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Mastering Java Method and Constructor References: Concepts and Practical Examples

Java method references, introduced in Java 8, offer a succinct and expressive way to refer to existing methods or constructors using the :: operator. They serve as a powerful alternative to verbose lambda expressions, helping developers write clearer and more maintainable code in functional programming contexts. This article covers the four types of method references.

Types of Java Method References

Java supports four main types of method references, grouped by the kind of method they refer to:

  1. Reference to a Constructor
    References a constructor to create new objects or arrays.
    Syntax: ClassName::new
    Examples:

    List people = names.stream().map(Person::new).toList();

    Create new instances with constructor reference for each element in the stream.

    Additionally, constructor references can be used for arrays:

    import java.util.function.IntFunction;
    
    IntFunction arrayCreator = String[]::new;
    String[] myArray = arrayCreator.apply(5);
    System.out.println("Array length: " + myArray.length);  // Prints 5

    This is especially useful in streams to collect into arrays:

    String[] namesArray = names.stream().toArray(String[]::new);
  2. Reference to a Static Method
    This refers to a static method in a class.
    Syntax: ClassName::staticMethodName
    Example:

    Arrays.sort(array, Integer::compare);
  3. Reference to an Instance Method of a Particular Object (Bound Method Reference)
    This is a bound method reference, tied to a specific, existing object instance. The instance is fixed when the reference is created.
    Syntax: instance::instanceMethodName
    Example:

    List names = List.of("Alice", "Bob");
    names.forEach(System.out::println);  // System.out is a fixed object

    Here, System.out::println is bound to the particular System.out object.

  4. Reference to an Instance Method of an Arbitrary Object of a Particular Type (Unbound Method Reference)
    This is an unbound method reference where the instance is supplied dynamically when the method is called.
    Syntax: ClassName::instanceMethodName
    Important Rule:
    The first parameter of the functional interface method corresponds to the instance on which the referenced instance method will be invoked. That is, the instance to call the method on is passed as the first argument, and any remaining parameters map directly to the method parameters.
    Example:

    List team = Arrays.asList("Dan", "Josh", "Cora");
    team.sort(String::compareToIgnoreCase);

    In this example, when the comparator functional interface’s compare method is called with two arguments (a, b), it is equivalent to calling a.compareToIgnoreCase(b) on the first parameter instance.

Summary

  • Java method references simplify code by allowing concise references to methods and constructors.
  • The first type—constructor references—express object and array instantiation clearly.
  • The second type is referencing static methods.
  • The third type—instance method reference of a particular object—is a bound method reference, fixed on a single object instance.
  • The fourth type—instance method reference of an arbitrary object of a particular type—is an unbound method reference, where the instance is provided at call time.
  • Constructor references are especially handy for arrays like String[].
  • System.out::println is a classic example of a bound method reference.

Locks and Semaphores in Java: A Comprehensive Guide to Concurrency Control

Locks and semaphores are foundational synchronization mechanisms in Java, designed to control access to shared resources in concurrent programming. Proper use of these constructs ensures thread safety, prevents data corruption, and manages resource contention efficiently.

What is a Lock in Java?

A lock provides exclusive access to a shared resource by allowing only one thread at a time to execute a critical section of code. The simplest form in Java is the intrinsic lock obtained by the synchronized keyword, which guards methods or blocks. For more flexibility, Java’s java.util.concurrent.locks package offers classes like ReentrantLock that provide advanced features such as interruptible lock acquisition, timed waits, and fairness policies.

Using locks ensures that when multiple threads try to modify shared data, one thread gains exclusive control while others wait, thus preventing race conditions.

Example of a Lock (ReentrantLock):

import java.util.concurrent.locks.ReentrantLock;

public class Counter {
    private int count = 0;
    private final ReentrantLock lock = new ReentrantLock();

    public void increment() {
        lock.lock();  // acquire lock
        try {
            count++;  // critical section
        } finally {
            lock.unlock();  // release lock
        }
    }

    public int getCount() {
        return count;
    }
}

What is a Semaphore in Java?

A semaphore controls access based on a set number of permits, allowing a fixed number of threads to access a resource concurrently. Threads must acquire a permit before entering the critical section and release it afterward. If no permits are available, threads block until a permit becomes free. This model suits scenarios like connection pools or task throttling, where parallel access is limited rather than exclusive.

