Tag Archives: EAST

Kim Dotcom Loses Megaupload Domain Names, Gets “Destroyed” Gaming Chair Back

Post Syndicated from Ernesto original https://torrentfreak.com/kim-dotcom-loses-megaupload-domain-names-gets-destroyed-gaming-chair-back-180117/

Following the 2012 raid on Megaupload and Kim Dotcom, U.S. and New Zealand authorities seized millions of dollars in cash and other property, located around the world.

Claiming the assets were obtained through copyright and money laundering crimes, the U.S. government launched separate civil cases in which it asked the court to forfeit bank accounts, servers, domain names, and other seized possessions of the Megaupload defendants.

One of these cases was lost after the U.S. branded Dotcom and his colleagues as “fugitives”.The defense team appealed the ruling, but lost again, and a subsequent petition at the Supreme Court was denied.

Following this lost battle, the U.S. also moved to conclude a separate civil forfeiture case, which was still pending at a federal court in Virginia.

The assets listed in this case are several bank accounts, including several at PayPal, as well as 60 servers Megaupload bought at Leaseweb. What has the most symbolic value, however, are the domain names that were seized, including Megaupload.com, Megaporn.com and Megavideo.com.

Mega’s domains

This week a U.S. federal court decided that all claims of Kim Dotcom, his former colleague Mathias Ortman, and several Megaupload-related companies should be stricken. A default was entered against them on Tuesday.

The same fugitive disentitlement argument was used in this case. This essentially means that someone who’s considered to be a fugitive from justice is not allowed to get relief from the judicial system he or she evades.

“Claimants Kim Dotcom and Mathias Ortmann have deliberately avoided prosecution by declining to enter or reenter the United States,” Judge Liam O’Grady writes in his order to strike the claims.

“Because Claimant Kim Dotcom, who is himself a fugitive under Section 2466, is the Corporate Claimants’ controlling shareholder and, in particular, because he signed the claims on behalf of the corporations, a presumption of disentitlement applies to the corporations as well.”

As a result, the domain names which once served 50 million users per day, are now lost to the US Government. The court records list 18 domains in total, which were registered through Godaddy, DotRegistrar, and Fabulous.

Given the legal history, the domains and other assets are likely lost for good. However, Megaupload defense lawyer Ira Rothken is not giving up yet.

“We are still evaluating the legal options in a climate where Kim Dotcom is being labeled a fugitive in a US criminal copyright case even though he has never been to the US, is merely asserting his US-NZ extradition treaty rights, and the NZ High Court has ruled that he and his co-defendants did not commit criminal copyright infringement under NZ law,” Rothken tells TorrentFreak.

There might be a possibility that assets located outside the US could be saved. Foreign courts are more open to defense arguments, it seems, as a Hong Kong court previously ordered the US to return several assets belonging to Kim Dotcom.

The Hong Kong case also brought some good news this week. At least, something that was supposed to be positive. On Twitter, Dotcom writes that two containers with seized assets were returned, but in a “rotten and destroyed” state.

“A shipment of 2 large containers just arrived in New Zealand. This is how all my stuff looks now. Rotten & destroyed. Photo: My favorite gaming chair,” Dotcom wrote.

According to Dotcom, the US Government asked him to pay for ‘climate controlled’ storage for more than half a decade to protect the seized goods. However, judging from the look of the chair and the state of some other belongings, something clearly went wrong.

Rotten & destroyed

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New AWS Auto Scaling – Unified Scaling For Your Cloud Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-auto-scaling-unified-scaling-for-your-cloud-applications/

I’ve been talking about scalability for servers and other cloud resources for a very long time! Back in 2006, I wrote “This is the new world of scalable, on-demand web services. Pay for what you need and use, and not a byte more.” Shortly after we launched Amazon Elastic Compute Cloud (EC2), we made it easy for you to do this with the simultaneous launch of Elastic Load Balancing, EC2 Auto Scaling, and Amazon CloudWatch. Since then we have added Auto Scaling to other AWS services including ECS, Spot Fleets, DynamoDB, Aurora, AppStream 2.0, and EMR. We have also added features such as target tracking to make it easier for you to scale based on the metric that is most appropriate for your application.

Introducing AWS Auto Scaling
Today we are making it easier for you to use the Auto Scaling features of multiple AWS services from a single user interface with the introduction of AWS Auto Scaling. This new service unifies and builds on our existing, service-specific, scaling features. It operates on any desired EC2 Auto Scaling groups, EC2 Spot Fleets, ECS tasks, DynamoDB tables, DynamoDB Global Secondary Indexes, and Aurora Replicas that are part of your application, as described by an AWS CloudFormation stack or in AWS Elastic Beanstalk (we’re also exploring some other ways to flag a set of resources as an application for use with AWS Auto Scaling).

You no longer need to set up alarms and scaling actions for each resource and each service. Instead, you simply point AWS Auto Scaling at your application and select the services and resources of interest. Then you select the desired scaling option for each one, and AWS Auto Scaling will do the rest, helping you to discover the scalable resources and then creating a scaling plan that addresses the resources of interest.

If you have tried to use any of our Auto Scaling options in the past, you undoubtedly understand the trade-offs involved in choosing scaling thresholds. AWS Auto Scaling gives you a variety of scaling options: You can optimize for availability, keeping plenty of resources in reserve in order to meet sudden spikes in demand. You can optimize for costs, running close to the line and accepting the possibility that you will tax your resources if that spike arrives. Alternatively, you can aim for the middle, with a generous but not excessive level of spare capacity. In addition to optimizing for availability, cost, or a blend of both, you can also set a custom scaling threshold. In each case, AWS Auto Scaling will create scaling policies on your behalf, including appropriate upper and lower bounds for each resource.

AWS Auto Scaling in Action
I will use AWS Auto Scaling on a simple CloudFormation stack consisting of an Auto Scaling group of EC2 instances and a pair of DynamoDB tables. I start by removing the existing Scaling Policies from my Auto Scaling group:

Then I open up the new Auto Scaling Console and selecting the stack:

Behind the scenes, Elastic Beanstalk applications are always launched via a CloudFormation stack. In the screen shot above, awseb-e-sdwttqizbp-stack is an Elastic Beanstalk application that I launched.

I can click on any stack to learn more about it before proceeding:

I select the desired stack and click on Next to proceed. Then I enter a name for my scaling plan and choose the resources that I’d like it to include:

I choose the scaling strategy for each type of resource:

After I have selected the desired strategies, I click Next to proceed. Then I review the proposed scaling plan, and click Create scaling plan to move ahead:

The scaling plan is created and in effect within a few minutes:

I can click on the plan to learn more:

I can also inspect each scaling policy:

I tested my new policy by applying a load to the initial EC2 instance, and watched the scale out activity take place:

I also took a look at the CloudWatch metrics for the EC2 Auto Scaling group:

Available Now
We are launching AWS Auto Scaling today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (Ireland), and Asia Pacific (Singapore) Regions today, with more to follow. There’s no charge for AWS Auto Scaling; you pay only for the CloudWatch Alarms that it creates and any AWS resources that you consume.

As is often the case with our new services, this is just the first step on what we hope to be a long and interesting journey! We have a long roadmap, and we’ll be adding new features and options throughout 2018 in response to your feedback.

Jeff;

Early Challenges: Managing Cash Flow

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/managing-cash-flow/

Cash flow projection charts

This post by Backblaze’s CEO and co-founder Gleb Budman is the eighth in a series about entrepreneurship. You can choose posts in the series from the list below:

  1. How Backblaze got Started: The Problem, The Solution, and the Stuff In-Between
  2. Building a Competitive Moat: Turning Challenges Into Advantages
  3. From Idea to Launch: Getting Your First Customers
  4. How to Get Your First 1,000 Customers
  5. Surviving Your First Year
  6. How to Compete with Giants
  7. The Decision on Transparency
  8. Early Challenges: Managing Cash Flow

Use the Join button above to receive notification of new posts in this series.

Running out of cash is one of the quickest ways for a startup to go out of business. When you are starting a company the question of where to get cash is usually the top priority, but managing cash flow is critical for every stage in the lifecycle of a company. As a primarily bootstrapped but capital-intensive business, managing cash flow at Backblaze was and still is a key element of our success and requires continued focus. Let’s look at what we learned over the years.

Raising Your Initial Funding

When starting a tech business in Silicon Valley, the default assumption is that you will immediately try to raise venture funding. There are certainly many advantages to raising funding — not the least of which is that you don’t need to be cash-flow positive since you have cash in the bank and the expectation is that you will have a “burn rate,” i.e. you’ll be spending more than you make.

Note: While you’re not expected to be cash-flow positive, that doesn’t mean you don’t have to worry about cash. Cash-flow management will determine your burn rate. Whether you can get to cash-flow breakeven or need to raise another round of funding is a direct byproduct of your cash flow management.

Also, raising funding takes time (most successful fundraising cycles take 3-6 months start-to-finish), and time at a startup is in short supply. Constantly trying to raise funding can take away from product development and pursuing growth opportunities. If you’re not successful in raising funding, you then have to either shut down or find an alternate method of funding the business.

Sources of Funding

Depending on the stage of the company, type of company, and other factors, you may have access to different sources of funding. Let’s list a number of them:

Customers

Sales — the best kind of funding. It is non-dilutive, doesn’t have to be paid back, and is a direct metric of the success of your company.

Pre-Sales — some customers may be willing to pay you for a product in beta, a test, or pre-pay for a product they’ll receive when finished. Pre-Sales income also is great because it shares the characteristics of cash from sales, but you get the cash early. It also can be a good sign that the product you’re building fills a market need. We started charging for Backblaze computer backup while it was still in private beta, which allowed us to not only collect cash from customers, but also test the billing experience and users’ real desire for the service.

