Rootine

rootine

Each time I read about a crypto hack on the internet, I usually heave a sigh of disappointment. I always ask myself whether there will ever be an end to the alarming rate of cyber attacks on blockchain protocols. For every crypto hack that has occurred, it feels like the cryptocurrency industry has taken ten steps backward, undermining its progress and worsening the industry’s image problem. Apparently, crypto hacks happen not because there are no effective cyber security measures in place but because they are either not enough or they are currently outpaced. Developing a superior cybersecurity technology must be seen as a race to beat hackers to their own game, and further improvements are required to match their tactics for tactics, so much so that cybersecurity technologies must be ten steps ahead of cyber threats. In this article, I will be delving into the efficacy of smart contract audits, highlighting the significance of a routine audit and the convergence of various approaches that are tailored towards a safer blockchain ecosystem. What Are Smart Contracts? Smart contracts are pieces of self-executing computer programs on a blockchain. They are designed to function when a particular set of conditions are fulfilled (i.e., minting an NFT), and the ultimate purpose they serve is to cut off intermediaries. It is hard to imagine today's world of decentralized applications(DApps) without its functionalities. However, deploying it on a blockchain protocol comes with potential risks. In other words, they are vulnerable to cyber attacks such as DOS attacks, phishing, flash loan attacks, and rentrancy attacks. One of the earliest events that proved that blockchain technology is not the gold standard for cybersecurity involved a smart contract. Many people remember this event as the infamous DAO hack, which happened in 2016 after hackers exploited a flaw in the code and stole over $50 million. The most recent example of this type of exploit is the Normie memecoin hack, which took place on the 26th of May 2024. This exploit not only resulted in the loss of over $800,000  but also saw the market capitalization of the memecoin slump to $35,000 from $41 million in a matter of days. Why Do Smart Contract Hacks Occur? There are bad actors in every dark corner of the web. Smart contract hacks occur because hackers are able to identify and exploit the vulnerabilities of a smart contract. In most cases, they are written by humans, making them prone to errors that hackers usually exploit. In another instance, hackers do this by tricking unsuspecting users into interacting with a malicious smart contract in order to steal their funds. Combating Smart Contract Hacks Blockchain protocols continue to suffer heavy financial loss due to the recurring exploit. To address this menace, a regular smart contract audit is usually recommended by cyber security experts. While this security measure might offer a semblance of efficacy, obtaining an audit certificate has never turned out to be the most assuring safety measure. As a matter of fact, a one-off audit is never enough because experience has shown that it must be carried out from time to time. Furthermore, the DeFi landscape has witnessed significant advances in blockchain security with the emergence of AI, so much so that AI is increasingly becoming a viable tool in the detection of anomalies. Leveraging AI can further enhance the ability of professional auditors to carry out a thorough risk assessment because it raises the bar for audit technology and reduces the dependence on the traditional manual process. The Significance of  Smart Contract Audit Auditing smart contracts is the process of running checks on the code to discover potential security flaws. While it is not the ultimate measure, it has grown in importance over the years, especially with the rise of professional audit firms. Regularly checking for vulnerability is an industry-standard procedure justified by collective wisdom: prevention is better than cure. It is also a reminder that cyber threats are always lurking around the corner. Most importantly, there is a need for blockchain protocols to diversify their cybersecurity strategies so that a regular audit is combined with other evolving techniques, such as artificial intelligence(AI). Commenting on the importance of a regular smart contract auditing UBET Sports CEO Daniel Jiwoong. Im said: “Smart contract auditing is essential for platform like UBET Sports operating in the decentralized finance (DeFi) space. These audits identify and mitigate security vulnerabilities that could be exploited by malicious actors, safeguarding user assets and maintaining the platform's integrity. Regular audits also build trust and confidence among users, reassuring them that the platform has been rigorously tested and verified by independent experts.” “Recently, UBET Sports underwent three comprehensive smart contract audits, demonstrating its commitment to security and reliability. The first audit coincided with the launch of UBET Sports' decentralized exchange (DEX) for sports betting, establishing a secure foundation. Subsequent audits focused on enhancing the Automated Market Maker (AMM) to improve slippage, optimizing gas fees, improving push settlements, and structuring revenue-sharing mechanisms for liquidity provision, ”he added. Is a routine smart contract audit worth it? “Building an on-chain product presents challenges, such as the need for audits before each release, preventing continuous integration and deployment. Despite these challenges and the high costs associated with multiple audits, we prioritized security to protect its users. With two more smart contract updates planned this year, we continue to make steady progress in the SportiFi space, merging sports and DeFi” Daniel further said. Conclusion Billions of dollars have been lost over the years to crypto hacks perpetrated by bad actors. Smart contract vulnerability can give rise to a potential hack, which can, in turn, undermine a blockchain protocol and drain the funds of its users. Promoting a secure blockchain ecosystem has been at the heart of every cybersecurity measure, like smart contract audits that are designed to combat the incidence of cyber-attacks. A smart contract audit, which typically involves a detailed examination of a protocol’s codebase, has been recognized as one of the standard techniques for combating potential crypto hacks. It has increasingly become a significant practice in blockchain security with the emergence of professional audit firms and sophisticated tools. Each time I read about a crypto hack on the internet, I usually heave a sigh of disappointment. I always ask myself whether there will ever be an end to the alarming rate of cyber attacks on blockchain protocols. For every crypto hack that has occurred, it feels like the cryptocurrency industry has taken ten steps backward, undermining its progress and worsening the industry’s image problem. Apparently, crypto hacks happen not because there are no effective cyber security measures in place but because they are either not enough or they are currently outpaced. Developing a superior cybersecurity technology must be seen as a race to beat hackers to their own game, and further improvements are required to match their tactics for tactics, so much so that cybersecurity technologies must be ten steps ahead of cyber threats. In this article, I will be delving into the efficacy of smart contract audits, highlighting the significance of a routine audit and the convergence of various approaches that are tailored towards a safer blockchain ecosystem. What Are Smart Contracts? Smart contracts are pieces of self-executing computer programs on a blockchain. They are designed to function when a particular set of conditions are fulfilled (i.e., minting an NFT), and the ultimate purpose they serve is to cut off intermediaries. It is hard to imagine today's world of decentralized applications(DApps) without its functionalities. However, deploying it on a blockchain protocol comes with potential risks. In other words, they are vulnerable to cyber attacks such as DOS attacks, phishing, flash loan attacks, and rentrancy attacks. One of the earliest events that proved that blockchain technology is not the gold standard for cybersecurity involved a smart contract. Many people remember this event as the infamous DAO hack, which happened in 2016 after hackers exploited a flaw in the code and stole over $50 million. The most recent example of this type of exploit is the Normie memecoin hack, which took place on the 26th of May 2024. This exploit not only resulted in the loss of over $800,000  but also saw the market capitalization of the memecoin slump to $35,000 from $41 million in a matter of days. Why Do Smart Contract Hacks Occur? There are bad actors in every dark corner of the web. Smart contract hacks occur because hackers are able to identify and exploit the vulnerabilities of a smart contract. In most cases, they are written by humans, making them prone to errors that hackers usually exploit. In another instance, hackers do this by tricking unsuspecting users into interacting with a malicious smart contract in order to steal their funds. Combating Smart Contract Hacks Blockchain protocols continue to suffer heavy financial loss due to the recurring exploit. To address this menace, a regular smart contract audit is usually recommended by cyber security experts. While this security measure might offer a semblance of efficacy, obtaining an audit certificate has never turned out to be the most assuring safety measure. As a matter of fact, a one-off audit is never enough because experience has shown that it must be carried out from time to time. Furthermore, the DeFi landscape has witnessed significant advances in blockchain security with the emergence of AI, so much so that AI is increasingly becoming a viable tool in the detection of anomalies. Leveraging AI can further enhance the ability of professional auditors to carry out a thorough risk assessment because it raises the bar for audit technology and reduces the dependence on the traditional manual process. The Significance of  Smart Contract Audit Auditing smart contracts is the process of running checks on the code to discover potential security flaws. While it is not the ultimate measure, it has grown in importance over the years, especially with the rise of professional audit firms. Regularly checking for vulnerability is an industry-standard procedure justified by collective wisdom: prevention is better than cure. It is also a reminder that cyber threats are always lurking around the corner. Most importantly, there is a need for blockchain protocols to diversify their cybersecurity strategies so that a regular audit is combined with other evolving techniques, such as artificial intelligence(AI). Commenting on the importance of a regular smart contract auditing UBET Sports CEO Daniel Jiwoong. Im said: “Smart contract auditing is essential for platform like UBET Sports operating in the decentralized finance (DeFi) space. These audits identify and mitigate security vulnerabilities that could be exploited by malicious actors, safeguarding user assets and maintaining the platform's integrity. Regular audits also build trust and confidence among users, reassuring them that the platform has been rigorously tested and verified by independent experts.” “Smart contract auditing is essential for platform like UBET Sports operating in the decentralized finance (DeFi) space. These audits identify and mitigate security vulnerabilities that could be exploited by malicious actors, safeguarding user assets and maintaining the platform's integrity. Regular audits also build trust and confidence among users, reassuring them that the platform has been rigorously tested and verified by independent experts.” “Recently, UBET Sports underwent three comprehensive smart contract audits, demonstrating its commitment to security and reliability. The first audit coincided with the launch of UBET Sports' decentralized exchange (DEX) for sports betting, establishing a secure foundation. Subsequent audits focused on enhancing the Automated Market Maker (AMM) to improve slippage, optimizing gas fees, improving push settlements, and structuring revenue-sharing mechanisms for liquidity provision, ”he added. “Recently, UBET Sports underwent three comprehensive smart contract audits, demonstrating its commitment to security and reliability. The first audit coincided with the launch of UBET Sports' decentralized exchange (DEX) for sports betting, establishing a secure foundation. Subsequent audits focused on enhancing the Automated Market Maker (AMM) to improve slippage, optimizing gas fees, improving push settlements, and structuring revenue-sharing mechanisms for liquidity provision, ”he added. Is a routine smart contract audit worth it? “Building an on-chain product presents challenges, such as the need for audits before each release, preventing continuous integration and deployment. Despite these challenges and the high costs associated with multiple audits, we prioritized security to protect its users. With two more smart contract updates planned this year, we continue to make steady progress in the SportiFi space, merging sports and DeFi” Daniel further said. “Building an on-chain product presents challenges, such as the need for audits before each release, preventing continuous integration and deployment. Despite these challenges and the high costs associated with multiple audits, we prioritized security to protect its users. With two more smart contract updates planned this year, we continue to make steady progress in the SportiFi space, merging sports and DeFi” Daniel further said. Conclusion Conclusion Billions of dollars have been lost over the years to crypto hacks perpetrated by bad actors. Smart contract vulnerability can give rise to a potential hack, which can, in turn, undermine a blockchain protocol and drain the funds of its users. Promoting a secure blockchain ecosystem has been at the heart of every cybersecurity measure, like smart contract audits that are designed to combat the incidence of cyber-attacks. A smart contract audit, which typically involves a detailed examination of a protocol’s codebase, has been recognized as one of the standard techniques for combating potential crypto hacks. It has increasingly become a significant practice in blockchain security with the emergence of professional audit firms and sophisticated tools.

