + GPU to Jax + TPU.

I agree with that, and I'm not sure they'll be able to improve the stack dramatically by themselves without the open-source community being more involved.