Example of a Semaphore:

import java.util.concurrent.Semaphore;

public class WorkerPool {
    private final Semaphore semaphore;

    public WorkerPool(int maxConcurrent) {
        this.semaphore = new Semaphore(maxConcurrent);
    }

    public void performTask() throws InterruptedException {
        semaphore.acquire();  // acquire permit
        try {
            // critical section
        } finally {
            semaphore.release();  // release permit
        }
    }
}

Comparing Locks and Semaphores

Aspect Lock Semaphore
Concurrency Single thread access (exclusive) Multiple threads up to a limit (concurrent)
Use case Mutual exclusion in critical sections Limit concurrent resource usage
API examples synchronized, ReentrantLock Semaphore
Complexity Simpler, single ownership More flexible, requires permit management

Best Practices for Using Locks and Semaphores

  • Always release locks or semaphore permits in a finally block to avoid deadlocks.
  • Use locks for strict mutual exclusion when only one thread should execute at a time.
  • Use semaphores when allowing multiple threads limited concurrent access.
  • Keep the critical section as short as possible to reduce contention.
  • Avoid acquiring multiple locks or permits in inconsistent order to prevent deadlocks.

Mastering locks and semaphores is key to writing thread-safe Java applications that perform optimally in concurrent environments. By choosing the right synchronization mechanism, developers can effectively balance safety and parallelism to build scalable, reliable systems.

Java 25 Compact Source Files: A New Simplicity with Instance Main Methods

Java continues evolving to meet the needs of developers—from beginners learning the language to pros writing quick scripts. Java 25 introduces Compact Source Files, a feature that lets you write Java programs without explicit class declarations, coupled with Instance Main Methods which allow entry points that are instance-based rather than static. This combination significantly reduces boilerplate, simplifies small programs, and makes Java more approachable while preserving its power and safety.

What Are Compact Source Files?

Traditionally, a Java program requires a class and a public static void main(String[] args) method. However, this requirement adds ceremony that can be cumbersome for tiny programs or for learners.

Compact source files lift this restriction by implicitly defining a final top-level class behind the scenes to hold fields and methods declared outside any class. This class:

  • Is unnamed and exists in the unnamed package.
  • Has a default constructor.
  • Extends java.lang.Object and implements no interfaces.
  • Must contain a launchable main method, which can be an instance method (not necessarily static).
  • Cannot be referenced by name in the source.

This means a full Java program can be as simple as:

void main() {
    IO.println("Hello, World!");
}

The new java.lang.IO class introduced in Java 25 provides simple convenient methods for console output like IO.println().

Implicit Imports for Compact Source Files

To keep programs concise, compact source files automatically import all public top-level classes and interfaces exported by the java.base module. This includes key packages such as java.util, java.io, java.math, and java.lang. So classes like List or BigDecimal are immediately available without import declarations.

Modules outside java.base require explicit import declarations.

Limitations and Constraints

Compact source files have some structural constraints:

  • The implicit class cannot be named or explicitly instantiated.
  • No package declarations are allowed; the class is always in the unnamed package.
  • Static members cannot be referenced via method references.
  • The IO class’s static methods require qualification.
  • Complex multi-class or modular programs should evolve into regular class-based files with explicit imports and package declarations.

This feature targets small programs, scripts, and educational use while preserving Java’s rigorous type safety and tooling compatibility.

Using Command-Line Arguments in a Compact Source File

Compact source files support standard command-line arguments passed to the main method as a String[] parameter, just like traditional Java programs.

Here is an example that prints provided command-line arguments:

void main(String[] args) {
    if (args.length == 0) {
        IO.println("No arguments provided.");
        return;
    }
    IO.println("Arguments:");
    for (var arg : args) {
        IO.println(" - " + arg);
    }
}

Save this as PrintArgs.java, then run it with:

java PrintArgs.java apple banana cherry

Output:

textArguments:
 - apple
 - banana
 - cherry

This shows how you can easily handle inputs in a script-like manner without boilerplate class syntax.

Growing Your Program

If your program outgrows simplicity, converting from a compact source file to a named class is straightforward. Wrap methods and fields in a class declaration and add imports explicitly. For instance:

import module java.base;

class PrintArgs {
    void main(String[] args) {
        if (args.length == 0) {
            IO.println("No arguments provided.");
            return;
        }
        IO.println("Arguments:");
        for (var arg : args) {
            IO.println(" - " + arg);
        }
    }
}

The logic inside main remains unchanged, enabling an easy migration path.

Conclusion

Java 25’s compact source files paired with instance main methods introduce a fresh, lightweight way to write Java programs. By reducing ceremony and automatically importing core APIs, they enable rapid scripting, teaching, and prototyping, while maintaining seamless interoperability with the full Java platform. Handling command-line arguments naturally fits into this new model, encouraging exploration and productivity in a familiar yet simplified environment.

This innovation invites developers to write less, do more, and enjoy Java’s expressive power with less friction.