The Importance Of a Routine Smart Contract Audit In Blockchain Security

MariaDB 11.4 introduced many advanced features. One that grabbed my attention is the general support of packages for stored routines. Although this was previously available by activating the Oracle compatibility mode, now, the feature is available generally out-of-the-box. This will help you to significantly enhance the organization of database development within a MariaDB environment. Packages provide a modular approach to managing database logic. This addition aligns MariaDB more closely with other advanced database systems that have long utilized packages, such as Oracle, and sets it apart in that regard from other open-source relational databases that don't support packages. Packages in MariaDB allow you to group related stored procedures, functions, variables, and other elements together into a single unit. This structure provides several benefits, including improved code organization, enhanced reusability, and simplified maintenance. Prior to this update, each stored procedure and function in MariaDB existed independently, which could lead to a cluttered schema and more complicated management of complex business logic when implemented in the database. Packages address this issue by providing a way to logically group related routines. Again, the primary advantage of using packages is the encapsulation of related routines. For example, in an e-commerce application, operations related to order processing can be grouped into a single package called OrderProcessing. This package might include procedures like PlaceOrder, CancelOrder, and UpdateOrderStatus, as well as functions such as GetOrderDetails. This logical grouping makes the database schema more organized and the codebase easier to navigate and hence to maintain. Creating Packages Creating and using packages in MariaDB 11.4 and later is straightforward. Simply use the CREATE PACKAGE statement to define a package and the CREATE PACKAGE BODY statement to implement the package’s routines. Here is a simplified example without the actual business logic implementation: DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT);
  PROCEDURE CancelOrder(order_id INT);
  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON;
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT)
  BEGIN
    -- Implementation code here
  END;

  PROCEDURE CancelOrder(order_id INT)
  BEGIN
    -- Implementation code here
  END;

  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON
  BEGIN
    -- Implementation code here
  END;
END;

$$
DELIMITER ; In this example, the OrderProcessing package is defined with two procedures and one function. The package body provides the implementation for these routines, encapsulating the logic related to order processing within a single package. Calling Packaged Stored Routines To call a procedure or function that was defined in a package, you use the "dot notation." Here's an example of how to call the CancelOrder procedure in the OrderProcessing package: CALL OrderProcessing.CancelOrder(7); The same applies to functions in packages: SELECT OrderProcessing.GetOrderDetails(7); You can also define package-level variables and constants, which are accessible to all routines within the package. This way, you can share common data without relying on global variables or passing around parameters between routines. By centrally managing shared data within the package, you reduce code duplication and minimize the risk of errors. The following is an example (don't take the variable names too seriously in this snippet of code): DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  -- procedure list (see previous example)
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  -- variable declarations
  DECLARE some_count INT DEFAULT 1;
  DECLARE some_total INT DEFAULT 0;

  -- procedure definitions (see previous example)
END;

$$
DELIMITER ; Other Benefits of Packages The introduction of packages in MariaDB 11.4 also brings the possibility of better version control and modularization of code. You can now manage stored routines more effectively, making it easier to track changes and updates. This modularization is particularly beneficial in large projects, where multiple developers might be working on different parts of the database logic simultaneously. By isolating different functionalities into packages, conflicts, and overlaps can be minimized, leading to a smoother development process. Moreover, packages support forward declarations, which means that the routines can be defined before their actual implementation. This feature allows for a more flexible and structured approach to coding, where developers can outline the package interface first and then fill in the details. This separation of interface and implementation can lead to cleaner, more understandable code, facilitating collaboration, and reducing the learning curve for new developers joining a project. For developers accustomed to working with Oracle databases, the inclusion of packages in MariaDB 11.4 will feel familiar and welcome. It bridges a functional gap between MariaDB and Oracle, making it easier to transition between these platforms. Packages vs Multiple Schemas It's important to note the distinction between packages and merely using multiple database schemas. While multiple schemas can help segregate different parts of a database, they do not offer the same level of organization and encapsulation as packages. Schemas are useful for dividing distinct areas of data and logic, but they do not inherently group related procedures and functions together in a way that enhances modularity and maintainability. Packages, on the other hand, allow for a more granular and cohesive approach, grouping related logic together within the same schema. This not only simplifies the management of routines but also improves the clarity and maintainability of the code. Try It Out As always, MariaDB continues to evolve. Try it out today by downloading the latest version of MariaDB, or if you have Docker running: docker run --name mariadb-11.4 -e MARIADB_ROOT_PASSWORD=my-secret-pw -e MARIADB_DATABASE=mydb -e MARIADB_USER=myuser -e MARIADB_PASSWORD=mypassword -d mariadb:11.4

docker exec -it mariadb-11.4 mariadb -p"mypassword" MariaDB 11.4 introduced many advanced features . One that grabbed my attention is the general support of packages for stored routines. Although this was previously available by activating the Oracle compatibility mode , now, the feature is available generally out-of-the-box. This will help you to significantly enhance the organization of database development within a MariaDB environment. MariaDB 11.4 advanced features Oracle compatibility mode Packages provide a modular approach to managing database logic. This addition aligns MariaDB more closely with other advanced database systems that have long utilized packages, such as Oracle, and sets it apart in that regard from other open-source relational databases that don't support packages. Packages in MariaDB allow you to group related stored procedures, functions, variables, and other elements together into a single unit. This structure provides several benefits, including improved code organization, enhanced reusability, and simplified maintenance. Prior to this update, each stored procedure and function in MariaDB existed independently, which could lead to a cluttered schema and more complicated management of complex business logic when implemented in the database . implemented in the database Packages address this issue by providing a way to logically group related routines. Again, the primary advantage of using packages is the encapsulation of related routines. For example, in an e-commerce application, operations related to order processing can be grouped into a single package called OrderProcessing . This package might include procedures like PlaceOrder , CancelOrder , and UpdateOrderStatus , as well as functions such as GetOrderDetails . OrderProcessing PlaceOrder CancelOrder UpdateOrderStatus GetOrderDetails This logical grouping makes the database schema more organized and the codebase easier to navigate and hence to maintain. Creating Packages Creating and using packages in MariaDB 11.4 and later is straightforward. Simply use the CREATE PACKAGE statement to define a package and the CREATE PACKAGE BODY statement to implement the package’s routines. Here is a simplified example without the actual business logic implementation: CREATE PACKAGE CREATE PACKAGE BODY DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT);
  PROCEDURE CancelOrder(order_id INT);
  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON;
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT)
  BEGIN
    -- Implementation code here
  END;

