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27
Jan

What is Litecoin? Here’s everything you need to know


As much as bitcoin is the cryptocurrency that has the most mainstream attention, there are hundreds upon hundreds of alternative coins which have been created over the years since it was born. While some alternatives like Ethereum and ZCash have become vogue in only the past couple of years though, some cryptocurrencies have been around for much longer, like Litecoin.

But what is Litecoin? In a nutshell, it’s a cryptocurrency like many others, built on a similar framework and ideal as bitcoin itself. Created by an ex-Google employee a few years after the first cryptocurrency, it’s now one of the most commonly traded crypto-coins out there, with a market value in the billions.

A crypto-what?

If you’ve heard of Litecoin, you’ve heard of bitcoin and have a reasonable idea of what the whole cryptocurrency scene is all about, but it never hurts to have a quick refresher. Cryptocurrencies are entirely digital currencies. Think of them as the way you operate your online banking. It’s not a ‘real’ or physical currency, but it can have real value. That currency can be transferred between users all over the world with low fees and far faster than most traditional currencies.

One key difference between your online bank account and cryptocurrencies is that they are (for the most part) entirely decentralized. That is, they aren’t controlled by any one authority. The ‘ledger,’ or “blockchain,” that records and confirms all transactions as valid is publicly viewable and editable by a large system of users all over the world.

In the case of Litecoin, those confirmations are created by a process known as “mining.” That’s a rather complicated topic in its own right, but suffice to say it involves performing complex mathematical calculations with powerful computing hardware. Miners who take part in it also create new Litecoins, which they are rewarded with for performing the service, along with a transaction fee.

Those calculations get more and more complicated as time goes on, limiting the influx of new Litecoin. There is also a hard limit of 84 billion Litecoin, which means there will come a day where no new tokens are created. Those factors create a scarcity which has helped drive up Litecoin’s value over the years, among other factors.

Second out the gate, but not second-fiddle

BTC Keychain/Flickr

Launched in October 2011, just under three years after the debut of its inspiration, bitcoin, Litecoin was created by former Google employee, Charlie Lee. Described by its creator as the “silver” to bitcoin’s gold, Litecoin is based on the Bitcoin Core client. Litecoin was designed to emulate its predecessor, extolling the same virtues of decentralization but with a few key features that arguably make it a more nimble alternative.

While bitcoin blocks can only be processed every ten minutes — part of the reason it has experienced longer confirmation times with the recent influx of users — Litecoin reduced that to a targeted 2.5 minutes per block. While that hasn’t always been possible throughout the cryptocurrency’s history, it is the average that makes transactions faster — and cheaper — to confirm, or validate.

The other key difference Lee employed with Litecoin’s creation, was in his choice of hashing algorithm. All cryptocurrency mining employs complicated algorithms. Most are based on the same SHA-256 algorithm that bitcoin uses, but Litecoin leveraged the Scrypt algorithm instead. Easier to compute, lighter on the workload, it’s what enables the faster confirmation of Litecoin transactions. There is an argument to be made that its enabling of faster transactions is a security issue, since less thorough checks of the data are required, but it hasn’t manifested in an obvious problem in the real world as of yet.

These two main differences from bitcoin make Litecoin very much its own cryptocurrency and more than just a pretender to the throne. Over the years it has garnered a base of thousands of owners all over the world, who between them trade millions of dollars worth of Litecoin every day.

Litecoin for transactions

Although cryptocurrencies (and the blockchain technology it’s built upon) could have serious potential for streamlining a variety of industries around the world — especially when you factor in smart contracts — they have two main functions as it stands. The first of those is in transactions.

Cryptocurrency, operating in the same manner as traditional, “fiat,” currencies can be used to pay for goods and services. Although cryptocurrencies have a reputation for being used on the darknet for drug transactions and facilitating ransomware attacks, a growing number of legitimate, legal outlets accept Litecoin as legal tender. Whether you’re looking for jewelry, clothing, or even luxury cars, there are many places you can spend Litecoin.

Litecoin is also a great cryptocurrency for giving ‘money’ to friends and family. Due to its shorter block time, fast confirmations and fees that rarely go north of a fraction of a dollar, Litecoin can be transferred to anyone quickly and cheaply if you have their wallet address.

Like some of the other alternative cryptocurrencies out there, interest in Litecoin as a transactional medium has increased in recent months thanks to bitcoin’s value spike and its escalating transaction fees. Although there is no guarantee that Litecoin won’t bump up against such problems itself should it see a large influx of new owners, for now at least it’s a great medium for transferring wealth quickly online.

