Bitcoin 2048 Faucet Game on Google Play Reviews Stats

A new whitepaper analysing the performance and scalability of the Streamr pub/sub messaging Network is now available. Take a look at some of the fascinating key results in this introductory blog

A new whitepaper analysing the performance and scalability of the Streamr pub/sub messaging Network is now available. Take a look at some of the fascinating key results in this introductory blog

Streamr Network: Performance and Scalability Whitepaper


https://preview.redd.it/bstqyn43x4j51.png?width=2600&format=png&auto=webp&s=81683ca6303ab84ab898c096345464111d674ee5
The Corea milestone of the Streamr Network went live in late 2019. Since then a few people in the team have been working on an academic whitepaper to describe its design principles, position it with respect to prior art, and prove certain properties it has. The paper is now ready, and it has been submitted to the IEEE Access journal for peer review. It is also now published on the new Papers section on the project website. In this blog, I’ll introduce the paper and explain its key results. All the figures presented in this post are from the paper.
The reasons for doing this research and writing this paper were simple: many prospective users of the Network, especially more serious ones such as enterprises, ask questions like ‘how does it scale?’, ‘why does it scale?’, ‘what is the latency in the network?’, and ‘how much bandwidth is consumed?’. While some answers could be provided before, the Network in its currently deployed form is still small-scale and can’t really show a track record of scalability for example, so there was clearly a need to produce some in-depth material about the structure of the Network and its performance at large, global scale. The paper answers these questions.
Another reason is that decentralized peer-to-peer networks have experienced a new renaissance due to the rise in blockchain networks. Peer-to-peer pub/sub networks were a hot research topic in the early 2000s, but not many real-world implementations were ever created. Today, most blockchain networks use methods from that era under the hood to disseminate block headers, transactions, and other events important for them to function. Other megatrends like IoT and social media are also creating demand for new kinds of scalable message transport layers.

The latency vs. bandwidth tradeoff

The current Streamr Network uses regular random graphs as stream topologies. ‘Regular’ here means that nodes connect to a fixed number of other nodes that publish or subscribe to the same stream, and ‘random’ means that those nodes are selected randomly.
Random connections can of course mean that absurd routes get formed occasionally, for example a data point might travel from Germany to France via the US. But random graphs have been studied extensively in the academic literature, and their properties are not nearly as bad as the above example sounds — such graphs are actually quite good! Data always takes multiple routes in the network, and only the fastest route counts. The less-than-optimal routes are there for redundancy, and redundancy is good, because it improves security and churn tolerance.
There is an important parameter called node degree, which is the fixed number of nodes to which each node in a topology connects. A higher node degree means more duplication and thus more bandwidth consumption for each node, but it also means that fast routes are more likely to form. It’s a tradeoff; better latency can be traded for worse bandwidth consumption. In the following section, we’ll go deeper into analyzing this relationship.

Network diameter scales logarithmically

One useful metric to estimate the behavior of latency is the network diameter, which is the number of hops on the shortest path between the most distant pair of nodes in the network (i.e. the “longest shortest path”. The below plot shows how the network diameter behaves depending on node degree and number of nodes.

Network diameter
We can see that the network diameter increases logarithmically (very slowly), and a higher node degree ‘flattens the curve’. This is a property of random regular graphs, and this is very good — growing from 10,000 nodes to 100,000 nodes only increases the diameter by a few hops! To analyse the effect of the node degree further, we can plot the maximum network diameter using various node degrees:
Network diameter in network of 100 000 nodes
We can see that there are diminishing returns for increasing the node degree. On the other hand, the penalty (number of duplicates, i.e. bandwidth consumption), increases linearly with node degree:

Number of duplicates received by the non-publisher nodes
In the Streamr Network, each stream forms its own separate overlay network and can even have a custom node degree. This allows the owner of the stream to configure their preferred latency/bandwidth balance (imagine such a slider control in the Streamr Core UI). However, finding a good default value is important. From this analysis, we can conclude that:
  • The logarithmic behavior of network diameter leads us to hope that latency might behave logarithmically too, but since the number of hops is not the same as latency (in milliseconds), the scalability needs to be confirmed in the real world (see next section).
  • A node degree of 4 yields good latency/bandwidth balance, and we have selected this as the default value in the Streamr Network. This value is also used in all the real-world experiments described in the next section.
It’s worth noting that in such a network, the bandwidth requirement for publishers is determined by the node degree and not the number of subscribers. With a node degree 4 and a million subscribers, the publisher only uploads 4 copies of a data point, and the million subscribing nodes share the work of distributing the message among themselves. In contrast, a centralized data broker would need to push out a million copies.

