Delivery management software brings these activities into one workflow. Instead of manually coordinating each stage, businesses can automate routine decisions and give dispatchers more time to handle exceptions that actually require human judgment.

Automate Order Intake and Dispatch

Manual order entry creates delays before a vehicle even leaves the depot.

Modern delivery systems can receive order data from ecommerce platforms, order management systems, warehouse software, or APIs. Addresses, delivery windows, package details, and customer instructions can then flow directly into the dispatch workflow.

FromDev has previously highlighted system integration as a core part of logistics automation, particularly when businesses need order and shipment information to move between multiple platforms without repeated manual entry.

Once orders are available centrally, dispatchers can assign work based on route, driver availability, capacity, or operating rules.

Use Software to Build Delivery Routes

Planning multi-stop routes manually becomes increasingly inefficient as stop counts rise.

A dispatcher may need to account for geography, delivery windows, vehicle capacity, driver schedules, service times, and priority orders simultaneously. Even a relatively small change, such as adding one urgent stop, can affect the rest of the route.

Using last mile delivery software allows businesses to move more of this planning into a structured digital workflow. Rather than manually plotting every stop, teams can use software to organize deliveries and manage changing routes as the day develops.

Apply Real Constraints to Route Optimization

The shortest route is not always the best route.

A useful delivery system needs more than geographic distance. It should be able to work with operational constraints that determine whether a route can actually be completed.

Important Routing Inputs Include

• Delivery time windows
• Driver working hours
• Vehicle capacity
• Package size or weight
• Stop priority
• Required service time
• Pickup and delivery dependencies
• Depot departure times

Businesses should configure these rules before expecting automation to improve performance.

Poor input data simply allows software to automate a bad process faster.

Give Drivers a Mobile Workflow

Drivers should not need printed route sheets, handwritten notes, and several separate apps to complete a delivery.

A mobile driver interface can provide the stop sequence, navigation details, customer instructions, package information, and delivery status controls in one place.

When the driver completes a stop, the status can be sent back to dispatch automatically.

This creates a shared operational record instead of forcing office staff to call drivers repeatedly for updates.

Mobile access is particularly important for real-time logistics because data only becomes useful when employees can update it while the work is happening. FromDev has identified mobile access and real-time tracking as important components of modern logistics systems.

Automate Customer Notifications

Customers often contact support because they do not know when an order will arrive.

Software can reduce those calls by triggering messages automatically as the delivery moves through different stages.

A customer might receive confirmation when the order is scheduled, another message when the driver begins the route, and an updated estimated arrival time as the vehicle approaches.

This information should come from actual delivery status rather than a manually entered estimate.

Automated communication is especially valuable when delays occur. Customers generally react better to a revised arrival time than to receiving no information until the original window has already passed.

Capture Proof of Delivery Digitally

A completed stop should create evidence that the delivery occurred.

Depending on the business, software can record a photograph, electronic signature, recipient name, timestamp, GPS position, or driver note.

Useful Proof-of-Delivery Records Include

• Completion time
• Delivery location
• Recipient information
• Signature where required
• Delivery photograph
• Exception notes
• Failed-attempt reason

These records make customer disputes easier to investigate.

They also provide operational data. Repeated failed deliveries at one type of location, for example, may indicate that customer instructions or delivery windows need to change.

Automate Exception Management

No delivery plan survives the entire day exactly as expected.

Traffic, customer cancellations, vehicle problems, incorrect addresses, and urgent orders create exceptions.

Software should make these problems visible quickly instead of leaving them buried in calls and text messages.

Dispatchers can then focus on decisions that cannot be fully automated, such as moving stops between drivers, contacting customers, or deciding whether a delayed order should be rescheduled.

FromDev has discussed the value of real-time logistics data for identifying disruptions and adapting routes instead of relying on static plans.

Automation works best when it handles routine work while making unusual situations easier for people to manage.

Connect Delivery Software With Other Systems

Delivery management should not become another isolated database.

Order data may originate in an ecommerce platform or ERP. Inventory status may come from warehouse software. Customer information may sit in a CRM.

APIs and other integrations can pass relevant information between these systems.

For example, once an order reaches a particular fulfillment status, it can automatically enter the delivery queue. After successful delivery, the final status can flow back into the order system.