Litecoin as a store of value

Although cryptocurrencies like Litecoin were originally intended to conduct transactions online, much like traditional currencies, their value does increase and decrease based on a number of market factors. Cryptocurrencies however, with their lack of governmental backing, tend to fluctuate far more — that’s why bitcoin and others have seen such an interest from mainstream investors in recent months.

Litecoin too has been on quite the tear and has made many people very wealthy in a relatively short period of time. Like many other cryptocurrencies in the past year, its value has increased exponentially. At the start of 2017 a single Litecoin was worth just $4. At its peak in December that same year it hit $371, correcting to $178 at the time of writing.

That’s an enormous increase that shows that just because bitcoins are worth thousands of dollars, that Litecoin can’t also be a great store of value. Some, like its creator, would argue that Litecoin has a greater potential as a cryptocurrency because of its better transactional abilities. While that might not necessarily affect its value directly, it could make it more popular, which in turn creates its own potential for a value increase over time due to demand.

Long-term relevance

When Litecoin was first created, it was just one of a handful of cryptocurrencies. Today it’s one of many — more than 1,300, with more being created every day. While it has greater name-recognition than most cryptocurrencies, its market cap of near $10 billion is far less than the biggest players and individual coins are worth much less too.

That shouldn’t put people off it though. It truly shines as a regular transactional medium, with only bitcoin, Ethereum, and Ripple seeing a greater daily trade volume. There may be leaner coins and some with more advanced features than Litecoin today, but it has firmly cemented itself as one of the most important cryptocurrencies. It might not quite be the silver to bitcoin’s gold anymore, but it is one of the most precious digital metals we have, and it doesn’t seem likely to fall from favor anytime soon.

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27
Jan

Ever wondered how lasers work? Here’s everything you need to know


Lasers! Clearly they are awesome — but how do they work, exactly? Why aren’t we all carrying them around in our pockets? Well, believe it or not, you probably are — thanks to modern electronics. Here’s the story of how a laser (light amplification by stimulated emission of radiation) operates, and what it does when it hits an object.

It’s all about electrons

Let’s spend a little time back in physics class: A laser owes its existence to electrons, which you may remember are those energetic particles that hover/exist around an atom, forming its “shell.”

Some electrons have the ability to absorb energy from outside sources and jump to higher-energy orbits, at least temporarily. However, the electrons quickly return to their normal orbits, and release the extra energy they used, which then ripples outside of the atom.

Electrons do this all the time—it’s how most radiation is created! Switch on a flashlight, and there’s a bunch of electrons cascading energy levels all over the place. But the reason that your flashlight is not a powerful laser beam (sorry) is that those electrons aren’t in sync. Instead, they hop all over the place, release energy randomly, and are hardly ever at the same wavelength or the same timing. In fact, electrons seem to naturally disperse their wavelengths and timing in these situations, which makes accidental lasers almost—but not entirely—impossible.

When creating a laser, engineers have to act like orchestra conductors for an innumerable number of electrons, getting them all to gain energy and release it in sync. When successful, this creates a coherent stream of photons that all move in the same manner, at the same time, in the same direction…and a laser is born. This happens thanks to a carefully constructed process and the right materials, which we will talk about in the next section!

Anatomy of a modern laser

Christian Delbert/123rf

Lasers come in all sizes, from tiny little lasers in microchips to vast lasers in science research facilities. However, most can be broken down into three very important parts that allow the laser to function.

Energy source: First, lasers require an energy source (also called pump sources or excitation mechanisms) to pump energy into the laser so that its electrons have a lot of juice to work with. There are several different popular types of energy sources, including direct electrical discharges, chemical reactions, and powerful sources of light like flash lamps.

Medium: The medium (typically called the gain medium or laser medium) is where the energy is directed. Its job is to gather that energy, get its electrons to jump around like crazy, and emit powerful bursts of light that are ready to be formed into a laser. Mediums cover a wide range of materials: Some are liquids, some are gases, and some are crystalline solids. Even a humble semiconductor can act as a laser medium.

Optical cavity: The optical cavity or resonator takes all the light released by the medium and focuses it. In the classic laser setup, it uses two mirrors to bounce that light back and forth to sync up the pulses, amplifying the energy and routing it toward a small opening where the laser is directed.

What happens when a laser hits something

When a laser strikes a material, it acts just like other radiation: Some is absorbed, some is reflected, and some may pass through or be transmitted. But that doesn’t tell us much about what a particular, focused laser actually does to the material, so let’s take a closer look at several major categories of practical laser uses, and how they work.

Illumination: In this case, lasers are simply used to illuminate something that’s hard to see. That’s right, sometimes even the trusty flashlight won’t do, especially at very long distances — or when teachers really want to use a laser pointer. And yes, this can be dangerous.

