Catch

catch

Table-driven testing is a testing paradigm where multiple test cases are defined in a structured format, typically as a collection of inputs and expected outputs. Instead of writing separate test functions for each test case, you define a single test function that iterates through the collection (or "table") of test cases. multiple test cases are defined in a structured format, typically as a collection of inputs and expected outputs define a single test function that iterates through the collection (or "table") of test cases This approach allows you to add new test cases by simply extending your test table rather than writing new test functions. The paradigm gets its name from how the test cases are organized - like rows in a table where each row represents a complete test case with inputs and expected outputs. rows in a table where each row represents a complete test case with inputs and expected outputs // Simplified example in Go

tests := []struct {
    name     string
    input    int
    expected int
}{
    {"positive number", 5, 5},
    {"negative number", -5, 5},
    {"zero", 0, 0},
}

for _, tc := range tests {
    t.Run(tc.name, func(t *testing.T) {
        result := Abs(tc.input)
        if result != tc.expected {
            t.Errorf("Abs(%d) = %d; expected %d", tc.input, result, tc.expected)
        }
    })
} // Simplified example in Go

tests := []struct {
    name     string
    input    int
    expected int
}{
    {"positive number", 5, 5},
    {"negative number", -5, 5},
    {"zero", 0, 0},
}

for _, tc := range tests {
    t.Run(tc.name, func(t *testing.T) {
        result := Abs(tc.input)
        if result != tc.expected {
            t.Errorf("Abs(%d) = %d; expected %d", tc.input, result, tc.expected)
        }
    })
} Why Table-Driven Testing Became Popular Table-driven testing became popular for several compelling reasons: Reduced code duplication: Instead of writing similar test functions with slight variations, you write a single function that processes multiple test cases.
Improved maintainability: When you need to change how tests are evaluated, you only need to update one function rather than multiple similar functions.
Better test coverage visibility: The table format makes it easy to see the range of inputs being tested, making it clearer which edge cases are covered.
Easier to add test cases: Adding new test cases is as simple as adding a new entry to the table, which encourages more comprehensive testing.
Self-documenting: The table structure itself documents what inputs are being tested and what outputs are expected.
Great fit for unit tests: It works particularly well for pure functions where different inputs should produce predictable outputs. Reduced code duplication: Instead of writing similar test functions with slight variations, you write a single function that processes multiple test cases. Reduced code duplication Improved maintainability: When you need to change how tests are evaluated, you only need to update one function rather than multiple similar functions. Improved maintainability Better test coverage visibility: The table format makes it easy to see the range of inputs being tested, making it clearer which edge cases are covered. Better test coverage visibility Easier to add test cases: Adding new test cases is as simple as adding a new entry to the table, which encourages more comprehensive testing. Easier to add test cases Self-documenting: The table structure itself documents what inputs are being tested and what outputs are expected. Self-documenting Great fit for unit tests: It works particularly well for pure functions where different inputs should produce predictable outputs. Great fit for unit tests Examples in Go Go's testing framework is particularly well-suited for table-driven testing. Here's a more comprehensive example: package calculator

import "testing"

func TestAdd(t *testing.T) {
    // Define table of test cases
    testCases := []struct {
        name     string
        a        int
        b        int
        expected int
    }{
        {"both positive", 2, 3, 5},
        {"positive and negative", 2, -3, -1},
        {"both negative", -2, -3, -5},
        {"zero and positive", 0, 3, 3},
        {"large numbers", 10000, 20000, 30000},
    }

    // Iterate through all test cases
    for _, tc := range testCases {
        // Use t.Run to create a named subtest
        t.Run(tc.name, func(t *testing.T) {
            result := Add(tc.a, tc.b)
            if result != tc.expected {
                t.Errorf("Add(%d, %d) = %d; expected %d", 
                    tc.a, tc.b, result, tc.expected)
            }
        })
    }
}

func TestCalculate(t *testing.T) {
    testCases := []struct {
        name     string
        a        int
        b        int
        op       string
        expected int
        expectErr bool
    }{
        {"addition", 5, 3, "+", 8, false},
        {"subtraction", 5, 3, "-", 2, false},
        {"multiplication", 5, 3, "*", 15, false},
        {"division", 6, 3, "/", 2, false},
        {"division by zero", 6, 0, "/", 0, true},
        {"invalid operation", 5, 3, "$", 0, true},
    }

    for _, tc := range testCases {
        t.Run(tc.name, func(t *testing.T) {
            result, err := Calculate(tc.a, tc.b, tc.op)
            
            // Check error expectations
            if tc.expectErr && err == nil {
                t.Errorf("Calculate(%d, %d, %s) expected error but got none",
                    tc.a, tc.b, tc.op)
                return
            }
            if !tc.expectErr && err != nil {
                t.Errorf("Calculate(%d, %d, %s) unexpected error: %v",
                    tc.a, tc.b, tc.op, err)
                return
            }
            
            // If we don't expect an error, check the result
            if !tc.expectErr && result != tc.expected {
                t.Errorf("Calculate(%d, %d, %s) = %d; expected %d",
                    tc.a, tc.b, tc.op, result, tc.expected)
            }
        })
    }
} package calculator

import "testing"

func TestAdd(t *testing.T) {
    // Define table of test cases
    testCases := []struct {
        name     string
        a        int
        b        int
        expected int
    }{
        {"both positive", 2, 3, 5},
        {"positive and negative", 2, -3, -1},
        {"both negative", -2, -3, -5},
        {"zero and positive", 0, 3, 3},
        {"large numbers", 10000, 20000, 30000},
    }

    // Iterate through all test cases
    for _, tc := range testCases {
        // Use t.Run to create a named subtest
        t.Run(tc.name, func(t *testing.T) {
            result := Add(tc.a, tc.b)
            if result != tc.expected {
                t.Errorf("Add(%d, %d) = %d; expected %d", 
                    tc.a, tc.b, result, tc.expected)
            }
        })
    }
}

func TestCalculate(t *testing.T) {
    testCases := []struct {
        name     string
        a        int
        b        int
        op       string
        expected int
        expectErr bool
    }{
        {"addition", 5, 3, "+", 8, false},
        {"subtraction", 5, 3, "-", 2, false},
        {"multiplication", 5, 3, "*", 15, false},
        {"division", 6, 3, "/", 2, false},
        {"division by zero", 6, 0, "/", 0, true},
        {"invalid operation", 5, 3, "$", 0, true},
    }

    for _, tc := range testCases {
        t.Run(tc.name, func(t *testing.T) {
            result, err := Calculate(tc.a, tc.b, tc.op)
            
