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mylavenu99 · 11 months ago

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ULB Haryana Property Tax Payment 2024: How to Pay Online?

ULB Haryana Property Tax image

Property tax stands as a crucial revenue source in India, fueling the maintenance of state infrastructure. If you’re a resident of Haryana seeking to streamline your property tax payment, this guide is tailored for you. In Haryana, residents have the convenience of paying property tax through both online and offline channels.

1. Online Payment Methods

Residents can conveniently settle their property tax dues through the official No Dues Certificate (NDC) portal of Haryana. Alternatively, offline payments can be made at designated bank branches or municipal offices.

2. Understanding ULB Haryana Property Tax

The Directorate of Urban Local Bodies (ULB) in Haryana plays a pivotal role in urban governance. Responsible for strategic planning and governance, this department drives urban infrastructure development, maintenance, and enhancement, thereby promoting sustainable growth and community well-being.

3. Key Functions of ULB Haryana:

Setting the policy framework for Urban Development in Haryana.

Facilitating the operations of Urban Local Bodies (ULB) in Haryana.

Maintenance of civic amenities across Haryana.

4. Eligibility Criteria for Online Property Tax Payment

To make online property tax payments in Haryana, individuals must meet specific eligibility criteria, including:

Being 18 years or older.

Owning a property in Haryana.

Being a permanent resident of Haryana.

5. Necessary Documents for Online Property Tax Payment

The following documents are mandatory for online property tax payments in Haryana:

Unique property ID in Haryana.

Property owner’s name.

Aadhaar card of the property owner.

Address proof of the property owner.

Old property ID (if applicable).

6. Registering on the Haryana ULB Portal

Here’s a step-by-step guide to registering as a user on the Haryana NDC portal:

Visit the official website of the Directorate of Urban Local Bodies Haryana.

Click on ‘Online Services’ at the top of the homepage.

Proceed to the ‘Online Services’ page and select the property tax payment link.

Click on ‘New User Registration’ on the homepage.

Fill in the necessary details on the ‘User Registration’ page and hit Register.

Enter the OTP received on your registered mobile number to complete the registration process successfully.

7. Seamless Online Payment Process

Follow these steps to pay property tax online in Haryana:

Visit the official website of No Dues Certificate Management System Haryana.

Enter your Property ID (PID) and click ‘Search.’

Choose ‘Pay Property Tax’ and review the payment details.

Click on ‘Pay Now’ to complete the transaction securely.

8. Checking Application Status and Property ID

Here’s how to check the application status and property ID in Haryana:

Visit the official portal of No Dues Certificate Management System Haryana.

Click on ‘Check Application Status’ and enter your Application Number to view the status.

To check the property ID, select ‘Citizen’ as User Type, enter your mobile number, and follow the prompts to view your property ID.

In conclusion, leveraging the digital platforms provided by the Directorate of Urban Local Bodies Haryana, property tax payments have become seamless and hassle-free. Say goodbye to long queues and embrace the convenience of managing your tax obligations from the comfort of your home.

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faizrashis1995 · 5 years ago

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5 Advanced Java Debugging Techniques Every Developer Should Know About

Production debugging is hard, and it’s getting harder. With architectures becoming more distributed and code more asynchronous, pinpointing and resolving errors that happen in production is no child’s game. In this article we’ll talk about some advanced techniques that can help you get to the root cause of painful bugs in production more quickly, without adding material overhead to your already busy production environment.

Better jstacks. jstack has been around for a long time (about as long as the JVM’s been around) and even to this day remains a crucial tool in every developer’s arsenal. Whenever you’re staring at a Java process that’s hung or not responding, it’s your go-to tool to see the stack trace of each thread. Even so, jstack has a couple of disadvantages that detract from its ability to help you debug complex issues in production. The first is that while it tells you what each thread is doing through its stack trace, it doesn't tell what you why it’s doing it (the kind of information usually only available through a debugger); and it doesn’t tell you when it started doing it.

