CI/CD Pipelines for Large Teams: How to Keep Velocity Without Breaking the Build

Posted on by Demir Yaman

Author: Sumit Saha is a Software Engineer at Microsoft with over 7 years of experience in distributed systems, data migrations, and system modernisation. He specialises in building scalable, reliable, and high-performance backend services that power global communication platforms.

Continuous Integration (CI) and Continuous Delivery (CD) are essential for modern software teams, as there is now a need for fast feature delivery and high-velocity improvements. However, achieving high speed may be difficult without compromising reliability, as the process involves coordinating among development teams working on different features of the same application simultaneously, product complexity, and possible bottlenecks. Nevertheless, even in this challenging case, proper practices and tools can help these teams succeed. 

Definition and purpose of CI/CD

Elaborating on the terms, CI refers to merging code changes into a shared repository where automated builds and tests validate each update. On the other hand, CD, besides these operations, also automates the releases of validated code to production or staging. CI/CD’s goal is to detect code-related issues, reduce manual efforts, and deliver new features as quickly as possible. 

Why velocity matters

Velocity, which measures how fast teams deliver working software, indicates their productivity. For example, high velocity rates show that teams deliver features and respond to customer needs fast. In contrast, low velocity highlights that a team needs to improve the pace of releases, bug fixes, or other items. Although velocity mainly concerns speed, it also demonstrates whether the tooling, processes, and team collaboration are suitable and whether developers can work efficiently. 

CI/CD-related challenges in large teams 

As the teams expand, the complexity of CI/CD also increases. Thus, the most common issues are: 

  1. Merge/integration conflicts occur more frequently because more developers make more code commits.
  2. Shared codebases can cause failures that will affect multiple teams. 
  3. Building and testing pipelines are critical for developers. If they are too slow, it reduces velocity. 
  4. As the requirements for speed are high, this may lead teams to cut corners to achieve them, which is risky in terms of production outcomes. 

Building a resilient CI/CD infrastructure 

Large teams need various approaches to pipelines and infrastructure to overcome the issues mentioned before. The following are examples of possible approaches. 

  1. Modular: it allows you to separate errors and fasten troubleshooting. For example, to build, then lint, unit test, integration test, and finally deploy.
  2. Parallelisation: running tests and builds simultaneously to reduce overall pipeline time. 
  3. Scalability: using containers (for example, Docker) and cloud infrastructure (GitLab CI, CircleCI) to scale pipelines’ storage capacity on demand. 
  4. Pipeline as a code: storing pipeline configurations in version control (YAML, Jenkins) to improve transparency. 
  5. Environment management: using temporary environments for feature testing. This approach will help reduce conflicts and failures when building in real-life conditions. 

Tips for collaboration and code quality 

There are tips for improving collaboration and code quality. For example, besides isolating changes, encourage peer review among developers and invest in testing in every stage (unit, integration, end-to-end). Moreover, require all pull requests to pass automated checks before merging and prevent unreviewed code from being released. If failures happen, analyse them without prejudice and learn from them.

Team communication also matters in large teams. For instance, developers should define what “done” means and ensure everyone knows how to escalate issues. For tracking pipeline health, use specialised dashboards (Datadog, Buildkite). Use email to receive notifications when builds fail. Lastly, continuously review and remove bottlenecks. 

Toolkit 

  1. CI/CD platforms: Jenkins, GitHub Actions, GitLab CI, CircleCI, Azure DevOps
  2. Infrastructure as code: Terraform, Ansible 
  3. Test orchestration: Cypress, Selenium Grid, Playwright 
  4. Monitoring/alerting: Prometheus, Sentry, PagerDuty 

Conclusion

In conclusion, maintaining velocity and reliability is possible by investing in a scalable architecture, pipeline health, segmentation, testing, and automation. Clear and compelling team communication, as well as reviewing bottlenecks, are necessary to build CI/CD pipelines as living systems. 

What is social engineering?

Posted on by Editorial Team

Social engineering is a fancy way of saying that hackers trick real people into giving away secrets they shouldn’t share. Instead of breaking through a locked computer system, these tricks play with human feelings, asking someone to click a sketchy link, wire money, or spill private data.

Picture an email that looks exactly like it came from your favorite co-worker, an urgent voicemail that seems to be from the IRS, or even a wild promise of riches from a distant royal. All of those messages are classic social-engineering scams because they don t bend code; they bend trust. That’s why experts sometimes call it human hacking.

Once criminals have the information they crave-email passwords, credit card numbers, or Social Security digits-they can steal a person’s identity in a heartbeat. With that stolen identity they can charge new buys, apply for loans, and even file phony unemployment claims while the real victim is left puzzled and broke.

A social engineering scheme often serves as the opening act in a much bigger cyber show. Imagine a hacker convincing a worker to spill her email password; the crook then slides that login into the door and drops ransomware onto the entire company’s network.

These tactics bedazzle criminals because they skip the heavy lifting usually needed to break through firewalls, antivirus programs, and other technical shields.

It’s one big reason social engineering sits at the top of network breaches today, as ISACAs State of Cybersecurity 2022 report makes clear. IBM’s Cost of a Data Breach also shows that attacks built on tricks like phishing or fake business emails rank among the priciest for companies to clean up.

How and why social engineering works

Social engineers dig into basic, everyday feelings to trick people into doing things they normally would never do. Instead of stealing software or breaking a lock, these attackers use goodwill, fear, and curiosity as their main tools.

Usually, an attack leans on one or more of these moves:

Spoofing a trusted brand: Crooks build near-perfect fake websites and emails that look almost identical to the real McCoy, letting them slip past busy eyes. Because victims already know the company, they follow instructions quickly, often without checking the URL or the sender. Hackers can buy kits online that make this cloning easy, so impersonating a huge brand has never been simpler.

Claiming to be an authority or government agency: Most of us listen when a badge or a big title speaks, even if we have never met the person. Scammers exploit that trust by sending notes that look like they came from the IRS, the FBI, or even a celebrity the victim admires, naming high-pressure deadlines or scary fines that push quick reactions.