            // Check error expectations
            if tc.expectErr && err == nil {
                t.Errorf("Calculate(%d, %d, %s) expected error but got none",
                    tc.a, tc.b, tc.op)
                return
            }
            if !tc.expectErr && err != nil {
                t.Errorf("Calculate(%d, %d, %s) unexpected error: %v",
                    tc.a, tc.b, tc.op, err)
                return
            }
            
            // If we don't expect an error, check the result
            if !tc.expectErr && result != tc.expected {
                t.Errorf("Calculate(%d, %d, %s) = %d; expected %d",
                    tc.a, tc.b, tc.op, result, tc.expected)
            }
        })
    }
} Go's testing framework provides t.Run() which creates a subtest for each test case, allowing individual cases to pass or fail independently. This also provides clear output about which specific test cases failed. t.Run() Implementing Table-Driven Testing in Python While table-driven testing originated in Go, the concept can be applied to any language. Python also supports table-driven testing, typically using frameworks like unittest or pytest: unittest pytest Using unittest import unittest

def add(a, b):
    return a + b

class TestAddition(unittest.TestCase):
    def test_add(self):
        # Define test cases as a list of tuples
        test_cases = [
            # (a, b, expected)
            (2, 3, 5),
            (0, 0, 0),
            (-1, 1, 0),
            (-1, -1, -2),
            (100, 200, 300)
        ]
        
        # Iterate through test cases
        for a, b, expected in test_cases:
            with self.subTest(a=a, b=b):
                result = add(a, b)
                self.assertEqual(result, expected, 
                                f"add({a}, {b}) returned {result} instead of {expected}")

if __name__ == '__main__':
    unittest.main() import unittest

def add(a, b):
    return a + b

class TestAddition(unittest.TestCase):
    def test_add(self):
        # Define test cases as a list of tuples
        test_cases = [
            # (a, b, expected)
            (2, 3, 5),
            (0, 0, 0),
            (-1, 1, 0),
            (-1, -1, -2),
            (100, 200, 300)
        ]
        
        # Iterate through test cases
        for a, b, expected in test_cases:
            with self.subTest(a=a, b=b):
                result = add(a, b)
                self.assertEqual(result, expected, 
                                f"add({a}, {b}) returned {result} instead of {expected}")

if __name__ == '__main__':
    unittest.main() The .subTest() context manager in unittest serves a similar purpose to Go's t.Run(), creating separate subtests for each test case. .subTest() unittest t.Run() Using pytest import pytest

def calculate(a, b, op):
    if op == '+':
        return a + b
    elif op == '-':
        return a - b
    elif op == '*':
        return a * b
    elif op == '/':
        if b == 0:
            raise ValueError("Division by zero")
        return a // b
    else:
        raise ValueError(f"Unknown operation: {op}")

# Define test cases
test_cases = [
    # a, b, op, expected
    (5, 3, '+', 8),
    (5, 3, '-', 2),
    (5, 3, '*', 15),
    (6, 3, '/', 2),
]

# Test function that pytest will discover
@pytest.mark.parametrize("a,b,op,expected", test_cases)
def test_calculate(a, b, op, expected):
    result = calculate(a, b, op)
    assert result == expected, f"calculate({a}, {b}, '{op}') returned {result} instead of {expected}"

# Test cases for exceptions
error_test_cases = [
    # a, b, op, exception
    (6, 0, '/', ValueError),
    (5, 3, '$', ValueError),
]

@pytest.mark.parametrize("a,b,op,exception", error_test_cases)
def test_calculate_exceptions(a, b, op, exception):
    with pytest.raises(exception):
        calculate(a, b, op) import pytest

def calculate(a, b, op):
    if op == '+':
        return a + b
    elif op == '-':
        return a - b
    elif op == '*':
        return a * b
    elif op == '/':
        if b == 0:
            raise ValueError("Division by zero")
        return a // b
    else:
        raise ValueError(f"Unknown operation: {op}")

# Define test cases
test_cases = [
    # a, b, op, expected
    (5, 3, '+', 8),
    (5, 3, '-', 2),
    (5, 3, '*', 15),
    (6, 3, '/', 2),
]

# Test function that pytest will discover
@pytest.mark.parametrize("a,b,op,expected", test_cases)
def test_calculate(a, b, op, expected):
    result = calculate(a, b, op)
    assert result == expected, f"calculate({a}, {b}, '{op}') returned {result} instead of {expected}"

# Test cases for exceptions
error_test_cases = [
    # a, b, op, exception
    (6, 0, '/', ValueError),
    (5, 3, '$', ValueError),
]

@pytest.mark.parametrize("a,b,op,exception", error_test_cases)
def test_calculate_exceptions(a, b, op, exception):
    with pytest.raises(exception):
        calculate(a, b, op) Pytest's parametrize decorator provides an elegant way to implement table-driven tests. It automatically generates a separate test for each set of parameters. parametrize In other languages: Java (using JUnit 5) import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertEquals;

class CalculatorTest {
    
    @ParameterizedTest
    @MethodSource("additionTestCases")
    void testAddition(int a, int b, int expected) {
        Calculator calculator = new Calculator();
        assertEquals(expected, calculator.add(a, b),
                     "Addition result incorrect");
    }
    
    // Method providing the test cases
    static Stream<Arguments> additionTestCases() {
        return Stream.of(
            Arguments.of(1, 1, 2),
            Arguments.of(0, 0, 0),
            Arguments.of(-1, 1, 0),
            Arguments.of(-1, -1, -2),
            Arguments.of(Integer.MAX_VALUE, 1, Integer.MIN_VALUE) // Overflow case
        );
    }
} import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
import java.util.stream.Stream;
import static org.junit.jupiter.api.Assertions.assertEquals;

class CalculatorTest {
    
    @ParameterizedTest
    @MethodSource("additionTestCases")
    void testAddition(int a, int b, int expected) {
        Calculator calculator = new Calculator();
        assertEquals(expected, calculator.add(a, b),
                     "Addition result incorrect");
    }
    
