Why hyper-personalised UX is the future – and how AI powers it

Posted on by Editorial Team

Author:  Victor Churchill, a Product Designer at Andela. He is an exceptional Product Designer with over 7 years of experience identifying and simplifying complexities with B2B, B2C and B2B2C solutions. Victor has a proven track record of conducting research analyses utilising these insights to drive business growth and impact.

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After taking the world by storm in just a few years AI has now come to change marketing and UX design. With people adapting to new advertising techniques too quickly the only way to really engage with potential customers is to create a meaningful connection on a deeper level. That’s where hyper-personalised user experiences step in.

In a world of marketing, hyper-personalised user experiences are a way to drive conversions by presenting to viewers highly relevant offers or content. At the heart of this technology lies powerful AI that crunches vast amounts of user data in order to tailor content to a specific user. To do this it goes through multiple sources of information like user behavior data (clicks, search queries, time spent on each page), demographic and profile data (age, location, language), and contextual data (device model, time of day, and browsing session length).

After gathering all the data it could collect, AI segments users into different categories based on the goals of the campaign: frequent buyers and one-time visitors, local and international shoppers, and so on. Algorithms then analyse potential ways for improving user experience. Based on the results, the software decides to prioritise one form of content or feature over the other for said user. For example, a fintech app notices that a user frequently transfers money internationally and starts prioritising currency exchange rates in their dashboard.

As Senior Product Designer at Waypoint Commodities, I always draw parallels between hyper-personalised user experiences and the way streaming platforms like Netflix and Spotify operate. These services personalise product recommendations based on the customer’s spending preferences and tastes. This way users get experiences that feel custom-made, which can dramatically increase engagement, time spent on platform, and conversion rates. A report from McKinsey revealed that 71 percent of consumers expected companies to deliver personalised interactions, and 76 percent got frustrated when it didn’t happen. The numbers are even higher if we speak about the US market, where up to 78% of customers are more likely to recommend brands with hyper-personalised interactions.

This trend is most visible in fintech and e-commerce, where user experience is critical for driving conversions, building trust, and keeping customers engaged. In these spheres additional friction such as irrelevant recommendations, or a lack of personalization can lead to lost revenue and abandoned transactions.

In order to create a hyper-personalised design it is important not to overstep. A study by Gartner revealed that poor personalisation efforts risk losing 38% of existing customers, emphasising the need for clarity and trust in personalisation strategies. The situation can backfire if users feel like they are being constantly watched. To avoid this, I always follow a few simple but essential principles when designing for personalisation.

Be transparent.

When you show something hyper-personalised to your customer, add a simple note saying ‘Suggested for you based on your recent purchases‘ or ‘Recommended for you based on your recent activity‘. This way users are informed about the channels you get information from, and your recommendations don’t come as a shock for them.

Don’t forget to leave some control to the user.

Even if you fine-tune your system to perfectly detect the needs of customers, some people can still find the recommendations irrelevant. This is why it’s important to allow customization through buttons like ‘Stop recommending this‘ and ‘Show more like this‘.

Don’t overuse personal data.

Even though sometimes it can feel like everybody is used to sharing data with advertisers, violating personal borders can usually lead to unsatisfying results. According to a survey by KPMG, 86% of consumers in the US expressed growing concerns about the privacy of their personal data. And 30% of participants said they are not willing to share any personal data at all.

Be subtle in your personalization and don’t implement invasive elements that mention past interaction too explicitly or use sensitive data. For example, don’t welcome a user with the words ‘Worried about your credit score?‘ or ‘Do you remember the shirt you checked out at 1:45 AM last night?‘.

Be clear about AI usage.

AI-driven personalisation lifts revenue by 10-15% on average, reports say. However, if the majority of the decisions in the recommendation system is made by artificial intelligence, people have a right to know that. Don’t put too much stress on it — just mention the important part with a little message saying that your suggestions are powered by AI. This way you can avoid misunderstanding.

Even though current systems already work well at detecting the needs of the customers, there’s still room for improvement. The hyper-personalised user experiences of the future could learn to read new data like voice, gestures and emotions or even anticipate needs before users even express them. It is clear that in the future AI-driven UX design will only become better, and now is the best time to embrace this technology.

How AI Detects Fraud in Banking

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What is AI fraud detection for banking?

AI in fraud detection focuses on using ML technologies to reduce fraudulent activities in the banking and financial services sector.

By leveraging data, AI models are trained to distinguish between concerning activities and normal transactions, which aids financial institutions in mitigating the chances of fraud – detecting patterns far earlier than any human agent could be capable of spotting.

To enhance decision-making processes as well as risk and fraud management, AI solutions are being integrated into new and legacy workflows within financial institutions. ML algorithms powered by AI and trained on past data are capable of recognizing and blocking transactions deemed suspicious automatically. Furthermore, AI technologies may need human agents to validate confirmed suspicious transactions by completing additional safety checks. Furthermore, AI can also employ predictive analytics to forecast the types of transactions individuals may carry out in the future and identify if the new behaviors are anomalous.

AI finance technology (fintech) can aid in safeguarding against phishing scams, identity theft, payment fraud, credit card fraud and other forms of banking fraud on an individual level. These systems mitigate losses from such fraudulent activities. 

Customer experience can be impacted with AI systems due to false positives. Regardless of the way fraudsters choose to commit financial crimes, be it through unauthorized charges or even more illicit activities like money laundering, ensuring client accounts are secure alongside abiding to regulatory compliance is the primary focus of financial institutions.

Both fintech and other financial institutions are depending on AI as a fraud mitigation tool. With constant improvement, AI mitigation service providers and leading institutions expect thwarting fraud attempts will be abetted on an unprecedented level through automation.

How AI Is Implemented For Financial Fraud Detection

AI technology provides systems the ability to perform activities such as learn, adapt, problem solve, and automate with human-like intelligence. Even though AI technologies lack human-like cognitive abilities, when dealing with well defined systems, an AI that is trained on distinct tasks can operate much quicker and at a far greater scale than humans. 

Supervised and Unsupervised Learning 

AI systems put into action for preventing banking fraud are automated to attend to well defined activities. These AI models go through a process of supervised learning whereby they are fed large amounts of specially selected data which refines the model to perform tasks. This approach helps forge models that are able to detect requisite patterns for predetermined tasks.

On the other end, unsupervised learning enables drawing inferences from past data without given training documents.

Unsupervised learning  

The gaps of supervised training models using anomaly detection techniques can be filled with unsupervised learning anomaly detection techniques. With the help of these technologies, AI models are capable of identifying previously unpredicted but still abnormal behavior patterns. AI systems that incorporate unsupervised learning capabilities can sift through data to identify potential fraud long before human analysts would consider such actions a possibility.

Both supervised and unsupervised learning enable banks to automate the verification process. AI can scan the database for known fraud patterns and trigger alerts when new and unknown patterns that suggest fraud are detected.

Various Uses Of AI Technology  

Social Media Chatbot serves as a powerful example of an application using AI technology. It is one of the most widely used bots since it uses AI technologies as customer service agents and gives basic information as per the user query. 