Latency scales logarithmically

To see if actual latency scales logarithmically in real-world conditions, we ran large numbers of nodes in 16 different Amazon AWS data centers around the world. We ran experiments with network sizes between 32 to 2048 nodes. Each node published messages to the network, and we measured how long it took for the other nodes to get the message. The experiment was repeated 10 times for each network size.
The below image displays one of the key results of the paper. It shows a CDF (cumulative distribution function) of the measured latencies across all experiments. The y-axis runs from 0 to 1, i.e. 0% to 100%.
CDF of message propagation delay
From this graph we can easily read things like: in a 32 nodes network (blue line), 50% of message deliveries happened within 150 ms globally, and all messages were delivered in around 250 ms. In the largest network of 2048 nodes (pink line), 99% of deliveries happened within 362 ms globally.
To put these results in context, PubNub, a centralized message brokering service, promises to deliver messages within 250 ms — and that’s a centralized service! Decentralization comes with unquestionable benefits (no vendor lock-in, no trust required, network effects, etc.), but if such protocols are inferior in terms of performance or cost, they won’t get adopted. It’s pretty safe to say that the Streamr Network is on par with centralized services even when it comes to latency, which is usually the Achilles’ heel of P2P networks (think of how slow blockchains are!). And the Network will only get better with time.
Then we tackled the big question: does the latency behave logarithmically?
Mean message propagation delay in Amazon experiments
Above, the thick line is the average latency for each network size. From the graph, we can see that the latency grows logarithmically as the network size increases, which means excellent scalability.
The shaded area shows the difference between the best and worst average latencies in each repeat. Here we can see the element of chance at play; due to the randomness in which nodes become neighbours, some topologies are faster than others. Given enough repeats, some near-optimal topologies can be found. The difference between average topologies and the best topologies gives us a glimpse of how much room for optimisation there is, i.e. with a smarter-than-random topology construction, how much improvement is possible (while still staying in the realm of regular graphs)? Out of the observed topologies, the difference between the average and the best observed topology is between 5–13%, so not that much. Other subclasses of graphs, such as irregular graphs, trees, and so on, can of course unlock more room for improvement, but they are different beasts and come with their own disadvantages too.
It’s also worth asking: how much worse is the measured latency compared to the fastest possible latency, i.e. that of a direct connection? While having direct connections between a publisher and subscribers is definitely not scalable, secure, or often even feasible due to firewalls, NATs and such, it’s still worth asking what the latency penalty of peer-to-peer is.

Relative delay penalty in Amazon experiments
As you can see, this plot has the same shape as the previous one, but the y-axis is different. Here, we are showing the relative delay penalty (RDP). It’s the latency in the peer-to-peer network (shown in the previous plot), divided by the latency of a direct connection measured with the ping tool. So a direct connection equals an RDP value of 1, and the measured RDP in the peer-to-peer network is roughly between 2 and 3 in the observed topologies. It increases logarithmically with network size, just like absolute latency.
Again, given that latency is the Achilles’ heel of decentralized systems, that’s not bad at all. It shows that such a network delivers acceptable performance for the vast majority of use cases, only excluding the most latency-sensitive ones, such as online gaming or arbitrage trading. For most other use cases, it doesn’t matter whether it takes 25 or 75 milliseconds to deliver a data point.

Latency is predictable

It’s useful for a messaging system to have consistent and predictable latency. Imagine for example a smart traffic system, where cars can alert each other about dangers on the road. It would be pretty bad if, even minutes after publishing it, some cars still haven’t received the warning. However, such delays easily occur in peer-to-peer networks. Everyone in the crypto space has seen first-hand how plenty of Bitcoin or Ethereum nodes lag even minutes behind the latest chain state.
So we wanted to see whether it would be possible to estimate the latencies in the peer-to-peer network if the topology and the latencies between connected pairs of nodes are known. We applied Dijkstra’s algorithm to compute estimates for average latencies from the input topology data, and compared the estimates to the actual measured average latencies:
Mean message propagation delay in Amazon experiments
We can see that, at least in these experiments, the estimates seemed to provide a lower bound for the actual values, and the average estimation error was 3.5%. The measured value is higher than the estimated one because the estimation only considers network delays, while in reality there is also a little bit of a processing delay at each node.