  PROCEDURE CancelOrder(order_id INT)
  BEGIN
    -- Implementation code here
  END;

  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON
  BEGIN
    -- Implementation code here
  END;
END;

$$
DELIMITER ; DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT);
  PROCEDURE CancelOrder(order_id INT);
  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON;
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  PROCEDURE PlaceOrder(customer_id INT, product_id INT, quantity INT)
  BEGIN
    -- Implementation code here
  END;

  PROCEDURE CancelOrder(order_id INT)
  BEGIN
    -- Implementation code here
  END;

  FUNCTION GetOrderDetails(order_id INT) RETURNS JSON
  BEGIN
    -- Implementation code here
  END;
END;

$$
DELIMITER ; In this example, the OrderProcessing package is defined with two procedures and one function. The package body provides the implementation for these routines, encapsulating the logic related to order processing within a single package. OrderProcessing Calling Packaged Stored Routines To call a procedure or function that was defined in a package, you use the "dot notation." Here's an example of how to call the CancelOrder procedure in the OrderProcessing package: CancelOrder OrderProcessing CALL OrderProcessing.CancelOrder(7); CALL OrderProcessing.CancelOrder(7); The same applies to functions in packages: SELECT OrderProcessing.GetOrderDetails(7); SELECT OrderProcessing.GetOrderDetails(7); You can also define package-level variables and constants, which are accessible to all routines within the package. This way, you can share common data without relying on global variables or passing around parameters between routines. By centrally managing shared data within the package, you reduce code duplication and minimize the risk of errors. The following is an example (don't take the variable names too seriously in this snippet of code): DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  -- procedure list (see previous example)
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  -- variable declarations
  DECLARE some_count INT DEFAULT 1;
  DECLARE some_total INT DEFAULT 0;

  -- procedure definitions (see previous example)
END;

$$
DELIMITER ; DELIMITER $$

CREATE OR REPLACE PACKAGE OrderProcessing
  -- procedure list (see previous example)
END;

CREATE OR REPLACE PACKAGE BODY OrderProcessing
  -- variable declarations
  DECLARE some_count INT DEFAULT 1;
  DECLARE some_total INT DEFAULT 0;

  -- procedure definitions (see previous example)
END;

$$
DELIMITER ; Other Benefits of Packages The introduction of packages in MariaDB 11.4 also brings the possibility of better version control and modularization of code. You can now manage stored routines more effectively, making it easier to track changes and updates. This modularization is particularly beneficial in large projects, where multiple developers might be working on different parts of the database logic simultaneously. By isolating different functionalities into packages, conflicts, and overlaps can be minimized, leading to a smoother development process. Moreover, packages support forward declarations, which means that the routines can be defined before their actual implementation. This feature allows for a more flexible and structured approach to coding, where developers can outline the package interface first and then fill in the details. This separation of interface and implementation can lead to cleaner, more understandable code, facilitating collaboration, and reducing the learning curve for new developers joining a project. For developers accustomed to working with Oracle databases, the inclusion of packages in MariaDB 11.4 will feel familiar and welcome. It bridges a functional gap between MariaDB and Oracle, making it easier to transition between these platforms. Packages vs Multiple Schemas It's important to note the distinction between packages and merely using multiple database schemas. While multiple schemas can help segregate different parts of a database, they do not offer the same level of organization and encapsulation as packages. Schemas are useful for dividing distinct areas of data and logic, but they do not inherently group related procedures and functions together in a way that enhances modularity and maintainability. Packages, on the other hand, allow for a more granular and cohesive approach, grouping related logic together within the same schema. This not only simplifies the management of routines but also improves the clarity and maintainability of the code. Try It Out As always, MariaDB continues to evolve . Try it out today by downloading the latest version of MariaDB, or if you have Docker running: MariaDB continues to evolve downloading docker run --name mariadb-11.4 -e MARIADB_ROOT_PASSWORD=my-secret-pw -e MARIADB_DATABASE=mydb -e MARIADB_USER=myuser -e MARIADB_PASSWORD=mypassword -d mariadb:11.4

docker exec -it mariadb-11.4 mariadb -p"mypassword" docker run --name mariadb-11.4 -e MARIADB_ROOT_PASSWORD=my-secret-pw -e MARIADB_DATABASE=mydb -e MARIADB_USER=myuser -e MARIADB_PASSWORD=mypassword -d mariadb:11.4

docker exec -it mariadb-11.4 mariadb -p"mypassword"

Packages for Store Routines in MariaDB 11.4: What You Need to Know

If a product is complex and releases are substantial, there are multiple ways to ensure quality. One key tool I use in our development process is team testing: stepping into the customer’s shoes, traversing various user journeys, and ensuring all key interactions work as intended. This approach offers numerous benefits, from engaging the entire team in understanding the product to correcting flaws in the original business logic. Despite its usefulness, team testing sessions tend to be tedious and generally disliked: building a product is far more enjoyable than repeatedly clicking through it. In this post, I will describe how I made team testing more engaging and somewhat enjoyable by incorporating ChatGPT into the process, transforming it into a competitive activity. How We Conduct Team Testing The process was straightforward: Deploy a feature to a testing environment.
Document what needs to be checked: main user journeys and definitions of success.
Gather on a call or in a room, use the product, discuss, and exchange feedback.
Organize and prioritize feedback, then act accordingly. As a result, the team has usually compiled a list of issues to address. Introducing ChatGPT With the rise of ChatGPT in mid-2023, I decided to integrate it into our testing sessions. Initially, I used it to analyze and count issue reports in Slack—since Slack lacks convenient export functionality. Here’s the prompt I tried: The text below is the log of slack channel.

It contains bugs reported by people, each bug report starts with a first name, then timestamp, then the message itself (normally 1 or 2 lines).

Strings containing ".png", IMG" or ".mov"  could be ignored.

Read the text, count the number of reports per person and rank them from highest ChatGPT struggled at first, glitching in about 60% of cases with verbose error messages. I realized I needed a different approach. Switching to a Script Since ChatGPT ran a Python interpreter for our task, I decided to directly utilize the Python code it wrote. I adjusted the script to account for the way Slack handles attached files as separate messages: # Initialize counters for each name based on the list provided
# The names should match the displayed names in Slack
name_counts = {
    "John": 0,
    "Jane": 0,
    "Rosy": 0,
}

# Open the file and count the occurrences of each name
with open("testing_session_log.txt", "r") as file:
    lines = file.readlines()

# Filtering out garbage lines and counting names
for line in lines:
    # Ignore garbage lines
    if any(garbage in line for garbage in [".png", ".IMG", ".mov"]):
        continue
    for name in name_counts.keys():
        if name in line:
            name_counts[name] += 1

print(name_counts) Returning to ChatGPT for Creativity and Recognition Implementing the script to generate a leaderboard immediately after the session introduced a sense of competition. I saw the engagement growing, and people reporting more bugs! So, I continued to use ChatGPT for crafting personalized praise based on the team’s reported issues, acknowledging their contributions: Using the attached testing session log, praise everyone for their impact and highligh one report of a choice for each person.

Participants names:
John
Jane
Rosy

Wrap the praise in a form of a short, sincere and inspirational message, no more than 4 sentences long. I found it deeply satisfying to read the praise from a machine: John: Your insight on UI details like the collapsed header buttons and gallery navigation enhances our project's usability.
Your contributions are crucial for a seamless user experience.

Jane: Your keen observations, especially on navigation issues, enhance our platform's intuitiveness.
Your ability to catch subtle bugs is invaluable.