Evoking fear or a sense of urgency: Pushing people to feel scared or rushed makes them move fast, often too fast. A lot of social-engineering scams feed off that shaky feeling. For example, a scammer might say a big credit charge got denied, a virus has locked a computer, or a picture online is breaking copyright rules. Those stories sound real enough to hook someone right away. That same fear-of-missing-out, or FOMO, is another trick, making victims act before they lose out on something special.

Grabbing Greed: The classic Nigerian Prince email-begging note from someone claiming to be an exiled royal and promising a huge payday if you share your bank details or send a small upfront fee-is perhaps the most famous scam that feeds on greed. Variants of this trick appear daily, especially when a fake authority figure shows up in the story and pushes an urgent deadline, creating twice the pressure to act. Though this scheme is nearly as old as e-mail, researchers say it still fleeced victims out of 700k dollars in 2018 alone.

Tapping Helpfulness and Curiosity: Not every con targets a dark impulse-some play on a softer side of human nature, and those may fool even cautious people. A fake message from a friend or spoofed social media alert can promise tech support, ask for survey votes, brag that your post went viral, then steer you to a phony page or silent malware download.

Types of social engineering attacks

Phishing

Phishing is the quick name we give to fake emails, text, or even phone calls designed to trick you into giving up private data, opening a dangerous download, or moving money somewhere it shouldn’t go. Scammers usually dress these messages up to look as if they come from a bank, a coworker, or any other name you would trust. In some cases, they may even copy a friend you talk to all the time so the alert radar never goes off.

Several kinds of phishing scams float around the Internet:

– Bulk phishing emails flood inboxes by the millions. They’re disguised to look like they come from trusted names-a big bank, a worldwide store, or a popular payment app. The message usually contains a vague alert like, “We can’t process your purchase. Please update your card information.” Most of the time, the email hides a sneaky link that sends victims to a fake site, where usernames, passwords, and card details are quietly stolen.

– Spear phishing zeroes in on one person- usually someone who has easy access to sensitive data, the company network, or even money. The crook spends time learning about the target, pulling details from LinkedIn, Facebook, or other social sites, then crafts a note that looks like it comes from a buddy or a familiar office issue. Whale phishing is just a fancy name for the same trick when the victim is a VIP-level person like a CEO or a high-ranking official. Business email compromise, often shortened to BEC, happens when a hacker gets hold of login info and sends messages straight from a trusted boss’s real account, so spotting the scam becomes a lot harder.

– Voice phishing – vishing, for short, is when scammers call you instead of sending an email. They often use recorded messages that sound urgent, even threatening, and claim to be from the FBI or other big names.

– SMS phishing, or smishing, happens when an attacker slips a shady link into a text message that seems like it comes from a friend or trusted company.

– In search-engine phishing, hackers build fake sites that pop up at the top of the results for hot keywords so that curious people land there and hand over private details without knowing they are being played.

– Angler phishing works over social media, where the con artist sets up a look-alike support account and talks to worried customers who think they are chatting with the real brand’s help team.

IBM’s X-Force Threat Intelligence Index says phishing is behind 41% of all malware incidents, making it the top way bad actors spread malicious code. The Cost of a Data Breach report shows that even among expensive breaches, phishing is almost always where the trouble first starts.

Baiting

Baiting is a trick where bad actors dangle something appealing-stuffed with malware or data-requesting links-so people either hand over private info or accidentally install harmful software.

The classic “Nigerian Prince” letter sits at the top of these scams, promising huge windfalls in exchange for a small advance payment. Today, free downloads for popular-looking games, tunes, or apps spread nasty code tucked inside the package. Other times the jobs are sloppier; a crook just drops an infected USB stick in a busy cafe and waits while curious patrons plug it in later because, well, it’s a “free flash drive.”

 Tailgating

Tailgating, sometimes called “piggybacking,” happens when someone who shouldn’t be there slips in behind a person who does have access. The classic example is a stranger trailing an employee through an unlocked door to a secure office. Trailgating can show up online, too. Think about someone walking away from a computer that’s still logged into a private email or network-the door was left open.

Pretexting

With pretexting, a scammer invents a reason that makes them look like the trustworthy person the victim should help. Ironically, they often claim the victim suffered a security breach and offer to fix it-for a password, a PIN, or remote access to the victims device. In practice, almost every social engineering scheme leans on some form of pretexting.

Quid Pro Quo

A quid pro quo scam works when a hacker offers something appealing, like a prize, in return for personal details. Think of fake contest wins or sweet loyalty messages, even a “Thanks for your payment, enjoy this gift!” These tactics sound helpful, but really they steal your info while you believe you are just claiming a reward.

Scareware

Scareware acts like malware, using pure fear to push people into giving up secrets or installing real threats. You might see a bogus police notice claiming you broke a law or a fake tech-support alert saying your device is crawling with viruses. Both pop-ups freeze your screen, hoping you panic and click something that deepens the problem.

Watering Hole Attack

The term watering hole attack comes from the idea of poisoning a spot where prey often drinks . Hackers sneak bad code onto a trusted site their target visits every day. Once the victim arrives, unwanted links or hidden downloads steal passwords or even install ransomware without the user ever realizing.

Social Engineering Defenses  

Because social engineering scams play on human emotions instead of code or wires, they are tough to block completely. That’s a big headache for IT teams: Inside a mid-sized company, one slip-up by a receptionist or intern can open the door to the entire corporate network. To shrink that risk, security experts suggest several common-sense steps that keep people aware and alert.  

– Security awareness training: The average employee has never seen a phishing email in a workshop, so it’s easy to miss the red flags. With so many apps asking for personal details, it feels normal to share a birthday or phone number; what people often forget is that that bit of info lets crooks crack a deeper account later. Regular training sessions mixed with clear, written policies arm staff with the Know-How to spot a con before it lands.

– Access control policies: Strong access rules-such as having users show a password and a second form of ID, letting devices prove their trust level, and following a Zero Trust mindset – weaken the power of stolen login details. Even if crooks land a username and passcode, these layered steps limit what they can see and do across a company’s data and systems.

