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More than 300 traps have been deployed in the French town of Hyères to catch mosquitos.

BBC Click's LJ Rich looks at the best tech news stories of the week.

The project has been developed by Alphabet's moonshot factory, X.

How better technology and regulation could cut the waste generated by the construction industry.

The inventor, entrepreneur and ZX Spectrum creator died at his London home following a long illness.

The prolific innovator invented the pocket calculator and the ill-fated Sinclair C5 electric car.

Piracy is once again in the spotlight as rights holders threaten legal action for illegal streaming.

The authorities are investigating the disappearance of Ms. Petito, 22, who had traveled to national parks with her fiancé, Brian Laundrie. Their social media accounts provide a glimpse into their trip.

Organizers insist the “Justice for J6” gathering on Saturday will be peaceful, but law enforcement, National Guard troops and residents around the Capitol are preparing for turmoil.

The storm is expected to bring dangerous surf conditions to the northeastern United States before unleashing strong winds and heavy rains in Newfoundland, forecasters said.

Threatened by extremists and under fire by politicians, election workers now have their own legal defense network. It’s a perk they never expected to need.

The findings of the inquiry mirrored a New York Times investigation of video evidence, along with interviews with more than a dozen of the driver’s co-workers and family members in Kabul.

William J. Walker has testified about how his requests for approval to deploy the National Guard on Jan. 6 went unanswered. Now it falls to him to prevent another rampage.

The committee said there was insufficient evidence to recommend third shots for all adult recipients of the vaccine, as Pfizer had requested.

Google has abused the dominant position of Android in India to illegally hurt competitors in the world’s second largest internet market, a two-year antitrust probe by the nation’s watchdog has found. The Android-maker reduced device manufacturing firms’ ability and incentive to develop — and sell — devices running alternative versions of Android (more popularly known […]

Unlike their neighbors in fintech, it’s assumed that edtech companies need to expand to a number of big markets in order to reach a scale that makes them attractive to VCs.

“I’ve seen startups spend thousands of dollars inefficiently as a result of not having optimal signal in their paid acquisition campaigns. I’ve also spent millions at companies such as Postmates refining our signal to the best possible state,” says growth marketer Jonathan Martinez in a guest column for Extra Crunch this week. “I’d like every […]

Hello and welcome to Daily Crunch for Friday, September 17th! What a week, ya’ll. It is now just days before Disrupt, which means the TechCrunch hive is buzzing. I’ll leave it by noting that Reid Hoffman is coming, which is going to be a treat.

Blumberg Capital, founded in 1991 by investor David Blumberg, has just closed its fifth early-stage venture fund with $225 million, a vehicle that Blumberg says was oversubscribed — he planned to raise $200 million — and that has already been used to invest in 16 startups around the world (the firm has small offices in […]

While the technology and business world worked towards the weekend, DevOps firm GitLab filed to go public. So we need to pause, digest the company's S-1 filing, and come to some early conclusions.

If you really want to video chat tonight with William Hung of retro American Idol fame… got twenty bucks to spare? Yesterday, Cameo launched its Cameo Calls products, which lets fans video chat for up to 15 minutes one-on-one with their favorite influencers and celebrities. The talent sets the duration, time, and price of their […]

