This article was brought to you by Kumar Harsh, draft.dev.

Where there is a CI/CD pipeline, there will be build logs. And while they’re important, anyone who’s stared at one knows the pain: thousands of lines of plain text, buried errors, and endless scrolling just to find out why something failed. What should be a quick diagnosis turns into a needle-in-a-haystack hunt.

Raw logs are useful, but they’re not enough. Developers don’t just need to know that a build failed; they need to know where, why, and how often it’s happening. That’s the difference between the text you read and the insights you act on.

In this article, we’ll look at how TeamCity goes beyond the build log. You’ll see how its structured log view, visual pipeline insights, and trend analysis help you navigate failures faster, spot performance regressions, and even anticipate recurring issues.

By the end, you’ll understand how TeamCity turns “just another log” into a tool for building better software faster.

The wall of text

Every developer has faced the dreaded wall of text. A build fails, and suddenly you’re staring at thousands of lines of console output. Somewhere inside is the clue you need: an error code, a failed test, or a timeout. But it’s buried under a flood of status messages and stack traces.

Searching helps, but you need to know exact keywords, and oftentimes you don’t know the exact error messages to search for. The longer the build, the longer its output, and the harder it gets to pinpoint issues.

Traditional CI systems don’t make this any easier. Take Jenkins. Its build logs are essentially flat text files. You can scroll, you can search, but there’s no real structure. A build step is just a line in the log, indistinguishable from all the noise around it.

If you want to know which step failed or how long each stage took, you’re left to manually scan through endless lines.

This lack of structure creates three major pain points:

• Flat logs with no hierarchy: There’s no easy way to jump between stages and steps or to test the results
• Zero visual cues: Errors don’t stand out. You have to read line by line.
• Hard-to-trace correlations: Connecting a failing test back to its stage or seeing how long a step ran becomes a lengthy, manual exercise.

The result is that debugging builds becomes a time sink. Instead of focusing on fixing issues, you waste cycles just trying to interpret the logs yourself.

That’s the problem TeamCity set out to solve.

What does a TeamCity build look like?

TeamCity rethinks how build information is presented. Instead of forcing you to scroll endlessly through output text, it structures results in a way that’s easy to navigate, interpret, and act on.

Think of it as moving from a raw server console to a dedicated dashboard built for surfing through logs. The build results page is designed to be a living view of your pipeline, complete with context, hierarchy, and visual cues.

You can see the entire flow of a build at a glance, drill into specific steps with a click, and watch logs update in real time as the build progresses.

This shift to a logs “browser” instead of a logs “dumper” is what makes TeamCity different. It focuses on highlighting problems so you can spend more time fixing issues than searching for them.

How does TeamCity structure its logs differently?

The first, most obvious thing you’ll notice in TeamCity is that logs aren’t just dumped into a giant text file. They’re organized hierarchically, following the natural flow of your pipeline.

Each layer is collapsible, which means you don’t have to scroll past hundreds of lines just to find the one step you care about.

Want to focus on a failing test step? Collapse everything else and zoom in on the problem area.

This structure also updates in real time. As your build runs, you can watch each step expand with fresh output while the rest of the log stays neatly tucked away. No more hunting for the latest lines in a never-ending scroll.

This means that, more often than not, you’ll pick up the error message and reason right in front of you as it happens instead of having to wade through lines after a build has failed and has dumped its logs.

How do TeamCity’s visual tools improve developer productivity?

Structured logs are a big step forward, but TeamCity doesn’t stop there. It layers visual context on top of the raw output, so developers can spot problems and patterns without needing to parse every line.

At a glance, you get a visual overview of the entire pipeline: each step, its status, and how long it took. This makes it easy to see whether a build failed fast or slowed down during a specific stage. Instead of you having to run a stopwatch in your head, TeamCity does the timing analysis for you.

Also, errors are highlighted in context, so they stand out immediately. No scrolling through lines of green “success” messages just to find the single red flag buried at the bottom.

The failed step jumps out, both on the build timeline and the results page. Click on the timeline to quickly scroll to the error output line:

The Tests tab gives you detailed insight into how your tests performed across build steps:

You can click on a failed test to see its output in the current run and the run where it failed for the first time:

And because builds rarely fail just once, TeamCity also gives you historical statistics for your builds. You can see trends across multiple runs, like how often your builds have failed across days or how often your tests have failed with each build.

What types of insights are considered “actionable”?

Not every log line deserves your attention – what you really need are insights that point directly to the next steps.

TeamCity’s build statistics help you surface exactly those kinds of actionable signals.

For example, if a build suddenly takes twice as long, TeamCity can help you pinpoint the slow step. You can set up charts for each step that show trends over time so you know whether it’s a new dependency, a misconfigured cache, or an overloaded test suite. Instead of guessing, you see the bottleneck right away.

The following pipeline has had about seven builds so far, some of which failed as well. The build duration history looks like this:

As you can see, after build #4 failed, build #5 succeeded, but it took way longer than usual to complete. The issue was somehow resolved in the next two build runs, #6 and #7.

This seems unusual at first. However, when you look at the stepwise build duration history for the build configuration, you find this:

It’s clear that on build #5, the Fetch Secrets step took thirty seconds, which is way off from its usual two-to-five-second runtime. Since fetching secrets usually involves making network requests to a remote secrets manager, this could indicate an issue with your third-party secrets manager or with the network setup.

And you were able to narrow down the cause to the scope of a single step by looking at only two graphs.TeamCity also helps with other build-wide trends, like artifact size, block-level/class-level/line-level/method-level code coverage, time spent in the queue, and more.

You can see the full list of available statistics here. If you want to add a custom statistic for your pipeline, you can use service messages and easily create charts and graphs out of them.

What are the benefits?

So why does this matter for developers and teams working under pressure?