Rosy:Your focus on mobile usability, from download behaviors to icon alignment, significantly polishes our mobile interface.
Your efforts make a substantial difference. Conclusion What I’ve implemented isn’t rocket science, but in the pre-LLM era, this would have taken considerably longer to achieve, if at all. While the future of AI-driven products is still shaping up, it’s clear that product teams can benefit greatly by integrating such technologies to simplify tasks, boost positive reinforcement, and free up time for critical projects. Image credit: Midjourney, prompt “team of engineers coding late at night, Hyper-realistic textures, Precise details --version 6.0 --weird 1000” If a product is complex and releases are substantial, there are multiple ways to ensure quality. One key tool I use in our development process is team testing: stepping into the customer’s shoes, traversing various user journeys, and ensuring all key interactions work as intended. This approach offers numerous benefits, from engaging the entire team in understanding the product to correcting flaws in the original business logic. Despite its usefulness, team testing sessions tend to be tedious and generally disliked: building a product is far more enjoyable than repeatedly clicking through it. In this post, I will describe how I made team testing more engaging and somewhat enjoyable by incorporating ChatGPT into the process, transforming it into a competitive activity. How We Conduct Team Testing The process was straightforward: Deploy a feature to a testing environment. Document what needs to be checked: main user journeys and definitions of success. Gather on a call or in a room, use the product, discuss, and exchange feedback. Organize and prioritize feedback, then act accordingly. Deploy a feature to a testing environment. Document what needs to be checked: main user journeys and definitions of success. Gather on a call or in a room, use the product, discuss, and exchange feedback. Organize and prioritize feedback, then act accordingly. As a result, the team has usually compiled a list of issues to address. Introducing ChatGPT With the rise of ChatGPT in mid-2023, I decided to integrate it into our testing sessions. Initially, I used it to analyze and count issue reports in Slack—since Slack lacks convenient export functionality. Here’s the prompt I tried: The text below is the log of slack channel.

It contains bugs reported by people, each bug report starts with a first name, then timestamp, then the message itself (normally 1 or 2 lines).

Strings containing ".png", IMG" or ".mov"  could be ignored.

Read the text, count the number of reports per person and rank them from highest The text below is the log of slack channel.

It contains bugs reported by people, each bug report starts with a first name, then timestamp, then the message itself (normally 1 or 2 lines).

Strings containing ".png", IMG" or ".mov"  could be ignored.

Read the text, count the number of reports per person and rank them from highest ChatGPT struggled at first, glitching in about 60% of cases with verbose error messages. I realized I needed a different approach. Switching to a Script Since ChatGPT ran a Python interpreter for our task, I decided to directly utilize the Python code it wrote. Python interpreter I adjusted the script to account for the way Slack handles attached files as separate messages: # Initialize counters for each name based on the list provided
# The names should match the displayed names in Slack
name_counts = {
    "John": 0,
    "Jane": 0,
    "Rosy": 0,
}

# Open the file and count the occurrences of each name
with open("testing_session_log.txt", "r") as file:
    lines = file.readlines()

# Filtering out garbage lines and counting names
for line in lines:
    # Ignore garbage lines
    if any(garbage in line for garbage in [".png", ".IMG", ".mov"]):
        continue
    for name in name_counts.keys():
        if name in line:
            name_counts[name] += 1

print(name_counts) # Initialize counters for each name based on the list provided
# The names should match the displayed names in Slack
name_counts = {
    "John": 0,
    "Jane": 0,
    "Rosy": 0,
}

# Open the file and count the occurrences of each name
with open("testing_session_log.txt", "r") as file:
    lines = file.readlines()

# Filtering out garbage lines and counting names
for line in lines:
    # Ignore garbage lines
    if any(garbage in line for garbage in [".png", ".IMG", ".mov"]):
        continue
    for name in name_counts.keys():
        if name in line:
            name_counts[name] += 1

print(name_counts) Returning to ChatGPT for Creativity and Recognition Implementing the script to generate a leaderboard immediately after the session introduced a sense of competition. I saw the engagement growing, and people reporting more bugs! So, I continued to use ChatGPT for crafting personalized praise based on the team’s reported issues, acknowledging their contributions: Using the attached testing session log, praise everyone for their impact and highligh one report of a choice for each person.

Participants names:
John
Jane
Rosy

Wrap the praise in a form of a short, sincere and inspirational message, no more than 4 sentences long. Using the attached testing session log, praise everyone for their impact and highligh one report of a choice for each person.

Participants names:
John
Jane
Rosy

Wrap the praise in a form of a short, sincere and inspirational message, no more than 4 sentences long. I found it deeply satisfying to read the praise from a machine: John: Your insight on UI details like the collapsed header buttons and gallery navigation enhances our project's usability.
Your contributions are crucial for a seamless user experience.

Jane: Your keen observations, especially on navigation issues, enhance our platform's intuitiveness.
Your ability to catch subtle bugs is invaluable.

Rosy:Your focus on mobile usability, from download behaviors to icon alignment, significantly polishes our mobile interface.
Your efforts make a substantial difference. John: Your insight on UI details like the collapsed header buttons and gallery navigation enhances our project's usability.
Your contributions are crucial for a seamless user experience.

Jane: Your keen observations, especially on navigation issues, enhance our platform's intuitiveness.
Your ability to catch subtle bugs is invaluable.

Rosy:Your focus on mobile usability, from download behaviors to icon alignment, significantly polishes our mobile interface.
Your efforts make a substantial difference. Conclusion What I’ve implemented isn’t rocket science, but in the pre-LLM era, this would have taken considerably longer to achieve, if at all. While the future of AI-driven products is still shaping up, it’s clear that product teams can benefit greatly by integrating such technologies to simplify tasks, boost positive reinforcement, and free up time for critical projects. Image credit: Midjourney, prompt “team of engineers coding late at night, Hyper-realistic textures, Precise details --version 6.0 --weird 1000” Image credit: Midjourney, prompt “team of engineers coding late at night, Hyper-realistic textures, Precise details --version 6.0 --weird 1000”

From Routine to Rewarding: Integrating ChatGPT Into Product Testing Workflow

As the world becomes increasingly digitalized, companies and individuals invest more time, effort, and money in Socializing, entertainment, money-making, and other online opportunities are a by-product of the world wide web — often, they benefit our lives meaningfully. cybersecurity. Alongside digitalization, cybersecurity is as much a top priority as it is a valid concern for most companies and individuals. From data protection to identity verification, the need for constant or routine revisions in safety procedures is of paramount importance to businesses and organizations that put internet safety first. Cybersecurity companies can be of tremendous help with tightening safety from the first step in the user journey, as they are well-equipped to guide internet users on how to protect their data. Most importantly, cybersecurity experts can help to forestall cyber attacks by routinely advising companies, the public sector, and individual users about online safety and by implementing the broader scope of their research and practices. Implementing Cybersecurity Measures For Users notes that companies have to meet the needs of underserved segments and improve technological services to offer an easy and user-friendly environment. As businesses put effort into protecting the high profit-yielding segments, the other part of the user base becomes vulnerable regarding safety. Constant education and features to support cybersecurity initiatives for every user highlight the focus on end-user safety that enables innovation. McKinsey While the cybersecurity market is forecasted to grow by by 2027, some companies are adopting new security measures to match the increasing demand for data protection, which continues to trend upwards. Businesses hire external consultants and use internal experts to monitor and improve their operations' cybersecurity. A blend of these practices can help businesses educate employees and end-users about such a topic, thus, improving their cybersecurity measures. $266.2 billion An excellent example of proper cybersecurity adoption is a communication and collaboration platform. With the rise to prominence during the COVID-19 pandemic, the company had to adapt to significant growth in user base and implement heightened security initiatives. Slack, All information about its end-user security measures is available on its website. The company lists its certificates, data protection layers, and privacy standards. Slack went through independent assessments from several organizations in different countries to ensure the security of its cloud service. Moreover, it enables users to encrypt their messages and choose data storage periods and permissions. Another important example to consider is a crowdsourced web intelligence network that continuously implements safety layers to deliver a reliable service to end users. It is focused on user consent and about what security measures apply at each step while the users share their internet resources for monetary rewards. Honeygain complies with GDPR and CCPA legislation to provide the highest standard for data protection so that every user can safely use the app. Honeygain, education While user consent on publicly available data gathering is the first step, every payout for the shared internet connection is managed by verified payment platforms as a second step. With multiple steps in strengthening the security of their users, Honeygain adopts various safety tools and standards. For example, they have implemented 2-factor authentication for payouts to strengthen the security surrounding monetary transactions alongside other cybersecurity efforts like its anti-cheat system. Honeygain chose it for its payments because it’s regarded as one of the most popular and user-friendly ways to increase online security — the multi-factor authentication system, which includes 2-factor authentication, reached a market value of $13 billion in 2022. For example, Google found that its 2-Step Verification via SMS sent to a recovery phone number stopped 100% of automated bots. Additionally, it can help companies save up to $3 million per data breach. Is 2FA really enough for cybersecurity? However, 2-factor authentication should be one of many security measures if you wish to protect your data and devices. Cybercriminals can target users who use passwords and 2-factor authentication to easily extract information like passwords themselves, secret questions, token-generated passwords, and even device ID data. Moreover, 2-factor authentication heavily relies on the user’s devices — it can’t be set up on a single device. Other security risks arise with 2-factor authentication, like SIM swapping, device theft, and different ways to gain control over users’ information. Thus, companies should adopt 2-factor authentication as an additional layer of security while simultaneously implementing other measures such as proper authorization and access systems, improved network security, and more. These examples demonstrate how important end-user safety has become for companies. While 2FA is a great tool in ensuring initial online security, it should be accompanied by additional safety measures like the examples of Slack and Honeygain suggest. Individual cybersecurity efforts are celebrated — but businesses must ensure additional customer security and support as the digital economy expands. If a security measure is popular and widely adapted, it doesn’t mean it’s foolproof. The best cybersecurity strategy consists of different procedures implemented in tandem with each other. Popular applications like Duolingo, LinkedIn, Tinder, and Dropbox offer multiple layers of cybersecurity to protect users and companies from illicit activities. When it comes to cybersecurity, every step is essential, whether it seems small or significant. Additionally, constant revisions and upgrades to current safety measures will ensure the company’s and its individual user’s online safety. Therefore, every effort in end-user security should be regarded as the top priority, just like the examples of Slack or Honeygain suggest.