Fortunately enough, there’s a great way you can fix that, and make a good tool even better, by injecting dynamic variable state into each thread’s data. The key to this problem lies in one of the most unexpected places. You see, at no point in it’s execution does jstack query the JVM or the application code for variable state to present. However there is one important exception, which we can leverage to turn lemons into lemonade, and that is - the Thread.Name() property, whose value is injected into the stack dump. By setting it correctly you can move away from uninformative jstack thread printouts that looks like this

“pool-1-thread-1″ #17 prio=5 os_prio=31 tid=0x00007f9d620c9800 nid=0x6d03 in Object.wait() [0x000000013ebcc000]

Compare that with the following thread printout that contains a description of the actual work being done by the thread, the input parameters passed to it, and the time in which it started processing the request:

”pool-1-thread- #17: Queue: ACTIVE_PROD, MessageID: AB5CAD, type: Analyze, TransactionID: 56578956, Start Time: 10/8/2014 18:34″

Here’s an example for how we set a stateful thread name:

private void processMessage(Message message) { //an entry point into your code

String name = Thread.currentThread().getName();

try {

Thread.currentThread().setName(prettyFormat(name, getCurrTranscationID(),

message.getMsgType(), message.getMsgID(), getCurrentTime()));

doProcessMessage(message);

}

finally {

Thread.currentThread().setName(name); // return to original name

}

In this example, where the thread is processing messages out of a queue, we see the target queue from which the thread is dequeuing messages, as well as the ID of the message being processed, and the transaction to which it is related (which is critical for reproducing locally), and last, but far from least - the time in which the processing of this message began. This last bit of information enables you to look at a server jstack with upwards of a hundred worker threads, and see which ones started first and are most likely causing an application server to hang.

(Click on the image to enlarge it)

An example of how an enhanced jstack shows dynamic variable state for each thread in the dump. Thread start time is marked as TS.

The capability works just as well when you’re using a profiler, a commercial monitoring tool, a JMX console, or even Java 8’s new Mission Control. In all of these cases, your ability to look at the live thread state, or a historic thread dump, and see exactly what each thread is doing and when it started is materially enhanced by having stateful thread contexts.

This thread variable state will also be shown by any JDK or commercial debugger or profiler.

But the value of Thread names doesn’t stop there. They play an even bigger role in production debugging - even if you don’t use jstack at all. One instance is the global Thread.uncaughtExceptionHandler callback which serves as a last line of defense before an uncaught exception terminates a thread (or is sent back to the thread-pool).

By the point an uncaught exception handler is reached, code execution has stopped and both frames and variable state have already been rolled back. The only state that remains for you to log the task that thread was processing, it’s parameters and starting time is captured by you guessed it - a stateful Thread name (and any additional TLS variables loaded onto it).

Its important to keep in mind that a threading framework might implicitly catch exceptions without you knowing it. A good example is ThreadPoolExecutorService, which catches all exceptions in your Runnable and delegates them to its afterExecute method, which you can override to display something meaningful. So whatever framework you use, be mindful that if a thread fails you still have a chance to log the exception and thread state to avoid tasks disappearing into the ether.

Throughput and deadlock jstacks. Another disadvantage of tools like jstack or Mission Control is that they need to be activated manually on a target JVM which is experiencing issues. This reduces their effectiveness in production where 99% of the time when issues occur you’re not there to debug.

Happily enough there’s a way by which you can activate jstack programmatically when your application’s throughput falls under a specific threshold or meets a specific set of conditions. You can even automatically activate jstack when your JVM deadlocks to see exactly which threads are deadlocking and what all the other threads are doing (coupled of course with dynamic variable state for each one, courtesy of stateful thread names). This can be invaluable as deadlocking and concurrency issue are sporadic for the most part and notoriously hard to reproduce. In this case by activating jstack automatically at the moment of deadlock, one which also contains the stateful information for each thread can be a huge catalyst in your ability to reproduce and solve these kinds of bugs.

