HN user
The way to manage this is to make your code more modular, to factor out subcomponents, to share and have them critiqued separately. For example, in machine learning frameworks, autograd is a separate package for automatic differentiation. I actively post questions and answers for subcomponents on Stack Overflow.
There are still idiots on Github using Python 2 for new projects. These are usually academics who don't actually share any love for technology. Their code will never be used by anyone.
You should be getting ideas from actual open source projects that actually exist, not from imaginary/fake ones. There are numerous serving packages that actually exist and offer ideas.
You will get a lot more ideas from other software that actually do exist. I can easily write a fake blog post with fake thoughts giving you fake ideas, but that's not what you want; you deserve ideas that work.
System76 does sell laptops with the 1080.
Uber's software doesn't exist in the public realm, and so there is nothing to learn from it. It is imaginary fluff. I have posted a list of the ones that do actually exist in another comment. And you should've at least got a 1080.
Are you serious? There is no code for us, so there is no tool! The article is as good as fake news.
Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me.
Meanwhile, here is a list of open source ML deployment packages:
https://github.com/oracle/graphpipe
https://github.com/eliorc/denzel
https://github.com/tensorflow/serving
https://github.com/ucbrise/clipper
https://github.com/tensorflow/adanet looks effectively modular to me, although I wouldn't consider it the epitome of the same. One needs to be able to merge, move, and remove modules too.
Learning Quickly to Plan Quickly Using Modular Meta-Learning https://arxiv.org/abs/1809.07878
Automatically Composing Representation Transformations as a Means for Generalization https://arxiv.org/abs/1807.04640
Modular meta-learning https://arxiv.org/abs/1806.10166
Omega: An Architecture for AI Unification https://arxiv.org/abs/1805.12069
Cortex Neural Network: learning with Neural Network groups https://arxiv.org/abs/1804.03313
Evolutionary Architecture Search For Deep Multitask Networks https://arxiv.org/abs/1803.03745
Is there even a software to go with this, or is it all talk? Where is the code?
I completely agree. For a DataStore in Python, I may often use a Pandas DataFrame or such. For random label-based access it can have an index too.
Unfortunately, when asked to design an OOP architecture in a job interview, if you don't adhere to its religious enterprisy notions, you can risk failing the interview.
Is he really? Why then did he fire managers who evidently preferred quality over speed? Time and again in 2018, Elon has proved to make bad decisions.
It's collisions that trigger a runaway Kessler syndrome. Collisions have happened before and they are a serious concern. Just ask the inhabitants of the space station, for example. Probability dictates that sooner or later, something or someone will run into you. I don't think Starlink is doing all that they can to safeguard their satellites, especially considering that managers were fired for not prioritizing quality over speed.
For a better article, definitely read https://mashable.com/2018/03/06/starlink-spacex-satellites-o...