Reflection: When lasers focus on reflection, they are typically transmitting information. The best example here is an optical disk drive found in Blu-ray players, computers, and so on. However, there are many smart device applications too.

Pyrolitic/photolytic reaction: Here, the laser is generally intended to change something…destructively. Pyrolitic versions heat a material, usually to melt it (and hey, sometimes zap birds). Photolytic versions break down chemical bonds within a material to accomplish similar goals.

Transmission: Here the laser is designed to pass down a code that encases valuable data, as in fiber optics.

State change: This is sort of a catch-all category, but in a number of cases the purpose of the laser is to change the material or change itself into a different type of energy (without burning anything). In this case, the material absorbs the laser and then undergoes an interesting transformation. For example, some lasers turn light into sound. Many such devices have valuable applications in everyday engineering.

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27
Jan

Deep learning vs. machine learning: Demystifying artificial intelligence


In recent months, Microsoft, Google, Apple, Facebook, and other entities have declared that we no longer live in a mobile-first world. Instead, it’s an artificial intelligence-first world where digital assistants and other services will be your primary source of information and getting tasks done. Your typical smartphone or PC are now your secondary go-getters.

Backing this new frontier are two terms you’ll likely hear often: machine learning and deep learning. These are two methods in “teaching” artificial intelligence to perform tasks, but their uses goes way beyond creating smart assistants. What’s the difference? Here’s a quick breakdown.

Computers now see, hear, and speak

With the help of machine learning, computers can now be “trained” to predict the weather, determine stock market outcomes, understand your shopping habits, control robots in a factory, and so on. Google, Amazon, Facebook, Netflix, LinkedIn, and more popular consumer-facing services are all backed by machine learning. But at the heart of all this learning is what’s known as an algorithm.

Simply put, an algorithm is not a complete computer program (a set of instructions), but a limited sequence of steps to solve a single problem. For example, a search engine relies on an algorithm that grabs the text you enter into the search field box, and searches the connected database to provide the related search results. It takes specific steps to achieve a single, specific goal.

Machine learning has actually been around since 1956. Arthur Samuel didn’t want to write a highly-detailed, lengthy program that could enable a computer to beat him in a game of checkers. Instead, he created an algorithm that enabled the computer to play against itself thousands of times so it could “learn” how to perform as a stand-alone opponent. By 1962, this computer beat the Connecticut state champion.

Thus, at its core, machine learning is based on trial and error. We can’t manually write a program by hand that can help a self-driving car distinguish a pedestrian from a tree or a vehicle, but we can create an algorithm for a program that can solve this problem using data. Algorithms can also be created to help programs predict the path of a hurricane, diagnose Alzheimer’s early, determine the world’s most overpaid and underpaid soccer stars, and so on.

Machine learning typically runs on low-end devices, and breaks a problem down into parts. Each part is solved in order, and then combined to create a single answer to the problem. Well-known machine learning contributor Tom Mitchell of Carnegie Mellon University explains that computer programs are “learning” from experience if their performance of a specific task is improving. Machine learning algorithms are essentially enabling programs to make predictions, and over time get better at these predictions based on trial and error experience.

Here are the four main types of machine learning:

Supervised machine learning

In this scenario, you are providing a computer program with labeled data. For instance, if the assigned task is to separate pictures of boys and girls using an algorithm for sorting images, those with a male child would have a “boy” label, and images with a female child would have a “girl” label. This is considered as a “training” dataset, and the labels remain in place until the program can successfully sort the images at an acceptable rate.

Semi-supervised machine learning

In this case, only a few images are labeled. The computer program will then use an algorithm to make its best guess regarding the unlabeled images, and then the data is fed back to the program as training data. A new batch of images is then provided, with only a few sporting labels. It’s a repetitive process until the program can distinguish between boys and girls at an acceptable rate.

Unsupervised machine learning

This type of machine learning doesn’t involve labels whatsoever. Instead, the program is blindly thrown into the task of splitting images of boys and girls into two groups using one of two methods. One algorithm is called “clustering” that groups similar objects together based on characteristics, such as hair length, jaw size, eye placement, and so on. The other algorithm is called “association” where the program creates if/then rules based on similarities it discovers. In other words, it determines a common pattern between the images, and sorts them accordingly.

Reinforcement machine learning

Chess would be an excellent example of this type of algorithm. The program knows the rules of the game and how to play, and goes through the steps to complete the round. The only information provided to the program is whether it won or lost the match. It continues to replay the game, keeping track of its successful moves, until it finally wins a match.

Now it’s time to move on to a deeper subject: deep learning.

