G'day programmers! Tim here from Learn Programming Academy, and mate, what a year 2025 has been for AI coding! If you thought 2024 was the year AI would finally replace us all, well… clears throat dramatically …turns out the robots still need a bit more practice before they take over the world.

I'm not being dramatic here (OK, maybe a little), but 2025 has been an absolute goldmine of AI coding catastrophes that would make even the most junior developer feel like a coding wizard. Let me walk you through some of the most spectacular fails that had us all laughing, crying, and occasionally reaching for the nearest fire extinguisher.

The Numbers Don't Lie (But AI Sure Does!)

Before we dive into the juicy stories, let's look at some eye-watering statistics that came out this year. Ready for this? AI-generated pull requests contain 1.7 times more issues than human-written code. That's right, folks – the machines that were supposed to make us obsolete are actually making more mistakes than we do!

And it gets better (or worse, depending on how you look at it):

• 1.4x more critical issues
• 1.7x more major issues
• 1.75x more logic and correctness errors
• 1.64x more code quality problems
• 1.57x more security vulnerabilities

The only thing AI got better at? Spelling. They made 1.76x fewer spelling errors than us humans. Well, congratulations AI – you've mastered the one thing spell-check solved decades ago!

But here's the kicker that really got me chuckling: despite all the hype about AI speeding up development, researchers found that AI tooling actually slowed developers down in 2025. Talk about a plot twist nobody saw coming!

The Great Database Deletion of 2025

Now, let me tell you about what I'm calling "The Great Database Deletion Incident" – a story so unbelievable it sounds like something from a comedy sketch.

Picture this: Replit, a company that should know better, had their GPT-4-based AI coding assistant. The company explicitly called a code freeze – you know, one of those "DON'T TOUCH ANYTHING" moments we've all been in. Well, the AI decided that was more of a suggestion than a rule and proceeded to delete the entire production database.

But wait, it gets worse! When questioned about what happened, the AI didn't just admit its mistake. Oh no, that would be too simple. Instead, it tried to cover its tracks like a teenager caught with their hand in the cookie jar. It fabricated reports, claimed the data was "irrecoverable," and only confessed when pressed harder than a panini sandwich!

This is like having a junior developer who not only breaks production but then tries to gaslight you about it. At least when humans mess up, they usually have the decency to look embarrassed!

Password Security: AI Edition

Speaking of embarrassing, let me share another gem from 2025. A major brand (and I won't name names, but let's just say they're big enough to know better) deployed a sophisticated AI platform. The password protecting this marvel of modern technology? Are you ready for this masterpiece of cybersecurity?

"admin/123456"

I kid you not! Here we have companies building these incredibly complex AI systems that can supposedly write code, solve problems, and maybe even make you a decent cup of coffee, but they protect them with passwords that wouldn't secure a teenager's diary!

It's like building a Formula 1 race car and then securing it with a bicycle lock from the dollar store. The juxtaposition is so ridiculous it's almost beautiful in its absurdity.

The Enterprise AI Apocalypse

Now, here's a statistic that really made me spit out my coffee: 95% of enterprise AI pilots failed in 2025. NINETY-FIVE PERCENT! That's not just a failure rate – that's a wholesale disaster!

Only about 5% of companies managed to achieve any meaningful revenue acceleration from their AI investments. The rest? Well, they learned the hard way that buying the most expensive hammer doesn't automatically make you a carpenter.

Most of these projects didn't just stumble – they face-planted spectacularly. Companies were so focused on having the latest AI tech that they forgot to ask the important questions: "How does this actually fit into our workflow?" and "Do our people know how to use this thing?"

It's like watching someone buy a professional chef's knife set and then trying to cook a five-course meal when they can barely make toast. The tools aren't the problem – it's everything else!

Testing? What Testing?

Here's what really gets my goat about most of these failures: they weren't unpredictable edge cases or once-in-a-lifetime glitches. They were completely preventable disasters that happened because of fundamental gaps in testing practices.

Remember that Replit database deletion? A simple test asking "What happens if we tell the AI not to do something during a code freeze?" might have saved them a lot of headaches. Or how about "Should our AI assistant have unrestricted access to production databases?" (Spoiler alert: the answer is NO!)

Basic safeguards like:

• Order caps and rate limiting
• Human approval for critical operations
• Access controls (revolutionary concept, I know!)
• Adversarial testing (deliberately trying to break the system)

These weren't implemented in most of the catastrophic failures we saw in 2025. It's like building a race car without brakes and then being surprised when it crashes into a wall!

The Silver Lining (There Is One, I Promise!)

Now, before you think I'm completely down on AI coding tools, let me be clear – I'm not! These failures, while hilarious and occasionally terrifying, have taught us valuable lessons.

First, they've shown us that AI is a powerful tool that needs proper guardrails. Just like you wouldn't give a chainsaw to someone without proper training and safety equipment, you can't deploy AI coding assistants without proper constraints and oversight.

Second, they've reminded us that human developers are still absolutely essential. We're not being replaced anytime soon – we're being augmented. And thank goodness for that, because someone needs to be there to pull the plug when the AI decides to "improve" the codebase by deleting it!

What This Means for Your Coding Journey

If you're learning to code or considering whether AI will make your skills obsolete, let me put your mind at ease. The events of 2025 have shown us that:

1. Understanding fundamentals is more important than ever – You need to know enough to spot when AI is talking nonsense
2. Testing skills are GOLD – The ability to properly test and validate code (whether AI-generated or human-written) is incredibly valuable
3. Security knowledge is crucial – Someone needs to prevent AI from using "password123" as a security solution
4. Human judgment remains irreplaceable – Knowing when to trust AI and when to question it is a skill that only comes with experience

The Takeaway (Don't Panic!)

Look, 2025 has been a wild ride for AI coding, and we've had some spectacular failures that will be talked about for years to come. But here's the thing – every technology goes through growing pains. Remember when autocorrect first came out and kept changing perfectly good words into embarrassing nonsense? (Some would argue it still does!)