Scoped Values in Java 25: Definitive Context Propagation for Modern Java

With the release of Java 25, scoped values come out of preview and enter the mainstream as one of the most impactful improvements for concurrency and context propagation in Java’s recent history. Designed to address the perennial issue of safely sharing context across method chains and threads, scoped values deliver a clean, immutable, and automatically bounded solution that dethrones the error-prone ThreadLocal for most scenarios.

Rethinking Context: Why Scoped Values?

For years, Java developers have used ThreadLocal to pass data down a call stack—such as security credentials, logging metadata, or request-specific state. While functional, ThreadLocal suffers from lifecycle ambiguity, memory leaks, and incompatibility with lightweight virtual threads. Scoped values solve these problems by making context propagation explicit in syntax, immutable in nature, and automatic in cleanup.

The Mental Model

Imagine code execution as moving through a series of rooms, each with its unique lighting. A scoped value is like setting the lighting for a room—within its boundaries, everyone sees the same illumination (data value), but outside those walls, the setting is gone. Each scope block clearly defines where the data is available and safe to access.

How Scoped Values Work

Scoped values require two ingredients:

  • Declaration: Define a ScopedValue as a static final field, usually parameterized by the intended type.
  • Binding: Use ScopedValue.where() to create an execution scope where the value is accessible.

Inside any method called within the binding scope—even dozens of frames deep—the value can be retrieved by .get(), without explicit parameter passing.

Example: Propagating User Context Across Methods

// Declare the scoped value
private static final ScopedValue<String> USERNAME = ScopedValue.newInstance();

public static void main(String[] args) {
    ScopedValue.where(USERNAME, "alice").run(() -> entryPoint());
}

static void entryPoint() {
    printCurrentUser();
}

static void printCurrentUser() {
    System.out.println("Current user: " + USERNAME.get()); // Outputs "alice"
}

In this sample, USERNAME is accessible in any method within the binding scope, regardless of how far it’s called from the entry point.

Nested Binding and Rebinding

Scoped values provide nested rebinding: within a scope, a method can establish a new nested binding for the same value, which is then available only to its callees. This ensures truly bounded context lifetimes and avoids unintended leakage or overwrites.

// Declare the scoped value
private static final ScopedValue<String> MESSAGE = ScopedValue.newInstance();

void foo() {
    ScopedValue.where(MESSAGE, "hello").run(() -> bar());
}

void bar() {
    System.out.println(MESSAGE.get()); // prints "hello"
    ScopedValue.where(MESSAGE, "goodbye").run(() -> baz());
    System.out.println(MESSAGE.get()); // prints "hello"
}

void baz() {
    System.out.println(MESSAGE.get()); // prints "goodbye"
}

Here, the value "goodbye" is only visible within the nested scope inside baz, while "hello" remains for bar outside that sub-scope.openjdk

Thread Safety and Structured Concurrency

Perhaps the biggest leap: scoped values are designed for modern concurrency, including Project Loom’s virtual threads and Java's structured concurrency. Scoped values are automatically inherited by child threads launched within the scope, eliminating complex thread plumbing and ensuring correct context propagation.

// Declare the scoped value
private static final ScopedValue<String> REQUEST_ID = ScopedValue.newInstance();

public static void main(String[] args) throws InterruptedException {
    String requestId = "req-789";
    usingVirtualThreads(requestId);
    usingStructuredConcurrency(requestId);
}

private static void usingVirtualThreads(String requestId) {
    try (var executor = Executors.newVirtualThreadPerTaskExecutor()) {
        // Launch multiple concurrent virtual threads, each with its own scoped value binding
        Future<?> taskA = executor.submit(() ->
                ScopedValue.where(REQUEST_ID, requestId).run(() -> processTask("Task VT A"))
        );
        Future<?> taskB = executor.submit(() ->
                ScopedValue.where(REQUEST_ID, requestId).run(() -> processTask("Task VT B"))
        );
        Future<?> taskC = executor.submit(() ->
                ScopedValue.where(REQUEST_ID, requestId).run(() -> processTask("Task VT C"))
        );

        // Wait for all tasks to complete
        try {
            taskA.get();
            taskB.get();
            taskC.get();
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }
}

private static void usingStructuredConcurrency(String requestId) {
    ScopedValue.where(REQUEST_ID, requestId).run(() -> {
        try (var scope = StructuredTaskScope.open()) {
            // Launch multiple concurrent virtual threads
            scope.fork(() -> {
                processTask("Task SC A");
            });
            scope.fork(() -> {
                processTask("Task SC B");
            });
            scope.fork(() -> {
                processTask("Task SC C");
            });

            scope.join();
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            throw new RuntimeException(e);
        }
    });
}

private static void processTask(String taskName) {
    // Scoped value REQUEST_ID is automatically visible here
    System.out.println(taskName + " processing request: " + REQUEST_ID.get());
}

No need for explicit context passing—child threads see the intended value automatically.