In the realm of customer service, there are multiple other applications which the banking sector use to incorporate AI technology for detecting and preventing fraud: 

  • Real time systems: Automated AI programs are tasked with processing vast volumes of transactions and determining account activities within different parameters in real time identification and flagging of account suspicious activities which is sometimes referred to as intelligent automated systems. 
  • Help desk operations: With the use of advanced AI algorithms, traditional human operators tasked with proactive fraud detection can now talk to LLM-based AI assistants and use natural language so they can analyze complicated policy documents and large data sets.
  • Compliance enforcement: Financial institutions are facing enormous scrutiny to remain compliant with regulations. AI technologies assist banks with policy implementation by enforcing KYC compliance through automated ID checking for errors or fraud. These technologies also assist in the enforcement of Anti Money Laundering (AML) policies by identifying and flagging accounts, behaviors, and transactions such as the transfer of the same monetary value between unrelated accounts which are linked to money laundering schemes.
  • Fraud Detection: AI technologies are beneficial for applications that involve recognition of complex patterns for anomaly detection. There are some differences in AI systems known as graph neural networks (GNN) that specialize in handling data that can be modeled in the form of graphs, such as data common in the banking sector. GNNs are capable of processing billions of records and detecting patterns within vast data sets to track and capture even the most intricate fraudulent activities.
  • Risk Evaluation: AI and machine learning models are created using risk-based data with assigned weights to estimate chances of an event occurring. They also evaluate what action to take with the highest probable outcomes. In this regard, these models can make evaluations based on transaction amounts and history, frequency, location and behavioral tendencies, making them ideal for measuring risk. AI systems are capable of estimating risk associated with specific transactions as well as the exposure involved with issuing a loan or a line of credit to fraud-prone applicants.
  • Fraud Network Identification: Suspicious relationships between entities or clusters can be analyzed through machine learning techniques like graph analysis to identify fraud networks.

Differences between AI-Powered Fraud Detection and Traditional Methods  

AI technologies are transforming how fraud is detected and secured in banking, significantly enhancing efficiencies over older techniques. Where modern systems are leaps ahead of their predecessors, they trace some of their foundations from traditional models.  

Pros of Traditional Fraud Detection Systems  

Implementation simplicity: Traditional methods rely on heuristics, making them easier to execute. For example, automatically flagging any new transactions that exceed a certain predetermined and stagnant threshold based on the account’s historical data.  

Domain Intuition: An experienced fraud analyst brings useful domain knowledge and hunches to problem-solving. There are cases where only a traditionally trained person can adjudicate the validity of certain transactions or identify a fraud attempt.

Problems Encountered in Traditional Fraud Detection Systems

Scope Problem: Traditional fraud detection systems which use heuristics tend to be static and rely on fixed patterns (if X then Y relationships). While there is some merit to heuristics, in this case, the lack of efficiency results from ignoring the many interactions within a set of complex data.

Processed Transaction Problem: The ever-increasing volume of transactions from users leads to problems in systems that were built and manually controlled by a fraud detection expert. With a rising set of every minute of everyday systems, there is an increase in unattended data that needs to be processed. You could throw money at the problem, but hiring additional staff is inefficient, both cost-wise and output-wise.

High error rate: The traditional systems used rely heavily on rules which are arbitrary, leading to very low fraud detection rates. Because the rules are so harsh and agnostic to context, even ambiguous signals of potential fraud tend to over-trigger non-responsive systems. If an account configured for zero tolerance withdrawals tries to increase its drawdown by attempting to withdraw $200, which is not more than double the “allowed limit”, this will almost certainly trigger a blockage. Although such behavior is certainly unusual, it is only unusual from the perspective of a faulty rule-based system that attempts to operate under the guise of fraud detection. In reality, such actions cannot be labeled as “suspicious and unprecedented.” If anything, a customer will only want to make a large withdrawal instead of the normal one. The end result of all this is extreme low detection rates but massive resource waste in unproductive investigations.

Fraud Detection Using AI Technology: Benefits

Recognizing patterns better: AI technology uses sophisticated algorithms to process highly detailed and intricate data. When AI systems analyze data, they spot anomalies that would otherwise go unnoticed.

Unprecedented expansion: AI systems automated transaction monitoring far beyond human capabilities. Automated AI fraud detection systems offer transaction analysis and verification on-the-go, responding instantly unlike traditional systems.

Flexibility: Adaptable Algorithms Artificial Intelligence systems are trained to execute specific tasks. Their learning does not stop after training. An active AI algorithm retrains itself to improve techniques to intercept different forms of fraud as its systems keep working.

Drawbacks of AI-based fraud detection 

Fraud detection powered by AI systems has its downfalls. First off, the model needs adequate data to draw assumptions from. In order to successfully train an AI model, vast amounts of data suffices. The data needs to be collected, made through a thorough process (synthetic data), and finally filtered. The accuracy of an AI model is successful due to well trained data.

Tougher system applicability: Integrating AI systems into pre-existing structures could pose a challenge and become a burden to work with. Initially, these systems do seem to possess a high level of complexity and are hard to deal with, while in the end, prove to respond to positive ROI in long durations.

Applications of AI for fraud detection in the banking sector  

The adoption of AI-based fraud detection systems has proven beneficial for many banks and other financial institutions. LSTM based AI models, for example, improved American Express’s fraud detection systems by 6%. PayPal AI systems also enhanced their real-time fraud detection by 10% through round-the-clock global surveillance.  

In the banking sector, practical applications of AI for fraud detection are growing rapidly. Below are some of these applications.

Crypto tracing

The anonymity of cryptocurrency makes it an easy fraudster’s target. However, the sophisticated AI tools designed to combat fraud can monitor blockchains for abnormal behaviors such as unidirectional, streamlined fund transfers and trace misplaced or illicit payments. 

Verification chatbot

Bots equipped with AI can provide customer service and conduct verification processes as well. Phishing as well as identity theft because of an obvious tell in a particular interaction can easily be picked by chatbots, hence bots can be used to fish out scammers through language and user behavior analysis.  

Ecommerce fraud detection

To protect their clients from ecommerce fraud, banks can scan the client’s activities and purchase history and cross-check that with device information such as location to note unconventional transactions and block them from going through. Moreover, algorithms and purchase history can help identify dishonest ecommerce websites so that users can be warned in advance before making purchases through disreputable stores.

Problems Encountered with AI Fraud Detection Systems in Banking

AI fraud detection technology, an innovation in itself, is now actively and dramatically changing the banking industry. What’s more, there is even greater room for further improvement. However, with AI comes the challenges that this technology may bring.

Mistakes and AI ‘hallucinations’

We know that AI algorithms are improving their efficacy daily. But like every other technology, they are imperfect. An AI model is capable of generating ‘hallucinatory’ results, results that are false or inaccurate in nature. Within banks, the damages that can arise from such inaccuracies can be solved by creating very hyper-specialized models – models aimed at performing very specific tasks. However, such models also stifle the potential value that AI brings. Hallucinations, although not common, are highly prevalent, thus turning accuracy in AI banking fraud protection critical. 

Bias in Data Set

The issue of bias has persisted even before technology was involved with science, dating long back to the earliest days of data analysis. As much work as has been done to eliminate bias and discrimination from governing lending and account protection, the issue still remains. But just as critical, creating AI models by biased designers and engineers adds risks of discrimination, making the AI prone to disabilities based on gender, race, religion or even disability.

Compliance

The considerations concerning data privacy are crucial in the banking sector. AI models need considerable data which has to be collected and handled ethically. Compliance with data privacy regulations is equally critical when it comes to the application of AI. Indeed, the pace of technological development is extremely fast which means that lawmakers and regulators will have to revisit the question of whether our legal framework is suitable for the protection of customer privacy.

15 Best Practices for Code Review in Product Engineering Teams

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A well-defined code review process within product teams is a powerful enabler for achieving high-quality software and a maintainable codebase. This allows for seamless collaboration among colleagues and an effortless interplay between various engineering disciplines.

With proper code review practices, engineering teams can produce a collaborative culture where learning happens organically, and where improvements to the code commit are welcomed not as a formality but as a step in the agile evolution journey. The importance of code review cannot be understated; however, it can be effectively addressed and underscored within the cyclic approach of the software development life cycle (SDLC) framework. This document seeks to aid teams with the provided recommended best practices to advance their review processes and product quality.

Mindbowser is one of the thought leaders in technology we turned to because they are known for their precise solutions. With years of experience integrating together insights from project work, they learn that quality code always guarantees innovative solutions and assures improved user experience.

Here at ExpertStack, we have developed a tailored list of suggestions which, when followed, enable code authors to maximize the advantages they can gain from participating in the review process. With the implementation of these suggested best practices for code reviews, organizations can cultivate a more structured environment that harnesses workforce collaboration and productive growth.  