Conclusion

The research has shown that the Streamr Network can be expected to deliver messages in roughly 150–350 milliseconds worldwide, even at a large scale with thousands of nodes subscribing to a stream. This is on par with centralized message brokers today, showing that the decentralized and peer-to-peer approach is a viable alternative for all but the most latency-sensitive applications.
It’s thrilling to think that by accepting a latency only 2–3 times longer than the latency of an unscalable and insecure direct connecion, applications can interconnect over an open fabric with global scalability, no single point of failure, no vendor lock-in, and no need to trust anyone — all that becomes available out of the box.
In the real-time data space, there are plenty of other aspects to explore, which we didn’t cover in this paper. For example, we did not measure throughput characteristics of network topologies. Different streams are independent, so clearly there’s scalability in the number of streams, and heavy streams can be partitioned, allowing each stream to scale too. Throughput is mainly limited, therefore, by the hardware and network connection used by the network nodes involved in a topology. Measuring the maximum throughput would basically be measuring the hardware as well as the performance of our implemented code. While interesting, this is not a high priority research target at this point in time. And thanks to the redundancy in the network, individual slow nodes do not slow down the whole topology; the data will arrive via faster nodes instead.
Also out of scope for this paper is analysing the costs of running such a network, including the OPEX for publishers and node operators. This is a topic of ongoing research, which we’re currently doing as part of designing the token incentive mechanisms of the Streamr Network, due to be implemented in a later milestone.
I hope that this blog has provided some insight into the fascinating results the team uncovered during this research. For a more in-depth look at the context of this work, and more detail about the research, we invite you to read the full paper.
If you have an interest in network performance and scalability from a developer or enterprise perspective, we will be hosting a talk about this research in the coming weeks, so keep an eye out for more details on the Streamr social media channels. In the meantime, feedback and comments are welcome. Please add a comment to this Reddit thread or email [[email protected]](mailto:[email protected]).
Originally published by. Henri at blog.streamr.network on August 24, 2020.
submitted by thamilton5 to streamr [link] [comments]

Another Bitcoin hard fork & potential civil war has been avoided - no suspensions will occur. Additional info on other forks including upcoming Bitcoin Cash fork and Bitcoin "Gold" also inside

Last month, we notified you that there was the potential for a civil war to break out as a direct result of another "OG" Bitcoin fork, named SegWit2X, particularly due to Bitcoin.org (affiliated with the Bitcoin Core development group) unusually breaking from its neutral stance and naming-and-shaming companies and organizations that supported SW2X even as an alternative, including Blockchain.info and Coinbase. The group behind the implementation of SegWit2X, which builds on the ideas of SegWit, has announced that its upcoming implementation around the middle of the month will not go on.
With this sudden change of course, we are now no longer preparing for suspending Bitcoin trading around the middle of the month. We'll also talk about two other forks that users have mentioned or will likely mention below.

Bitcoin Cash is forking - use additional caution

Unrelated to the above dispute between SegWit2X and SegWit without 2X, Bitcoin Cash developer groups are currently planning on updating their difficulty adjustment algorithm in a few days on November 13th. One of the main differences of the Cash chain is that it has a slightly different method of adjusting difficulty, where if there's a period with huge gaps between new blocks, an emergency difficulty adjustment will occur to rapidly lower difficulty ahead of the standard difficulty retargeting of every 2048 2016 blocks. This helped stabilize it in its early days in August and September, but has later proven to be rather erratic. With the new difficulty adjustment algorithm selected, difficulty retargets will occur with every block similar to other altcoins like Ethereum and Vertcoin, based on a moving average of how long the last 144 blocks took to make. In Portal terms, stable hashrate goes in, stable difficulty comes out. Rapid hashrate changes goes in, rapid difficulty changes comes out.
To implement this change, a hard fork is necessary. As seemingly all parties are in agreement with the change, and with supporters of the Cash chain like exchanges and online wallets preparing for this, we will not suspend Bitcoin Cash trading on /GlobalOffensiveTrade due to a low risk of an accidental chain split and economic impact. It's not an unwise idea to use extra caution during this time, but if you're a Cash enthusiast, you're probably already prepared. An eye will be kept on the transition just in case something does go wrong.