27
Jan

Deep learning vs. machine learning: Demystifying artificial intelligence


In recent months, Microsoft, Google, Apple, Facebook, and other entities have declared that we no longer live in a mobile-first world. Instead, it’s an artificial intelligence-first world where digital assistants and other services will be your primary source of information and getting tasks done. Your typical smartphone or PC are now your secondary go-getters.

Backing this new frontier are two terms you’ll likely hear often: machine learning and deep learning. These are two methods in “teaching” artificial intelligence to perform tasks, but their uses goes way beyond creating smart assistants. What’s the difference? Here’s a quick breakdown.

Computers now see, hear, and speak

With the help of machine learning, computers can now be “trained” to predict the weather, determine stock market outcomes, understand your shopping habits, control robots in a factory, and so on. Google, Amazon, Facebook, Netflix, LinkedIn, and more popular consumer-facing services are all backed by machine learning. But at the heart of all this learning is what’s known as an algorithm.

Simply put, an algorithm is not a complete computer program (a set of instructions), but a limited sequence of steps to solve a single problem. For example, a search engine relies on an algorithm that grabs the text you enter into the search field box, and searches the connected database to provide the related search results. It takes specific steps to achieve a single, specific goal.

Machine learning has actually been around since 1956. Arthur Samuel didn’t want to write a highly-detailed, lengthy program that could enable a computer to beat him in a game of checkers. Instead, he created an algorithm that enabled the computer to play against itself thousands of times so it could “learn” how to perform as a stand-alone opponent. By 1962, this computer beat the Connecticut state champion.

Thus, at its core, machine learning is based on trial and error. We can’t manually write a program by hand that can help a self-driving car distinguish a pedestrian from a tree or a vehicle, but we can create an algorithm for a program that can solve this problem using data. Algorithms can also be created to help programs predict the path of a hurricane, diagnose Alzheimer’s early, determine the world’s most overpaid and underpaid soccer stars, and so on.

Machine learning typically runs on low-end devices, and breaks a problem down into parts. Each part is solved in order, and then combined to create a single answer to the problem. Well-known machine learning contributor Tom Mitchell of Carnegie Mellon University explains that computer programs are “learning” from experience if their performance of a specific task is improving. Machine learning algorithms are essentially enabling programs to make predictions, and over time get better at these predictions based on trial and error experience.

Here are the four main types of machine learning:

Supervised machine learning

In this scenario, you are providing a computer program with labeled data. For instance, if the assigned task is to separate pictures of boys and girls using an algorithm for sorting images, those with a male child would have a “boy” label, and images with a female child would have a “girl” label. This is considered as a “training” dataset, and the labels remain in place until the program can successfully sort the images at an acceptable rate.

Semi-supervised machine learning

In this case, only a few images are labeled. The computer program will then use an algorithm to make its best guess regarding the unlabeled images, and then the data is fed back to the program as training data. A new batch of images is then provided, with only a few sporting labels. It’s a repetitive process until the program can distinguish between boys and girls at an acceptable rate.

Unsupervised machine learning

This type of machine learning doesn’t involve labels whatsoever. Instead, the program is blindly thrown into the task of splitting images of boys and girls into two groups using one of two methods. One algorithm is called “clustering” that groups similar objects together based on characteristics, such as hair length, jaw size, eye placement, and so on. The other algorithm is called “association” where the program creates if/then rules based on similarities it discovers. In other words, it determines a common pattern between the images, and sorts them accordingly.

Reinforcement machine learning

Chess would be an excellent example of this type of algorithm. The program knows the rules of the game and how to play, and goes through the steps to complete the round. The only information provided to the program is whether it won or lost the match. It continues to replay the game, keeping track of its successful moves, until it finally wins a match.

Now it’s time to move on to a deeper subject: deep learning.

27
Jan

We need restrictions on government surveillance, not limits on Google Assistant or Alexa


Android-figures.jpg?itok=JOwVsINE

We all want to preserve our civil liberties, so let’s tackle the real offenders instead of Assistant and Alexa.

Last week, Ava Kofman wrote an interesting yet terrifying piece in The Intercept about Voice RT. You’ve probably never heard of Voice RT before because it’s been one of those things the U.S. Government does in secret; in this case, it was developing technology that can positively identify someone by the sound of their voice. Be sure to read it. It’s important information everyone needs to know.

Our voice is the perfect biometric identifier; it’s stable and unique.

