Most businesses don’t think about recovery until they’re living through an outage. That’s backward. The companies that bounce back fastest are the ones who tested their systems before disaster struck, not after. This is where comprehensive IT service solutions earn their keep. They’re not just about keeping the lights on day to day. They’re about making sure you have a real plan when things go sideways, and the infrastructure to execute it under pressure.

So let’s talk about what a 24-hour downtime test actually looks like, and whether your setup would pass.

What Downtime Really Costs You

It’s not just lost sales. Downtime costs you customer trust, employee productivity, and sometimes your reputation for good. A single day offline can mean missed deadlines, frustrated clients, and staff sitting idle waiting for systems to come back.

Some numbers worth knowing:

• Small businesses often lose thousands per hour of downtime, not just per day
• Recovery time matters more than most people expect, since delays compound
• Customers remember outages longer than companies think

The Questions You Should Be Asking

Before you assume you’re covered, ask yourself a few things. Do you know where your backups actually live? Have you tested restoring from them recently, not just confirmed they exist? Does your team know who does what when systems go down?

If you hesitated on any of those, that’s useful information. It tells you exactly where to focus.

Building a Recovery Plan That Works

A real recovery plan isn’t a document that sits in a drawer. It’s something you’ve practiced. Start with the basics.

You need clear backup schedules. Daily is often the minimum for anything critical. You need documented recovery steps that don’t rely on one specific person’s memory. And you need a communication plan, because your team and your customers will want answers fast.

Test your backups regularly. Not annually. Regularly. A backup you haven’t verified is just a hope, not a plan.

Running Your Own 24 Hour Test

Here’s a simple way to check your readiness without waiting for an actual crisis.

Pick a system, maybe your file server or your customer database, and simulate losing it. Time how long it takes to restore from backup. Then ask your team to walk through the steps they’d take if this were real. Watch where they get stuck. That’s your weak point.

Do this once, and you’ll learn more than a dozen disaster-planning meetings ever could.

What Good Recovery Looks Like

Strong recovery isn’t about being fancy. It’s about being fast and predictable. You want systems that fail gracefully, backups that restore cleanly, and a team that knows their role without needing a meeting first.

Redundancy helps too. If one server or one location goes down, having a second option ready cuts your downtime dramatically. Cloud backups, offsite storage, and failover systems all play a part here.

Moving Forward

You don’t need to overhaul everything overnight. Start small. Test one system. Fix one gap. Then move to the next.

The businesses that recover fastest aren’t lucky. They planned ahead, tested their assumptions, and fixed problems before they became emergencies. Give your IT stack the same chance. Run the test, see what breaks, and fix it now while you still have the luxury of time.

The post The 24-Hour Downtime Test: Can Your IT Stack Recover? first appeared on FROMDEV.

AI glasses are not a new concept, but the current generation has moved far enough along that they deserve a practical re-evaluation. Not as a novelty. Not as a gadget review. As a workday tool that does a few things genuinely well for developers, and a few things still not quite well enough to bet on entirely.

Here is what you actually get.

The Problem They Are Solving

Developers and engineers lose more time to context-switching than most productivity metrics capture. Attention research from UC Irvine found it takes over 23 minutes to fully regain focus after a single digital interruption — a finding that maps directly onto what happens every time a Slack ping or phone buzz pulls you out of flow. Research on workplace AI adoption has found that even modest reductions in task-switching friction can produce measurable time savings across a week. The gains compound across a dense sprint schedule.

AI glasses address a specific slice of that problem: the moments when you need to receive or act on information without breaking concentration. A message comes in mid-compile. A call lands during a code review. A reminder fires while you are writing a function. The phone stays in your pocket, and a narrow but useful layer of communication keeps moving without pulling you out of what you are doing.

What AI Glasses With Camera Actually Do

The core feature set across most current models is consistent. Open-ear audio handles calls and content playback without blocking ambient sound — useful in open offices and co-working spaces where full isolation creates its own social friction. A built-in AI assistant handles real-time queries through voice: reminders, quick lookups, message replies, timer controls. Notifications come through the speakers rather than a screen you have to unlock and navigate.

The camera extends the value. Ai glasses with camera add a first-person capture layer that is more useful in practice than it sounds. You can record a walkthrough of a UI bug without staging a screen recording, capture a whiteboard session at a client site without stopping the conversation, or document a hardware setup or server environment in a data centre where pulling out a phone is awkward or restricted. For developers who move between environments — office, client site, co-working space, field — that is a capability gap a phone does not fill as cleanly.

The AI assistant integration varies by model, but the better implementations summarise messages, draft short replies, and manage reminders in real time. That is not going to replace a setup built around deep technical work, but it handles the low-stakes reactive layer of a developer’s day without demanding a screen. Pair that with broader daily AI tools in your stack and the notification layer becomes something that largely runs itself.

Where They Actually Earn Their Place

The use cases that come up most consistently for technical users are:

Commutes and transit. A long train or bus commute that previously required pulling out a phone to triage messages can run almost entirely through audio. Replies go out, reminders get set, and stand-up prep happens without opening a laptop or fighting for phone signal in a crowded carriage.

On-site and field work. Server rooms, client offices, lab environments, hardware setups — anywhere that a phone in-hand is inconvenient, creates a security concern, or signals distraction. Hands-free audio and quick point-of-view capture cover documentation and communication without interrupting the physical task.

Back-to-back standups and code reviews. When the calendar is packed and gaps are five minutes, catching a notification or logging a quick note between sessions without sitting back down at a workstation matters more than it sounds. The cost of missing a context switch in a dense review cycle adds up faster than most developers track.

Long audio sessions. Extended coding runs with music or a podcast in the background, walking breaks, commuting on foot — anything where earbuds fall out or a phone in a pocket is a minor but persistent friction. AI glasses handle audio stably across longer sessions without ear canal pressure.

Where They Still Fall Short

Battery life on most current models runs between four and six hours of active use. That is a real ceiling for full-day reliance. They work well as a defined-window tool, not as an all-day phone replacement.

Camera resolution is functional rather than excellent. It handles documentation and reference capture well, but not broadcast-quality recording. If polished video is the goal, dedicated AI video tools are a better fit for that side of the workflow — the glasses handle capture, not production.