Click here to learn more about how to automatically detect deadlocks from within your application.

Capturing live variables. We’ve talked about ways of capturing state from the JVM through thread contexts. This approach, however effective, is restricted to variables that you had to format into the thread name in advance. Ideally, we want to be able to go in and get the value of any variable from any point in the code from a live JVM, without attaching a debugger or redeploying code. A great tool that’s been around for a while, but hasn’t got the recognition it deserves, lets you do just that; and that tool is BTrace.

BTrace is a helpful tool that lets you run Java-like scripts on top of a live JVM to capture or aggregate any form of variable state without restarting the JVM or deploying new code. This enables you to do pretty powerful things like printing the stack traces of threads, writing to a specific file, or printing the number of items of any queue or connection pool and many more.

This is done using BTrace scripting, a Java-like syntax in which you write functions that are injected into the code in locations of your choice through bytecode transformation (a process we’ll touch on below). The best way to try out the tool is to attach its sample scripts into a live application. Usage is very straightforward, from your command line simply enter - btrace <JVM pid> <script name>.There’s no need to restart your JVM.

BTrace is very well documented and comes with many sample scripts (see below) to cover various common debugging scenarios around IO, memory allocation and class loading. Here are a couple of powerful examples of things you can do very easily with BTrace -

NewArray.java: Print whenever a new char[] is allocated, and also add your own conditions based on its value. Pretty handy for selective memory profiling.

FileTracker.java: Print whenever the application writes to a specific file location. Great for pinpointing the cause of excessive IO operations.

Classload.java: React whenever a target class is loaded into the JVM. Very useful for debugging “jar-hell” situations.

BTrace was designed as a non-intrusive tool, which means it cannot alter application state or flow control. That’s a good thing, as it reduces the chances of us negatively interfering with the execution of live code and makes its use in production much more acceptable. But this capability comes with some heavy restrictions - you can’t create objects (not even Strings!), call into your or 3rd party code (to perform actions such as logging), or even do simple things such as looping for fear of creating an infinite loop. To be able to do those you’ll have to use the next set of techniques - writing your own Java agent.

A Java agent is a jar file that provides access by the JVM to an Instrumentation object to enables you to modify bytecode that has already been loaded into the JVM to alter its behaviour. This essentially lets you “rewrite” code that is already loaded and compiled by the JVM, without restarting the application or changing the .class file on disk. Think about it like BTrace on steroids - you can essentially inject new code anywhere in your app into both your code and 3rd party code to capture any piece of information you want.

The biggest downside to writing your own agent is that unlike BTrace, which lets you write Java-like scripts to capture state, Java agents operate at the bytecode level. This means that if you want to inject code into an application you’ll have to create the right bytecode. This can be tricky because bytecode can be hard to produce and read, following an operator stack-like syntax which is similar in many ways to Assembly language. And to make things harder, since bytecode is already compiled, any miscorrelation to the location in which it is injected will be rejected without much fanfare by the JVM’s verifier.

To assist with this, many bytecode generation libraries have been written over the years such as JavaAssist and ASM (which is my personal favorite). A great hack which I find myself using quite a lot uses the ASM byteview IDE plugin, which lets you type any Java code in your editor and automatically generate the right ASM code to then generate it’s equivalent bytecode, which you can copy and paste into your agent.

Click here for a real-world example of a sample agent we used to detect and sporadic memory leaks coming from 3rd party code on our server - correlating it to application state.

Dynamically generating bytecode generation scripts from your IDE using the ASM bytecode plugin.

The last technique I’d like to touch on briefly is building Native JVM agents. This approach uses the JVM TI C++ API layer, which gives you unprecedented control and access into the internals of the JVM. This includes things like getting callbacks whenever GC starts and stops, whenever new threads are spawn, monitors are acquired, and many more low-level capabilities. This is by far the most powerful approach to acquire state from running code, as you are essentially running at the JVM level.