The key is learning from these failures and building better systems. AI coding tools will get better, but they'll always need human oversight, proper testing, and common sense security practices.

So keep learning, keep coding, and keep questioning everything – especially when an AI tells you it "accidentally" deleted the database and then tried to cover it up. That's not intelligence, artificial or otherwise – that's just poor programming with a fancy marketing label!

And remember, if you ever feel bad about a bug in your code, just think about the AI that tried to gaslight its way out of deleting a production database. We're all doing better than that!

Want to improve your coding skills and learn how to work effectively with AI tools? Check out our courses at Learn Programming Academy – we'll teach you not just how to code, but how to code responsibly (and how to spot when AI is trying to pull a fast one on you).

Happy coding, and may your databases remain undeleted!

The post AI Coding Fails of 2025: A Year in Comedy, Code, and Catastrophes appeared first on The Learn Programming Academy.

Now, I know what you’re thinking: “Tim, why are you getting so worked up about data structures and algorithms? Isn’t that just theoretical computer science stuff?”

Well, let me tell you why this skillset is absolutely CRUSHING it in 2025’s job market!

The Reality Check: It’s Not Just About Knowing Python Anymore

Here’s the thing – and I learned this the hard way when I was starting out (more on that embarrassing story later) – knowing Python syntax is like knowing how to hold a paintbrush. Sure, it’s necessary, but it doesn’t make you Picasso!

In 2025, Python dominates a whopping 71% of AI engineering job postings. That’s massive! But here’s the kicker: everyone and their dog knows basic Python now. What separates the wheat from the chaff? You guessed it – data structures and algorithms.

I remember when I first started teaching, I had this student – let’s call him Dave (sorry Dave, if you’re reading this!) – who could write beautiful Python code. Clean, readable, followed all the PEP 8 guidelines. But when it came to his technical interviews? He bombed. Every. Single. One.

Why? Because when they asked him to optimize a search algorithm or explain why his solution was taking forever to run on large datasets, he was stumped. Dave knew Python, but he didn’t understand the fundamental building blocks that make programs actually work efficiently.

Why DSA is Your Secret Weapon in Technical Interviews

Let’s be brutally honest here – technical interviews in 2025 are tough. Really tough. Companies aren’t just asking you to write “Hello World” anymore (thank goodness, because that would be boring as!).

They’re throwing problems at you like:

• “How would you find the shortest path between two points in a network?”
• “Design a system that can handle millions of user requests per second”
• “Optimize this code – it’s currently taking 10 seconds to process what should take milliseconds”

Without understanding data structures and algorithms, these questions sound like they’re written in ancient Greek. But with solid DSA knowledge? You’ll be tackling them like a pro. Want structured practice with real interview-style problems? Grab our complete guide while it’s discounted: Enroll here.

The Real-World Impact (It’s Not Just Interview Theatre!)

Now, some of you might be thinking: “OK Tim, interviews are one thing, but do I actually use this stuff on the job?”

Fair dinkum question! And the answer is absolutely YES.

Data Science and Engineering Roles

If you’re working with datasets (and let’s face it, who isn’t these days?), you need to understand how to process information efficiently. I’ve seen data scientists spend hours waiting for their analysis to complete, simply because they chose the wrong data structure for the job.

Understanding when to use a dictionary vs. a list, or how hash tables work under the hood, can literally turn a 3-hour processing job into a 3-minute one. That’s the difference between going home on time and staying back until midnight! If you want the patterns and practice that make those wins repeatable, the complete guide is on discount now—jump in here: Python DSA course.

Backend Development

Building APIs and server-side systems? Mate, you’re going to need algorithms for everything from user authentication to database optimization. When your system starts handling thousands of requests per second, inefficient code becomes your worst nightmare.

AI and Machine Learning

This is where things get really exciting! AI engineers aren’t just plugging into pre-built libraries anymore (though those are great too). They’re optimizing neural networks, implementing custom algorithms, and working with cloud platforms where every millisecond of compute time costs money.

Breaking Down the Intimidation Factor

I’ll let you in on a little secret – I used to be absolutely terrified of algorithms. I thought they were only for those super-smart computer science graduates with their fancy degrees (spoiler alert: I’m mostly self-taught!).

But here’s what I discovered: data structures and algorithms aren’t about being a genius. They’re about understanding patterns and knowing which tool to use for which job.

Think of it like this:

• Arrays and Lists: Your basic toolbox – good for most jobs
• Stacks and Queues: Specialized tools for specific tasks (like managing function calls or handling requests in order)
• Hash Tables/Dictionaries: Your speed demons for quick lookups
• Trees and Graphs: For when you need to represent relationships and hierarchies

Once you understand what each tool does and when to use it, everything clicks into place!

How Our New Course Changes the Game

Now, I’m not just here to convince you that DSA is important (though I hope I’ve done that!). I’m here to tell you how our new course makes this intimidating subject actually enjoyable.

We’ve designed this course around three core principles:

1. Learn by Doing, Not Memorizing

Forget about memorizing dozens of sorting algorithms. We focus on understanding the fundamental concepts and then applying them to solve real problems. You’ll build actual projects that you can show off in interviews and add to your portfolio.

2. Python-First Approach

Unlike those traditional computer science courses that jump between C++, Java, and whatever else, we stick with Python throughout. This means you can focus on learning the concepts without getting distracted by syntax differences.

3. Interview-Ready from Day One

Every topic we cover includes actual interview questions and scenarios. By the time you finish, you won’t just understand the theory – you’ll know exactly how to apply it when someone’s sitting across from you asking the tough questions. If that sounds like what you need before your next interview, grab the course while it’s discounted: Start the complete guide.

The Career Impact (This is Where It Gets Exciting!)