Services — if you’re a service company and customers are paying you for that, great. You can effectively scale for the number of hours available in a day. As demand grows, you can add more employees to increase the total number of billable hours.

Note: If you’re a product company and customers are paying you to consult, that can provide much needed cash, and could provide feedback toward the right product. However, it can also distract from your core business, send you down a path where you’re building a product for a single customer, and addict you to a path that prevents you from building a scalable business.

Investors

Yourself — you likely are putting your time into the business, and deferring salary in the process. You may also put your own cash into the business either as an investment or a loan.

Angels — angels are ideal as early investors since they are used to investing in businesses with little to no traction. AngelList is a good place to find them, though finding people you’re connected with through someone that knows you well is best.

Crowdfunding — a component of the JOBS Act permitted entrepreneurs to raise money from nearly anyone since May 2016. The SEC imposes limits on both investors and the companies. This article goes into some depth on the options and sites available.

VCs — VCs are ideal for companies that need to raise at least a few million dollars and intend to build a business that will be worth over $1 billion.

Debt

Friends & Family — F&F are often the first people to give you money because they are investing in you. It’s great to have some early supporters, but it also can be risky to take money from people who aren’t used to the risks. The key advice here is to only take money from people who won’t mind losing it. If someone is talking about using their children’s college funds or borrowing from their 401k, say ‘no thank you’ — even if they’re sure they want to loan you money.

Bank Loans — a variety of loan types exist, but most either require the company to have been operational for a couple years, be able to borrow against money the company has or is making, or be able to get a personal guarantee from the founders whereby their own credit is on the line. Fundera provides a good overview of loan options and can help secure some, but most will not be an option for a brand new startup.

Grants

Government — in some areas there is the potential for government grants to facilitate research. The SBIR program facilitates some such grants.

At Backblaze, we used a number of these options:

• Investors/Yourself
We loaned a cumulative total of a couple hundred thousand dollars to the company and invested our time by going without a salary for a year and a half.
• Customers/Pre-Sales
We started selling the Backblaze service while it was still in beta.
• Customers/Sales
We launched v1.0 and kept selling.
• Investors/Angels
After a year and a half, we raised $370k from 11 angels. All of them were either people whom we knew personally or were a strong recommendation from a mutual friend.
• Debt/Loans
After a couple years we were able to get equipment leases whereby the Storage Pods and hard drives were used as collateral to secure the lease on them.
• Investors/VCs
Ater five years we raised $5m from TMT Investments to add to the balance sheet and invest in growth.

The variety and quantity of sources we used is by no means uncommon.

GAAP vs. Cash

Most companies start tracking financials based on cash, and as they scale they switch to GAAP (Generally Accepted Accounting Principles). Cash is easier to track — we got paid $XXXX and spent $YYY — and as often mentioned, is required for the business to stay alive. GAAP has more subtlety and complexity, but provides a clearer picture of how the business is really doing. Backblaze was on a ‘cash’ system for the first few years, then switched to GAAP. For this post, I’m going to focus on things that help cash flow, not GAAP profitability.

Stages of Cash Flow Management

All-spend

In a pure service business (e.g. solo proprietor law firm), you may have no expenses other than your time, so this stage doesn’t exist. However, in a product business there is a period of time where you are building the product and have nothing to sell. You have zero cash coming in, but have cash going out. Your cash-flow is completely negative and you need funds to cover that.

Sales-generating

Starting to see cash come in from customers is thrilling. I initially had our system set up to email me with every $5 payment we received. You’re making sales, but not covering expenses.

Ramen-profitable

But it takes a lot of $5 payments to pay for servers and salaries, so for a while expenses are likely to outstrip sales. Getting to ramen-profitable is a critical stage where sales cover the business expenses and are “paying enough for the founders to eat ramen.” This extends the runway for a business, but is not completely sustainable, since presumably the founders can’t (or won’t) live forever on a subsistence salary.

Business-profitable

This is the ultimate stage whereby the business is truly profitable, including paying everyone market-rate salaries. A business at this stage is self-sustaining. (Of course, market shifts and plenty of other challenges can kill the business, but cash-flow issues alone will not.)

Note, I’m using the word ‘profitable’ here to mean this is still on a cash-basis.

Backblaze was in the all-spend stage for just over a year, during which time we built the service and hadn’t yet made the service available to customers. Backblaze was in the sales-generating stage for nearly another year before the company was barely ramen-profitable where sales were covering the company expenses and paying the founders minimum wage. (I say ‘barely’ since minimum wage in the SF Bay Area is arguably never subsistence.) It took almost three more years before the company was business-profitable, paying everyone including the founders market-rate.

Cash Flow Forecasting

When raising funding it’s helpful to think of milestones reached. You don’t necessarily need enough cash on day one to last for the next 100 years of the company. Some good milestones to consider are how much cash you need to prove there is a market need, prove you can build a product to meet that need, or get to ramen-profitable.

Two things to consider:

1) Unit Economics (COGS)

If your product is 100% software, this may not be relevant. Once software is built it costs effectively nothing to deliver the product to one customer or one million customers. However, in most businesses there is some incremental cost to provide the product. If you’re selling a hardware device, perhaps you sell it for $100 but it costs you $50 to make it. This is called “COGS” (Cost of Goods Sold).

Many products rely on cloud services where the costs scale with growth. That model works great, but it’s still important to understand what the costs are for the cloud service you use per unit of product you sell.

Support is often done by the founders early-on in a business, but that is another real cost to factor in and estimate on a per-user basis. Taking all of the per unit costs combined, you may charge $10/month/user for your service, but if it costs you $7/month/user in cloud services, you’re only netting $3/month/user.

2) Operating Expenses (OpEx)

These are expenses that don’t scale with the number of product units you sell. Typically this includes research & development, sales & marketing, and general & administrative expenses. Presumably there is a certain level of these functions required to build the product, market it, sell it, and run the organization. You can choose to invest or cut back on these, but you’ll still make the same amount per product unit.

Incremental Net Profit Per Unit

If you’ve calculated your COGS and your unit economics are “upside down,” where the amount you charge is less than that it costs you to provide your service, it’s worth thinking hard about how that’s going to change over time. If it will not change, there is no scale that will make the business work. Presuming you do make money on each unit of product you sell — what is sometimes referred to as “Contribution Margin” — consider how many of those product units you need to sell to cover your operating expenses as described above.

Calculating Your Profit

The math on getting to ramen-profitable is simple:

(Number of Product Units Sold x Contribution Margin) - Operating Expenses = Profit

If your operating expenses include subsistence salaries for the founders and profit > $0, you’re ramen-profitable.

Improving Cash Flow

Having access to sources of cash, whether from selling to customers or other methods, is excellent. But needing less cash gives you more choices and allows you to either dilute less, owe less, or invest more.

There are two ways to improve cash flow:

1) Collect More Cash

The best way to collect more cash is to provide more value to your customers and as a result have them pay you more. Additional features/products/services can allow this. However, you can also collect more cash by changing how you charge for your product. If you have a subscription, changing from charging monthly to yearly dramatically improves your cash flow. If you have a product that customers use up, selling a year’s supply instead of selling them one-by-one can help.

2) Spend Less Cash

Reducing COGS is a fantastic way to spend less cash in a scalable way. If you can do this without harming the product or customer experience, you win. There are a myriad of ways to also reduce operating expenses, including taking sub-market salaries, using your home instead of renting office space, staying focused on your core product, etc.

Ultimately, collecting more and spending less cash dramatically simplifies the process of getting to ramen-profitable and later to business-profitable.

Be Careful (Why GAAP Matters)

A word of caution: while running out of cash will put you out of business immediately, overextending yourself will likely put you out of business not much later. GAAP shows how a business is really doing; cash doesn’t. If you only focus on cash, it is possible to commit yourself to both delivering products and repaying loans in the future in an unsustainable fashion. If you’re taking out loans, watch the total balance and monthly payments you’re committing to. If you’re asking customers for pre-payment, make sure you believe you can deliver on what they’ve paid for.

Summary

There are numerous challenges to building a business, and ensuring you have enough cash is amongst the most important. Having the cash to keep going lets you keep working on all of the other challenges. The frameworks above were critical for maintaining Backblaze’s cash flow and cash balance. Hopefully you can take some of the lessons we learned and apply them to your business. Let us know what works for you in the comments below.

The post Early Challenges: Managing Cash Flow appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Hollywood Wins ISP Blockade Against Popular Pirate Sites in Ireland

Post Syndicated from Ernesto original https://torrentfreak.com/hollywood-wins-isp-blockade-against-popular-pirate-sites-in-ireland-180116/

Like many other countries throughout Europe, Ireland is no stranger to pirate site blocking efforts.

The Pirate Bay was blocked back in 2009, as part of a voluntary agreement between copyright holders and local ISP Eircom. A few years later the High Court ordered other major Internet providers to follow suit.

However, The Pirate Bay is not the only ‘infringing’ site out there. The Motion Picture Association (MPA) has therefore asked the Commercial Court to expand the blockades to other sites.

On behalf of several major Hollywood studios, the group most recently targeted a group of the most used torrent and streaming sites; 1337x.io, EZTV.ag, Bmovies.is, 123movieshub.to, Putlocker.io, RARBG.to, Gowatchfreemovies.to and YTS.am.

On Monday the Commercial Court sided with the movie studios ordering all major Irish ISPs to block the sites. The latest order applies to Eircom, Sky Ireland, Vodafone Ireland, Virgin Media Ireland, Three Ireland, Digiweb, Imagine Telecommunications and Magnet Networks.