Cybersecurity technologies: Reliable anti-spam tools and secure-email gateways block many phishing emails before workers ever click them. Traditional firewalls and up-to-date antivirus programs slow down any harm that creeps past those front lines. Regularly patching everyday operating systems seals popular holes that attackers exploit through social tricks. On top of that, modern detection-and-response systems-like endpoint detection and response (EDR) and the newer extended detection and response (XDR)-give security teams fast visibility so they can spot and shut down threats that sneak in under a social-engineering mask.

Data science is crucial for shielding biometric authentication systems from evolving threats

Posted on by Editorial Team

Biometric authentication  systems are now commonplace in everything from smartphones to smart locks, moving far beyond simple face and fingerprint scans. Their growing adoption creates a pressing need for continual, rigorous protection.

Data science drives this need, revealing how biometric verification can fortify privacy while streamlining access. The pressing question is how these scans translate into a safer digital world.

Biometric fusion is layered verification

Most of us have unlocked a phone with a fingerprint or a face scan, but attackers also know that single traits can be spoofed. Biometric fusion answers this by demanding multiple identification traits at once, so access is granted only when several independent points are satisfied.

By expanding the set of factors a system weighs, fusion raises the bar on fabrication success; studies confirm that multimodal cues slash the odds of attacker victory. Devices can stack visual traits with behavioral signals, movements, or keystroke patterns, soon expanding to the rhythm of a user’s speech or the pressure of a press.

This makes the 33% of users  who now find traditional two-factor prompts a chore much more likely to engage. Behavioural metrics can be captured through accelerometers, microphones, or subtle signal processing, creating a seamless shield that continues to verify without interrupting the user’s flow.

Innovations in models and algorithms are steadily raising recognition accuracy in biometric systems 

Data scientists are exploring varied approaches. One long-serving technique is principal component analysis (PCA), which compresses the user’s most significant identifying characteristics into a slimmed-down computational form. Though PCA’s image extrapolation is fast, the recognition precision it delivers still invites fine-tuning.  

Emerging alongside PCA, artificial firefly swarm optimization leverages a different logic. When this algorithm identified and matched faces, it hit 88.9% accuracy, comfortably ahead of PCA’s 80.6%. The swarm imitates colonies of fireflies, tracking the dynamics of light and shadow across facial landmarks and treating these fluctuations as cues to the face’s changing proportions.  

Armoring accuracy against critical use-cases is essential. As biometric, AI, and other technologies edge into sensitive arenas like law enforcement, the stakes rise. Courts and correctional facilities trial facial recognition to scan criminal records, yet earlier models struggled, leaving 45% of adults wary of the same system spreading in policing.

Adaptive biometrics acknowledge the constant march of time

Someone who keeps the same device for ten years may find their biometric traits drifting beyond the algorithms’ reach. Authentication systems will face growing trouble with distinctive shifts like:

  •  Long-term health changes that loosen the ridges of a fingerprint
  •  Clouded vision from cataracts that distort the iris’ geometric signature
  •  Hand joints that drift and enlarge from arthritis, altering the geometry of a palm
  •  A voice that drops or broadens from changes in lung function or the voice-cracking years of adolescence

Most of these changes can’t be postponed or masked. Data scientists are investigating adaptive models that learn to accommodate them. A smooth adaptive response keeps doors from slamming shut on travelers whose traits are still theirs, just a little altered. Avoiding service interruptions and phantom alerts is a matter of preserving the everyday trust users deserve.

Both the developers of these systems and the users who depend on them must reckon with the long arc of biometric evolution. Like all defenses, they will be probed, spoofed, and stretched. Every breakthrough invites a fresh wave of inventive attacks, therefore a layered, device-spanning security net remains the only wise posture. Strong passwords, continuous phish awareness, and now adaptive biometrics must all be rehearsed with equal vigilance – even as the threats keep mutating with the passage of years.

Securing data can cut down false positives 

Misidentifications can arise from shifting light, mask-wearing, or sunglasses. Engineers have refined biometric data storage so the system learns these variations. The result is sharper accuracy and fewer chances of false acceptance.  

Differential privacy safeguards sensitive traits while tuning authentication performance, especially for fingerprints. It gathers biometric samples in noisy visuals or weak signal zones. Later, the verifier matches the true person without confusing them for a fake, achieving solid recognition without giving up safety.

Biometric authentication can align seamlessly with anomaly detection enhanced by machine and deep learning systems. As the framework matures, it continually assimilates the subtle variations that define the legitimate user, retaining defensive integrity all the while.  

Incorporating behavioural biometrics enriches this multilayered approach. Suppose a user seldom requests enrolment in a particular country. The authentication engine can flag that attempt as anomalous even though the extracted face or fingerprint otherwise meets the enrolment standard. Similarly, an unusual cadence of retries – say, a user suddenly trying every hour instead of every week—triggers the model, suggesting that the same face or voice print, while technically correct, is accompanied by a behavioural signal that demands a second factor or a cooling-off period. Each flagged instance reinforces the model, sharpening its ability to discern between legitimate variability and fraught deviations.

Data science strengthens biometric authentication

Cybersecurity analysts and data specialists know that biometric protection requires a variety of strategies. In the future, biometric security technologies will become increasingly effective in terms of accurate data analysis and expanding the capabilities of other security strategies. The application of biometric authentication will become more flexible than ever, making electronics more secure in any environment.

Breaking Language Barriers in Podcasts with OpenAI-Powered Localization

Posted on by Editorial Team

Author: Rustam Musin, Software Engineer

Introduction

Content localization is key to addressing broader audiences in the globalized world of today. Podcasts, as a rapidly emerging medium, present a unique challenge which is maintaining tone, style, and context while translating from one language to another. In this article we outline how to automate the task of translating English-language podcasts into Russian counterparts with the help of OpenAI’s API stack. With a pipeline based on Kotlin with Whisper, GPT-4o, and TTS-1, we present an end-to-end solution for automated podcast localization with high quality.