    // Method providing the test cases
    static Stream<Arguments> additionTestCases() {
        return Stream.of(
            Arguments.of(1, 1, 2),
            Arguments.of(0, 0, 0),
            Arguments.of(-1, 1, 0),
            Arguments.of(-1, -1, -2),
            Arguments.of(Integer.MAX_VALUE, 1, Integer.MIN_VALUE) // Overflow case
        );
    }
} When Not to Use Table-Driven Testing Despite its advantages, table-driven testing isn't suitable for all testing scenarios: Complex setup requirements: When each test requires complex, unique setup and teardown procedures.
Testing side effects: When you're testing functions that produce side effects like file I/O or database modifications that are difficult to represent in a table.
Sequence-dependent tests: When tests must run in a specific order because they depend on state changes from previous tests.
Complex assertions: When verifying results requires complex logic that can't be easily expressed in a table format.
UI or integration testing: These typically require more complex interactions and verifications that don't fit well into a simple input/output table. Complex setup requirements: When each test requires complex, unique setup and teardown procedures. Complex setup requirements Testing side effects: When you're testing functions that produce side effects like file I/O or database modifications that are difficult to represent in a table. Testing side effects Sequence-dependent tests: When tests must run in a specific order because they depend on state changes from previous tests. Sequence-dependent tests Complex assertions: When verifying results requires complex logic that can't be easily expressed in a table format. Complex assertions UI or integration testing: These typically require more complex interactions and verifications that don't fit well into a simple input/output table. UI or integration testing Conclusion Table-driven testing offers a powerful, maintainable approach to testing functions with multiple input/output combinations. Its structured format reduces code duplication, improves test clarity, and makes it easier to add new test cases. While it works exceptionally well with pure functions and unit tests, it may not be suitable for more complex testing scenarios involving side effects or sequence-dependent operations. Go's testing framework provides particularly elegant support for table-driven testing, but as we've seen, the paradigm can be implemented effectively in many programming languages using appropriate testing frameworks.

The Testing Hack That Makes Bugs Easier to Catch (and Code Easier to Read)

AI Gone Wrong? This Guy's Job Is to Catch It Before It Happens

In this article, we aim to map out the fee landscape surrounding Solana, providing a solid foundation for understanding how the fee mechanism and market in Solana operate. Recap: Transaction Lifecycle in Solana A transaction is the transmission of data across the blockchain. In blockchain networks, transaction fees are a key piece in maintaining network security, incentivizing validators to process transactions swiftly, and preventing spam. Solana, a popular blockchain known for its high-throughput and fast transaction processing speed fueled by its parallel execution and local market mechanism, and a record of having a transaction speed of 300 milliseconds. A simple Solana transaction could be a trade on a DeFi protocol, an NFT mint, or a token transfer that happens on the Solana blockchain, which forces users to pay a certain fee for each successful transaction. On Solana, transactions tend to be fast and happen instantaneously. The transaction lifecycle in Solana can be summarized as follows: Immediately after the user signs the transaction in their wallet, dApp, or any interface, the signed transaction is sent to a Solana RPC server.
RPC servers act as entry points for transactions; after verifying the leader schedule, forward the leader schedule to the current one.
The leader is responsible for validating and organizing signed transactions within its designated slot before execution.
Solana validators use a block-building algorithm called Prio-Graph, which prioritizes transactions based on priority fees and vote transactions.
The RPC server establishes a fast and secure connection to the leader node’s Transaction Processing Unit (TPU) using Quick UDP Internet Connections (QUIC).
The TPU processes transactions in multiple stages: SigVerify strategy, Banking stage, and Broadcast stage.
Solana has no mempool and instead uses Gulfstream, meaning transactions are locally ordered in each thread’s queue.
Transactions specify which state must be read-locked and write-locked, and those unable to obtain the required locks are re-queued.
Validators synchronize their states inside Solana clusters to maintain a consistent ledger.
A block is deemed complete when 31 or more verified blocks have been constructed onto it. Immediately after the user signs the transaction in their wallet, dApp, or any interface, the signed transaction is sent to a Solana RPC server. RPC servers act as entry points for transactions; after verifying the leader schedule, forward the leader schedule to the current one. The leader is responsible for validating and organizing signed transactions within its designated slot before execution. Solana validators use a block-building algorithm called Prio-Graph, which prioritizes transactions based on priority fees and vote transactions. The RPC server establishes a fast and secure connection to the leader node’s Transaction Processing Unit (TPU) using Quick UDP Internet Connections (QUIC). The TPU processes transactions in multiple stages: SigVerify strategy, Banking stage, and Broadcast stage. Solana has no mempool and instead uses Gulfstream, meaning transactions are locally ordered in each thread’s queue. Transactions specify which state must be read-locked and write-locked, and those unable to obtain the required locks are re-queued. Validators synchronize their states inside Solana clusters to maintain a consistent ledger. A block is deemed complete when 31 or more verified blocks have been constructed onto it. For a deeper dive into the Solana Transaction Lifecycle, check out my previous article here. here here In each signed transaction on Solana, be it $1 to $100, a fixed transaction fee of 0.000005 SOL is incurred. This means that users experience low-cost and fast transaction fees and speed, respectively, compared to other blockchains like Ethereum. This design of a static fee model in Solana contributes to its high-throughput and cos-efficient architecture, allowing thousands of users to perform bundles of transactions without incurring heavy transaction fee costs. This is unlike other blockchains, where certain scenarios like network congestion or transaction complexity can contribute to heavy transaction fees. However, this fixed transaction (base fee) is just a fraction of Solana’s fee structure. Solana also has priority fees that determine the order of transactions in a leader queue; these kinds of fees are optional and depend on whether the user wants a faster transaction processing speed. The next section will go over in detail the types of fees in Solana. A Brief Overview of Compute Units Compute units (CU) in Solana measure are the computational resources needed for a transaction to be executed. In Solana, each transaction is deducted from the compute unit budget: Maximum Limit: A transaction can use up to 1.4 million compute units.
Default Limit: By default, each instruction is limited to 200,000 compute units.
Custom Limit: You can request a specific compute unit limit by including a SetComputeUnitLimit instruction in your transaction. Maximum Limit: A transaction can use up to 1.4 million compute units. Default Limit: By default, each instruction is limited to 200,000 compute units. Custom Limit: You can request a specific compute unit limit by including a SetComputeUnitLimit instruction in your transaction. SetComputeUnitLimit Solana's transaction fees are based on compute units. This means that transactions that use more computational resources will incur higher fees. However, vote transactions are sometimes overcharged compared to their actual CU usage, leading to inefficiencies and centralization risks. Types of Fees in Solana Solana fee system is made up of two kinds: Base fees and Priority fees. Base Fee Solana transactions have a fixed base fee of 0.0000005 SOL. These base fees are paid by users not relative to any kind of transactions being made on the network, and it gives users the right to write to program accounts. The fee payer must be an account owned by the system program. These fees are paid upfront without regard for the amount of compute units needed to ensure the transaction is added to the block, which makes the transaction fee independent of resources used. Transactions in Solana require a certain amount of compute units (200,000 CU) for each transaction, and if this number is exceeded, the transaction fails and can only be successful if the user decides to pay extra fees (priority fees). Solana's base transaction fee is split 50/50, with 50% burned to reduce supply and 50% rewarded to the validator who processed the transaction. Priority Fee Priority Fee on Solana is an optional additional fee paid by users to validators to increase the probability of their transaction being processed and inclusion into the block. This is non-deterministic as it does not guarantee inclusion in the block due to scenarios like block space limits, high-competiting bids, compute unit constraints, and leader behaviors. The incentives for validators to accept priority fees to include transactions in the block exist outside the Solana runtime (the Solana runtime is only responsible for enforcing transaction execution rules). The Priority fee Indeterminism for transactions with higher fees is based on the Solana scheduler. The Solana scheduler operates with four execution cores, while two additional cores are reserved for vote transactions. Each thread maintains its queue, processing transactions independently without awareness of other threads’ operations. Threads cycle through their queues, locking and executing transactions before collecting new ones. Due to this design, a high-priority transaction in one thread may compete with a lower-priority transaction in another thread if both involve the same account, leading to unpredictable execution ordering. Source: Fees in Theory and Practice (Helius) Source: Fees in Theory and Practice (Helius) Fees in Theory and Practice Note: Vote transactions do not have an associated priority fee and are treated differently than standard transactions. The fee is a fixed fee of 0.9 SOL per day, which is paid by validators. Fee Calculations in Solana? Recall: The Solana base fee has a fixed transaction fee of 0.000005 SOL (5,000 lamports) per transaction. It is independent of the resources used to execute the transaction. Recall: Solana Tx Base Fee  =  (5,000 Lamports)  x  (# of Signatures in Tx) Solana Tx Base Fee  =  (5,000 Lamports)  x  (# of Signatures in Tx) More so, Solana users can add optional priority fees to improve their inclusion probability. Priority fees are priced per CU requested for a transaction. To understand how it is calculated, Solana transactions have the following Compute Budget instructions: SetComputeUnitLimit: Transactions can set the maximum amount of CUs that they are allowed to consume (with a max of 1.4M CUs per transaction). When executed, the transaction can be used up to that requested CU limit. If no SetComputeUnitLimit instruction is provided, the transaction’s CU limit is calculated as (# of instructions in the transaction) x (200k CUs). SetComputeUnitLimit: Transactions can set the maximum amount of CUs that they are allowed to consume (with a max of 1.4M CUs per transaction). When executed, the transaction can be used up to that requested CU limit. If no SetComputeUnitLimit instruction is provided, the transaction’s CU limit is calculated as (# of instructions in the transaction) x (200k CUs). SetComputeUnitLimit SetComputeUnitPrice: # of micro-lamports per CU requested that the transaction offer pay in its priority fee. SetComputeUnitPrice: # of micro-lamports per CU requested that the transaction offer pay in its priority fee. SetComputeUnitPrice This means that the priority fee on Solana is determined by the amount of compute units (CUs) a transaction requests and the price the user is willing to pay per CU. Tx Priority Fee = (Tx CU Limit)  x  (CU Price) Tx Priority Fee = (Tx CU Limit)  x  (CU Price) Shortcomings of Solana Transaction Fee Mechanism (TFM) The potential shortcomings of Solana Transaction Fee Mechanism are: Poor CU Usage Solana base fees do not account for the use of compute units and lead to the overestimation of compute units needed for a given block and a loss in efficiency in the usage of compute units. This is a shortcoming for Solana TFM, as base fees do not incentivize transactions to use and request compute units effectively. Absence of Privileged Transactions Vote transaction fees in Solana are fees that must be paid by validators (privately) as they are included in the block. This creates an unfair system for validators, as sometimes vote transactions are overcharged relative to the amount of compute units consumed. This creates a centralizing economic effect as the total voting constant is fixed for all validators, while the rewards earned are proportional to the stake. This list is not an exhaustive list of the shortcomings of Solans’s TFM rather focused on those related to the Solana fee market. To further understand the shortcomings of Solana TFM, check Solana & Ethereum Transaction Fee Mechanism by Eclipse. Solana & Ethereum Transaction Fee Mechanism Solana & Ethereum Transaction Fee Mechanism Wrapping Up In this report, we explored the Solana transaction lifecycle, examined its fee market, and discussed the shortcomings of the Solana transaction fee mechanism.