According to Justice Brian McGovern, the movie studios had made it clear that the sites in question infringed their copyrights. As such, there are “significant public interest grounds” to have them blocked.

Irish Examiner reports that none of the ISPs opposed the blocking request. This means that new pirate site blockades are mostly a formality now.

MPA EMEA President and Managing Director Stan McCoy is happy with the outcome, which he says will help to secure jobs in the movie industry.

“As the Irish film industry is continuing to thrive, the MPA is dedicated to supporting that growth by combatting the operations of illegal sites that undermine the sustainability of the sector,” McCoy says.

“Preventing these pirate sites from freely disturbing other people’s work will help us provide greater job security for the 18,000 people employed through the Irish film industry and ensure that consumers can continue to enjoy high quality content in the future.”

The MPA also obtained similar blocks against movie4k.to, primewire.ag, and onwatchseries.to. last year, which remain in effect to date.

The torrent and streaming sites that were targeted most recently have millions of visitors worldwide. While the blockades will make it harder for the Irish to access them directly, history has shown that some people circumvent these measures or simply move to other sites.

Several of the targeted sites themselves are also keeping a close eye on these blocking efforts and are providing users with alternative domains to bypass the restrictions, at least temporarily.

As such, it would be no surprise if the Hollywood studios return to the Commercial Court again in a few months.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Eevee gained 2791 experience points

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/15/eevee-gained-2791-experience-points/

Eevee grew to level 31!

A year strongly defined by mixed success! Also, a lot of video games.

I ran three game jams, resulting in a total of 157 games existing that may not have otherwise, which is totally mindblowing?!

For GAMES MADE QUICK???, glip and I made NEON PHASE, a short little exploratory platformer. Honestly, I should give myself more credit for this and the rest of the LÖVE games I’ve based on the same codebase — I wove a physics engine (and everything else!) from scratch and it has held up remarkably well for a variety of different uses.

I successfully finished an HD version of Isaac’s Descent using my LÖVE engine, though it doesn’t have anything new over the original and I’ve only released it as a tech demo on Patreon.

For Strawberry Jam (NSFW!) we made fox flux (slightly NSFW!), which felt like a huge milestone: the first game where I made all the art! I mean, not counting Isaac’s Descent, which was for a very limited platform. It’s a pretty arbitrary milestone, yes, but it feels significant. I’ve been working on expanding the game into a longer and slightly less buggy experience, but the art is taking the longest by far. I must’ve spent weeks on player sprites alone.

We then set about working on Bolthaven, a sequel of sorts to NEON PHASE, and got decently far, and then abandond it. Oops.

We then started a cute little PICO-8 game, and forgot about it. Oops.

I was recruited to help with Chaos Composer, a more ambitious game glip started with someone else in Unity. I had to get used to Unity, and we squabbled a bit, but the game is finally about at the point where it’s “playable” and “maps” can be designed? It’s slightly on hold at the moment while we all finish up some other stuff, though.

We made a birthday game for two of our friends whose birthdays were very close together! Only they got to see it.

For Ludum Dare 38, we made Lunar Depot 38, a little “wave shooter” or whatever you call those? The AI is pretty rough, seeing as this was the first time I’d really made enemies and I had 72 hours to figure out how to do it, but I still think it’s pretty fun to play and I love the circular world.

I made Roguelike Simulator as an experiment with making something small and quick with a simple tool, and I had a lot of fun! I definitely want to do more stuff like this in the future.

And now we’re working on a game about Star Anise, my cat’s self-insert, which is looking to have more polish and depth than anything we’ve done so far! We’ve definitely come a long way in a year.

Somewhere along the line, I put out a call for a “potluck” project, where everyone would give me sprites of a given size without knowing what anyone else had contributed, and I would then make a game using only those sprites. Unfortunately, that stalled a few times: I tried using the Phaser JS library, but we didn’t get along; I tried LÖVE, but didn’t know where to go with the game; and then I decided to use this as an experiment with procedural generation, and didn’t get around to it. I still feel bad that everyone did work for me and I didn’t follow through, but I don’t know whether this will ever become a game.

veekun, alas, consumed months of my life. I finally got Sun and Moon loaded, but it took weeks of work since I was basically reinventing all the tooling we’d ever had from scratch, without even having most of that tooling available as a reference. It was worth it in the end, at least: Ultra Sun and Ultra Moon only took a few days to get loaded. But veekun itself is still missing some obvious Sun/Moon features, and the whole site needs an overhaul, and I just don’t know if I want to dedicate that much time to it when I have so much other stuff going on that’s much more interesting to me right now.

I finally turned my blog into more of a website, giving it a neat front page that lists a bunch of stuff I’ve done. I made a release category at last, though I’m still not quite in the habit of using it.

I wrote some blog posts, of course! I think the most interesting were JavaScript got better while I wasn’t looking and Object models. I was also asked to write a couple pieces for money for a column that then promptly shut down.

On a whim, I made a set of Eevee mugshots for Doom, which I think is a decent indication of my (pixel) art progress over the year?

I started idchoppers, a Doom parsing and manipulation library written in Rust, though it didn’t get very far and I’ve spent most of the time fighting with Rust because it won’t let me implement all my extremely bad ideas. It can do a couple things, at least, like flip maps very quickly and render maps to SVG.

I did toy around with music a little, but not a lot.

I wrote two short twines for Flora. They’re okay. I’m working on another; I think it’ll be better.

I didn’t do a lot of art overall, at least compared to the two previous years; most of my art effort over the year has gone into fox flux, which requires me to learn a whole lot of things. I did dip my toes into 3D modelling, most notably producing my current Twitter banner as well as this cool Star Anise animation. I wouldn’t mind doing more of that; maybe I’ll even try to make a low-poly pixel-textured 3D game sometime.

I restarted my book with a much better concept, though so far I’ve only written about half a chapter. Argh. I see that the vast majority of the work was done within the span of a single week, which is bad since that means I only worked on it for a week, but good since that means I can actually do a pretty good amount of work in only a week. I also did a lot of squabbling with tooling, which is hopefully mostly out of the way now.

My computer broke? That was an exciting week.


A lot of stuff, but the year as a whole still feels hit or miss. All the time I spent on veekun feels like a black void in the middle of the year, which seems like a good sign that I maybe don’t want to pour even more weeks into it in the near future.

Mostly, I want to do: more games, more art, more writing, more music.

I want to try out some tiny game making tools and make some tiny games with them — partly to get exposure to different things, partly to get more little ideas out into the world regularly, and partly to get more practice at letting myself have ideas. I have a couple tools in mind and I guess I’ll aim at a microgame every two months or so? I’d also like to finish the expanded fox flux by the end of the year, of course, though at the moment I can’t even gauge how long it might take.

I seriously lapsed on drawing last year, largely because fox flux pixel art took me so much time. So I want to draw more, and I want to get much faster at pixel art. It would probably help if I had a more concrete goal for drawing, so I might try to draw some short comics and write a little visual novel or something, which would also force me to aim for consistency.

I want to work on my book more, of course, but I also want to try my hand at a bit more fiction. I’ve had a blast writing dialogue for our games! I just shy away from longer-form writing for some reason — which seems ridiculous when a large part of my audience found me through my blog. I do think I’ve had some sort of breakthrough in the last month or two; I suddenly feel a good bit more confident about writing in general and figuring out what I want to say? One recent post I know I wrote in a single afternoon, which virtually never happens because I keep rewriting and rearranging stuff. Again, a visual novel would be a good excuse to practice writing fiction without getting too bogged down in details.

And, ah, music. I shy heavily away from music, since I have no idea what I’m doing, and also I seem to spend a lot of time fighting with tools. (Surprise.) I tried out SunVox for the first time just a few days ago and have been enjoying it quite a bit for making sound effects, so I might try it for music as well. And once again, visual novel background music is a pretty low-pressure thing to compose for. Hell, visual novels are small games, too, so that checks all the boxes. I guess I’ll go make a visual novel.

Here’s to twenty gayteen!

Pirate Streaming on Facebook is a Seriously Risky Business

Post Syndicated from Andy original https://torrentfreak.com/pirate-streaming-on-facebook-is-a-seriously-risky-business-180114/

For more than a year the British public has been warned about the supposed dangers of Kodi piracy.

Dozens of headlines have claimed consequences ranging from system-destroying malware to prison sentences. Fortunately, most of them can be filed under “tabloid nonsense.”

That being said, there is an extremely important issue that deserves much closer attention, particularly given a shift in the UK legal climate during 2017. We’re talking about live streaming copyrighted content on Facebook, which is both incredibly easy and frighteningly risky.

This week it was revealed that 34-year-old Craig Foster from the UK had been given an ultimatum from Sky to pay a £5,000 settlement fee. The media giant discovered that he’d live-streamed the Anthony Joshua v Wladimir Klitschko fight on Facebook and wanted compensation to make a potential court case disappear.

While it may seem initially odd to use the word, Foster was lucky.

Under last year’s Digital Economy Act, he could’ve been jailed for up to ten years for distributing copyright-infringing content to the public, if he had “reason to believe that communicating the work to the public [would] cause loss to the owner of the copyright, or [would] expose the owner of the copyright to a risk of loss.”

Clearly, as a purchaser of the £19.95 pay-per-view himself, he would’ve appreciated that the event costs money. With that in mind, a court would likely find that he would have been aware that Sky would have been exposed to a “risk of loss”. Sky claim that 4,250 people watched the stream but the way the law is written, no specific level of loss is required for a breach of the law.

But it’s not just the threat of a jail sentence that’s the problem. People streaming live sports on Facebook are sitting ducks.