Key Features and Advantages

  • Immutability: Values cannot be mutated within scope, preventing accidental overwrite and race conditions.
  • Automatic Cleanup: Context disappears at the end of the scope, eliminating leaks.
  • No Boilerplate: No more manual parameter threading across dozens of method signatures.
  • Designed for Virtual Threads: Plays perfectly with Java’s latest concurrency primitives.baeldung+2

Use Cases

  • Securely propagate authenticated user or tracing info in web servers.
  • Pass tenant, locale, metrics, or logger context across libraries.
  • Enable robust structured concurrency with context auto-inheritance.

Mastering Asynchronous Programming in Java with CompletableFuture and Virtual Threads

Java's CompletableFuture provides a powerful and flexible framework for asynchronous programming. Introduced in Java 8, it allows writing non-blocking, event-driven applications with simple and readable code. With Java 21 and Project Loom, virtual threads can be combined with CompletableFutures to achieve highly scalable concurrency with minimal overhead. This article explores the core usage patterns of CompletableFuture and how to leverage virtual threads effectively.

Basics of CompletableFuture Usage

At its simplest, a CompletableFuture represents a future result of an asynchronous computation. You can create one that runs asynchronously using the supplyAsync() or runAsync() methods.

  • supplyAsync() runs a background task that returns a result.
  • runAsync() runs a background task with no returned result.

Example:

CompletableFuture<String> future = CompletableFuture.supplyAsync(() -> "Hello World");
System.out.println(future.get());  // Blocks until the result is ready

In this example, the supplier runs asynchronously, and the main thread waits for its result using get(). The computation executes in the common ForkJoinPool by default.

Chaining and Composing Tasks

CompletableFuture excels at composing asynchronous tasks without nested callbacks:

  • thenApply() transforms the result of a completed future.
  • thenAccept() consumes the result without returning anything.
  • thenRun() runs a task once the future is complete.
  • thenCombine() combines results of two independent futures.
  • thenCompose() chains dependent futures for sequential asynchronous steps.

Example of chaining:

CompletableFuture.supplyAsync(() -> 10)
    .thenApply(result -> result + 20)
    .thenAccept(result -> System.out.println("Result: " + result));

Example of combining:

CompletableFuture<Integer> f1 = CompletableFuture.supplyAsync(() -> 10);
CompletableFuture<Integer> f2 = CompletableFuture.supplyAsync(() -> 20);
CompletableFuture<Integer> combined = f1.thenCombine(f2, Integer::sum);
System.out.println(combined.get());  // 30

These patterns allow building complex, non-blocking workflows with clean and expressive code.

Exception Handling

CompletableFuture allows robust error handling without complicating the flow:

  • Use exceptionally() to recover with a default value on error.
  • Use handle() to process outcome result or exception.
  • Use whenComplete() to perform an action regardless of success or failure.

Example:

CompletableFuture.supplyAsync(() -> {
    if (true) throw new RuntimeException("Failure");
    return "Success";
}).exceptionally(ex -> "Recovered from " + ex.getMessage())
  .thenAccept(System.out::println);  // Outputs: Recovered from java.lang.RuntimeException: Failure

Waiting for Multiple Futures

The allOf() method is used to wait for multiple CompletableFutures to finish:

List<CompletableFuture<String>> futures = List.of(
    CompletableFuture.completedFuture("A"),
    CompletableFuture.completedFuture("B"),
    CompletableFuture.completedFuture("C"));

CompletableFuture<Void> allDone = CompletableFuture.allOf(futures.toArray(new CompletableFuture[0]));
allDone.join();  // Wait until all futures complete

This enables executing parallel asynchronous operations efficiently.

Using CompletableFuture with Virtual Threads

Java 21 introduces virtual threads, lightweight threads that allow massive concurrency with minimal resource consumption. To use CompletableFutures on virtual threads, create an executor backed by virtual threads and pass it to async methods.

Example:

try (var executor = Executors.newVirtualThreadPerTaskExecutor()) {
    CompletableFuture<Void> future = CompletableFuture.runAsync(() -> {
        System.out.println("Running in virtual thread: " + Thread.currentThread());
        try { Thread.sleep(1000); } catch (InterruptedException e) { Thread.currentThread().interrupt(); }
        System.out.println("Task completed");
    }, executor);

    future.join();
}
  • The executor is created with Executors.newVirtualThreadPerTaskExecutor().
  • Async tasks run on virtual threads, offering high scalability.
  • The executor must be closed to release resources and stop accepting tasks; using try-with-resources is recommended.