Solana’s Transaction Fees Are Insanely Cheap—But Here’s the Catch

Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try, catch, and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch, you can: Catch specific errors.
Display user-friendly messages.
Log errors for debugging. Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Using the $Error Variable To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user.
Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Here's a refined version of how you might handle errors using $Error: try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} Errors in a PowerShell script can prevent successful execution. Implementing try-catch blocks enables error management and response. This post covers try/catch basics and how to handle custom error messages in PowerShell. Effective error handling saves troubleshooting time and improves user experience. PowerShell offers robust options for exception management with try , catch , and finally blocks. Learn to use try-catch for graceful error handling and to add custom error messages for enhanced feedback. PowerShell script PowerShell script try catch finally try-catch error error Why Use Exception Handling in PowerShell? Scripts can fail for many reasons: missing files, invalid input, or network issues, to name a few. With exception handling, you can capture these issues, inform users in a friendly way, and potentially recover from errors without crashing your script. Using try-catch , you can: try-catch Catch specific errors. Display user-friendly messages. Log errors for debugging. Catch specific errors. Display user-friendly messages. Log errors for debugging. Syntax overview of Try/Catch Syntax overview of Try/Catch Like similar in other programming languages, the try-catch block syntax is very simple and syntax will be the same. It is framed with two sections enclosed in curly brackets (the first block is a try and the second is the catch block). try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} try { 

#Functionality within try block 

} 

catch 

{ 

#Action to do with errors 

} The main purpose of using the try-catch block, we can start to manipulate the error output and make it more friendly for the user. Example 1: Example 1: After executing the below script, the below error will be shown on the screen as output and it would occupy some space and the problem may not be immediately visible to the User. So you can use a try-catch block to manipulate the error output and make it more friendly. without Try-Catch block without Try-Catch block without Try-Catch block Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” Get-content -Path “C:\dotnet-helpers\BLOG\TestFiled.txt” with Try Catch block with Try Catch block with Try Catch block In the below script, we added the ErrorAction parameter with a value of Stop to the command. Not all errors are considered “terminating”, so sometimes we need to add this bit of code in order to properly terminate into the catch block. ErrorAction Stop try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} try {

Get-content -Path “C:\dotnet-helpers\BLOG\TestFile.txt” -ErrorAction Stop

}

catch { 

Write-Warning -Message “Can’t read the file, seem there is an issue”

} Example 2: Example 2: Using the $Error Variable Using the $Error Variable Using the $Error Variable $Error To improve the code and follow best practices, consider the following improvements: Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0], consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Use More Descriptive Error Messages: When handling errors, it's useful to log or display meaningful error messages rather than generic ones. If you mention a custom message, ensure it provides useful information to the user. Use More Descriptive Error Messages: Avoid Hardcoding Indexes: Instead of hardcoding the index to access $Error[0] , consider using .First() for better readability and understanding of what exactly you're accessing (especially in PowerShell). Avoid Hardcoding Indexes: $Error[0] .First() Here's a refined version of how you might handle errors using $Error : $Error try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
} try{
 