In Foster’s case, the fight he streamed was watermarked, which means that Sky put a tracking code into it which identified him personally as the buyer of the event. When he (or his friend, as Foster claims) streamed it on Facebook, it was trivial for Sky to capture the watermark and track it back to his Sky account.

Equally, it would be simplicity itself to see that the name on the Sky account had exactly the same name and details as Foster’s Facebook account. So, to most observers, it would appear that not only had Foster purchased the event, but he was also streaming it to Facebook illegally.

It’s important to keep something else in mind. No cooperation between Sky and Facebook would’ve been necessary to obtain Foster’s details. Take the amount of information most people share on Facebook, combine that with the information Sky already had, and the company’s anti-piracy team would have had a very easy job.

Now compare this situation with an upload of the same stream to a torrent site.

While the video capture would still contain Foster’s watermark, which would indicate the source, to prove he also distributed the video Sky would’ve needed to get inside a torrent swarm. From there they would need to capture the IP address of the initial seeder and take the case to court, to force an ISP to hand over that person’s details.

Presuming they were the same person, Sky would have a case, with a broadly similar level of evidence to that presented in the current matter. However, it would’ve taken them months to get their man and cost large sums of money to get there. It’s very unlikely that £5,000 would cover the costs, meaning a much, much bigger bill for the culprit.

Or, confident that Foster was behind the leak based on the watermark alone, Sky could’ve gone straight to the police. That never ends well.

The bottom line is that while live-streaming on Facebook is simplicity itself, people who do it casually from their own account (especially with watermarked content) are asking for trouble.

Nailing Foster was the piracy equivalent of shooting fish in a barrel but the worrying part is that he probably never gave his (or his friend’s…) alleged infringement a second thought. With a click or two, the fight was live and he was staring down the barrel of a potential jail sentence, had Sky not gone the civil route.

It’s scary stuff and not enough is being done to warn people of the consequences. Forget the scare stories attempting to deter people from watching fights or movies on Kodi, thoughtlessly streaming them to the public on social media is the real danger.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

AWS Glue Now Supports Scala Scripts

Post Syndicated from Mehul Shah original https://aws.amazon.com/blogs/big-data/aws-glue-now-supports-scala-scripts/

We are excited to announce AWS Glue support for running ETL (extract, transform, and load) scripts in Scala. Scala lovers can rejoice because they now have one more powerful tool in their arsenal. Scala is the native language for Apache Spark, the underlying engine that AWS Glue offers for performing data transformations.

Beyond its elegant language features, writing Scala scripts for AWS Glue has two main advantages over writing scripts in Python. First, Scala is faster for custom transformations that do a lot of heavy lifting because there is no need to shovel data between Python and Apache Spark’s Scala runtime (that is, the Java virtual machine, or JVM). You can build your own transformations or invoke functions in third-party libraries. Second, it’s simpler to call functions in external Java class libraries from Scala because Scala is designed to be Java-compatible. It compiles to the same bytecode, and its data structures don’t need to be converted.

To illustrate these benefits, we walk through an example that analyzes a recent sample of the GitHub public timeline available from the GitHub archive. This site is an archive of public requests to the GitHub service, recording more than 35 event types ranging from commits and forks to issues and comments.

This post shows how to build an example Scala script that identifies highly negative issues in the timeline. It pulls out issue events in the timeline sample, analyzes their titles using the sentiment prediction functions from the Stanford CoreNLP libraries, and surfaces the most negative issues.

Getting started

Before we start writing scripts, we use AWS Glue crawlers to get a sense of the data—its structure and characteristics. We also set up a development endpoint and attach an Apache Zeppelin notebook, so we can interactively explore the data and author the script.

Crawl the data

The dataset used in this example was downloaded from the GitHub archive website into our sample dataset bucket in Amazon S3, and copied to the following locations:

s3://aws-glue-datasets-<region>/examples/scala-blog/githubarchive/data/

Choose the best folder by replacing <region> with the region that you’re working in, for example, us-east-1. Crawl this folder, and put the results into a database named githubarchive in the AWS Glue Data Catalog, as described in the AWS Glue Developer Guide. This folder contains 12 hours of the timeline from January 22, 2017, and is organized hierarchically (that is, partitioned) by year, month, and day.

When finished, use the AWS Glue console to navigate to the table named data in the githubarchive database. Notice that this data has eight top-level columns, which are common to each event type, and three partition columns that correspond to year, month, and day.

Choose the payload column, and you will notice that it has a complex schema—one that reflects the union of the payloads of event types that appear in the crawled data. Also note that the schema that crawlers generate is a subset of the true schema because they sample only a subset of the data.

Set up the library, development endpoint, and notebook

Next, you need to download and set up the libraries that estimate the sentiment in a snippet of text. The Stanford CoreNLP libraries contain a number of human language processing tools, including sentiment prediction.

Download the Stanford CoreNLP libraries. Unzip the .zip file, and you’ll see a directory full of jar files. For this example, the following jars are required:

  • stanford-corenlp-3.8.0.jar
  • stanford-corenlp-3.8.0-models.jar
  • ejml-0.23.jar

Upload these files to an Amazon S3 path that is accessible to AWS Glue so that it can load these libraries when needed. For this example, they are in s3://glue-sample-other/corenlp/.

Development endpoints are static Spark-based environments that can serve as the backend for data exploration. You can attach notebooks to these endpoints to interactively send commands and explore and analyze your data. These endpoints have the same configuration as that of AWS Glue’s job execution system. So, commands and scripts that work there also work the same when registered and run as jobs in AWS Glue.

To set up an endpoint and a Zeppelin notebook to work with that endpoint, follow the instructions in the AWS Glue Developer Guide. When you are creating an endpoint, be sure to specify the locations of the previously mentioned jars in the Dependent jars path as a comma-separated list. Otherwise, the libraries will not be loaded.

After you set up the notebook server, go to the Zeppelin notebook by choosing Dev Endpoints in the left navigation pane on the AWS Glue console. Choose the endpoint that you created. Next, choose the Notebook Server URL, which takes you to the Zeppelin server. Log in using the notebook user name and password that you specified when creating the notebook. Finally, create a new note to try out this example.

Each notebook is a collection of paragraphs, and each paragraph contains a sequence of commands and the output for that command. Moreover, each notebook includes a number of interpreters. If you set up the Zeppelin server using the console, the (Python-based) pyspark and (Scala-based) spark interpreters are already connected to your new development endpoint, with pyspark as the default. Therefore, throughout this example, you need to prepend %spark at the top of your paragraphs. In this example, we omit these for brevity.

Working with the data

In this section, we use AWS Glue extensions to Spark to work with the dataset. We look at the actual schema of the data and filter out the interesting event types for our analysis.

Start with some boilerplate code to import libraries that you need:

%spark

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext

Then, create the Spark and AWS Glue contexts needed for working with the data:

@transient val spark: SparkContext = SparkContext.getOrCreate()
val glueContext: GlueContext = new GlueContext(spark)

You need the transient decorator on the SparkContext when working in Zeppelin; otherwise, you will run into a serialization error when executing commands.

Dynamic frames

This section shows how to create a dynamic frame that contains the GitHub records in the table that you crawled earlier. A dynamic frame is the basic data structure in AWS Glue scripts. It is like an Apache Spark data frame, except that it is designed and optimized for data cleaning and transformation workloads. A dynamic frame is well-suited for representing semi-structured datasets like the GitHub timeline.

A dynamic frame is a collection of dynamic records. In Spark lingo, it is an RDD (resilient distributed dataset) of DynamicRecords. A dynamic record is a self-describing record. Each record encodes its columns and types, so every record can have a schema that is unique from all others in the dynamic frame. This is convenient and often more efficient for datasets like the GitHub timeline, where payloads can vary drastically from one event type to another.

The following creates a dynamic frame, github_events, from your table:

val github_events = glueContext
                    .getCatalogSource(database = "githubarchive", tableName = "data")
                    .getDynamicFrame()

The getCatalogSource() method returns a DataSource, which represents a particular table in the Data Catalog. The getDynamicFrame() method returns a dynamic frame from the source.

Recall that the crawler created a schema from only a sample of the data. You can scan the entire dataset, count the rows, and print the complete schema as follows:

github_events.count
github_events.printSchema()

The result looks like the following:

The data has 414,826 records. As before, notice that there are eight top-level columns, and three partition columns. If you scroll down, you’ll also notice that the payload is the most complex column.

Run functions and filter records

This section describes how you can create your own functions and invoke them seamlessly to filter records. Unlike filtering with Python lambdas, Scala scripts do not need to convert records from one language representation to another, thereby reducing overhead and running much faster.

Let’s create a function that picks only the IssuesEvents from the GitHub timeline. These events are generated whenever someone posts an issue for a particular repository. Each GitHub event record has a field, “type”, that indicates the kind of event it is. The issueFilter() function returns true for records that are IssuesEvents.

def issueFilter(rec: DynamicRecord): Boolean = { 
    rec.getField("type").exists(_ == "IssuesEvent") 
}

Note that the getField() method returns an Option[Any] type, so you first need to check that it exists before checking the type.

You pass this function to the filter transformation, which applies the function on each record and returns a dynamic frame of those records that pass.

val issue_events =  github_events.filter(issueFilter)

Now, let’s look at the size and schema of issue_events.

issue_events.count
issue_events.printSchema()

It’s much smaller (14,063 records), and the payload schema is less complex, reflecting only the schema for issues. Keep a few essential columns for your analysis, and drop the rest using the ApplyMapping() transform:

val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                 ("actor.login", "string", "actor", "string"), 
                                                 ("repo.name", "string", "repo", "string"),
                                                 ("payload.action", "string", "action", "string"),
                                                 ("payload.issue.title", "string", "title", "string")))
issue_titles.show()

The ApplyMapping() transform is quite handy for renaming columns, casting types, and restructuring records. The preceding code snippet tells the transform to select the fields (or columns) that are enumerated in the left half of the tuples and map them to the fields and types in the right half.