• Faster root cause analysis: The most obvious benefit is speed. TeamCity’s structured approach means you spend less time hunting and more time fixing. In high-velocity environments where every minute of downtime delays releases, this faster feedback loop makes a tangible difference.
• Understanding build performance: A failed test is one problem, but a slow build can be just as damaging. TeamCity’s step-by-step duration breakdown lets you spot build performance bottlenecks at a glance. Maybe a secrets fetch is dragging, or a test suite’s runtime has doubled. By surfacing this information, TeamCity gives you a starting point for optimization. You don’t just know that a build is slow; you know why it’s slow, and where to focus your efforts.
• Detecting patterns and preventing repeats: Another benefit is pattern recognition. Builds rarely fail for the first time out of nowhere. Often, you’ll see the same flaky test appear intermittently across runs or the same misconfigured environment variable pop up in different branches. Traditional logs leave you to connect those dots manually, but TeamCity makes those patterns visible through historical comparisons.
• Supporting proactive improvement: All these insights shift the developer mindset from reactive to proactive. Maybe you notice a steady increase in build duration, or memory consumption keeps varying without reason. TeamCity gives you the data to intervene before those issues become blockers.
• Laying the groundwork for AI-powered insights: TeamCity’s structured data and historical awareness lay the foundation for what’s coming next. For instance, the upcoming AI Build Analyzer will analyze builds from multiple angles to suggest likely root causes and possible fixes. You won’t just read logs anymore but collaborate with an intelligent system to solve problems even faster.

Conclusion

Build logs will always be an important part of CI/CD debugging workflows, but they aren’t enough on their own. Raw text logs leave developers to do the heavy lifting of interpretation, slowing down feedback loops and burying critical issues in noise.

What developers need are insights: structured, visual, and actionable signals that point directly to the next step.

That’s what TeamCity delivers out of the box. From hierarchical logs and visual pipeline overviews to historical trends and pattern detection, TeamCity turns builds into a source of continuous learning rather than just reactive debugging.

The result is faster root-cause analysis, improved build performance, and a smoother path from code commit to deployment. And with innovations like the new AI Build Analyzer, the future of build intelligence looks even brighter.

Will AI kill Open Source? Is it already happening? Or is this just another clickbait title? Well, let’s see. First of all, I am writing this by hand without the help of any artificial intelligence. There is only human intelligence involved here. I will leave it up to you to judge the quality of it, but at least it is real.

I don’t think AI will kill Open Source. It is not about it not being capable of it. I think it is more that we are not going to allow it to happen. Why should we suddenly abandon all practices of reuse and use of proven implementations and libraries over reinventing the wheel ourselves? Why would we let AI rewrite algorithms and functionality that are already implemented in open source projects, verified, and proven to work? That’s where human intelligence comes in. Abandoning all sound practices of software engineering just because we suddenly have a new developer-kid-on-the-block that can vomit out code faster than any human in history? I don’t think so.

After all, human intelligence is human

After all, human intelligence is human. We know that we sometimes make mistakes. AI doesn’t. AI is never wrong unless a human points out that it is. How does this relate to open source again? What if we didn’t have to point out to the AI that it was wrong? What if we got the AI to use components and building blocks from open source libraries and APIs that are verified to be correct? Isn’t that the strength of open source? That multiple human brains have been involved in creating it in collaboration. So, in order to feed the AI with secure, stable, correct building blocks, we need open source.

If there is one thing AI is good at, it is following specifications. Maybe implementations can be generated by AI if the specifications are well-defined enough. Especially if the specifications come with a comprehensive test suite to verify that an implementation implements it correctly. Wouldn’t it be nice if we had a set of high-quality, widely adopted, interoperable specifications with associated test suites?

Luckily, we do! And that is what Jakarta EE is all about. I will elaborate more on this in future posts. I see that this post is starting to get a little long, so it may be that this will be the first in a series of posts on this topic.

Ivar Grimstad

Mon, 2026-02-23 06:58

I’ve spent over two decades in the trenches of user experience design. I remember the transition from table-based layouts to CSS, the pivot to responsive design when the iPhone launched, and the rise of the “attention economy.” But as we navigate 2026, the industry is facing its most significant shift yet. We are moving past the era of “design at any cost” into the era of Sustainable UX.

It’s not something most designers think about, including myself, until I was prompted by hearing about this as a concept. For years, we have treated the internet as an ethereal, weightless cloud. We have assumed that digital products were “green” simply because they weren’t printed on paper. I used to think that too, and before the concept of climate change emerged, it was more about saving trees.

We were wrong. The cloud is a physical infrastructure, a sprawling network of data centres, undersea cables, and cooling systems that hum 24/7. While AI-focused data centers match the power consumption of massive aluminum smelters, their high geographic density creates an even more intense and localised environmental strain.

As UX designers, we are the architects of this energy consumption. Every high-resolution hero image, every auto-playing background video, and every complex JavaScript animation we approve is a direct instruction to a processor to consume power. If we want to build a future that lasts, we must stop designing for “wow” and start designing for efficiency.

Dark Mode

In the early 2000s, white backgrounds were the standard because they mimicked the familiarity of paper. However, the hardware has evolved, and our design philosophy must follow. The shift from LCD to OLED (Organic Light Emitting Diode) technology has fundamentally changed how colour impacts energy.

The Logic

Unlike traditional LCD screens, which require a backlight that is always on (even when displaying black), OLED screens illuminate each pixel individually. When a pixel is set to true black (#000000), that specific diode is turned completely off. It draws zero power.

By designing interfaces that favour darker palettes, we aren’t just following a trend; we are physically reducing the energy requirement of the user’s device.

The Data

The energy savings are far from negligible. A landmark study by Purdue University in 2021, which has become the gold standard for this discussion, revealed that at 100% brightness, switching from light mode to dark mode can save an average of 39% to 47% of battery power. On a global scale, if every major app defaulted to dark mode, the reduction in grid demand would be astronomical.

The Design Goal

In 2026, Dark Mode should no longer be a secondary “theme” tucked away in a settings menu. We should be designing with a “Dark-First” mentality. This doesn’t mean every site must look like The Matrix, but it does mean prioritising high-contrast dark themes as the default system-preferred state. This extends the hardware lifespan of the device and lowers the carbon footprint of every interaction.

I personally prefer Light-Mode for reading, so it makes sense to have both light and dark mode options available. There are also accessibility considerations with providing both options.