The Importance of Routine Cybersecurity Practices: Learning From Slack And Honeygain

Many testers and developers use Postman for both manual and automated API testing. However, not all Postman users fully exploit its capabilities to simplify API-related tasks. In this article, I will demonstrate a few basic examples of how to make API testing faster, more efficient, and reduce time spent on various routine actions when using Postman. Let's dive in and uncover some tips and tricks that will make your API testing experience with Postman faster, more efficient, and less prone to repetitive tasks. Let’s start with Environment. Environment In Postman, the Environment feature allows you to manage environment variables during the execution of test scenarios. It serves as a container where you can define and store a set of variables that will be used in all requests and scripts associated with that environment. Using the Environment feature in Postman provides flexibility and reusability. Instead of hard-coding values or repeating them across multiple requests and scripts, you can define variables in the environment and reference them wherever needed. This way, you can easily update values in one central location without modifying each individual request. Indeed, each variable in an Environment has a name and a value. You can utilize these variables to store data such as URLs, authentication tokens, API keys, and other values that can be used in your requests Let's consider an example: We have a regular backend service with a REST API, , that we want to test. Let's create the first environment. https://reqres.in To create an environment, open the "Environments" tab in the left menu of Postman, and click on the " " button. + Now enter the name of the first environment, let's call it "Production." In an Environment, variables are typically created to be reused across requests. Let's create the first variable: Name it "service_url" and set its value to " ". https://reqres.in After creating the variable, click on "Save" to save all the changes. In the top right corner, locate "No environment" and click on it. Then, select our created environment, "Production". Now, we can use the variables from the selected environment. Let's go to Collections and create a new request. In the URL field, enter the following: “ ”. Now, you can click on "Send" and see the result: {{service_url}}/api/users Our request has been completed successfully. The request was made to the address " " which was stored in the environment variable " " and we appended " " to it. https://reqres.in service_url /api/users By utilizing the " " variable, we can easily reuse it across all our requests. This allows for flexibility and makes it convenient to manage and update the base URL in a single place, saving time and effort in case the URL needs to be modified. service_url But the real power comes when we create multiple environments. Let's go back to "Environments" and create another environment called "Stage". In this environment, let's create the " " variable again, but this time, with the value "https:// .reqres.in". service_url stage Now, in the top right corner, change the Environment to "Stage." Go back to the previous request and execute it again: We receive an error in the response because the domain "stage.reqres.in" does not exist. However, this is not a problem as it confirms that the URL has changed to the value of the " " variable from the "Stage" environment, and everything is functioning correctly. service_url By creating multiple environments with the same variables but different values, we can easily switch between them as needed. In the projects I participate in, we typically create the following environments: : Used to test requests in the live production environment. Production : Used to test requests in the development/staging environment. Stage : Created specifically for testing API branches or feature branches. Branch : Created for testing the API on a locally deployed service. It is typically used by developers to test and debug the API on their local machines. Local For each environment, variables are created and shared across requests. These variables can include API keys, URLs, user agents, and other parameters that differ from one environment to another. By utilizing environments, you can easily switch between different environments (such as Dev, Production, Branch, or Local) with just a couple of clicks. Now, let's move on to another tool that helps speed up the creation of new requests. Request Header Presets In most services, each request has a set of headers that can include the following: Content-Type: Specifies the media type of the request payload, such as application/json or application/x-www-form-urlencoded. Authorization: Provides authentication credentials or tokens to access protected resources. User-Agent: Identifies the client making the request, typically the name and version of the software or application. And other custom headers specific to different services Indeed, manually filling in all the headers for each new request can be time-consuming, especially when there are numerous headers involved. Two functions come to our rescue in such situations: : This function allows you to quickly create a new request by copying an existing one and then modifying it to suit your needs. It is a fast method but may not always be suitable if certain requests have unique headers that should not be sent in other requests. Duplicate : This is the function we will be discussing now. Header presets To access the header presets feature, follow these steps: Open any request in Postman. Go to the "Headers" section. On the right-hand side, you will find a button labeled "Presets." After clicking on the "Presets" button, select "Manage presets", and in the opened menu, click on "Add". You will then see the following screen: Here, we can add headers for presets. Let's create the first header: Key: "Accept-Language" Value: "en" Description: "Preferred content language code" It is a good practice to provide descriptions for headers in presets so that every team member understands the purpose of each header. Let's name the preset "Standard Headers", and click on "Add." Now, open the "Headers" tab of the request, and click on "Presets" once again. In the list, you will see the preset we created. Select it, and the header from the preset will be added to the list of headers: So we have added our header from the preset to the current list of headers. That way, we can create multiple presets for different types of requests. For example, in the projects I participate in, we usually create three presets: one for authenticated requests, one for unauthenticated requests, and one for internal requests. When using presets, there are a few things to keep in mind: If the request's header list already contains headers from a preset, applying the preset will not overwrite those headers. The preset will simply add all the headers to the request's header list. Therefore, it's important to check the list for duplicates. You can use variables from the Environment in the header values. This makes presets even more powerful, as you can dynamically set values based on the environment. Now, let's move on to examples of automating routine tasks using tests. Automation of Passing Authentication to All Requests After Login At many services, there are endpoints that require authentication. Let's take Bearer authentication as an example, where each request needs to include the "Authorization" header with an authentication token. This token needs to be fresh because it has a specific expiration date. For testing requests with authentication, there are multiple approaches. I will demonstrate what I consider the simplest and quickest method, in my opinion. To illustrate this, let's take the service as an example again. To access an authenticated endpoint, Bearer authentication is required. reqres.in The service has an endpoint " " that provides an authentication token. Let's make a request to this endpoint in Postman: /login After a successful request, the service returns an authentication token. We need to configure Postman to automatically include this token value in all requests that require authentication. To achieve this, navigate to the "Tests" tab (located below the URL input), and on the right side, under "Snippets", select "Set environment variable". The following code snippet will be generated: pm.environment.set("variable_key", "variable_value"); To achieve this, navigate to the "Tests" tab (located below the URL input), and on the right side, under "Snippets", select "Set environment variable". Replace "variable_key" with "authorization_token" and replace "variable_value" with "pm.response.json().token" so that it looks as follows: pm.environment.set("authorization_token", pm.response.json().token); Now, execute the request. If it is successful, click on the eye icon in the top-right corner. You will see a screen displaying the list of variables in your environment, which includes the "authorization_token" variable with the value extracted from the response of the request. What happened here: We used a test script. Tests are always executed after the request is made. You can learn more about tests here: . Postman - Writing Scripts is a function used to set the value of an environment variable, and that's how we set the value of the variable. You can even set the value of a variable that doesn't exist in the environment yet, as it will be automatically created. pm.environment.set() authorization_token retrieves the JSON object from the response of the request. Then we use to extract the value from the desired field. pm.response.json() .token As a result, we have an environment variable that will automatically be set whenever we use the ` ` endpoint. This allows us to have the authorization token automatically included in every subsequent request that requires authentication. /login Now all we need to do is go to the relevant requests and add the "Authorization" header with the value " ". This way, the authorization will be automatically included in those requests using the value of the " " variable: Bearer {{authorization_token}} authorization_token As a result, we have written a small script that eliminates the need to manually set the authorization in our environment. It will automatically be applied when a successful login request is made. This kind of automation allows us to simplify working with requests that require data sharing between them. Let's move on to the bonus part — generating various fields. Generating Random Data for Requests Quite often, when testing different APIs, we may need various data, such as: User data: names, surnames, phone numbers, company names, etc. Unique UUIDs Current date and time and much more Some of these things can be invented and written manually each time, while others need to be generated using services, websites, etc. But why bother doing that when Postman can do it all for you? Postman has a wide range of data options that it can generate. You can explore all of them by typing " " in the URL input field. {{ The menu scrolls and contains over 100 different options, such as: Current timestamp (UTC or based on your timezone) Random phone number UUID Random locale Random color And many more If you frequently test APIs, such as when modifying user data, sometimes it's much more beneficial to use random values instead of hardcoded ones. This allows you to cover unexpected scenarios as it introduces variability. By using random values, you can test edge cases and uncover potential issues that may not be apparent with fixed values. Well, Conclusion So, we have covered several simple examples of automating routine tasks with the features of Postman. These pretty little things greatly simplify working with APIs and reduce the amount of manual work and time spent on testing. Enjoy using them! 🙂