In the remaining parts of this article, we will outline best practices to assist code authors serve their submissions to peer reviews and eloquently navigate the complex review process. We’ll provide tried-and-true methods alongside some of our newest strategies, allowing authors to learn the art of submitting reviews and integrating feedback on revisions.

What is the Role of Code Review in Software Development Success?

Enhancing Quality and Identifying Defects

A code review is a crucial step toward fixing bugs and achieving logic error goals in software development. Fixing these issues before a production-level deployment can save software developers a significant amount of money and resources since any bugs will be eliminated before the end users are affected.

Reviewers offer helpful comments which assist in refactoring the code to make it easy to read and maintain. With improved readability comes low-effort comprehensible documentation that can save fellow team members time when maintaining the codebase.

Encouraging sharing and collective learning within teams  

Through code reviews, developers learn different ways of coding and problem-solving which enhances sharing of knowledge within the team. They build upon each other’s understanding, leading to an improvement in the entire team’s proficiency.  

Furthermore, code reviews enable developers to improve their competencies and skills. Learning cultures emerge as a result of team members providing feedback and suggestions. Improvement becomes the norm, and team-wide skills begin to rise.

Identifying and Managing Compliance and Security Risks

Using code reviews to build an organization’s security posture proactively enhances identification and mitigation of security issues and threats in the software development life cycle. In addition, reviews of the software code aid in verifying that the appropriate industry standards were adhered to, thereby certifying that the software fulfills critical privacy and security obligations.

Boosting Productivity in Development Efforts

Through progressive feedback, code reviews are helpful in augmenting productivity in software development by resolving difficulties at the primary stages of development instead of erasing hard-won progress with expensive bug-fixing rounds later on in the project timeline.

Moreover, team members acquire new skills and expertise together through participation in collaborative sessions, making the development team more skilled and productive by enabling them to generate higher-quality code more rapidly thanks to shared skills cultivation.

15 Tips for Creating Code Reviews That Are More Effective

Here are some effective and useful strategies to follow when performing code reviews:

1. Do a Pre-Review Self Assessment

Complete a self-review of the code prior to submission. Fixing simple problems on your own means the reviewer can focus on the more difficult alterations, making the process more productive.

Reviewing changes helps identify oversights, and enables self-optimizing in dealing with a given problem. Utilize code review software like GitHub, Bitbucket, Azure DevOps, or Crucible to aid authors during reviews. These applications let you check the differences between the present version of your code and the most recent one.

These applications let you assess the version that is being compared, where the focus is on changes made. This mindset strengthens evaluation and improvement. Taking the self-review path with advanced recourse aids promotes collaborative and constructive code development and is almost non negotiable for a DevOps culture.

2. Look at the Changes Incrementally  

As review size increases, the value of feedback also decreases in proportion. Conducting reviews across huge swathes of code is quite challenging from both an attention and time perspective; the reviewer is likely to miss detail alongside potential problems. In addition, the risk of review delays may stagnate the work.  

You should try to think of reworking a whole codebase as an iterative process instead. A good example of this is when the code authors submit proposals for new features centered around a module; these can be submitted in the form of smaller review requests for better focus. The advantages of this approach are simply too good to be passed upon.  

The approach provides maximum attention and it becomes much simpler to discover useful feedback. In addition, the work becomes easy and relevant to the developer’s skill level, meaning incorporation becomes much easier. Finally, it reduces the chances of bugs in a simplified modular codebase while paving the way for simpler updates and maintenance down the line.

3. Triage the Interconnected Modifications  

The submission of numerous modifications in a single code review can be overwhelming for the reviewers, making it difficult for them to give detailed and insightful feedback. This type of review exhaustion compounds deconstructive large code reviews with unrelated modifications, providing suboptimal feedback laced with inefficiency.

Nevertheless, addressing this challenge is possible through grouping-related changes. Structuring the modifications by purpose helps in organizing the review to be manageable in scope and focus. Concentrated context enables reviewers to get the required situational awareness, thereby making the feedback more useful and constructive. In addition, concentrated purposive reviews can be easily assimilated into the main codebase thereby facilitating smoother development.

4. Add Explanations

Invest time crafting descriptions by providing precise and comprehensive explanations for the code modifications that are being submitted for review. Commenting or annotating code helps capture its intent, functioning, and the reasoning behind its modifications, aiding reviewers in understanding its purpose.

Following this code review best practice streamlines the code review workflow, improves the overall quality and usefulness of feedback received, and increases engagement rates in regard to code reviews. Interestingly, multiple studies showed that reviewers appreciate a description of the code changes and want people to include descriptions more when requested to submit code for review.

Illustrate the elements simply but provide surrounding context related to the problem or task the changes try to resolve. This provides an impression of the problem resolving the concern. Describe how the modification will resolve the concern and mention how it will impact other components or functions as a cue to flag dependencies or regressions to the reviewers. Add information in regards to other documents, resources, or tickets.

5. Perform Comprehensive Evaluation Tests

For tests, verify your changes to the code with the necessary tests before submitting them for evaluation. It tends to be counterproductive both to the reviewer and the author if broken code is sent for evaluation. Validation of change helps verify if the change is working optimally so that everything is working perfectly. This has resulted in a drop in production defects which is the purpose of  test driven code reviews.

Automated unit tests should be incorporated that will run on their own during the review of the code. Also execute regression tests to confirm the processes functions as required without introducing new problems. For essential parts or changes that are sensitive to performance, do not forget to carry out performance tests in the course of the code review.

6. Automated Code Reviews

In comparison to automated code review, a manual code review may take longer to complete due to human involvement in the evaluation process. In big projects or those with limited manpower, there may be bottlenecks within the code review process. The development timeline might be extended due to unnecessary wait times or red tape.  

Using a tool such as Codegrip for code review automation allows for real-time feedback as well as coherency within the review processes collaboration automation accelerates responses and streamlines reviews. Grade-A automated tools ensure TM-perfection through speed; they check for grade B issues and self-resolve, leaving loopholes for experts to sort the complex grade-A problems.

Using style checkers, automated static analysis tools, and syntax analyzers can improve the quality of the code. This allows you to ensure that reviewers do not spend time commenting on issues that can be resolved automatically, which enables them to provide important insights. In turn, this will simplify the code review process, which fosters more meaningful collaboration between team members.  

Use automated practices which verify compliance with accepted industry standards and internal policies on coding. Use style guidelines specific code formatting software that automatically enforces uniform styling on the code. Add automated verification for defined unit tests triggered during the code review which checks the code change’s functionality.  

Set up Continuous Integration (CI) that uses automated code review processes embedded within the development workflow. CI guarantees that every code change goes through an automated evaluation prior to integration.

7. Fine-Tune Your Code Review Process by Selectively Skipping Reviews

The process of reviewing every single code piece developed by an employee juxtaposes the unique workflow of each company and can quickly gather momentum into a time intensive avalanche of redundancy slamming productivity. Depending on the structure of an organization, skipping certain code reviews may be acceptable. The guideline to disregard code reviews pertains exclusively to trivial alterations that won’t affect any logical operations. These include up-vote comments, basic formatting changes, superficial adjustments, and renaming inline variables.

More significant changes or alterations still require a review to uphold the quality of the code and to guarantee that all concerns are fixed prior to releasing potential hazards.

Set up objectives and rules around the specific criteria that will be established guiding code review bypassing. Use a grade scale to administer a risk-based code review system. Striking a review balance on complicated or pivotal code changes should take precedence over low complexity or straightforward changes. Establish limits or thresholds concerning the scale of modification, impact, or size that will require mandatory code reviews.

Presumably, any minor updates that fall below the designated threshold can be deemed exempt. While having the flexibility not conducting formal reviews, there should always be sufficient counterbalancing measures in place to ensure that there isn’t a steady stream of bypasses resulting in formal review chaos.

