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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.

This sounds really interesting, I'm not a prospect but want to learn more about it. Can I get in touch with you somehow?

How did you come up with the initial list of phrases? Did you do some kind of analysis of other horoscopes?

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.

Page up/Page down buttons?

Maybe try the vimium extension, d/u jumps half a page down/up

Here's the original post https://news.ycombinator.com/item?id=27016630 This guy is a genius

reddit and twitch also

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

According to their hacker one program they pay $7,700 for account takeover exploits

Do you have any examples of talks that you've edited?

Nearly all public repos of the devloper are using mainly JS, so I assume he's a JS dev and thus just uses the tools he knows the best.

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.

What kind of software would be needed

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?

How big was the initial investment? What your background?

Can you link some products that have your research as a basis?

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.

[0] http://www.kdd.org/kdd2017/papers/view/struc2vec-learning-no... [1] https://arxiv.org/abs/1603.07185

What exactly do you mean with "they let the models train their own word embeddings", can you elaborate more on this or are there any current papers about this topic?

It's an interesting topic, basically the question is who is responsible for the actions.

A somewhat related topic, an artist created a bot that randomly bought stuff from the darknet, drugs, fake shoes and a passport. But the artist did't get into any trouble.

According to Google the Stockholm area is 188km^2 with 760km bike lanes and NY is 789km^2 with 1609km bike lanes.

I'm currently working on something similar, these were my first two ideas:

Try to train your own word2vec model on a twitter dataset and then you could use the weighted tf-idf average of these vectors. You get a vector for each tweet, and tweets that are about the same topic should be next to each other. Then try clustering algorithms, you can use the cosine distance to find the nearest X tweets.

Second Idea would be to train doc2vec with twitter data.

Another worthwhile idea could be to use LDA, haven't tried it myself

This is an interesting topic, at my first job I've developed some tool to help our customer support, it's a topic which is quite interesting for me.

On what data do you train your models? Do you train them individually for each customer with their own data, or do you take the data of all your customers and train "universal" model?

Another question about the broader categories, are they defined by you? I guess you're doing some supervised learning. Is it possible for the customer to add own categories?

The last time I've really touched JavaScript is about a year ago, I'm just working with Python these days. Thinking back, I had 2/3 newsletters about JavaScript and whenever I've read them there was a headline "why X is better than Y", "Y has no chance against Z" "Good old X is better than Z,Y".

I was always feeling, that I'm missing out on something and that I needed to learn new things. Switching to python actually made writing code enjoyable again.