Automation of Tester's Routine Using Postman

No, You Don't Need a Highly Specialized Team to Build Your SaaS Application 

TL;DR This isn’t another blog full of pie-in-the-sky theories or frameworks that make you feel like you’re getting a crash course in advanced calculus from Jackson Pollock. No, this is real talk from someone who’s been in the trenches—often clueless, occasionally brilliant. So, strap in. As of writing this, I’ve only been a “real” product manager for about a year and three months, and somehow, at 31, I’m Head of Product at a gamification platform serving Fortune 500 retail customers. And while I swear I have no idea what I’m doing most of the time, I’m also somehow looked up to as the most experienced member of the product team. Welcome to the startup life. Here’s how I survive (and sometimes even thrive) in this sadistic world of startup product management. The Myth of the Average Day Let’s start with a reality check: If you’re still searching for what an “average day” looks like in product management, just… stop. PMs tell a lot of stories—almost half of which are true—so believe me when I say, no two days are alike. I’m either deep into financial forecasting (seriously, why am I the one doing this?) or whiteboarding design ideas that probably won’t make it past launch lunch. It’s chaos, but hey, it keeps me on my toes. Action Item: Embrace the madness. If you’re waiting for a routine, you’re going to be waiting for your way into getting RIF’d. Learn to jump from task to task like you’re on an obstacle course and get comfy with being uncomfy. Communication: It’s Not Mind-Reading When I first started, I thought I was so good at communicating. Turns out, You can’t be good at communicating on day one, month one, or even year one. Every company has its own culture and nuances. It’s like assuming that just because you’re an English-speaking American from Idaho that you’re basically also a Londoner (The answer is a big fat “no” by the way, speaking from experience). Making sure all parts of the org—devs, designers, QA, and external business units—are on the same page is basically your full-time job as a PM. You have to know how your teams best absorb information and influence that into your company’s culture. Since we’re a gamification company, we communicate visually so I explain and facilitate communications visually and interactively. I practically live on Miro, where my team’s litter flows and wireframes with sticky notes and comments in real-time like a caffeinated ant colony. Action Item: Talk. Like, actually talk to your team. Use tools like Miro, but remember, you’re the one responsible for making sure everyone gets heard. Think of yourself as the ambassador to each department, minus the diplomatic immunity (brace yourself). Design, Iterate, Break It, Fix It, Repeat “The definition of insanity is doing the same thing over and over again and expecting different results” — Not Albert Einstein Design something, test it, break it, fix it, and do it all again next week. I’m constantly tinkering with designs and ideas, but here’s the thing: As a PM, it’s my job to pump the brakes when people start getting too excited. Not every idea deserves a sprint, even if it sounds like the next big thing. Action Item: Keep iterating, but don’t let your team jump off the deep end without checking if there’s water in the pool first. You’ve got to validate, or you’ll end up building a beautiful feature nobody wants. RICE: How to Cook It and Then Break It Down Look, I like the RICE priority matrix framework as much as the next PM, but let’s be real—it’s too vague sometimes. Quick Tip: When you see the word “framework,” think of it as a mound of clay in pottery class. It’s there for you to shape into something useful to your specific case. Please, please—for the love of Marty Cagan—do NOT use a vanilla PM framework without tailoring it to your product team’s nuanced needs. For example: What exactly is Impact? For my company, it makes sense to break it down into four things: strategic, objective, financial, and market demand. And when it comes to Effort? Oh, you bet I’ve got that sliced up into four things as well: dev, QA, UX, and business. Tailor RICE (or any framework) to your company. Action Item: Don’t worship at the PM framework altar. Make them work for you by getting specific. Break things down into chunks that actually reflect your team’s reality. You’re the one who has to explain this to your Founder/CEO, after all. Stakeholder Alignment: Like Herding Minotaurs in a Labyrinth Aligning stakeholders is like trying to get eight territorial bull-human hybrids to play nice in the yard without destroying the hedges—good luck. The business team wants everything done yesterday, while devs are (rightly) freaking out about timelines and burnout. I try to make the roadmap and all documents as visible as possible and involve stakeholders in Slack huddles, but everyone is busy, and not everyone will see the product as the number one priority. You just have to smile, nod, and then do whatever actually needs to be done. Action Item: Involve stakeholders early and often, but don’t let them run wild. You’re the ringmaster of this circus, so keep the roadmap from turning into a chaotic free-for-all. You might feel like you’ll get fired for saying “no” to senior stakeholders, but the alternative is getting fired for building a bloated, buggy, unusable heap of garbage (always keep your PM resume up-to-date 😉). AI: The Admin Hack You Didn’t Know You Needed Confession time: AI has saved my life. Well, okay, maybe not literally, but it’s definitely saved my sanity. I’m not building AI into my product (yet), but I am using it to make my work life a whole lot easier. From automating Jira tickets to handling bug reports, AI has freed me from hours of admin work. Quick Tip: Whatever it is you’re trying to automate, someone has already done it. Find a YouTube video, follow along, and then use ChatGPT to refine and fill in the gaps. Now, I actually have time to do product-y things, like thinking about big-picture strategy. Weird, right? Action Item: Let AI handle the boring stuff. Automate the grunt work so you can put on the black turtleneck and sound smart and inspiring in meetings (or at least pretend to). Relationships Matter More Than Any Framework Sure, frameworks are great, but guess what? People are more important. Some devs just want to code in peace, while others want to be part of the product discussion. My job is to make sure the ones who want to hide in their cave are left alone, while the ones who want to collaborate have a seat at the table. Action Item: Get to know your team. Understand their quirks, and manage them accordingly. Build trust, or be prepared to have your grand plans completely ignored. Bottom Line Product management isn’t about memorizing frameworks or pretending you’ve got it all figured out—don’t be that person. Play dumb. Play curious. Play caring. But don’t play smart(er) than anyone else. Embrace the mess, keep your team on track, and figure it out as you go. Make peace with the fact that you’ll always be a noob—I have. So, if you’ve learned anything from this, take this advice as a high-level guide, twist it, tweak it, and tailor it to your specific product situation. Because product “management” isn’t about theory—it’s about strategy and execution. And if all else fails, just let AI handle your Jira tickets and Confluence PRDs. K Bye! © James Effarah You've reached the end. TL;DR This isn’t another blog full of pie-in-the-sky theories or frameworks that make you feel like you’re getting a crash course in advanced calculus from Jackson Pollock. No, this is real talk from someone who’s been in the trenches—often