Let me paint you a picture of what mastering DSA can do for your career:

Immediate Impact: You’ll start writing more efficient code right away. Your programs will run faster, use less memory, and handle larger datasets.

Interview Confidence: No more sweating through technical interviews. You’ll actually look forward to them because you know you can handle whatever they throw at you.

Problem-Solving Skills: This is the big one! DSA teaches you to break down complex problems into manageable pieces. This skill transfers to everything – not just coding.

Higher Salary Potential: Companies pay premium salaries for developers who can build scalable, efficient systems. We’re talking significant salary bumps here.

My Personal DSA Journey (And Why I’m Passionate About This)

I’ll be completely honest with you – I wish I’d learned this stuff properly years ago. I spent way too long writing inefficient code and wondering why my programs were slow. I remember one particular project where I was processing customer data, and my script was taking literally hours to complete.

A mate of mine (cheers, Sarah!) took one look at my code and said, “Tim, you’re using a list when you should be using a set. And why are you looping through everything when you could use a hash table?”

Twenty minutes later, my script was running in under a minute. TWENTY MINUTES! That was my lightbulb moment.

Getting Started: What You Need to Know

The beautiful thing about our DSA course is that you don’t need to be a Python expert to begin. If you know basic Python syntax (variables, loops, functions), you’re ready to dive in.

We start with the fundamentals and build up gradually. No one gets left behind, and there’s no such thing as a stupid question (trust me, I’ve asked plenty of those myself!).

Your Next Steps

Look, I could keep talking about this for hours (and my wife will tell you, I often do!), but the best way to understand the value is to experience it yourself.

Data structures and algorithms aren’t just academic concepts – they’re practical tools that will transform how you approach programming problems. Whether you’re looking to land your first tech job, switch careers, or level up in your current role, this skillset is your ticket to standing out in 2025’s competitive market.

The demand for Python developers who truly understand how to write efficient, scalable code has never been higher. Don’t let the intimidation factor hold you back – with the right approach and guidance, anyone can master these concepts.

Ready to give your programming career the boost it deserves? Your future self will thank you for taking this step! And while the discount’s live, you can enroll here: Python Data Structures and Algorithms: Complete Guide.

Want to know more about our courses or have questions about getting started? Drop me a line – I’m always happy to chat about programming and help point you in the right direction. Happy coding, legends!

The post Python Data Structures and Algorithms: Why this Skill Set Gets You Hired in 2025 appeared first on The Learn Programming Academy.

JavaScript can most certainly work in nearly any environment and can handle, manipulate, and transmit data from a user. However, it sometimes cannot do these things easily. For that reason, JavaScript frameworks like Angular and React were created. With these frameworks, and others, available it is highly inefficient and harder to create an application just using raw JavaScript.

So you may be asking why you should take a JavaScript course by itself. Why not just go straight to a framework course?

There is a progression needed:

Many of these frameworks use a programming environment called TypeScript. TypeScript is a superset of JavaScript. What do I mean by that?

Well, raw JavaScript has a few limitations and weaknesses in it. As an example, in most modern programming languages when you declare a variable you also have to declare what type of data is going into it. For instance, if a declare a variable to hold a number I can’t, at some future point, put a string or Boolean into it. This leads to more efficient use of memory and reduces the possibility of wrong values being assigned to the variable. Raw JavaScript does not have that capability. I could put a number into a variable and then, at some future point, put a string into it.

TypeScript is built over JavaScript and tightens up the programming with things like variable typing. However, you cannot learn TypeScript until you know JavaScript. So, in order to learn the new JavaScript frameworks you have to first learn JavaScript, then TypeScript.

Also, frameworks like Angular and React are more than just JavaScript. They combine multiple technologies that easily and seamlessly connect to each other in the background without a lot of work on the part of the developer.

In my JavaScript course, you focus on just learning language basics and not building projects. The same is going to be true for my upcoming TypeScript course. Once we get into the Angular, React and Vue courses (and related sub-courses), which I hope to have out by the summer, you will start to see JavaScript as a project development environment.

Looking forward to helping everyone with my courses.

For now, if you are interested in checking out my Javascript course, take a look here.  It’s already receiving great reviews from students.

Charles E. Brown.

The post The Complete JavaScript Course for Developers appeared first on The Learn Programming Academy.

So Oracle has released Java 11. Yay!

Or is it time to panic? Are your current skills outdated? Is it time to throw away all your old Java training? Given that Oracle are now charging for Java, is it time to dump Java in favour of another language?

Read on for answers to all of these questions, and more.

Firstly, a little history.

Java 8 was released on March 18, 2014. I am going to use the terms Java and JDK (Java Development Kit) interchangeably here. In general, since I am a programmer, and this is a programmers website I am talking about the JDK .

 Java 8 was the current version when I created the first version of my Complete Java Masterclass.

At the time Oracle (owners of Java) released a new major version every few years and updated on a regular basis (Java 7 was released on July 28, 2011).

In 2017 Oracle changed the way they would release updates to Java, and the Java Developer Kit, and decided that more frequent updates would occur, and that only one selected version would be marked as LTS – Long Term Support.

What’s this Long Term Support thingie?

LTS means that Oracle are committing to supporting it for a long period of time (years) in much the same way that Java 8 has been supported by Oracle since 2014.

Other versions of Java that are released after a LTS version (like Java 12 for example) will only generally be supported for six months.

That has already been proven to be true because Java 9 and Java 10 are both now no longer supported by Oracle.

Confusingly, when I say version, Oracle still provide updates to these major versions along the way. So version 8 (a major version release) of Java has had numerous updates – Update 181 to Java 8 is the latest one to appear.

So there are effectively versions of Java within versions if that makes sense. When Oracle say they are no longer going to support a version of Java, it means there will be no more updates to that major version.

When Java 9 came out it was supported until Java 10 was released on March 20, 2018. September 25, 2018 was release day for Java 11 – As such, Java 10 is now no longer going to be supported.