Voice control accuracy has improved significantly, but in noisy environments — open plan offices, conference floors, data centres with fan noise — error rates climb. Some inputs are still faster through a screen, and forcing voice commands in a shared space creates its own friction.

The Practical Verdict

AI glasses work best as a complement to an existing setup. Developers getting real value from them are not trying to replace their phone or workstation — they are offloading one layer: the reactive, notification-driven communication that previously required constant physical interaction with a device mid-task.

The multi-device model — where lightweight tools each handle a narrow slice of cognitive load rather than one device doing everything — is where this hardware fits cleanest. If your work is entirely desk-based, the value is limited. If you move between environments, travel frequently, or work in physical spaces where pulling out a phone is disruptive, there is a solid case here worth exploring past the spec sheet.

The post Hands-Free and Always-On: What AI Glasses Actually Do for Your Workday first appeared on FROMDEV.

A 404 page usually gets treated like the internet equivalent of a “my bad”. It’s a little annoying at times, and, if it’s a common issue on a site, may act as a deterrent to return visits. But, for the most part (as average internet users) we just do the surfing equivalent of a u-turn, then we move on.

But does everyone? 

A lot of people don’t realise, but a forgotten 404 can reveal quite a bit of information about how a website is structured, like which services have been retired, and whether old subdomains or external services are still connected behind the scenes. Sometimes the page itself is harmless but the problem lies in what it points towards.

What a 404 can reveal

Even after a page has disappeared, its URL can stick around in search results, old documents, emails, social posts, or third-party systems. There is a trail of bread crumbs that isn’t dusted away just because the gingerbread house is gone. 

Old subdomains are particularly interesting because a company might have once used ‘events.example.com’, ‘help.example.com’, or ‘campaign.example.com’, then quietly moved the content somewhere else. The DNS record can remain long after the project has been forgotten.

That’s where things get uncomfortable: if the subdomain still points towards an external platform, cloud service, hosting provider, or application that the organisation no longer controls, an attacker might be able to register the abandoned resource and serve their own content with your name still attached to it.

The trust you’ve built with customers and the web algorithms is doing a lot of heavy lifting not just for someone else, but for someone who has bad intentions. 

The forgotten-subdomain problem

DNS records gather ‘dust’ just as easily as a stack of old paper files. Subdomain takeover can start with an old campaign, a discontinued app, or a developer experiment that nobody remembered to clean up, not some dramatic breach of the main website. 

Under someone else’s control, the consequences range from phishing pages and malicious redirects to cookie theft attempts and damage to the organisation’s reputation.

And because the address bar still looks legitimate, the social-engineering part becomes considerably easier.

Your 404 monitoring needs to go further

Checking whether a page returns a 404 isn’t a comprehensive review, since checking only tells you that the requested resource isn’t there and not why the URL exists. You also can’t glean what used to sit behind it, or whether its DNS configuration has been left dangling.

If you’re auditing your infrastructure, you need to know how to avoid subdomain takeover and understand how these abandoned references can become exploitable. A proper review should include old subdomains, DNS records, external service dependencies, expired projects, and assets belonging to previous campaigns.

Clean up the digital leftovers

Start with a current inventory of your subdomains. Compare it against DNS records, cloud services, SaaS platforms, and internal documentation, then remove records for services that have been decommissioned, verify ownership of anything hosted externally, and keep an eye on old campaign domains before they become somebody else’s problem. It’s good hygiene for any business with an online presence. 

That forgotten 404 might still be telling you something that you need to listen to.

The post Your 404 Pages Are a Goldmine for Hackers first appeared on FROMDEV.

For a lot of tech companies, it’s not a matter of being aware of the value of documentation, but rather working out how to manage it alongside product development without losing momentum. Here’s how to decide whether or not you should outsource your technical documentation.

If Your Engineering Team Has No Time for Writing Docs

If your developers find themselves regularly writing guides rather than working on new features, this is an obvious sign that it’s time to think about bringing in outside help with documentation.

Your developers may have the technical wherewithal to explain a feature, but producing good documentation goes beyond that. You also need to understand what users are trying to achieve and structure the information in a way that’s easy to follow. If documentation is constantly competing with your development priorities, then bringing in someone to handle it can be a smart choice.

If Your Documentation Is Struggling to Keep Up with Product Updates

Your company most likely moves fast to get new features and updates out the door, and when you’re working at speed, you can quickly find that your documentation is struggling to keep up. 

Having gaps in documentation is a problem for users who rely on your guides to do their jobs, and it also puts pressure on the support teams who have to answer questions that clear documentation could’ve resolved. In this scenario, having someone on the outside who specialises in documentation can give your team a much-needed boost.

If Your Docs Need a Refresh Because They’re Not Good Enough

Technical knowledge is just the first step to producing good documentation. If your current documentation is too complicated or lacks consistency, then bringing in a professional writer can help you sort out the clarity and structure.

Working with a company offering specialist technical writing services, like Devdocs, can give you access to people who really understand how to take complex product information and turn it into useful documentation that makes sense to users.

If Your Support Team Is Getting Slogged Down with Repeat Questions

According to Zendesk’s trends report on customer experience, more and more customers are expecting businesses to give them quick access to information and self-service options. There’s a good chance you can save your support team some work by providing the exact information that users need in your documentation itself.

Reviewing your support tickets can be a great way to work out where your documentation needs improvement. If customers keep asking the same question over and over, then it’s a sign that your guides just aren’t doing a good enough job.

Treat Documentation as a Core Part of Your Product

The National Institute of Standards and Technology highlights that clear documentation and processes are key to effective technology management and cybersecurity practices. And the more you can make your documentation work for you, the stronger your software is likely to be. 

Ultimately, knowing when to bring in outside expertise gives your team the best chance to keep up quality without slowing down development.

The post How to Decide When to Outsource Your Technical Documentation first appeared on FROMDEV.