This reduces duplicate data entry and helps prevent situations where one platform shows an order as delivered while another still shows it as pending.

Track Performance Automatically

Once delivery activity is captured digitally, businesses can measure performance without manually assembling reports.

Useful metrics include route completion time, miles per stop, deliveries per driver hour, first-attempt success rate, on-time percentage, service time per stop, and failed delivery frequency.

Software can also compare planned activity with actual results.

If routes repeatedly finish later than predicted, managers can investigate whether the issue comes from unrealistic service-time assumptions, loading delays, traffic patterns, or poor address information.

This is where automation becomes more than a scheduling tool. It becomes a source of operational data.

Automate the Repetitive Work First

Businesses do not need to automate every delivery decision immediately.

Start with repetitive processes that consume staff time and create avoidable errors. Order import, route creation, driver assignments, status updates, customer notifications, and proof-of-delivery capture are logical starting points.

Then review the resulting data and automate additional workflows where the benefit is clear.

Delivery management software works best when it removes repetitive coordination without removing human oversight.

The goal is not to eliminate dispatchers. It is to stop skilled employees from spending their day copying addresses, rebuilding route sheets, chasing driver updates, and preparing reports manually.

When software handles those tasks consistently, delivery teams can manage more volume without increasing administrative work at the same rate.

The post How Software Can Automate Delivery Management first appeared on FROMDEV.

The post Test first appeared on FROMDEV.

Many people use Fotor to get product images instantly and without putting much effort. However, over time, people start looking for better Fotor alternatives that will provide professional-quality photos that can be easily used for ecommerce. 

If you are looking for alternative tools for product photos, continue reading this article, which will help you learn more about them and choose the one that will help your products stand out. 

Why Look for Fotor Alternatives? 

Before moving to the tools, let’s first see what we should look for in Fotor alternatives and why people are generally looking for other tools. 

• Free exports that Fotor offers usually include watermarks and have limited credits, which is a negative factor if you have a limited budget. 
• When you aim to do multiple actions on the tool, it can lag and affect consistency badly. 
• It can be difficult to provide more realistic lighting and shadows; therefore, we need to look for a tool that specializes in it. 

Now, bearing this in mind, let’s start discovering the tools and their main offerings. 

Overview Chart 

3 Best Fotor Alternatives Reviewed 

Krea.ai

Krea.ai is a creative suite transforming basic product shots into ready-to-use images for e-commerce and lifestyle. It can work on the background and apply any styles you aim to use. The tool offers creative flexibility for advanced AI scenes that provide high-resolution photos and is a great alternative to Fotor AI. 

Main Highlights 

• You can create professional product photos with people holding your product, which is great for social media and e-commerce. 
• You can enter any products that come to mind, including gadgets, makeup, and food, and let the tool make product images with them. 
• The tool provides photos in a matter of seconds, saving you time and nerves. 
• After uploading your product photo, you can describe what you want the background to look like and receive a realistic outcome. 
• You can sharpen details, enhance the image resolution, and fix textures.
• You can train the AI for custom style, coloring, and lighting to maintain consistency among the photos. 

Pricing 

The tool offers the following pricing plans: 

• Free Tier: $0/a month-  100 units per day, limited image upscaling and editing. 
• Basic: $5/a month- 5000 units per month, all image models, LoRA training, image upscaling, commercial license, etc. 
• Pro: $21/month-20,000 units per month, all image and video models, app builder, LoRA training, access to new features, bulk discounts, and so on. 
• Max: $63/month – 60,000 units per month, image upscaling, Krea nodes, and unlimited relaxed generations. 

As a result, Krea is one of the top Fotor alternatives that offers advanced editing and upscaling for your product images and free use of the platform. 

Flair 

Flair is an AI-powered design platform for product imagery, which is a great option for e-commerce sellers. It can turn basic photos that were taken with a phone into high-resolution ones ready to use in marketplaces. 

Main Highlights 

• You can build custom human models that match your brand aesthetics and even choose their body type, hair color, and more. 
• Ability to generate ads for the products, maintaining brand consistency while using the templates available. 
• You can remove, regenerate, and modify the image background. 
• Using the editing tools, edit and refine the images to have the perfect outcome for your case. 