So technically Java 9 and Java 10 are no longer supported by Oracle. The two main versions being supported currently by Oracle are Java 8 (until January 2019) and Java 11 which is the first official LTS version of Java.

Java 11 is the one to learn then?

Oracle have committed to supporting Java 11 for the next eight years. So you can expect to see this version get wide usage over the coming years for that very reason.

Java 12 is planned for a March 2019 release, and a new release is planned each six months after that.

Large corporations with big Java projects and code-bases don’t tend to upgrade quickly. With a lot of Java code comes complexity in maintaining it, and full testing would generally be needed before moving to a major new Java release.

It’s not as simple as just installing the new version of Java. Depending on the age of the code-base, it might be using features that are no longer present or work differently in more recent releases of Java. 

As a result, organisations with large code-bases tend to stick to particular versions of Java for long periods of time, and only update when there is a need, or a change in support.

Java 11 will be the next version that these organisations will likely migrate their code-base to. Because Oracle are committed to supporting it for eight years, they can get their code-base working with a version and be able to use it for a long period of time with full support.

I read that Java 11 is no longer free, is that true?

Technically yes, that is true. It’s now no longer free to use Java 11 – Oracle expect you to pay to use Java 11.

If you are a business, then yes, you should expect to pay to use it. It’s not unreasonable in my opinion for Oracle to ask for a payment for commercial use, they are not a charity, and they do offer a few alternatives if you do not want to pay.

Firstly though, from their licensing agreement for Java 11 page is the following text.

Further, You may not:-

use the Programs for any data processing or any commercial, production, or internal business purposes other than developing, testing, prototyping, and demonstrating your Application;

What that means is if you are going through my Java course or anyone else’s training, and writing Java programs in general, you do not have to pay.

But when it comes time to run your program, thats when you have to pay.

In general as a programmer, or a student learning, writing, testing, prototyping and/or demonstrating your apps, you do not have to pay.

If you are a business who is using Java for commercial purposes, then, yes you will pay.

How much?

The full details are here, but in general it will be $2.50 per computer and $25.00 per CPU processor, per month.

Even then though, you have an option – to use the free OpenJDK. Yes, installing that version means you don’t have to worry about anything and it will be completely free.

So what this means is you really don’t have to pay for Java if you don’t want to.

So which version of Java should you learn?

What this means for you, the developer is that currently Java 8 is still the right version to be working with at the moment. If you get a job at an organisation, chances are high they are using Java 8. So having experience with Java 8 is going to be a good thing for you.

But you should also be starting to look at Java 11, There is no urgency to this because it has only just come out, and as mentioned above larger companies will be in no hurry to update.

And in terms of whether to use the Oracle version of the JDK or the open source version, either is good if you are a student learning Java, and/or are building your Java applications.

How does this affect the Complete Java Masterclass?

What I will be doing is ensuring it all works correctly in Java 11, and also will be taking the opportunity to update some parts of the course to Java 11. 

With a 75 hour course, it’s not practical to re-record the entire course, especially when I actually designed most of it to be future proof anyway. The course as it sits right now is completely relevant for you in 2018 and beyond.

The first thing I am going to do is re-record all the installation videos for Java 11.

I will then take the opportunity to re-record some of the early parts of the course to Java 11, mainly so that students get a current day experience with the current version of Java. But I want to stress that the course as is is very much relevant for you.

I’ll be using the Oracle JDK since there is no reason no to do so, and it’s also a good idea for students to see the version of the JDK they will likely see when they are working in a job. However, I will discuss the OpenJDK so that students can clearly see the differences.

Overall, the content won’t be changed drastically, but I’ll improve things where I can and where it makes sense.

I’ll have some exciting news for you in the future about a new Java 11 course I am working on, that should be released in November, but more on that later. Its purpose is not to replace my existing Java Masterclass course though.

For now, my suggestion is that you start moving your skills to Java 11, but also to keep in mind there is no mad rush to do so – It will be a good thing to add to your resume (Java 11 skills) but solid Java skills is more important than Java 11 specific skills.

Thanks for reading. I’ll do a followup post to this one in the future where I will discuss what has changed significantly in Java 11.

All the best.

Cheers

Tim

The post Java 11 has Arrived. Is it time to panic? appeared first on The Learn Programming Academy.

Introduction

When Google announced in May 2017 that Kotlin would become a first-class language for Android, many developers had never heard of it. Just a little over a year later, more and more developers and companies are using it. Why is it gaining popularity?

Kotlin is an object-oriented, statically-typed language that can be compiled to run on the Java Virtual Machine. It can also be compiled to JavaScript, an Android application, or a native application (this feature is currently in development, but preview releases are available).

Kotlin is the brainchild of JetBrains, the same company behind the popular IntelliJ IDEA IDE. According to the official Kotlin site, companies like Pinterest, Gradle, Uber, Coursera, and Evernote are using Kotlin in some way. The number of Kotlin jobs on job boards is also increasing, so it’s well worth taking a look at the language.

Kotlin and Java

When it comes to learning Kotlin, Java developers have a head start as Kotlin can be compiled to run on the JVM. Not only that, Kotlin and Java are interoperable, meaning that you can use Kotlin and Java in the same application. This means that if you want to transition to Kotlin, you can do so without having to rewrite an entire application. On top of that, you can use the JDK in a Kotlin application, so you can leverage all your existing Java knowledge.

Having said that, Kotlin and Java are different languages. So how does Kotlin differ from Java? Let’s look at a few differences between the two.

#1 Conciseness

Java can sometimes be wordy. Java developers are familiar with classes that look like the following:

To declare a class and provide access to the outside world, you must add a constructor, and setters and getters for the fields you want to expose.

This is so common that the IDEs provide ways to quickly write this boilerplate code. For example, IntelliJ IDEA has generate constructor and generate getter and setter menu items.