Bitcoin "Gold" - is banned on /GlobalOffensiveTrade

As the new dispute between SW w/o 2X and SW2X broke out, another fork of Bitcoin, called Bitcoin Gold, was beginning to be talked about. Its main idea is to shove the middle finger at ASICs (special boxes meant for mining) like Litecoin (early on, at least) and Vertcoin and similar altcoins, and to restore the viability of mining with typical graphics cards, as was commonplace during the first Bitcoin boom of 2012/2013/early 2014 and with Ethereum in early/mid 2017, with both instances causing widespread supply shortages. This was also an idea that some supporters of the Bitcoin Core developer group were tossing around a little while before the Cash fork occurred, but there were no solid plans as Gold was not a thing yet.
On paper, Gold would seem like a good fork for those that wanted to wave the middle finger at ASICs, but problems quickly became apparent with its execution. Namely, a premine operation was uncovered, where the developers of the Gold chain would be the exclusive miners of Gold until public release, and then presumably resell those coins after public release as a means to fund development, or just to make quick Benjamins. Coinbase, in its FAQ on its position on Gold, also stated that the source code was not open for public viewing for public review, thereby making it a security risk.
As a result, we will be banning Bitcoin Gold on /GlobalOffensiveTrade.

An overview of what's going on with Bitcoin and /GlobalOffensiveTrade:

  • "OG" Bitcoin trading will continue to be allowed now that SegWit2X implementation has been called off
  • Bitcoin Cash trading will continue to be allowed through an upcoming fork to alter how difficulty is determined. Use extra caution, but there are next to nil risks of significant economic impacts
    • "Bcash" is not a valid or official name, and will still result in your post or comment being removed
  • Bitcoin Gold trading is banned due to security concerns stemming from the code not being open source, as well as extensive pre-mining to the tune of 100,000 coins - your post or comment will be removed
  • Our bait-and-switch policies involving Bitcoin remains unchanged: if you do not explicitly specify which variant of Bitcoin you want, it will be assumed that you want original Bitcoin and not Bitcoin Cash (including if you say just BTC). If you want Bitcoin Cash instead of original Bitcoin, you must explicitly say so, either by stating "Bitcoin Cash", "BCC", or "BCH"
    • Bait-and-switch attempts involving sending Bitcoin Cash instead of original Bitcoin, or vice versa, are still bannable under rule 4
submitted by wickedplayer494 to GlobalOffensiveTrade [link] [comments]

Here's a list of 158 free online programming/CS courses (MOOCs) with feedback(i.e. exams/homeworks/assignments) that you can start this month (September 2015)