Estimating sentiment using Stanford CoreNLP

To focus on the most pressing issues, you might want to isolate the records with the most negative sentiments. The Stanford CoreNLP libraries are Java-based and offer sentiment-prediction functions. Accessing these functions through Python is possible, but quite cumbersome. It requires creating Python surrogate classes and objects for those found on the Java side. Instead, with Scala support, you can use those classes and objects directly and invoke their methods. Let’s see how.

First, import the libraries needed for the analysis:

import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

The Stanford CoreNLP libraries have a main driver that orchestrates all of their analysis. The driver setup is heavyweight, setting up threads and data structures that are shared across analyses. Apache Spark runs on a cluster with a main driver process and a collection of backend executor processes that do most of the heavy sifting of the data.

The Stanford CoreNLP shared objects are not serializable, so they cannot be distributed easily across a cluster. Instead, you need to initialize them once for every backend executor process that might need them. Here is how to accomplish that:

val props = new Properties()
props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
props.setProperty("parse.maxlen", "70")

object myNLP {
    lazy val coreNLP = new StanfordCoreNLP(props)
}

The properties tell the libraries which annotators to execute and how many words to process. The preceding code creates an object, myNLP, with a field coreNLP that is lazily evaluated. This field is initialized only when it is needed, and only once. So, when the backend executors start processing the records, each executor initializes the driver for the Stanford CoreNLP libraries only one time.

Next is a function that estimates the sentiment of a text string. It first calls Stanford CoreNLP to annotate the text. Then, it pulls out the sentences and takes the average sentiment across all the sentences. The sentiment is a double, from 0.0 as the most negative to 4.0 as the most positive.

def estimatedSentiment(text: String): Double = {
    if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
    val annotations = myNLP.coreNLP.process(text)
    val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
    sentences.foldLeft(0.0)( (csum, x) => { 
        csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
    }) / sentences.length
}

Now, let’s estimate the sentiment of the issue titles and add that computed field as part of the records. You can accomplish this with the map() method on dynamic frames:

val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
    val mbody = rec.getField("title")
    mbody match {
        case Some(mval: String) => { 
            rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
            rec }
        case _ => rec
    }
})

The map() method applies the user-provided function on every record. The function takes a DynamicRecord as an argument and returns a DynamicRecord. The code above computes the sentiment, adds it in a top-level field, sentiment, to the record, and returns the record.

Count the records with sentiment and show the schema. This takes a few minutes because Spark must initialize the library and run the sentiment analysis, which can be involved.

issue_sentiments.count
issue_sentiments.printSchema()

Notice that all records were processed (14,063), and the sentiment value was added to the schema.

Finally, let’s pick out the titles that have the lowest sentiment (less than 1.5). Count them and print out a sample to see what some of the titles look like.

val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))
pressing_issues.count
pressing_issues.show(10)

Next, write them all to a file so that you can handle them later. (You’ll need to replace the output path with your own.)

glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions("""{"path": "s3://<bucket>/out/path/"}"""), 
                              format = "json")
            .writeDynamicFrame(pressing_issues)

Take a look in the output path, and you can see the output files.

Putting it all together

Now, let’s create a job from the preceding interactive session. The following script combines all the commands from earlier. It processes the GitHub archive files and writes out the highly negative issues:

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext
import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

object GlueApp {

    object myNLP {
        val props = new Properties()
        props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
        props.setProperty("parse.maxlen", "70")

        lazy val coreNLP = new StanfordCoreNLP(props)
    }

    def estimatedSentiment(text: String): Double = {
        if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
        val annotations = myNLP.coreNLP.process(text)
        val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
        sentences.foldLeft(0.0)( (csum, x) => { 
            csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
        }) / sentences.length
    }

    def main(sysArgs: Array[String]) {
        val spark: SparkContext = SparkContext.getOrCreate()
        val glueContext: GlueContext = new GlueContext(spark)

        val dbname = "githubarchive"
        val tblname = "data"
        val outpath = "s3://<bucket>/out/path/"

        val github_events = glueContext
                            .getCatalogSource(database = dbname, tableName = tblname)
                            .getDynamicFrame()

        val issue_events =  github_events.filter((rec: DynamicRecord) => {
            rec.getField("type").exists(_ == "IssuesEvent")
        })

        val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                         ("actor.login", "string", "actor", "string"), 
                                                         ("repo.name", "string", "repo", "string"),
                                                         ("payload.action", "string", "action", "string"),
                                                         ("payload.issue.title", "string", "title", "string")))

        val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
            val mbody = rec.getField("title")
            mbody match {
                case Some(mval: String) => { 
                    rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
                    rec }
                case _ => rec
            }
        })

        val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))

        glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions(s"""{"path": "$outpath"}"""), 
                              format = "json")
                    .writeDynamicFrame(pressing_issues)
    }
}

Notice that the script is enclosed in a top-level object called GlueApp, which serves as the script’s entry point for the job. (You’ll need to replace the output path with your own.) Upload the script to an Amazon S3 location so that AWS Glue can load it when needed.

To create the job, open the AWS Glue console. Choose Jobs in the left navigation pane, and then choose Add job. Create a name for the job, and specify a role with permissions to access the data. Choose An existing script that you provide, and choose Scala as the language.

For the Scala class name, type GlueApp to indicate the script’s entry point. Specify the Amazon S3 location of the script.

Choose Script libraries and job parameters. In the Dependent jars path field, enter the Amazon S3 locations of the Stanford CoreNLP libraries from earlier as a comma-separated list (without spaces). Then choose Next.

No connections are needed for this job, so choose Next again. Review the job properties, and choose Finish. Finally, choose Run job to execute the job.

You can simply edit the script’s input table and output path to run this job on whatever GitHub timeline datasets that you might have.

Conclusion

In this post, we showed how to write AWS Glue ETL scripts in Scala via notebooks and how to run them as jobs. Scala has the advantage that it is the native language for the Spark runtime. With Scala, it is easier to call Scala or Java functions and third-party libraries for analyses. Moreover, data processing is faster in Scala because there’s no need to convert records from one language runtime to another.

You can find more example of Scala scripts in our GitHub examples repository: https://github.com/awslabs/aws-glue-samples. We encourage you to experiment with Scala scripts and let us know about any interesting ETL flows that you want to share.

Happy Glue-ing!

 


Additional Reading

If you found this post useful, be sure to check out Simplify Querying Nested JSON with the AWS Glue Relationalize Transform and Genomic Analysis with Hail on Amazon EMR and Amazon Athena.

 


About the Authors

Mehul Shah is a senior software manager for AWS Glue. His passion is leveraging the cloud to build smarter, more efficient, and easier to use data systems. He has three girls, and, therefore, he has no spare time.

 

 

 

Ben Sowell is a software development engineer at AWS Glue.

 

 

 

 
Vinay Vivili is a software development engineer for AWS Glue.

 

 

 

Epic Games Sues Cheater Over ‘Stealing’ Fortnite V-Bucks

Post Syndicated from Ernesto original https://torrentfreak.com/epic-games-sues-cheater-over-stealing-fortnite-v-bucks-180112/

Last fall, Epic Games released Fortnite’s free-to-play “Battle Royale” game mode for the PC and other platforms, generating massive interest among gamers.

This also included thousands of cheaters, many of whom were subsequently banned. Epic Games then went a step further by taking several cheaters to court for copyright infringement.

While the initial targets were people who coded, used or promoted cheats to gain a clear competitive advantage, this week Epic sued a different type of cheater. In a complaint filed at a California Federal court, the game publisher accuses a New Zealander of creating an exploit that allows users to get free V-bucks.

V-bucks are the game’s currency and can be bought through an online store, starting at $9.99. The virtual coins allow players to purchase skins for their characteras well as other game tools.

According to Epic, people who create and use these kinds of free-money exploits are stealing from the game publisher.

“Players who search for and promote exploits ruin the game experience for others and undermine the integrity of Fortnite. Players who use exploits to avoid paying for items in Fortnite are stealing from Epic,” the complaint reads.

V-bucks

The alleged perpetrator is identified as Yash Gosai, who’s a resident of Auckland, New Zealand. Epic believes that Gosai developed the exploit which was then promoted through YouTube.

“On information and belief, Gosai developed an exploit for Fortnite’s Battle Royale mode that enables players to obtain V-bucks without paying for them. Gosai created and posted a video on YouTube to advertise, promote and demonstrate the exploit,” the complaint reads.

While the game company managed to get the video taken down, they’re not done with the New Zealander. They accuse Gosai of copyright infringement, breach of contract, as well as conversion.

“Defendant’s videos demonstrating the exploit infringe Epic’s copyrights in Fortnite by copying, reproducing, preparing derivative works from, and/or displaying Fortnite
publicly without Epic’s permission, the company writes.

Epic asks the court for damages and wants the defendant to destroy all Fortnite copies and any related works.

As mentioned before, this is not the first lawsuit Epic has filed against a cheater. Thus far, it has reached at least three settlements behind closed doors. Minnesota resident Charles Vraspir signed an agreement early December. Philip Josefsson from Sweden and Artem Yakovenko from Russia followed soon after.

