Implementations across cloud providers are going to be different, and I don't know how AWS vs GCP vs Azure is handling failure, so now it's your responsibility.
Now the problem has grown from just write a few lines of bash script to, "create a script that can handle failure and reverts it so a known state", this is a more complex problem than just creating a resource. And now multiply this for all different resources, EC2, AKS, RDS, Security Groups ... and keep up with the API.
And if somebody joins your team, and wants to contribute to the solutions, they're going to have to understand the codebase.
To get data in Terraform you have outputs and you can display the data.
Terraform helps you to have a unified way to manage your resources, sure the bash scripts works for you, but what happens if you leave the company? Somebody else has to maintain your shell script.
What happens if somebody else is changing the infrastructure and they're not familiar with your shell script, they need time to dig in to figure things out and then update it, and in best case test it.
And you need to keep your scripts up to date, you need to build in fault tolerance, you need to think how you're going to deploy new resources. How are you going to handle destroying resources?
And on top of that you also need to learn the cloud Provider CLI tools or API to know what kind of calls to execute.
It just provides a standardised way to manage your infra.
Can you elaborate more on the TTS? Did you prerecord fragments (how many did you actually do?) and you just stich them together? So there is a may.mp3, 22.mp3 and your scripts just puts them together?
As a Solutions Engineer I've talked with a lot of companies, we're selling software to enterprises. A lot of prospects take Gartner as a starting point and evaluate multiple solutions from there.
Email market is massive, so carving out a tiny niche could be enough to have a profitable product. There was recently a thread about missive https://news.ycombinator.com/item?id=26713949
weren't they VC founded from the start? If I remember the story correctly, they didn't release a product for (1-2?) years while having multiple developers in the bay area.
A little bit off topic, we were actually thinking about implementing federated learning for a client but in the end didn't went through with it, I've just read about it in a research environment.
What are your use cases for it, are you using it commercially or just for research?
Thank you for providing such a great tool, I'm excited to try version 2.0. I've also played around with Prodigy. SpaCy was my start in NLP, I really hope it is going to stay around.
We've developed a great product for our customer with SpaCy, it wouldn't be possible without SpaCy.
Word embeddings are not just useful for text, they can be applied whenever you have relation between "tokens". You can use them to identifying nodes in graphs that belong to the same group[0]. Another, in my opinion, really interesting idea is to apply them to relational databases[1], you can simply ask for similar rows.
It's a interesting article but the author didn't really provide good arguments why I should stop using w2v.
Now the problem has grown from just write a few lines of bash script to, "create a script that can handle failure and reverts it so a known state", this is a more complex problem than just creating a resource. And now multiply this for all different resources, EC2, AKS, RDS, Security Groups ... and keep up with the API.
And if somebody joins your team, and wants to contribute to the solutions, they're going to have to understand the codebase.