As a developer, there are times where you may end up with two or more dictionaries that you need to combine into one master dictionary. There are lots of different ways to merge dictionaries in the Python programming language. In this tutorial, you will look at a few of the old ways to merge dictionaries and then look at the latest method that was added in Python 3.9. Here are the methods you will learn about: Using dict.update() Merging with ** Merging with the Union Operator You will start your journey with the update() method! Using dict.update() Python’s dictionary has many different methods. One of those methods can be used to merge two dictionaries together. That method is called update(). Here is an example: >>> first_dictionary = {"name": "Mike", "occupation": "Python Teacher"} >>> second_dictionary = {"location": "Iowa", "hobby": "photography"} >>> first_dictionary.update(second_dictionary) >>> first_dictionary {'name': 'Mike', 'occupation': 'Python Teacher', 'location': 'Iowa', 'hobby': 'photography'} This worked perfectly! The only problem with this method is that it modifies one of the dictionaries. If you want to create a third dictionary without modifying one of the input dictionaries, then you’ll want to check out one of the other merging methods in this article. You are now ready to learn about using **! Merging with ** When you use the double-asterisk, it is sometimes called “unpacking”, “expanding” or “splatting” a dictionary. The ** is used in Python with kwargs in functions too. Here is how you can use the ** to merge two dictionaries: >>> first_dictionary = {"name": "Mike", "occupation": "Python Teacher"} >>> second_dictionary = {"location": "Iowa", "hobby": "photography"} >>> merged_dictionary = {**first_dictionary, **second_dictionary} >>> merged_dictionary {'name': 'Mike', 'occupation': 'Python Teacher', 'location': 'Iowa', 'hobby': 'photography'} This syntax looks a little weird, but it works great! Now you are ready to learn about the latest way to merge two dictionaries! Merging with the Union Operator Starting in Python 3.9, you can use Python’s union operator, |, to merge dictionaries. You can learn all the nitty-gritty details in PEP 584. Here is how you can use the union operator to merge two dictionaries: >>> first_dictionary = {"name": "Mike", "occupation": "Python Teacher"}  >>> second_dictionary = {"location": "Iowa", "hobby": "photography"} >>> merged_dictionary = first_dictionary | second_dictionary >>> merged_dictionary {'name': 'Mike', 'occupation': 'Python Teacher', 'location': 'Iowa', 'hobby': 'photography'} This is the shortest method yet for merging two dictionaries into one. Wrapping Up You now know three different methods that you can use to merge multiple dictionaries into one. If you have access to Python 3.9 or greater, you should use the union operator as that is arguable the cleanest looking method of combining dictionaries. However, if you are stuck on an older version of Python, you needn’t despair as you now have two other methods that should work! The post Merging Dictionaries with the Union Operator appeared first on Mouse Vs Python.

I already covered unravelling the with statement, and async with is not much different. Much like with, the language reference for async with gives an example of the statement already destructured. Based on that and the fact that async with is just with with asynchronous versions of __enter__ and __exit__ (__aenter__ and __aexit__, respectively), I&aposm just going to jump straight to the unravelled version and keep this post short.async with a as b: cExample use of async withbecomes:_enter = type(a).__aenter__ _exit = type(a).__aexit__ b = await _enter(a) try: c except: if not await _exit(a, *sys.exc_info()): raise else: await _exit(a, None, None, None) Example aync with unravelledIt&aposs with unravelled but changed by:__enter__ to __aenter__.__exit__ to __aexit__.All calls to those methods being preceded with await.