Pricing 

Flair’s pricing plans are:

• Free: $0- one custom model, 5 generated images, and 1 video generation. 
• Pro: $8/a month- 2 video generations, image upscaler, faster rendering. 
• Pro+: $26/ a month- 8 standard or 2 fast custom models, 80 generated images, 3 video generations, image upscaler, etc. 
• Scale: $38/a month- 15 standard or 4 fast custom models, 150 generated images, 5 video generations, priority customer support, commercial license, etc. 

Overall, Flair is a good alternative for Fotor since it offers an advanced platform for editing product images and creating ones that match your overall brand consistency. 

Claid AI 

Claid AI is an AI-powered design platform that helps to turn basic product photos into professional images and conducts background removal, upscaling, and generative scene creation. 

Main Highlights 

• The tool adjusts product quality automatically and removes the background to create clean and consistent shots. 
• You can fix lighting, brighten dark images, adjust colors, and get professional-looking product photos. 
• Maintains the original details of the photos, including product proportions, edges, logos, etc. 

Pricing 

The pricing plans that Claid AI offers are the following: 

• Free: $0- access to standard tools, 50 general credits, 50 API credits.
• Essentials: $9/ a month- 500 credits, standard resolution, toolset for product imagery.
• Pro: $34/month- 2000 credits, full access to premium tools, higher upload limits. 
• Business: You can create custom scalable plans for your company. 

For its customisation, model photoshoots, and free tier, Claid becomes another interesting alternative to Fotor. 

The Bottomline

As a result, different modern tools for product photography become a great alternative to Fotor. 

These tools provide advanced free plans, numerous editing options, and give an opportunity to adjust any existing elements or add new ones to achieve a unified image. In addition, different AI models can be integrated into product photos to make them even more suitable for e-commerce. 

Choose the right platform for product imagery for your own case and start creating images that will instantly grab customers’ attention. 

The post Top Fotor AI Alternatives for Product Photos first appeared on FROMDEV.

Figure: In our test, TapVid turned three ordered MetricFlow screenshots into a reviewable product-demo brief while preserving the requested labels and figures.

The short answer: match the format to the buyer’s job

Use a screen recorder when the viewer needs to see an exact action performed in the live interface. It is fast, literal, and ideal for support replies, bug reports, internal handoffs, and highly specific tutorials.

Use a product demo video maker when the viewer needs a guided story. That usually means a landing page, sales follow-up, launch announcement, paid campaign, feature overview, or partner presentation. The job is not merely to display the interface. It is to connect a problem, a workflow, and an outcome in a sequence that is easy to follow.

Use an interactive demo when the buyer benefits from clicking through a controlled simulation. This is useful deeper in the evaluation process, especially when a sales team wants prospects to explore several paths without receiving a full product account.

These formats can work together. A strong SaaS content system may use a recorder for source capture, a demo maker for narrative packaging, and an interactive demo for self-directed evaluation.

What a screen recorder does well

A screen recorder has one decisive advantage: it captures the interface as it exists at that moment. If a support engineer needs to show a user where a setting lives, the fastest responsible answer may be a 45-second recording with a cursor and a short voiceover.

Recorders also reduce production friction. The presenter can open the product, perform the steps, trim the start and finish, and share the file. There is little scripting and no separate storyboard. That speed matters for low-stakes content with a short useful life.

The tradeoff is that the recording inherits every hesitation, notification, loading pause, typo, cursor detour, and irrelevant part of the screen. A raw capture often explains how to click, but not why the workflow matters. It can also become hard to update. If the final button label changes, the team may need to repeat the whole performance.

A recorder is usually sufficient when all four conditions are true:

• The audience already understands the problem.
• The workflow is narrow and linear.
• The interface itself is the main proof.
• The content does not require extensive brand treatment or multiple channels.

If one of those conditions fails, the buying decision starts to favor a demo maker.

What a product demo video maker adds

A product demo video maker separates source proof from the final presentation. The source may be screenshots, product images, short clips, approved copy, or a recorded walkthrough. The production layer then organizes those materials into scenes, adds narration and captions, highlights relevant regions, and controls timing.

That separation creates several practical advantages.

First, the team can define the message before it defines every transition. A landing-page video may open with the user problem, show three exact product steps, and close with the next action. A recorder tends to begin wherever the presenter happens to begin.

Second, the maker can focus attention. A dense dashboard is meaningful to the product team but overwhelming to a new buyer. Crops, callouts, staged reveals, and concise voiceover can tell the viewer what to notice without redrawing the interface.

