Client communication often feels harder than it should. Olha Tvardovska, a Project Manager, shares common communication pitfalls and habits worth building. The guidance applies to managers and engineers, including those who haven’t yet worked directly with clients.

Why Communication Problems Arise

Not everyone enjoys working with clients. Some people just want a ticket, materials, and access so that they can focus solely on execution. The reasons are usually straightforward:

1. Uncertainty: It’s hard to predict a client’s questions or where a conversation might lead
2. English Skills: Many don’t feel confident in English and struggle to understand and express their thoughts.
3. On-the-spot promises: It’s easy to agree during a call without considering the consequences.

Clients notice communication problems, even if they remain polite. The rules below can help, and the examples will show how small changes can prevent bigger ones.

1. Avoid the Word “Impossible”

Most of us know the rule: don’t tell a client something is “impossible.” There are good reasons for it.

First, how sure are we that the request truly can’t be done?

A colleague once told me about a client with an unusual request. The team reviewed it from every angle and said, “We can’t do it the way you want. Here’s an alternative.”
The client asked for time. A week later, they returned with their own technical team, who clearly showed how the idea could be implemented.

The partnership survived, but it could have ended right there. Being proven wrong in front of a client is not pleasant.

There’s also a psychological side. Think of a time someone told you something you cared about wasn’t possible, such as renting an apartment with a pet, getting a job you wanted, or anything. It never feels good.

Clients are often emotionally attached to their ideas. They may have worked on them for a long time. A blunt “impossible” can crush that. And what’s at stake may be serious: a promotion, a project tied to a loan, or simply something they don’t have the technical knowledge to judge themselves. They rely on you. A harsh refusal can feel devastating.

What to Do

• Before saying “impossible,” pause and look at the situation from the client’s perspective. Often, this is enough to rethink your approach or find better wording.
• If you must decline, explain why clearly. Details matter. The more transparent you are, the more the client trusts you. Without that, they may accept your answer once or twice, and then quietly end the collaboration.

2. Don’t Say Yes to Everything

Saying “impossible” too often is one problem. Saying yes to everything is just as dangerous.

Overcommitting puts pressure on the team, causing overtime and missed deadlines. Clients pay more, receive less, and service quality drops.

This leads to frustration on both sides. You may resolve things with your team, but fixing a damaged relationship with a client is much harder. They won’t simply walk away; they’ll warn others about their experience.

What to Do

Be open from the start. Say if you haven’t worked with the technology or task before, if your experience only partly matches the requirements, or if someone on the team has related expertise, but it’s hard to judge how relevant it will be.

Suggest starting with a trial iteration. If it goes well, continue. If not, help transfer the project to another vendor. The client may still leave after the initial scope, but you’ll part on good terms — and they’ll recommend you.

Why not offer the trial iteration without explaining your limited experience?
Because the client must decide whether to invest in research, they need complete information, including risks. When you honestly describe your background and concerns, the client won’t feel misled. Even if the project fails, they’ll remember your honesty.

3. Remember the Team

When you communicate with a client, remember that any commitment you make becomes the responsibility of the whole team. Always share the results of your conversations with your colleagues.

Here’s a real example. A client often approached our team lead with questions about how long specific changes might take. That seemed harmless; they wanted to plan better.

But soon they started asking for extra changes to be added into a sprint or release, saying, “It’s really important” or “It’s just a small thing.”

Our team lead tried to help the client and kept agreeing to extra work, often working overtime. Over time, he became exhausted, irritable, and less communicative, but he still delivered more than planned.

Minor “quick fixes” kept coming. The team lead blamed himself for not refusing them. The team felt unsupported. Meanwhile, the client grew used to getting extra work for the same budget and timeline.

What to Do

To fix the situation, we first made it clear that all changes must go through the project manager. No exceptions, even for minor edits. This alone made the client think twice before asking for new requests. All changes returned to the standard process.

Next, the team lead took a vacation. Many don’t know how to balance work and rest. We explained the situation to the team, reviewed what happened, and restored stability.

The client isn’t the enemy. They were under pressure and had no budget for more work, so they asked for favors. Remember that responsibility is shared, team commitments matter, and even small changes mean extra work for your teammates.

4. Explain Your Estimates

A clear explanation of how the team’s time is used helps avoid misunderstandings and long message threads.

It’s easy to imagine a conversation like this:

— What are we spending time on?
 — On the feature you requested.

— Ideal hours or real ones?
 — Uh… real ones, I think.

— So that includes standups, lunch, and coffee? Great. And did you tell the client this is development time only? That a QA still needs to test it? That bugs are possible? That stabilization may take longer?
 — …

Ideally, a project manager or team lead should confirm estimates with the client. But sometimes the client asks for rough numbers on the spot. They see the number, multiply it by the rate, and remember the cost. Later, when they read the detailed explanation, they may get upset. Try to avoid putting them in that situation.

What to Do

Be precise:

• Write “X hours for development only.” Make the number realistic.
• Give optimistic and pessimistic scenarios.
• Ask teammates to adjust estimates for their part of the work.
• Involve QA.
• Collect assumptions: seniority level, whether backend work is on your side, and so on.

If you’re unsure or worried about saying too much, be honest:

“I need to check with the team and will get back to you shortly.”

Then follow up quickly. It’s better to message the group chat or mailing list than wait for one specific person to come online.

Remember: the client will not know which details you left out. They may start planning based on the numbers you mentioned casually. It’s your job to prevent that.

You likely know all this already. If you also consistently follow it, I’ll give you my respect and a virtual high‑five.

5. Maintain the Level of Service

Clients come not only for solutions but also for a certain level of service, especially in outsourcing. In practice, this is often overlooked.

Here are a few examples:

• A project ends, but the client still has your contact. They send an email and get no reply. You’re busy, so you forward the message to the project manager, but they might be on vacation or out sick. In the end, the client is ignored. Some developers even block stakeholders after delivery: “The project is done, contact management.”
• The project is sporadic, or your involvement is limited. You handle tasks without understanding the client’s business or goals, choosing the quickest fix. You follow the specification blindly and avoid thinking ahead. When the client later asks for help with a promo campaign, you fail to take the initiative and don’t warn them that the system may not handle increased traffic.
• Even in a full-time project, it’s possible to ignore requests, choose tasks on your own, or answer everything with a simple yes, no, or maybe. Some people think, “They came for solutions, we’ll decide what’s important.”

What to Do

With this approach, the current project may still succeed. But the client will not be quick to sign the next contract. To avoid this, remember that the client didn’t come to you only for a technical result. They want things to work better with you than without you.

Conclusion

Each of these rules works even on its own, but together they depend on one thing: being willing to communicate.

Many people avoid client interaction out of habit or insecurity, not because of a lack of skill. The difference between those who struggle and those who don’t isn’t talent, but practice.

And then some people felt completely at ease. Talking to clients became routine for them. They easily switched communication styles, using a formal tone with clients and an informal one with the team.

Client communication is worth developing. It reduces friction, increases trust, and makes your work easier over time. Start small, build consistency, and don’t aim for perfection. Clear intent and steady habits matter more than flawless delivery, and you already know that.

DataArt’s BAs work across a wide range of industries and project types rather than staying on a single product. This accelerates development and rewards those who combine solid requirements-engineering fundamentals with clear communication.

We assess BAs across ten capability areas: Mindset, Communication, Elicitation, Specification, UI Prototyping, Requirement Management, Scope Management, Stakeholder Management, SDLC, and People Management. Growth across levels is about deepening these capabilities and increasing independence and ownership.