Performance Cliffs A common bad property of many different JIT compilers is that of a "performance cliff": A seemingly reasonable code change, leading to massively reduced performance due to hitting some weird property of the JIT compiler that's not easy to understand for the programmer (e.g. here's a blog post about the fix of a performance cliff when running React on V8). Hitting a performance cliff as a programmer can be intensely frustrating and turn people off from using PyPy altogether. Recently we've been working on trying to remove some of PyPy's performance cliffs, and this post describes one such effort. The problem showed up in an issue where somebody found the performance of their website using Tornado a lot worse than what various benchmarks suggested. It took some careful digging to figure out what caused the problem: The slow performance was caused by the huge functions that the Tornado templating engine creates. These functions lead the JIT to behave in unproductive ways. In this blog post I'll describe why the problem occurs and how we fixed it. Problem After quite a bit of debugging we narrowed down the problem to the following reproducer: If you render a big HTML template (example) using the Tornado templating engine, the template rendering is really not any faster than CPython. A small template doesn't show this behavior, and other parts of Tornado seem to perform well. So we looked into how the templating engine works, and it turns out that the templates are compiled into Python functions. This means that a big template can turn into a really enormous Python function (Python version of the example). For some reason really enormous Python functions aren't handled particularly well by the JIT, and in the next section I'll explain some the background that's necessary to understand why this happens. Trace Limits and Inlining To understand why the problem occurs, it's necessary to understand how PyPy's trace limit and inlining works. The tracing JIT has a maximum trace length built in, the reason for that is some limitation in the compact encoding of traces in the JIT. Another reason is that we don't want to generate arbitrary large chunks of machine code. Usually, when we hit the trace limit, it is due to inlining. While tracing, the JIT will inline many of the functions called from the outermost one. This is usually good and improves performance greatly, however, inlining can also lead to the trace being too long. If that happens, we will mark a called function as uninlinable. The next time we trace the outer function we won't inline it, leading to a shorter trace, which hopefully fits the trace limit. In the diagram above we trace a function f, which calls a function g, which is inlined into the trace. The trace ends up being too long, so the JIT disables inlining of g. The next time we try to trace f the trace will contain a call to g instead of inlining it. The trace ends up being not too long, so we can turn it into machine code when tracing finishes. Now we know enough to understand what the problem with automatically generated code is: sometimes, the outermost function itself doesn't fit the trace limit, without any inlining going on at all. This is usually not the case for normal, hand-written Python functions. However, it can happen for automatically generated Python code, such as the code that the Tornado templating engine produces. So, what happens when the JIT hits such a huge function? The function is traced until the trace is too long. Then the trace limits stops further tracing. Since nothing was inlined, we cannot make the trace shorter the next time by disabling inlining. Therefore, this happens again and again, the next time we trace the function we run into exactly the same problem. The net effect is that the function is even slowed down: we spend time tracing it, then stop tracing and throw the trace away. Therefore, that effort is never useful, so the resulting execution can be slower than not using the JIT at all! Solution To get out of the endless cycle of useless retracing we first had the idea of simply disabling all code generation for such huge functions, that produce too long traces even if there is no inlining at all. However, that lead to disappointing performance in the example Tornado program, because important parts of the code remain always interpreted. Instead, our solution is now as follows: After we have hit the trace limit and no inlining has happened so far, we mark the outermost function as a source of huge traces. The next time we trace such a function, we do so in a special mode. In that mode, hitting the trace limit behaves differently: Instead of stopping the tracer and throwing away the trace produced so far, we will use the unfinished trace to produce machine code. This trace corresponds to the first part of the function, but stops at a basically arbitrary point in the middle of the function. The question is what should happen when execution reaches the end of this unfinished trace. We want to be able to cover more of the function with machine code and therefore need to extend the trace from that point on. But we don't want to do that too eagerly to prevent lots and lots of machine code being generated. To achieve this behaviour we add a guard to the end of the unfinished trace, which will always fail. This has the right behaviour: a failing guard will transfer control to the interpreter, but if it fails often enough, we can patch it to jump to more machine code, that starts from this position. In that way, we can slowly explore the full gigantic function and add all those parts of the control flow graph that are actually commonly executed at runtime. In the diagram we are trying to trace a huge function f, which leads to hitting the trace limit. However, nothing was inlined into the trace, so disabling inlining won't ensure a successful trace attempt the next time. Instead, we mark f as "huge". This has the effect that when we trace it again and are about to hit the trace limit, we end the trace at an arbitrary point by inserting a guard that always fails. If this guard failure is executed often enough, we might patch the guard and add a jump to a further part of the function f. This can continue potentially several times, until the trace really hits and end points (for example by closing the loop and jumping back to trace 1, or by returning from f). Evaluation Since this is a performance cliff that we didn't observe in any of our benchmarks ourselves, it's pointless to look at the effect that this improvement has on existing benchmarks – there shouldn't and indeed there isn't any. Instead, we are going to look at a micro-benchmark that came out of the original bug report, one that simply renders a big artificial Tornado template 200 times. The code of the micro-benchmark can be found here. All benchmarks were run 10 times in new processes. The means and standard deviations of the benchmark runs are: Implementation Time taken (lower is better) CPython 3.9.5 14.19 ± 0.35s PyPy3 without JIT 59.48 ± 5.41s PyPy3 JIT old 14.47 ± 0.35s PyPy3 JIT new 4.89 ± 0.10s What we can see is that while the old JIT is very helpful for this micro-benchmark, it only brings the performance up to CPython levels, not providing any extra benefit. The new JIT gives an almost 3x speedup. Another interesting number we can look at is how often the JIT started a trace, and for how many traces we produced actual machine code: Implementation Traces Started Traces sent to backend Time spent in JIT PyPy3 JIT old 216 24 0.65s PyPy3 JIT new 30 25 0.06s Here we can clearly see the problem: The old JIT would try tracing the auto-generated templating code again and again, but would never actually produce any machine code, wasting lots of time in the process. The new JIT still traces a few times uselessly, but then eventually converges and stops emitting machine code for all the paths through the auto-generated Python code. Related Work Tim Felgentreff pointed me to the fact that Truffle also has a mechanism to slice huge methods into smaller compilation units (and I am sure other JITs have such mechanisms as well). Conclusion In this post we've described a performance cliff in PyPy's JIT, that of really big auto-generated functions which hit the trace limit without inlining, that we still want to generate machine code for. We achieve this by chunking up the trace into several smaller traces, which we compile piece by piece. This is not a super common thing to be happening – otherwise we would have run into and fixed it earlier – but it's still good to have a fix now. The work described in this post tiny bit experimental still, but we will release it as part of the upcoming 3.8 beta release, to get some more experience with it. Please grab a 3.8 release candidate, try it out and let us know your observations, good and bad!