8. Optimize Code Reviews Using A Smaller Team Of Reviewers

Choose an optimal number of reviewers based on your code modification. The right number of reviewers is necessary; having too many can be an issue since the review could become disjointed due to little accountability. Too many code reviewers can slow workflow efficiency, communication, and productivity.

Narrowing down the reviewer list to a select few who are knowledgeable fosters precision and agility during the review process without compromising on quality.

Limit participation to those with requisite qualifications as regards the code and the changes undertaken, including knowledge of the codebase. Break down bigger teams into smaller focused teams based on modules or fields of specialization. Focused groups can manage reviews within their designated specialties.

Allow all qualified team members to be lead reviewers but set boundaries that encourage rotation to prevent review burnout. Every team member should be designated to be a lead reviewer at some time. The only role is to plan the review and merge the input for the review.

9. Clarify Expectations

There’s less confusion and better productivity when everyone knows what’s expected in a code review; developers and people reviewing the code are more productive when every aspect of the order is well understood. The overall code review’s effectiveness may be compromised with unclear expectations. Helping reviewers set firm expectations streamlined priority-based task completion and boosted overall speed for the process.

It’s vital to set and communicate expectations before the review begins, such as setting objectives for what a reviewer should achieve beyond simply looking at the code. Along with those goals, set expectations on how long the review would take. Having an estimated range will allow for the boundaries of the review to be set as well as noting which portions of the code are evaluated and which ones need the most focus. 

State if the reviews are scheduled for FP (feature based), sprints, or after important changes are made to code.

Providing review authors and reviewers instruction together with defined objectives aids in reaching common goals around process productivity, along with providing proper guidance towards steps needed to work towards successful completion. Clear guidance on intended outcomes fosters better defined goals for the process which can be shared with all participants leading to sensible improvements and concrete actions, and thereby strengthening outcomes with good suggestions.

10. Add Experienced Reviewers  

The effectiveness of code review is always different due to the knowledge and experience level of the specific reviewers. The review process without experienced reviewers will not be impactful as many crucial details will be missed due to the lack of informed insights. A better rate of recognition of errors improves the standard of code.  

Pick reviewers who have expertise in the area affiliated with the modifications. Have seasoned developers instruct and lead review sessions for junior team members so they learn and improve. Bring senior developers and technical leads for critical and complex reviews so that their insights can be used..  

Allow developers from other teams or different projects to join in on the review process because they will bring a distinct perspective. The inclusion of expert reviewers will permit shifts in the quality of responses given to the developers. Their insights are instrumental as they will tell the developer where vague problems exist, thus enforcing change.

11. Promote Learning

Make sure you involve junior reviewers in the code review process, as it fosters training and learning. Think about putting reviewers who are not familiar with the code to benefit from the review feedback. Code reviews are important from a learning perspective and without some form of motivation are often ignored.

If there is no effort aimed at learning, developers risk overlooking opportunities to gain fresh insights, adopt better industry practices, be more skilled, and advance professionally.

Ask reviewers to give better feedback with useful explanations of industry best practices, alternative methods, and gaps that can be closed. Plan to encourage discussions or presentations about knowledge that needs to be shared. More competent team members can actively mentor the less competent ones.

12. Alert Specific Stakeholders  

Notifying key stakeholders like managers, team members, and team leads regarding the review process helps maintain transparency during development. Often, including too many people in the review notifications causes chaos because reviewers have to waste time figuring out whether the code review is relevant to them.  

Identify stakeholders that need to be notified about the review process and manage expectations as to where reviewers decide whether to notify testers or just provide updates. Utilize tools that allow setting relevant roles for stakeholders and automate notifications via emails or texts.  

Do not send notifications to everyone or scope hands, rather, limit the scope to those who actually benefit from the information at hand.

13. Submit an Advance Request  

Effective scheduling of code reviews helps mitigate any possible bottlenecks in the development workflow. Review requests that are not planned may pose a challenge to reviewers since they may not have ample time to conduct a detailed analysis of the code.

Reviewers receive automatic alerts about the pending reviews well in advance which allocates specific time to their schedules for evaluation. When coding within a large team on intricate features, adjust your calendar for frequent check-in dates.  

Elaborate on the timeframes of the code review to maximize efficiency and eliminate lag time. Investigate if it’s possible to implement review queues. Review queues allow reviewers to select code reviews depending on their schedule. Establish a review structure that increases predictability, benefitting both coders and reviewers.  

Even during the time-sensitive review requests for critical coding that requires priority scrutiny, framework and structure are essential.

14. Accept Reviews to Synergize and Improve Further

Things like additional or different review comments tend to make many people uncomfortable due to how strange they may appear. Teams might become protective and ignore suggestions, which causes blockers to improve efforts.

Accepting feedback with an open mindset allows for code quality change to foster collaboration within the team and culture improves over time. Code feedback acceptance positivity by teams lead to increase in morale and job satisfaction as well as 20% code quality improvement which was noticed by one researcher.

Stay open to reviewer suggestions plus their reasoning, and to the points they put forth because they are worth dropping attempts to increase the code quality instead. Talk to reviewers about their suggestions or comments with the aim of clarification where needed. 

Assist reviewers to sustain coded quality of their feedback and seek suggestions from impacted individuals to actively look to make posed suggestive change result maintaining high as gratitude.

15. Thank Contributors for In-Depth Review of Code Critiques

Reviewers often feel demotivated for putting time into the review and feedback process. If appreciated, it motivates them to continue engaging with the review process. Expressing thanks to reviewers not only motivates them but also helps cultivate a positive culture and willingness to engage with feedback.

Concisely, express thanks in team meetings to the respective reviewers or send a dedicated thank you to the group. Inform all of the team members to notify the reviewers on the feedback implementation after the actions and decisions are made regarding the feedback. As a form of gratitude for their hard work, periodically award small tokens of appreciation to the reviewers.

AI in Cybersecurity: Principles, Mitigation Frameworks, and Emerging Developments

Posted on by Editorial Team

What is AI in Cybersecurity?

The use of intelligent algorithms and multitasking models to AI determining the cyber threat scenarios deals with electronic warfare intelligently. It is called Artificial Intelligence (AI). The sophisticated cybersecurity frameworks powered by AI are not only capable of preemptively analyzing and responding within split seconds but also detecting massive volumes of incoming data, categorizing relevant information, and sifting through troves of data.

AI’s capabilities alongside responding to other security measures is Supporting Measures can be understood in the following ways. Processing tasks such as log review and vulnerability scans can be executed with ease. With AI, the cybersecurity personnel can focus on more complex tasks as they are provided with agile bots who are able to take care of time level, strategy deployment, and simulation plans. Real time attack alerts, AI’s role in automation plays an important role in threat detection also with advanced detection AI systems, threats can be dealt with in real time. Quieter and emergency response solutions can be set. In addition, the evolving nature of threats enables AI systems to be adaptable.

AI in cybersecurity boosts vulnerability management and reinforces the ability to counter emerging cyber attacks. Real-time monitoring and proactive readiness helps mitigate damages, AI technologies shift through behavioral patterns and automates phishing detection and monitoring. AI learns from previous changes and identifies emerging bases to emerging bases, thus enhancing defensive posture and claiming the sensitive information.

How Can AI Assist in Avoiding Cyberattacks?

AI in cybersecurity enhances cyber threat intelligence and allows security professionals to:

  • Look for signs of looming cyberattack
  • Improve their cyber defenses
  • Examine usage data like fingerprints, keystrokes, and voices to confirm user identity
  • Uncover evidence  – or clues – about specific cyber attackers and their true identity

Is Automating Cybersecurity a Risk?

Currently, monitoring systems require more human resources than necessary. AI technology can assist in this area and greatly improves multitasking capabilities. Using AI to track threats will optimize time management for organizations under constant pressure to identify new threats, further enhancing their capabilities. This is especially important in light of modern cyberattacks becoming more sophisticated. 