Building the Localization Pipeline

Purpose and Goals

The primary aim of this system is to automatically localize podcasts while not affecting the original content’s authenticity. The challenge lies in maintaining the speaker’s tone, smooth translations, and natural speech synthesis. Our solution minimizes manual labor to a bare minimum, enabling it to scale up to high amounts of content.

Architecture Overview

The system follows a linear pipeline structure:

  1. Podcast Downloader: Fetches podcast metadata and audio using Podcast4j.
  2. Transcription Module: Converts speech to text via Whisper.
  3. Text Processing Module: Enhances transcription and translates it using GPT-4o.
  4. Speech Synthesis Module: Converts the translated text into Russian audio with TTS-1.
  5. Audio Assembler: Merges audio segments into a cohesive episode.
  6. RSS Generator: Creates an RSS feed for the localized podcast.

For instance, a Nature Podcast episode titled “From viral variants to devastating storms…” undergoes this process to become “От вирусных вариантов до разрушительных штормов…” in its Russian adaptation.

Technical Implementation

Technology Stack

Our implementation leverages:

  • Kotlin as the core programming language.
  • Podcast4j for podcast metadata retrieval.
  • OpenAI API Stack:
    • Whisper-1 for speech-to-text conversion.
    • GPT-4o for text enhancement and translation.
    • TTS-1 for text-to-speech synthesis.
  • OkHttp (via Ktor) for API communication.
  • Jackson for JSON handling.
  • XML APIs for RSS feed creation.
  • FFmpeg (planned) for improved audio merging.

By combining Kotlin with OpenAI’s powerful APIs, our system efficiently automates podcast localization while maintaining high-quality output. Each component of our technology stack plays a crucial role in ensuring smooth processing, from retrieving and transcribing audio to enhancing, translating, and synthesizing speech. Moreover, while our current implementation delivers reliable results, future improvements like FFmpeg integration will further refine audio merging, enhancing the overall listening experience. This structured, modular approach ensures scalability and adaptability as we continue optimizing the pipeline.

Key Processing Stages

Each stage in the pipeline is critical for ensuring high-quality localization:

  • Podcast Download: Uses Podcast4j to retrieve episode metadata and MP3 files.
  • Transcription: Whisper transcribes English speech into text.
  • Text Enhancement & Translation: GPT-4o corrects punctuation and grammar before translating to Russian.
  • Speech Synthesis: TTS-1 generates Russian audio in segments (to comply with token limits).
  • Audio Assembly: The segments are merged into a final MP3 file.
  • RSS Generation: XML APIs generate a structured RSS feed containing the localized metadata.

By leveraging automation at every step, we minimize manual intervention while maintaining high accuracy in transcription, translation, and speech synthesis. As we refine our approach, particularly in audio merging and RSS feed optimization, the pipeline will become even more robust, making high-quality multilingual podcasting more accessible and scalable.

Overcoming Core Technical Challenges

Audio Merging Limitations

When it comes to merging MP3 files, it presents challenges such as metadata conflicts and seeking issues. Our current approach merges segments in Kotlin but does not fully resolve playback inconsistencies. A future enhancement will integrate FFmpeg for seamless merging.

Handling Large Podcast Files

Whisper has a 25 MB file size limit, which typically accommodates podcasts up to 30 minutes. For longer content, we plan to implement a chunk-based approach that divides the podcast into sections before processing.

Translation Quality & Tone Preservation

To ensure accurate translation while preserving tone, we use a two-step approach:

  1. Grammar & Punctuation Fixing: GPT-4o refines the raw transcript before translation.
  2. Style-Preserving Translation: A prompt-based translation strategy ensures consistency with the original tone.

Example:

  • Original: “Hi, this is my podcast. We talk AI today.”
  • Enhanced: “Hi, this is my podcast. Today, we’re discussing AI.”
  • Translated: “Привет, это мой подкаст. Сегодня мы говорим об ИИ.”\

Addressing these core technical challenges is key to providing a fluent and natural listen for localized podcasts. While our current methods represent a solid standard, upcoming refinements such as introducing support for FFmpeg to enable more advanced audio merging, implementing chunk-based transcription to handle longer episodes, and rendering smoother translation requests will help continue to bring the system further towards increased efficiency and quality. Moreover, through continued building out of such solutions, our vision is an uninterrupted, automatic pipeline that does not sacrifice either accuracy or authenticity based on language.

Ensuring Natural Speech Synthesis

On another note, in order to ensure high-quality, natural-sounding speech synthesis in podcast localization, it is essential to address both the technical and content-specific challenges. This includes fine-tuning voice selection and adapting unique podcast elements, such as intros, outros, and advertisements, to preserve the integrity of the original message while making the content feel native to the target language audience. Below are the key aspects of how we ensure natural speech synthesis in this process:

Voice Selection Constraints

TTS-1 currently provides Russian speech synthesis but retains a slight American accent. Future improvements will involve fine-tuning custom voices for a more native-sounding experience.

Handling Podcast-Specific Elements

Intros, outros, and advertisements require special handling. Our system translates and adapts these elements while keeping sponsor mentions intact.

Example:

  • Original Intro: “Welcome to the Nature Podcast, sponsored by X.”
  • Localized: “Добро пожаловать в подкаст Nature, спонсируемый X.”

Demonstration & Results

Sample Podcast Localization

We put our system to the test by localizing a five-minute snippet from the Nature Podcast and here’s how it performed:

  1. Accurate transcription with Whisper: The system effectively captured the original audio, ensuring no key details were lost.
  2. Fluent and natural translation with GPT-4o: The translation was smooth and contextually accurate, with cultural nuances considered.
  3. Coherent Russian audio output with TTS-1: The synthesized voice sounded natural, with a slight improvement needed in accent fine-tuning.
  4. Fully functional RSS feed integration: The podcast’s RSS feed worked seamlessly, supporting full localization automation.

As you can see, our system demonstrated impressive results in the localization of the Nature Podcast, delivering accurate transcriptions, fluent translations, and coherent Russian audio output. 