The 10 best online coding classes for kids in 2026:

1. CodeYoung — Best for live 1:1 coding classes from block-based to Python/Java/web dev (K-12)
   
2. Scratch — Best free visual programming intro for beginners (ages 8-16)
   
3. Code.org — Best free full K-12 computer science curriculum
   
4. Tynker — Best for transitioning from block coding to real text-based languages (K-8)
   
5. CodeCombat — Best game-based way to learn real Python or JavaScript (ages 4-12)
   
6. CodeWizardsHQ — Best live small-group classes for structured, social learning (ages 8-18)
   
7. Juni Learning — Best premium 1:1 tutoring for advanced learners and AP CS prep (ages 7-18)
   
8. Swift Playgrounds — Best free way to build real iOS/iPadOS apps (ages 4-12+)
   
9. Khan Academy — Best free intro to JavaScript, HTML, and SQL for teens
   
10. freeCodeCamp — Best free, full web development curriculum for older kids and teens
    

Comparison Table

Pricing reflects publicly listed rates at the time of publication and varies by plan, course, and region. Always confirm current pricing directly with each provider.

Methodology

This list mixes free self-paced tools, gamified apps, and live instructor-led classes on purpose, since kids typically progress through more than one of these as they go from block-based basics to real text-based languages. Picks were chosen for age-range coverage, whether they teach transferable skills (real languages, not just proprietary game mechanics), and pricing transparency.

1. CodeYoung

CodeYoung offers live 1:1 online coding classes for kids from kindergarten through grade 12, structured across three tracks: block-based programming (Scratch, MIT App Inventor), text-based programming (HTML/CSS, JavaScript, ReactJS), and real-world Python development with AI integration. Course tracks are built for specific age bands (5-9, 10-13, 14+), with a gamified platform offering 1,000+ exercises and a project-based, mastery-focused curriculum. Unlike most entries on this list, it’s live and personalized rather than self-paced, which matters most once a child moves past drag-and-drop blocks into real syntax and needs someone to debug alongside them. The program is STEM.org accredited, with mentors described as “top 1%” and holding advanced degrees.

Best For: Kids who need live, 1:1 guidance progressing from visual blocks to real programming languages.

Pros:

• Covers a wide range — Scratch, Python, Java, HTML/CSS, JavaScript/ReactJS, and MIT App Inventor — under one program
  
• Live 1:1 format means real-time debugging help, not just pre-recorded lessons
  
• Age-banded tracks (5-9, 10-13, 14+) match curriculum to developmental stage
  
• Free trial class available before committing
  

Cons:

• Costs more per session than free self-paced tools
  
• Requires scheduling a fixed class time
  
• Full package pricing beyond the starting rate isn’t published upfront
  

2. Scratch

Scratch, built by MIT, is the standard entry point for most kids learning to code — a free, browser-based, drag-and-drop visual programming environment where kids build games, animations, and interactive stories without writing syntax. It has a large, active community where kids can remix and share each other’s projects.

Best For: A completely free, zero-pressure first introduction to programming logic.

Pros:

• Completely free with no account limitations or paywalls
  
• Visual block interface removes syntax errors as a barrier for beginners
  
• Massive project community for inspiration and sharing
  

Cons:

• Block-based format doesn’t directly transfer to real text-based language syntax
  
• No live instructor if a child gets stuck on a specific logic problem
  
• Can feel too open-ended for kids who want more structured lessons
  

3. Code.org

Code.org is a nonprofit offering a completely free, full K-12 computer science curriculum, often used in schools but equally accessible at home. Courses range from drag-and-drop basics for younger kids up through real JavaScript for older students, frequently using licensed themes like Minecraft to keep lessons engaging.

Best For: Families who want a complete, structured, no-cost curriculum rather than a single standalone tool.

Pros:

• Entirely free, nonprofit-run, with no premium tier
  
• Full K-12 progression in one place, from early blocks to real JavaScript
  
• Popular licensed themes (Minecraft, Star Wars) keep younger kids engaged
  

Cons:

• Self-paced with no live instructor for real-time troubleshooting
  
• Some courses are designed around classroom use, which can feel less tailored at home
  
• Progression through the full curriculum takes significant time and consistency
  

4. Tynker

Tynker starts kids in block-based coding and progressively transitions them into real text-based languages like Python and JavaScript, with a notable feature letting kids code actual Minecraft mods. It covers roughly kindergarten through grade 8.

Best For: Kids who want a smoother, structured bridge from visual blocks into real code.

Pros:

• Deliberate progression path from blocks to real Python/JavaScript syntax
  
• Minecraft modding feature is a strong hook for kids who already play the game
  
• Offers live classes and 1:1 tutoring options alongside the self-paced app
  

Cons:

• Full feature access requires a paid subscription (~$120-180/year)
  
• Self-paced core app has no live instructor by default
  
• Aimed at K-8; older teens will outgrow the core curriculum
  

5. CodeCombat

CodeCombat teaches real Python or JavaScript through an RPG-style game where kids write actual code to move characters, battle enemies, and solve dungeon puzzles — not a simplified, proprietary syntax. It’s aimed at roughly ages 4-12 depending on the specific course level.

Best For: Kids who are motivated by games and want to write real code, not just drag blocks, from early on.

Pros:

• Teaches actual Python/JavaScript syntax inside a genuinely fun game format
  
• Free tier offers real, substantial gameplay and learning, not just a demo
  
• Strong option for kids ready to move past pure block-based tools
  

Cons:

• Full course library and classroom features require a paid plan (~$99/year)
  
• Game framing may not suit kids who prefer a more direct, non-game learning style
  
• No live instructor for real-time help when stuck on a specific bug
  

6. CodeWizardsHQ

CodeWizardsHQ runs live, small-group coding classes (4-6 students) for ages 8-18, structured across Elementary, Middle School, and High School program tracks. The social, cohort-based format is a deliberate alternative to fully 1:1 or fully self-paced options.

Best For: Kids who learn well in a small group setting with peers, not just 1:1 or solo.

Pros:

• Live instruction with real-time instructor feedback, not pre-recorded content
  
• Small-group format (4-6 students) adds social motivation and group discounts
  
• Clear age-banded program tracks for elementary through high school
  

Cons:

• Group format means less individualized pacing than a 1:1 class
  
• Full course pricing isn’t listed without going through enrollment
  
• Scheduling around a fixed group class time is less flexible than self-paced tools
  

7. Juni Learning

Juni Learning offers premium 1:1 online coding tutoring for ages 7-18, taught by instructors typically drawn from computer science backgrounds, covering everything from beginner Scratch-style work through Python, Java, web development, and AP Computer Science prep.