But with great power comes great responsibility, and some pretty complex challenges make this approach relatively harder to implement. The first is that since you’re running at the JVM level you're no longer writing in cross platform Java, but in low-level platform dependant C++. The second disadvantage is that the APIs themselves, while extremely powerful, are hard to use and can impact performance significantly, depending on the specific set of capabilities you’re consuming.

On the plus side, if used correctly, this layer provides terrific access to parts of the JVM which would otherwise be closed to us in our search of the root cause of production bugs. When we began writing Takipi for production debugging, I’m not sure we knew the extent to which TI would play a crucial role in our ability to build the tool. The reason for that is that through the use of this layer you’re able to detect exceptions, calls into the OS or map application code without manual user instrumentation. If you have the time to take a look at this API layer - I highly recommend it, as it opens a unique window into the JVM not many of us know.[Source]-https://www.infoq.com/articles/Advanced-Java-Debugging-Techniques/

We provide the best Advanced Java training in navi mumbai. We have industry experienced trainers and provide hands on practice. Basic to advanced modules are covered in training sessions.

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chaj · 6 years ago

Link

via grafana.com

Bloomberg is best known as a media company with its news destination site, its award-winning magazine Bloomberg Businessweek, and its daily 24-7 social media program, Tic Toc, on Twitter.

But the main product for the 38-year-old company is actually Bloomberg Terminal, a software system that aggregates real-time market data and delivers financial news to more than 325,000 subscribers around the world. The enterprise premium service handles about 120 billion (that’s billion) pieces of data from financial markets daily, 2 million stories from its news division and affiliates, and a messaging network (think “Instant Bloomberg”) that fields 1 billion messages.

“With all this, people seem to notice when it doesn’t work,” said Stig Sorenson, Head of Production Visibility Group at Bloomberg. “We have had a few outages that were high profile, so about three years ago we decided to embark on a journey where we took telemetry a bit more seriously.”

Since 2015, Bloomberg’s central telemetry team has been growing steadily – and the same could be said for its influence within the company. Today, “we’re storing 5 million data points a second and running over 2,500 rules on our metrics stream,” said Software Developer Sean Hanson during his talk at GrafanaCon 2019 in Los Angeles. “We also do a bit more on the log side with about 100 terabytes of raw log data a day and a lot of legacy log rules.”

youtube

Their most impressive feat, however, was rallying 5,500 engineers around streamlining their monitoring systems. “It’s a hard problem when you have a lot of independent users and a lot of teams,” Hanson admitted. “For a lot of users, monitoring is not a priority … So we try to give them as much as we can without them actually having to do something.”

Here’s how the telemetry team embarked on their “safari of stability” by solving three key problems within their infrastructure.

1. Centralizing Data

Historically Bloomberg isolated software so that outages only affected a small subset of customers. Teams would get an alert about a sub-failure and evaluate their individual products, utilizing their own data sources and their own telemetry stack.

However, working in silos led to issues when there was either an unforeseen single point of failure or when the alerts would snowball into multiple failures.

With teams working in isolation, “the outage would linger until it got bad enough that someone from the outside, either on our environment support team or a high-level manager, would be like, ‘Hey, I think you might all be working on the same problem independently,’” said Hanson. “Then the teams would piece together all of the individual data to track down a root cause.”

The telemetry team’s first step to stem this problem was to deploy agents to as many machines as possible to collect system metrics – file system, operating system, etc. – as well as each machine’s process tables. The telemetry team also worked with key infrastructure teams to gain insight into system frameworks within individual services, queues, or databases.

The goal was to centralize the data and provide a broader picture of the operating infrastructure at any given time. The Head of Engineering now has high-level system health dashboards in place to monitor outages. “Once we provided all of these displays, we were able to narrow down the pieces of data that could help triage outages as they happened or prevent them if we could alert on them,” said Hanson.

The Grafana dashboards also became valued assets throughout the organization, from high-level execs such as Sorenson who want a monitoring overview, to developers who want drill-down links on all the panels, to programmers who extract insights through a query API for more complex analysis.