Zen of python, import this, the hidden easter egg with the joke, source code of Zen of python disobey itself

 Tereza Iofciu, Data Science coach, PyLadies Hamburg organizer, and PSF Code of Conduct working group member has been awarded the Python Software Foundation 2021 Q1 community service award.RESOLVED, that the Python Software Foundation award the Q1 2021 Community Service Award to Tereza Iofciu. Tereza is a PSF Code of Conduct WG member and has done a wonderful job helping, participating, and driving the Code of Conduct WG discussions. Tereza formed and continues to help organize the PyLadies event in 2021. Tereza is also a member of the newly formed PSF Diversity & Inclusion WG.We interviewed Tereza to learn more about her inspiration and work with the Python community. Georgi Ker, a close associate of Tereza also speaks about Tereza. The Origin StoryCan you tell us about your origin story? Like how you got into tech?I got into tech quite traditionally, I studied Computer Science in Bucharest, Romania, but I chose that not for a particular love for Informatik. I was good at Math and Physics in high school but I couldn't study those as I didn't want to become a teacher, seeing how teachers were treated in school.  In the year 2000, Computer Science seemed like a thing for the future. After that I kind of went with the flow, and the flow got me to Germany and doing a Ph.D. in Information Retrieval as the field of Data Science was emerging. After that, I worked as a Data Scientist, Data Engineer, Product Management, Leadership, and now I am teaching (ha! the irony) Data science at the Neuefische Bootcamp. Involvement with the Python Community and InspirationWhat was your earliest involvement with the Python community? I would say in 2018 I saw on Twitter a friend of mine posting she was looking for a new job where diversity was part of the culture.  Through her, I discovered the PyLadies Berlin meetups and I realized that I was missing such a community in Hamburg. We had lots of meetups in the city (things used to still be in-person back then), but most were talks and networking, and not so much about teaching and learning.  It took a while to set it up but then I started the PyLadies Hamburg that year, which I wrote about here. You have been a volunteer coordinator and organizer of PyLadies Hamburg. You are also a member of the PSF Code of Conduct WG, and the Diversity & Inclusion WG. This is amazing. What drives and inspires you into volunteering your time and resources in the Python Community? I often felt that a normal day job doesn't fulfill all my needs, one gets paid for work and it is hard for companies to be consistent in providing other goals. Business is business and in the end, things come down to profit.  So one rarely gets the opportunity to be surrounded at work by like-minded people all the time.  I have volunteered in other organizations, but I found that the PyLadies does attract people who, while they are active in it, are very passionate and inspiring about making tech accessible to more than the majority. So in the end PyLadies was also a refuge and an energy top-up.  It is like finding your village in the world!  Tech companies in Germany are still very behind with diversity.. and changing that needs all the help it can get, women and people from underrepresented groups need a space where they can learn and grow and get inspired without invisible glass ceilings. How has your involvement within the Python community helped your career? Being involved helped my career in several ways - I've discovered that I learn better when I teach, that is I cannot be bothered to learn a new thing when it is just for the sake of me learning it.  This ultimately led to me believing I would succeed in my current role, and thus I took the opportunity.  We've organized a lot of events - meetups, full-day workshops (IoT workshop at PyCon DE 2019), and conferences like Python Pizza Hamburg in 2019 and 2020, and International Women's Day PyLadies over 3 timezones.  One learns a lot from organizing and it can also be lots of fun. Also, I have been in a leadership role since 2019, and part of the job is to inspire people to get out of their comfort zone, present their work, organize workshops, do meetups and this is something that I was already practicing within the community.  And the network, being around inspiring people is inspiring, and in the end, one is part of an inspiration loop - people also come back with stories on how their life got better with PyLadies. Impact of Covid in the Python CommunityHow has Covid affected your work with the Python community and what steps are you taking to push the community forward during this trying time? We moved pretty quickly to remote events, nobody really felt like being responsible for spreading covid and now there is the remote everywhere.  Aside from the fatigue of the pandemic, going remote has greatly made the events accessible to more people, people from other cities, countries, or people who have to take care of other people and wouldn't have been able to travel to a meetup.  We had this year’s workshops with speakers from the US and Canada. This would have not been possible previously. On the PyLadies Hamburg side, we try to keep to the rhythm of monthly events.  And the International Women's Day event became a three timezone event quite randomly, I posted about organizing one event in Hamburg and looking for speakers among the PyLadies organizers, then Lorena Mesa from Chicago saw it and asked if she could do a joint one in Chicago and then I asked her if she knows anyone on the other side of the globe for symmetry, and she said Georgi Ker in Bangkok who said: "of course." This year I also attended for the first time PyCon US and I was part of the panel presenting the Diversity & Inclusion Workgroup, and we were geographically spread all over the world.Georgi Ker Speaks on Tereza Iofciu's ImpactGeorgi Ker, who had the opportunity of working together with Tereza and Lorena Mesa in organizing the online International Women’s Day 2021 event, speaks on Tereza’s impact.Tereza is everywhere! I don't even know where to start. She was the one who initiated organizing the PyLadies IWD - International Women's Day - event in different time zones. Making the event accessible for more people.Apart from involvement in the Interim Global Council, she is also one of the PyLadies moderators to ensure that PyLadies stays as a safe environment for everyone.Tereza is like the guardian of PyLadies and PSF protecting the gates of the Python community caring for people.We at the Python Software Foundation wish to once again congratulate and celebrate Tereza Iofciu for her amazing contributions to PyLadies and the wider Python community.