Best For: Advanced or ambitious learners, especially teens preparing for AP Computer Science or more serious CS study.

Pros:

• Covers a genuinely advanced range, up through AP CS-level material
  
• Consistent 1:1 instructor relationship across sessions
  
• Strong fit for older, motivated students rather than casual dabbling
  

Cons:

• The most expensive option on this list, starting around $300/month for one weekly session
  
• Overkill for younger kids just starting with basic block-based coding
  
• Requires a recurring monthly commitment rather than pay-as-you-go
  

8. Swift Playgrounds

Swift Playgrounds is Apple’s free app for learning to code in Swift, letting kids build real apps that can run on iPhone and iPad rather than a simulated teaching environment. It’s designed to be approachable for younger kids while scaling up to genuinely useful app-building skills.

Best For: Kids interested specifically in building real iOS/iPadOS apps, not just general programming logic.

Pros:

• Completely free with no subscription or in-app purchases
  
• Teaches Swift, a real, actively used production programming language
  
• Projects result in apps that genuinely run on Apple devices, which is highly motivating
  

Cons:

• Requires an iPad or Mac — not available on Android or Windows
  
• Narrower focus on Swift/Apple ecosystem rather than general-purpose languages
  
• No live instructor component
  

9. Khan Academy

Khan Academy’s computer programming courses are a free, nonprofit-run introduction to JavaScript, HTML/CSS, and SQL, aimed at older kids and teens who are ready to move past visual blocks into real, industry-relevant syntax.

Best For: Teens ready for a genuinely free, structured intro to real front-end and database languages.

Pros:

• Completely free with no premium tier or ads
  
• Teaches real, widely used languages (JavaScript, HTML/CSS, SQL)
  
• Backed by Khan Academy’s broader, well-established instructional reputation
  

Cons:

• Better suited to older kids/teens than younger beginners
  
• Self-paced with no live instructor
  
• Less gamified or engaging than kid-focused platforms like CodeCombat or Tynker
  

10. freeCodeCamp

freeCodeCamp is a free, nonprofit, project-based curriculum covering full-stack web development, from HTML/CSS basics through JavaScript frameworks, data structures, and more. It’s aimed at teens and up who are serious about building genuinely job-relevant coding skills.

Best For: Older kids and teens ready to work toward real, portfolio-worthy web development projects.

Pros:

• Entirely free, comprehensive curriculum covering real, in-demand web development skills
  
• Project-based structure results in an actual portfolio, not just completed exercises
  
• Large, active community and forum for getting unstuck
  

Cons:

• Text-heavy, self-directed format is a poor fit for younger or less independent learners
  
• No live instructor or personalized feedback
  
• Steeper learning curve than kid-focused, gamified platforms
  

FAQ

What is the best way for a kid to start learning to code?

Most kids start with a free visual tool like Scratch or Code.org to build programming logic before moving into real text-based languages. From there, the right next step depends on the child — self-paced tools like Tynker or CodeCombat for independent learners, or live classes like CodeYoung or CodeWizardsHQ for kids who benefit from real-time instruction.

At what age should a child start learning to code?

Tools like ScratchJr and Code.org’s early courses are designed for kids as young as 4-5, focusing on basic sequencing and logic before introducing full block-based programming around ages 7-8, and real text-based languages typically from around age 10 onward.

Are free coding tools enough, or does my child need a class?

Free tools like Scratch, Code.org, and Khan Academy are genuinely useful for building foundational logic and exploring interest, but many kids hit a point where they need real-time help debugging actual code — that’s where a live class, such as CodeYoung’s 1:1 sessions, tends to accelerate progress faster than self-paced tools alone.

What programming language should kids learn first?

Most educators recommend starting with a visual, block-based tool like Scratch to learn programming logic before introducing a text-based language. Python is a common first real language due to its readable syntax, followed by JavaScript for kids interested in web development.

Is live 1:1 coding instruction worth the extra cost over free apps?

It depends on the goal. Free apps are excellent for exploration and building initial interest at low cost. Live 1:1 instruction becomes more valuable once a child is working with real code and needs a person to help debug specific errors and explain concepts a different way when the first explanation doesn’t land.

The post Best Online Coding Classes for Kids in 2026 first appeared on FROMDEV.

Scala is not the obvious choice for every application, and its smaller talent pool can make staffing more expensive. But when a product has complicated business rules, high transaction volumes, demanding reliability requirements, or a long roadmap, Scala can move some of the cost from ongoing maintenance into more deliberate engineering upfront.

The real question is whether its characteristics can lower the total cost of ownership over the product’s expected lifespan. Teams evaluating Scala development services should make that calculation before treating the language itself as the solution.

The first release is only the beginning of the cost

The initial build is only one line in the budget. Once the product is live, engineers fix defects, handle incidents, and maintain integrations. Business rules change, dependencies need upgrades, and the team eventually runs into architectural decisions that were reasonable when the system was smaller but now get in the way.

For a short-lived product, optimizing for initial speed can be rational. A core business platform has a different cost curve: a shortcut that saves three weeks in the first release becomes expensive if every later change requires regression testing or specialist knowledge.

That is why the economics of a backend technology choice should be measured across the product lifecycle, not against the first project estimate.

Scala starts to pay off when the domain gets complicated

Scala’s strongest financial argument is not that it makes simple software cheaper, but that it gives teams strong tools for dealing with complicated software.

Scala combines object-oriented and functional programming under a static type system that catches many mistakes before code reaches production. Instead of leaving business rules scattered through mutable state and implicit assumptions, developers can use immutable values, pattern matching, algebraic data types, and explicit error-handling patterns to make those rules easier to see and reason about. (Scala Documentation)

That matters where the rules keep multiplying. A platform handling orders, pricing, eligibility, or payments can start with a handful of conditions and end up with hundreds of interacting cases as new products introduce exceptions, regulations change, and several teams touch the same domain.

A strongly modeled domain does not prevent bugs, but it makes invalid states harder to represent and intended behavior easier to understand.