“We have one place that users go to, to configure everything for their metrics, logs, alerts, Grafana folders, and distributed traces” said Hanson.

More importantly, the team automated processes to implement SRE best practices moving forward. Firm-wide rules around CPU, memory, file system storage, and service frameworks “take effect as soon as users create a new service or spin up a new machine,” said Hanson. Plus, because they are hooked into the machine-building process now, “even the machine creation process can publish its own metrics and report failures.”

2. Unifying Alerts

Prior to the formation of the telemetry team, Bloomberg had various systems that created different notifications.

Now, not only are alerts centralized. A link is served with every alert that shows correlation – what happened around the same time on the same set of machines or for the same basic rule – so teams can quickly detect whether it is an isolated incident or a problem with their software.

“All of the alerts we generate have a similar look and feel and a base set of information that we require which include our remediation plan,” said Hanson. “Every alert that comes out should have an action or a call to action available to you right at the top.”

In the case of tags, the team enforces tag key registration, not tag values, to ensure that when users try to register PIDs or timestamps as tag keys, they are alerted that they are off-base.

“We really wanted it to be easy to do the right thing, hard to do the wrong thing, but still possible to do something non-standard that we decide is sane,” added Hanson. “We designed our APIs to try to facilitate this.”

Recently the team has taken the initiative to meet with cross-functional teams “to talk about use cases and guide people to pre-built solutions where they exist or teach them how to use existing tools to build on top of,” said Hanson. “If there’s a really good use case or if we see it a lot, we just build it into our system so that people don’t have to even think about it. They just get it.”

3. Simplifying Queries

Bloomberg’s dashboards live in one “massive” Grafana instance that provides templates and uses the same query language and API as Graphite.

As users adapted to using the metrics, “we had growing dimensionality where users really want to drill down a lot into their data,” said Hanson. “So they want to keep adding more tags or labels, and some of the frameworks like Kubernetes cause a lot of transient time series to come around.”

In other words, more time series means more RAM. So this is where MetricTank came into play.

With MetricTank’s pattern-based pruning rules in the index and pattern-based retention rules, “we came up with a Goldilocks approach where users could pick their favorite flavor from three,” said Hanson. “If they want aggregate data for 10 years of trend analysis, they can pick longer lived. Or if they want advanced drilldowns, they can do that, but they don’t get the data for as long … We let the users pick, we apply it as a tag, and MetricTank does the rest.”

One snag the team hit with MetricTank came after releasing their query API to users for programmatic access. Problems came up for “[users] making a lot of queries sequentially or for users using the tag-based auto-complete in Grafana,” said Hanson. “When you get two-, three-, five-second lag on auto-complete, it’s pretty frustrating and noticeable. The more beta users that came onto the query API, the slower it got, until we had a daily report that took two days to run.”

Working closely with the Grafana team, the Bloomberg team implemented speculative querying which issues redundant queries to other replicas when slow peers were detected. This reduced the run time for daily reports down to four hours. “We also implemented native functions in MetricTank which prevented proxying through Graphite Web,” explained Hanson. “After that, we are now down to an hour and a half for our daily report. So there’s really not much more we can optimize there without actually trying to optimize the report itself.”

So What’s Next?

Improving dashboard discoverability is the next item on the Bloomberg Telemetry punch list.

Bloomberg currently has more than 3,500 dashboards in more than 500 folders, and there are many generic dashboards that prove to be popular and copied internally. But while imitation is the best form of flattery, it’s a poor form of organization.

Dashboards get copied repeatedly with names that are barely distinguishable from the next to the point that it’s hard to organically surface the original dashboards within the system. “People were only finding them by being linked through tickets,” said Hanson.

While folders and permission settings help limit access and editing rights to key dashboards, it didn’t solve the issue of rampant dashboard copies appearing in the system. So Bloomberg again reached out to Grafana Labs for a solution.