Python is one of the most popular programming languages worldwide. It often ranks high in surveys—for instance, it claimed the first spot in the Popularity of Programming Language index and came second in the TIOBE index.

A dictionary in python is a data structure that stores data in the form of key-value pairs. The key-value pairs are also called items. The key-value pairs in each dictionary are separated by a colon “:” and each item in the dictionary is separated by a comma “,”. In this article, we will look at different ways to add an item to a dictionary in python. Add an item to a dictionary using subscript notation If we have a dictionary named myDict and a key-value pair having values myKey and myValue, then we can add the key-value pair to the dictionary using the syntax myDict [ myKey ] = myValue. This can be done as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) myDict["niche"] = "programming" print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} In the example above, we have added a new key “niche” with value “programming” associated with it. Remember that if the key of the item which is being added to the dictionary already exists in it, the value for the key will be overwritten with a new value. This can be seen in the following example. myDict = {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} print("Original Dictionary is:", myDict) myDict["niche"] = "python programming" print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'python programming'} In the example above, the key “niche” already exists in the dictionary with value “programming” associated with it. When we try to add the key-value pair with “niche” as key and “python programming” as associated value, the value associated with “niche” is updated to the new value. Add an item to a dictionary using the update() method in Python We can add an item to a dictionary using the update() method. The update() method when invoked on a dictionary, takes a dictionary or an iterable object having key-value pairs as input and adds the items to the dictionary. We can give a  new dictionary as an input to the update() method and add the items to a given dictionary as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) myDict.update({'niche': 'programming'}) print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} We can give a list of tuples having key-value pairs as an input to the update() method and add items to a given dictionary as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) items = [("niche", "programming")] myDict.update(items) print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} We can also pass key-value pairs as keyword parameters to the update() method to add elements to the dictionary. Here the keys will be used as keyword parameters and values will be assigned as an input to the keyword parameters as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) myDict.update(niche="programming") print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} Add an item to a dictionary using the ** operator in Python A double asterisk (**) operator is used to pass variable length keyword parameters to a function. We can also use the ** operator to add a key-value pair to another dictionary. When we apply the ** operator to a dictionary, it deserializes the dictionary and converts it to a collection of key-value pairs. This collection of key-value pairs can be again converted to a dictionary. To add an item to a dictionary, we will first create a dictionary with only that item. Then we will use the ** operator to merge the new dictionary and the dictionary to which the item had to be added as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) newDict = {'niche': 'programming'} myDict = {**myDict, **newDict} print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} Add an item to a dictionary using the __setitem__() method We can also add an item to the dictionary using the __setitem__() method. The __setitem__() method, when invoked on a dictionary, takes the new key and value as its first and second parameters respectively and adds the key-value pair to the dictionary as follows. myDict = {"name": "PythonForBeginners", "acronym": "PFB"} print("Original Dictionary is:", myDict) myDict.__setitem__('niche', 'programming') print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} If the key already exists in the dictionary, the value associated with it is overwritten with the new value. This can be seen in the following example. myDict = {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} print("Original Dictionary is:", myDict) myDict.__setitem__('niche', 'python programming') print("Modified Dictionary is:", myDict) Output: Original Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'programming'} Modified Dictionary is: {'name': 'PythonForBeginners', 'acronym': 'PFB', 'niche': 'python programming'} Conclusion In this article, we have seen different ways to add an item to a dictionary in python. To read more about dictionaries in python you can read this article on dictionary comprehension or this article on how to merge two dictionaries in python. The post Add an item to a dictionary in Python appeared first on PythonForBeginners.com.