Third, the output can fit distribution. A sales deck may need landscape video, while a social post may need a vertical cut. A narrated demo can also include subtitles for silent viewing. These variations are easier when the narrative and assets are managed as components instead of one continuous capture.

Fourth, review becomes more specific. A stakeholder can approve the wording, screenshot order, number, and visual correspondence before final rendering. That is important when the video includes pricing, metrics, plan names, compliance language, or product claims.

Product demo video maker decision matrix

The most expensive buying mistake is choosing a format based on visual polish alone. Score the job across five dimensions: viewer control, interface fidelity, narrative support, update frequency, and distribution.

Figure: Choose the format according to the viewer’s task, not according to which tool has the longest feature list.

Choose a screen recorder when

• Exact pointer movement is part of the instruction.
• The content answers one support or enablement question.
• Fast publishing matters more than reusable structure.
• The presenter can record a clean path without sensitive data.
• The recording will not need many aspect ratios or language versions.

Choose a product demo video maker when

• The asset will support acquisition, sales, onboarding, or a launch.
• The workflow needs a beginning, a reason to care, and a next step.
• Product labels, screenshots, pricing, or metrics require review.
• The same source material may become several versions.
• The team expects scene-level edits after stakeholder feedback.

Choose an interactive demo when

• The buyer needs to explore instead of watch.
• Several branches matter to evaluation.
• Sales wants a controlled sandbox without provisioning an account.
• Completion and click behavior are useful buying signals.
• The team can maintain the simulated experience as the product changes.

An interactive demo is not automatically a replacement for video. Video controls sequence and emphasis. Interactive experiences transfer control to the viewer. Many teams need both at different stages.

A documented TapVid test with SaaS interface assets

For our hands-on evaluation, we built a fictional MetricFlow workflow with three 1600 by 900 interface screenshots. The first showed a Revenue overview and monthly recurring revenue of $48,200. The second showed the Last 30 days filter. The third showed a revenue report and the filename Revenue-30D.csv.

We asked TapVid to create a concise product demo, preserve the three-step order and exact labels, and avoid inventing interface elements, metrics, or product claims. The initial upload test surfaced a boundary worth knowing: the Assets to Video workflow accepted PNG, JPG, JPEG, GIF, WebP, and TIFF assets in this session, but it rejected the MP4 screen recording we had also prepared. We therefore used the three original screenshots as the documented test input.

The generated brief identified the asset as a SaaS Product Demo and organized the narrative into the requested three steps: open Revenue Overview, select the date range, and export the report. It repeated the four strict label constraints, named the screenshot sequence, and stated that no invented UI, metrics, or external marketing claims should be added.

That result is useful for a buyer because it creates a review gate before creative direction and rendering. A product marketer can compare the brief against approved source material while corrections are still cheap. It does not remove the need for human review, and it should not be treated as a guarantee of zero errors. It does show a source-first workflow in which the relevant wording and correspondence are visible before production continues.

The test also clarifies product fit. If your essential evidence is continuous cursor movement inside a live application, begin with a recorder. If your essential evidence can be represented by approved screenshots and copy, a structured explainer workflow can provide more control over narrative and review.

Build the source package before choosing the tool

The output quality of any demo system depends on the input package. Do not begin with a vague instruction such as “make our product look exciting.” Start with a compact evidence set.

1. Define one viewer and one decision. A homepage prospect, an existing customer, and a technical evaluator do not need the same demo.
2. Write one approved core message. State what the workflow does without adding unverified performance claims.
3. Capture only the screens required to prove that message. Remove personal data, test notifications, irrelevant navigation, and stale values.
4. Record exact wording that cannot change. Include prices, feature names, file names, dates, numerical values, and legal language.
5. Map each sentence to an asset. This prevents the narration for one feature from appearing over another screen.
6. Define the next action. The demo should lead naturally to a trial, booking, comparison, documentation page, or product step.

This package lets you evaluate competing products on the same material. Without a fixed fixture, a flashy sample can hide weak source handling.

A practical production workflow for SaaS teams

Treat the first video as a pilot, not as a company-wide tool migration. Pick one commercially useful workflow with a clear owner and a known source of truth.