Now the idea of recognizing someone by their voice is easy to grasp. We do it every day when we talk to the people close to us. A person’s voice is pretty unique and it doesn’t take a lot of processing power — either the organic kind in our heads or the silicon kind in our gadgets — to know who you’re talking to just from hearing them speak. But the NSA was able to take things to the extreme. They have the ability to listen to everything, everywhere. If you’re using a pay phone in the middle of nowhere they can listen. They might even have the authority to do it, and that means we might have a big problem sitting on our nightstands or coffee tables called Google Home and Amazon Echo.

Speaking with The Verge, Albert Gidari, director of privacy at the Stanford Center for Internet and Society, said these products “are vulnerable to government demands for access and disclosure; I think the government could obtain a technical assistance order to facilitate the scan, and under FISA, perhaps to build the tool, too.” I’m sure Gidari is right because the government can already subpoena our phones, our computers, and even our televisions to facilitate an investigation. Senators Wyden (D., Oregon) and Paul (R., Kentucky) think that FISA might be abused in this way, too.

And it’s not an issue of a government agency spying on what we are saying or doing when we talk to Alexa or Google Home. They don’t need that information; all they want is a recording of our voices.

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Both the Echo and Google Home store recordings of the things we say when talking to them. Both also transmit voice data to a cloud server for processing, too. Thankfully, each only records after the hot word is detected and any data that leaves your device is encrypted and anonymized. Whether they could intercept and decrypt the data, demand it through the courts, or pull it right from the device all they’ll hear is people like you and me telling Assistant or Alexa to change the channel or give a weather forecast — mundane information that you aren’t trying to keep secret. Police in Bentonville Arkansas found just that when an Amazon Echo was subpoenaed in a 2016 murder case. But again, that’s not what the NSA was collecting with Voice RT; they just wanted some sample data of a voice so they could match it while doing the spying thing in real time.

It’s not what you say. It’s the fact that it is you saying it that makes it interesting to the NSA.

Digging through the documents leaked by Edward Snowden, Kofman found that the NSA has been collecting voice recognition data for years. This technology was used to identify Saddam Hussein and match some past recordings that were captured. Voiceprints for Osama Bin Laden and other high-ranking Al Qaeda members were created, and one memo tells how Abu Musab al-Zarqawi was found to be the speaker in online audio files that the CIA was very interested in. These classified documents show that between 2004 and 2012 the NSA refined and used their speaker recognition technology in counterterrorism ops and international drug arrests.

If you think the country’s top spy agency collecting random recordings of what people say to assist when fighting terrorism or drug trafficking is OK, not many people would argue with you. Google and Facebook (and probably every other internet company) scan collected data for things like child pornography or abuse, copyright infringement, and terroristic activity because someone feels it’s for the greater good. I won’t argue. But Snowden also revealed that the NSA plans to deploy the same tech to prevent whistleblowers like him from exposing their misdeeds years before Executive Order 13587 was signed.

The NSA has plans to use the tactics that help catch Bin Laden to surveil Americans.

That’s going too far, and it has people like former White House adviser to the Director of National Intelligence Timothy Edgar worried about our privacy and the repercussions from an overbearing government agency if they think we have said or done something they don’t like. We’ve all heard cases where a very thin line was walked and crossed and everything wasn’t quite legal according to the spirit of the Constitution. When it comes to our voices, civil liberty experts think Google and Amazon need to change how Home and the Echo work so that no voice data is retained.

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Our voices are stable; something I said 10 years ago can be positively identified through a voiceprint as coming from me today. With voice recordings at hand, even though they are files saying something like “OK Google, dim the living room lights to 50%” it’s easy to use them against us for any investigation or operation. I imagine the NSA has plenty of voice data that can identify a person who served drinks to a wanted terrorist at a hotel, or the agent at the ticket counter in an airport. I also imagine these innocents were questioned and then served with a gag order, even though they were innocent of any crimes and the NSA and CIA and FBI knew they were innocent.

Don’t believe the talk that programs like FISA are designed and used solely to protect us. We have plenty of evidence to the contrary.

This needs to be unmasked and debated in public. Technology that can be used to track journalists or expose their sources is dangerous when not used correctly and shouldn’t be hidden behind a wall of government classification. While groups like the EFF and Freedom of the Press Foundation try to make that happen, there is pressure on Google and Amazon to stop saving and analyzing voice data the way they do now. That puts quite the damper on a system designed to analyze everything and get smarter. Machine learning needs copious amounts of data to analyze over and over to “learn.” Google and Amazon need to worry about collecting and storing it all in a way that keeps it anonymous instead of worrying about how their learning machines can learn without the data they need.

A machine needs data to learn. A lot of data repeated over and over and over again.