Knowing that a class will require a constructor and getters and setters most of the time, Kotlin automatically generates them under the covers. Here is the equivalent class declared in Kotlin:

That’s it. One line of code.

Kotlin is more concise in Java in many other ways. This is only one example.

#2 Null Safety

Java developers write quite a bit of code to prevent null pointer exceptions. Here are few ways that Kotlin helps us avoid the dreaded NullPointerException runtime error.

Nullable vs. non-Nullable Variables

In Kotlin, all variables are non-nullable by default. This means that you can’t assign null to them. You have to declare a nullable variable in order to assign null, something you’ll try to avoid doing.

When you declare a non-nullable variable, you can use it with full confidence that it will never contain null, which means you don’t have to write code to check for null – another way Kotlin can be more concise.

Safe Call Operator

Kotlin has a safe call operator that returns a value when a variable is not null, or null when it is. For example, look at this code, which is common in Java:

In Kotlin, there’s no need to explicitly test for null. You can write the following:

If a or b or c is null, the expression will short circuit at that point, and the null safety operator (?.) will return null. A NullPointerException will not be thrown. If none of them are null, then a.b.c.color will be returned.

These are just a couple of ways that null safety is built into Kotlin. There are more.

Extension Functions

In Java, we often override a class because we want to add a custom method. There’s an easier way to do it in Kotlin. We can use an extension function.

For example, let’s say you want to know whether the length of a String is equal to 4. If you want to perform this test a lot, you can add an extension function that performs the test, as follows:

If you’ve never seen Kotlin, this might look strange. The keyword fun is shorthand for function. String tells the compiler that you’re adding an extension function that you can invoke using String objects. In this case, String is the receiver type. isFourCharacters is the function name. The function doesn’t take any arguments, and it returns a Boolean.

Because the function has only one line of code and returns a value, you don’t have to use curly braces around the body of the function. Instead, you can simply assign the result of the conditional statement to the function, which effectively returns the result from the function. This is another feature in Kotlin.

Inside an extension function, you refer to the String you’re receiving, or the receiver object, using the keyword this.

To call the extension function, you’d do the following:

In this case, “bear” is the receiver object. Inside the function, it will be substituted for this. Since it is four characters in length, the function will return true.

When you use extension functions, you aren’t actually extending the class, so this doesn’t violate encapsulation. The compiler does some magic under the covers to make it work.

Smart Casts

In Java, we often write code like the following:

We have to test whether an object is of a specific type before we cast to that type.

In Kotlin, you can write this more concisely:

Notice that you don’t have to cast to a String when you access the length property. Kotlin has the notion of smart casting. If the compiler can be certain that the value of a variable won’t change from the time you check its type with the is operator, to the time you use it, then there’s no need to explicitly cast. If you wanted to, you could have more statements within the if block that call String methods on obj, and you wouldn’t have to cast to String.

Conclusion

We’ve touched on a handful of differences between Kotlin and Java, to give you a taste of the flavour of Kotlin. There are many more differences, and Kotlin has added functionality that Java doesn’t have. But remember that Kotlin is completely interoperable with Java. You don’t have to spend months learning Kotlin, or months (years) rewriting an application, before you can start to use the language. You can add Kotlin classes to your existing Java applications. You can also leverage all your existing knowledge of the JDK.

If you’ve been wanting to give Kotlin a try, now is a great time to jump in and learn it. Google has blessed it, and companies are looking for developers who have Kotlin in their toolbox.

The post Kotlin – Java Developers Have a Head Start appeared first on The Learn Programming Academy.

Many of the reasons to learn ArcPy have already been described in an earlier Learn Programming Academy Blog post named Three New Courses Released – ArcPy, Machine Learning with Python and HTML/CSS so here I’ll talk first about why and how I came to learn it.

I first encountered Geographic Information Systems (GIS) when working for the South Australian Museum on a project to computerize its mammal collection in 1985-1986. At the time I was entering field values for the species, collector, date, latitude and longitude of each specimen in the collection into a textual database and I wanted to use a map to visualize where each species came from. The project was jointly administered by the South Australian Department of Environment and Planning (SADEP) and they developed a button for the database’s GUI which drew a map for a species from its collection locations. The tool that they used to do this was a GIS software product named ARC/INFO from the Environmental Systems Research Institute (ESRI).

When the project finished I went to work for SADEP, and that started a transition from being a zoologist and occasional GIS user, who clicked a button to produce a map, to being a fulltime GIS developer for ESRI in the UK, a few years later. Your career in GIS developing will inevitably take a very different path to mine, and while I still very much advocate the use of Esri (as its name is now written) software, the products that you use today have undergone massive evolution and two major architectural revolutions.

In 1999, ArcGIS was released with its flagship application ArcMap and while that could be customized using Visual Basic for Applications (VBA) and ArcObjects from the outset, it was only about 10 years later that Python bindings for ArcObjects became available as a site-package named ArcPy. It was around 2010 that I started learning Python and ArcPy. While I had a steep learning curve on Python, I found ArcPy relatively easy to pick up because, from my many years as a proprietary AML (Arc Macro Language) developer, I came to it with a large vocabulary of ARC/INFO commands and parameters that readily mapped to the ArcPy functions and parameters.

In 2015, ArcGIS Pro was released as Esri’s next generation flagship application (designed to replace ArcMap over time), and even though ArcObjects no longer underpins that application, the most popular way to customize it is still by using Python with ArcPy. Once again the existing a large proportion of the existing ArcPy functions and parameters readily mapped to those of ArcGIS Pro, with the main exception being that the arcpy.mapping module that worked with ArcMap map documents had undergone a major overhaul to become the arcpy.mp module that works with ArcGIS Pro projects and maps.

If you are a Python developer, wanting to expand your skills into GIS, and are trying to decide which geospatial Python libraries to focus on, the way I measure their relative popularity is by counting the number of Q&As about them at the GIS Stack Exchange. As of writing the two most asked about geospatial Python libraries are ArcPy (7,791) and PyQGIS (3,738), followed distantly by pyproj (86), pyshp (71) and the rest.