Trainee Business Analyst

Trainees are at the start of their BA journey. They need a sufficient level of English and an analytical mindset. At this stage, the focus is on learning and gaining initial experience through training tasks or non-critical project work. A curator supports and takes responsibility for results, ensuring guided onboarding and development.

Junior Business Analyst

Junior BAs have basic knowledge of requirements engineering and work under supervision, typically with up to six months of experience or participation in 0–1 discoveries. There is no direct client communication at this stage.

• Mindset: Continuously learn from BA-related materials and understand how requirements connect to architecture. Deliver consistent outputs with mentor support.
• Communication: Keep communication clear and structured, both verbally and in writing. Participate in team discussions and run simple presentations.
• Elicitation: Use at least three elicitation techniques (e.g., interviews, workshops, surveys) and apply basic NFR types such as Availability, Compatibility, Localization, or Performance.
• Specification: Write requirements with review cycles. Understand user stories, Use Cases, acceptance criteria, and INVEST principles.
• UI Prototyping: Create simple wireframes using tools like Balsamiq or Visio.
• Requirement Management: Manage requirement attributes (ID, name, status, priority, stakeholders, traceability) and maintain traceability in tools like Confluence or JIRA.
• Scope Management: Support decomposition and structuring of requirements.
• Stakeholder Management: Understand stakeholder roles and collaborate within the team.
• SDLC: Understand the lifecycle and BA responsibilities in Agile approaches.

Strong Junior Business Analyst

Strong Junior BAs build on Junior-level knowledge with 6–12 months of experience in RE and 1–2 discoveries. They work more independently and begin to engage in limited client interaction.

• Mindset: Continue learning and attend industry events such as conferences, IT talks, or workshops periodically.
• Communication: Participate in larger meetings and contribute to discussions with clients when appropriate.
• Elicitation: Understand the “why” behind requirements. Expand NFR knowledge and plan elicitation activities effectively.
• Specification: Reduce rework cycles and improve clarity of requirements.
• UI Prototyping: Understand differences across platforms and create basic process diagrams.
• Requirement Management: Manage priorities using at least one technique and support demos.
• Scope Management: Maintain backlog consistency and handle requirement conflicts.
• Stakeholder Management: Identify key stakeholders and manage simple conflicts.
• SDLC: Apply lifecycle knowledge in real project scenarios.

Middle Business Analyst

Middle BAs work independently on non-complex projects and communicate directly with clients. They typically have 1–3 years of experience or 3–5 discoveries.

• People Management: Handle conflicts using negotiation and mediation techniques.
• Mindset: Demonstrate technical awareness and process large amounts of information effectively.
• Communication: Lead meetings and collaborate with technical stakeholders across the project lifecycle.
• Elicitation: Use multiple techniques, perform self-onboarding, and work with legacy systems when needed.
• Specification: Produce precise requirements using structured formats such as user stories, Use Cases, and BDD.
• UI Prototyping: Define detailed screen specifications with business rules and controls
• Requirement Management: Manage scope boundaries, prioritize effectively, and maintain traceability systems.
• Scope Management: Prevent scope creep and perform structured decomposition independently.
• Stakeholder Management: Adjust approach based on stakeholder needs and manage conflicts.
• SDLC: Tailor processes, estimate work, and perform risk analysis.

Strong Middle Business Analyst

Strong Middle BAs take ownership of complex projects, define BA processes, and lead other analysts. They also develop domain expertise and contribute to project strategy. Typically, they have 3–5 years of experience and 5–10 discoveries.

• People Management: Lead BAs, mentor team members, and contribute to methodology development.
• Mindset: Continuously learn and actively participate in the professional community.
• Communication: Drive communication across teams and project phases.
• Elicitation: Lead discovery, pre-sales, and solution design phases. Define key artifacts such as product vision and roadmap.
• Requirement Management: Tailor processes independently to fit project needs.
• SDLC: Lead BA methodology setup and planning activities.
• Specification, UI Protyping, Scope Management, and Stakeholder Management are the same as for Middle BAs.

Senior Business Analyst

Senior BAs operate at a strategic level. They lead large, complex initiatives, provide consulting across projects, and mentor other analysts. Typically, they have more than five years of experience and extensive discovery involvement.

They combine deep expertise with strong leadership, shaping both project outcomes and BA practices. Senior BAs proactively engage stakeholders, influence business decisions, and continuously improve delivery processes across the organization.

Progression in this matrix is not about checking every box — it’s about increasing your ability to operate independently, handle complexity, and drive outcomes. If you already meet most expectations at your target level, you’re likely closer than you think.

*The article was initially published on the DataArt Team blog.

A small disclaimer before we begin: the advice in this article applies only to the Claude Certified Architect Foundations (CCA-F) certification. Anthropic offers three additional certifications, each with different content and target audiences.

Why This Certification?

I decided to pursue the certification for several reasons:

• Bonus: At the time, DataArt offered a bonus to all candidates who successfully passed the exam. Many companies use similar incentives to encourage participation.
• Novelty: It’s a relatively new technology that not many people know how to work with.
• Personal Development: It’s a great opportunity to explore new use cases and tools available to developers.
• Career Growth: It looks good on a CV and LinkedIn profile.

I’m sure these motivations can apply to many of you as well.

Experience

Before taking the exam, I had been using Claude for day-to-day software development tasks for several months. You don’t need extensive prior knowledge, although familiarity with the tools is helpful.

But I would place greater value on practical experience building your own AI solutions rather than simply using LLMs. For example, projects where you’ve integrated the Anthropic API into automated pipelines are particularly valuable. Using an LLM as a support chatbot for solving refund requests would be a good example.

If you are a developer who only uses Claude Code, that alone won’t be enough. You should complete the recommended courses to learn concepts you may not encounter in day-to-day work. Even if you have practical experience with the Anthropic API, the courses can help you understand niche topics such as tool design and how data and conversations are sent to models.

Preparation Time

I learned about the exam close to the deadline, so I only had the weekend to prepare. Although I managed to pass, I would recommend allowing yourself around two weeks so you can spread the workload more evenly across weekdays and weekends.

My Strategy

My primary preparation resource was the official Anthropic Partner Academy. The Academy provides:

• Official courses
• Certification exam guides
• Practice exams
• Learning resources aligned with certification objectives
• Exam registration information

On the certifications page, you’ll find a recommended list of courses to complete before registering. That’s exactly what I did, and it proved sufficient.

The courses are free, and most importantly, the practice questions closely resemble those on the actual exam. Anthropic’s recommendations proved highly accurate, so I strongly recommend following them.

Also, be sure to download their official exam guide. Many candidates consider it their most valuable preparation resource. It explains the exam topics in detail and includes sample questions with answers. The explanations are especially useful because they reveal the reasoning behind the correct responses.

Understanding the reasoning behind answers is critical. The exam is highly opinionated about what constitutes the “correct” solution. Some questions present multiple options that seem equally valid, but the right answer aligns with Anthropic’s preferred approach and best practices. Studying official resources helps you understand that perspective.

Advice for Future Candidates

Below, you can find the advice we compiled thanks to feedback from our colleagues.

Focus on the Official Exam Guide

It should be your primary preparation resource. Many candidates report that understanding recommended approaches, patterns and best practices described in the guide improved their exam performance.

Complete the Recommended Courses

The official courses available through the Anthropic Partner Academy provides a strong foundation for preparation. They help you develop the vocabulary, concepts, and mental model required for the exams. So, combining official courses, the exam guide, and practice exams is the most reliable path to success.