The savings are incremental rather than dramatic. A refactoring that can be completed confidently is cheaper than one requiring weeks of investigation; a business rule explicit in the type system is easier to review; a predictable function is easier to test. For a small application those differences may be irrelevant; over ten years of development, they become a meaningful part of the cost model.

Performance is useful when throughput actually affects the business

Scala is sometimes sold through performance claims too broad to be useful. The JVM does not make every application fast, and Scala does not automatically outperform Java, Go, or other backend languages. Database queries, network calls, serialization, caching, and capacity planning usually have a larger effect than the choice between two competent JVM languages.

The case is different when concurrency and throughput are fundamental requirements. Scala’s standard library includes abstractions such as Future for asynchronous computation, and the wider ecosystem covers distributed systems and data processing, all on the JVM’s mature runtime. (Scala Documentation)

For a system processing large volumes of events or transactions, those capabilities can matter to infrastructure costs and operational reliability—and they are worth evaluating as part of the architecture, not as a benchmark offered as proof of business value.

Java interoperability gives Scala room to evolve

Scala runs on the JVM, and the official documentation treats Java interoperability as a core part of the platform: Scala code can work directly with Java classes and interfaces, while teams continue using established Java libraries and collections alongside Scala. (Scala Documentation)

For an organization with an existing Java estate, that changes the migration equation. A company can introduce Scala for a new service without replacing the Java applications around it, keeping shared infrastructure and libraries in place. Development teams that work with both languages—SysGears has been building JVM-based systems since 2010—typically introduce Scala service by service rather than through one clean rewrite, because a product usually has to evolve while customers continue using it.

The result is less architectural lock-in than the language’s relatively small developer community might suggest.

The hiring problem is real

There is a straightforward downside to Scala: fewer developers know it. A market with ten suitable Java developers for every Scala developer means paying more or waiting longer to fill a position.

The developer market is large: Stack Overflow’s 2025 Developer Survey gathered more than 49,000 responses from 177 countries. Scala draws from a much smaller slice of it, so finding experienced engineers takes more time and money than hiring for mainstream stacks. (Stack Overflow Developer Survey)

This makes team strategy part of the architecture decision. A company starting from zero needs a credible plan for building expertise: hiring senior engineers, training developers with JVM experience, or working with an established Scala partner such as SysGears while the internal team develops ownership.

The comparison is not simply “Scala developers cost more” but whether the premium buys something the product genuinely needs. If not, use another language.

Long-lived Scala systems need a version strategy

Choosing Scala for a decade-long product means planning upgrades before the first production release. Scala’s current documentation lists Scala 3.8 as requiring JDK 17 or newer, while Scala 3.3 LTS through 3.7 support JDK 8 and above. Scala 3.3.8 LTS, released in June 2026, added support for JDK 26 and backported fixes from the newer Scala line. (Scala Documentation)

A long-lived product should have a regular dependency and JDK upgrade process; otherwise, upgrades that could have been routine become modernization projects.

Scala 2 applications also need a migration plan. Scala 3 interoperates with Scala 2.13, but the official migration guidance identifies obstacles such as unported macro libraries, unsupported compiler plugins, and dependencies on scala-reflect. (Scala Documentation) That is a manageable problem, but not one to discover mid-project.

Scala is not a good investment for every product

There are plenty of cases where Scala is difficult to justify financially. A small internal CRUD application with simple business rules probably does not need its type system or functional abstractions. A prototype testing a market hypothesis has different priorities from future core infrastructure. A company with no JVM expertise may reasonably prefer a stack it can staff more easily.

The same is true when a team adopts the language for prestige rather than a concrete engineering requirement. Scala is expressive, but expressiveness becomes a liability when developers use abstractions that make the code harder for the rest of the organization to understand.

The language should earn its place in the architecture.

The strongest case is a product that gets harder every year

The financial case for Scala becomes more convincing when the product’s complexity is expected to grow faster than its team. That pattern is common in systems central to a business: a platform accumulates integrations, customer-specific rules, compliance requirements, and operational constraints until the cost of a change depends as much on understanding the existing system as on implementing the new feature. It is also the pattern SysGears sees most often in projects where Scala has clearly justified itself—platforms whose domain logic outgrew every early estimate.

A technology that helps engineers express domain rules precisely can have value that never appears in the first year’s budget. The benefit may show up three years later: a core component refactored without rewriting half the application, a new engineer understanding the domain model without reading thousands of lines of defensive code, fewer production incidents caused by state-management errors.

Make the decision using the whole lifecycle

Scala makes the strongest financial case when a product will stay in use for years and grow in complexity along the way—and much less sense when the application is simple, short-lived, or easy to replace.

The evaluation should go beyond development speed: estimate the likely cost of staffing, infrastructure, maintenance, upgrades, incidents, and major refactoring, then compare it with the realistic alternatives. For a decade-long product, the cheapest first release is rarely the cheapest system to own.

Scala is a credible option when its type safety, JVM ecosystem, functional model, and support for complex domains address problems the product will actually face. The tradeoff is a smaller talent pool and a language that rewards teams willing to learn its more advanced concepts.

That is a reasonable trade for the right system. It is an unnecessary one for the wrong system.

The post When Scala Makes Financial Sense for a Long-Lived Product first appeared on FROMDEV.

Your customer does not see your teams, tools or workflows; they simply feel whether each connected experience makes sense from one touchpoint to the next.

Brands can no longer work in silos because customers expect one seamless journey, and disconnected teams often create friction, inconsistent experiences and unnecessary frustration.

In this article, we’ll look at why customer expectations have changed, where connected experiences often break down and how you can build smoother journeys.

Let’s get into what makes connected experiences work in the real world. 

Why Customers Expect One Continuous Experience Across Every Touchpoint

The biggest shift is not that customers have more ways to interact with brands, it’s that they no longer see apps, websites, call centres and physical stores as separate experiences. They simply expect every interaction to continue where the last one ended.

This expectation becomes obvious during everyday moments that businesses often overlook. Someone might upgrade a mobile plan in an app, check billing later that day and contact support at night, all while expecting the brand to already understand the full picture.

Customers are also becoming less patient with delays between systems. If a SIM activation is complete in one channel but still pending in another, customers immediately notice the disconnect because digital experiences now move much faster than internal processes.