This is not the complete list of MOOCs starting in September 2015, just the ones relevant to this community. The complete list of courses starting in September 2015 can be found over at Class Central. I maintain a much bigger list of these courses over at Class Central
BEGINNER(35)
Course Name Start Date Length (in weeks) Provider Rating
BJC.1x: The Beauty and Joy of Computing, Part 1 8th Sep, 2015 NA edX ☆☆☆☆☆
HTML, CSS and JavaScript Sep, 2015 NA Coursera ☆☆☆☆☆
Data Analysis and Statistical Inference 14th Sep, 2015 10 Coursera ★★★★★ (22 reviews)
Usable Security 14th Sep, 2015 6 Coursera ★★★★☆ (3 reviews)
Programming and the Web for Beginners Sep, 2015 NA Coursera ☆☆☆☆☆
Ruby on Rails: An Introduction Sep, 2015 NA Coursera ☆☆☆☆☆
Introduction to HTML5 Sep, 2015 NA Coursera ☆☆☆☆☆
Getting Started with Python Sep, 2015 NA Coursera ☆☆☆☆☆
DEV208x: Introduction to jQuery 15th Sep, 2015 3 edX ☆☆☆☆☆
Louv1.1x: Paradigms of Computer Programming – Fundamentals 21st Sep, 2015 5 edX ☆☆☆☆☆
CAMS.2x: Computing: Art, Magic, Science 22nd Sep, 2015 7 edX ☆☆☆☆☆
CODAPPS: Coding mobile apps for entrepreneurs Sep, 2015 NA Coursera ☆☆☆☆☆
Intro to Computer Science Self paced 12 Udacity ★★★★★ (47 reviews)
Intro to Java Programming Self paced 16 Udacity ★★★☆☆ (10 reviews)
Mobile Web Development Self paced 6 Udacity ☆☆☆☆☆
DB: Introduction to Databases Self paced 11 Stanford OpenEdx ★★★★★ (5 reviews)
Make Your Own 2048 Self paced NA Udacity ★★★★☆ (3 reviews)
Programming Foundations with Python Self paced 6 Udacity ★★★★☆ (2 reviews)
Intro to HTML and CSS Self paced 3 Udacity ★★★★☆ (10 reviews)
JavaScript Basics Self paced 3 Udacity ★★★★☆ (7 reviews)
How to Use Git and GitHub Self paced 3 Udacity ★★★★★ (15 reviews)
CS50x: Introduction to Computer Science Self paced NA edX ★★★★★ (33 reviews)
Intro to Relational Databases Self paced 4 Udacity ★★★☆☆ (1 review)
Networking: Introduction to Computer Networking Self paced 11 Stanford OpenEdx ★★★★★ (5 reviews)
CS101: Computer Science 101 Self paced 6 Stanford OpenEdx ★★★☆☆ (5 reviews)
CS002x: Programming in Scratch Self paced 6 edX ★★★★☆ (3 reviews)
CS001x: MyCS: Computer Science for Beginners Self paced 6 edX ★★★☆☆ (1 review)
DEV203x: Introduction to Bootstrap – A Tutorial Self paced 3 edX ★★☆☆☆ (5 reviews)
LFS101x.2: Introduction to Linux Self paced 8 edX ★★★★☆ (16 reviews)
CS005x: CS For All: Introduction to Computer Science and Python Programming Self paced 14 edX ★★★★★ (2 reviews)
CS101.1x: Introduction to Computer Programming, Part 1 Self paced 9 edX ★★☆☆☆ (9 reviews)
COMP102.1x: Introduction to Java Programming – Part 1 Self paced 5 edX ★★★★☆ (3 reviews)
Learn Swift Programming Syntax Self paced 3 Udacity ☆☆☆☆☆
Linux Command Line Basics Self paced 1 Udacity ☆☆☆☆☆
Computer Science 101 Self paced NA Coursera ★★★★☆ (11 reviews)
INTERMEDIATE(97)
Course Name Start Date Length (in weeks) Provider Rating
EX101x: Data Analysis: Take It to the MAX() 1st Sep, 2015 8 edX ★★★☆☆ (10 reviews)
Python Data Structures Sep, 2015 NA Coursera ☆☆☆☆☆
Programming Mobile Applications for Android Handheld Systems: Part 1 2nd Sep, 2015 4 Coursera ★★★★☆ (27 reviews)
Programming Mobile Applications for Android Handheld Systems: Part 2 2nd Sep, 2015 4 Coursera ★★★★★ (7 reviews)
Algorithms, Part I 4th Sep, 2015 6 Coursera ★★★★☆ (24 reviews)
Algorithmic Thinking (Part 1) 5th Sep, 2015 4 Coursera ★★★☆☆ (3 reviews)
Statistical Inference 7th Sep, 2015 4 Coursera ★★☆☆☆ (9 reviews)
Regression Models 7th Sep, 2015 4 Coursera ★★★☆☆ (8 reviews)
Practical Machine Learning 7th Sep, 2015 4 Coursera ★★★☆☆ (10 reviews)
Developing Data Products 7th Sep, 2015 4 Coursera ★★★★☆ (4 reviews)
Introduction to Genomic Technologies 7th Sep, 2015 4 Coursera ★★★☆☆ (6 reviews)
Bioconductor for Genomic Data Science 7th Sep, 2015 4 Coursera ☆☆☆☆☆