A copy of the complaint against Gosai is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

timeShift(GrafanaBuzz, 1w) Issue 29

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/12/timeshiftgrafanabuzz-1w-issue-29/

Welcome to TimeShift

intro paragraph


Latest Stable Release

Grafana 4.6.3 is now available. Latest bugfixes include:

  • Gzip: Fixes bug Gravatar images when gzip was enabled #5952
  • Alert list: Now shows alert state changes even after adding manual annotations on dashboard #99513
  • Alerting: Fixes bug where rules evaluated as firing when all conditions was false and using OR operator. #93183
  • Cloudwatch: CloudWatch no longer display metrics’ default alias #101514, thx @mtanda

Download Grafana 4.6.3 Now


From the Blogosphere

Graphite 1.1: Teaching an Old Dog New Tricks: Grafana Labs’ own Dan Cech is a contributor to the Graphite project, and has been instrumental in the addition of some of the newest features. This article discusses five of the biggest additions, how they work, and what you can expect for the future of the project.

Instrument an Application Using Prometheus and Grafana: Chris walks us through how easy it is to get useful metrics from an application to understand bottlenecks and performace. In this article, he shares an application he built that indexes your Gmail account into Elasticsearch, and sends the metrics to Prometheus. Then, he shows you how to set up Grafana to get meaningful graphs and dashboards.

Visualising Serverless Metrics With Grafana Dashboards: Part 3 in this series of blog posts on “Monitoring Serverless Applications Metrics” starts with an overview of Grafana and the UI, covers queries and templating, then dives into creating some great looking dashboards. The series plans to conclude with a post about setting up alerting.

Huawei FAT WLAN Access Points in Grafana: Huawei’s FAT firmware for their WLAN Access points lacks central management overview. To get a sense of the performance of your AP’s, why not quickly create a templated dashboard in Grafana? This article quickly steps your through the process, and includes a sample dashboard.


Grafana Plugins

Lots of updated plugins this week. Plugin authors add new features and fix bugs often, to make your plugin perform better – so it’s important to keep your plugins up to date. We’ve made updating easy; for on-prem Grafana, use the Grafana-cli tool, or update with 1 click if you’re using Hosted Grafana.

UPDATED PLUGIN

Clickhouse Data Source – The Clickhouse Data Source plugin has been updated a few times with small fixes during the last few weeks.

  • Fix for quantile functions
  • Allow rounding with round option for both time filters: $from and $to

Update

UPDATED PLUGIN

Zabbix App – The Zabbix App had a release with a redesign of the Triggers panel as well as support for Multiple data sources for the triggers panel

Update

UPDATED PLUGIN

OpenHistorian Data Source – this data source plugin received some new query builder screens and improved documentation.

Update

UPDATED PLUGIN

BT Status Dot Panel – This panel received a small bug fix.

Update

UPDATED PLUGIN

Carpet Plot Panel – A recent update for this panel fixes a D3 import bug.

Update


Upcoming Events

In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

Women Who Go Berlin: Go Workshop – Monitoring and Troubleshooting using Prometheus and Grafana | Berlin, Germany – Jan 31, 2018: In this workshop we will learn about one of the most important topics in making apps production ready: Monitoring. We will learn how to use tools you’ve probably heard a lot about – Prometheus and Grafana, and using what we learn we will troubleshoot a particularly buggy Go app.

Register Now

FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. There is no need to register; all are welcome.

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Carl Bergquist – Quickie: Monitoring? Not OPS Problem

Why should we monitor our system? Why can’t we just rely on the operations team anymore? They use to be able to do that. What’s currently changing? Presentation content: – Why do we monitor our system – How did it use to work? – Whats changing – Why do we need to shift focus – Everyone should be on call. – Resilience is the goal (Best way of having someone care about quality is to make them responsible).

Register Now

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Leonard Gram – Presentation: DevOps Deconstructed

What’s a Site Reliability Engineer and how’s that role different from the DevOps engineer my boss wants to hire? I really don’t want to be on call, should I? Is Docker the right place for my code or am I better of just going straight to Serverless? And why should I care about any of it? I’ll try to answer some of these questions while looking at what DevOps really is about and how commodisation of servers through “the cloud” ties into it all. This session will be an opinionated piece from a developer who’s been on-call for the past 6 years and would like to convince you to do the same, at least once.

Register Now

Stockholm Metrics and Monitoring | Stockholm, Sweden – Feb 7, 2018:
Observability 3 ways – Logging, Metrics and Distributed Tracing

Let’s talk about often confused telemetry tools: Logging, Metrics and Distributed Tracing. We’ll show how you capture latency using each of the tools and how they work differently. Through examples and discussion, we’ll note edge cases where certain tools have advantages over others. By the end of this talk, we’ll better understand how each of Logging, Metrics and Distributed Tracing aids us in different ways to understand our applications.

Register Now

OpenNMS – Introduction to “Grafana” | Webinar – Feb 21, 2018:
IT monitoring helps detect emerging hardware damage and performance bottlenecks in the enterprise network before any consequential damage or disruption to business processes occurs. The powerful open-source OpenNMS software monitors a network, including all connected devices, and provides logging of a variety of data that can be used for analysis and planning purposes. In our next OpenNMS webinar on February 21, 2018, we introduce “Grafana” – a web-based tool for creating and displaying dashboards from various data sources, which can be perfectly combined with OpenNMS.

Register Now

GrafanaCon EU | Amsterdam, Netherlands – March 1-2, 2018:
Lock in your seat for GrafanaCon EU while there are still tickets avaialable! Join us March 1-2, 2018 in Amsterdam for 2 days of talks centered around Grafana and the surrounding monitoring ecosystem including Graphite, Prometheus, InfluxData, Elasticsearch, Kubernetes, and more.

We have some exciting talks lined up from Google, CERN, Bloomberg, eBay, Red Hat, Tinder, Automattic, Prometheus, InfluxData, Percona and more! Be sure to get your ticket before they’re sold out.

Learn More


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Nice hack! I know I like to keep one eye on server requests when I’m dropping beats. 😉


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

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Announcing our new beta for the AWS Certified Security – Specialty exam

Post Syndicated from Janna Pellegrino original https://aws.amazon.com/blogs/architecture/announcing-our-new-beta-for-the-aws-certified-security-specialty-exam/

Take the AWS Certified Security – Specialty beta exam for the chance to be among the first to hold this new AWS Certification. This beta exam allows experienced cloud security professionals to demonstrate and validate their expertise. Register today – this beta exam will only be available from January 15 to March 2!

About the exam

This beta exam validates that the successful candidate can effectively demonstrate knowledge of how to secure the AWS platform. The exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection.

The exam validates:

  • Familiarity with regional- and country-specific security and compliance regulations and meta issues that these regulations embody.
  • An understanding of specialized data classifications and AWS data protection mechanisms.
  • An understanding of data encryption methods and AWS mechanisms to implement them.
  • An understanding of secure Internet protocols and AWS mechanisms to implement them.
  • A working knowledge of AWS security services and features of services to provide a secure production environment.
  • Competency gained from two or more years of production deployment experience using AWS security services and features.
  • Ability to make tradeoff decisions with regard to cost, security, and deployment complexity given a set of application requirements.
  • An understanding of security operations and risk.

Learn more and register >>

Who is eligible

The beta is open to anyone who currently holds an Associate or Cloud Practitioner certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

How to prepare

We have training and other resources to help you prepare for the beta exam:

AWS Security Fundamentals Digital| 3 Hours
This course introduces you to fundamental cloud computing and AWS security concepts, including AWS access control and management, governance, logging, and encryption methods. It also covers security-related compliance protocols and risk management strategies, as well as procedures related to auditing your AWS security infrastructure.

Security Operations on AWS Classroom | 3 Days
This course demonstrates how to efficiently use AWS security services to stay secure and compliant in the AWS Cloud. The course focuses on the AWS-recommended security best practices that you can implement to enhance the security of your data and systems in the cloud. The course highlights the security features of AWS key services including compute, storage, networking, and database services.

Online resources for Cloud Security and Compliance

Review documentation, whitepapers, and articles & tutorials related to cloud security and compliance.

Learn more and register >>

Please contact us if you have questions about exam registration.

Good luck!

Europol Hits Huge 500,000 Subscriber Pirate IPTV Operation

Post Syndicated from Andy original https://torrentfreak.com/europol-hits-huge-500000-subscriber-pirate-iptv-operation-180111/

Live TV is in massive demand but accessing all content in a particular region can be a hugely expensive proposition, with tradtional broadcasting monopolies demanding large subscription fees.

For millions around the world, this ‘problem’ can be easily circumvented. Pirate IPTV operations, which supply thousands of otherwise subscription channels via the Internet, are on the increase. They’re accessible for just a few dollars, euros, or pounds per month, slashing bills versus official providers on a grand scale.

This week, however, police forces around Europe coordinated to target what they claim is one of the world’s largest illicit IPTV operations. The investigation was launched last February by Europol and on Tuesday coordinated actions were carried out in Cyprus, Bulgaria, Greece, and the Netherlands.

Three suspects were arrested in Cyprus – two in Limassol (aged 43 and 44) and one in Larnaca (aged 53). All are alleged to be part of an international operation to illegally broadcast around 1,200 channels of pirated content worldwide. Some of the channels offered were illegally sourced from Sky UK, Bein Sports, Sky Italia, and Sky DE

If initial reports are to be believed, the reach of the IPTV service was huge. Figures usually need to be taken with a pinch of salt but information suggests the service had more than 500,000 subscribers, each paying around 10 euros per month. (Note: how that relates to the alleged five million euros per year in revenue is yet to be made clear)

Police action was spread across the continent, with at least nine separate raids, including in the Netherlands where servers were uncovered. However, it was determined that these were in place to hide the true location of the operation’s main servers. Similar ‘front’ servers were also deployed in other regions.