Pointing the finger at Alexa and Assistant as the root of the problem isn’t good for anyone who finds them useful. We need to focus on roping in our government and putting a stop to unwarranted surveillance of Americans. And this isn’t just a U.S.A. thing; Interpol and Britain’s GCHQ have “worked closely” with the NSA and credit programs like Voice RT as “playing an important part in our relationship with NSA.” China has been said to have the same type of program and is now able to positively identify tens or perhaps hundreds of thousands of Chinese citizens by the sound of their voice automatically.

I don’t trust the NSA to have secret technology that can identify any of us at any time and not use it inappropriately. I agree with civil liberty experts that this is a dangerous path and should be made public. I don’t agree with pressuring Google and Amazon to stop innovating while we wait. We’re seeing the future unfold before our eyes and until the “smart” machines Google and Amazon are using to bring it to us show that they need reigning in, let’s not hold the future back.

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27
Jan

Can changing the color hue of your room help with tracking for your PlayStation VR?


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If you want the best tracking for your PlayStation VR it might be time to change a few lightbulbs

Reddit user Pavlovs_Human argues that changing the color hue of your room to green helps improve the tracking to your PlayStation VR. So I went ahead and tested this theory to see if your tracking will improve. Whether it’s worth your time to make these changes in your room is up to you.

How does your PlayStation VR track in the first place?

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Your PlayStation Eye Camera tracks where you are moving while you play your PSVR. As I’m sure you’re aware, there’s a very obvious glowing bulb at the end of your PlayStation Controllers. The bulbs use LED lights that can use an endless possibility of colors for your PlayStation Eye to see. Meaning during the set up it will go through its array of possibilities and ensure your two controllers are colors that combat the area surrounding you.

There is also a blue glow radiating from your headset itself. The headset also has motion trackers it does also use the lights to help with tracking. While the LEDs in the controllers can turn any color, the headset will always remain blue. This means that, even if you are in a room that is the same shade of blue, the headset will remain the same color. Because of these settings, it’s probably best to find the best color set up in your play space to ensure your console can find the best configuration.

Did changing to a green hue help tracking?

There was a notable difference in the tracking when I changed the lighting in my room to green. I found that green worked best because it gave the Move Controllers a solid color to combat against, as opposed to many colors around the room. To elaborate, let’s decide your Move controllers calibrate to an orange color to combat a mostly white room you have. Well, what happens when you have to wave your hand in front of an orange painting in the background? Combating those small area’s is why I painted my room a solid color with LED floodlights.

The tracking was most certainly improved with this setup, and I do recommend it, especially if you are having tracking issues you can’t seem to shake. On that note, I also recommend having a different light stand for your green bulbs. This will avoid the hassle of having to constantly switch them out for your original lights. The best types of each to use are listed below!

Which type of bulb and stands to use

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The type of Green bulb you’re going to get and the stand you use to display it matters quite a bit. You want to make sure the color is solid and spread evenly among the room. Meaning spotlights will not work in this situation. Most green LED bulbs will do you just fine, as long as you make sure they are also floodlights and a medium base. (If they are it will certainly be specified on the box).

See on Amazon

Now, onto stands. I’ve seen cases of the usual lamp posts working for other users. But, in my case, I found the light fixture I’ve pictured above worked best. Not only was it on the ceiling to ensure an even spread of light, the three directions allowed for some personal configuration in case I saw an area that didn’t have enough light, or perhaps too much.

See on Amazon

Thoughts?

Have you tried setting up a color hue change in your VR room? What were your results, or what does your setup look like? Tell us in the comments below!

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27
Jan

Apple Shares First Series of HomePod Ads With Focus on Music


Apple today shared its first series of HomePod ads on its official YouTube channel, titled Bass, Beat, Distortion, and Equalizer.

The four ads are each 15-second clips that show the word HomePod animated in various ways, with the actual speaker only appearing in brief flashes. Apple also highlights that the speaker is now available to order ahead of its February 9th launch in the United States, United Kingdom, and Australia.

The music-focused ads are each set to their own song, including Ain’t I by Lizzo, DNA by Kendrick Lamar, Holy Water by Hembree, and All Night by Big Boi. Apple continues to position the HomePod as a “breakthrough speaker” first and “intelligent home assistant” second in the description of each video.





The four ads follow Apple’s teaser video titled Introducing HomePod shared back in June, after the speaker was previewed at WWDC 2017.

Apple has primarily positioned the HomePod as a speaker that can stream Apple Music, but with built-in Siri, users can also send messages, set timers, play podcasts, check the news, control HomeKit-enabled smart home accessories, and complete several other tasks without needing to take out their iPhone.

The high-fidelity speaker is equipped with spatial awareness and Apple-engineered audio technology, including a seven‑tweeter array and high-excursion woofer. It stands nearly seven inches tall and is powered by Apple’s A8 chip.