Understand Concepts and Reasoning, Not Just Tools

The exam focuses heavily on:

• AI system design principles
• Agent-based workflows
• Prompt engineering
• Context management
• Claude best practices
• Safety and governance
• Evaluation methodologies

Knowing how a feature works is important, but understanding when and why Anthropic recommends using it is even more valuable.

Take the Practice Exam Multiple Times

The practice exams are the most effective preparation tools available.

The recommended approach is to:

• Take the practice exam more than once
• Review questions you answered incorrectly
• Analyze why specific answers are preferred
• Revisit difficult topics before the actual exam

This process helps you recognize patterns that frequently appear on the real exam.

Keep in mind that the practice questions are generally shorter and simpler than those on the real exam

Learn Anthropic Terminology and Patterns

Many questions are scenario-based and depend on understanding Anthropic-specific concepts and terminology.

Colleagues frequently mentioned the following areas:

• Agent design patterns
• Tool use and orchestration
• Context management strategies
• Prompting approaches
• Workflow design
• Evaluation frameworks
• Reliability and safety considerations

Beware of Solution-design Questions

The most difficult questions are typically solution-design questions rather than technical ones. They’re often opinion-based and require you to think the way Anthropic approaches problem-solving and solution architecture.

Use AI-Generated Study Materials Carefully

Even though AI-generated summaries and guides can be helpful for reviewing concepts, but they should remain a secondary source because they may:

• Misinterpret Anthropic terminology
• Recommend approaches that differ from official guidance
• Miss important exam objectives

Allow Enough Preparation Time

The amount of preparation required depends on your experience level. Users who did integrations with Claude API or built custom solutions involving LLMs may need only a few focused days of study, while others may prefer one to two weeks

Manage Your Time During the Exam

The exam is reading-intensive. The questions are longer and more complicated than those during practice.

Candidates recommend:

• Reading questions carefully
• Avoiding spending too much time on a single question
• Flagging difficult questions for review
• Leaving enough time for the final section of the exam

A strong understanding of Anthropic’s recommended practices, combined with repeated exposure to official preparation materials, gives you the best chance of success. Focus on the reasoning behind the answers, not just the answers themselves, and you’ll be well prepared for exam day. Good luck!

*The article was initially published on DataArt’s Team blog.

Levels of AI Usage in Cyberattacks

AI is revolutionizing cybersecurity, but also makes attackers more dangerous. Here are some ways attackers use AI during an attack:

1. Info Source: Using AI as a knowledge base to learn how to start a brute force attack.
2. Assistant: Integrating AI into the workflow to support specific steps. AI can generate password lists or draft phishing emails.
3. Autonomous Attacker: Automating parts of the attack. Instead of prompting every step, attackers can issue a single instruction.
4. Full Automation of the Attack: Selecting a target and instructing AI to identify vulnerabilities and launch an attack.
5. Automation of Cybercriminal Campaigns: AI handles the entire process: choosing a target, finding a weakness, and attacking.

DarkLLM

DarkLLMs are models trained specifically for cybercrime and sold on the dark web. They can:

• Generate grammatically correct phishing emails in any language
• Simulate customer service conversations
• Write malicious code
• Bypass spam and security filters
• Create fake identities, documents, videos, and voice recordings

Some services can even bypass identity verification systems using deepfake videos that mimic real user behavior (e.g., smiling or turning the head). What once seemed like science fiction is now a commercial service available on underground forums.

EXA AI

This is an open-source tool that anyone can use, including attackers. It retrieves targeted information about people and companies more efficiently than traditional search engines.

While not malicious itself, in the wrong hands it becomes a powerful reconnaissance tool. Attackers can uncover email addresses, workplaces, interests, and social profiles within seconds.

OSINT

OSINT (Open Source Intelligence) gathers information from publicly available resources. This often includes:

• Social media profiles
• Public records
• Forums and communities
• News and company websites

Even small details, location tags, job updates, or shared photos help attackers build a profile. This lets them create highly personalized and convincing attacks.

Have I Been Pwned

You can check whether your email has been exposed in data breaches using haveibeenpwned.com.

If your email has been compromised, take these steps:

• Change your password immediately
• Use a unique password for each service
• Enable Multi-Factor Authentication (MFA)

Most people will find their email in at least one breach. This means attackers may already have access to some of their data.

Why Sharing Your Birthday Matters

Sharing your birthday on social media introduces risks:

• Security Questions: Many services use birth dates for account recovery.
• Identity Verification: Banks and government services may rely on it

Attackers can use this information to impersonate you or create convincing phishing messages, including targeted promotions timed to your birthday.

Digital Footprint

A digital footprint is the data trail you leave online. It includes:

• Active Footprint: Information you intentionally share (social media posts, photos, comments, reviews, and LinkedIn updates.
• Passive Footprint: Data collected without explicit action (cookies, IP address, location, browsing history)

How Attackers Build Your Profile?

Attackers combine multiple data points to build a detailed profile:

• Identification Data: Name, phone number, address, email
• Professional Information: Workplace, job titles, colleagues
• Social Connections: Friends, family, interactions
• Behavioral Patterns: Online activity, habits, travel
• Interests and Triggers: Hobbies, favorite brands, preferences
• Technical Context: Devices, operating systems, browsers

This enables hyper-personalized attacks that reference real aspects of your life.

For example, if you post on LinkedIn that you’re looking for a job, you might receive a fake onboarding email with a malicious link.

Checking your digital footprint is important to lower your risk.

Security Checklists

These practical steps help reduce your risk.

Access points

• Access Points: email, phone number, and single sign-on accounts are gateways to other services. If compromised, attackers can reset passwords. Enable MFA or 2FA wherever possible.
• Check whether your email appears in data breaches at haveibeenpwned.com and subscribe to alerts.
• Review connected SSO (Single Sign-On) accounts (Google, Apple, Facebook) and remove unused ones.
• Use authenticator apps (Microsoft Authenticator, Google Authenticator, Authy) instead of SMS-based codes.

Financial and Sensitive Services

• Avoid accessing banking accounts over public Wi-Fi, like at cafes, hotels, or airports. Attackers can intercept your data over unsecured networks. If necessary, use a VPN.
• Do not store passwords or credit cards in your browser.
• Enable 2FA for cloud storage (Google Drive, iCloud) and remove sensitive files when possible
• Use a password manager (Bitwarden, 1Password, KeePass) with a strong master password.

Privacy and Social Media

• Review privacy settings on Instagram, Facebook, TikTok, LinkedIn, and other social media platforms. Limit access to friends/followers only.
• Remove unnecessary personal data from profiles:
• Phone number
• Email
• Date of birth
• Workplace
• Enable 2FA on social media and massaging apps, as this significantly reduces the risk of account takeover.
• Disable automatic media downloads (photos, videos, documents) in messengers (Telegram, WhatsApp…) to reduce the risk of device infection from malicious content.
• Review third-party apps, subscriptions, and bots that have access to your profile. Remove the ones you haven’t used in a while or don’t trust.
• Don’t post real-time information about your location, trips, or vacations. Unless you want to invite burglars to your home. Also, remove geotags from photos taken at sensitive locations (like your home address)

Devices

• Use screen locks (password, PIN, or biometrics)
• Enable remote tracking and wipe features (Find My iPhone, Android Device Manager)
• Install antivirus software
• Keep systems and apps updated
• Download apps only from official stores (Google Play, App Store)

Browsers and Online Behavior

• Disable password auto-save in your browser.
• Review and remove unnecessary extensions
• Avoid clicking suspicious links
• Verify website security (HTTPS) before logging in
• Use private browsing for sensitive activity

Final Tip

Improving your security doesn’t require doing everything at once. Start with three actions today, then add more over time.