clueless, occasionally brilliant. So, strap in. As of writing this, I’ve only been a “real” product manager for about a year and three months, and somehow, at 31, I’m Head of Product at a gamification platform serving Fortune 500 retail customers. And while I swear I have no idea what I’m doing most of the time, I’m also somehow looked up to as the most experienced member of the product team. swear Welcome to the startup life. Welcome to the startup life. Here’s how I survive (and sometimes even thrive) in this sadistic world of startup product management. The Myth of the Average Day The Myth of the Average Day Let’s start with a reality check: If you’re still searching for what an “average day” looks like in product management, just… stop. PMs tell a lot of stories—almost half of which are true—so believe me when I say, no two days are alike. I’m either deep into financial forecasting (seriously, why am I the one doing this?) or whiteboarding design ideas that probably won’t make it past launch lunch. whiteboarding design ideas launch It’s chaos, but hey, it keeps me on my toes. Action Item: Embrace the madness. If you’re waiting for a routine, you’re going to be waiting for your way into getting RIF’d . Learn to jump from task to task like you’re on an obstacle course and get comfy with being uncomfy. RIF’d Communication: It’s Not Mind-Reading Communication: It’s Not Mind-Reading When I first started, I thought I was so good at communicating. Turns out, You can’t be good at communicating on day one, month one, or even year one. Every company has its own culture and nuances. It’s like assuming that just because you’re an English-speaking American from Idaho that you’re basically also a Londoner (The answer is a big fat “no” by the way, speaking from experience). “no” “no” Making sure all parts of the org—devs, designers, QA, and external business units—are on the same page is basically your full-time job as a PM. You have to know how your teams best absorb information and influence that into your company’s culture. Since we’re a gamification company, we communicate visually so I explain and facilitate communications visually and interactively. I practically live on Miro , where my team’s litter flows and wireframes with sticky notes and comments in real-time like a caffeinated ant colony. Miro Action Item: Talk. Like, actually talk to your team. Use tools like Miro, but remember, you’re the one responsible for making sure everyone gets heard. Think of yourself as the ambassador to each department, minus the diplomatic immunity (brace yourself). Design, Iterate, Break It, Fix It, Repeat Design, Iterate, Break It, Fix It, Repeat “The definition of insanity is doing the same thing over and over again and expecting different results” — Not Albert Einstein Not Albert Einstein Design something, test it, break it, fix it, and do it all again next week. I’m constantly tinkering with designs and ideas, but here’s the thing: As a PM, it’s my job to pump the brakes when people start getting too excited. Not every idea deserves a sprint, even if it sounds like the next big thing. Action Item: Keep iterating, but don’t let your team jump off the deep end without checking if there’s water in the pool first. You’ve got to validate, or you’ll end up building a beautiful feature nobody wants. RICE: How to Cook It and Then Break It Down RICE: How to Cook It and Then Break It Down Look, I like the RICE priority matrix framework as much as the next PM, but let’s be real—it’s too vague sometimes. RICE priority matrix Quick Tip: When you see the word “framework,” think of it as a mound of clay in pottery class. It’s there for you to shape into something useful to your specific case. Quick Tip: Please, please—for the love of Marty Cagan —do NOT use a vanilla PM framework without tailoring it to your product team’s nuanced needs. Marty Cagan For example: What exactly is Impact ? is Impact For my company, it makes sense to break it down into four things: strategic, objective, financial, and market demand. And when it comes to Effort ? Effort Oh, you bet I’ve got that sliced up into four things as well: dev, QA, UX, and business. Tailor RICE (or any framework) to your company. Action Item: Don’t worship at the PM framework altar. Make them work for you by getting specific. Break things down into chunks that actually reflect your team’s reality. You’re the one who has to explain this to your Founder/CEO, after all. PM framework Stakeholder Alignment: Like Herding Minotaurs in a Labyrinth Stakeholder Alignment: Like Herding Minotaurs in a Labyrinth Aligning stakeholders is like trying to get eight territorial bull-human hybrids to play nice in the yard without destroying the hedges—good luck. The business team wants everything done yesterday, while devs are (rightly) freaking out about timelines and burnout. I try to make the roadmap and all documents as visible as possible and involve stakeholders in Slack huddles, but everyone is busy, and not everyone will see the product as the number one priority. You just have to smile, nod, and then do whatever actually needs to be done. Action Item: Involve stakeholders early and often, but don’t let them run wild. You’re the ringmaster of this circus, so keep the roadmap from turning into a chaotic free-for-all. You might feel like you’ll get fired for saying “no” to senior stakeholders, but the alternative is getting fired for building a bloated, buggy, unusable heap of garbage ( always keep your PM resume up-to-date 😉). always keep your PM resume up-to-date AI: The Admin Hack You Didn’t Know You Needed AI: The Admin Hack You Didn’t Know You Needed Confession time: AI has saved my life. Well, okay, maybe not literally, but it’s definitely saved my sanity. I’m not building AI into my product (yet), but I am using it to make my work life a whole lot easier. From automating Jira tickets to handling bug reports, AI has freed me from hours of admin work. automating Jira tickets Quick Tip: Whatever it is you’re trying to automate, someone has already done it. Quick Tip: Find a YouTube video, follow along, and then use ChatGPT to refine and fill in the gaps. Now, I actually have time to do product-y things, like thinking about big-picture strategy. Weird, right? Action Item: Let AI handle the boring stuff. Automate the grunt work so you can put on the black turtleneck and sound smart and inspiring in meetings (or at least pretend to). Relationships Matter More Than Any Framework Relationships Matter More Than Any Framework Sure, frameworks are great, but guess what? People are more important. Some devs just want to code in peace, while others want to be part of the product discussion. My job is to make sure the ones who want to hide in their cave are left alone, while the ones who want to collaborate have a seat at the table. Action Item: Get to know your team. Understand their quirks, and manage them accordingly. Build trust, or be prepared to have your grand plans completely ignored. Bottom Line Product management isn’t about memorizing frameworks or pretending you’ve got it all figured out—don’t be that person. Play dumb. Play curious. Play caring. But don’t play smart(er) than anyone else. Embrace the mess, keep your team on track, and figure it out as you go. Make peace with the fact that you’ll always be a noob—I have. So, if you’ve learned anything from this, take this advice as a high-level guide, twist it, tweak it, and tailor it to your specific product situation. Because product “management” isn’t about theory—it’s about strategy and execution. “management” “management” And if all else fails, just let AI handle your Jira tickets and Confluence PRDs. K Bye! © James Effarah You've reached the end.