What often surprises businesses is that customers rarely remember the biggest innovations. They remember being asked to verify their identity multiple times, receiving conflicting answers from different teams or getting a generic roaming message after they have already purchased a roaming pack.

Most importantly, customers no longer separate your organisation into departments. Every interaction contributes to one connected customer experience, which means one broken handover can shape how they feel about the entire brand. 

How Organisational Silos Break the Customer Journey

Most customer journeys do not fail because teams are underperforming; they fail because billing, support, sales, digital and network teams often work with different goals, systems and customer information that never fully connect.

With that in mind, here are the common organisational gaps that quietly disrupt the customer journey long before customers decide to leave:

• Customers unknowingly become the bridge between disconnected teams. They repeat plan changes, billing concerns and support history because each team only sees part of the journey. This turns a simple request into extra work the customer never expected to do.
• Customer journeys are designed around departments instead of real-life moments. A person trying to activate a SIM, fix roaming or change a plan does not care which team owns each step. They only notice when the journey feels harder than the task itself.
• Temporary fixes quietly become permanent ways of working. A manual approval, spreadsheet or extra form may solve one urgent problem at the time. Months later, it can become the hidden reason the journey feels slow or inconsistent.
• Teams only optimise the part they can see. One team may make plan upgrades faster while another receives more billing questions afterwards. Without a wider view, local improvements can still create journey-level friction.
• Problems are solved one interaction at a time instead of fixing the root cause. Support may clear the ticket, but the real problem might have started when the SIM failed, the payment did not sync or the service message arrived too late. That means the issue gets closed, while the broken step stays in place.
• Internal complexity eventually becomes visible to customers. They may never see your systems, approval steps or team structure. They feel it through delays, repeated checks and answers that do not match across channels. 

5 Components of A Connected Customer Experience That Customers Notice Most

Customers may never see the systems, teams or technology working behind the scenes, but they immediately notice when an experience feels simple, consistent and built around what they are trying to do.

These five components often determine whether a connected customer experience feels effortless or frustrating:

1. Context That Follows Every Interaction

The best experiences preserve momentum, so when someone upgrades a plan, buys a roaming pack or starts a support request, every touchpoint already knows where they are in the journey.

People no longer expect brands to simply remember information; they expect progress to move with them so they never feel like they’re starting over. 

2. Consistent Information Wherever They Go

People question the entire experience when a plan price in the app does not match the website or when an agent gives different roaming guidance.

Consistency builds trust because customers should never have to double-check which channel has the correct answer before making a decision.

3. Faster Resolution With Fewer Steps

Customers rarely remember how fast something was resolved, but they always remember how many obstacles they had to overcome first. Every extra login, verification step or transfer makes a simple task feel unnecessarily difficult.

The best experiences remove hidden effort instead of simply increasing speed. Changing a plan or updating account details should take a few steps, not multiple channels and repeated actions. 

4. Relevant Support Before Problems Escalate 

The strongest connected experiences reduce uncertainty before it turns into frustration, using existing signals to offer support at the right moment instead of waiting for someone to search for help.

For example, someone travelling overseas could automatically receive roaming guidance, data alerts and support options before any service issues arise. 

5. Smooth Transitions Between Digital and Human Support

When customers move between channels to solve an issue, the experience should continue naturally without losing progress.

The agent taking over should already see the earlier conversation, including what was asked, what was tried and why the person still needs help. 

Why a Shared Digital Foundation is Essential for Connected Experiences

Many organisations discover that improving individual touchpoints only solves part of the problem. The bigger challenge is making sure every team works from the same source of truth.

Building a connected customer experience often starts with a digital telecom platform that unifies customer data, digital services and customer interactions across multiple touchpoints. This creates a single view that can be used consistently across the organisation.

Without a shared foundation, each new app, feature or service can become another layer the organisation has to patch together later. That is why some digital projects look modern to customers, but still feel clunky when they move from self-service to support.

A stronger foundation gives teams reusable building blocks for plan changes, onboarding, billing and service updates. Instead of rebuilding the same journey for every new offer, teams can launch faster while keeping the customer experience connected across channels. 

How Connected Experiences Create Long-Term Business Value

Connected experiences are often discussed as customer initiatives, but their biggest impact usually happens behind the scenes through faster decisions, fewer inefficiencies and stronger long-term relationships.

Below are the business outcomes that start showing up when connected experiences become normal, not occasional:

• Churn risk becomes easier to reduce. People are less likely to leave when plan changes, billing, support and service updates feel joined up. Over time, that reliability can matter more than another short-term discount.
• Support demand becomes easier to control. Fewer broken handovers mean fewer repeat calls about activations, payments or unresolved cases. Your support team gets more room to deal with the messy cases that actually need human judgement.
• New offers can launch faster. Teams can introduce plans, add-ons and digital services without redesigning the customer journey each time. This shortens launch cycles and makes it easier to respond to changing market demands.
• Cross-sell and upsell moments feel more relevant. Offers land better when they are based on what people are actually doing, not broad customer segments. A roaming offer before travel is useful, while the same offer after the trip feels careless.
• Operational waste starts to drop. Less manual checking, chasing and correcting gives teams more breathing room. The business spends less energy holding the journey together and more energy improving it.
• Customer lifetime value becomes stronger. People stay longer when the experience keeps getting easier. Small connected moments build confidence, and confidence is what makes customers more open to buying again.

Conclusion 

The brands pulling ahead are building a connected customer experience, not simply adding more channels, so every interaction feels like one journey.

That matters because customers notice gaps quickly, even when they cannot see the systems, teams or decisions behind them.

The real work is removing the small handovers, delays and mixed messages that make a journey feel harder than it should.

Brands that close those gaps become easier to trust, easier to stay with and harder to replace when better options appear. 

The post Connected Customer Experience: Why Brands Can No Longer Work In Silos first appeared on FROMDEV.

Walk into almost any office these days and it feels like a gadget convention exploded all over the place. Smartphones, docking stations, cloud instances, old servers humming away in corners—keeping up with all of it is enough to make even the most organized IT pro want to throw their hands up. The sheer volume of stuff that needs tracking, maintaining, and eventually replacing has gotten completely out of hand. That is precisely why adopting a proper system for managing the whole lifecycle of technology assets has stopped being a luxury and started being a survival tactic. Without something solid in place to handle IT asset lifecycle management software duties, companies end up throwing money down the drain and leaving digital doors wide open for trouble to stroll right in.