Security is about reducing risk, not achieving perfection. Attackers look for easy targets. So basic precautions make you significantly harder to exploit.

*The article has been initially published on the DataArt Team blog.

Front-end development has transformed dramatically over the past few years. What once focused primarily on rendering UI now increasingly requires an understanding of distributed architectures, AI-assisted workflows, and even AI-native interfaces. In this article, Bogdan Turko, Senior Full-Stack TypeScript Engineer at DataArt, outlines the practical skills engineers need to stay relevant. His perspective helps developers understand not just what’s changing — but also how to adapt.

The 4 Key Forces Reshaping Front-End

Front-end development is being shaped by several major forces that are changing how products are built, how users interact with interfaces, and what skills engineers need to stay relevant. Below are, in my opinion, the main ones.

1. Low-code/ No-code Platforms

The low-code and no-code market is projected to reach $100 billion, driven by platforms such as Shopify, Webflow, Wix, and Mendix. These tools enable teams to quickly build websites, dashboards, internal tools, APIs, mobile applications with visual editors and built-in deployment infrastructure.

Modern platforms increasingly support responsive design and production-ready hosting workflows, making them attractive for rapid product delivery. However, they still have limitations when it comes to highly customized user experiences, performance-critical applications, advanced real-time interactions, and complex business logic or enterprise-scale integrations.

Rather than replacing front-end development as a profession, low-code platforms are mostly reducing demand for routine website implementation. In practice, they are creating a new layer of engineering work focused on integrations, extensibility, customization, and building capabilities beyond what visual tools can provide.

2. Conversational Apps (Messengers)

Messaging platforms, especially in China, are increasingly becoming an additional layer of user interaction. Applications such as WhatsApp and WeChat demonstrate how communication, commerce, and services can be combined into a single experience.

Today, users can purchase products, book services, receive support, and interact with businesses directly within messaging environments, reducing the need to navigate traditional websites. However, conversational interfaces still complement rather than fully replace traditional front-end experiences.

3. Declining User Demand for Browsing

Traditional browsing patterns may gradually evolve as AI-powered agents begin assisting users with repetitive and common tasks. Rather than manually navigating websites, users may increasingly rely on AI systems to complete actions such as filling out forms, comparing products, finding better offers, or planning travel itineraries.

This shift does not eliminate the need for interfaces, but may significantly reduce the number of routine browser interactions and change how users engage with digital products.

4. AI/ML Code Generation Agents

AI assistants are reshaping modern development workflows by becoming part of the engineering toolchain rather than replacing engineers. Developers increasingly use AI directly inside IDEs to accelerate implementation, generate boilerplate, explore solutions, automate repetitive tasks, and coordinate multi-step workflows through prompts and AI-assisted tooling.

This shift is moving engineering work higher up the abstraction stack: instead of focusing exclusively on writing code line by line, engineers are spending more time defining requirements, validating outputs, designing architecture, and making product and technical decisions.

At the same time, AI-generated code can introduce inaccuracies, hidden complexity, security risks, and technical debt if applied without review. As a result, software engineering is evolving from pure code production toward code supervision, system design, and responsible integration of AI into development processes.

Trends in Front-End Technologies

As these forces reshape user expectations, front-end technologies are adapting to meet new demands around performance, scalability, resilience, and developer productivity.

Local-first/Offline-first

Local-first applications prioritize storing and processing data on the client side, allowing users to work with their data directly on their device while synchronizing changes in the background. This approach offers several advantages, including improved user experience, instant data access, and independence from network connectivity.

It can also deliver smoother performance on lower-end devices by reducing reliance on loading states and server round-trips. In addition, local-first architectures can support better compliance with data sovereignty requirements while improving overall system resilience and fault tolerance.

Examples of this approach include tools like Figma and Superhuman. However, despite its benefits, local-first design introduces significant complexity, particularly around data synchronization, conflict resolution, and maintaining consistency across multiple devices.

Backend for Front-End (BFF)

The Backend for Frontend (BFF) pattern introduces specialized API layers designed for specific client types, such as mobile, web, or wearable applications. Each BFF layer works with underlying microservices, aggregating and transforming data to match the needs of a particular frontend.

This approach can enable faster and more focused development, allow independent deployments across different platforms, and improve both performance and flexibility in client-specific scenarios. However, it also introduces trade-offs, including increased system complexity, potential code duplication across BFF layers, higher infrastructure and maintenance costs, and a risk of overengineering if applied without clear necessity.

Performance-Oriented Front-End Approaches

Modern front-end technologies are exploring different approaches to improving performance by reducing unnecessary client-side JavaScript execution and optimizing how interactivity is delivered to users.

The Qwik framework focuses on resumability and minimizing hydration costs. The SolidJS library improves efficiency through fine-grained reactivity and highly targeted DOM updates, while the HTMX library takes a different approach, shifting much of the interactivity to the server using an HTML-over-the-wire model.

These technologies are particularly valuable in contexts where fast time-to-interactive and efficient resource usage are critical, such as content-heavy platforms and e-commerce applications, where even small performance gains can meaningfully influence user experience and conversion metrics.

At the same time, they are not direct replacements for mainstream frameworks like React or Angular, but rather specialized tools that are most effective in scenarios where performance constraints or architectural simplicity are a primary design priority.

Micro-frontends and Islands Architecture

Micro-frontends split applications into independently developed components, allowing teams to work with different frameworks (e.g., React, Vue). Islands architecture renders most of the page statically on the server while activating only dynamic components where necessary.

Micro-frontends improve scalability by allowing teams to develop and maintain independent parts of an application in parallel, reducing coordination overhead and enabling faster delivery across larger organizations.

In contrast, islands architecture focuses on performance optimization by minimizing the amount of JavaScript executed on the client and selectively hydrating only interactive components. This leads to faster load times and improved overall page responsiveness.

Full-stack Shift

Front-end engineers are increasingly expanding their responsibilities into areas that were traditionally considered part of back-end development, including server-side rendering approaches such as SSG, SSR, and ISR, as well as working directly with meta-frameworks like Next.js, Remix, Astro, and SvelteKit.

Their role now often extends to concerns such as caching strategies, performance optimization, and server-side debugging, along with a growing familiarity with DevOps practices and CI/CD pipelines. In addition, concepts like React Server Components and server-driven data fetching are further blurring the line between client and server logic.

As a result, the distinction between front-end and back-end development is becoming less rigid, evolving toward a more integrated full-stack-oriented workflow.

UI/UX Designer’s Trends

AI is also transforming design workflows. Tools like Figma AI, Motiff AI, VisilyAI, and Claude Design can generate mockups aligned with project styles.

Future workflows may include fully synchronized ecosystems across tools like Storybook, Figma, and Style Dictionary, reducing inconsistencies and enabling continuous design validation.

Other Frontend Trends

Beyond core architectural and tooling changes, several broader trends are shaping the future of front-end development. One of them is the rise of personalized and adaptive interfaces, where AI-driven systems adjust UI elements based on user behavior and context to create more tailored experiences.

Another emerging direction is the growth of conversational and voice-based interfaces, where natural language interaction increasingly complements or replaces traditional input methods such as clicks and forms.