Image And Video Optimisation

We have become lazy designers. With high-speed 5G and fibre optics, we’ve stopped worrying about file sizes. The average mobile page weight has increased by over 500% in the last decade, largely due to unoptimized visual assets.

The Logic

The “Digital Fat” of a website (those 4MB Unsplash photos and 15MB background videos) is the single largest contributor to page-load energy. Every megabyte transferred from a server to a client requires electricity for the transmission, the server’s processing, and the user’s rendering engine. When we use massive files, we are essentially “burning” energy to show a picture that could have been just as effective at a fraction of the size. Not to mention, you are also providing a better user experience with a page that loads much faster.

The Data

According to the HTTP Archive, images and video consistently account for the lion’s share of a page’s total weight. However, the shift to modern formats like AVIF and WebP can reduce image weight by up to 50% compared to JPEG, without any perceptible loss in quality.

Although these formats are not as familiar to me as JPG and PNG, I am definitely looking forward to using them to reduce page size.

The Design Goal

I recently led a redesign for a cybersecurity platform. By implementing a “Before and After” audit, we discovered that their homepage was loading 5.5MB of data. By replacing high-res photography with SVG (Scalable Vector Graphics) art and using clever CSS gradients instead of image assets, we dropped the load to 1.2MB. That is a 78% reduction in energy load! As a designer, your first question should always be:

“Do I need a photo for this, or can I achieve the same emotional resonance with code?”

Intentional Motion: Cutting “Loud” Animations

We live in an era of “scroll-jacking” and complex 3D Parallax effects. While these might win awards on Awwwards.com, they are often ecological disasters.

The Logic

Animation is not free. To render a complex animation, the device’s GPU (Graphics Processing Unit) must work at high capacity. This increases the CPU temperature, triggers cooling fans (in laptops), and drains battery rapidly. “Loud” animations that run constantly in the background or trigger massive re-paints of the browser are the energy equivalent of leaving your car idling in the driveway.

The Data

Google’s Material Design guidelines emphasize “Meaningful Motion.” They argue that animation should be used only to orient the user or provide feedback. And using WebP instead of JPEG can save 25-50% of data on a page.

The Design Goal

We must adopt Meaningful Motion. If an animation doesn’t help a user complete a task or understand a hierarchy, it is a waste. We should favour CSS transitions over heavy JavaScript libraries like GSAP or Lottie where possible, as CSS is hardware-accelerated and far more efficient for the browser to calculate.

As a UX designer, I can’t argue this approach. This not only helps reduce data waste but also improves UX for our users.

Setting A “Data Budget” For Every Project

In my 20+ years of UX, the most successful projects have generally been the ones with the tightest constraints.

Just as a project has a financial budget, it should also have a carbon and data budget.

The Logic

A Data Budget is a hard cap on the total size of a page (e.g., “This landing page cannot exceed 1MB”). This forces the design team to make difficult, intentional choices. If you want to add a new tracking script or a fancy font weight, you have to “pay” for it by optimising or removing something else. This prevents “feature creep” from turning into “carbon creep.”

The Data

The Sustainable Web Design model, developed by pioneers like Wholegrain Digital, provides a formula to calculate the CO2 per page view. The average website produces about 0.5 grams of CO2 per view. For a site with 1 million monthly views, that’s 6 metric tons of CO2 a year, equivalent to driving a car 15,000 miles.

The Design Goal

The Sustainable UX Checklist

• Reduce Images
  Question the necessity of every visual and use the smallest resolution and most efficient file formats (like AVIF) to minimize data transfer.
• Optimise Video
  Eliminate auto-playing media and prioritise highly compressed, short loops to ensure energy is only spent on content the user intends to view.
• Limit Fonts
  Use a maximum of two web font weights or stick to classic system fonts to remove unnecessary server requests and rendering bloat.
• Recycle Assets
  Repurpose a single image or video multiple times using CSS filters and overlays to create visual variety without increasing the total page weight.
• Choose Green Hosting
  Host your digital products on servers verified by The Green Web Foundation to ensure they are powered by renewable energy sources.
• Minimize Data Distance
  Select server locations geographically close to your primary audience to reduce the energy required for data to travel through physical infrastructure.

The Business Case For Eco-friendly Design

Some might argue that “Green UX” sounds like a compromise on quality. On the contrary, it is a competitive advantage. Sustainable design is performance design.

When you reduce page weight, your site loads faster. When your site loads faster, your Core Web Vitals improve. When your Core Web Vitals improve, your SEO ranking goes up. Furthermore, users on older devices or slower data plans (especially in emerging markets) can actually access your product. This is the definition of “Inclusive Design.”

By cutting the “digital fat,” we create a leaner, faster, and more accessible web. We are moving away from the “disposable design” of the 2010s toward a more permanent, respectful digital architecture.

Conclusion: The Future Of “Clean” Design

In my two decades of design, I’ve seen many trends come and go. Skeuomorphism, Flat Design, Neumorphism — they were all aesthetic choices. But sustainable UX isn’t a trend; it’s now a necessity. We are the first generation of designers who have to reckon with the physical consequences of our digital work.

Sustainable UX is a “win-win-win.” It’s better for the planet because it reduces energy consumption. It’s better for the user because it results in faster, more responsive interfaces. And it’s better for the business because it lowers hosting costs AND improves conversion rates.

The era of “unlimited pixels” is over. In 2026, the most sophisticated design is the one that leaves the smallest footprint. We are no longer just designers; we are the guardians of the user’s battery, their data plan, and ultimately, the environment.

The Call To Action

I challenge you to audit just one page of your current project today. Use a tool like the Website Carbon Calculator to see its impact. Then, look for the “invisible waste.” Can that image be an SVG? Can that video be a static hero? Can that “loud” animation be silenced?

Start small. The most elegant solution is often the one with the fewest bytes.