The information security field sits on a treasure trove of prior cases in automation technology, which have made ample use of AI elsewhere in business operations. Thus there is no danger in using AI for automating cybersecurity. For instance, in automating the onboarding process, Human Resources grant new employees access to company assets and provide them the resources requisite to execute their roles using sophisticated software tools. 

AI solutions allow companies with limited numbers of expert security personnel to maximize their expenditures on cybersecurity through automation. Organizations can now fortify their operations and improve efficiency without having to find qualified skilled personnel.

The advantages of implementing AI automation in cybersecurity are:

  • Saving on costs: The integration of AI technology with cybersecurity enables the faster collection of data which aids in the incident response management, making it more agile. Furthermore, the need for security personnel to perform monotonous manual work is eliminated, allowing them to engage in more strategic tasks that are advantageous to the company. 
  • Elimination of oversight: A common weakness of conventional security systems is the reliance on an operator which is always prone to error. AI technology in cybersecurity eliminates most of the security processes that require intervention by people. Resources that are truly in demand can then be allocated where they are needed most, resulting in superior outcomes.
  • Improved strategic thinking: Automated systems in cybersecurity assist an organization in pinpointing gaps in its security policies and rectifying them. This allows the establishment of procedures aimed at achieving a more secure IT infrastructure.  

Despite all of this, organizations must understand that cybercriminals adapt their tactics to counter new AI-powered cybersecurity measures. Cybercriminals use AI to launch sophisticated and novel attacks and introduce next-generation malware designed to compromise both traditional systems and those fortified with AI.

The Role of AI in Cybersecurity

1. Password safeguards and user authentication  

Cybersecurity AI implements advanced protective measures for safeguarding passwords and securing user accounts through effective authentication processes. Logging in using web accounts is commonplace nowadays, especially for users who wish to obtain products or for those who want to submit sensitive information using forms. These online accounts need to be protected using sophisticated authentication mechanisms to ensure sensitive information does not fall into the wrong hands.  

Automated validation systems using AI technologies such as CAPTCHA, Facial Recognition, and Fingerprint Scanners allow organizations to confirm whether a user trying to access a service is actually the account owner. These systems counter cybercrime techniques like brute-force attacks and credential stuffing which could otherwise jeopardize the entire network of an organization.

2. Measures to Detect and Prevent Phishing 

Phishing shows up on the business risk radar as a threat that many industries have to deal with, which makes them susceptible within any business. AI has the ability to help firms discover malice and determine anomalies in messages through email security solutions. It has the ability to analyze emails both in context and content to determine in a fraction of time whether they are spam, phishing masquerades or genuine emails. AI makes identifying signs of phishing fast and easy through spoofing, forged senders and domain name misspellings.

Understanding how users communicate, their typical behavior, and the wording that they use becomes easier for the AI that has already gotten past the ML algorithm techniques training period. An advanced spear phishing threat is more challenging to tackle, as the attackers impersonate high-profile companies such as company CEO’s, and it becomes critical how you prevent it. To stop the access of leading corporate account incursion, AI has the ability to identify irregularities in user activity that can cause such damage, and thereby suppress possibilities of spear phishing.

3. Understanding Vulnerability Management 

Each year, newly discovered vulnerabilities are on the rise because of the smarter ways cybercriminals use to hack. With the high volume of new vulnerabilities everyday, businesses struggle to use their traditional systems to keep high risk threats at bay. 

UEBA (User and Entity Behavior Analytics), an AI-driven security solution, allows businesses to monitor the activities of users, devices, and servers. This enables detection of abnormal activities which can be potential zero day attacks. AI in cybersecurity gives businesses the ability to defend themselves from unpatched vulnerabilities, long before they are officially reported and patched.

4. Network Security

Network security requires the creation of policies and understanding the network’s topography, both of which are time-intensive processes. An organization can enact processes for allowing connections that are easily verified as legitimate and scrutinizing those that require deeper inspection for possible malice after policies are set. Organizations can also implement and enforce a zero trust approach to security due to the existence of these policies.  

On the other hand, policies across different networks need to be created and managed, which is manual and very time-consuming. Lack of proper naming conventions for applications and workloads means that security teams would spend considerable time figuring out which workloads are tied to specific applications. Over time, AI is capable of learning an organization’s network traffic patterns, enabling it to recommend relevant policies and workloads.

5. Analyzing actions

Analyzing actions allows firms to detect emerging risks alongside recognized weaknesses. Older methods of threat detection monitoring security perimeters with attack patterns and compromise indicators are inefficient due to the ever-growing amount of attacks launched by cyber criminals each year.  

To bolster an organization’s threat hunting capabilities, behavioral analytics can be implemented. It processes massive amounts of user and device information by creating profiles of applications with AI models which operate on the firm’s network. Such profiles enable firms to analyze incoming data and detect activities that can be harmful.

Leading Cybersecurity Tools Enhanced by AI Technology  

The application of AI technology is now commonplace in various cybersecurity tools to boost their efficient defensive capabilities. These include:  

1. AI-Enhanced Endpoint Security Tools  

These tools help prevent malware, ransomware, and other malicious activity by using AI to detect and mitigate threats on laptops, desktops, and mobile phones.  

2. AI Integrated NGFW  

AI technologies into Next-Generation Firewalls (NGFW) increase their capabilities in threat detection, intrusion prevention, and application control safeguarding the network.  

3. SIEM AI Solutions  

The AI-based SIEM solutions help contextualize multiple security logs and events, making it easy for security teams to streamline threat detection, investigation, and response which traditionally would take longer.  

4. AI-Enhanced Cloud Security Solutions  

These tools use AI to enforce protective measures on data and applications hosted in the cloud, ensuring safety, compliance and data sovereignty.  

5. AI Enhanced Cyber Threat Detection NDR Solutions  

Cyber Threat Detection NDR Solutions that have AI abilities enabled monitor network traffic for sophisticated threats to ensure efficient response inline with network security policies.

The Upcoming Trends Of AI In Cybersecurity  

The use of technologies such as machine learning and AI are increasingly pivotal in dealing with threats in cyber security. This is mainly because cybernetic technologies are capable of learning aid functions from any pieces of information fed to them. More so, the steps and measures put in place need to make sure they have adapted to the unique challenges brought in by new vulnerabilities.

How To Implement Generative Artificial Intelligence In Cybersecurity  

Modern companies are adopting generative Technology and AI systems to strengthen existing cybersecurity plans. The use of generative technology mitigates risks by creating new data while ensuring the existing data is preserved.  

  • Effective Testing Of Cybersecurity Systems: Generative technologies can be used by organizations to create and simulate a variety of new data which can be used in testing incident response plans  and different classes of cyber attack defense strategies. Identifying system deficiencies through prior testing greatly increases a firm’s preparedness in case a real attack is launched.  
  • Anticipating Attacks Through Historical Data: Previous historical data containing attack and response tactics can be used to generate predictive strategies through the use of generative AI. These custom-built models are tailored to the unique requirements of a given firm aiding the firm stay a step ahead aloof from malicious hackers. 
  • Providing Advanced Security Techniques: Augmenting the current mechanisms for threat detection by applying predictive analysis for the creation of hypothetical scenarios that mimic real offense strategies improves a model’s ability to detect real life cases while flagging even the faintest and newest suspicious activities.

Generative AI is powerful in the modern-day battleground of technology in fighting cyber threats. Its ability to simulate situations, foresee possible attacks, and increase threat detection helps defenders of an organization be one step ahead of danger.

Advantages of Artificial Intelligence (AI) in the Mitigation of Cyber Risks

Adopting AI tools in cybersecurity offers organizations enormous capabilities intended to help in risk management. Some of the advantages include: 

Continuous education: AI learning is one of its powerful features. Technologies such as deep learning and ML provide AI the means to understand the existing normal operations and detect deviations from the norm which are so neural and malignant behaviors. AI technology makes it increasingly challenging for hackers to circumvent an organization’s defenses which increases the level of ongoing learning on the systems. 