At the same time, the web is becoming more ubiquitous, extending beyond browsers into wearables, vehicles, and embedded devices, effectively turning web technologies into a cross-platform interface layer.

Finally, mixed-reality browsing is gradually evolving, with AR and VR technologies exploring new ways for users to interact with digital environments and potentially redefining the concept of a web interface itself.

Trends for Frontend Tools

As development workflows become more performance-oriented, distributed, and AI-assisted, languages, standards, and developer tools continue to adapt to support new engineering requirements.

TC39 JS Proposals

There have been conceptual discussions around a possible future evolution of JavaScript that separates the language into two layers: a minimal core (JS0) and a higher-level syntax layer (JSSugar) that would compile down to it.

In this model, JS0 would represent a lightweight, performance-oriented foundation focused on essential runtime capabilities, while JSsugar would provide developer-friendly syntactic enhancements that improve readability and ergonomics without changing the underlying execution model.

Rather than an actual direction adopted by the JavaScript ecosystem, this idea is best understood as an exploratory proposal illustrating how language design could potentially balance performance constraints with developer experience.

Signals & Decorators

Signals are a proposed model for fine-grained reactivity that introduces JavaScript-native reactive primitives, improving performance and making state management more predictable and easier to reason about in complex applications, although standardization remains experimental.

Decorators, on the other hand, represent an ongoing standardization effort aimed at providing a JavaScript-native way to extend and modify class behavior in a declarative manner, similar in spirit to what is already widely used in TypeScript-based ecosystems.

TypeScript: What’s next?

Future directions for TypeScript may include very significant performance optimizations, with the possibility of substantial speed improvements in certain workflows. There is also ongoing speculation about deeper integration with native browser environments, although this remains an open and uncertain area of development.

At the same time, TypeScript continues to expand its role across backend runtimes such as Node.js, Deno, and Bun, reinforcing its position as a cross-platform language layer. It also remains a strong influence on the evolution of ECMAScript, with many JavaScript features being shaped by patterns popularized in TypeScript ecosystems.

CSS is changing

CSS is continuing to evolve at a fast pace, introducing modern features such as container queries, native nesting, parent :has()selector, and improved control over accent colors.

Together, these capabilities reduce the need for JavaScript in certain styling and UI-related logic, allowing more behavior and layout decisions to be handled directly within CSS.

How to survive?

To stay relevant in this fast-changing environment:

• Embrace continuous learning
• Build knowledge beyond front-end, including AI, architecture, DevOps
• Focus on problem-solving rather than tools
• Learn how to effectively work with AI systems

Engineers who adapt will remain valuable. Those who expand beyond traditional front-end boundaries will help lead the next wave of development.

*The article was initially published on the DataArt Team blog.

The IT job market is changing faster than ever, driven largely by the rapid adoption of AI across industries. For fresh graduates, this creates both new opportunities and new challenges. Understanding where demand is growing, which skills are becoming essential, and how to prepare strategically can make the difference between a long job search and a successful start to an IT career.

This article looks at the current impact of AI on the job market, what it means for entry-level candidates, and the concrete steps graduates can take to stay competitive.

The Current State of AI in the Job Market

AI has already created 1.3 million jobs globally, particularly in data, automation, and AI operations. Around one in five tech hires today is an entry-level AI role. By 2030, an estimated 97 million new AI- and digital-jobs are expected to emerge.

At the same time, routine tasks such as data entry and basic testing are increasingly being automated. Roles that rely heavily on repetitive work are becoming less future-proof and should be approached with caution.

AI literacy is becoming a baseline skill. Industries including healthcare, finance, e-commerce, manufacturing, and cybersecurity are actively adopting AI, making them strong targets for job seekers.

However, technical skills alone are not enough. Critical thinking, communication, and domain knowledge remain essential and are becoming even more valuable as AI takes over routine work.

What’s improving

• AI Development Roles: Demand is rising for ML Engineers, AI Developers, NLP Engineers, and AI Research Scientists.How to Break into the IT Job Market in the AI Era: a Practical Plan for Juniors
• Data Roles: Data Engineer, Data Scientist, and Data Analyst remain in high demand.
• AI-Augmented Development: AI tools are now embedded in the software development process, and most engineers use at least one AI tool daily.
• Automation and Analytics: As data volumes grow, roles focused on automation and analytics are becoming more critical.

You don’t need to become an AI expert, but you must understand:

• How AI tools work
• How to use AI to improve productivity, even if it is not required for your job.
• Where AI fits in your domain and how it can strengthen your expertise

IT Market for New Graduates

Entry-level AI roles now make up around 20% of new tech hires, reflecting the deep integration of AI into products and processes.

To improve your chances of landing an IT job, focus on these three aspects:

• AI Skills: Graduates with AI skills have an advantage over general IT graduates, especially in data science and ML roles.
• Education Mix: The best combination is Python + data skills + one AI specialization + strong portfolio projects targeting AI-augmented roles.
• Certifications: Certifications such as AWS Cloud Practitioner and Google Data Analytics signal genuine interest and commitment to recruiters.

Key Trends

Hiring is Becoming Skills-Oriented, not Degree-Oriented

Degrees are common; skills are what differentiate candidates

Practical Knowledge Matters More Than Certifications

A certification has little value if you can’t demonstrate real-world understanding.

Internships and Live Projects are Important as Ever

They provide tangible evidence of hands-on experience.

Competition is Fiercer Due to Global Hiring

Candidates now compete with talent from around the world.

What companies expect from entry-level candidates:

• Strong programming fundamentals and understanding core principles.
• Problem-solving ability, often tested through practical tasks.
• Basic knowledge of cloud, cybersecurity, or AI, which is becoming the norm
• Communication skills and a learning mindset, essential for teamwork and adaptation

Steps to Enter the IT Market

Pick a Smart Entry Role.

Avoid overly advanced or niche positions early on. For example, aspiring Data Engineers can start as Data Analysts and grow into more specialized roles over time.

Build Strong Fundamentals.

Learn the key skills needed to become an expert in the field, which include:

• Programming basics
• Data Structures
• Databases
• Networking basics

Create 2–3 GitHub Projects

Examples include a data dashboard, a web add, or an automation script. A solid GitHub portfolio can significantly strengthen your resume.

Earn One Relevant Certification.

Choose a language or platform aligned with your target role, such as Python, Java, or .NET.

Tailor Your Resume to Each Job

Many recruiters use AI-based resume screening or an ATS (Applicant Tracking System), so align your resume with the job description to improve screening results.

Optimize LinkedIn:

• Photo: Use a professional image
• Keyword Headline: Let recruiters know your status, whether you are a fresh graduate, and what roles you are looking for
• Projects: Share GitHub work, problem-solving ideas, or technical posts to show active interest

Apply Consistently

Set a weekly application target to improve your odds in a competitive market.

Network Daily

• Connect with around 20 recruiters
• Join at least 5 IT communities
• Message job posters directly to show initiative

Prepare for Technical and HR Interviews

Practice coding problems, do mock interviews, and consider using AI tools to simulate interview scenarios.

Track Your Progress

For example:

200 applications →10 interviews→3 offers→1 job

Regularly review what’s working and adjust your approach

Before You Go

The job market in the AI era is challenging, especially for fresh graduates. But with the right skills, consistent effort, and a clear strategy, it’s entirely possible to succeed. Keep learning, stay adaptable, and focus on building practical experience — the opportunities are there for those who prepare for them.

*The article was initially published on The DataArt Team blog.