Code Snippets

To give you a deeper understanding of how the system works, here are some key implementation highlights demonstrated through code snippets:

  • Podcast Downloading:

fun downloadPodcastEpisodes(
    podcastId: Int,
    limit: Int? = null
): List<Pair<Episode, Path>> {
    val podcast = client.podcastService.getPodcastByFeedId(podcastId)
    val feedId = ByFeedIdArg.builder().id(podcast.id).build()
    val episodes = client.episodeService.getEpisodesByFeedId(feedId)

    return episodes
        .take(limit ?: Int.MAX_VALUE)
        .mapNotNull { e ->
            val mp3Path = tryDownloadEpisode(podcast, e)
            mp3Path?.let { e to mp3Path }
        }
}
  • Transcription with Whisper:

suspend fun transcribeAudio(audioFilePath: Path): String {
    val audioFile = FileSource(
        KxPath(audioFilePath.toFile().toString())
    )

    val request = TranscriptionRequest(
        audio = audioFile,
        model = ModelId("whisper-1")
    )

    val transcription: Transcription = withOpenAiClient {
        it.transcription(request)
    }
    return transcription.text
}

Conclusion

This automated process streamlines podcast localization by employing AI software to transcribe, translate, and generate speech with minimal human intervention. While the existing solution successfully maintains the original content’s integrity, further enhancements like FFmpeg-based audio processing and enhanced TTS voice training will make the experience even smoother. Finally, as AI technology continues to advance, the potential for high-quality, hassle-free localization grows. So the question remains, can AI be the driving force that makes all global content accessible to everyone?

15 Best Practices for Code Review in Product Engineering Teams

Posted on by Editorial Team

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.

Observability at Scale

Posted on by Editorial Team 
Authored by Muhammad Ahmad Saeed, Software Engineer

This article has been carefully vetted by our Editorial Team, undergoing a thorough moderation process that includes expert evaluation and fact-checking to ensure accuracy, and reliability.

***

In today’s digital world, businesses operate on complex, large scale systems designed to handle millions of users simultaneously. What is the challenge one might wonder? Keeping these systems reliable, performant, and user friendly at all times. For organizations that rely on microservices, distributed architectures, or cloud native solutions, downtime can have disastrous consequences.

This is where observability becomes a game changer. Unlike traditional monitoring, which focuses on alerting and basic metrics, observability offers a deeper understanding of system behavior by providing actionable insights from the system’s output. It empowers teams to diagnose, troubleshoot, and optimize systems in real time, even at scale. When it comes to engineers, observability isn’t just a tool for them , it’s rather a lifeline for navigating the complexity of modern infrastructure.

What Is Observability?

Observability is the ability to deduce the internal states of a system by analyzing the data it produces during operation. This concept, originally derived from control theory, which focuses on the principle that a system’s behavior and performance can be understood, diagnosed, and optimized without directly inspecting its internal mechanisms. In the realm of modern software engineering, observability has transformed into a foundational practice for managing complex, distributed systems. In order to fully understand observability, let’s unpack its three pillars:

  1. Logs: Logs are immutable, time stamped records of events within your system. They help capture context when errors occur or when analyzing specific events. For example, a failed login attempt might produce a log entry with details about the request.
  2. Metrics: Metrics are quantitative measurements that indicate system health and performance. Examples include CPU usage, memory consumption, and request latency. These metrics are great for spotting trends and anomalies.
  3. Traces: Traces map the journey of a request through a system. They show how services interact and highlight bottlenecks or failures. Tracing is especially valuable in microservices environments, where a single request can touch dozens of services.

Collectively, these components provide a view of the entire behavior of a system, making it possible for teams to be able to address important questions, such as why a certain service is slower than it should be, what triggered an unexpected rise in errors, and whether certain identifiable patterns have led up to system failures.

While observability can significantly improve reliability, achieving it at scale presents some  challenges. Since as systems grow in size and complexity, so does the volume of data they generate. Therefore, managing and interpreting this data effectively requires robust strategies and tools to address several key challenges, some of which are presented next.

One major hurdle is the massive volume of data produced by large scale systems. Logs, metrics, and traces accumulate rapidly, creating significant demands on storage and processing resources. Without efficient aggregation and storage strategies, organizations risk escalating costs while making it increasingly difficult to extract meaningful insights.

Another challenge arises from context loss in distributed systems. In modern architectures like microservices, a single request often traverses numerous services, each contributing a piece of the overall workflow. If context is lost at any point, whether due to incomplete traces or missing metadata, debugging becomes an error prone task. 

Finally, distinguishing the signal from the noise is a persistent problem. Not all data is equally valuable, and the sheer quantity of information can obscure actionable insights. Also, advanced filtering, prioritization techniques, and intelligent alerting systems are essential for identifying critical issues without being overwhelmed by less relevant data.

Addressing these challenges requires both technological innovation and thoughtful system design, ensuring observability efforts remain scalable, actionable, and cost effective as systems continue to evolve. Let’s take Netflix as an example, which streams billions of hours of content to users worldwide. Their system comprises thousands of microservices, each contributing logs and metrics, so without a robust observability strategy, pinpointing why a particular user is experiencing buffering would be nearly impossible. This streaming platform overcomes this by using tools like Atlas (their in-house monitoring platform) to aggregate, analyze, and visualize data in real time.

Best Practices for Achieving Observability at Scale

As modern systems grow increasingly complex and distributed, achieving effective observability becomes critical for maintaining performance and reliability. However, scaling observability requires more than just tools, it actually demands strategic planning and best practices. Below, we explore five key approaches to building and sustaining observability in large scale environments.