The main servers behind the IPTV operation were located in Petrich, a small town in Blagoevgrad Province, southwestern Bulgaria. No details have been provided by the authorities but TF is informed that the website of a local ISP, Megabyte-Internet, from where pirate IPTV has been broadcast for at least the past several months, disappeared on Tuesday. It remains offline this morning.

The company did not respond to our request for comment and there’s no suggestion that it’s directly involved in any illegal activity. However, its Autonomous System (AS) number reveals linked IPTV services, none of which appear to be operational today. The ISP is also listed on sites where ‘pirate’ IPTV channel playlists are compiled by users.

According to sources in Cyprus, police requested permission from the Larnaca District Court to detain the arrested individuals for eight days. However, local news outlet Philenews said that any decision would be postponed until this morning, since one of the three suspects, an English Cypriot, required an interpreter which caused a delay.

In addition to prosecutors and defense lawyers, two Dutch investigators from Europol were present in court yesterday. The hearing lasted for six hours and was said to be so intensive that the court stenographer had to be replaced due to overwork.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Validate Your IT Security Expertise with the New AWS Certified Security – Specialty Beta Exam

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/security/validate-your-it-security-expertise-with-the-new-aws-certified-security-specialty-beta-exam/

AWS Training and Certification image

If you are an experienced cloud security professional, you can demonstrate and validate your expertise with the new AWS Certified Security – Specialty beta exam. This exam allows you to demonstrate your knowledge of incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. Register today – this beta exam will be available only from January 15 to March 2, 2018.

By taking this exam, you can validate your:

  • Familiarity with region-specific and country-specific security and compliance regulations and meta issues that these regulations include.
  • Understanding of data encryption methods and secure internet protocols, and the AWS mechanisms to implement them.
  • Working knowledge of AWS security services to provide a secure production environment.
  • Ability to make trade-off decisions with regard to cost, security, and deployment complexity when given a set of application requirements.

See the full list of security knowledge you can validate by taking this beta exam.

Who is eligible?

The beta exam is open to anyone who currently holds an AWS Associate or Cloud Practitioner certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

How to prepare

You can take the following courses and use AWS cloud security resources and compliance resources to prepare for this exam.

AWS Security Fundamentals (digital, 3 hours)
This digital course introduces you to fundamental cloud computing and AWS security concepts, including AWS access control and management, governance, logging, and encryption methods. It also covers security-related compliance protocols and risk management strategies, as well as procedures related to auditing your AWS security infrastructure.

Security Operations on AWS (classroom, 3 days)
This instructor-led course demonstrates how to efficiently use AWS security services to help stay secure and compliant in the AWS Cloud. The course focuses on the AWS-recommended security best practices that you can implement to enhance the security of your AWS resources. The course highlights the security features of AWS compute, storage, networking, and database services.

If you have questions about this new beta exam, contact us.

Good luck with the exam!

– Sara

Graphite 1.1: Teaching an Old Dog New Tricks

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/11/graphite-1.1-teaching-an-old-dog-new-tricks/

The Road to Graphite 1.1

I started working on Graphite just over a year ago, when @obfuscurity asked me to help out with some issues blocking the Graphite 1.0 release. Little did I know that a year later, that would have resulted in 262 commits (and counting), and that with the help of the other Graphite maintainers (especially @deniszh, @iksaif & @cbowman0) we would have added a huge amount of new functionality to Graphite.

There are a huge number of new additions and updates in this release, in this post I’ll give a tour of some of the highlights including tag support, syntax and function updates, custom function plugins, and python 3.x support.

Tagging!

The single biggest feature in this release is the addition of tag support, which brings the ability to describe metrics in a much richer way and to write more flexible and expressive queries.

Traditionally series in Graphite are identified using a hierarchical naming scheme based on dot-separated segments called nodes. This works very well and is simple to map into a hierarchical structure like the whisper filesystem tree, but it means that the user has to know what each segment represents, and makes it very difficult to modify or extend the naming scheme since everything is based on the positions of the segments within the hierarchy.

The tagging system gives users the ability to encode information about the series in a collection of tag=value pairs which are used together with the series name to uniquely identify each series, and the ability to query series by specifying tag-based matching expressions rather than constructing glob-style selectors based on the positions of specific segments within the hierarchy. This is broadly similar to the system used by Prometheus and makes it possible to use Graphite as a long-term storage backend for metrics gathered by Prometheus with full tag support.

When using tags, series names are specified using the new tagged carbon format: name;tag1=value1;tag2=value2. This format is backward compatible with most existing carbon tooling, and makes it easy to adapt existing tools to produce tagged metrics simply by changing the metric names. The OpenMetrics format is also supported for ingestion, and is normalized into the standard Graphite format internally.

At its core, the tagging system is implemented as a tag database (TagDB) alongside the metrics that allows them to be efficiently queried by individual tag values rather than having to traverse the metrics tree looking for series that match the specified query. Internally the tag index is stored in one of a number of pluggable tag databases, currently supported options are the internal graphite-web database, redis, or an external system that implements the Graphite tagging HTTP API. Carbon automatically keeps the index up to date with any tagged series seen.

The new seriesByTag function is used to query the TagDB and will return a list of all the series that match the expressions passed to it. seriesByTag supports both exact and regular expression matches, and can be used anywhere you would previously have specified a metric name or glob expression.

There are new dedicated functions for grouping and aliasing series by tag (groupByTags and aliasByTags), and you can also use tags interchangeably with node numbers in the standard Graphite functions like aliasByNode, groupByNodes, asPercent, mapSeries, etc.

Piping Syntax & Function Updates

One of the huge strengths of the Graphite render API is the ability to chain together multiple functions to process data, but until now (unless you were using a tool like Grafana) writing chained queries could be painful as each function had to be wrapped around the previous one. With this release it is now possible to “pipe” the output of one processing function into the next, and to combine piped and nested functions.

For example:

alias(movingAverage(scaleToSeconds(sumSeries(stats_global.production.counters.api.requests.*.count),60),30),'api.avg')

Can now be written as:

sumSeries(stats_global.production.counters.api.requests.*.count)|scaleToSeconds(60)|movingAverage(30)|alias('api.avg')

OR

stats_global.production.counters.api.requests.*.count|sumSeries()|scaleToSeconds(60)|movingAverage(30)|alias('api.avg')

Another source of frustration with the old function API was the inconsistent implementation of aggregations, with different functions being used in different parts of the API, and some functions simply not being available. In 1.1 all functions that perform aggregation (whether across series or across time intervals) now support a consistent set of aggregations; average, median, sum, min, max, diff, stddev, count, range, multiply and last. This is part of a new approach to implementing functions that emphasises using shared building blocks to ensure consistency across the API and solve the problem of a particular function not working with the aggregation needed for a given task.

To that end a number of new functions have been added that each provide the same functionality as an entire family of “old” functions; aggregate, aggregateWithWildcards, movingWindow, filterSeries, highest, lowest and sortBy.

Each of these functions accepts an aggregation method parameter, for example aggregate(some.metric.*, 'sum') implements the same functionality as sumSeries(some.metric.*).

It can also be used with different aggregation methods to replace averageSeries, stddevSeries, multiplySeries, diffSeries, rangeOfSeries, minSeries, maxSeries and countSeries. All those functions are now implemented as aliases for aggregate, and it supports the previously-missing median and last aggregations.

The same is true for the other functions, and the summarize, smartSummarize, groupByNode, groupByNodes and the new groupByTags functions now all support the standard set of aggregations. Gone are the days of wishing that sortByMedian or highestRange were available!

For more information on the functions available check the function documentation.

Custom Functions

No matter how many functions are available there are always going to be specific use-cases where a custom function can perform analysis that wouldn’t otherwise be possible, or provide a convenient alias for a complicated function chain or specific set of parameters.

In Graphite 1.1 we added support for easily adding one-off custom functions, as well as for creating and sharing plugins that can provide one or more functions.

Each function plugin is packaged as a simple python module, and will be automatically loaded by Graphite when placed into the functions/custom folder.

An example of a simple function plugin that translates the name of every series passed to it into UPPERCASE:

from graphite.functions.params import Param, ParamTypes

def toUpperCase(requestContext, seriesList):
  """Custom function that changes series names to UPPERCASE"""
  for series in seriesList:
    series.name = series.name.upper()
  return seriesList

toUpperCase.group = 'Custom'
toUpperCase.params = [
  Param('seriesList', ParamTypes.seriesList, required=True),
]

SeriesFunctions = {
  'upper': toUpperCase,
}

Once installed the function is not only available for use within Grpahite, but is also exposed via the new Function API which allows the function definition and documentation to be automatically loaded by tools like Grafana. This means that users will be able to select and use the new function in exactly the same way as the internal functions.

More information on writing and using custom functions is available in the documentation.

Clustering Updates

One of the biggest changes from the 0.9 to 1.0 releases was the overhaul of the clustering code, and with 1.1.1 that process has been taken even further to optimize performance when using Graphite in a clustered deployment. In the past it was common for a request to require the frontend node to make multiple requests to the backend nodes to identify matching series and to fetch data, and the code for handling remote vs local series was overly complicated. In 1.1.1 we took a new approach where all render data requests pass through the same path internally, and multiple backend nodes are handled individually rather than grouped together into a single finder. This has greatly simplified the codebase, making it much easier to understand and reason about, while allowing much more flexibility in design of the finders. After these changes, render requests can now be answered with a single internal request to each backend node, and all requests for both remote and local data are executed in parallel.

To maintain the ability of graphite to scale out horizontally, the tagging system works seamlessly within a clustered environment, with each node responsible for the series stored on that node. Calls to load tagged series via seriesByTag are fanned out to the backend nodes and results are merged on the query node just like they are for non-tagged series.