General principles, thought‑provoking questions, instructive stories, and even a bit of wild fiction await anyone who picks up the titles recommended by our DevOps Engineer Eugene Maly. Each title approaches the discipline from a different angle, spanning culture, automation, delivery pipelines, and organizational change.

1. Terraform: Up and Running: Writing Infrastructure as Code, 3rd Edition

This book introduces Terraform, a key player in defining, launching, and managing IaC across various cloud and virtualization platforms like AWS, Google Cloud, Aure, etc.

The author uses clear code examples to explain how Terraform’s declarative language helps you deploy and manage infrastructure with just a few commands. Beginners can start with the basics and move up to running a full stack that can handle heavy traffic and even support a large team.

2. Infrastructure as Code: Dynamic Systems for the Cloud Age, 2nd Edition

This practical guide shows how to use principles and patterns from DevOps teams to manage modern cloud infrastructure.

The updated edition explains how to use cloud and automation tools to make changes that are safe, fast, and easy to undo. It teaches you how to define everything as code and use software design practices to build systems from small, independent parts.

The book covers:

• The basics of infrastructure as code
• Working with infrastructure stacks
• Managing servers and other platforms
• Supporting large systems and teams

3. The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win, 4th Edition

The Phoenix Project is written for IT professionals and anyone curious about DevOps. It tells a story about technical teams facing and solving tough challenges.

The book follows two groups, developers and operations, who often clash even though they share the same goal. Their disagreements cause more problems, and the main character has to solve them to keep everyone from losing their jobs.

This story demonstrates ways to motivate teams and organize their work. While it does not go into technical details, it clearly supports modern DevOps practices and provides a practical example of building a reliable, predictable software delivery.

4. Effective DevOps: Building a Culture of Collaboration, Affinity, and Tooling at Scale

Effective DevOps focuses on the culture behind DevOps and explores how to work well with different people. Instead of being a technical manual, it explains how strong relationships help teams succeed.

The authors highlight that DevOps is mainly about people, not just tools. The book uses questions, examples, and stories to show how to build trust and teamwork within teams and with clients.

5. The DevOps 2.0 Toolkit: Automating the Continuous Deployment Pipeline with Containerized Microservices, 1st Edition

The DevOps 2.0 Toolkit is a good starting point for anyone beginning their DevOps journey. The author presents a list of technologies and practical ways to use them, including Docker, microservices, CI/CD, and blue‑green deployment. Each chapter is independent of the others, explains why a particular architecture or technology stack was chosen, and includes code examples. In a way, the book is a series of workshops. A strong underlying message emerges: automate everything you can.

Since the book was published in 2016, some examples may now be outdated. It’s worth cross‑checking its recommendations against your own experience.

6. Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, 1st Edition

This book covers the main ideas behind continuous delivery, such as:

• The purpose of configuration management
• When to use E2E tests, smoke tests, and other automated testing
• How to design deployment pipelines (blue‑green, canary, etc.)
• Why performance monitoring and alerting matter

Some tools in the book may be outdated, but the main principles remain sound. This makes it a good starting point for anyone learning or reviewing continuous delivery.

7. The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations, 2nd Edition

This book explains how companies such as Google, Capital One, Target, Netflix, and Etsy use DevOps principles.

It includes practical guidance on:

• Moving code from repository to production
• Automating tests
• Collecting metrics and logs
• Detecting and fixing issues
• Recovering from failures
• Removing process bottlenecks
• Helping leadership support improvements
• Driving change in resistant organizations

The book is full of real-world examples and describes how people deal with real problems, the actions they take, and whether those choices work.

Whether you are new to DevOps or looking to expand your existing skill set, these books offer proven ideas, practical frameworks, and real-world lessons. Together, they provide a solid foundation for building reliable systems, improving collaboration, and strengthening delivery practices across your organization.

*The article was initially published on DataArt’s Team blog.

Traditional UI test automation tools like Selenium and Playwright rely on rigid selectors. Every UI change breaks scripts and forces tedious refactoring. But what if a tester could define a goal in natural language, and the AI found the path on its own?

We built a framework based on browser-use and Large Language Models (LLMs) to realize this premise. Instead of writing code, we feed the agent Markdown files. The agent navigates the application independently, makes decisions, and verifies the results.

Project Evolution: From MVP to the Cloud

We divided the architecture development into three phases:

1. Local PoC: A basic Agent ran locally, clicking through the application. Its work was evaluated and reported in Allure.
2. CI/CD Integration: We integrated the framework with the API of a cloud-based test management platform. The agent began fetching entire test suites autonomously and sending back execution statuses within the pipeline.
3. Exploratory Testing: We removed strict assertions and gave the agent a base URL, a search goal, and the freedom to explore system behavior.

Main Engineering Challenges (And How We Solved Them)

Moving from concept to production exposed several operational problems.

1. The Butterfly Effect in Prompts

We had about 70 tests. Attempting to improve the global prompt (master_prompt.md) to fix one test caused unexpected failures in tests that had previously worked.

Solution: We implemented pre-pre prompting. The agent pulls micro-context from the test management platform's comments section, giving it task-specific instructions. This keeps global prompts stable while ensuring relevance at the test level.

2. The War of Models and Cloud Time Limits

OpenAI models struggled with complex application logic. Switching to Gemini 2.5 Pro stabilized around 50 simple tests, but 20 complex flows still failed. Meanwhile, the full regression suite exceeded six hours, hitting timeout limits.

Solution (Routing and Concurrency): We built a parallel architecture with four jobs (two workers each). The 20 most complex tests were routed to Gemini 3.1-pro preview, while the remaining 50 ran on Gemini 2.5 Pro. This reduced the total regression time from six hours to about two hours.

3. Technical Debt in QA Documentation

The AI agent revealed a major issue: manual test documentation was outdated. While human testers could infer that “Payments” had been renamed to “Finance,” the AI could not. It searched for non-existent elements and failed accordingly. We had to update and revise large portions of business process documentation before achieving stability.

4. Cost and Resource Usage (API vs. Engineering Time)

AI eliminates hours of maintaining test scripts but introduces API costs. A single full regression run cost over $20 in tokens. Running tests multiple times per day increased expenses significantly. However, compared to the engineering effort required to maintain brittle selectors, the trade-off proved favorable.

5. Where the Approach Fails: iFrames and Payment Gateways

While the AI performs well on dynamic pages, it struggles with restrictive UI elements such as secure payment gateways (e.g., Braintree iFrames). These often block standard mouse interactions and require precise keyboard navigation (e.g., forcing the use of Tab). AI was having a hard time mimicking this hardware-level behavior.

Solution: We extended the framework with custom Tools. By injecting specialized functions into the agent’s toolkit, we enabled it to bypass UI constraints and execute precise programmatic actions when standard interaction failed.

6. Vision of the Future: 2026 Multi-Agent Architecture

Working on advanced solutions in QE laboratories — where tools for semantic code analysis and vector-based risk assessment of deployed changes are designed daily — it is clear that an isolated browser agent is just the beginning. The evolution points to multi-agent ecosystems managing the entire Software Testing Life Cycle (STLC):

• Requirement-Mapper: Analyzes project management platforms and knowledge bases to generate requirement maps.
• Scenario-Architect: Builds test scenarios using bounded drift — allowing controlled tolerance for minor discrepancies to prevent unnecessary failures.
• Script-Transpiler: Converts logical flows into executable formats (e.g., HTTPx for API tests).
• Risk-Auditor: Replaces manual screenshot reviews with concise, synthesized risk insights.