  1. Implement Distributed Tracing
     Distributed tracing tracks requests as they flow through multiple services, allowing teams to pinpoint bottlenecks or failures. Tools such as OpenTelemetry and Zipkin make this process seamless.
  2. Use AI-Powered Observability Tools
     At scale, manual monitoring becomes impractical. AI-driven tools like Datadog and Dynatrace use machine learning to detect anomalies, automate alerting, and even predict potential failures based on historical patterns. 
  3. Centralize Your Data
     A fragmented observability approach where logs, metrics, and traces are stored in separate silos, leads to inefficiencies and miscommunication. However, centralized platforms like Elastic Stack or Splunk enable teams to consolidate data and access unified dashboards.
  4. Adopt Efficient Data Strategies
     Realistically, collecting and storing every piece of data is neither cost effective nor practical. The best approach is to implement data sampling and retention policies to store only the most relevant data, ensuring scalability and cost optimization.
  5. Design for Observability from the Start
     Observability shouldn’t be an afterthought. It is best to build systems with observability in mind by standardizing logging formats, embedding trace IDs in logs, and designing APIs that expose meaningful metrics.

To sum up, observability at scale is not just a good-to-have but an absolute must have in today’s fast moving and complex technical environment. Organizations will be able to ensure seamless performance and rapid problem resolution by following best practices like distributed tracing, AI-powered tooling, centralization of data, efficient strategies, and designing systems for observability. 

The Business Benefits of Observability

Although the journey to robust observability is not easy, improvements in reliability, decreased debugging time, and a better user experience are priceless. Besides the key approaches tackled above, there is also effective observability that extends far beyond technical gains, where it has measurable impacts on business outcomes:

  • Reduced Downtime: Proactive issue detection minimizes the time systems remain offline, saving millions in potential revenue loss.
  • Faster Incident Resolution: Observability tools empower teams to identify and fix issues quickly, reducing mean time to resolution (MTTR).
  • Better User Experience: Reliable, responsive systems enhance user satisfaction and retention.

For example, Slack, the widely used messaging platform, leverages observability to maintain its 99.99% uptime and ensure seamless communication for businesses worldwide. By implementing automated incident detection and proactive monitoring, Slack can identify and address issues in real time, minimizing disruptions. Their resilient microservices architecture further contributes to maintaining reliability and uptime.

Conclusion: 

To conclude, in an era defined by ever evolving large scale systems, observability has shifted from being a luxury to a necessity. Teams must deeply understand their systems to proactively tackle challenges, optimize performance, and meet user expectations. Through practices like distributed tracing, AI-driven analytics, centralized data strategies, and designing systems for observability from the ground up, organizations can transform operational chaos into clarity.

However, the true value of observability extends beyond uptime or issue resolution. It represents a paradigm shift in how businesses interact with technology, offering confidence in infrastructure, fostering innovation, and ultimately enabling seamless scalability. As technology is constantly evolving, the question is no longer whether observability is necessary, but whether organizations are prepared to harness its full potential. 

A $41,200 humanoid robot was unveiled in China

Posted on by Editorial Team

The Chinese company UBTech Robotics presented a humanoid robot for 299,000 yuan ($41,200). This is reported by SCMP.

Tien Kung Xingzhe was developed in collaboration with the Beijing Humanoid Robot Innovation Center. It is available for pre-order, with deliveries expected in the second quarter.

The robot is 1.7 meters tall and can move at speeds of up to 10 km/h. Tien Kung Xingzhe easily adapts to a variety of surfaces, from slopes and stairs to sand and snow, maintaining smooth movements and ensuring stability in the event of collisions and external interference.

The robot is designed for research tasks that require increased strength and stability. It is powered by the new Huisi Kaiwu system from X-Humanoid. The center was founded in 2023 by UBTech and several organizations, including Xiaomi. He develops products and applications for humanoids.

UBTech’s device is a step towards making humanoid robots cheaper, SCMP notes. Unitree Robotics previously attracted public attention by offering a 1.8-meter version of the H1 for 650,000 yuan ($89,500). These robots performed folk dances during the Lunar New Year broadcast on China Central Television in January.

EngineAI’s PM01 model sells for 88,000 yuan ($12,000), but it is 1.38 meters tall. Another bipedal version, the SA01, sells for $5,400, but without the upper body.

In June 2024, Elon Musk said that Optimus humanoid robots will bring Tesla’s market capitalization to $25 trillion.

Elon Musk Blames ‘Massive Cyber-Attack’ for X Outages, Alleges Ukrainian Involvement

Posted on by Editorial Team

Elon Musk has claimed that a “massive cyber-attack” was responsible for widespread outages on X, the social media platform formerly known as Twitter. The billionaire suggested that the attack may have been orchestrated by a well-resourced group or even a nation-state, potentially originating from Ukraine.

X Faces Hours of Service Disruptions
Throughout Monday, X experienced intermittent service disruptions, preventing users from loading posts. Downdetector, a service that tracks online outages, recorded thousands of reports, with an initial surge around 5:45 AM, followed by a brief recovery before another wave of disruptions later in the day. The majority of issues were reported on the platform’s mobile app.

Users attempting to load tweets were met with an error message reading, “Something went wrong,” prompting them to reload the page.

Musk addressed the situation in a post on X, stating:

We get attacked every day, but this was done with a lot of resources. Either a large, coordinated group and/or a country is involved.”

However, Musk did not provide concrete evidence to support his claims.

Musk Suggests Ukrainian Involvement
Later in the day, during an interview with Fox Business, Musk doubled down on his allegations, suggesting that the attack may have originated from Ukraine.

“We’re not sure exactly what happened, but there was a massive cyber-attack to try and bring down the X system with IP addresses originating in the Ukraine area,” Musk stated.

The claim comes amid Musk’s increasingly strained relationship with the Ukrainian government. Over the weekend, he asserted that Ukraine’s “entire front line” would collapse without access to his Starlink satellite communication service. Additionally, he criticized U.S. Senator Mark Kelly, a supporter of continued aid to Ukraine, labelling him a “traitor.”

A Pattern of Unverified Cyber-Attack Claims
Musk has previously attributed X outages to cyber-attacks. When his live-streamed interview with Donald Trump crashed last year, he initially claimed it was due to a “massive DDoS attack.” However, a source later told The Verge that no such attack had occurred.

Broader Challenges for Musk’s Businesses
The disruptions at X add to a series of recent setbacks for Musk’s ventures.