Get-content -Path "G:\dotnet-helpers\BLOG\TestFiled.txt" -ErrorAction Stop
}
# It will execute if a specific file is not found in a specific Directory
Catch [System.IO.DirectoryNotFoundException] {
 
Write-Warning -Message "Can't read the file, seems there is an issue"
Write-Warning $Error[0]
}
# It will execute if the specified driver is not found in the specified path
Catch [System.Management.Automation.DriveNotFoundException]{
Write-Warning -Message "Custom Message: Specific driver is not found"
Write-Warning $Error[0]
 
}
#Execute for Un-Handled exception - This catch block will run if the error does not match any other catch block exception.
Catch{
Write-Warning -Message "Oops, An un-expected Error Occurred"
#It will return the exception message for the last error that occurred.
Write-host $Error[0].Exception.GetType().FullName
}

How to Stop Your Scripts from Crashing with Try-Catch Magic

The HackerNoon Newsletter: 21 Cranium-Catching Bitcoin Marketing Ideas For Dummies  Savvyies (11/13/2024)

21 Cranium-Catching Bitcoin Marketing Ideas For Dummies & Savvyies

21 хващащи черепа биткойн маркетингови идеи за манекени и разбирачи

21 ideas de marketing de Bitcoin que harán que los principiantes y los entendidos se destaquen