This evolution extends beyond UI testing:

• AI Risk Analysis Systems: Use semantic indexing of defect history to assign risk scores to new code before testing begins.
• Data Environment Agents: Operate within data environments to accelerate architecture design and reduce operational overhead.

Combining backend analytics with frontend navigation creates robust, non-flaky end-to-end testing.

Despite this automation, humans remain essential. We currently operate in a human-in-the-loop model, where AI executes tests and provides initial analysis while experts review and validate outcomes. As systems mature, this may shift toward a human-in-the-lead model, where Quality Architects define strategy and guide AI systems rather than oversee execution.

Summary and Results

After months of optimization, we achieved our goal. The autonomous ecosystem stably executes regression. Exploratory testing by the agent uncovered defects that previously went unnoticed by the manual testing team.

Conclusion

LLMs do not replace QA — they redefine it. The era of maintaining CSS and XPath selectors is ending. Quality Engineers are evolving into Quality Architects, focusing on business process modeling while AI agents handle execution at scale.

*The article was initially published on DataArt Team blog.

Taking English classes is still the best and quickest way to learn the language, as an instructor provides structure, clear explanations, and real interaction that helps learners build confidence and fluency. However, learning doesn’t happen only in the classroom. To make real progress, learners should keep practicing and engaging with the language outside lessons.

ELSA Speak

Pros:

1. Good pronunciation coach: highlighted word parts that are mispronounced, articulation accuracy score, sound-focused lessons, lessons on intonation, and stress patterns
2. Acts as a conversation coach, offering dialogues, real-life scenarios, and listening exercises.

Cons:

1. Limited focus on grammar; it assumes a basic level of knowledge. After explanations, it expects you to actively use new structures in speech.
2. Level difficulty can feel inconsistent. Lower levels might use some sophisticated words, while higher levels use simpler ones.
3. The UI can be overwhelming.

TalkPal

Pros:

1. A variety of voice-based practice modes:

Chat: One-on-one conversations with an AI tutor

Call: Practice calls or client meetings

Sentence: Build sentences from given words

Dialogue: Learn conversational structure

Word: Learn new vocabulary with images, making learning more visual and intuitive

Photo: Describe what you see

Debate: for practicing argumentation

2. Feedback on pronunciation

3. Courses from beginner to proficiency, as well as professional tracks (business, law, medicine)

4. Supports multiple languages

5. Realistic conversation practice

6. Daily pick for guided learning

7. User-friendly interface

Cons:

1. No structured grammar learning or exercises
2. Practice modes and professional courses lack level differentiation
3. Limited functionality in the free version

ELSA speak vs TalkPal

AI as a Speaking Simulator

You can use AI tools like ChatGPT or Gemini in voice mode to simulate real client interactions.

Example prompt:

Act as a US-based client. I am explaining a production bug. After my answer, give feedback on clarity and tone.

Example response improvement:

• Original: “Basically, there was a problem in the payment module, and we redeployed, and now it works.”
• Improved: “The issue originated from cache inconsistency in the payment module. We deployed a hotfix, validated transaction stability, and implemented monitoring to prevent recurrence.”

This approach allows you to practice realistic technical communication and receive instant, structured feedback — something traditional learning rarely offers.

Difficult Client Scenario

You can also simulate high-pressure situations. The conversation might go something like this:

You:
Simulate a stressed client. Ask me one question about a critical system outage. I will answer. After my answer, analyze my confidence level and any filler words. Also, give short feedback on my answer.

AI:
Okay, I will play the role of a stressed client. Here we go. Hi, I’m really concerned about this critical system outage. Can you tell me what exactly caused it and how long it will take to fix it?

You:
Well, we’re looking into it right now. I don’t know. Maybe something’s wrong with one of the services. I don’t know. We’re still trying to confirm it. It could maybe be related to the last deploy, but we’re not totally sure. Basically, we’re working on it, and we’re trying to fix it as soon as possible. So, yeah, maybe it’ll be resolved in a couple of hours.

AI:
Thanks for your response. I can tell you are working through the details, which is completely understandable given the situation. I noticed a few filler words like “uh” and “I don’t know”, which can be a sign of uncertainty. You might try to be a bit more specific, even if it’s just an estimate to reassure the client. Overall, you are definitely on the right track.

This helps you:

• Reduce filler words
• Improve clarity under pressure
• Build confidence in real conversations

Like flight simulators for pilots, these exercises let you rehearse before real interactions.

Writing Tone Upgrade

In daily communication (Teams, email, Jira), your tone can unintentionally sound too direct, vague, or passive. Tools like DeepL can help refine tone.

Example 1

• Before: We can’t do this because it wasn’t in scope.
• After: This wasn’t included in the original scope. We can review it and provide an estimate for the additional effort.

Example 2

• Before: You didn’t send the requirements.
• After: It seems we haven’t received the updated requirements yet. Could you please confirm?

The Smart Rule

Avoid blindly copying AI-generated content; analyze it instead.

Real learning happens when you ask yourself:

• What changed?
• Is it softer?
• More structured?
• More collaborative?

This is how AI becomes a learning tool rather than a crutch.

Your Meetings are Data

AI tools can analyze real meeting recordings. For example, Yoodli provides insights such as:

• Filler words
• Speaking speed
• Interruptions
• Talk-time ratio
• Confidence markers

You may discover patterns, such as overusing certain phrases or dominating conversations. Use this data to improve your communication style.

Vocabulary Mining

Another power move is to do vocabulary mining. After meetings, paste transcripts into ChatGPT and extract reusable phrases and terminology.

Important note: Treat all content as strictly confidential under NDA. Do not include or expose sensitive or identifiable information in the output. Generalize or anonymize details where needed.

Tools for Understanding Complex Texts and Independent Learning

ChatGPT

ChatGPT is useful for:

• Explaining complex technical documentation
• Summarizing long texts or API descriptions
• Extracting key terminology and definitions
• Rewriting text in simpler English

Example prompts:

• Explain this as if I were a junior developer.
• Summarize this API documentation in bullet points.
• Highlight key terminology I must understand.
• Give me a list of important words from this text.

Rewordify

It often happens when working with documentation, compliance, contacts, etc. We encounter some words or phrases that slow us down. This is where Rewordify comes in and simplifies complex texts by replacing difficult words with clearer alternatives or explanations.

It helps you:

• Understand unfamiliar vocabulary in context
• Highlight unknown words
• Build vocabulary through flashcards and quizzes
• Track learning progress

Natural Reader

Natural Reader converts written content into audio, allowing you to practice pronunciation using your own materials (presentations, emails, notes).

Let’s imagine we are preparing for a short presentation. We input the text and select from a range of voices. Then press play and listen to what it could sound like.

Natural Reader is perfect for shadowing:

• Listen to a sentence
• Pause
• Repeat it, matching pronunciation and rhythm

YouGlish

YouGlish shows real-life video examples of words and phrases in context.

Key features:

• Multiple accents (American, British, Australian)
• Playback speed control
• Subtitles with highlighted keywords

It helps connect sound, context, and meaning.

What happens when We Rely Too Much on AI

Despite its advantages, over-reliance on AI can be problematic. If learners depend too heavily on AI-generated content, they may stop actively thinking in the target language. As a result, there is a risk to:

• Create a gap between your real ability and the AI-generated output
• Stop learning from mistakes
• Develop overly formal or artificial speech patterns
• Trust explanations that may not always be correct.
• Lose confidence when AI is unavailable.