Python for Genomic Data Science 7th Sep, 2015 4 Coursera ★★★★☆ (4 reviews)
Statistics for Genomic Data Science 7th Sep, 2015 4 Coursera ☆☆☆☆☆
Concepts in Game Development 7th Sep, 2015 4 Open2Study ★★★★☆ (14 reviews)
The Data Scientist’s Toolbox 7th Sep, 2015 4 Coursera ★★★☆☆ (79 reviews)
R Programming 7th Sep, 2015 4 Coursera ★★★☆☆ (128 reviews)
Getting and Cleaning Data 7th Sep, 2015 4 Coursera ★★★☆☆ (23 reviews)
Exploratory Data Analysis 7th Sep, 2015 4 Coursera ★★★★☆ (13 reviews)
Reproducible Research 7th Sep, 2015 4 Coursera ★★★★☆ (10 reviews)
SPD2x: Systematic Program Design - Part 2: Arbitrary Sized Data 8th Sep, 2015 5 edX ☆☆☆☆☆
Analysis of Algorithms 11th Sep, 2015 6 Coursera ★★★★☆ (1 review)
Automata 12th Sep, 2015 6 Coursera ★★★★☆ (7 reviews)
Mining Massive Datasets 12th Sep, 2015 7 Coursera ★★★★★ (6 reviews)
Software Security 14th Sep, 2015 6 Coursera ★★★★★ (11 reviews)
Cryptography 14th Sep, 2015 7 Coursera ★★★★★ (4 reviews)
Responsive Website Basics: Code with HTML, CSS, and JavaScript Sep, 2015 NA Coursera ☆☆☆☆☆
Introduction To Swift Programming Sep, 2015 NA Coursera ☆☆☆☆☆
Foundations of Objective-C App Development Sep, 2015 NA Coursera ☆☆☆☆☆
Database Management Essentials Sep, 2015 NA Coursera ☆☆☆☆☆
Introduction to Big Data Sep, 2015 NA Coursera ☆☆☆☆☆
Data Management and Visualization Sep, 2015 NA Coursera ☆☆☆☆☆
Introduction to Software Product Management Sep, 2015 NA Coursera ☆☆☆☆☆
Object Oriented Programming in Java Sep, 2015 NA Coursera ☆☆☆☆☆
Design of Computer Programs Self paced 8 Udacity ★★★★☆ (9 reviews)
Web Development Self paced 12 Udacity ★★★★★ (16 reviews)
Programming Languages Self paced 8 Udacity ★★★☆☆ (7 reviews)
Intro to Algorithms Self paced 16 Udacity ★☆☆☆☆ (1 review)
Software Testing Self paced 4 Udacity ★★★★☆ (5 reviews)
Software Debugging Self paced 8 Udacity ★★★★☆ (2 reviews)
Intro to Theoretical Computer Science Self paced 8 Udacity ★★★★★ (1 review)
Intro to Artificial Intelligence Self paced 16 Udacity ★★★★☆ (4 reviews)
HTML5 Game Development Self paced 8 Udacity ★★★☆☆ (4 reviews)
Learning from Data (Introductory Machine Learning course) Self paced 10 Others ★★★★☆ (2 reviews)
Intro to Hadoop and MapReduce Self paced 4 Udacity ★★★★☆ (8 reviews)
CS 8802, Artificial Intelligence for Robotics: Programming a Robotic Car Self paced NA Udacity ★★★★★ (1 review)
Intro to Data Science Self paced 8 Udacity ★★★★☆ (7 reviews)
Data Wrangling with MongoDB Self paced 8 Udacity ★★★★☆ (3 reviews)
Data Analysis with R Self paced 8 Udacity ★★★★★ (11 reviews)
Parallel Programming Concepts Self paced NA openHPI ★★★★☆ (3 reviews)
Website Performance Optimization Self paced 1 Udacity ★★★★☆ (2 reviews)
UX Design for Mobile Developers Self paced 6 Udacity ★★★★☆ (1 review)
Developing Scalable Apps in Java Self paced 8 Udacity ★★★★☆ (1 review)
Developing Android Apps Self paced 10 Udacity ★★★★★ (2 reviews)
Software Development Process Self paced 12 Udacity ☆☆☆☆☆
Computer Networking Self paced 12 Udacity ★★★★★ (2 reviews)
Practical Numerical Methods with Python Self paced 17 Others ★★★★★ (2 reviews)
Object-Oriented JavaScript Self paced 5 Udacity ★★★★★ (6 reviews)
Intro to AJAX Self paced 2 Udacity ★★★★★ (1 review)
Data Visualization and D3.js Self paced 7 Udacity ★★★☆☆ (1 review)
HTML5 Canvas Self paced 2 Udacity ★★★★☆ (2 reviews)
Intro to iOS App Development with Swift Self paced 4 Udacity ★★★★★ (4 reviews)
Intro to jQuery Self paced NA Udacity ★★★★☆ (2 reviews)
Intro to Machine Learning Self paced 10 Udacity ★★★★☆ (5 reviews)
Full Stack Foundations Self paced 3 Udacity ☆☆☆☆☆
Responsive Web Design Fundamentals Self paced 2 Udacity ★★★★☆ (3 reviews)
Health Informatics in the Cloud Self paced 5 Udacity ☆☆☆☆☆
JavaScript Testing Self paced 2 Udacity ★★★★★ (1 review)