Where All the Money Vanishes

Here is a little secret that finance departments hate admitting out loud—businesses lose an absolute fortune on equipment they cannot even find anymore. These so-called ghost assets just sit there on the books, quietly eating up budget for support contracts and insurance, while the actual hardware has probably ended up in somebody’s closet at home or a landfill somewhere. A decent lifecycle tracking system acts like a detective with a magnifying glass, sniffing out every single endpoint and forcing procurement teams to face reality before they order another batch of monitors nobody actually needs. When visibility improves that dramatically, the savings show up fast, and suddenly the CFO stops giving IT that skeptical side-eye during budget meetings.

Navigating the License Maze Without a Map

Software licensing is an absolute jungle, plain and simple. Between Microsoft’s ever-shifting rules and Oracle’s infamous audit teams, one wrong move can cost more than a luxury car. Keeping tabs on who uses what, when, and whether those seats are actually active is a nightmare if spreadsheets are the only tool in the arsenal. This is where good management software earns its keep by acting like an obsessive librarian, flagging unused licenses for cancellation and catching overages before they turn into penalty fees. The peace of mind alone is worth the price of admission, knowing that an unexpected auditor showing up at the door will not result in total financial devastation.

Stopping Problems Before They Start

There is something deeply satisfying about fixing a problem before anybody even notices it exists. Modern tracking tools make that possible by paying close attention to performance dips and warning signs that humans might miss. Instead of waiting for a loud crash in the middle of the workday, the system quietly suggests that a particular machine might need attention soon, allowing the team to schedule repairs during off-hours. This kind of foresight keeps everyone happier, because nothing kills productivity like staring at a blue screen while the IT person frantically runs around trying to find a replacement.

Keeping the Bad Guys Out

Cybersecurity folks love to say that visibility is everything, and they are not wrong. Hackers thrive on the unknown, slipping into networks through forgotten devices that nobody bothered to patch or update properly. Lifecycle software shines a bright light into every corner, automatically flagging machines that are running outdated systems or missing critical security fixes. Enforcing consistent policies across hundreds or thousands of devices becomes almost effortless, and the attack surface shrinks considerably when every single endpoint follows the same rules. That is a comforting thought, especially with ransomware gangs getting more aggressive by the day.

Doing Right by the Planet

Here is something that does not get discussed enough—responsible IT management is also good for the environment. Throwing away perfectly usable hardware just because it is a couple of years old is incredibly wasteful, both financially and ecologically. Smart tracking allows organizations to cascade devices down the chain, so a marketing director’s old laptop becomes a perfectly fine machine for the reception desk. This extends usable life significantly, delays expensive recycling trips, and gives the company a nice little sustainability talking point to share with environmentally conscious clients and partners.

Getting People on Board

Rolling out any new system inevitably triggers some eye-rolling and grumbling from staff who worry about being watched too closely. The key to smooth adoption is framing the whole thing differently—this is not a surveillance tool, it is a convenience tool. When employees see that their machines run faster and break down less often because of proactive maintenance, they start appreciating the system rather than resenting it. Nobody enjoys working on a sluggish laptop, and nobody wants their afternoon ruined by an unexpected crash. Frame it that way, and even the biggest skeptics come around eventually.

Smarter Choices for Tomorrow

Looking back at all the data collected over time reveals patterns that are incredibly valuable for future planning. Instead of guessing how long certain equipment should last or what specs the next batch of purchases should have, leadership can make decisions backed by real evidence. This transforms IT from a department that constantly asks for more money into a strategic partner that demonstrates clear value through smarter spending and better outcomes. And honestly, that shift in perception alone makes all the effort worthwhile.

The post Why IT Asset Management Actually Matters first appeared on FROMDEV.

Artificial intelligence has revolutionised workplace productivity, with AI meeting tools promising many things such as automatic transcription, searchable notes and instant summaries, all with the idea that they will keep teams aligned. But while the technology has advanced rapidly, the user experience hasn’t always kept pace. For developers, engineering managers and technical teams already juggling countless apps and workflows, many AI meeting tools still feel more like an extra task than a genuine time-saver.

The issue isn’t a lack of innovation; in fact, it’s far from it. Today’s AI meeting platforms are packed with impressive capabilities, from speaker recognition to automated action items and integrations with popular workplace software. However, when these features are hidden behind cluttered dashboards, confusing settings, or complicated workflows, they can quickly become more of a distraction than a benefit. In fast-paced engineering environments, where efficiency is everything, even small moments of friction can have a noticeable impact on productivity. 

More often than not, developers aren’t looking for endless features; they mostly want a simple meeting workflow that lets them record conversations, access accurate transcripts and review clear summaries without unnecessary clicks or complexity.

Why Developers Find AI Meeting Tools More Frustrating Than Helpful

What a lot of developers find is that they spend much of their day switching between code editors, documentation, project management platforms, and messaging apps, like Teams. Meetings already interrupt valuable periods of focused work, so the software supporting those meetings needs to be as seamless as possible.

Unfortunately, many AI meeting tools introduce unnecessary friction. Cluttered dashboards, confusing recording controls, unclear permissions, and lengthy setup processes (we’ve all been there where we think setting up will be five minutes, but 30 minutes later, it’s still not working) can make even the simplest meeting feel overly complicated. Instead of focusing on the discussion, users are left wondering whether the meeting is recording correctly, who can access the transcript, or where the AI-generated summary has been saved.

What Makes a Great AI Meeting Tool?

There are many things that can make an AI meeting tool great. The most effective AI meeting tools get out of the user’s way, allowing them to concentrate on the conversation rather than the software itself. A clear one-click recording process, visible transcription status, and predictable sharing permissions immediately create a smoother experience. Developers also benefit from summaries that highlight key decisions, action points and next steps, rather than long blocks of text that require additional editing before they’re useful.

Good UX also relies on progressive disclosure, a design principle that keeps everyday tasks simple while making advanced settings available when needed. Privacy controls, retention policies, and integration options remain important, but they shouldn’t overwhelm users who simply want to record a meeting and review the outcomes afterwards.