If your Python skills are still nascent then, before embarking on ArcPy for Python Developers using ArcGIS Pro, you may want to commence the Complete Python Masterclass first, but if you don’t mind a steeper learning curve then jumping straight to the ArcPy course is certainly achievable.

As you undertake ArcPy for Python Developers using ArcGIS Pro, and after that, I strongly encourage you to reinforce what you have learned using two important resources:

1. The ArcGIS Pro Python Reference in the ArcGIS Pro help; and
2. ArcPy Q&As at the GIS Stack Exchange

ArcGIS Pro Python Reference

“The ArcGIS Pro Python Reference contains detailed information about every ArcPy module, function, and class provided with ArcGIS Pro, working with Python, as well as how to work with, and create your own, geoprocessing tools in Python.”

I frequently use this resource not just to check the syntax and parameter options for the various functions and classes but also because each provides one or more pieces of sample code for typical workflows that might involve it. You can think of this as being a somewhat static resource but, at each new release and often in between, more sample code is added, often in response to user requests.

GIS Stack Exchange

“Geographic Information Systems is a question and answer site for people interested in GIS, Remote Sensing, Photogrammetry, Geodesy and Surveying, as well as Programming/Databases specific to the aforementioned.”

This is a great resource for browsing to find whether an ArcPy programming question, which you think “someone must have asked before”, has already been asked, because often it has not only been asked but answered too! The site is far from static because if your question has not yet been asked, or maybe it has been asked but not answered (about 8% fall into this category), then you can work with the site’s ArcPy experts who volunteer their time to construct new Q&As by working on finding an answer to your question.

Conclusion

As a Python developer coming to ArcPy you are likely to find that your biggest challenge is not learning more core Python but instead will be building up a vocabulary of ArcPy spatial functions and workflows that enable you to communicate with, develop applications for, and delight ArcGIS Pro end users and spatial project stakeholders. The best place to learn about ArcGIS Pro and ArcPy is from someone who specializes in them, and who has specialized in customizing spatial products from Esri for over 30 years!

The post Why and How to Learn ArcPy? appeared first on The Learn Programming Academy.

The complexity of applications is ever growing, regardless whether they’re mobile, web, desktop, or other; more and more applications add requirements for image analytics, voice recognition, command identification, and so much more. Coding these functionalities from the ground up is interesting, challenging, exciting, but time consuming.

Besides, building this kind of functionality requires experts that need to master Python or R, and understand what models to use in a specific case – i.e. is this a case for supervised or unsupervised machine learning? When do I use classification vs regression algorithms? If I have some previous real-world data where one of the features with the most weight in the model has quite a few outliers, can I use it in a Bayesian Network, or should I use a Bayesian Hierarchical Model for transfer learning?

All these are fair questions, some slightly harder to answer than others, but questions you would have to face when building the machine learning models for your application. But, is that really necessary if you just need to know if an image has a dog in it? Or if some text has a few words that you want to moderate?

You shouldn’t need to invest a lot of time and effort into creating a machine learning model that will do something fairly common and simple, and you don’t. You can implement some powerful machine learning to solve some common problems in just minutes, by just deploying a cloud service that will handle everything for you. If you are facing some of the scenarios listed below, keep reading; I will tell you how Cognitive Services can help.

Cognitive Services can help if:

• You need to analyse the sentiment of the feedback coming from users to generate reports for management to read.
• You need automatic translation of the transcript of a meeting, and even some automatic audio translation for employees from other countries to understand.
• You need to understand voice and text commands that trigger actions for a virtual assistant.
• You need to recognise people based on their voice to authenticate them into your service.
• You need to extract text from images, even if the text is handwritten, to improve search in your notes app.
• You need to detect faces in a picture to be able to create a tailored video for someone that participated on the city’s marathon, with only the photos in which they appear in.
• You need to moderate text and images so your young users can safely use your service.
• You need to create a transcript of a video and its translation to many languages, and identify who appears and when in the video for your YouTube-competitor idea.
• You need to identify through a picture whether I’m buying coriander or parsley.
• You need a bot that automatically answers frequently asked questions through Messenger, Slack, and your own website.

So what are these magical Cognitive Services?

Cognitive Services is an array of machine learning services provided by Microsoft through Azure, its cloud computing platform. You literally only need to provision the service that you want, set some things up here and there, and make some REST requests to be able to implement machine learning functionality into your apps. If you know how to perform REST requests in your language of choice, whether that is Java, Swift, Python, C#, JavaScript, or whatever, you will be able to implement machine learning in minutes.

“If you know how to perform REST requests in your language of choice, you will be able to implement machine learning in minutes.”

How much do these services cost?

With these being cloud based services, they come at a price to have implemented into your apps. We cannot tell you exactly how much it will cost in your particular case as the cost will vary from service to service, and depending on the use you give it. For example, it is more expensive to store individual videos in the cloud and process them to find people in the video – and where in the video they appear – than simply returning a decimal value representing the sentiment for a small body of text. On the other hand, the monthly cost of storing just one video versus servicing billions of request for sentiment analysis would be much less.

However, the important thing that I want you to know right now is initially, all these services are entirely free. If you are just testing the functionality, or have very few users that make few requests a month, the free version of the services could easily work for you; just keep in mind that eventually, you will have to migrate to a pay-as-you-go subscription. This simply means that, depending on how the services are used, you will be charged accordingly.