Microsoft's Surface event: How to watch new hardware debut live on Sept. 22  CNET

AMD Chipset Vulnerability Leaks Passwords, Patch Available  Tom's Hardware

New PS5 Model Heat Debate Has Finally Been Settled | Screen Rant  Screen Rant

Someone Made Ubuntu Look Just Like Windows 11  Tom's Hardware

PrintNightmare Windows Patch Reportedly Breaks Network Printing Altogether  Tom's Hardware

PC gaming is becoming way harder to get into, and that sucks  PC Gamer

Intel Goes on Game Dev Hiring Spree Before Alchemist Gaming GPU Launch  Tom's Hardware

The PHP team is pleased to announce the release of PHP 8.1.0, RC 2. This is the second release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 2 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the third release candidate (RC 3), planned for 30 September 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 1. This is the first release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 1 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the second release candidate (RC 2), planned for 16 September 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, Beta 3. This is the third beta release, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, Beta 3 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the first release candidate (RC 1), planned for 2 September 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the first beta release of PHP 8.1.0, Beta 2. This continues the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, Beta 2 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be Beta 3, planned for Aug 19 2021.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the first beta release of PHP 8.1.0, Beta 1. This continues the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0 Beta 1 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be Beta 2, planned for Aug 5 2021.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the third testing release of PHP 8.1.0, Alpha 3. This continues the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0 Alpha 3 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be Beta 1, planned for Jul 22 2021.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

The PHP team is pleased to announce the second testing release of PHP 8.1.0, Alpha 2. This continues the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki.For source downloads of PHP 8.1.0 Alpha 2 please visit the download page.Please carefully test this version and report any issues found in the bug reporting system.Please DO NOT use this version in production, it is an early test version.For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive.The next release will be Alpha 3, planned for 8 Jul 2021.The signatures for the release can be found in the manifest or on the QA site.Thank you for helping us make PHP better.

PSH highlights and indents source code using highlight configuration files which are similar to Ultraedit highlighting files As such it currently supports C C Java Perl PHP Pascal Lisp Mumps The post PHP Syntax Highlighter appeared first on PHPBuilder.

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By Ying Zhang on July 30, 2000 This document describes how to display safely formatted output from user input. We will discuss the dangers of displaying unfiltered output and then provide a safe means of displaying formatted output. Download the attachment and extract it into your web documents directory. Dangers... The post Displaying Formatted User Input appeared first on PHPBuilder.

This script will display a progress bar. It's beneficial when you are performing many tasks on the back end that take a while to complete. In these The post Displaying Dynamic Progress Bars appeared first on PHPBuilder.

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Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 www.choopa.com Linux 0:00:00 0.000 0.262 0.003 0.020 0.020 2 Aruba Linux 0:00:00 0.000 0.412 0.005 0.026 0.026 3 Bigstep Linux 0:00:00 0.000 0.200 0.062 0.125 0.125 4 CWCS Managed Hosting Linux 0:00:00 0.000 0.250 0.068 0.134 0.134 5 Hyve Managed Hosting Linux 0:00:00 0.000 0.143 0.073 0.148 0.148 6 ServerStack Linux 0:00:00 0.000 0.214 0.100 0.201 0.201 7 www.flexential.com Linux 0:00:00 0.000 0.248 0.104 0.208 0.208 8 Pair Networks Linux 0:00:00 0.000 0.369 0.118 0.235 0.235 9 GoDaddy.com Inc Linux 0:00:00 0.007 0.415 0.005 0.025 0.027 10 Rackspace Linux 0:00:00 0.007 0.485 0.007 0.017 0.017 See full table In August 2021, Choopa had the most reliable hosting company site: it responded to all of Netcraft’s requests, with an average connection time of 3ms. Choopa has appeared in the top 10 table five times in 2021 so far, also coming top in February and April of this year. Customers can choose from a range of cloud and managed solutions as well as register domain names. Aruba, Bigstep and CWCS Managed Hosting appear in second, third and fourth places. Aruba came close to Choopa in average connection time, averaging 5ms. Bigstep and CWCS Managed Hosting were both slower, averaging 62ms and 68ms. Aruba provides hosting, cloud and digital signature services, fiber optic internet, digital preservation, and much more, with data centers across Europe in the UK, Germany, Czechia, Poland, Italy and France. Bigstep’s bare metal cloud hosting provides the flexibility of cloud hosting without the associated overhead and performance reductions of virtualization. The bare metal offerings are available in data centres in the UK and Romania. CWCS provides dedicated servers along with cloud services, as well as a variety of other solutions. CWCS has data centres across the UK, as well as North America. All of the top 10 hosting company sites used Linux in August, making Linux clearly dominant in the top 10 throughout this year.