UIKit Fundamentals Self paced 8 Udacity ☆☆☆☆☆
LPL: Language, Proof and Logic Self paced 15 Stanford OpenEdx ☆☆☆☆☆
Responsive Images Self paced 2 Udacity ★★★★☆ (2 reviews)
Machine Learning Self paced NA Coursera ★★★★★ (100 reviews)
iOS Networking with Swift Self paced 12 Udacity ☆☆☆☆☆
Authentication & Authorization: OAuth Self paced 2 Udacity ★★★★☆ (2 reviews)
Browser Rendering Optimization Self paced 4 Udacity ★★★★☆ (1 review)
Developing Scalable Apps in Python Self paced 6 Udacity ★★★★☆ (1 review)
iOS Persistence and Core Data Self paced 8 Udacity ☆☆☆☆☆
How to Make an iOS App Self paced 12 Udacity ☆☆☆☆☆
Android Performance Self paced 4 Udacity ☆☆☆☆☆
Android Development for Beginners Self paced 4 Udacity ★★★★☆ (5 reviews)
Advanced Android App Development Self paced 6 Udacity ☆☆☆☆☆
Android Design for Developers Self paced 4 Udacity ☆☆☆☆☆
Android Ubiquitous Computing Self paced 4 Udacity ☆☆☆☆☆
Google Play Services Self paced 8 Udacity ☆☆☆☆☆
Gradle for Android and Java Self paced 6 Udacity ★★★★★ (1 review)
DEV201x: Introduction to TypeScript Self paced 6 edX ☆☆☆☆☆
DEV202.1x: Building Cloud Apps with Microsoft Azure – Part 1 Self paced 4 edX ★★★☆☆ (1 review)
DEV204x: Programming with C# Self paced 6 edX ★★★★☆ (5 reviews)
AMRx: Autonomous Mobile Robots Self paced NA edX ★★★☆☆ (3 reviews)
Xcode Debugging Self paced 3 Udacity ☆☆☆☆☆
PH525.1x: Statistics and R for the Life Sciences Self paced 4 edX ★★★★☆ (6 reviews)
KIexploRx: Explore Statistics with R Self paced NA edX ★★★★☆ (7 reviews)
Intro to DevOps Self paced 3 Udacity ☆☆☆☆☆
Configuring Linux Web Servers Self paced 1 Udacity ☆☆☆☆☆
Learn Backbone.js Self paced 2 Udacity ☆☆☆☆☆
Android TV and Google Cast Development Self paced 1 Udacity ☆☆☆☆☆
JavaScript Design Patterns Self paced 6 Udacity ★★★★★ (1 review)
ADVANCED(26)*
Course Name Start Date Length (in weeks) Provider Rating
Bitcoin and Cryptocurrency Technologies 4th Sep, 2015 7 Coursera ☆☆☆☆☆
Genomic Data Science with Galaxy 7th Sep, 2015 4 Coursera ★★☆☆☆ (4 reviews)
Command Line Tools for Genomic Data Science 7th Sep, 2015 4 Coursera ☆☆☆☆☆
Algorithms for DNA Sequencing 7th Sep, 2015 4 Coursera ★★★★★ (8 reviews)
Hardware Security 14th Sep, 2015 6 Coursera ★★★☆☆ (6 reviews)
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center 15th Sep, 2015 7 Coursera ★★★☆☆ (1 review)
Machine Learning Foundations: A Case Study Approach Sep, 2015 NA Coursera ☆☆☆☆☆
DAT203x: Data Science and Machine Learning Essentials 24th Sep, 2015 5 edX ☆☆☆☆☆
Machine Learning Self paced NA Udacity ★★★★★ (1 review)
Artificial Intelligence for Robotics Self paced 8 Udacity ★★★★★ (11 reviews)
Applied Cryptography Self paced 8 Udacity ★★★★☆ (2 reviews)
Intro to Parallel Programming Self paced 12 Udacity ★★★★☆ (2 reviews)
Interactive 3D Graphics Self paced 8 Udacity ★★★★★ (2 reviews)
Functional Hardware Verification Self paced 8 Udacity ★★★★★ (1 review)
Machine Learning 1—Supervised Learning Self paced NA Udacity ★★★★☆ (1 review)
Machine Learning 2—Unsupervised Learning Self paced NA Udacity ★★★★☆ (1 review)
Machine Learning 3—Reinforcement Learning Self paced 4 Udacity ★★★☆☆ (2 reviews)
In-Memory Data Management In a Nutshell Self paced NA openSAP ★★☆☆☆ (1 review)
Real-Time Analytics with Apache Storm Self paced 2 Udacity ★★★★☆ (1 review)
Model Building and Validation Self paced 8 Udacity ★★★☆☆ (3 reviews)
Advanced Operating Systems Self paced 5 Udacity ★★★★★ (1 review)
High Performance Computer Architecture Self paced NA Udacity ☆☆☆☆☆
Computability, Complexity & Algorithms Self paced NA Udacity ☆☆☆☆☆
Knowledge-Based AI: Cognitive Systems Self paced 7 Udacity ☆☆☆☆☆
Introduction to Recommender Systems Self paced NA Coursera ★★★★☆ (10 reviews)
Machine Learning Self paced 16 Udacity ☆☆☆☆☆
submitted by dhawal to learnprogramming [link] [comments]