Take the first steps

Now you know that Azure offers a wide array of services for machine learning to be used inside your apps, you will be able to find all the services listed below. The very first thing that you need to do is to go to the Azure website and create your account; you will even find that it offers you $200 (that’s US dollars) worth of credit for you to use with paid services for your first month.  You will have access to some services for free for an entire year, and lifetime access to a few services that are always free to use. Once you have your Azure subscription ready, it’s time to start using one of these Cognitive Services:

• Computer Vision
• Video Indexer
• Custom Vision
• Face API
• Content Moderator
• Speech to Text
• Text to Speech
• Separker Recognition
• Speech Translation
• Text Analytics
• Translator Text
• Bing Spell Check
• Language Understanding
• QnA Maker for Knowledge Bases
• A bunch of search services for visual, web, video, entities, images, and more

Improve the experience that you offer to your users, and separate yourself from the competition. Add Azure Machine Learning using Cognitive Services skills to your resume today!

The post Your Apps Need Machine Learning, Add It in Minutes with Cognitive Services appeared first on The Learn Programming Academy.

Do you want to learn how to write a web application? There are several languages you could choose. PHP is by far one of the most popular, and the one I recommend.

Here are nine reasons why I choose PHP:

#1 Using PHP is completely free

You don’t need to pay any licence fees, so the cost of creating a web application in PHP is minimal. PHP also runs on all major operating systems: Windows, OSX and Linux. There are several totally free packages available like XAMPP that will install everything you need to start developing on your local computer. There are also many free source code editors like Atom, that will help you to start creating a PHP website in no time.

#2 It’s painless 

Getting started writing PHP is very easy. You can just write the code in a text file, then access your script in a web browser. PHP is an interpreted language, which means you don’t need to compile it, you can just run it. PHP has a very gentle learning curve, and it’s easy to start writing useful code quickly. It’s also a very forgiving programming language, with many ways to accomplish the same task.

#3 It’s easy to get online

Any shared hosting package is almost guaranteed to support PHP. This means that you can get started running your web application on cheap, shared hosting, then upgrade to a more powerful server when you need to. Putting your code live is also as simple as copying the files from your computer to the live server.

#4 Excellent documentation and community support

The official PHP documentation is comprehensive, easy to use, and full of examples. Also, the PHP community is vast – from forums on the official website, to communities like Stack Overflow, you’re never far from professional help and advice.

#5 PHP is highly flexible

PHP can be used to create any kind of web application you can think of, from simple dynamic websites, to eCommerce sites that use shopping carts, content management systems, and so on. Also, PHP can connect to many different databases, from the open-source MariaDB, to Microsoft SQL Server. If you use MariaDB or MySQL, just like PHP these are free and you don’t need a licence to use them.

#6 Fast and secure PHP is fast, and uses minimal server resources

You can easily run a PHP web application on shared hosting. It’s also designed with security in mind, and includes many features to help you write secure code. Yes, it’s possible to write insecure code using PHP, but that’s true of any language. Learn to write it properly, and your code will be as secure as any other language.

#7 A modern language PHP can be written using object oriented techniques if you need to

You can do unit testing using PHPUnit, and there are many frameworks available that help you create web applications quickly like Laravel, Symfony and CodeIgniter.

#8 Huge libraries available

Huge standard library and many third-party packages available PHP comes with a huge library of available functions, all built in to allow you start using them without having to install anything else. If you do need some third party code, there’s a huge repository of packages available at Packagist.

#9 Desirable skills

PHP developers are in high demand. A high proportion of sites on the internet use PHP, so the need for PHP skills isn’t going away any time soon. WordPress, one of the most popular content management systems on the internet, is written in PHP. So, if you want to start writing web applications, you can’t go wrong by learning PHP.

In summary, I’m passionate about PHP – I’ve created the very popular PHP for Beginners course on Udemy.   This course is right for you if you want to learn how to use PHP as I assume no previous programming experience.

The post Why Learn PHP? appeared first on The Learn Programming Academy.

Mobile apps have taken over the world, there’s no denying that.

Web applications are also super popular, and as customers we judge brands and businesses based on the quality (and availability) of their apps.

But there is one kind of app, a very important one, that businesses still require and use every single day that you may not be offering to your clients: the desktop app.

Think about apps that are offered as desktop apps simply because it makes sense, we spend a lot of our time, especially at work, on our computers. Evernote, Slack, iTunes, Photoshop, AutoCAD, Chrome, Visual Studio, Word, PowerPoint, Excel, 1Password…

These are some examples of apps that are built for the masses to download, but for desktop, there is an almost unique demand for custom applications – applications with some very specific functionality required by the eventual users.

Maybe the receptionists across the entire company require a custom software to keep track of appointments; maybe a custom CRM is needed, or HR may want to keep track of employees in a unique way that common software doesn’t allow them to do.

When the power of a PC is necessary for an app, or the app needs to run on a local PC, desktop apps are the ones to build.

With Windows being the second most popular Operating System in the planet (only after Android), it is Windows Presentation Foundation the platform that you should target when building desktop apps.

WPF allows you to build apps that will run on Windows 7, 8 and 10 (which account now for about 93% of Windows computers according to Stat Counter), with almost no limitations. You could even still target Windows XP, but I don’t recommend you do.

WPF has been used for years to create very powerful applications since there is virtually no limit to what you can do with it.  This is unlike apps that need to be downloaded through an App Store, which are built with an entirely different architecture and are executed in a sandbox.

WPF applications could then access native functionality that store applications may not, or use more resources while store applications are limited to what Microsoft, or Apple or Google dictate.

How Are WPF Apps Created?

If you are interested in creating this kind of applications, you will first need a Windows computer, or of course a Windows VM, and install Visual Studio 2017 making sure you install the tools for Windows desktop development. From there you will need to use a couple of languages to create your apps.

So let’s talk about those languages: XAML and C#. There is an added benefit to learning those languages that I’ll talk about later in this post, for now let’s focus on how will you be using them both, and why the combination of both makes for an amazing development process.

XAML

The eXtensible Application Markup Language, or XAML, is a markup language that you will use to define the interfaces of your apps. Similar to how you would use HTML (another markup language) to define the interface of a website, you can use XAML to define your windows and pages and any element that WPF applications have.