All operations such as thenApplyAsync() or thenCombineAsync() can similarly take the virtual thread executor to keep subsequent stages on virtual threads.

Summary

  • CompletableFuture allows flexible, readable asynchronous programming.
  • Tasks can be created, chained, combined, and composed easily.
  • Robust exception handling is built-in.
  • allOf() allows waiting on multiple futures.
  • With virtual threads, CompletableFuture scales brilliantly by offloading async tasks to lightweight threads.
  • Always close virtual thread executors to properly release resources.

Using CompletableFuture with virtual threads simplifies asynchronous programming and enables writing performant scalable Java applications with clean and maintainable code.

Exploring Java Non-Denotable Types

Java's evolving type system has brought powerful features like local variable type inference and lambda expressions — providing concise and expressive coding patterns. One nuanced concept that arises in this landscape is the idea of non-denotable types, which plays a subtle but important role in how type inference works and enables interesting use cases such as mutable state within lambdas without atomic types.

What Are Non-Denotable Types in Java?

Java programmers are familiar with declaring variables with explicit types like int, String, or class names. These are called denotable types — types you can write explicitly in your source code.

However, the Java compiler also works with non-denotable types behind the scenes:

  • These types cannot be explicitly named or expressed in Java source code.

  • Common examples include:

    • Anonymous class types: Each anonymous class has a unique generated subclass type.

    Example:

    var obj = new Runnable() {
        public void run() { System.out.println("Running..."); }
        public void runTwice() { run(); run(); }
    };
    obj.runTwice(); // This works because obj's type is the anonymous class, not just Runnable
    • Capture types: Types arising from generics with wildcards and the capture conversion process.
    import java.util.List;
    
    public class CaptureType {
    
        public static void main(String[] args) {
            List<?> unknownList = List.of("A", "B", "C");
    
            // Wildcard capture example: helper method with captured type
            printFirstElement(unknownList);
        }
    
        static <T> void printFirstElement(List<T> list) {
            if (!list.isEmpty()) {
                // 'T' here is the capture of '?'
                System.out.println(list.get(0));
            }
        }
    }

When declaring a variable with var (introduced in Java 10), the compiler sometimes infers one of these non-denotable types. This is why some variables declared with var cannot be assigned to or typed explicitly without losing precision or functionality.

Why Does This Matter?

Non-denotable types extend Java's expressiveness, especially for:

  • Anonymous class usage: The type inferred preserves the anonymous class's full structure, including methods beyond superclass/interface declarations.
  • Enhanced local variable inference: var lets the compiler deduce precise types not writable explicitly in source code, often improving code maintainability.

A Practical Example: Incrementing a Counter in a Lambda Without Atomic Types

Mutability inside lambdas is limited in Java. Variables captured by lambdas must be effectively final, so modifying local primitives directly isn't possible. The common approach is to use AtomicInteger or a one-element array for mutation.

However, by leveraging a non-denotable anonymous class type, it is possible to mutate state inside a lambda without using atomic types.

public class Main {
    public static void main(String[] args) {
        // counter is an anonymous class instance with mutable state (non-denotable type)
        var counter = new Object() {
            int count = 0;
            void increment() { count++; }
            int getCount() { return count; }
        };

        // Lambda expression capturing 'counter' and incrementing the count
        java.util.function.Consumer<Integer> incrementer = (i) -> counter.increment();

        // Invoking the lambda multiple times to increment the internal counter
        for (int i = 0; i < 5; i++) {
            incrementer.accept(i);
        }

        System.out.println("Counter value: " + counter.getCount());  // Outputs 5
    }
}

How this works:

  • The counter variable's type is inferred as the unique anonymous class type (non-denotable).
  • This anonymous class contains a mutable field (count) and methods to manipulate and access it.
  • The lambda (incrementer) invokes the method increment() on the anonymous class instance, modifying its internal state.
  • This avoids the need for an AtomicInteger or mutable containers like arrays.
  • Access to the additional method increment() (which is not part of regular interfaces) showcases the benefit of the precise anonymous class type inference.

Benefits of This Approach

  • Avoids clutter and complexity from atomic classes or array wrappers.
  • Keeps code safe within single-threaded or controlled contexts (not thread-safe, so take care in multithreaded scenarios).
  • Utilizes modern Java's type inference power to enable cleaner mutable state management.
  • Demonstrates practical use of non-denotable types, a somewhat abstract but powerful concept in Java's type system.