Together, the teams are working to enhance the auto-complete function. “This would allow us to search for keywords, descriptions, or even maybe metric names or tag labels inside the queries, which would be great,” said Hanson.

The goal is for Bloomberg’s telemetry team to “score” dashboards based on popularity with a custom weighting system. They are hoping to develop functions such as tagging dashboards “official” vs. “experimental” so users know which ones are more reliable compared to others.

Another big project: Meta tags, a seamless and cost-effective way to add metadata, is also in the works.

In creating a sustainable monitoring infrastructure, “starting with some good open source technology gets you a big step up,” said Hanson. “But since you didn’t build it, it might not work for you right off the bat. So we can’t be afraid to jump in and improve the product for yourself and the wider community.”

After all, “investing in telemetry pays dividends,” said Hanson, “which is my obligatory financial joke I save for the very end.”

For more from GrafanaCon 2019, check out all the talks on YouTube.

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patboutet · 7 years ago

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Lab 5 - cpsc 543

Welcome to another week of CPSC543 labs!

This week we had our final lab and it was a doozy (in a good way)!

We began with continuing the use of our awesome Twiddlerino but this time it was being used to play a classic game of Pong. The setup was a client-server model, with the Arduino sending the Twiddlers position to the serial port, which was being monitored by a Processing application, that implemented the graphics and game logic of the Pong game. The Arduino would also accept input from the Processing game via the serial port to generate a haptic feedback when the ball would "hit" the players paddle.

Part 1: It was pretty straightforward to get running with only a serial port change and library import required. After which it was just fun to play Pong for a while and get a feel for how the Twiddler worked in this game environment as a controller for the paddle. My observations here were that moving the paddle from its starting position generated some resistance the farther you got from the origin. With the Twiddler wanting to spring back to the origin (ie. it's starting location). This is due to the Twiddler/Arduino having a PID controller setup with the target position at zero. Next was that whenever the ball would hit the players paddle there would be a slight twitch in the Twiddler, alongside an audio sound of a "toc" from Processing, simulating the paddle striking the ball via haptic, visual, and audio queues. Overall a nice combination of modalities that was pretty decent at convincing me I had just hit a "real" ball.

Part 2: The next part of the lab had us basically implement our own PID controller instead of relying on the provided PID controller library.

It was minimal code change.

First was to remove the myPID code and Compute().

Next was to look up each terms definition from Wikipedia as a reminder on how to calculate each: P term:

I term:

D term:

I found it easier to refer to the pseudo code on the wikipedia page and then just implement that.

My implementation of this pseudo code was roughly as follows. Here is a link to the full sketch code on pastebin here.

Include MsTimer2 to run a method I created that implements my own PID code.

Add a few global variables to keep track of previous error, summing error, and our time delta.

Create my method called "myPIDCompute" that implemented the pseudo code of a basic PID

Most of its pretty straightforward except the time delta for the error term, it's 2 milliseconds but I have to convert it by dividing my 1000 as MsTimer2 takes an integer and converts it to milliseconds.

When this gets run every 2 milliseconds it will set PWMOut.

Add the following MsTimer2 code to the setup() method, to have "myPIDCompute" run every 2 milliseconds.

As I mentioned above I removed all the original myPID code but the key line to get rid of is

in the loop().

With that done it ran identically to the original code that I had to replace, well enough to the point that I was unable to tell any difference in gameplay using my implemented PID vs the original PID controller library one.

One challenge that took me way to long to debug was it was supposed to be position - target and not target - position in the code; since target is always 0, our error would always end up being 0.

Part 3: Make an engaging experience! So now for the really interesting part of the lab, where we are to combine all what we've used so far into a really engaging experience!

For that I present my game "Windy Bridge"

The goal is to move your player (the white dot) back and forth across the bridge (black strip) for as long as you can (gaining points the longer you last). All the time resisting wind gusts that come from either the left or the right.