Conclusion: AI as a Partner, not a Substitute

AI tools have transformed language learning by making it more interactive, personalized, and efficient. They enable learners to practice speaking, refine writing, analyze communication patterns, and understand complex material — all within their daily workflow.

However, the key to success lies in using AI wisely. It should serve as a learning partner, not a replacement for real English classes, effort, and critical thinking. By combining AI tools with consistent practice and clear goals, learners can significantly improve their language skills and communication confidence.

*The article was originally published on DataArt Team blog.

My Path Into the Salesforce Ecosystem

I’ve worked at different outsourcing and outstaffing companies, both in local and remote teams, with clients from many countries and industries. Some used Salesforce for internal systems, others for customer service, and some for ISV development. Since I first started with Salesforce in July 2017, I’ve earned nine certifications and retaken two exams.

About Salesforce

Salesforce is known as a global leader in CRM. It is the first cloud-based CRM system and promotes the idea of no software, even using 1–800-NO-SOFTWARE as its phone number. From the beginning, Salesforce was built as a SaaS platform that is flexible, scalable, and easy to extend.

Salesforce offers many ready-made products for different business needs, including Sales Cloud, Service Cloud, Experience Cloud (formerly Community Cloud), CPQ, Force.com sites, B2B Marketing (Pardot), B2C clouds, and more.

Even if you haven’t used Salesforce, you’ve probably heard of it. The company has acquired major platforms like Slack, Heroku, and Tableau, along with dozens of smaller ones. Salesforce consistently ranks among the top employers in the United States and invests heavily in its customer and professional communities. The result is one of the most active ecosystems in enterprise software.

Its scale and the clear separation of products by purpose explain why Salesforce puts so much focus on certification. The platform is expensive, and companies rarely buy many products at once. As a result, they often look for specialists who can solve specific business problems with the exact cloud they use, not just general Salesforce developers.

Does Salesforce Certification Actually Matter?

In my experience, IT certification is a mixed topic. Many developers are skeptical about formal education, exams, and official certificates. They trust hands‑on experience and doubt that any document, physical or digital, can prove real skill.

That said, certifications are highly valued in several fields:

• Project managers — PMP
• Cybersecurity specialists — OSCP, OSCE, CISSP, SANS
• Salesforce specialists

Even among business analysts, opinions vary.

What You Actually Get From Being Certified

The answer depends on who you ask. Here’s how it breaks down from three angles.

1. Why Does Salesforce Need You Certified?

• Certification brings them revenue: Exams cost either $150 or $400, depending on the level. Retakes are $150 or $200. Becoming a Salesforce CTA (Certified Technical Architect) costs $6,000, or $3,000 for a retake. These fees help support a certification system that requires significant resources to maintain.
• A structured process for training and validating also helps Salesforce customers find people who can set up and extend their systems. This makes the platform more appealing despite its high cost.

2. Why Does Your Employer Care?

If you work at a product company, your employer may not care about Salesforce certifications unless the company builds Salesforce extensions (ISVs).

But for outsourcing or outstaffing companies, certification is important. Salesforce has a partner program for subcontractors. The higher the partnership level, the more leads they get from Salesforce recommendations, the faster Salesforce Support responds, and the more free certification vouchers they receive. This makes the partnership even stronger. Certifications can also matter to clients.

One practical benefit: if you work in outsourcing or outstaffing, your employer will typically cover exam costs. It's also worth researching which Salesforce roles are most in demand and what compensation looks like across the board.

3. Why Do You Personally Need It?

• Your certificates belong to you, not your company.
• Even if you're skeptical, certifications show you're willing to learn. They also confirm that you meet a basic level of knowledge.
• Certifications help with salary reviews and future job opportunities. Some clients require them, and certified Salesforce developers are always in short supply.

You can also print your certificates and hang them on the wall (not that I did!) Salesforce’s badges, rank, and title system make it fun and motivating!

Choosing the Right Certification

Start by signing up for a free Developer Edition. This is where you’ll practice and experiment with Salesforce.

Next, review the list of Salesforce roles and pick the one that best fits you. You can explore the career paths on the official site.

After you choose a role, decide which certification you want to earn first. The career path chart can help you make this choice.

Most people start with the Salesforce Certified Administrator exam. Some start with Salesforce Certified Platform App Builder instead. App Builder requires a broader skill set and a bit more preparation. The two certifications overlap significantly, so it’s more efficient to take them close together to avoid relearning the same topics.

In many cases, “Salesforce Certified Administrator” is required before taking more advanced consultant exams.

Building Your Study Plan

1. Once you choose the certification you want, make sure you meet all requirements. Some certificates have none, but check anyway. You can do this by registering on Webassessor.
2. Mindset matters. Don’t aim to win. Go in prepared to pass, but not afraid to fail, especially if this is your first Salesforce exam. Many people don’t pass on their first try, and that’s a normal part of the process!
3. Create a study plan. Give yourself at least a month, especially if Salesforce is new to you.
4. Once you have a plan, register for the exam. Without a deadline, your plan will stay a plan. If you’re not ready by exam day, you can reschedule for free up to 24 hours before the exam.
5. Start studying.

Preparation Tips

Pick a certification that genuinely connects to your actual work or career direction. Motivation matters. I learned that firsthand after having to retake an exam twice.

I broke the preparation process into four steps:

Step 1 (especially important if you lack practical experience):

Get to know Trailhead, Salesforce’s official learning platform. Start with trails labeled “Quick Start” or “Quick Look”.

Step 2:
Review the exam outline. You can find it by selecting a role and the exam you’re targeting.

Step 3:
Find a solid book or video course — many are free. Trailhead is useful, but it’s not enough to get you across the finish line. A structured course keeps you focused and cuts the time you’d otherwise lose browsing blogs or documentation.

Step 4: Review
Revisit the topics you understand least.

Bonuses:

• Ask a colleague who has already taken the exam for tips.
• Use one or two question banks for your certificate. Questions often overlap.
• You might know the theory well, but still have trouble with tricky wording. Reviewing typical tasks helps you get used to the format.
• Don’t memorize questions and answers. Focus on understanding.

Useful Links:

• https://quizlet.com — Enter the exam name in the search bar to find the flashcards.
• https://t.me/SalesforceA — Several Telegram groups share updated Salesforce exam practice materials for free.
• https://focusonforce.com — Offers theory, practice, and explanations for every question. Paid, but worth it.

On the Exam

You'll get 65 questions, five of which are unscored. Why does this matter? If you're unsure about a question, remember that it might be one of those five. Salesforce uses them to test new wording for future exams.

A few things worth keeping in mind on exam day:

• Don't panic. Salesforce is very customer‑oriented and will help with any technical issues. I’ve had power outages and software freezes during exams, and they’ve always been fixed.
• Manage your time. Don’t get stuck on one question — you still have many others. Mark the hard ones and come back to them later.
• Use elimination if you don’t know the answer. And if you’re still unsure, trust your intuition. It’s not just guessing; it’s using what you know to make the best choice.
• Never leave a question unanswered. Even a random guess gives you a chance.
• Review your answers before submitting.

Also, share your experience with colleagues. Even a bad experience can help someone else avoid mistakes!

One Last Thing

It sounds simple, but it matters: trust yourself. Every setback is part of the process. If a failed Salesforce exam is one of yours, the next attempt is already closer than it feels.

May clear skies follow you, or at least leave room for a small Salesforce cloud to shade you on a hot day.

Good luck with your exams!

*The article was originally published on the DataArt Team blog.