Some best practice advice please. Bought a second key (v4) and want to start using it/them in anger. (Long-ish but simple questions).

(I'm labelling so that you have easy ways to refer to shite - not for any real reason. By way of background, I'm not dedicated IT but reasonable competent - just beyond that little knowledge is dangerous phase; I'm involved in law, tech and finance for my sins). I'm running Linux - reasonably competent.
I saw ECC keys have been nixed by the NSA but I think that's where I want to go anyway. Will review the decision when I review the subkeys.
a. I have an older yubikey-neo v3 (2048/RSA max) and a newer nano 4. The v4 is effectively virgin.
b. v3 Neo: I have been using slot 2 as a static password and the U2F for GitHub. I need to start using my yubikeys properly.
c. Can I copy different versions of the key content between versions? Couldn't see it on the neo-manager or personalisation progs. How do you manage your backup/primary keys? (didn't see much on the net about them). Guess masterkeys stay safe on USB somewhere and just copy the masterless subkeys to the two yubi's?
d. I think I want to start using my old yubikey as the backup. I don't have a problem yet with the smaller key length implementation on the v3 vs v4. Lawyers email stuff as plain text ..! I'm not doing anything super wonderful, it's the client stuff locally not being quite best practice I worry about.
e I need to create replacement SSH keys around. Usual thing: cloud-based and real servers. They're in a mess on my HD at the moment. I know I experimented with ECC keys a while back but don't have proper notes.
f. I guess I should create a few. Key Key risk ..? Is ECC a thing for sshd, widespread/debugged? (suggestions, tips etc, best practice)
g. I desperately need to up my PGP game. I have none worthy of the name. Again, is PGP ECC widespread/debugged enough to use them (as much as pgp is widespread :) ). Openpgp is a reasonable version number now.
h. I'd like to sign stuff emails, git for the moment. File encryption is OK with basic LUKS - haven't investigated tying that to PGP or such.
i. My biggest problem here is the sodding yubikey docs. I don't find them very good at all. Perhaps it's just me, but the net is ok.
j. I can't find a PGP email client I like. Decided finally last week not to try to build a mail server (for PGP signing) myself and just go with zoho on DO.
k. I'm investigating accepting bitcoin payments, but know nothing about blockchain/bitcoin. Views on using yubi with that? don't expect to keep a balance, and close out the FX risk ASAP; much bid-offer spread on bitcoin?
Links appreciated. I have gather far too many and am confusing myself about best practice ATM.
Thank you all very much for whatever time you can spend deciphering this (pun intended).
submitted by throwawaylifespan to yubikey [link] [comments]

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