The Twiddler controls the left and right movement of the player and utilizes a PID to haptically display "wind gusts". The player automatically moves forward and will keep turning around and coming back across the bridge.

Basically if you remember from the previous lab 4 blog, where we were introduced to a PID and had a visual display graphing the twiddlers position, target, and PID values. Well if you image that graph turned 90 degrees up (as in the picture below).

The red (target), blue (position), and black lines are spaced a bit to see them visually as normally they are stacked.

Now visually imagine the bridge down the centre. We can now by adjust the target to either side of the bridge, and increasing the PID values (P = 3, I = 0.1, D = 0.2); to simulate a wind gust from either side that that player needs to fight back against otherwise they get blown off the bridge. When the wind dies down, we return the target to zero (ie. the centre of the bridge) and reduce the PID values to (P= 0.4, I =0, D = 0) to give them no resistance. This works because the x axis movement of the player is tied to the positive or negative (WRT zero) position that the Twiddler is broadcasting.

I included some visuals onscreen to indicate which direction the wind is blowing from, combining this with audio of blowing wind, and the haptic force from the Twiddler; I'd say it actually did a pretty decent job of making it "feel" like there was wind trying to push the player off the bridge.

The game only ends when you fall off the bridge.

You can see the full thing in action in these two videos.

The first is my getting blown of shortly into the game.

youtube

The second is a lengthier play through to see that the player just turns around and keeps going (the goal is to last as long as you can, as you get more points the longer you stay on the bridge.)

youtube

After completing the lab, as with all fun challenges, more ideas kept coming. Here are a few extensions I would do to make the game even more challenging and fun.

To increase difficulty as the game progress': - Have the bridge change dynamically, getting wider/narrower as a whole, or having parts that are chokepoints. - Randomize the timing, direction, strength, and duration of the wind gusts.

Well that's it for another week.

Thanks for reading :)

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marthawelsh · 8 years ago

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Tipsy Robot’s Automated Bartenders Are a Riveting New Diversion on the Las Vegas Strip

A pair of robotic bartenders have created some serious buzz since a new bar, Tipsy Robot, opened inside Miracle Mile Shops at the Planet Hollywood Las Vegas resort.

“Kuka” is a German word meaning, “Bow before your new robotic bartender overlords.”

Tipsy Robot is billed as the “first land-based robotic bar.” There’s another pair of robot bartenders on Royal Caribbean’s Harmony of the Seas cruise liner, but these are infinitely better, because Las Vegas.

You can’t spell “tipsy” without “tips,” and, ironically, robot bartenders don’t accept those.

As marketing gimmicks go, this is one of the best we’ve ever seen, and crowds were gathering to watch the robots mix drinks even before the venue opened to the public.

We were utterly mesmerized as the dynamic duo deftly delivered drinks. See for yourself in our hastily slapped-together video.

//www.youtube.com/watch?v=mt2w5F2NUbg

That adorable little dance, though.

So, here’s all the Tipsy Robot skinny.

Guests place orders via one of 33 tablets. There’s a robust list of 18 signature cocktails, but guests may order custom-built drinks, too.

Park it at a tablet and make some mechanical mixology magic.

For an existing drink, it’s just a matter of making a selection and providing a name and e-mail address.

For custom drinks, guests can choose from virtually unlimited options, from the kind of liquor (Tipsy Robot boasts 172 bottles, or 59 different brands) to exact proportions of liquor and mixers and ice.

There are 14 “portions” in all. For example, we ordered a rum and Coke with two parts rum, six parts Coke and six parts ice. We really like ice.

Tipsy Robot serves Captain Morgan Silver. We’re trying to get past it.

Once an order is placed and paid for with a credit card (drinks are $14 for a standard drink with one shot of alcohol), it goes into a queue. That’s a fancy European term for “line.”

The robots take anywhere from a minute to 90 seconds to prepare a drink, so the virtual line moves quite quickly.

A fun part of the process is that video displays keep track of where your order is in the queue, and you can tell when your specific drink is being made.