Quality Assurance (QA) software tester courses in 2026 teach practical skills required to test modern applications, identify defects early, automate repetitive testing tasks, and ensure software reliability. These courses focus on manual testing, automation testing, API testing, AI-assisted testing, real-world project experience, and industry tools used in real IT environments. By learning these skills, students can prepare for roles such as QA Tester, Automation Tester, Software Test Engineer, and QA Analyst.
Software testing has evolved significantly. Today’s QA professionals are not just testers; they are quality engineers who work closely with developers, DevOps teams, and business stakeholders to ensure software works correctly, securely, and efficiently.
Why QA Software Testing Skills Are Critical in 2026
Software is everywhere – mobile apps, web platforms, cloud systems, AI tools, and enterprise applications. Every application must work correctly, securely, and smoothly. QA testers ensure this happens.
Here’s why QA skills are in high demand:
Companies release software faster using Agile and DevOps

Automation testing reduces manual effort

AI-powered applications require specialized testing

Businesses need high-quality, bug-free applications

Cybersecurity and performance testing are essential

According to industry trends, software testing remains one of the most accessible entry points into IT, especially for beginners and career switchers.
Core Skills You Will Learn in a QA Software Tester Course
A comprehensive QA software tester course focuses on both technical and practical skills used in real-world software testing environments.

1. Software Testing Fundamentals
   Before learning advanced tools, students first understand the foundation of software testing.
   Key concepts include:
   What is software testing and why it is important

Software Development Life Cycle (SDLC)

Software Testing Life Cycle (STLC)

Testing levels: Unit, Integration, System, and User Acceptance Testing

Testing types: Functional and Non-functional testing

Bug lifecycle and defect tracking

These concepts help students understand where testing fits in the software development process.
Example:
If a login button doesn’t work, a tester identifies the issue, reports it, and verifies the fix.

1. Manual Testing Skills
   Manual testing is the starting point for every QA career. It helps testers understand application behavior and user workflows.
   Skills learned include:
   Writing test cases

Creating test scenarios

Executing test cases

Identifying bugs

Reporting defects clearly

Performing functional testing

Performing regression testing

Example:
Testing an e-commerce website checkout process manually to ensure payments work correctly.
Manual testing builds strong analytical thinking and attention to detail.

1. Automation Testing Skills
   Automation testing is one of the most valuable skills in 2026. It helps test applications faster and more efficiently.
   Students learn:
   Selenium WebDriver

Automation frameworks

Writing automation scripts

Test execution automation

Automation reporting

Automation testing reduces repetitive work and increases testing efficiency.
Example:
Automatically testing login functionality across different browsers using Selenium.
Automation testers are highly in demand and earn higher salaries compared to manual testers.

1. Programming Basics for Testers
   QA testers learn basic programming to create automation scripts and understand application logic.
   Common programming languages include:
   Java

Python

JavaScript

Students learn:
Variables and data types

Conditional statements

Loops

Functions

Object-oriented concepts

Programming helps testers automate tests and collaborate with developers.
Example:
Writing a script that automatically verifies website functionality.

1. API Testing Skills
   Modern applications use APIs (Application Programming Interfaces). API testing ensures backend systems work correctly.
   Students learn:
   What APIs are

REST API testing

Using tools like Postman

Sending requests and validating responses

Testing data flow between systems

Example:
Testing whether a login API returns correct user information.
API testing is critical because many systems depend on backend communication.

1. Test Automation Frameworks
   Automation frameworks organize automation scripts efficiently.
   Students learn frameworks such as:
   Data-driven framework

Keyword-driven framework

Hybrid framework

Page Object Model (POM)

These frameworks improve test maintainability and scalability.
Example:
Using a framework to test multiple user login scenarios automatically.

1. Bug Tracking and Test Management Tools
   QA testers use tools to track bugs and manage test cases.
   Common tools include:
   Jira

TestRail

Bugzilla

Azure DevOps

Students learn:
Reporting bugs

Tracking bug status

Writing clear defect reports

Managing test execution

Example:
Reporting a bug when a mobile app crashes during login.
These tools are essential in real-world projects.

1. Agile and Scrum Methodology
   Most companies use Agile methodology for software development. QA testers work in Agile teams.
   Students learn:
   Agile principles

Scrum framework

Sprint cycles

Daily standup meetings

Sprint planning and retrospective

Example:
Testing new features released in every sprint.
Understanding Agile helps testers work efficiently in modern teams.

1. Web Application Testing Skills
   Web applications are widely used across industries.
   Students learn to test:
   Login systems

Forms

Navigation

User interface

Browser compatibility

Example:
Testing a website on Chrome, Firefox, and Edge browsers.
This ensures applications work correctly across different environments.

1. Mobile Application Testing Skills
   Mobile app testing is essential because millions of users use mobile apps daily.
   Students learn:
   Android testing

Mobile test scenarios

Device compatibility testing

App functionality testing

Example:
Testing a banking app on different mobile devices.
Mobile testing skills increase job opportunities.

1. Database Testing Skills
   Applications store data in databases. QA testers verify data accuracy.
   Students learn:
   SQL basics

Writing SQL queries

Validating database records

Checking data integrity

Example:
Verifying user registration data stored correctly in the database.
Database testing ensures backend accuracy.

1. Performance Testing Basics
   Performance testing ensures applications work well under heavy load.
   Students learn:
   Performance testing concepts

Load testing basics

Response time analysis

Example:
Testing whether a website handles 1,000 users simultaneously.
This skill helps improve application reliability.

1. AI and Modern Testing Skills in 2026
   AI is transforming software testing. Modern QA courses now include AI-related testing skills.
   Students learn:
   AI-assisted testing tools

Smart test automation

Test case generation using AI

Self-healing automation scripts

AI reduces manual effort and improves testing accuracy.
Example:
Using AI tools to identify test scenarios automatically.
AI testing skills are becoming highly valuable.

1. Real-World Project Experience
   One of the most important parts of QA training is working on real projects.
   Students learn:
   Testing real applications

Writing real test cases

Automation project implementation

Bug reporting in real environments

Example:
Testing an actual e-commerce or banking application.
This experience prepares students for real job roles.

1. Communication and Documentation Skills
   QA testers communicate with developers, managers, and teams.
   Students learn:
   Writing clear test cases

Writing bug reports

Communication skills

Reporting testing results

Good communication improves teamwork and efficiency.