Python 3 & Django 1.11 Support

Graphite 1.1 finally brings support for Python 3.x, both graphite-web and carbon are now tested against Python 2.7, 3.4, 3.5, 3.6 and PyPy. Django releases 1.8 through 1.11 are also supported. The work involved in sorting out the compatibility issues between Python 2.x and 3.x was quite involved, but it is a huge step forward for the long term support of the project! With the new Django 2.x series supporting only Python 3.x we will need to evaluate our long-term support for Python 2.x, but the Django 1.11 series is supported through 2020 so there is time to consider the options there.

Watch This Space

Efforts are underway to add support for the new functionality across the ecosystem of tools that work with Graphite, adding collectd tagging support, prometheus remote read & write with tags (and native Prometheus remote read/write support in Graphite) and last but not least Graphite tag support in Grafana.

We’re excited about the possibilities that the new capabilities in 1.1.x open up, and can’t wait to see how the community puts them to work.

Download the 1.1.1 release and check out the release notes here.

Judge Issues Devastating Order Against BitTorrent Copyright Troll

Post Syndicated from Ernesto original https://torrentfreak.com/judge-issues-devastating-order-bittorrent-copyright-troll-180110/

In recent years, file-sharers around the world have been pressured to pay significant settlement fees, or face legal repercussions.

These so-called “copyright trolling” efforts have been a common occurrence in the United States since the turn of the last decade.

Increasingly, however, courts are growing weary of these cases. Many districts have turned into no-go zones for copyright trolls and the people behind Prenda law were arrested and are being prosecuted in a criminal case.

In the Western District of Washington, the tide also appears to have turned. After Venice PI, a copyright holder of the film “Once Upon a Time in Venice”, sued a man who later passed away, concerns were raised over the validity of the evidence.

Venice PI responded to the concerns with a declaration explaining its data gathering technique and assuring the Court that false positives are out of the question.

That testimony didn’t help much though, as a recently filed minute order shows this week. The order applies to a dozen cases and prohibits the company from reaching out to any defendants until further notice, as there are several alarming issues that have to be resolved first.

One of the problems is that Venice PI declared that it’s owned by a company named Lost Dog Productions, which in turn is owned by Voltage Productions. Interestingly, these companies don’t appear in the usual records.

“A search of the California Secretary of State’s online database, however, reveals no registered entity with the name ‘Lost Dog’ or ‘Lost Dog Productions’,” the Court notes.

“Moreover, although ‘Voltage Pictures, LLC’ is registered with the California Secretary of State, and has the same address as Venice PI, LLC, the parent company named in plaintiff’s corporate disclosure form, ‘Voltage Productions, LLC,’ cannot be found in the California Secretary of State’s online database and does not appear to exist.”

In other words, the company that filed the lawsuit, as well as its parent company, are extremely questionable.

While the above is a reason for concern, it’s just the tip of the iceberg. The Court not only points out administrative errors, but it also has serious doubts about the evidence collection process. This was carried out by the German company MaverickEye, which used the tracking technology of another German company, GuardaLey.

GuardaLey CEO Benjamin Perino, who claims that he coded the tracking software, wrote a declaration explaining that the infringement detection system at issue “cannot yield a false positive.” However, the Court doubts this statement and Perino’s qualifications in general.

“Perino has been proffered as an expert, but his qualifications consist of a technical high school education and work experience unrelated to the peer-to-peer file-sharing technology known as BitTorrent,” the Court writes.

“Perino does not have the qualifications necessary to be considered an expert in the field in question, and his opinion that the surveillance program is incapable of error is both contrary to common sense and inconsistent with plaintiff’s counsel’s conduct in other matters in this district. Plaintiff has not submitted an adequate offer of proof”

It seems like the Court would prefer to see an assessment from a qualified independent expert instead of the person who wrote the software. For now, this means that the IP-address evidence, in these cases, is not good enough. That’s quite a blow for the copyright holder.

If that wasn’t enough the Court also highlights another issue that’s possibly even more problematic. When Venice PI requested the subpoenas to identify alleged pirates, they relied on declarations from Daniel Arheidt, a consultant for MaverickEye.

These declarations fail to mention, however, that MaverickEye has the proper paperwork to collect IP addresses.

“Nowhere in Arheidt’s declarations does he indicate that either he or MaverickEye is licensed in Washington to conduct private investigation work,” the order reads.

This is important, as doing private investigator work without a license is a gross misdemeanor in Washington. The copyright holder was aware of this requirement because it was brought up in related cases in the past.

“Plaintiff’s counsel has apparently been aware since October 2016, when he received a letter concerning LHF Productions, Inc. v. Collins, C16-1017 RSM, that Arheidt might be committing a crime by engaging in unlicensed surveillance of Washington citizens, but he did not disclose this fact to the Court.”

The order is very bad news for Venice PI. The company had hoped to score a few dozen easy settlements but the tables have now been turned. The Court instead asks the company to explain the deficiencies and provide additional details. In the meantime, the copyright holder is urged not to spend or transfer any of the settlement money that has been collected thus far.

The latter indicates that Venice PI might have to hand defendants their money back, which would be pretty unique.

The order suggests that the Judge is very suspicious of these trolling activities. In a footnote there’s a link to a Fight Copyright Trolls article which revealed that the same counsel dismissed several cases, allegedly to avoid having IP-address evidence scrutinized.

Even more bizarrely, in another footnote the Court also doubts if MaverickEye’s aforementioned consultant, Daniel Arheidt, actually exists.

“The Court has recently become aware that Arheidt is the latest in a series of German declarants (Darren M. Griffin, Daniel Macek, Daniel Susac, Tobias Fieser, Michael Patzer) who might be aliases or even fictitious.

“Plaintiff will not be permitted to rely on Arheidt’s declarations or underlying data without explaining to the Court’s satisfaction Arheidt’s relationship to the above-listed declarants and producing proof beyond a reasonable doubt of Arheidt’s existence,” the court adds.

These are serious allegations, to say the least.

If a copyright holder uses non-existent companies and questionable testimony from unqualified experts after obtaining evidence illegally to get a subpoena backed by a fictitious person….something’s not quite right.

A copy of the minute order, which affects a series of cases, is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

RuTracker Reveals Innovative Plan For Users to Subvert ISP Blocking

Post Syndicated from Andy original https://torrentfreak.com/rutracker-reveals-innovative-plan-for-users-to-subvert-isp-blocking-180110/

As Russia’s largest torrent site and one that earned itself a mention in TF’s list of most popular torrent sites 2018, RuTracker is continuously under fire.

The site has an extremely dedicated following but Russia’s telecoms watchdog, spurred on by copyright holders brandishing court rulings, does everything in its power to ensure that people can’t access the site easily.

As a result, RuTracker’s main domains are blocked by all ISPs, meaning that people have to resort to VPNs or the many dozens of proxy and mirror sites that have been set up to facilitate access to the popular tracker.

While all of these methods used to work just fine, new legislation that came into force during October means that mirror and proxy sites can be added to block lists without copyright holders having to return to court. And, following legislation introduced in November, local VPN services are forbidden from providing access to blocked sites.

While RuTracker has always insisted that web blockades have little effect on the numbers of people sharing content, direct traffic to their main domains has definitely suffered. To solve this problem and go some way towards mitigating VPN and proxy bans, the site has just come up with a new plan to keep the torrents flowing.

The scheme was quietly announced, not on RuTracker’s main forum, but to a smaller set of users on local site Leprosorium. The idea was that a quieter launch there would allow for controlled testing before a release to the masses. The project is called My.RuTracker and here’s how it works.

Instead of blocked users fruitlessly trying to find public circumvention methods that once seen are immediately blocked, they are invited to register their own domains. These can be single use, for the person who registers them, but it’s envisioned that they’ll be shared out between friends, family, and online groups, to better make use of the resource.

Once domains are registered, users are invited to contact a special user account on the RuTracker site (operated by the site’s operators) which will provide them with precise technical details on how to set up their domain (.ru domains are not allowed) to gain access to RuTracker.

“In response, after a while (usually every other day), a list of NS-addresses will be sent to the registrar’s domain settings. Under this scheme, the user domain will be redirected to the RuTracker site via a dynamic IP address: this will avoid blocking the torrent tracker for a particular IP address,” the scheme envisages.

According to local news resource Tjournal, 62 personal mirrors were launched following the initial appeal, with the operators of RuTracker now planning to publicly announce the project to their community. As more are added, the site will keep track of traffic from each of the personal “mirrors” for balancing the load on the site.

At least in theory, this seems like a pretty innovative scheme. Currently, the authorities rely on the scale and public awareness of a particular proxy or mirror in order to earmark it for blocking. This much more decentralized plan, in which only small numbers of people should know each domain, seems like a much more robust system – at least until the authorities and indeed the law catches up.

And so the cat-and-mouse game continues.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

[$] A look at the handling of Meltdown and Spectre

Post Syndicated from jake original https://lwn.net/Articles/743363/rss

The Meltdown/Spectre debacle has,
deservedly, reached the mainstream press
and, likely, most of the public that has even a remote interest in computers
and security. It only took a day or so from the accelerated disclosure
date of January 3—it was originally scheduled for
January 9—before the bugs
were making big headlines. But Spectre has been known for at least six
months and Meltdown for nearly as long—at least to some in the industry.
Others that were affected were completely blindsided by the
announcements and have joined the scramble to mitigate these hardware bugs
before they bite users. Whatever else can be said about Meltdown and Spectre,
the handling (or, in truth, mishandling) of this whole incident has been a
horrific failure.