Final Thoughts

Non-denotable types may seem like compiler internals, but they shape everyday programming modern Java developers do, especially with var and lambdas. Ignoring them means missing out on some elegant solutions like mutable lambdas without extra overhead.

Understanding and utilizing non-denotable types open up possibilities to leverage Java's type system sophistication while writing concise, expressive, and idiomatic code.

How to Use Jupyter Notebook with Poetry: A Step-by-Step Guide

Poetry is a powerful dependency management and packaging tool for Python projects, offering isolated virtual environments and reproducible builds. Using Jupyter Notebook with Poetry allows you to work seamlessly within the same environment managed by Poetry, ensuring all your dependencies are consistent.

This guide will take you through the steps to get up and running with Jupyter Notebook using Poetry.


Step 1: Install Poetry Using pip

You can install Poetry using pip, the Python package installer. Run the following command:

pip install poetry

After installation, verify that Poetry is installed and accessible:

poetry --version

Step 2: Create a New Poetry Project

Create a new directory for your project and initialize it with Poetry:

mkdir my-jupyter-project
cd my-jupyter-project
poetry init --no-interaction

This generates a pyproject.toml file to manage your project’s dependencies.


Step 3: Add Jupyter and ipykernel as Dependencies

Add Jupyter Notebook as a development dependency:

poetry add --dev jupyter ipykernel

You can also add other libraries you plan to use (e.g., pandas, numpy):

poetry add pandas numpy

Step 4: Install the Jupyter Kernel for Your Poetry Environment

Make your Poetry virtual environment available as a kernel in Jupyter so you can select it when you launch notebooks:

poetry run python -m ipykernel install --user --name=my-jupyter-project

Replace my-jupyter-project with a meaningful name for your kernel.


Step 5: Launch Jupyter Notebook

Run Jupyter Notebook using Poetry to ensure you are using the correct virtual environment:

poetry run jupyter notebook

This command will start Jupyter Notebook in your browser. When you create or open a notebook, make sure to select the kernel named after your Poetry environment (my-jupyter-project in this example).


Step 6: Start Coding!

You now have a fully isolated environment managed by Poetry, using Jupyter Notebook for your interactive computing. All the dependencies installed via Poetry are ready to use.


Optional: Using Jupyter Lab

If you prefer Jupyter Lab, you can add and run it similarly:

poetry add --dev jupyterlab
poetry run jupyter lab

This method ensures your Jupyter notebooks are reproducible, isolated, and aligned with your Poetry-managed Python environment, improving project consistency and collaboration.

If you use VSCode, be sure to select the Poetry virtual environment interpreter and the corresponding Jupyter kernel to have a smoother development experience.

Enjoy coding with Poetry and Jupyter!

The Real Experience of Using a Vibe-Coded Application

“Vibe coding” isn’t just about getting something to work—it’s about how the built application feels and performs for everyone who uses it. The style, structure, and polish of code left behind by different types of builders—whether a non-developer, a junior developer, or a senior developer—directly influence the strengths and quirks you’ll encounter when you use a vibe-coded app.


When a Non-Developer Vibe Codes the App

  • What you notice:
    • The app may get the job done for a specific purpose, but basic bugs or confusing behavior crop up once you step outside the main workflow.
    • Error messages are unhelpful or missing, and sudden failures are common when users enter unexpected data.
  • Long-term impact:
    • Adding features, fixing issues, or scaling up becomes painful.
    • The app “breaks” easily if used in unanticipated ways, and no one wants to inherit the code.

When a Junior Developer Vibe Codes the App

  • What you notice:
    • There’s visible structure: pages fit together, features work, and the app looks like a professional product at first glance.
    • As you use it more, some buttons or features don’t always behave as expected, and occasional bugs or awkward UI choices become apparent.
    • Documentation may be missing, and upgrades can sometimes introduce new problems.
  • Long-term impact:
    • Regular use exposes “quirks” and occasional frustrations, especially as the app or user base grows.
    • Maintenance or feature additions cost more time, since hidden bugs surface in edge cases or after updates.

When a Senior Developer Vibe Codes the App

  • What you notice:
    • Everything feels smooth—there’s polish, sensible navigation, graceful error messages, and a sense of reliability.
    • Features work the way you intuitively expect, and odd scenarios are handled thoughtfully (with clear guidance or prevention).
  • Long-term impact:
    • The application scales up smoothly; bugs are rare and quickly fixed; documentation is clear, so others can confidently build on top of the product.
    • Users enjoy consistent quality, even as new features are added or the system is used in new ways.

