ujal

See also https://github.com/davidkimai/Context-Engineering

Are there similar projects for the Ethereum blockchain? Imagine a censorship resistant Darknet.

You also have hdom & co. coming from the i-am-not-a-framework side - https://github.com/thi-ng/umbrella/

Production size and evaluation times are a thing with react apps.

Whats the canonical way to deduce the types in Rust from JSON? JSON Schemas?

I will probably never understand IQ and its purpose for real life problems. It is either me who doesn't get it or all the people out there who lack introspection to understand what exactly it was that enabled them to grasp some new concept.

Perhaps you don't give a f* anymore?

"If you wish to derive a commercial advantage by not releasing your application under the GPLv3 or any other compatible open source license, you must purchase a non-exclusive commercial SOD license. By purchasing a commercial license, you do not longer have to release your application's source code." --

~400 (Machine Learning) vs ~900 (Frontend).

Apparently 30% of Web Devs have < 5 years experience. Time to start specializing in some other domain.

There are now more ML related jobs on stackoverflow than AngularJS.

Thats what you get with Google TPUs on reference models. The ImageNet numbers are from RiseML, the rest is from here - https://youtu.be/zEOtG-ChmZE?t=1079

This is what we know from Google about Duplex:

"To train the system in a new domain, we use real-time supervised training. This is comparable to the training practices of many disciplines, where an instructor supervises a student as they are doing their job, providing guidance as needed, and making sure that the task is performed at the instructor’s level of quality. In the Duplex system, experienced operators act as the instructors. By monitoring the system as it makes phone calls in a new domain, they can affect the behavior of the system in real time as needed. This continues until the system performs at the desired quality level, at which point the supervision stops and the system can make calls autonomously." --

I don't get the sentiment of the article either. I can't speak for researchers but software engineers are living through very exciting times.

  State of the art in numbers:
  Image Classification - ~$55, 9hrs (ImageNet)
  Object Detection - ~$40, 6hrs (COCO)
  Machine Translation - ~$40, 6hrs (WMT '14 EN-DE)
  Question Answering - ~$5, 0.8hrs (SQuAD)
  Speech recognition - ~$90, 13hrs (LibriSpeech)
  Language Modeling - ~$490, 74hrs (LM1B)

"If you think Deep (Reinforcement) Learning is going to solve AGI, you are out of luck" --

I don't know. Duplex equipped with a way to minimize his own uncertainties sounds quite scary.

Lisp is perfectly suited for this task due to the minimal and familiar syntax to non-programmers. Math should be taught in prefix notation if u ask me.

I've grokked programming only after being exposed to Lisp. Before Lisp, learning how to programme seemed like a never ending exercise. After, I realised I already know everything I need to know - data in, data out…

I completely agree. Machine Learning is on the way to become a field like Web Development. There is a huge supply/demand gap that will only get wider.

"The Netherlands used to keep track of people’s religion as part of the public records. The intent was noble as always: by keeping track of how many Jews, Catholics, and Protestants there were in a city and its different parts, you would be able to plan for an appropriate amount of synagogues, Protestant churches, and Catholic churches, their proportion to one another, and so on.

Then, World War II came around.

There were almost no Jews at all in the Netherlands after World War II. According to Wikipedia, less than 10% survived (14,346, compared to an earlier population of 154,887). As it turns out, it was very convenient for the… new administration… to have access to the collected data, and it was indeed used against the citizens, as it always is in the end." --

"The 207,130 images collected were reduced to the 108,312 OCT images (from 4686 patients) and used for training the AI platform. Another subset of 633 patients not in the training set was collected based on a sample size requirement of 583 patients to detect sensitivity and specificity at 0.05 marginal error and 95% confidence. The test images (n = 1000) were used to evaluate model and human expert performance." --

A curated list of awesome remote jobs and resources - https://github.com/lukasz-madon/awesome-remote-job

In my case the verbosity of Typescript or Flow stops me from adopting it. Still waiting for Elm/Haskell-style type signatures for Javascript.

In this case I was the child of my parent. But given that people over there are selling their cattle with bitcoins it seems not too far off.

In Zimbabwe you can.

It is possible to hash the data and make that hash part of the transaction [0].

[0] https://arxiv.org/abs/1502.04015

Well, good luck getting even the real inflation rate from a corrupt government.

It does use bitcoin to verify the transactions. Point is, bitcoin - currently being the biggest blockchain deployed - is more trustworthy than most governments and therefore most currencies.

I am not an economist though and regulations seem to be necessary in the real world. Any ideas as to what technology, giving widespread adoption, would make them obsolete?

At the minimum you can't fake its value.

Sure, but not everyone has the privilege of living in a first world country. See for example the Republic of Georgia utilising blockchain tech to secure real estate transactions [0]. Now I am actually less afraid of investing my money as I don't trust their government.

[0] https://www.forbes.com/sites/laurashin/2017/02/07/the-first-...

Countries and their economies collapsing now and then are not that rare.

cd/m^2?

On the other hand skills like object permanence require the development outside the womb. I am sure nature would have found a way if it would be possible otherwise.

Same with me and I wish I would have been exposed to philosophy earlier in my life. Science provides us with the tools to denounce certain religious facts but it can not give us our confidence back. This also explains the rise of pseudo-intellectuals (people publicly arguing outside of their field of expertise) who are filling this gap with nonsense.