The US and others may have withdrawn from Afghanistan, but many Afghan Government websites and email addresses under the .gov.af top-level domain are still very much dependent on services hosted outside of the country – mostly in the US. By taking control of Afghanistan, the Taliban has inherited these government domains and now shares web hosting and mail servers with several other governments around the world, including the UK Government. In many cases, emails sent to .gov.af domains will be routed through US-hosted servers, presenting intelligence opportunities if the new Taliban government were to continue using them.

Bagram, formerly the site of the largest US military base in Afghanistan. Over the past few weeks, the Taliban have taken control of substantially the whole of Afghanistan, with just Kabul Airport and the Panjshir Valley presently controlled by the US Military and the National Resistance Front of Afghanistan respectively. Yet the situation with Afghanistan’s internet infrastructure is quite different to what anyone following the mainstream media might reasonably expect, as Afghanistan’s key internet resources – domains, IP addresses, routing and government communications – are controlled by a diverse set of entities subject to Western jurisdictions. Who is in control of the .af domain? Presently, .af’s DNS is run using Anycast DNS services from Packet Clearing House, a San Francisco based not-for-profit organisation, and Gransy, a Czech registrar and registry services provider. Packet Clearing House provides free Anycast DNS services to “developing-country ccTLD registries”, and Gransy provides free Anycast DNS services to ccTLDs with fewer than 10,000 domains – .af has around 6K domains and is well within Gransy’s criteria for a free service.

In the August 2021 survey we received responses from 1,211,444,849 sites across 263,733,974 unique domains and 11,327,711 web-facing computers. This reflects a loss of 4.99 million sites, but a gain of 1.64 million domains and 67,600 computers. The number of unique domains powered by the nginx web server grew by more than a million this month, while Apache's count fell by 916,000. This has extended nginx's lead in the domains metric, giving it a 29.8% share compared with Apache's 25.5%. OpenResty gained 234,000 domains, but its market share remained static at 14.5%, while Cloudflare gained 726,000 domains and increased its market share to 7.72%. The number of web-facing computers using nginx has continued to increase, this month by 49,000 (+1.18%). There are now 4.19 million web-facing computers running nginx, compared with 3.52 million that run Apache. Microsoft follows in third place with 1.38 million computers. The web-facing computers metric has painted a remarkably stable trend over the past several years, as is evident in the graph below, with both Microsoft and Apache steadily falling while nginx has progressively climbed to first overtake Microsoft in 2017, and then Apache during 2020. There has also been a rise in "Other" web servers, which includes several nginx-based spinoffs such as OpenResty and Tengine. Websites in Afghanistan The Taliban offensive in Afghanistan has obvious potential to upset the country's internet infrastructure, but the extent of any changes may be limited. Afghanistan has had a relatively small presence on the web throughout the past 20 years, and many of its sites were already hosted outside of the country and used generic top-level domains to avoid interference from the Taliban. This month's survey found only 8,031 websites hosted in Afghanistan, and 23,205 sites that use Afghanistan's .af country-code top-level domain (ccTLD). More than two-thirds of the latter are hosted in the US, and more than 2,000 are hosted in Germany – although any site that relies on a .af domain would still be vulnerable to interruption by the country's new government, should it desire. Nearly 1,000 of the .af sites are Afghan Government websites that fall under the .gov.af second-level domain – such as president.gov.af and kabul.gov.af – but surprisingly, less than half of these are hosted in Afghanistan, with the rest being hosted in the US, Germany, Singapore, France, Canada, UK, Netherlands, Ireland and India. Even more surprisingly, dozens of the .gov.af sites hosted in the US and Germany are used to host webmail services, potentially putting Afghan Government communications in easy reach of external intelligence agencies. Other vendor and hosting news Microsoft has announced the general availability of Azure Government Top Secret. The new air-gapped Azure regions are intended to handle national security workloads at the US Top Secret level. Microsoft also announced its new Azure Healthcare APIs, which provide pipelines to manage protected health information data at scale. Statistics collected by Azure DDoS Protection showed a shift towards attacks against web applications in the first half of 2021. Apache Tomcat 10.0.10 was released on 5 August, followed by Tomcat 10.1.0-M4 (alpha) and Tomcat 9.0.52 on 6 August, and Tomcat 8.5.70 on 16 August. All four of these releases correct the regression of an HTTP/2 flow control bug in their previous versions. OpenResty 1.19.9.1 was released on 6 August. This version of the web platform based on nginx and LuaJIT now uses nginx 1.19.9 (a mainline release from 30 March) as its core, and also includes some LuaJIT fixes. DeveloperJuly 2021PercentAugust 2021PercentChange nginx444,524,63136.54%441,930,79136.48%-0.06 Apache311,567,36825.61%305,180,85825.19%-0.42 OpenResty75,464,8746.20%75,516,2186.23%0.03 Cloudflare54,611,8564.49%55,830,6304.61%0.12