21 idee di marketing Bitcoin da urlo per principianti e esperti

21 רעיונות שיווק ביטקוין תופסי גולגולת עבור בובות ומנוסות

初心者から上級者まで、ビットコインマーケティングの21の魅力的なアイデア

21 kaukolę užvaldanti Bitcoin rinkodaros idėja manekenams ir išminčiai

21 Cranium-Ifata Bitcoin Ibitekerezo byo Kwamamaza Dummies & Savvyies

21 ไอเดียการตลาด Bitcoin ที่น่าสนใจสำหรับคนโง่และคนฉลาด

21 Cranium-Catching Bitcoin Marketing Ideas Para sa Mga Dummies at Savvyies

Aptallar ve Bilgililer İçin 21 Kafatası Yakalayan Bitcoin Pazarlama Fikri

21 Cranium-Catching Bitcoin Miehleketo Ya Vuxavisi Bya Dummies & Savvyies

Dummies va Savvyies uchun 21 Boshsuyagi-ushlab bitcoin marketing g'oyalar

With the exponential growth of stablecoins in 2024, it's safe to say that RWA tokenization is the next big thing. According to RWA.xyz, stablecoins recorded a massive 3800% growth between 2020 and 2024, surging from $4 billion to over $156 billion and bringing its total RWA market share to over 90%. This article provides fresh insights into the state of RWA tokenization in 2024, focusing on the growth statistics, its key players, market predictions and emerging trends. State of RWA in brief The brave new world of RWA tokenization has attracted significant attention in traditional finance where–according to a recent survey by Tokenized Assets Coalition–about 86% of Fortune 500 executives recognize its potential benefits. Moreover, there has been a consistent institutional narrative about its projected growth, with leading market analysts estimating trillions of dollars of tokenized assets by 2030. RWA tokenization has recently emerged as a fast growing trend with the potential to shape the global investment landscape, thanks to the surging stablecoin volume and a fast paced institutional adoption. While payment stablecoin is leading the charge, real estate is the sleeping giant of RWA tokenization. Other use cases such as treasury bonds, private credits, commodities and stocks add significantly to the industry’s trillions of dollars worth of total addressable markets. RWA Tokenization Growth The growth pattern of RWA tokenization has inspired an interesting narrative about its huge potential. According to the recent report by Tokenized Assets Coalition, the current market size of tokenized assets is valued at $186 billion with a 32% year-to-date increase and over 150 tokenized asset issuers around the globe. Based on a sector specific analysis, stablecoin is responsible for a large percentage of the growth of the RWA total market size, while others sectors such as real estate, commodities(ie gold, silver, etc), and treasury bonds,  have not only gained significant traction but have also shown tremendous growth potential as well. For instance, the tokenized real estate market is poised to experience a rapid growth in the years ahead as it continues to attract significant attention from traditional institutions lured by its potential to usher in liquidity and open up access to retail investors globally. According to Block square, the tokenized real estate sector is estimated to reach $26 billion by 2034. Furthermore, there are some other notable events that point to the growth and mainstream recognition of the RWA tokenization. These include MakerDOA's $1 billion investment in tokenized US treasuries, Goldman Sachs’s RWA tokenization projects, BlackRock's investment in US treasuries through its $500 million BUIDL fund, PricewaterhouseCoopers’ 2024 RWA Tokenization cohort and the US tokenized treasures reaching a $1 billion milestone. Most importantly, the growth of RWA tokenization is very much tied to the increasing role of payment stablecoin in traditional finance as evident in its ability to bring about instant settlement and low fees.  The billion dollar moves by key players such as Goldman Sachs, MakerDAO, Ripple, Coinbase, BlackRock, etc. also contribute to the growth trajectory and future outlook. Key Institutional Players There is a growing list of key institutional players that are genuinely interested in RWA tokenization’s potential to shape the global investment landscape. According to a recent Coinbase report, 35% of Fortune 500 companies are gravitating towards tokenization projects. Below are some of the key players shaping the future of tokenized assets: BlackRock World's largest asset manager, BlackRock, announced its foray into RWA tokenization through its BUIDL fund. A significant investment in the US tokenized treasuries. Now worth over $600 million, BlackRock's BUIDL fund allows investors to invest in tokenized US treasuries, which is a game changer for fund management and the institutional adoption of blockchain technology. MakerDAO MakerDAO is the company behind DAI, an overcollateralized stablecoin, and a key player in the RWA space. It recently announced a $1 billion investment in the US tokenized treasuries. Goldman Sachs As a leading investment bank, Goldman Sachs has demonstrated a visionary leadership financial innovation through its proposed tokenization of institutional-grade assets targeting the US and EU markets. It seeks to provide  institutional clients with investment options in tokenized securities, real estate and commodities. Coinbase As a leading crypto exchange, Coinbase is spearheading a major foray into RWA tokenization. It recently entered a partnership with eNor Securities to offer tokenized assets to retail investors in Latin America, as part of its broader expansion strategy in the region. Market Predictions Market predictions are consistently estimating trillions of dollars of tokenized assets by 2030 and beyond. In a recent report by the Tokenized Assets Coalition (TAC), Mckinsey & Company, Boston Consulting Group(BCG) and Citibank predicted that there will be $2, $16 and $5 Trillion worth of tokenized assets by 2030, while Standard Chartered estimated that the total market size will grow to $30 Trillion by 2034. Latest Development: Brickken Partners With BNB Chain For RWA Tokenization Brickken, a leading RWA tokenization company, recently announced its collaboration with BNB Chain. This makes Brickken an official provider of RWA tokenization services within the BNB ecosystem. Brickken has  continued to record giant strides in the RWA space. In May, it was selected as one of the 11 participants of PricewaterhouseCoopers’ RWA Tokenization Cohort – out of 700 applicants . The partnership with BNB Chain, one of the leading blockchains, will allow it to leverage BNB’s while taking maximum advantage of its low transaction costs, high scalability and to provide secure, cost effective solutions to investors and businesses seeking to tokenize real world assets on the protocol's platform. Additionally, BNB Chain's vast DeFi ecosystem consisting of decentralised applications provides Brickken with market access thus increasing liquidity by opening up access to both retail and institutional investors. Following the announcement, Brickken was chosen by Hydrokken, a clean energy tokenization project, to tokenize its shares in natural hydrogen amplifying the potential of RWA tokenization in driving clean energy initiative. Conclusion: Real world asset(RWA) tokenization offers new ways to invest in real-world assets like real estate, commodities, and government bonds. Stablecoin has emerged as the leading use case for RWA tokenization, controlling over 90% of the total market share. Other sectors in the RWA space such as real estate, commodities, and tokenised treasuries have continued to attract significant attention, especially from traditional institutional like Goldman Sachs and BlackRock, which recently announced their foray into the brave new world of RWA tokenization. This year, the RWA space has experienced significant growth thanks to the surging market capitalization of stablecoins.  Traditional institutions have also contributed enormously to its growth success through their investments, partnerships and confidence in the potential of blockchain technology to shape the future of finance. With the exponential growth of stablecoins in 2024, it's safe to say that RWA tokenization is the next big thing. According to RWA.xyz, stablecoins recorded a massive 3800% growth between 2020 and 2024, surging from $4 billion to over $156 billion and bringing its total RWA market share to over 90%. RWA tokenization 90%. This article provides fresh insights into the state of RWA tokenization in 2024, focusing on the growth statistics, its key players, market predictions and emerging trends. State of RWA in brief The brave new world of RWA tokenization has attracted significant attention in traditional finance where–according to a recent survey by Tokenized Assets Coalition–about 86% of Fortune 500 executives recognize its potential benefits. Moreover, there has been a consistent institutional narrative about its projected growth, with leading market analysts estimating trillions of dollars of tokenized assets by 2030. RWA tokenization has recently emerged as a fast growing trend with the potential to shape the global investment landscape, thanks to the surging stablecoin volume and a fast paced institutional adoption. While payment stablecoin is leading the charge, real estate is the sleeping giant of RWA tokenization. Other use cases such as treasury bonds, private credits, commodities and stocks add significantly to the industry’s trillions of dollars worth of total addressable markets. RWA Tokenization Growth The growth pattern of RWA tokenization has inspired an interesting narrative about its huge potential. According to the recent report by Tokenized Assets Coalition, the current market size of tokenized assets is valued at $186 billion with a 32% year-to-date increase and over 150 tokenized asset issuers around the globe. Based on a sector specific analysis, stablecoin is responsible for a large percentage of the growth of the RWA total market size, while others sectors such as real estate, commodities(ie gold, silver, etc), and treasury bonds ,  have not only gained significant traction but have also shown tremendous growth potential as well. real estate, commodities(ie gold, silver, etc), and treasury bonds For instance, the tokenized real estate market is poised to experience a rapid growth in the years ahead as it continues to attract significant attention from traditional institutions lured by its potential to usher in liquidity and open up access to retail investors globally. According to Block square, the tokenized real estate sector is estimated to reach $26 billion by 2034. Furthermore, there are some other notable events that point to the growth and mainstream recognition of the RWA tokenization. These include MakerDOA's $1 billion investment in tokenized US treasuries, Goldman Sachs’s RWA tokenization projects, BlackRock's investment in US treasuries through its $500 million BUIDL fund, PricewaterhouseCoopers’ 2024 RWA Tokenization cohort and the US tokenized treasures reaching a $1 billion milestone. Most importantly, the growth of RWA tokenization is very much tied to the increasing role of payment stablecoin in traditional finance as evident in its ability to bring about instant settlement and low fees.  The billion dollar moves by key players such as Goldman Sachs, MakerDAO, Ripple, Coinbase, BlackRock, etc. also contribute to the growth trajectory and future outlook. Key Institutional Players There is a growing list of key institutional players that are genuinely interested in RWA tokenization’s potential to shape the global investment landscape. According to a recent Coinbase report, 35% of Fortune 500 companies are gravitating towards tokenization projects. Below are some of the key players shaping the future of tokenized assets: Coinbase Coinbase BlackRock World's largest asset manager, BlackRock, announced its foray into RWA tokenization through its BUIDL fund. A significant investment in the US tokenized treasuries. Now worth over $600 million, BlackRock's BUIDL fund allows investors to invest in tokenized US treasuries, which is a game changer for fund management and the institutional adoption of blockchain technology. MakerDAO MakerDAO is the company behind DAI, an overcollateralized stablecoin, and a key player in the RWA space. It recently announced a $1 billion investment in the US tokenized treasuries. Goldman Sachs As a leading investment bank, Goldman Sachs has demonstrated a visionary leadership financial innovation through its proposed tokenization of institutional-grade assets targeting the US and EU markets. It seeks to provide  institutional clients with investment options in tokenized securities, real estate and commodities. Coinbase As a leading crypto exchange, Coinbase is spearheading a major foray into RWA tokenization. It recently entered a partnership with eNor Securities to offer tokenized assets to retail investors in Latin America, as part of its broader expansion strategy in the region. Market Predictions Market predictions are consistently estimating trillions of dollars of tokenized assets by 2030 and beyond. In a recent report by the Tokenized Assets Coalition (TAC), Mckinsey & Company, Boston Consulting Group(BCG) and Citibank predicted that there will be $2, $16 and $5 Trillion worth of tokenized assets by 2030, while Standard Chartered estimated that the total market size will grow to $30 Trillion by 2034. Latest Development: Brickken Partners With BNB Chain For RWA Tokenization Brickken, a leading RWA tokenization company, recently announced its collaboration with BNB Chain. This makes Brickken an official provider of RWA tokenization services within the BNB ecosystem. Brickken has  continued to record giant strides in the RWA space. In May, it was selected as one of the 11 participants of PricewaterhouseCoopers’ RWA Tokenization Cohort – out of 700 applicants . The partnership with BNB Chain, one of the leading blockchains, will allow it to leverage BNB’s while taking maximum advantage of its low transaction costs, high scalability and to provide secure, cost effective solutions to investors and businesses seeking to tokenize real world assets on the protocol's platform. Additionally, BNB Chain's vast DeFi ecosystem consisting of decentralised applications provides Brickken with market access thus increasing liquidity by opening up access to both retail and institutional investors. Following the announcement, Brickken was chosen by Hydrokken, a clean energy tokenization project, to tokenize its shares in natural hydrogen amplifying the potential of RWA tokenization in driving clean energy initiative. Conclusion: Real world asset(RWA) tokenization offers new ways to invest in real-world assets like real estate, commodities, and government bonds. Stablecoin has emerged as the leading use case for RWA tokenization, controlling over 90% of the total market share. Other sectors in the RWA space such as real estate, commodities, and tokenised treasuries have continued to attract significant attention, especially from traditional institutional like Goldman Sachs and BlackRock, which recently announced their foray into the brave new world of RWA tokenization. This year, the RWA space has experienced significant growth thanks to the surging market capitalization of stablecoins.  Traditional institutions have also contributed enormously to its growth success through their investments, partnerships and confidence in the potential of blockchain technology to shape the future of finance.