Identifying undiscovered risks: Threats that are unknown can be detrimental to any given organization. With the introduction of AI, all mapped risks together with the ones that have not been identified can be subsequently addressed before said risks become an issue, which provide a remedy to these security gaps that software providers have yet to patch.

Vast volumes of data: AI systems are capable of deciphering and understanding large volumes of data people in the security profession may not be able to comprehend. As a result, organizations are able to automatically detect new sophisticated threats hidden within enormous datasets and amounts of traffic.

Improved vulnerability management: Besides detecting new threats, AI technology allows many organizations to improve the management of their vulnerabilities. It enables more effective assessment of systems, enhances problem-solving, and improves decision-making processes. AI technology can also locate gaps within networks and systems so that organizations can focus on the most critical security tasks.

Enhanced overall security posture: The cumulative risks posed by a range of threats from Denial of Service (DoS) and phishing attacks to ransomware are quite complex and require constant attention. Manually controlling these risks is very tedious. With AI, organizations are now able to issue real-time alerts for various types of attacks and efficiently mitigate risks. 

Better detection and response: AI in Cyber Security aids in the swift detection of untrusted data and with more systematic and immediate responses to new threats, aids in protection of the data and networks. Cyber Security Systems powered by AI enables faster detection of threats, thus improving the systemic reaction to emerging dangers.

IT vs OT Cybersecurity

Posted on by Editorial Team

Defining Operational Technology (OT)

Operational technology (OT) refers to the use of software and hardware to control and maintain processes within industries. OT supervises specialized systems, also termed as high-tech specialist systems, in sectors such as power generation, manufacturing, oil and gas, robotics, telecommunication, waste management, and water control.  

One of the most common types of OT is industrial control systems (ICS). ICS are used to control and monitor industrial processes and integrate real-time data gathering and analysis systems, like SCADA systems. These systems often employ PLCs, which control and monitor devices like productivity counters, temperature sensors, and automatic machines using data from various sensors or devices.  

Overall access to OT devices is best limited to small organizational units and teams. Due to the specialized nature of OT, it often operates on tailored software rather than generic Windows OS.  

Safeguarding the OT domain employs SIEM solutions for real-time application and network activity oversight, event security, application monitoring, and even advanced firewalls which manage influx and outflux traffic to the main control network.

Defining Information Technology (IT)  

Technology is a field that involves the creation, administration and use of the hardware and software systems, networks, as well as the computer utilities. Nowadays, the application of IT is essential to automations in business processes as it facilitates communication and interaction between human beings and systems as well as between various machines.  

IT can be narrowed down to three core focuses:  

  • Operations: Routine supervision and administration of the IT departments which has their issues ranging from hardware and network support to application and system security support auditing to technical support help desk services.  
  • Infrastructure maintenance: Setting up and maintaining infrastructure equipment which includes cabling, portable computers, voice telephone and telephone systems as well as physical servers.  
  • Governance: This deals with aligning the information technology policies and the services with the IT needs of the organization and with its demand.

The Importance of Cybersecurity in OT and IT

Both operational technology (OT) and information technology (IT) focus on the security of devices, networks, systems, and users.  

In IT, cybersecurity protects data, enables secure user logins, and manages potential cyber threats. Similarly, OT systems also require cybersecurity in place to safeguard critical infrastructures and mitigates the risk of unanticipated delays. Manufacturing plants, power plants, and water supply systems rely heavily on continuous uptime, and any unexpected pauses can cost unexpected downtime.  

The security needs become vital with increased interconnectivity of these systems. New cybercriminal exploits are continuously emerging, permitting access to industrial networks. Increased attempts to breach these systems are rising; more than ninety percent of organizations operating OT systems reported experiencing at least one significant security breach within two years of deployment, according to a Ponemon Institute study. Additionally, over fifty percent of these organizations reported their OT system infrastructure sustained cyber-attacks causing the equipment or plant to go offline.  

The World Economic Forum classifies cyber-attacks involving OT systems and critical infrastructures as one of the five major threats to global risks, next to climate change, geopolitical tensions, and natural disasters.

OT Security vs IT Security: An Overview  

The distinction between OT security and IT security is becoming increasingly vague as OT systems introduce connected devices, and due to the rise of IoT (Internet of Things) and IIoT (Industrial Internet of Things) which interlinks the devices, machines, and sensors sharing real-time information within enterprises.  

As with everything in cybersecurity, there are unique differentiations of concerns to IT security and OT security. These differ from the systems in question to the risks at hand.

Differences Between OT and IT Cybersecurity  

There are marked differences in OT and IT. Firstly, OT systems are autonomous, self-contained, isolated, and run on proprietary software. Whereas, IT systems are connected, do not possess autonomy, and usually operate on iOS and Windows.  

1. Operational Environment  

IT and OT cybersecurity have differences in operational regions. OT cybersecurity protects industrial environments known to incorporate tooling, PLCs, and intercommunication using industrial protocols. OT systems are not built on standard operating systems, and most lack traditional security hardware and software. They are heterogeneously programmed unlike most computers.   

On the other hand, IT cybersecurity safeguards peripherals like desktops, laptops, PC speakers, desktop printers, and mobile phones. It protects environments like the cloud and servers using bespoke antivirus and firewall solutions. Communication protocols used include HTTP, RDP, and SSH.

2. Safety vs Confidentiality  

Confidentiality and safety are two distinctive sectors of an organization’s IT and OT Security Practices. Information Technology (IT) security concentrates more on confidentiality of information transmitted by the organization. OT cyber security focuses on protecting critical equipment and processes. The automation systems in any industry demand high attention supervision to avoid breakdown and maintain operational availability.  

3. Destruction vs. frequency  

There is a cyber security focus which sets up protection against different types of security incidents. Cyber security for OT (Operational Technology) is designed to safeguard against catastrophic incidents. The OT systems usually have limited access points. The consequence of a breach, however, is severe. Even minor incidents have the potential to cause widespread devastation; for instance, plunging an entire nation into a power outage or contaminating water systems.  

Unlike OT, IT systems have numerous gateways and touchpoints because of the internet, all of which can be exploited by cyber criminals. This presents an abundance of security risks and vulnerabilities.

4. Frequency of Patching

Both OT and IT systems differ significantly. Furthermore, their patching requirements also differ greatly. Due to the specialized nature of OT networks, they are patched infrequently; doing so typically means a full stop of production workflow. Because of this, not all components need to be updated, which allows components to operate with unpatched vulnerabilities along with an increased risk of a successful exploit. 

In contrast, IT components undergo rapid changes in technology, requiring frequent updates. IT vendors often have set dates for patches and providers like Apple and Microsoft update their software systems periodically to bring their clients to current versions.

Overlapping Characteristics of OT and IT Cybersecurity

Although they are fundamentally different, IT vs OT Cyber Security both relate to the ever-emerging convergence of both worlds.

OT devices were secured previously by keeping them offline and only accessible to employees through internal networks. Recently, IT systems have been able to control and monitor OT systems, interfacing them remotely over the internet. This helps organizations to more easily operate and monitor the performance of components in ICS devices, enabling proactive replacement of components before extensive damage occurs.

IT is also very important for providing the real-time status of OT systems and correcting errors instantaneously. This mitigates safety industrial risks and resolves OT problems before they impact an entire plant or manufacturing system.

Why IT And OT Collaboration Is Important

The integration of ICS into an organization enhances efficiency and safety; however, it elevates the importance of IT vs. OT security collaboration. The absence of adequate cybersecurity in OT systems poses risks of cyber threats as organizations increase the levels of connectivity. This is especially true in today’s cyberspace where hackers develop sophisticated methods for exploiting system vulnerabilities and bypassing security defences.