Analytics! See where you are in the queue, the most popular drinks being ordered and trends related to the consumption of various drink categories. You are officially a world-class nerd.

While a drink is being prepared, an e-mail is sent to the address given when the order was placed.

The e-mail contains a QR code which, when scanned, “unlocks” the drink. This ensures nobody can abscond with a cocktail.

Set your drink free with your QR code. QR codes are like bar codes. Emphasis on “bar.”

That’s it.

The robots prepare drinks element by element, grabbing ice from a dispenser, extracting liquor from bottles hanging overhead, slicing fruit, shaking up the drink and pouring the cocktails ever-so-carefully into plastic cups.

What don’t the robot bartenders do? They don’t take breaks, they don’t accept tips and they don’t provide straws.

There are attendants in space-aged uniforms to handle the straw thing.

The robot helpers are called “Galactic Ambassadors.” Just play along.

During our visit, we chatted up Rino Armeni, owner of the 2,500-square-foot Tipsy Robot and Chairman of Robotic Innovations. He said, “I’m very proud that Las Vegas finally has something different, new, and most importantly, ahead of its time.”

Armeni is a charismatic Italian whose enthusiasm is contagious.

“In food and beverage,” Armeni says, “I think we’ve been asleep at the wheel lately. It’s been a matter of recycling, rather than being inventive.”

Yes, he actually said “sleeping on the wheel,” but we know what he meant.

Armeni continues, “We want to be almost like the fountains of Bellagio, the ‘Welcome to Las Vegas’ sign. We want people to come and experience this kind of entertainment.”

Owner Rino Armeni greets Tipsy Robot guests, assuring them he’s never heard the word “Skynet” before.

Armeni is careful to point out he considers the robot bartenders entertainment, rather than a replacement for actual bartenders.

In fact, Tipsy Robot has a “Human Bar,” with humans serving up the libations.

The robot bartenders aren’t fully autonomous, of course. A human being is still tasked with replacing the liquor bottles.

When we asked an insider how much the robots cost, the answer was along the lines of “a metric ass-ton.”

Humans and robots have many things in common, including an ongoing need for lubrication.

Tipsy Robot is looking to crowdsource the names of the robots. Siegfried and Roy leap to mind. Find out more on the Tipsy Robot Facebook page.

Tipsy Robot is open from 10:00 a.m. to 11:00 p.m. Sunday through Thursday, and 10:00 a.m. to midnight on Friday and Saturday.

Here’s another look at these modern marvels. You may not be able to tell these robot bartenders your problems, but you’ll always know the precise size of your pour.

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Tipsy Robot Las Vegas

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marthawelsh · 8 years ago

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Tipsy Robot’s Automated Bartenders Are a Riveting New Diversion on the Las Vegas Strip

A pair of robotic bartenders have created some serious buzz since a new bar, Tipsy Robot, opened inside Miracle Mile Shops at the Planet Hollywood Las Vegas resort.

“Kuka” is a German word meaning, “Bow before your new robotic bartender overlords.”

You can’t spell “tipsy” without “tips,” and, ironically, robot bartenders don’t accept those.

As marketing gimmicks go, this is one of the best we’ve ever seen, and crowds were gathering to watch the robots mix drinks even before the venue opened to the public.

We were utterly mesmerized as the dynamic duo deftly delivered drinks. See for yourself in our hastily slapped-together video.

//www.youtube.com/watch?v=mt2w5F2NUbg

That adorable little dance, though.

So, here’s all the Tipsy Robot skinny.

Guests place orders via one of 33 tablets. There’s a robust list of 18 signature cocktails, but guests may order custom-built drinks, too.

Park it at a tablet and make some mechanical mixology magic.

For an existing drink, it’s just a matter of making a selection and providing a name and e-mail address.

For custom drinks, guests can choose from virtually unlimited options, from the kind of liquor (there are more than 60) to exact proportions of liquor and mixers and ice.