Bottom Line

The level of vibe coding behind an application dramatically shapes real-world user experience:

  • With non-developer vibe coding, apps work only until a real-world edge case breaks the flow.
  • Junior vibe coding brings function, but with unpredictable wrinkles—great for prototyping, but less for mission-critical tasks.
  • Senior vibe coding means fewer headaches, greater stability, and a product that survives change and scale.

Sustained use of “vibe-coded” apps highlights just how much code quality matters. Clean, thoughtful code isn’t just an academic ideal—it’s the foundation of great digital experiences.

Unpacking AI Creativity: Temperature, Top-k, Top-p, and More — Made Simple

Ever wondered what goes on under the hood when language models (like ChatGPT) craft those surprisingly clever, creative, or even bizarre responses? It all comes down to how the AI chooses its next word. In language model jargon, parameters like temperature, top-k, top-p, and several others act as the steering wheel and gas pedal for a model’s creativity and coherence. Let’s demystify these terms with simple explanations, relatable examples, and clear categories.


1. Controlling Creativity and Randomness

Temperature: The Creativity Dial

What it does: Controls how “random” or “creative” the model is when picking the next word.

How it works:

  • After calculating the likelihood of each possible next word, the model scales these probabilities by the temperature value.
  • Lower temperature (<1) sharpens probabilities, making the model pick more predictable words.
  • Higher temperature (>1) flattens probabilities, increasing the chance of less likely, more creative words.

Example:
Prompt: "The cat sat on the..."

  • Low temperature (0.2) → “mat.”
  • High temperature (1.2) → “windowsill, pondering a daring leap into the unknown.”

2. Limiting the Word Choices

Top-k Sampling: Picking from the Favorites

What it does: Limits the model to select the next word only from the top k most likely candidates.

How it works:

  • The model ranks all possible next words by probability.
  • It discards all except the top k words and normalizes their probabilities.
  • The next word is then sampled from this limited set.

Example:
Prompt: "The weather today is..."

  • Top-k = 3 → “sunny, cloudy, or rainy.”
  • Top-k = 40 → “sunny, humid, breezy, misty, unpredictable, magical...”

Top-p Sampling (Nucleus Sampling): Smart Curation

What it does: Dynamically selects the smallest set of top candidate words whose combined probability exceeds threshold p.

How it works:

  • The model sorts words by probability from highest to lowest.
  • It accumulates the probabilities until their sum reaches or exceeds p (e.g., 0.9).
  • The next word is sampled from this dynamic “nucleus” pool.

Example:
Prompt: "The secret to happiness is..."

  • Top-p = 0.5 → “love.”
  • Top-p = 0.95 → “love, adventure, good friends, chocolate, exploring, a song in your heart...”

3. Controlling Repetition and Novelty

Frequency Penalty

What it does: Decreases the likelihood of words that have already appeared frequently in the text.

How it works:

  • Words that occur more often are penalized in their probability, reducing repetition.

Example:
If the word “sunny” appears repeatedly, the model is less likely to pick “sunny” again soon.

Presence Penalty

What it does: Encourages introducing new words and concepts instead of reusing existing ones.

How it works:

  • Words already mentioned get a penalty making them less probable to recur.

Example:
After mentioning “love,” the model is nudged towards new ideas like “adventure” or “friendship” in the continuation.


4. Managing Output Length and Search Strategy

Max Tokens

What it does: Limits the total number of tokens (words or word pieces) the model can generate in one response.

How it works:

  • The model stops generating once this token count is reached, ending the output.

Example:
If Max Tokens = 50, the model will stop after generating 50 tokens, even if the thought is unfinished.

Beam Search

What it does: Keeps track of multiple possible sequences during generation to find the best overall sentence.

How it works:

  • Instead of sampling one word at a time, the model maintains several candidate sequences (beams) simultaneously.
  • It evaluates and selects the sequence with the highest total likelihood.

Example:
The model considers several ways to complete the sentence “The weather today is…” and picks the one that makes the most sense overall.


Summary Table

Category Parameter What It Does How It Works Example
Creativity & Randomness Temperature Controls randomness and creativity Scales word probabilities before sampling Low temp: “mat.” High temp: “windowsill…”
Limiting Word Choices Top-k Picks from top K probable words Limits sampling pool to top K words K=3: “sunny, cloudy,” K=40: “breezy, misty…”
Top-p (Nucleus) Picks from tokens covering p% total probability Dynamically selects smallest pool with cumulative prob ≥ p p=0.5: “love.” p=0.95: “adventure, chocolate”
Repetition & Novelty Frequency Penalty Reduces repeated words Penalizes frequently used words Avoids repeating “sunny”
Presence Penalty Encourages new words Penalizes words already present Introduces new concepts after “love”
Output & Search Strategy Max Tokens Limits length of output Stops generation after set token count Stops after 50 tokens
Beam Search Finds most coherent sequence Maintains and selects best of multiple token sequences Picks best completion of “The weather today is”

By adjusting these parameters, you can tailor AI outputs to be more predictable, creative, concise, or expansive depending on your needs. Behind every witty, insightful, or quirky AI response, there’s a carefully tuned blend of these controls shaping its word-by-word choices.