Related Roundup: HomePodTag: Apple ads
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27
Jan

Ask Engadget: What’s the best way to unify my music collection?


The support shared between readers in the comments section is one of the things we love most about the Engadget community. Over the years, we’ve known you to offer sage advice on everything from Chromecasts and cameras to drones and smartphones. In fact, our community’s knowledge and insights are a reason why many of you participate in the comments.

We truly value the time and detail you all spend in responding to questions from your fellow tech-obsessed commenters, which is why we’ve decided to bring back the much-missed “Ask Engadget” column. This week’s question revolves around creating a master music collection from a variety of sources. Weigh in with your advice in the comments — and feel free to send your own questions along to ask@engadget.com!

Regardless of format, I have long been a music collector. In the 80’s I had a cassette collection in the hundreds, and in the 2000s my CD collection amounted to thousands of discs. Currently, my listening habits are split between iTunes (where I have roughly 80GB of my CD collection) and Spotify (where I have over 100 playlists); iTunes has all my older music, while the Spotify playlists contain all the newer stuff I’ve been checking out. Obviously, I don’t own those songs on Spotify and therefore, they’re not backed up anywhere.

I hate the idea of “losing’” all my music on Spotify. Ideally, I’d like to have everything in one place, but I’m unsure of the best way to do that. So, my question is two-fold: One, is there a way to have my digital collection in the same place I stream from? And two, how does one consolidate and back up their collection in the era of streaming services?

Terrence O'Brien

Terrence O’Brien
Managing Editor

Google Play Music: You can upload your CDs, digital copies purchased elsewhere, etc. so that all your music is there. It integrates seamlessly with your unlimited streaming account. Additionally, if you’re afraid of “losing” music should you cancel your subscription, you can buy songs and albums through Google, which, again, is tightly integrated with your online library. And you can download any music you’ve uploaded or purchased straight from the web app. You can even bulk-download your whole library (well, the parts of it you’ve purchased or uploaded, at least).

It is available on iOS, though I personally can’t speak to the quality of the app. It’s $10 a month or $15 for family plan (up to six family members). You also get YouTube Red (no ads). You can upload up to 50,000 songs for free if you just want to test it out, but you can’t download them without the paid plan.

27
Jan

A ‘Westworld’ mobile game is in the works


HBO’s version of Westworld seems tailor-made for a video game: it’s a fully-realized robot theme park with plenty of opportunities for disaster. And sure enough, you’re about to get one. In the wake of a teaser on the website of the show’s fictitious Delos Incorporated, Warner Bros. Interactive Entertainment has confirmed to Engadget that a mobile game is in development. It’s not saying much about the mechanics of the game itself, but it’s working with Dead by Daylight developer Behavior Interactive and the TV series’ Kilter Films. The title is “currently being tested in limited release,” a spokesperson said.

The teaser itself may offer enough clues as it is. It describes a “Delos Park Training Simulation” that has you managing a “miniature Westworld,” ranging from creating and caring for hosts (i.e. robots) to “managing guest satisfaction.” Naturally, you can expect problems with robots that don’t follow the program — just hope that your park doesn’t fare as badly as the one in the show. The interface might not look exactly like that in the teaser, but it may well give you an idea of what to expect.

Via: io9

Source: Delos Incorporated

27
Jan

‘Monster Hunter: World’: Tips and tricks for solo adventurers


Monster Hunter: World is designed with multiplayer in mind. For the first time in the series, every quest can be tackled with a party of up to four hunters. The game encourages you to team up with friends or search for a hunting party via the matchmaking system. The emphasis on multiplayer lends itself to the belief that solo play may not be ideal in Monster Hunter: World. Rest assured, though, you can find much success as a lone hunter — it just requires you to put your best hunting foot forward. Here’s how to flourish in Monster Hunter: World as a solo player on PlayStation 4 and Xbox One.

Always come prepared

Before accepting a quest and heading out on your way, you need to prepare. Early on, you’ll start to acquire herbs and other materials that turn into useful consumables like potions and antidotes. You can hold up to 10 of each consumable in your item pouch at one time. Since you won’t have teammates to heal you, you’ll want to max those out.

If you don’t have enough resources to craft potions, you can buy them for next to nothing at the provisions stockpile at headquarters. We suggest buying plenty and sending them to storage. Before too long, you can use them to craft mega potions (potion + honey = mega potion) which restore much more health. If you have close to 10 with you at all times, you will be in good shape.