1. CI/CD and DevOps Testing Basics
   Modern companies use CI/CD pipelines for faster software delivery.
   Students learn:
   Continuous Integration basics

Continuous Testing concepts

Automation integration with CI/CD tools

Example:
Running automation tests automatically during software deployment.
This skill is highly valuable in DevOps environments.
Tools You Will Learn in a QA Software Tester Course
Students typically learn industry-standard tools such as:
Selenium

Postman

Jira

TestNG

Jenkins

Git

SQL tools

Browser Developer Tools

These tools are widely used in real IT companies.
Job Roles You Can Get After Learning QA Skills
After completing a QA software testing course, students can apply for roles such as:
QA Tester

Software Test Engineer

Automation Tester

QA Analyst

Test Automation Engineer

Quality Engineer

These roles exist in almost every IT company.
Salary Expectations for QA Testers in 2026
QA tester salaries depend on experience and skills.
Typical salary ranges:
Entry-level QA Tester:
$60,000 to $80,000 per year
Automation Tester:
$80,000 to $110,000 per year
Senior QA Engineer:
$110,000 to $140,000 per year
Automation and AI testing skills increase earning potential significantly.
Who Should Learn QA Software Testing?
QA software testing is suitable for:
Beginners with no IT experience

Career switchers

Fresh graduates

Manual testers upgrading to automation

IT professionals switching roles

It is one of the easiest ways to enter the IT industry.
How QA Skills Prepare You for Real Jobs
QA courses prepare students through:
Hands-on practice

Real-world projects

Industry tools training

Automation experience

Interview preparation

These skills help students become job-ready.
Future Scope of QA Software Testing in 2026 and Beyond
Software testing continues to grow because software continues to expand.
Future trends include:
AI-powered testing

Automation-first testing

Cloud testing

Security testing integration

DevOps testing integration

QA professionals will remain essential in software development.
Conclusion
A Quality assurance tester course in 2026 teaches essential skills such as manual testing, automation testing, API testing, programming basics, database testing, Agile methodology, and real-world project experience. These skills prepare students for high-demand QA roles across industries. With the rise of automation, AI, and cloud applications, QA testing remains one of the most stable and accessible IT career paths.
By learning modern testing tools, automation frameworks, and industry practices, students can build strong careers as QA testers and contribute to delivering high-quality software used by millions of users worldwide.

Since the advent of Spring Boot, it has been the De-Facto Backend Framework for Java Web Developers, thanks to its Developer friendly ecosystem, Autoconfiguration Magic and ready to code setup (Spring starters).

But, This is the age of cloud. Several other frameworks have mushroomed up, such as Micronaut and Quarkus.

We will talk about Quarkus, how is it different from Spring Boot, how cloud has made companies to rethink their strategy of choosing the framework, and what is Spring’s answer to Quarkus.

For me, Quarkus was difficult. Despite Quarkus having similar ready to code setup, similar style of writing code, there was far less developer support and exhaustive documentation when compared to that of Spring boot’s detailed documentation and other online forums.

Spring uses reflection heavily for autoconfiguration. It provides amazing developer experience.

Stereotypes, Component Scan, Configuration files declaring beans for classes outside the container using @Bean, Spring Data JPA interfaces, all of this is autoconfigured using reflection. Spring creates proxies at runtime to setup the Spring Container. This results in high usage of RAM and slower application startup.

To relate to this, lets go through a small example. You might have seen BeanCreationException while working with Spring, refer here. This happens when Spring tries to create Application Context at start up. It tries to resolve all the dependencies and create Beans in their respective scopes.

This can help you understand that Spring Beans are created at runtime, when the Application Context initialization is attempted.

Now that we have established that Spring has a high memory footprint, lets understand, why is it a problem.

Organizations have been moving to Microservices architecture because of its Reliability and Scalability. Systems are moving to cloud environments, reducing costs compared to On Premises Infrastructure and plenty of out of the box options for various use cases that these systems may need.

A monolith is divided into multiple microservices based on domain driven design principles. Each microservice has multiple replica instances and each replica gets its own computing and memory resources.

Imagine a system where hundreds, if not thousands of microservices run on cloud, each pod consumes memory and computing resources, the cloud bill increases.

Now imagine an Alternative that is approximately 10x faster on average and uses 1/10th of memory resources, saves a ton of money, the trade off is Developer experience. This is what Quarkus brings to the table.

Here we discussed briefly how Spring works internally, what challenges does it face when integrating with cloud.

In the next part, we will discuss as to how Quarkus solves the cloud challenge and what Spring’s team is doing to mitigate the same.

The era of conversational AI chatbots is officially giving way to the era of agentic AI—systems that don’t just talk to you, but actually do the work for you. If you have been following the rapid developments in early 2026, you already know that Anthropic and the open-source community are releasing tools that are actively disrupting industries.

But with so many new tools hitting the market, the terminology can get confusing. If you are trying to figure out the difference between OpenClaw, Claude Cowork, and Claude Code, you are in the right place.

This comprehensive guide will break down what each tool does, who it is built for, and which one you should integrate into your daily workflow to maximize your productivity.

1. Claude Code: The Developer’s Powerhouse

Launched as an agentic coding tool, Claude Code is Anthropic’s terminal-native assistant designed specifically for software engineers. Instead of making you copy and paste code snippets from a browser window, Claude Code lives directly in your terminal, IDE, or browser, and has deep access to your entire codebase.

Key Features

Autonomous Engineering: Claude Code doesn’t just suggest code; it reads your repository, plans an approach, writes across multiple files, runs tests, and can even create pull requests.

Claude Code Security: A recently launched (and highly disruptive) feature that scans production codebases to find and patch complex vulnerabilities. It reads code like a human security researcher, tracing data flows rather than just matching known threat patterns.

Legacy Modernization: It is exceptionally proficient at deciphering and modernizing legacy languages like COBOL, automating the grueling analysis phases of enterprise server migrations.

Who is it for? Software developers, security analysts, and DevOps engineers who are comfortable working in a Command Line Interface (CLI) and want an autonomous AI pair programmer.

1. Claude Cowork: The Desktop Digital Employee

While Claude Code is incredible, terminal interfaces are intimidating for non-developers. Enter Claude Cowork—Anthropic’s answer to “Claude Code for the rest of us.” Released as a desktop application feature (currently for macOS), Cowork operates as a highly capable digital employee.