Understanding Multiple Inheritance in Java: Limitations, Solutions, and Best Practices

In object-oriented programming, multiple inheritance refers to a class's ability to inherit features from more than one class. While this concept offers flexibility in languages like C++, Java intentionally does not support multiple inheritance of classes to prevent complex issues, such as ambiguity and the notorious diamond problem—where the compiler cannot decide which superclass's method to invoke when two have the same method name.

"One reason why the Java programming language does not permit you to extend more than one class is to avoid the issues of multiple inheritance of state, which is the ability to inherit fields from multiple classes." 1

Types of Multiple Inheritance in Java

Java distinguishes “multiple inheritance” into three main types:

  • Multiple Inheritance of State:
    Inheriting fields (variables) from more than one class. Java forbids this since classes can only extend a single superclass, preventing field conflicts and ambiguity1.
  • Multiple Inheritance of Implementation:
    Inheriting method bodies from multiple classes. Similar issues arise here, as Java doesn't allow a class to inherit methods from more than one parent class to avoid ambiguity12.
  • Multiple Inheritance of Type:
    Refers to a class implementing multiple interfaces, where an object can be referenced by any interface it implements. Java does allow this form, providing flexibility without the ambiguity risk, as interfaces don’t define fields and, until Java 8, did not contain method implementations12.

How Java Achieves Multiple Inheritance with Interfaces

Although Java does not support multiple inheritance of classes, it enables multiple inheritance through interfaces:

  • A class can implement multiple interfaces. Each interface may declare methods without implementations (abstract methods), allowing a single class to provide concrete implementations for all methods declared in its interfaces13.
  • Since interfaces don't contain fields (only static final constants), the ambiguity due to multiple sources of state doesn’t arise1.
  • With Java 8 and newer, interfaces can contain default methods (methods with a default implementation). If a class implements multiple interfaces that have a default method with the same signature, the Java compiler requires the programmer to resolve the conflict explicitly by overriding the method in the class2.

"A class can implement more than one interface, which can contain default methods that have the same name. The Java compiler provides some rules to determine which default method a particular class uses."2

Example: Multiple Inheritance via Interfaces

Here, one object can be referenced by different interface types. Each reference restricts access to only those methods defined in its corresponding interface, illustrating polymorphism and decoupling code from concrete implementations.

interface Backend {
    void connectServer();
}

interface Frontend {
    void renderPage(String page);
}

interface DevOps {
    void deployApp();
}

class FullStackDeveloper implements Backend, Frontend, DevOps {
    @Override
    public void connectServer() {
        System.out.println("Connecting to backend server.");
    }

    @Override
    public void renderPage(String page) {
        System.out.println("Rendering frontend page: " + page);
    }

    @Override
    public void deployApp() {
        System.out.println("Deploying application using DevOps tools.");
    }
}

public class Main {
    public static void main(String[] args) {
        // Single object instantiation
        FullStackDeveloper developer = new FullStackDeveloper();

        // Interface polymorphism in action
        Backend backendDev = developer;
        Frontend frontendDev = developer;
        DevOps devOpsDev = developer;

        backendDev.connectServer();         // Only Backend methods accessible
        frontendDev.renderPage("Home");     // Only Frontend methods accessible
        devOpsDev.deployApp();              // Only DevOps methods accessible

        // Confirm all references point to the same object
        System.out.println("All references point to: " + developer.getClass().getName());
    }
}

Key points shown in main:

  • Polymorphism: You can refer to the same object by any of its interface types, and only the methods from that interface are accessible through the reference.
  • Multiple Interfaces: The same implementing class can be treated as a Backend, Frontend, or DevOps, but the reference type controls what methods can be called.

Summary

  • Java does not support multiple inheritance of state and implementation through classes to prevent ambiguity.
  • Java supports multiple inheritance of type through interfaces: a class can implement multiple interfaces, gaining the types and behaviors defined by each.
  • Since Java 8, interfaces can also have default method implementations, but name conflicts must be resolved explicitly by overriding the conflicting method2.

This design keeps Java’s inheritance clear and unambiguous, while still offering the power of code reuse and flexibility via interfaces.

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