Why User Experience Will Define the Future of AI Productivity Tools

As AI becomes a standard feature across workplace software, the technology itself is becoming less of a differentiator. Most platforms can already generate transcripts, identify speakers and create summaries. What increasingly sets products apart is how easy those features are to use.

For engineering teams, consistency and predictability build trust. Clear recording indicators, transparent permissions and summaries that surface important decisions without manual reorganisation encourage long-term adoption because they remove unnecessary friction from an already busy working day.

Ultimately, successful AI meeting tools won’t be the ones with the longest list of features. They’ll be the platforms that quietly capture conversations, organise information and integrate naturally into existing workflows without demanding constant attention.

The post Too Many Buttons, Too Little Time: Why AI Meeting Tools Keep Frustrating Busy Developers first appeared on FROMDEV.

Unlike transaction-focused companies, the main criterion of success for customer-centric organizations is not the result of a single interaction or transaction, but the value created at each step of the customer journey. Thus, these companies manage to create differentiation, improve customer retention, and outplay the competition, which fails to get deeper into customer needs.

Customer centricity defined

Customer centricity is more than friendly customer service or prompt responses to requests. Unlike customer-friendly organizations, customer-centric ones build their entire strategy and processes in order to satisfy customer needs.

A product-centric company is focused on improving features, efficiency, and launching new products or services without checking if the changes solve the problem. Meanwhile, a customer-centric one starts all its decisions with a deep understanding of customer goals, challenges, and needs.

Customer centricity is a complex concept that requires companies to put aside their own short-term gains and start thinking about lifetime relationships with customers. Besides Customer Lifetime Value, customer centricity involves customer behavior, feedback, experience analysis, and continuous improvement of the product or service.

In the world of digital commerce, customer centricity is especially beneficial because customers can choose alternatives and move away to another brand easily. Organizations that actively listen to customers using voice of the customer Programs, feedback surveys, reviews nd analyze customer behavior get the edge over their competitors.

Why do customer-centric businesses perform better than others?

The performance of customer-centric businesses is better due to their ability to provide good experiences. Companies focused on customer relationships usually show higher profitability, customer loyalty, and growth compared to organizations focused on products and transactions.

The main advantage of being customer-centric is high customer satisfaction. Companies that understand customer needs and provide personalized experiences have better chances of building long-term relationships. Customers’ positive experience results in repeat purchases and increased loyalty. Meanwhile, a bad customer experience can push away even loyal customers – 89% of people are ready to change brands after a bad customer experience.

Another benefit of customer-centric companies is customer advocacy. Customer advocacy occurs when a customer feels valued and appreciated, and he or she is ready to share a positive opinion about a brand with friends, family members, and online communities. Positive word of mouth makes customer-centric companies visible and attracts new customers without high acquisition costs. About 72% of consumers are ready to share their positive experience of online shopping.

Personalization also helps customer-centric organizations to stand out. Customers expect companies to know their preferences and provide a personalized experience. According to Epsilon, 80% of consumers are more likely to buy from personalized brands.

Another benefit of being customer-centric is the adaptability of the company. Customer feedback and interactions influence marketing strategies and help to understand market changes in time. As per the findings of Salesforce, customer interactions and feedback significantly affect marketing strategies of marketers.

Thus, the combination of better customer experiences, increased customer loyalty, customer advocacy, personalization, and adaptability leads to business growth. According to the research conducted by Fred Reichheld, a 5% customer retention improvement leads to a 25-95% profit improvement in different industries.

Steps to become a customer-centric organization

To build a customer-centric organization requires not only technological implementation. First of all, you should make sure that all teams in your organization understand that the goal of each interaction is customer success.

Understand your customers deeply.

The main point of customer centricity is understanding customer needs and challenges. To get knowledge of your customers, it is important to interact with them directly, using surveys, interviews, focus groups, and customer journey mapping.

Meanwhile, it is also important to use analytics and artificial intelligence in order to see patterns in customer behavior. With the help of AI solutions,s you can analyze vast amounts of data, see customer preferences, and create personalized experiences. Combining customer feedback and data analytics gives you the opportunity to predict customer needs and solve customer problems in advance.

Foster empathy across all teams.

Customer-centricity starts with employees. Marketing, sales, product development, and customer service departments should understand the customer perspective and the way how their work influences customer satisfaction.

Empathy can be fostered through sharing customer stories, customer feedback, and examples of success. You can train your employees and teach them to communicate better with the help of special tools. For example, conversational AI assistants can give recommendations to employees, show sentiment analysis, and let them understand customers better. A culture of empathy makes teams work closer together and makes each customer touchpoint reflect the values of the company.

Streamline customer experience

Customers expect an easy, convenient, and consistent experience on any touchpoint. Complex processes, delays, and disconnections destroy relationships with customers.

It is important to make the customer journey simpler using website optimization, customer service process optimization, and providing various channels of support. Using solutions like a service configurator, click-to-call functionality, and automated recovery workflows, you can make customer goals easier to reach. A seamless customer experience improves customer satisfaction and builds trust.

Close the feedback loop.

Collecting customer feedback is the first step. But what matters most is action after getting feedback. When customers see that their opinion is used as part of the decision-making process, they trust the company and are engaged with it. Closing the feedback loop turns customer insights into improvements.

Equip teams with the right tools.

Technological solutions play an important role in making the business customer-centric. Customer Relationship Management platforms, omnichannel experience solutions,d and e-commerce solutions help you track interactions with the customer, understand the customer journey, and provide a personalized experience.

But technology should help employees to connect better with customers. The best solutions help employees to make informed decisions and provide a personalized customer experience.

Conclusion

Customer-centric organizations outperform others because they understand that business success is dependent on customer success. Focusing on customer needs, personalization, continuous listening, and employee empowerment allows organizations to build strong relationships and achieve sustainable growth.

In the world of digital commerce, organizations putting customers at the core of their operations and decisions have competitive advantages over their competition. Customer-centricity is not only a tool for improving experience, but also the foundation of business success.

The post Why Customer-Centric Organizations Outperform Their Competitors first appeared on FROMDEV.