IT security can mitigate OT vulnerabilities by using its own systems for monitoring cyber threats as well as the mitigation strategies deployed to them. In addition, the integration of OT systems brings a reliance on baseline IT security controls due to the need to minimize the impacts of attacks.

IT Sector Sees Mass Layoffs as Automation and Profitability Pressures Mount

Posted on by Editorial Team

The global IT industry is undergoing significant workforce reductions, with over 52,000 employees laid off in the first months of 2025 alone. According to Layoff.fyi – which tracks publicly reported job cuts across 123 technology companies – nearly 25,000 of those layoffs occurred in April 2025.

Intel has announced plans for the year’s largest downsizing: cutting 20% of its workforce, or roughly 22,000 positions, out of approximately 109,000 employees worldwide. This move echoes a broader pattern of layoffs that began in mid-2024, when more than 25,000 IT workers lost their jobs in August 2024 and 34,000 in January 2024. Over all of 2024, the industry averaged about 12,700 layoffs per month, compared to 22,000 monthly cuts in 2023.

Normalization, Not Decline, Experts Say


Analysts describe the trend as a “normalization” of employment levels rather than evidence of an industry downturn. They note that a surge of investor funding in recent years fueled rapid hiring – often outpacing companies’ ability to turn a profit. As unprofitable ventures folded or restructured, staff were inevitably released back into the labor market.

Automation’s Growing Role


Approximately 30% of these layoffs are attributed to the swift advancement of automation technologies – beyond just AI. For instance, automated design tools now enable individual designers to build and maintain websites that once required entire teams of developers. As these tools become more capable and widespread, the demand for certain roles continues to shrink, reshaping the IT workforce landscape.

Zuckerberg Predicts AI Will Replace Mid-Level Developers in 2025

Posted on by Editorial Team

Meta CEO Mark Zuckerberg believes artificial intelligence is quickly advancing to the point where it can handle the work typically done by mid-level software developers – potentially within the year.

Speaking on The Joe Rogan Experience podcast, Zuckerberg noted that Meta and other major tech companies are developing AI systems capable of coding at a mid-tier engineer’s level. However, he acknowledged current limitations, such as AI occasionally generating incorrect or misleading code – commonly known as “hallucinations.”

Other tech leaders are equally optimistic. Y Combinator CEO Garry Tan has praised the rise of “vibe coding,” where small teams leverage large language models to build complex apps that once needed large engineering teams.

Shopify CEO Tobi Lütke has gone as far as requiring managers to justify new hires if AI could perform the same tasks more efficiently. Anthropic co-founder Dario Amodei has made a bold prediction: within a year, AI will be capable of writing nearly all code.

At Google, CEO Sundar Pichai recently revealed that over 25% of new code is now AI-generated. Microsoft CEO Satya Nadella reported a similar trend, with a third of the company’s code produced by AI.

Despite the enthusiasm, some experts urge caution. Cambridge University AI researcher Harry Law warns that over-reliance on AI for coding could hinder learning, make debugging harder, and introduce security risks without proper human oversight.

LinkedIn Replaces Keywords With AI, Enhancing Job Search Efficiency

Posted on by Editorial Team

LinkedIn has unveiled a transformative update to its job search functionality, phasing out traditional keyword-based searches in favour of an advanced AI-driven system. This shift promises to deliver more precise job matches by leveraging natural language processing, fundamentally changing how job seekers and employers connect.

AI-Powered Job Matching

Gone are the days of rigid keyword searches. LinkedIn’s new AI system dives deeper into job descriptions, candidate profiles, and skill sets to provide highly relevant matches. According to Rohan Rajiv, LinkedIn’s Product Manager, the platform now interprets natural language queries with greater sophistication, enabling users to search with conversational phrases rather than specific job titles or skills.

For instance, job seekers can now input queries like “remote software engineering roles in fintech” or “creative marketing jobs in sustainable fashion” and receive tailored results. This intuitive approach eliminates the need to guess exact keywords, making the process more accessible and efficient.

Enhanced Features for Job Seekers

The update introduces several user-centric features designed to streamline the job search experience:

  • Conversational Search: Users can describe their desired role in natural language, and the AI will interpret and match based on context, skills, and preferences.
  • Application Transparency: LinkedIn now displays indicators when a company is actively reviewing applications, helping candidates prioritise opportunities with higher response potential.
  • Premium Perks: Premium subscribers gain access to AI-powered tools, including interview preparation, mock Q&A sessions, and personalised presentation tips to boost confidence and performance.

A New Era of Job Search Philosophy

LinkedIn’s overhaul reflects a broader mission to redefine job searching. With job seekers outpacing available roles, mass applications have overwhelmed recruiters. The platform’s AI aims to cut through the noise by guiding candidates toward roles that align closely with their skills and aspirations, fostering quality over quantity.

“AI isn’t a magic fix for employment challenges, but it’s a step toward smarter, more meaningful connections,” Rajiv said. By focusing on precision matching, LinkedIn hopes to reduce application fatigue and improve outcomes for both candidates and employers.

Global Rollout and Future Plans

Currently, the AI-driven job search is available only in English, but LinkedIn has ambitious plans to expand to additional languages and markets. The company is also exploring further AI integrations to enhance profile optimisation and career coaching features.

This update marks a significant leap toward a more intelligent, user-friendly job search ecosystem, positioning LinkedIn as a leader in leveraging AI to bridge the gap between talent and opportunity.

The Future of Blockchain Efficiency: Maximising Throughput Using Rollups

Posted on by Editorial Team

Author: Asutosh Mourya is Engineering Manager at Trili Tech – a blockchain research and development hub focused on the open-source Tezos blockchain.

Blockchain technologies are already part of our daily lives, even if we’re not consciously aware of them. They are applied in such areas as secure online transactions, supply chain management, identity verification, and even in creating and trading digital assets like non-fungible tokens (NFTs). Understanding how exactly blockchain works can shed light on its practical implications for enhancing efficiency and transparency in different aspects of our daily interactions.

In the article, I will share my views on how to maximise blockchain efficiency using rollups. Join me on this journey.

What is Blockchain

In terms of functionality, blockchain can be described as a series of data blocks linked in an uneditable digital chain. These blocks are stored in a decentralised environment, where each block’s information is verifiable by all participating computers. The decentralised structure ensures trust, validity, and usability, departing from traditional hierarchical systems.

Simply put you can think of a blockchain as a digital necklace made up of individual beads, with each bead representing a piece of information. These beads are linked together to form the necklace, just like blocks are linked to form the chain. Each bead holds details about transactions, who sent money and when it was sent.

In a blockchain, blocks not only contain transaction data but also a crucial element known as a hash. These cryptographic hash functions are fundamental to the blockchain’s operation. Hashes are represented by a unique series of characters like:

X23G9K1H4P8Q6L2V5

They act as the digital signature for a block, generated from its data. A key feature is that each block includes the hash of the previous block, forming a linked chain. This interconnectedness through hashes ensures the integrity of the blockchain network. Any attempt to modify the content within a block would alter the hash, signalling potential tampering to the network.

The incorporation of hashes creates a self-regulated network in the blockchain, eliminating the need for intermediaries. This design prevents third parties from monitoring or interfering with transactions, bolstering the security and reliability of the system.

What Makes Blockchain Special

Blockchains set themselves apart from other digital databases in distinct ways. Firstly, they operate as distributed databases, spreading data across multiple servers situated in various physical locations. This decentralisation enhances reliability, performance, and transparency compared to traditional databases. 

What is more, blockchains employ open-source software, allowing the entire network community to scrutinise the underlying code collaboratively. This transparency facilitates the detection and resolution of bugs, glitches, or flaws. 

Notably, once verified, new information can only be added to the blockchain; it cannot be altered. Security and trustworthiness are upheld by requiring majority consensus from network participants, promoting a shared responsibility model instead of relying on a single, central entity.

Current Limitations 

Though blockchain is getting widely adopted, it faces some challenges. One of them is scalability. In blockchain, it means handling more transactions without making things slower or less secure. To understand the grounds of this problem we need to get acquainted with the following concepts.