RWA Tokenization:Catching Up With The Numbers, The Institutional Players, And The Market Predictions

Do I Even Need a Bio? Yes, you do. And do people actually read other people’s bios on social media? Also affirmative. First impression is what matters and lasts. Additionally, I find it useful to try to squeeze my professional career story into a few short sentences. It helps me get a better overview of what my strengths and areas of expertise actually are. Maybe even inspires me to make some changes and get a bit closer to some professional goals. And probably most importantly – it is a brief yet comprehensive source of information about me for future employers, employees, or any new connections in my network. What Does a ‘Good’ Bio Include? The length of a bio varies among different social media platforms. Probably the longest one I’ve written was for a conference application form. Searching through various LinkedIn bios and writing tips, I came up with a few conclusions about its elements and main features: Length For most of my social media accounts, I keep my bio short - 2 or 3 sentences maximum. For event applications or ‘about me’ sections with more space on some of my accounts – I allow 5 well-structured sentences. Content A short social media bio includes information on my job position, the company I work for, the main areas of my expertise, and the biggest achievements. For a longer ‘about me’ I add a few more personal elements – my motivations and hobbies. You can also include your valuable experience. Tone In a short bio, I keep the language clear, precise, and rather formal. For a longer personal summary, I remember to stay as authentic as possible. I make the language slightly more conversational, however, still professional. Practical Prompt for a Catchy Bio With ChatGPT First, I want to create a short bio – useful for example for a LinkedIn headline. Copy and paste input: Write a 2-3 sentences long professional bio for my social media account.
Remember to make sentences short and stick to the given limit.
Use simple verbs, make language simple.
Include only relevant information, do not add pompous quotes.
Keep grammar structure consistent.
Use following information:
My job position: (fill in with your information)
The company I work for: (fill in with your information)
My main responsibilities: (name 1 or 2 areas of your expertise)
My main achievements: (include the achievements you’re most proud of) My input: Write a 2-3 sentences long professional bio for my social media account.
Remember to make sentences short and stick to the given limit.
Use simple verbs, make language simple.
Include only relevant information, do not add pompous quotes.
Keep grammar structure consistent.
Use following information:
My job position: Executive Assistant and Technical Writer
The company I work for: sentimatic
My main responsibilities: writing articles about being productive with AI on https://productiveimpact.com/
My main achievements: running a small English tutoring business at https://anna-tomyofenglish.carrd.co What I got: Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co Now, I want to develop my short bio into a longer professional summary and add some more personal information. I can then use it for conference applications or any longer description sections on my social media pages. I type: Now change my short bio into a longer one - stick to the 5 sentences limit.
Use simple verbs and keep sentences short and concise.
Keep it on a level comprehensible for a B1 speaker of English.
Do not add pompous quotes.
Keep grammar structure consistent.
Write only about the information below, nothing else.
Add information including:
My motivations: (in short name the main professional goal of your actions)
My hobbies: (name 2 or 3 hobbies)
My valuable expertise (add anything you find worth the attention or write ‘doesn’t concern’) And this is the final version of my longer bio: Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co. I aim to automate tasks to have more time for creativity. Outside of work, I enjoy watching French movies, exploring psychology, and learning new languages. Now What? Now, whenever something changes in my professional life, I can easily adapt my bio and keep the people within my network updated. With a clear and catchy bio, I gain a higher following and attract people who simply fit my professional goals and aspirations better. Improve your personal brand with the help of ChatGPT, and start using these prompts for an effective social media bio. For any other social media content tips, check my other articles and subscribe to my newsletter! Thank you for supporting produtiveimpact! Check @productiveimpct Do I Even Need a Bio? Yes, you do. And do people actually read other people’s bios on social media? Also affirmative. First impression is what matters and lasts. Additionally, I find it useful to try to squeeze my professional career story into a few short sentences. It helps me get a better overview of what my strengths and areas of expertise actually are. Maybe even inspires me to make some changes and get a bit closer to some professional goals. And probably most importantly – it is a brief yet comprehensive source of information about me for future employers, employees, or any new connections in my network. What Does a ‘Good’ Bio Include? The length of a bio varies among different social media platforms. Probably the longest one I’ve written was for a conference application form. Searching through various LinkedIn bios and writing tips, I came up with a few conclusions about its elements and main features: Length Length Length Length For most of my social media accounts, I keep my bio short - 2 or 3 sentences maximum. For event applications or ‘about me’ sections with more space on some of my accounts – I allow 5 well-structured sentences. Content Content Content Content A short social media bio includes information on my job position, the company I work for, the main areas of my expertise, and the biggest achievements. For a longer ‘about me’ I add a few more personal elements – my motivations and hobbies. You can also include your valuable experience. Tone Tone Tone Tone In a short bio, I keep the language clear, precise, and rather formal. For a longer personal summary, I remember to stay as authentic as possible. I make the language slightly more conversational, however, still professional. Practical Prompt for a Catchy Bio With ChatGPT First, I want to create a short bio – useful for example for a LinkedIn headline. Copy and paste input: Write a 2-3 sentences long professional bio for my social media account. Remember to make sentences short and stick to the given limit. Use simple verbs, make language simple. Include only relevant information, do not add pompous quotes. Keep grammar structure consistent. Use following information: My job position: (fill in with your information) The company I work for: (fill in with your information) My main responsibilities: (name 1 or 2 areas of your expertise) My main achievements: (include the achievements you’re most proud of) Write a 2-3 sentences long professional bio for my social media account. Remember to make sentences short and stick to the given limit. Use simple verbs, make language simple. Include only relevant information, do not add pompous quotes. Keep grammar structure consistent. Use following information: My job position: (fill in with your information) The company I work for: (fill in with your information) My main responsibilities: (name 1 or 2 areas of your expertise) My main achievements: (include the achievements you’re most proud of) My input: Write a 2-3 sentences long professional bio for my social media account. Remember to make sentences short and stick to the given limit. Use simple verbs, make language simple. Include only relevant information, do not add pompous quotes. Keep grammar structure consistent. Use following information: My job position: Executive Assistant and Technical Writer The company I work for: sentimatic My main responsibilities: writing articles about being productive with AI on https://productiveimpact.com/ My main achievements: running a small English tutoring business at https://anna-tomyofenglish.carrd.co Write a 2-3 sentences long professional bio for my social media account. Remember to make sentences short and stick to the given limit. Use simple verbs, make language simple. Include only relevant information, do not add pompous quotes. Keep grammar structure consistent. Use following information: My job position: Executive Assistant and Technical Writer The company I work for: sentimatic My main responsibilities: writing articles about being productive with AI on https://productiveimpact.com/ https://productiveimpact.com/ https://productiveimpact.com/ My main achievements: running a small English tutoring business at https://anna-tomyofenglish.carrd.co https://anna-tomyofenglish.carrd.co https://anna-tomyofenglish.carrd.co What I got: Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co https://productiveimpact.com/ https://productiveimpact.com/ https://anna-tomyofenglish.carrd.co https://anna-tomyofenglish.carrd.co Now, I want to develop my short bio into a longer professional summary and add some more personal information. I can then use it for conference applications or any longer description sections on my social media pages. I type: Now change my short bio into a longer one - stick to the 5 sentences limit. Use simple verbs and keep sentences short and concise. Keep it on a level comprehensible for a B1 speaker of English. Do not add pompous quotes. Keep grammar structure consistent. Write only about the information below, nothing else. Add information including: My motivations: (in short name the main professional goal of your actions) My hobbies: (name 2 or 3 hobbies) My valuable expertise (add anything you find worth the attention or write ‘doesn’t concern’) Now change my short bio into a longer one - stick to the 5 sentences limit. Use simple verbs and keep sentences short and concise. Keep it on a level comprehensible for a B1 speaker of English. Do not add pompous quotes. Keep grammar structure consistent. Write only about the information below, nothing else. Add information including: My motivations: (in short name the main professional goal of your actions) My hobbies: (name 2 or 3 hobbies) My valuable expertise (add anything you find worth the attention or write ‘doesn’t concern’) And this is the final version of my longer bio: Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co. I aim to automate tasks to have more time for creativity. Outside of work, I enjoy watching French movies, exploring psychology, and learning new languages. Executive Assistant & Technical Writer at sentimatic. I write articles on AI productivity for https://productiveimpact.com/ and manage a small English tutoring business at https://anna-tomyofenglish.carrd.co . I aim to automate tasks to have more time for creativity. Outside of work, I enjoy watching French movies, exploring psychology, and learning new languages. https://productiveimpact.com/ https://productiveimpact.com/ https://anna-tomyofenglish.carrd.co https://anna-tomyofenglish.carrd.co Now What? Now, whenever something changes in my professional life, I can easily adapt my bio and keep the people within my network updated. With a clear and catchy bio, I gain a higher following and attract people who simply fit my professional goals and aspirations better. Improve your personal brand with the help of ChatGPT, and start using these prompts for an effective social media bio. For any other social media content tips, check my other articles and subscribe to my newsletter! Thank you for supporting produtiveimpact! Check @productiveimpct @productiveimpct