How To Product Manage (Startup Edition) 😈 7 Steps To Sanity

Tech professionals face heavy workloads and tight deadlines, which can take a serious toll on their well-being. Recent data from Blind, an anonymous community of tech professionals, reveals that 57% of tech employees report feeling burned out, with common stressors including long hours and constant pressure to maintain operational reliability. While top tech firms like Google and LinkedIn have implemented Site Reliability Engineering (SRE) practices to address system reliability and minimize burnout, there’s a growing consensus that more must be done to support developers on an individual level. This is where Alexandr Hacicheant, who is in charge of Reliability Engineering at Mayflower, steps in with a solution that prioritizes people first. “We often think of engineers as endlessly resilient problem-solvers, but they’re also people who need balance to thrive,” Alexandr explains. By focusing on developer well-being, Alexandr’s methods aim to build healthier, more sustainable tech teams, positioning work-life balance as a business imperative rather than an afterthought. A major part of Alexandr’s journey at Mayflower has been related to creating a workplace where developers could excel without sacrificing their well-being. “My goal wasn’t just to solve system reliability issues; it was to provide engineers with a supportive environment where they felt valued and balanced,” he shares. His approach centers on enhancing productivity through team autonomy, clear performance metrics, and streamlined workflows, helping engineers stay engaged and focused without constant firefighting. For Alexandr, preventing burnout starts with giving developers the resources and structure they need to manage their time effectively. “By reducing the amount of reactive work and empowering engineers to work on high-impact projects, we’re creating a space where they can actually enjoy the work they’re doing,” he adds. One of Alexandr’s key initiatives has been to address one of the most common sources of burnout in tech: the on-call shift. “On-call duty can be incredibly stressful for developers, especially when it disrupts their time off,” he explains. At Mayflower, his team implemented automated systems that handle routine maintenance and alert engineers only for critical issues, allowing them to have uninterrupted downtime. “Reducing unnecessary disruptions lets engineers fully recharge, which is crucial for both their health and productivity,” Alexandr notes. This approach has proven successful in decreasing stress levels among Mayflower’s engineers, enabling them to return to work with renewed focus and motivation. For Alexandr, the reduction of burnout isn’t simply a benefit to individual engineers; it’s a core business strategy. “Healthy engineers are productive engineers, and productive engineers drive a company’s success,” he emphasizes. While Alexandr’s efforts at Mayflower have yielded tangible improvements in both system reliability and employee satisfaction, he believes the industry as a whole needs to prioritize developer well-being. “If we’re going to make tech sustainable, we need to make engineers’ well-being a priority, not just system uptime,” he argues. By adopting a “People first” philosophy, Alexandr has set a standard that other leaders can look to, creating a more supportive environment for developers across the industry. Alexandr has begun sharing his insights more broadly through industry discussions, workshops, and publications. His message is simple but powerful: focusing on work-life balance isn’t just a perk; it’s an essential part of building resilient teams that can meet modern tech challenges. “When we put people first, we create a work environment that supports engineers and business success alike,” he says. Looking forward, Alexandr envisions a tech industry where developer well-being is a primary focus. “We have to shift our thinking from ‘What’s the latest tech trend?’ to ‘How do we create a sustainable workplace for our engineers?’” he reflects. His future goals include refining his “People first” approach further within Mayflower and encouraging its adoption across the industry. As Alexandr puts it, “The future of tech depends on more than innovation—it depends on creating environments where people can thrive.” By championing a work culture that respects work-life balance, he aims to inspire leaders across the industry to foster not just productive, but genuinely healthy, teams. Tech professionals face heavy workloads and tight deadlines, which can take a serious toll on their well-being. Recent data from Blind, an anonymous community of tech professionals, reveals that 57% of tech employees report feeling burned out, with common stressors including long hours and constant pressure to maintain operational reliability. While top tech firms like Google and LinkedIn have implemented Site Reliability Engineering (SRE) practices to address system reliability and minimize burnout, there’s a growing consensus that more must be done to support developers on an individual level. This is where Alexandr Hacicheant, who is in charge of Reliability Engineering at Mayflower, steps in with a solution that prioritizes people first. “We often think of engineers as endlessly resilient problem-solvers, but they’re also people who need balance to thrive,” Alexandr explains. By focusing on developer well-being, Alexandr’s methods aim to build healthier, more sustainable tech teams, positioning work-life balance as a business imperative rather than an afterthought. A major part of Alexandr’s journey at Mayflower has been related to creating a workplace where developers could excel without sacrificing their well-being. “My goal wasn’t just to solve system reliability issues; it was to provide engineers with a supportive environment where they felt valued and balanced,” he shares. His approach centers on enhancing productivity through team autonomy, clear performance metrics, and streamlined workflows, helping engineers stay engaged and focused without constant firefighting. For Alexandr, preventing burnout starts with giving developers the resources and structure they need to manage their time effectively. “By reducing the amount of reactive work and empowering engineers to work on high-impact projects, we’re creating a space where they can actually enjoy the work they’re doing,” he adds. One of Alexandr’s key initiatives has been to address one of the most common sources of burnout in tech: the on-call shift. “On-call duty can be incredibly stressful for developers, especially when it disrupts their time off,” he explains. At Mayflower, his team implemented automated systems that handle routine maintenance and alert engineers only for critical issues, allowing them to have uninterrupted downtime. “Reducing unnecessary disruptions lets engineers fully recharge, which is crucial for both their health and productivity,” Alexandr notes. This approach has proven successful in decreasing stress levels among Mayflower’s engineers, enabling them to return to work with renewed focus and motivation. For Alexandr, the reduction of burnout isn’t simply a benefit to individual engineers; it’s a core business strategy. “Healthy engineers are productive engineers, and productive engineers drive a company’s success,” he emphasizes. While Alexandr’s efforts at Mayflower have yielded tangible improvements in both system reliability and employee satisfaction, he believes the industry as a whole needs to prioritize developer well-being. “If we’re going to make tech sustainable, we need to make engineers’ well-being a priority, not just system uptime,” he argues. By adopting a “People first” philosophy, Alexandr has set a standard that other leaders can look to, creating a more supportive environment for developers across the industry. Alexandr has begun sharing his insights more broadly through industry discussions, workshops, and publications. His message is simple but powerful: focusing on work-life balance isn’t just a perk; it’s an essential part of building resilient teams that can meet modern tech challenges. “When we put people first, we create a work environment that supports engineers and business success alike,” he says. Looking forward, Alexandr envisions a tech industry where developer well-being is a primary focus. “We have to shift our thinking from ‘What’s the latest tech trend?’ to ‘How do we create a sustainable workplace for our engineers?’” he reflects. His future goals include refining his “People first” approach further within Mayflower and encouraging its adoption across the industry. As Alexandr puts it, “The future of tech depends on more than innovation—it depends on creating environments where people can thrive.” By championing a work culture that respects work-life balance, he aims to inspire leaders across the industry to foster not just productive, but genuinely healthy, teams.

How Alexandr Hacicheant Prioritizes Engineers’ Well-Being with SRE Practices

Как Alexander Hacicheant дава приоритет на благосъстоянието на инженерите с SRE практики

আলেকজান্ডার হ্যাসিচ্যান্ট কীভাবে এসআরই অনুশীলনের সাথে ইঞ্জিনিয়ারদের সুস্থতাকে অগ্রাধিকার দেয়

Cómo Alexandr Hacicheant prioriza el bienestar de los ingenieros con prácticas de SRE

Kuinka Alexandr Hacicheant asettaa etusijalle insinöörien hyvinvoinnin SRE-käytäntöjen avulla

Comment Alexandr Hacicheant donne la priorité au bien-être des ingénieurs grâce aux pratiques SRE

Alexandr Hacicheant が SRE プラクティスでエンジニアの幸福を優先する方法

Jak Alexandr Hacicheant stawia na pierwszym miejscu dobre samopoczucie inżynierów dzięki praktykom SRE

څنګه الکساندر هیچینټ د SRE تمرینونو سره د انجینرانو هوساینې ته لومړیتوب ورکوي

Imaynatachus Alexandr Hacicheant Ingenierokunap Allin Kawsayninta SRE Ruwaykunawan ñawpaqman churan

Maitiro eAlexandr Hacicheant Anotungamira Kugarika kweInjiniya neSRE Maitiro

Ndlela leyi Alexandr Hacicheant a Rhangisaka Vuhlayiseki bya Vanjhiniyara ha yona hi Maendlelo ya SRE

Alexandr Hacicheant 如何通过 SRE 实践优先考虑工程师的福祉

Crypto Tool or Data Thief? How Meme-Token-Hunter-Bot and Its Clones Steal from macOS Users

ክሪፕቶ መሣሪያ ወይስ ዳታ ሌባ? Meme-Token-Hunter-Bot እና ክሎኖቹ ከማክሮስ ተጠቃሚዎች እንዴት እንደሚሰርቁ

¿Herramienta criptográfica o ladrón de datos? Cómo el Meme-Token-Hunter-Bot y sus clones roban a los usuarios de macOS

כלי קריפטו או גנב נתונים? כיצד Meme-Token-Hunter-Bot והשיבוטים שלו גונבים ממשתמשי macOS

暗号ツールかデータ泥棒か？Meme-Token-Hunter-BotとそのクローンがmacOSユーザーから盗む方法

Крипто алатка или крадец на податоци? Како Meme-Token-Hunter-Bot и неговите клонови крадат од корисниците на macOS

Alat Kripto atau Pencuri Data? Bagaimana Meme-Token-Hunter-Bot dan Klonnya Mencuri daripada Pengguna macOS

د کریپټو وسیله یا ډیټا چور؟ څنګه Meme-Token-Hunter-Bot او د هغې کلونونه د macOS کاروونکو څخه غلا کوي

¿Cripto Herramientachu icha Datos Suwachu? Imaynatachus Meme-Token-Hunter-Bot chaymanta Clonesnin macOS Usuariokunamanta suwakunku

Kryptoverktyg eller datatjuv? Hur Meme-Token-Hunter-Bot och dess kloner stjäl från macOS-användare

Асбоби крипто ё дузди маълумот? Чӣ тавр Meme-Token-Hunter-Bot ва клонҳои он аз корбарони macOS медузданд

Kripto Aracı mı yoksa Veri Hırsızı mı? Meme-Token-Hunter-Bot ve Klonları macOS Kullanıcılarından Nasıl Çalıyor?

Ithuluzi le-Crypto noma Isela Ledatha? Indlela i-Meme-Token-Hunter-Bot nama-Clones ayo antshontsha ngayo kubasebenzisi be-macOS

The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development.
AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs.
Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged.
Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes.
Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code.
Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers.
IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks.
Technological advancements create new roles and opportunities.
AI requires human oversight and ethical direction.
Creativity, critical thinking, and empathy remain irreplaceable.
Upskilling and adaptability are key to staying relevant.
AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future. The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. AI-powered tools 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. World Economic Forum reports World Economic Forum reports 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. USA Today report USA Today report Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future. Digital Transformation in the Age of AI, Digital Transformation in the Age of AI,

AI Isn't Here to Steal Your Job, Techies—Proven Points to Understand

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