Key Features

Direct File System Access: You can grant Claude Cowork permission to specific local folders. It runs in a secure, containerized sandbox (using Apple’s Virtualization Framework) to protect your system.

Workflow Automation: Point it at a messy “Downloads” folder and tell it to organize files by type, rename them, and extract data from receipt PDFs into an Excel spreadsheet. It handles the entire multi-step process autonomously.

Progressive Skills: It utilizes Anthropic’s “Agent Skills” to natively interact with office file formats like XLSX, DOCX, and PPTX without needing external software.

Who is it for? Knowledge workers, project managers, and administrators who want the power of an autonomous AI agent without ever having to open a developer terminal. It is currently available for Claude Pro ($20/mo) and Max subscribers.

1. OpenClaw: The Open-Source “Personal OS”

Formerly known as Clawdbot/Moltbot, OpenClaw is a viral, open-source, “local-first” AI agent created by developer Peter Steinberger. Unlike Anthropic’s official tools, OpenClaw is designed to be an always-on, 24/7 personal assistant that you communicate with via standard messaging apps like Telegram, WhatsApp, or Discord.

Key Features

Conversation-First Interface: You don’t need a complex UI. You just text your agent on WhatsApp to clear your inbox, check you in for a flight, or summarize a project status.

Continuous Memory & Proactivity: Because it runs continuously on your local machine (or a dedicated cloud server/Raspberry Pi), OpenClaw remembers your past interactions, learns your preferences, and can execute scheduled tasks (cron jobs) without being prompted.

Community Skills: It features a massive marketplace (“ClawHub”) of over 3,000 community-built extensions, allowing it to connect to almost any API or service.

Model Agnostic: While it runs beautifully with Claude Code under the hood, you can also run it completely locally on NVIDIA RTX GPUs using open-source models to ensure absolute data privacy.

Who is it for? Tech-savvy tinkerers, automation enthusiasts, and anyone looking to build a highly personalized, deeply integrated “second brain” that acts proactively on their behalf.

Feature Comparison at a Glance

Which AI Agent Should You Choose?

The decision ultimately comes down to your technical comfort level and daily workflows:

If you write code for a living: Claude Code is the undisputed champion. Its ability to navigate complex repositories and autonomously debug makes it a mandatory tool for modern development.

If you handle documents, spreadsheets, and messy files: Claude Cowork is your best bet. It provides enterprise-grade AI automation wrapped in a safe, intuitive, and sandboxed desktop interface.

If you want a 24/7 proactive life assistant: OpenClaw offers unparalleled flexibility. It requires a bit of setup (often via Docker or WSL), but the ability to text an AI on WhatsApp and have it manage your actual desktop calendar and emails is the closest thing we have to Artificial General Intelligence (AGI) today.

I watched a teammate spend 20 minutes complaining that Copilot “doesn’t understand our codebase.” Then I looked at the repo. No README. No architecture docs. No module descriptions. Just code.

Here’s the uncomfortable truth: most AI code quality problems aren’t AI problems. They’re context problems.

The experiment that changed my mind

I took the same task — “add pagination to the users endpoint” — and tried it two ways:

Round 1: Just the prompt. AI generated something that technically worked but used the wrong ORM pattern, wrong error handling style, and a pagination approach nobody on the team uses.

Round 2: Same prompt, but I added a 40-line AGENTS.md file describing our project conventions: ORM patterns, error handling approach, pagination style, test expectations.

The difference was night and day. Not because the AI got smarter between attempts — the context did.

Why this matters more than model upgrades

Everyone’s waiting for GPT-5 or Claude Next or whatever to “finally get it right.” But I’ve found that well-documented context with a mediocre model outperforms zero context with a frontier model.

Think about it like onboarding a new developer. You wouldn’t drop a senior engineer into your codebase with zero documentation and expect them to match your team’s patterns on day one. Why do we expect that from AI?

What actually works: the AGENTS.md pattern

I keep a simple markdown file at the project root that describes:

# AGENTS.md

## Project Overview
Express API with PostgreSQL, using Knex for queries.

## Conventions
- Error handling: wrap in try/catch, use AppError class
- Pagination: cursor-based, not offset
- Tests: co-located with source, use test factories
- Naming: camelCase for JS, snake_case for DB columns

## Common Gotchas
- Don't use the `users` table directly — go through UserService
- Rate limiting is middleware-level, don't add it per-route

That’s it. 30 lines. Takes maybe an hour to write well.

The key insight: this file is portable. I’ve used variations of it with Cursor, Copilot, and Claude Code. The format changes slightly, but the content — your project’s actual knowledge — stays the same.

The trade-off nobody talks about

I won’t pretend this is free. The setup cost is real — maybe 2-3 days to get your context files right for a large project. And they need maintenance. When your patterns evolve, your AGENTS.md needs to evolve too.

It also doesn’t solve everything. Greenfield projects where you don’t have established patterns yet? AI is still going to hallucinate conventions. And for high-stakes code — auth, payments, data migrations — I still do manual review regardless of how good the context is.

But for the 80% of code that follows established patterns? Context files are the highest-leverage investment I’ve found.

The question I’m still working through

Here’s what I haven’t figured out: how do you keep context files in sync with a fast-moving codebase?

I’ve tried pre-commit hooks that validate AGENTS.md against actual code patterns. It sort of works. But I’m curious — has anyone found a better approach? Or do you just accept some drift and do periodic manual updates?

P.S. I’ve been packaging the workflow patterns I use daily into a toolkit — project templates, AGENTS.md examples, verification scripts. If you’re interested, I share more at updatewave.gumroad.com.

Rovo

• I’m ________ and ________ happened.
• Search the whole company, restricted to _____ Jira project, _____ confluence space, and ______ github repo.
• Answer my question: ________
• Reply very concisely and abruptly, with no background information or definitions <— this doesn’t fully work with Rovo!

Copilot

You’re an expert __________ with comprehensive knowledge of ________.
Help me ______.
I already know ______.
I want you to ________.