SpaceX Mishap: On Friday, a SpaceX rocket exploded mid-flight, scattering debris near the Bahamas.
Tesla Under Pressure: A growing “Tesla takedown” movement has led to protests at dealerships, while Tesla’s stock price continues to slide, hitting its lowest point in months.
Political Tensions: Musk’s meeting with Donald Trump last week reportedly grew tense, with Trump hinting at curbing the billionaire’s influence over government agencies.

The Bottom Line
While Musk attributes X’s outages to a large-scale cyber-attack, no independent evidence has surfaced to confirm this claim. Given his history of making similar allegations without substantiation, the true cause of the disruption remains unclear. Meanwhile, mounting challenges across Musk’s business empire suggest that cyber-attacks may not be the only crisis he is facing.

The Role of Digital Twins in Building the Next Generation of Data Centers

Posted on by Editorial Team 
contributed by Aleksandr Karavanin, Production Engineer at Meta

With increasing numbers of new-age businesses relying on online services, data centers have become the backbone of global operations. However, it has become increasingly difficult to maintain them, with complexities such as power efficiency, downtime of systems, and real-time monitoring. In an effort to address these problems, Digital Twin technology has become a game-saver, which allows organizations to create virtual representations of their data centers to achieve maximum performance, predict failures, and improve operational efficiency.

Understanding Digital Twins in Data Centers

A Digital Twin  is a virtual representation of a physical system, continuously updated with real-time data to reflect the actual conditions of the infrastructure. For data centers, digital twins merge Internet of Things (IoT) sensors, Artificial Intelligence (AI), and machine learning algorithms to monitor and replicate real world conditions in authentic-to-life depiction.

Data center management has moved from manual monitoring and reactive maintenance to AI-driven automation. The transition enables IT teams to make data-driven decisions to achieve maximum resource utilization, zero downtime, and improved performance.

One of the greatest advantages of digital twins is that they provide real-time insight into data center operations. By constantly consuming data from power usage, cooling systems, and hardware performance, the virtual replicas provide a comprehensive view of the health of the facility. Virtual simulations allow organizations to experiment with different configurations, optimizing energy efficiency and reducing operational risks.

On the other hand, one of the key benefits of digital twins is their ability to enable proactive decision-making through real-time monitoring. By continuously analyzing incoming data from critical systems, digital twins offer IT teams unparalleled visibility into the health and efficiency of the data center.

Benefits of Real-Time Monitoring:

Real-time monitoring is a crucial aspect of data center management, ensuring efficiency and preventing interruptions. Digital twins provide a real-time flow of information from various infrastructure components, allowing IT personnel to detect inefficiencies, predict resource needs, and solve potential issues ahead of time. Leveraging this real-time visibility, organizations can enhance performance and reduce operational risks.

  • Faster Issue Detection and Troubleshooting

Digital twins enable IT personnel to identify and fix system failures before they are an issue. By constantly monitoring cooling system data, power usage, and server performance, they trigger instantaneous alerts when issues are detected, allowing for immediate response.

  • Increased Capacity Planning

By analyzing data trends, organizations are able to predict when additional resources will be required, scaling seamlessly. It helps businesses scale their data center operations in a cost-effective way, preventing bottlenecks and optimizing resource utilization.

These benefits are not just theoretical, leading tech companies are already leveraging digital twins to transform their data center operations. One standout example is Thésée DataCenter, which has successfully implemented digital twin models to optimize its cooling systems.

Thésée DataCenter opened the first fully interactive digital twin in a colocation environment in 2022. The digital twin provides customers with a 3D view of their IT equipment, power usage, and operating conditions, with real-time visibility on performance and service levels. By enabling precise knowledge of infrastructure capacity and risk-free planning of future installations, Thésée DataCenter has simplified capacity planning and anticipated necessary changes to cooling infrastructure, achieving aggressive energy performance objectives.

Apart from real-time monitoring and capacity planning, digital twins also play a critical part in predictive maintenance and proactive incident management. Rather than addressing issues after they happen, digital twin technology allows organizations to shift from a reactive to a predictive maintenance approach, reducing the likelihood of surprise failures.

Predictive Maintenance and Proactive Incident Response

Traditional data center maintenance often follows a reactive approach, addressing issues only after they cause disruptions. Digital twins, however, enable a shift toward predictive maintenance, where AI-driven analytics detect potential failures before they occur.

By analyzing historical and real-time data, digital twins identify patterns that indicate impending hardware failures or cooling inefficiencies. This predictive capability reduces the risk of sudden outages, minimizing downtime and repair costs.

Beyond predicting failures, digital twins also enhance proactive incident response, it is a crucial advantage of digital twin technology in data center management.Through AI-based automation and real-time analytics, digital twins allow organizations to detect possible risks early and respond instantly, minimizing disruptions and ensuring continuity of operations.

Automated Risk Detection

AI constantly monitors hardware performance, power fluctuations, and security threats, analyzing massive amounts of information in real-time. Preemptive monitoring enables IT personnel to identify anomalies that can predict impending failures, such as overheated servers, power supply irregularities, or attempts at unauthorized entry. By detecting these threats before they occur, organizations prevent cascading failures that can trigger downtime or security incidents.

For example, if a digital twin detects unusual power consumption in a server rack, it can warn of a potential power supply issue before it results in an outage. Similarly, in security scenarios, AI-driven monitoring can flag suspicious access patterns, enabling IT personnel to take action before a security breach occurs.

However, detecting anomalies is only the first step, timely alerts and swift response mechanisms are equally critical to preventing disruptions. This is where AI-driven alerts come into play, ensuring that IT teams receive real-time notifications and can take immediate corrective action.

AI-Driven Alerts and Immediate Response

Digital twins not only detect issues but also generate automated alerts based on predefined thresholds and AI-driven insights. These alerts provide IT teams with real-time notifications about potential risks, enabling them to take immediate corrective action.