With your healing items situated, visit the Smithy on the second level of headquarters. It’s a always good idea to check to see if you can upgrade any of your weapons or armor with the materials earned from the last quest. Also, after taking down a new monster, you can craft its themed armor — which almost always has better stats than what you currently have on. You won’t always have the materials needed to upgrade your armor and weapons after each mission, but it pays off to check. Even small increases in attack and defense go a long way during the lengthy hunts.

Lastly, visit the canteen on the third level of the headquarters (the cat chef), and purchase a meal. The Chef’s Choice is your best bet, as it almost always provides significant health and stamina boosts, along with the occasional attack/defense bump.

Seriously, don’t forget to eat before the hunt. It’s the most important aspect of your preparation, especially as a solo hunter.

Focus on the main objective

Monster Hunter World‘s quests have a time limit, which ups the thrills and keeps you on your toes. You have 50 minutes to complete a main objective. If the clock runs out, you’ll have to start all over again.

It’s a lot less time than it sounds. You need to make the most of every minute, especially on missions that task you with killing a single boss. Focus on tracking the monster, and sticking with it until the beast falls. There’s nothing worse than inflicting life-threatening damage, only for the mission to end before you can finish the monster off.

You aren’t so pressed for time that you can’t stop to pick up resources in your path toward the objective, but avoid pursuing smaller monsters or going off the beaten trail. Just avoid taking in the sights when there’s a giant dragon to be killed. You can always return to a map on untimed expeditions to gather resources in the same exact area.

Patience and repetition is key

Monster Hunter veterans know that targets don’t succumb easily — hence, the 50-minute time limit. To survive long enough to win, it’s important to avoid taking any unnecessary risks. To avoid risk, you have to know what your enemy is going to do before they do it.

Each monster you fight has its own set of moves, and most of them follow a very rigid pattern. They also tend to have “tells,” which let you know what move will come next. Identifying these patterns and learning when and where to capitalize on them leads to success.

Even when a pattern isn’t clear, it helps to know a monster’s move set. Most have at least one “lunge” toward you, and a set of close-range maneuvers.

As a solo player, rushing into a battle and hacking away as quickly as possible is a surefire way to get yourself killed. Before you even try to inflict damage, you should spend a short time watching your foe’s behavior. That way you can look for an opening, go in for the kill, and go back on the defensive without getting hit.

In the vast majority of missions, you can only faint three times before you have to start over. If you aren’t patient and deliberate, death comes quicker than you’d think.

Wield the environment

There’s one significant advantage in World‘s move to PS4 and Xbox One? The environments are much larger and more diverse. You may think a larger playground sets up better for a team of four, but solo players also can and should still try and make use of the world around them.

Remember, you’re a lot smaller than the monsters you take on. Use the size difference to your advantage by getting behind trees, boulders, anything around you that thwarts an enemy attack. Not only can you use the environment for defense, but you can often use the terrain to create openings or even damage your prey. For example, when monsters collide with structures, they frequently need to regroup, giving you a prolonged opportunity to attack.

Also, if you’re having trouble avoiding a monster’s attacks, try finding a patch of tall grass to conceal yourself. You won’t be able to sneak attack all the time, but when entering new areas, you can catch your enemy off guard by hiding in the shadows and keeping the noise to a minimum.

Consider ranged weapons

Multiple hunters can swarm on a large monster and take hack after hack, weakening it to a standstill. Solo hunters don’t have that luxury. You can absolutely come out on top with any of the 14 weapons on solo hunts, but we’ve found that ranged weapons — particularly light bowguns — seem to provide the best balance of offense and defense.

For starters, you only have to concentrate on avoiding the monster’s long-range attacks when it’s at full strength. You can continuously fire on the beast from afar, stopping only when you need to dodge attacks, scurry away, and repeat.

Ranged weapons also allow you to survey the landscape and use the environment to your advantage more often.

So while hacking away with a large samurai sword or hammer may feel better, you can dole out just as much damage with a string of projectiles while eliminating the threat of at least a few of each monster’s attacks.

Don’t forget to camp

Monster Hunter World is the first entry in the series that lets players manage items and refuel at camp mid-mission. As a solo player, you should absolutely keep this in mind. When the message comes up on screen that you can eat again, fast travel to the nearest camp and eat to get those health and stamina bonuses back.

And if you find yourself low on potions, visit your tent and restock from your item storage. You can also change weapons if you find that your current choice isn’t getting the job done (say it with us: light bowgun).

Editors’ Recommendations

  • ‘Monster Hunter: World’ beginners guide
  • ‘Monster Hunter: World’ review
  • ‘Dauntless’ hands-on preview
  • Prowl the Ancient Forest in this weekend’s ‘Monster Hunter World’ beta for PS4
  • ‘Monster Hunter: World’ costumes will be up for grabs in ‘Street Fighter V’


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