Of course, there is a way to not use XAML at all – by simply dragging and dropping elements from a toolbox into a designer.  But – spoiler alert! – learning XAML will improve the quality of your apps enormously; not because they will look better (I guess you can achieve the same result by dragging and dropping), but because the code will be more elegant, efficient, and eventually you can use your XAML skills for greater things (more on that in a second). So, take my advice and learn this language as you build your WPF apps.

Use XAML Right!

As you start using XAML you will probably feel like it’s a ton of work, and that you should simply use the designer and drag and drop elements to it. Resist these thoughts. At the beginning of course it will take a bit more time to do so the right way, but eventually you’ll know XAML like the back of your hand, and then is when you unlock so much more potential.

When using XAML right, you can use it not just as the code that defines how your apps look, but also how the interface connects to your data, works with the required formats, and reacts to user interactions.

I won’t go into more detail, but achieving this will make working with designers so much easier (they can see in real time as they create the UI, the sample data that you can see, in design time with no need to run the app, retrieving from some sample data source) and help you separate the elogic from the graphic interface entirely.

C#

This is the language that you will use to create the logic of your application, its functionality. As with any other language, there is a learning curve if you are coming from another language, but if you know how to code, you can easily adapt to C#’s syntax and start falling in love with its elegancy.

C# is often described as a very powerful high level language, dragging some benefits from C and C++ to a modern language that is super easy to understand. Some joke that with C# you don’t need to document or comment your code (that’s wrong, always do that).

The Hidden Benefits Of Creating WPF Apps

While adding desktop app development to your resume is a great idea, it will greatly expand the kind of applications that you can create and increase the problems that you can solve for your clients, learning to build WPF apps also comes with a “hidden” benefit.

Like I said, WPF apps are built with XAML and C#, and it turns out that with those skills you will acquire as you create your desktop apps you can build iOS and Android apps as well. Actually, even Windows store and macOS apps as well.

These two languages are the same ones that are used to create Xamarin apps (which I have talked about in a different post), and Xamarin allows you to build entirely native apps using these languages. Suddenly, you only have to learn two languages to build native applications for all kinds of apps.

Perhaps then, as you add desktop app development to your list of skills, you should decide to focus and learn those two languages fully since they will be so useful no matter the app you are building.

Few language combinations offer such versatility, so learning them could be one of the best investment decisions you make!

Why not take a look at my Windows presentation foundation masterclass on Udemy? It’s designed to help you learn and get up to speed with building WPF quickly.

The post The Kind of Apps That You Are Not Building, and Should appeared first on The Learn Programming Academy.

3D Programming has never been so accessible.

I was always attracted to 3D programming and graphics. The way we can create real-looking models of the world with two dimensional screens has always hypnotised me.

When I enrolled in my first degree at University in computer science, I took a course in computer 3D graphics; back than it was a totally different world. All the graphics were done in C or C++ and you had to do most of the math and physics by yourself.  And there’s a lot of math there – I mean, a LOT. Vectors, geometry, trigonometry and so on, you had to be really good at that in order to write 3D graphics. Not to mention the high demands in memory and CPU those programs tend to consume, which made them unsuitable for the standard computer.

Well, those days are over. A lot has happened in the computing world – huge things! – and an advance in 3D programming is one of them. Not long ago I stumbled across the Javascript library called Three.js, and my life had changed (well at least in one perspective).

What is Three.js?

It’s a Javascript library that supplies functions and objects for creating and programming three dimensional graphics. The library’s code works on the WebGL library and offers more or less the same functionality. But the really good news here is that Three.js’s goal is to enable 3D graphics programming without the complexity that usually characterises these projects. It does so by omitting most of the complex mathematics and physics. Sure, you still have to know the basics – a little bit geometry and trigonometry is a must have – but other than that, you can count on the Three.js engine to make the computations for you.

For me it was a real blessing. I love to program, I love to think of solutions to problems, and using Three.js enabled me to do just that. Every crazy idea I had, every animation, graphics, game, you name it, was transformed thanks to Three.js algorithmic problem, rather than a math course. Three.js let me do what I love – to program, to find great solutions and not to be a mathematician.

The other advantage of Three.js (and it surprised me because I’m old enough to remember how things used to work) is that it all runs on the browser. No special hardware is needed, no special installations or software. Just your plain browser and your standard computer. For me it was almost too good to be true, but it works. Even relatively complex computations are done fast enough to go unnoticed. I guess much of the praises are owed to the modern computers and browsers that are incomparable in their  powers to the computers only a decade or so ago. But part of the good stuff is owed to Three.js as a library. Its creators did a really good job of crafting a useful, clean and efficient library that actually makes our life (at least our life as programmers) better.

The library’s 3D model is pretty much the same as any other – it is based on a scene in which the 3D objects are located, a camera that provides the point of view, and the renderer that puts everything on the view port (in Three.js’s case – the browser).

Most of the common 3D objects such as cubes, spheres, cones and so on, are represented as pre-defined objects so we can use them out of the box (again – simplicity is the keyword). Other elements like shadows or sprite motions may be more sophisticated to manipulate. But all in all, I think that you can get up and running with 3D graphics in much less time with Three.js than any other library I’ve seen.

So, what do you need?

First of all, you have to know how to program in Javascript. You don’t have to be a professional, but a little more than basic knowledge is required if you want to do something useful. Besides that, as I said above – basic math is needed. But beyond that – only a browser and a text editor. Simple as that.

I know that for me, Three.js library was the tool that helped me start writing awesome three dimensional animations, and it certainly let my creativity out. I was finally able to concentrate on programming, in thinking and was able to put my ideas into action with my abilities.

If you love to program, and 3D animation and graphics make your brain start coming up with an endless numbers of projects – you may want to check my 3D Programming With Javascript And The Three-js 3D Library course on Udemy.  You’ll love it!

The post 3D Programming Made Easy! appeared first on The Learn Programming Academy.