  • Real-Time Notifications: Digital twins send instant alerts through dashboards, emails, or integrated management systems, ensuring IT personnel are informed the moment an issue arises.
  • Automated Mitigation Actions: In some cases, AI can trigger automated responses, such as redistributing workloads to prevent overheating, adjusting cooling parameters, or isolating compromised systems to mitigate security threats.
  • Incident Prioritization: By analyzing the severity of detected issues, digital twins help IT teams prioritize responses, ensuring critical problems are addressed first while routine maintenance tasks are scheduled accordingly.

This proactive approach reduces downtime, optimizes resource utilization, and enhances the overall resilience of data center operations. But how effective is this in practice?

A premier cloud services company leveraged digital twin technology to improve data center reliability and reduce operational costs, resolving for unexpected server failures that caused costly downtime and increased maintenance costs. By integrating digital twins in its infrastructure, the company created virtual replicas of its physical servers, cooling systems, and power distribution networks that allowed real-time monitoring of the critical parameters such as CPU temperature, workload balancing, power fluctuations, airflow efficiency, and security threats. 

With AI-powered predictive analytics, the digital twin picked up early warning signs of the potential failures before they had turned into critical problems. This deployment caused a reduction of 30% in downtime, with AI detecting anomalies in server performance, triggering real-time alerts and enabling IT teams to replace or repair components before disruption. Automated mitigation strategies, such as workload redistribution, also ensured continued service continuity. 

Predictive maintenance also lowered maintenance costs by 20%, with fewer emergency repairs, optimized scheduling of routine maintenance, and improved efficiency of cooling systems to lower energy consumption. The enhanced monitoring and proactive incident response also raised service reliability, allowing IT teams to divert their energies away from reactive problem-solving and towards strategic innovation, and ultimately, improving uptime and customer satisfaction. 

In this change, the cloud services provider demonstrated how AI-driven predictive analytics and digital twins can significantly enhance infrastructure resilience and cost efficiency.

Future of Digital Twins in Data Centers

The previous case study highlights the huge benefits of AI-driven digital twins in enhancing data center operations. As we can see, the use of digital twin technology has led to stunning decreases in downtime, maintenance costs, and overall improvements in service reliability. These advantages highlight the huge potential digital twins hold to transform data centers today. Looking ahead, the future of digital twins in data centers seems even more promising.

As AI and machine learning continue to advance, the capabilities of digital twins will expand, offering even greater automation and efficiency in data center operations. The rapid integration of edge computing and high speed mobile networks will further enhance real-time data processing, enabling faster decision-making and improved latency management.

However, the widespread adoption of digital twins is not without challenges. Data security concerns, high implementation costs, and system complexity remain potential obstacles. Consequently, organizations must ensure robust cybersecurity measures and assess the return on investment before deploying digital twin solutions at scale.

Conclusion

In conclusion, digital twins are transforming data center management by enabling real-time simulation, predictive maintenance, and proactive incident response. As organizations strive for smarter, self-optimizing and self-healing data centers, digital twin technology will play a crucial role in ensuring efficiency, reliability, and sustainability.

Looking ahead, businesses that embrace digital twins will gain a competitive advantage, reducing operational risks and improving resource management. Finally, as technology evolves, the future of data centers will be defined by intelligent automation, setting the stage for a new era of digital infrastructure.

How scam HR can run virus on your PC

Posted on by Editorial Team

Imagine getting an offer for your dream job, but handing over your computer to a hacker in the process.

This isn’t a plot from a cybersecurity thriller. It’s the reality of a growing threat in the digital recruitment space, where job scams have evolved from phishing emails to full-blown remote code execution attacks disguised as technical assessments. We invited Akim Mamedov, a CTO to share his experience and recommendations.

***

For quite some time there were rumors that a new type of scam emerged in hiring, especially in platforms like LinkedIn. I didn’t pay enough attention until I encountered this scam scheme personally.

The truth is that almost every scam relies on social engineering, e.g., to lure a person in performing some action without paying enough attention. This kind is similar, the desired outcome is running malicious code on the user ‘s computer. Now let’s dive deep in the details and explore how the schema works and how bad guys attempt to do their dirty business.

After surfing on LinkedIn I’ve received a message from the guy about an interesting job offer. He described the role in detail, promised a good salary and was actively asking for my attention.

Before switching to Telegram I checked the profile of the guy and it looked pretty decent – good working experience, extensive profile information, linked university and company where he supposedly works.

After proceeding to telegram we decided to schedule a call.

On the call, I had a chance to see him in person – it was an Indian guy with a long beard. I hadn’t opportunity to take screenshots because he immediately turned his camera off. This is when it started to look suspicious as hell so I’ve started making screenshots of everything.

He asked a couple of quick questions like tell me about a project and confirm that you’ve worked with this and with that. At the end of the call he said that there is still a small test task which I have to solve and then they will hire me.

That’s where the interesting part begins. I’ve opened the archive and started checking the code.

Meanwhile I’ve messaged a couple of questions to HR so he got the feeling that i’m aware about the malware and deleted messages in telegram and linkedin. Now let’s focus on what the code does.

From the first glance, it’s a simple javascript backend project.

But what @el3um4s/run-vbs and python-shell does inside this simple js test task?

After quick search of usages i’ve found a file where this package is actually used

There are 2 files – one for Windows OS and the other for any other OS with python installed. Let’s check one with python code.

Inside the file with python code we have a script which collects some computer information and sends it to the server. Response from that server could contain instructions which go directly to the exec() function thus executing arbitrary code in the system. This looks like a botnet script which keeps an endless connection to the attacker server and waits until the server responds to perform some actions. Needless to say that running this script means passing your system to an attacker thus allowing reading sensitive data, tinkering with OS services and utilizing computer resources.

This is the opinion of ChatGPT regarding the code in that file.

The impact of this scheme could possibly be big enough to infect thousands of computers. Sure there are a lot of arrogant developers who consider this test task too easy for spending more than a couple of minutes and will try to finish it fast. Junior developers are at risk too – lured with high salaries and non-demanding job descriptions, they will run the project without properly understanding it.

In conclusion, be mindful of the code you’re trying to run, always check any source code and script you’re running.