Figure: A repeatable demo process freezes proof first, then records, explains, reviews, and distributes it.

1. Freeze the proof

Confirm the interface state, data, wording, and screenshots that are approved for external use. If the product is changing weekly, choose a workflow that is stable enough to survive the campaign.

2. Capture the source

Record the exact path when cursor behavior matters. Capture clean screenshots when the main requirement is visual fidelity and precise labels. Use both if the tool and workflow support them.

3. Add explanation

Write narration that explains the user problem and the value of each step. Do not simply read every visible label. The screen supplies evidence; the voiceover supplies context.

4. Review correspondence

Check every sentence against the displayed asset. Verify numbers, plan names, filenames, dates, and buttons character by character. Confirm that a sentence about one feature is never paired with another feature’s screen.

5. Distribute and learn

Publish the pilot in one channel and define the signal that matters there. A sales follow-up may be judged by replies or next meetings. A landing page may be judged by engagement and qualified conversion. Avoid claiming a causal lift unless the test design supports it.

How to compare vendors before you buy

Run the same fixture through every shortlisted tool and score the result before discussing an annual plan. A useful pilot should test the risks that create real rework.

• Asset fidelity: Are screenshots and product images preserved instead of redrawn?
• Information fidelity: Are exact labels, numbers, prices, and filenames unchanged?
• Correspondence: Does each line of narration match the correct screen?
• Reviewability: Can the team inspect a brief, script, or storyboard before rendering?
• Edit scope: Can one changed line or screenshot be corrected without rebuilding unrelated scenes?
• Format fit: Does the workflow support the channels your team actually uses?
• Operational boundary: Which file types, durations, aspect ratios, and batch workflows are supported today?
• Commercial terms: What do the current plan, contract, and website say about usage rights, data handling, and limits?

Ask the vendor to show the failed cases as well as the polished examples. A credible pilot reveals both capability and boundary.

The cost question is really a rework question

Subscription price is only one component of production cost. Include the time spent preparing assets, recording clean takes, correcting facts, gathering approvals, producing alternate formats, and updating an outdated interface.

A cheap recorder can be the lowest-cost option for a short support answer. It can become expensive if a campaign asset needs six retakes and three aspect ratios. A demo maker can justify a higher tool cost when it reduces repeated performance and makes individual scenes easier to revise. An interactive platform can be worthwhile when a buyer must explore branches, but it also creates a simulated product surface that someone must maintain.

The correct calculation is cost per approved, usable asset, not cost per exported file.

Frequently Asked Questions

Is a screen recorder a product demo video maker?

It can produce a simple product demo, but the categories are not identical. A recorder captures an interaction. A product demo video maker usually adds scripting, scene structure, narration, callouts, captions, branding, and review controls around source assets.

Should a SaaS homepage use a recording or a produced demo?

Use a recording when the interface action is self-explanatory and the page needs literal proof. Use a produced demo when a new visitor needs context, selective emphasis, or a guided path from problem to outcome. Test the asset in the actual page layout before deciding.

Can a product demo video maker preserve exact UI text?

It should be tested, not assumed. Provide a fixture with difficult labels, numbers, prices, and filenames. Review the brief, script, storyboard, and final output against the sources. TapVid’s source-first approach is designed around preserving supplied assets and wording, but human review remains part of responsible production.

When is an interactive demo better than video?

An interactive demo is stronger when prospects need to choose paths and learn by doing. Video is stronger when the team must control sequence, pacing, and emphasis. They often serve different funnel stages.

What should we test before buying a product demo video maker?

Test one representative workflow for asset fidelity, exact text, screen-to-script correspondence, review gates, scene-level revision, supported formats, and export requirements. Use the same fixture across vendors so the comparison is fair.

Final recommendation

Do not force one format to carry every SaaS communication job. Keep a screen recorder for fast, literal instruction. Use a product demo video maker when explanation, review, brand consistency, and reusable versions affect commercial performance. Add an interactive demo when buyer-controlled exploration becomes part of evaluation.

For a practical pilot, choose one stable workflow, freeze the approved proof, and ask each tool to preserve the same screens and exact language. The winner is not the product that creates the most motion. It is the one that produces the clearest approved asset with the least risky rework.

The post Product Demo Video Maker vs Screen Recorder: What SaaS Teams Actually Need first appeared on FROMDEV.

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.