• I need you to reason step by step and explain your thought process internally before giving the final answer.
• Think through trade-offs before deciding.
• Perform internal reasoning before answering.
• Provide the final output only after complete reasoning.

Gemini

As of 22nd January 2026, ____________?

• Answer Yes or No, with sources.
• Do not rely on memory, verify against current official docs.
• If you are not sure, say ‘unknown’ instead of guessing.
• State whether this comes from official docs, community reports, or inference.

[critical thinking]

• I need you to reason step by step and explain your thought process internally before giving the final answer.
• Think through trade-offs before deciding.
• Perform internal reasoning before answering.
• Provide the final output only after complete reasoning.
• Do not rely on memory, verify against actual content.
• If you are not sure, say ‘unknown’ instead of guessing.
• State whether this comes from official docs, community reports, or inference.

Overview: What are extension members?

Extension members allow you to define additional members for existing types without modifying their definitions. With them, you can add functionality to existing types you don’t have access to or don’t control, for example, built-in types or types from an API or commercial library. 

Extension methods have been a feature of C# since version 3.0 in 2007, so the concept has been around for some time in .NET. However, traditional extension methods were just that – methods only. Extensions could not be created for properties, fields, or operators. You couldn’t create static extensions and they couldn’t easily participate in interfaces. However, new syntax in C# 14 allows both instance and static properties and methods, as well as operators.

Classic extension methods

Let’s quickly review what a classic extension method looks like. We’ll extend the DateTime structure to check the first Monday of any quarter. You might see code like this in manufacturing scenarios where production runs need to start on a specific day, such as the first Monday of a quarter. The code looks something like this:

    public static DateTime FirstMondayOfQuarter(this DateTime dateTime, int quarter)
    {
        if (quarter is < 1 or > 4)
            throw new ArgumentOutOfRangeException(nameof(quarter), 
                "Quarter must be between 1 and 4.");

        var year = dateTime.Year;
        var firstMonth = (quarter - 1) * 3 + 1;

        var date = new DateTime(year, firstMonth, 1);

        var offset = ((int)DayOfWeek.Monday - (int)date.DayOfWeek + 7) % 7;
        return date.AddDays(offset);
    }

Notice that to make the extension method you must make the class and method static, and use the this keyword to indicate which type to extend. While the definition uses the static keyword, it’s not a static member.

Code to use this extension method looks like the following:

DateTime myDate = DateTime.Now;

for (var i = 1; i <= 4; i++)
{
    Console.WriteLine(myDate.FirstMondayOfQuarter(i).ToShortDateString());
}

Because it’s not a static method, you can’t just call DateTime.FirstMondayOfQuarter(2). Calling DateTime.Now (or any DateTime member) creates a new instance of a DateTime.

Extension members in C# 14

Use the new extension block inside a static class to define extensions. The extension block accepts the receiver type (the type you want to make an extension for), and optionally, a receiver parameter name for instance members. Adding the parameter name is recommended for clarity. Here’s the syntax:

extension(Type) { … }    // plain extension block

extension(Type parameterName) { … }   // extension block with a parameter name

If we want to convert a classic extension method to a new extension member, we can use Rider. Rider has a handy intention action for this, just press Alt + Enter and choose Move to extension block:

The code to use it doesn’t change. However, you can now call the code without having to create an instance first, like this:

Console.WriteLine(DateTime.Now.FirstMondayOfQuarter(i).ToShortDateString());

So you won’t need to change any calling code unless you want to.

To create an extension property, use an extension block like you would for any extension member. The rest of the code looks very natural like regular C# code.

public static class DateTimeExtensions
{
    extension(DateTime date)
    {
        public static bool isWeekend(DateTime d) => 
                d.DayOfWeek is DayOfWeek.Saturday or DayOfWeek.Sunday;
    }
}

// To use it:

if (DateTime.Today.IsWeekend)
{
    // No work today, yay!
}

Notice that in the extension block you define methods, properties, and other members without using the this parameter syntax for each member.

A goal of the C# team was to ensure that existing code doesn’t break, so then the syntax you use becomes a matter of style. There’s no need to change any of your existing extension methods, but Rider’s handy intention action makes it fast and easy to do so.

In Summary

Extension members are beneficial for several scenarios, including transforming helper methods into properties, organizing related extensions, incorporating static constants or factories into existing types, defining operators on external types, and making third-party APIs feel more integrated or native.

As of February 2026, Databao Agent ranks #1 in the Spider 2.0–DBT benchmark. This ranking measures how well agents can operate in a real dbt project, including reading the repository, understanding what’s broken, implementing the missing models, and validating everything by actually running code.

Our team ended up achieving the highest score in the benchmark, but we didn’t do it just because “we used a better model.” We got the biggest gains by treating the agent the same way you would mentor a junior colleague – providing better context, restricting chaos, and enforcing a reliable workflow.

This post is a practical account of what we changed and why it mattered. Read on to learn about the engineering decisions that made the difference, including how we reduced uncertainty, upgraded context, tightened up tool discipline, and rewrote a messy pile of prompts into a clear policy the agent could follow. The lessons we learned the hard way are that reliability beats cleverness, and prompts alone don’t buy you reliability – you have to design for it.

What is a dbt project?

dbt (data build tool) treats analytics like software. Instead of ad-hoc SQL embedded in dashboards and notebooks, data transformations live in a version-controlled repository, are reviewed like code, and can reliably rebuild the same analytics layer.

The main unit of work in dbt is a model: an .sql file that defines a dataset (usually a table or a view) built from other datasets. Models depend on other models, and dbt builds them in dependency order, turning the project into a directed graph rather than a pile of disconnected queries.

A typical dbt repository contains the following parts:

• The models/ directory with SQL models (often organized into layers, such as staging → intermediate → marts).
• YAML files that document the project and add tests and constraints (sources, descriptions, uniqueness tests, freshness, etc.).
• A workflow built around commands like dbt run or dbt build. These commands materialize models, run tests, and tell you what failed, where, and why.

Working with dbt means navigating a codebase, respecting conventions and dependencies, iterating, and not declaring victory until the build is green. The Spider 2.0–DBT benchmark asks agents to do exactly that.

