rjusher

Not so obvious alternative uses to certain gadgets to improve productivity

My project for this quarantine has been https://iober.com/ .

Yet Another Focus Music generator to solve many of my problems using other services throughout the years.

What works best for me to focus is a combination of many services that exists on the market, so at the end I had to tune into many web apps on my browser which hogged my memory and I had to pay for many subscriptions making it a really expensive solution.

So I created this service, is still a work in progress, but I have been running to polish everything during this quarantine, right now I feel it is at a 90%.

I do think it matters where and how you store your data, but I really like how he puts it simple, is not a battle between to opposing sides, simply there is a need for a data store that is not that structured, it is much simplier to prototype and achieve an mvp with a NoSQL Storage, but once you are in the real world you need to provide some type of guarantee to your users, in terms of data integrity.

In my opinion everybody needs to understand the differences and use each when it is the most convenient, use the best tool for the right job.

But maybe we will start to see some type of mixup between this two systems, because as he mentions in the article, OpenSource is driving the future of the databases, and due to this, the databases of the future would be more dynamic and more synchronized to the needs of the developer.

I do understand that .net is far more powerfull, far more everything, but the best tool not always is the one that conquers the world. I would truthfully love to see Microsoft bring WPF to other platforms.

So Kubernetes compliments Docker, but how it complements it.

I had tested Docker just for fun, thinking that maybe I could implement it in the way I work, and sure it is a super tool for developing (far better than Virtual Machines), but deploying was kind of nightmerish, for what I understood Docker wasn't at the time ready for being a deployment tool.

Does Kubernetes fixes or extends Docker in this way

I am really curious at how is the scene for .NET developers, with the rise of things like nw.js(node-webkit) and electron(github's atom shell).

I do know that the framework is stronger and more test proven but the traction this type of tools are earning is important.

Will the .net framework will conquer the heart of developers with the release of some of their projects as open source, and will create the best cross platform desktop development kit?

Or is it doomed to be a Windows only beutiful thing.

I might come across as ignorant but what is the relationship between Kubernetes and Docker, because when I was reading the article I tought of it as a Docker competitor, but further down in the comments, there is one that says they do different jobs.

And that confused me.

You are completely right, even though I would like to own a car for my daily use, and a big car for trips and etc, is not feasible, for me at least, so I have to buy the best of both worlds.

I disagree with the 1/4 of the time charging, with a solution like the Tesla's battery swap, it would be far less(I could be completely wrong in this).

What do you mean with not very green, I do know they carry chemicals inside, but the not very green part is the manufacturing or the battery itself.

But Tesla is also working in a big battery factory, and I read this plant is very green.

But how is the batteries less green than cars producing contamination. (The batteries could be worst, that's is something I haven't even considered)¿?

Would you recommend any approach or I should go undust my high school and college books in the search for study material. Or is this too basic material.

You are right it is not the best example of an open source project, and for what you say it neither is a good python project example. But is there any other place you can get the hold of working async with python, it may be hard, but you would learn a lot.

But maybe there are other async python projects that I don't know of. If you know of any please post them, I would also like to learn more about the subject.

One thing I don't understand is the position of the big car makers(BMW, MB, AUDI) being a passive observer in this field.

Tesla is getting so much, that if any automaker that can deliver a car with half the specs of a model S, and keeping their model's prices as a mass produced car, would deliver a big punch to Tesla, and would greatly move the market forward.

Is it the investment necessary for building a network of charging stations?

I highly doubt it is because Tesla has more money for R&D than any other car maker.

Is getting a Model S, earns you the title of being an early adopter. Because I believe the market already shifted towards this type of vehicles, but I may be polarized, because I already desire an electric car.

This is like saying that the Formula 1, exists for nothing. This is the way to test all the engineering efforts, it also leaves that knowledge in the company for future and further improvements, which directly influences the design and execution of the Model 3.

But I have to agree that waiting for an affordable Tesla car is getting anoying, and they do need to create an income stream from a lower priced model for creating a sustainable company, and take advantage of the wow factor that still surrounds Tesla and evertything they make.

But they have also innovated in other areas like the house batteries, which for me was a surprise (for me it seems odd that Tesla makes batteries for the average household) but they are generating other income streams, so it seems that Teslas agenda is not only the Model 3.

I would also add

*Twisted (https://github.com/twisted/twisted)

For async python.

What I am struggling with, is the use case of a library like this.

Is this oriented towards gaming or substitude something like d3.js or is it has a simplier api, for the developer than other libraries. Or is it a library to showcase the cool stuff that can be done with new technologies.

I think the main difference and the selling point is that it is "renderer agnostic" but I don't understand the benefits of that.

But how do you become a good data scientist, instead of a technical person, that knows how to apply an algorithm in Python/R.

What I am trying to ask is how do you become good at setting your start point(formulate your hypotheses), communicating your insights and selecting which tools apply where, because if your are good at coding and have experience in things related to computer science you have the abilities to handle a dataset(SQL Knowledge) and the data tools(Python, Pandas, etc), but that doesn't earn you the title of data scientist.