How to Make Your Social Media Bio Catchy and Memorable With ChatGPT

All You Need To Know About Try Catch in C#

As the very first crypto, Bitcoin was designed to encourage financial equality - it was a way to make online payments from one party to another without needing a ‘third party’, and it gave anyone with internet access a chance to participate in the financial ecosystem. But for all of the industry’s meteoric growth, optimism, and progress - this isn’t enjoyed by everyone and crypto is still in catch-up mode when it comes to inclusivity In this post, Janine Grainger - Founder and CEO of Easy Crypto - shares three fundamentals the industry needs to focus on if we stand a chance of delivering on crypto’s original vision… #1 Radical Representation - Diversity Starts at the ‘Top’ Although the crypto industry is gradually embracing diversity, we have to remember that the space itself was born from two very male-dominated industries: finance and technology. Late last year, a of 50 prominent crypto and blockchain firms revealed that women hold just 6 percent of CEO positions, painting what it called a ‘disheartening picture’ of underrepresentation. study Unsurprisingly - this has affected both product design and who (and how) products are marketed to industry-wide. Unsurprisingly, a large percentage of women are ‘crypto curious’; but ultimately, hesitant to participate because it feels like a ‘boy’s club.’ But as one of the industries that has been arguably the slowest to respond to a call for diversity, there’s the impetus to be the most radical about change - including leadership appointments and creating work environments in which all types of talent can flourish. We need companies to go ‘all in’ on hiring female talent, and to be unapologetic about doing so. Companies need to commit to gender diversity and send a strong message about inclusivity and equality. This will foster an environment where diverse perspectives can shape the company's future, from product development to strategic direction. #2 Education and Empowerment - Giving Everyone the Confidence they Need to Invest With more and more women starting to take responsibility for their own financial freedom, the industry’s focus needs to be on empowering everyone to become an investor easily (within minutes) in alternative assets, with the understanding and confidence to do so. By providing users with the knowledge and confidence to navigate the crypto space, innovators are breaking down barriers and encouraging more women to participate in the financial revolution. Driving education and information at every step of the process for those who use their platform builds confidence and ultimately, platform loyalty. #3 Product Innovation - Recognizing the Need for Tailored Products Although there is now much wider recognition that crypto investors aren't all young, hoodie-wearing Silicon Valley males who mine Bitcoin from their basements, a lot of products out there still cater to this demographic and have limited the industry's appeal to a broader audience. To counter this, smart companies are focusing on products that address some of the key concerns associated with the crypto industry. This includes user-friendly wallets designed to make the digital world as accessible as traditional apps - with an emphasis on security alongside user-friendly features that do away with complexities such as seed phrases. In addition, a new breed of stablecoin marries the trustworthiness of traditional fiat assets with the benefits of blockchain (without the volatility). These stablecoins provide an easy ‘on-ramp’ to the world of crypto, facilitating instant, low-cost, global, and programmable payments and will become a catalyst for greater crypto adoption and the digitization of financial products and services. Importantly, improving representation, education, empowerment, and product innovation is work that needs to happen day in and day out. Beyond Women’s Day, these focuses must become intrinsic to how the industry operates if it wants to support meaningful change. If not, we risk not delivering on the full potential of crypto for a large portion of society.

Crypto Is In ‘Catch-Up’ Mode When It Comes to Inclusivity

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