What Spider 2.0–DBT evaluates

The Spider 2.0–DBT benchmark turns a day-to-day dbt workflow into an evaluation. In the version we ran, the benchmark had 68 tasks. Each of them was a folder containing:

• An incomplete dbt project (models were missing or incorrect).
• A DuckDB database file with the available data.

The agent’s job was to behave like a careful data engineer:

1. Read the repository to understand what the repo is trying to produce. 
2. Identify what’s missing or wrong.
3. Implement the missing SQL models or fixes.
4. Run dbt.
5. Keep iterating until the project builds.

The evaluation compares the produced database with a “golden database” and checks whether the agent produced the right tables and columns.

Even though it may sound like simple SQL generations that many LLMs can do well, the hard part is operating in a repository environment. Some tasks are large – like, “data warehouse” sized – tables with 2,500+ columns, dozens of models in a single task, and thousands of lines of SQL across the project. 

This scale forces the agent to behave like a real contributor. You can’t paste the entire repository and schema into a single prompt and expect consistent reasoning. The agent has to navigate the project, read selectively, build a mental map of the project, and stay oriented after each run.

Where we started: Baselines and the real enemy

We didn’t start from scratch – our first agent was based on a popular LLM and could inspect a data project, run commands, and make edits using standard data tools. Surprisingly enough, its performance right out of the gate wasn’t too shabby – it could solve about a quarter of the tasks in our benchmark.

Encouraged, we built a more flexible version of the agent by giving it some more tools not available in default setups of other agents. This gave us more control and room to experiment. On paper, these were all improvements. But in practice, consistency was sorely lacking. The agent behaved a little differently each time we ran it. It would nail one task, then completely whiff on the next.

This inconsistency turned out to be the real enemy. When we looked closer, the issue wasn’t that the agent couldn’t write SQL or “do data stuff.” The problem was that it struggled to behave consistently and to understand what the actual task was – something a careful data or analytics engineer wouldn’t have any issues with.

Important kinds of uncertainty

As we dug deeper, we realized there were two main culprits behind the agent’s randomness.

The first was missing or unclear context. The agent often didn’t have enough visibility into how the project was structured, what tables existed, or what conventions were being followed. This uncertainty is fixable. If you provide better, targeted context, the agent stops guessing.

The second was natural ambiguity. Human language is fuzzy by nature. Even with good instructions, there can be multiple reasonable ways to solve a task, but only one of them matches the benchmark’s expected answer. You can’t fully eliminate this kind of uncertainty.

Understanding this distinction changed what we worked on. Once we did, we were able to re-allocate our efforts, focusing less on fixing the model and more on fixing the environment around it.

Our strategy shift: From model tuning to workflow engineering

Early on, we gave the agent lots of freedom and lots of tools. That felt powerful, but failed in predictable ways: the agent wandered around, tried random actions, undid its own work, and generally got lost.

So, we changed our mindset. Instead of asking, “What can this agent do?” we asked, “What would a human engineer actually do here?”

We focused on two things:

1. Better context: Make the right information easy to access and hard to miss.
2. A clear, disciplined workflow: Reduce chaos by forcing a specific order of operations.

Better context

We made sure the agent didn’t have to hunt for information.

We showed the important project files upfront, so the agent wouldn’t waste time opening the wrong things, and added a quick database overview at the beginning, so the agent knew which tables already existed. These fixed a surprising number of failures, especially on tasks where the correct action was to do nothing at all.

We also helped the agent connect the dots between requirements and data sources instead of guessing names. When it ran data builds, we summarized the results instead of dumping long, noisy logs. This kept the agent focused on what mattered next.

The result? Fewer blind mistakes and fewer “I didn’t find the right thing” failures.

A clear, disciplined workflow

Context helped, but it didn’t solve the failures entirely, so we tightened up the rules.

In the first version, we gave the agent access to many tools. It could read, write, edit, and add any file in the dbt project, and it had unrestricted access to the terminal. In theory, this was supposed to make the agent powerful, but unfortunately, the agent used its power to break things.

We removed the general scope tools and limited access to a narrow set of specific commands, such as dbt run or dbt build. File edits were restricted so that the agent could mostly edit .sql files in specific directories. We also gave the agent a clear checklist: inspect first, make minimal changes, verify, and only then declare success.

In several tasks, the agent didn’t inspect the database state carefully and could unintentionally overwrite existing tables with incorrect results. To prevent this, we added a few hard rules like never touching tables that already exist but aren’t part of the project, and never submitting an answer unless the final validation step succeeds.

This dramatically reduced chaotic behavior, loops, and premature “I’m done!” moments.

What we learned: Stability over cleverness

It goes without saying that not every idea paid off. Adding more clever mechanisms (e.g., re-running the agent several times and choosing the “best” output, simulating human reviewers, or layering on extra tools) often gave us even less reliable results.

And then there was the “prompt onion” problem. Initially, whenever we wanted to improve performance or change the logic, we added another rule or clarification. But soon enough, rules started overlapping and conflicting, and the execution flow became murky.In the end, stability beat cleverness. We took a step back and rewrote everything into a clean, human-readable policy. Redundancies and contradictions were removed, and the workflow became linear and predictable, leaving less room for interpretation – for both humans and the agent.

How this translates to real agents (Databao)

The biggest takeaway was about behavior, not SQL or models. Agents work best when:

• They can clearly see their environment.
• They follow a human-like workflow.
• Their freedom is intentionally limited.

In real systems, prompts alone aren’t enough. Safety and reliability need to be enforced at the tool and system levels, not just in the instructions.

What’s next: Reducing variance and catching errors automatically

Ranking #1 on the benchmark wasn’t the finish line for us. We’re already working on reducing variance, implementing smarter error detection, and splitting responsibilities across multiple specialized agents.

If data agents interest you, you can get involved. The open-source data agent code is already available on GitHub, and support for dbt will be added soon.

If you’d rather use agents than develop them, you can build Databao into your workflow or join us in building a proof of concept together. We’ll work with you to understand your use case, define a context-building process, and give the agent access to a selected group of business users. Together, we’ll evaluate the quality of the responses and overall satisfaction with the results.

TALK TO THE TEAM