FluBot has built up a community of compromised Android phones in the UK since April and in the past 24 hours has commenced monetising them by sending overlays for British Banks. FluBot first appeared in 2020, targeting mainly Spanish banks, but recently it has spread its reach, with Australian, German and Polish banks all affected within the last few weeks. UK banks are now firmly in its sights, with HSBC and Santander the first to be affected, and Lloyds and Halifax following shortly after.

The coronavirus pandemic resulted in the closure of many bricks and mortar retail stores, forcing UK consumers to adopt online shopping more than ever before. This trend has largely continued in spite of many stores since reopening, as millions of consumers have become accustomed to the practical benefits of online shopping. Along with this increased volume of online shopping came a new trend of phishing attacks where cybercriminals impersonate parcel delivery companies in an attempt to steal financial details from their victims. Royal Mail and Hermes were popular targets for these types of attack, but most new attacks now impersonate the Post Office. A typical text message used to lure victims to a phishing site that impersonates the Post Office. These attacks are typically disseminated via text message, informing the victim that they have missed a delivery. Sometimes the messages say up front that the recipient must rebook the delivery by paying a small surcharge. The relatively small surcharge is often sufficient to trick victims into believing the phishing site is legitimate, or at least that any risk is minimal, allowing the phisher to obtain the victim’s details and potentially steal a much larger amount. As most of the attacks are orchestrated via text message, the phishing sites are usually hosted with purpose-bought domain names that include the targeted company’s name in an attempt to be convincing. Some examples include: myhermes-youritem.com reschedule-postoffice.com royalmail-customer.com dpd.delivery.150227811923.com Some messages instead use generic URL shorteners to take victims to the phishing sites, but this would not necessarily be viewed as suspicious by all recipients, as the use of URL shorteners is commonplace even in legitimate text messages. Most of the phishing kits used in these attacks also attempt to evade detection by blocking unwanted clients such as bots and anti-phishing organisations, but Netcraft successfully circumvents these checks. Navigating through a simple Hermes phishing site that is optimised for mobile devices. The victim is redirected to the real Hermes website after their details have been stolen. After impersonating the delivery company, some of these phishing attacks proceed to also impersonate one of several UK banks. This gives the criminal an opportunity to steal additional credentials that are specific to each bank, such as online banking security codes and other tokens that would likely be used to gain unauthorised access to the victim’s bank account. Some attacks - particularly those that do not use the phishing site to directly impersonate the victim’s bank - are followed up by a phone call from the cybercriminal, who will use the information stolen by the phishing site to convince the victim that it is a genuine call from their bank regarding the payment they just made. This provides a more interactive opportunity for the criminal to obtain the information required to gain access to the victim’s bank account, including time-sensitive OTP codes.

Netcraft’s research into the Android banking malware FluBot confirms that its operations are expanding rapidly, with a spike in the number of malware distribution pages deployed, and finance applications affected in greater numbers. In recent days new overlays have been distributed that target a number of Polish and German banks, only days after news that FluBot has begun to target Australian banks. FluBot is distributed in the first instance using text messages, containing links to so-called “lure” pages: web pages unintentionally hosted by compromised web servers, commonly impersonating parcel tracking services, or voicemail notifications. Lure pages attempt to induce visitors to download the malware. Text messages impersonating delivery companies, directing victims to FluBot lure sites

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