Block Time

This term refers to the average time it takes for a new block to be added to the blockchain. Different blockchain networks have varying block times. For example, Bitcoin has a block time of around 10 minutes, while Ethereum aims for a shorter block time of around 15 seconds. The challenge is to strike a balance: a shorter block time can lead to faster transactions, but it may also increase the likelihood of forks (divergent branches in the blockchain) and reduce security.

Transactions per Second

This metric indicates the number of transactions a blockchain can process in one second. The TPS varies widely among different blockchain networks. Traditional payment systems like Visa can handle thousands of transactions per second, whereas many blockchain networks, especially those focused on decentralisation and security, may have lower TPS.

For Bitcoin, the most used blockchain, the problem is its small 1MB block size, limiting the number of transactions and making fees higher. They suggested a solution called Segregated Witness (SegWit), but it’s not widely adopted yet. Ethereum, another big blockchain, has a 15-second block time, which is faster but also limits transactions per block. They plan to fix this with Ethereum 2.0, using Proof of Stake (PoS) and sharding. Other blockchains explore ideas like side chains or off-chain methods (like Lightning Network) to handle more transactions without slowing down the main blockchain. Solving scalability is crucial for making blockchains work better.

What are Rollups

Blockchain rollups are a Layer 2 solution for scaling cryptocurrencies, involving the consolidation of multiple transactions on a secondary blockchain (Layer 2). These transactions are then bundled into a unified piece of data, broadcasted onto the primary blockchain (Layer 1). 

In simpler terms, rollups extract transactions from the main blockchain, process them off-chain, compile them into a single data unit, and reintegrate them onto the primary chain. This process is why rollups are often referred to as ‘off-chain scaling solutions.’

At the most fundamental level, layer 1 scaling involves enhancing the scalability of the primary blockchain. On the other hand, layer 2 scaling entails relocating transactions from the main blockchain layer to a distinct layer that can interact with the primary chain.

Why Blockchain Rollups

Typical blockchain blocks have limited space, causing delays and increased costs as networks grow busier. Blockchain rollups solve this by consolidating transactions into one data piece off-chain, making processing more efficient.

How Do Blockchain Rollups Work?

Blockchain can store two types of information: transactions and data. While processing transactions on-chain is heavy, data resulting from transactions is lighter. Rollups merge transactions off-chain, submitting consolidated data to the mainnet, reducing the burden and enabling multiple transactions in one data piece. This enhances blockchain scalability.

A Step-by-Step Rollups Mechanics 

  1. Conducting Off-Chain Transactions

   – Engage in transactions directly on the rollup chain, acting as a blockchain platform.

   – Transaction processing occurs on the rollup chain, overseen by the “sequencer,” who validates, constructs L2 blocks, and submits transaction data with proofs to the primary L1 chain.

  1. Aggregating Batched Transactions

   – The sequencer organises multiple transactions into batches, collectively presenting them to the main L1 chain.

   – Batched transactions not only streamline processing but also reduce gas fees, providing a cost-effective experience for end-users.

  1. Ensuring On-Chain Security

   – Post-batching, the rollup chain delivers transaction data to a dedicated smart contract on the L1 chain.

   – Upon finalisation of the L1 block containing rollup transactions, the data becomes immutable, safeguarded against modification or censorship, ensuring constant data availability for verification.

  1. Generating Verifiable Proofs

   – Some rollups enhance transaction data with cryptographic “summaries” or “proofs.”

   – These proofs, serving as cryptographic assurances, are deposited on the L1 chain, validating the successful execution of the designated batch of transactions by the rollup.

Types of Blockchain Rollups

1. ZK Rollups (Zero-Knowledge)

 – ZK-SNARK: Uses short proofs for quick transaction processing and enhanced security but has vulnerabilities to certain hacks.

 – ZK-STARK: More scalable and transparent than ZK-SNARK, with larger proof sizes, providing improved security.

2. Optimistic Rollups

 – Assume all transactions are valid by default, approving them to the mainnet without extensive validation.

 – Use fraud-proving mechanisms to identify illegitimate transactions and penalise validators accordingly.

 – Depend on Ethereum mainnet for security, making them easier to implement and cost-effective compared to ZK-rollups.

Here are examples of operational rollup blockchains that simplify complex blockchain technology:

  • Optimism: an Ethereum optimistic rollup with a TVL of $700 million in November 2023. Optimism stands out for its standardised and open-source development stack, the OP Stack. Developers use it to launch their blockchains, and the native token is known as OP.
  • Base: An Ethereum optimistic rollup developed by Coinbase, one of the world’s largest crypto exchanges. Base does not have its own native token.
  • StarkNet: An Ethereum ZK-rollup leveraging zero-knowledge technology (STARK) for transaction computation and verification. The native token is called STRK.
  • Polygon Hermez: Polygon provides a suite of ZK-rollup solutions, Polygon Hermez is one of them, it employs Proof of Efficiency (PoE) consensus, allowing anyone to be a sequencer or aggregator.  Sequencers compile transactions, while aggregators validate and provide proofs to Ethereum. Polygon Hermez incentivizes honest behaviour and boasts decentralisation.

Benefits of Rollups

High Throughput

As can be seen from above, rollups, as a scaling solution for blockchains, deliver a remarkable boost in throughput. By efficiently processing and bundling multiple transactions together, they significantly enhance the overall capacity of the network. This surge in throughput ensures that a more extensive volume of transactions can be seamlessly handled, contributing to a smoother and more efficient blockchain experience.

Reduced Wait Time

Another prominent benefit ushered in by rollups is the substantial reduction in transaction wait times. Through the aggregation of transactions into batches, users experience quicker confirmation and processing.

Limitations of Rollups

Layer 2 blockchain solutions, while improving transaction speed and lowering costs, still face some important limitations. One concern is the risk of fraud by validators in Layer 2. Additionally, these solutions often sacrifice a bit of decentralisation for efficiency. Withdrawing from Layer 2 can be slow, as seen in plasma chains, and might involve added costs. 

Moreover, implementing Layer 2 solutions demands substantial computational power, making certain options less cost-effective for scenarios with lower activity. Despite these challenges, ongoing development in Layer 2 solutions remains vital for addressing blockchain scalability issues, playing an important role in the blockchain ecosystem’s future growth.

Microsoft: AI Now Constitutes 30% of Company Code, Estimated to Reach 95% by 2030

Posted on by Editorial Team

The coding landscape at Microsoft is undergoing swift change owing to the evolving application of artificial intelligence. As outlined by Satya Nadella, the company’s CEO, AI makes up about 20 to 30 percent of the code within company repositories, and that figure could jump to 95% by 2030, especially for AI’s Python Language.

During the ‘LlamaCon’ conference in a dialogue with Mark Zuckerberg, Nadella also remarked on AI’s increasing prominence in software engineering task automation. He pointed out that Python retains the lead in AI-generated code, while languages such as C++ tend to lag far behind due to complexities in adoption.

Microsoft’s Chief Technology Officer Kevin Scott shares this view, predicting a long-term shift where AI will substantially dominate code writing, calling this an inevitable change in development workflows.

A Broader Industry Trend  

Microsoft isn’t the only one to experience this change. Just last week, Google’s CEO Sundar Pichai said that over 30 percent of Google’s code is also being AI generated. Neither of the tech companies, however, provided any insight on how those numbers are calculated, which opens them up to some interpretation.  

The concern with not measuring the contributions of AI accurately is that AI code generation is not uniform. Equality could be measured by how companies measure contributions—whether that’s by lines committed, code accepted, pull requests merged, etc.

The Main Takeaway

Although it’s possible to argue about the precise figures, one thing is clear: AI is increasingly becoming integrated within software engineering at leading tech companies. If the current trends continue, it seems we may be heading towards a time in the future where human developers engage more with problem-solving and design while AI does most of the coding.