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Author: Creative2

When Custom Software Makes More Sense Than Another Subscription

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SaaS analytics platforms are built to serve millions of customers at once. That’s exactly why, eventually, they stop serving you.

Organizations depend on analytics software to understand how customers interact with their business. Whether it’s tracking website traffic, measuring marketing campaigns, or monitoring product usage, SaaS platforms have made sophisticated analytics accessible to companies of every size.

For many businesses, those tools are exactly what they need.

As organizations grow, however, the questions they want answered often become more specific than the software was designed to address.

At this point, the focus shifts from simply purchasing another subscription to developing a tailored solution that meets the business’s unique needs.

SaaS Is Built for Scale, Not Specificity

Commercial analytics platforms succeed because they solve common problems for thousands of customers. They deliver reliable reporting without the need for organizations to develop and maintain their software.

That’s also where their limitations begin.

Every SaaS company must make product decisions that cater to a broad user base. If a feature serves only a small segment of customers, it often doesn’t get included.

That isn’t a flaw in SaaS. It’s simply the economics of building software for millions of users.

Over time, organizations with unique reporting needs have to adapt outside the bounds of general analytics software and find the metrics that matter to their business.

Analytics Needs Can Grow Beyond SaaS Offerings

As organizations mature, analytics becomes less about counting page views and more about understanding behavior.

Marketing teams begin asking different questions, while operations teams want reporting that spans multiple systems. Leadership wants visibility into metrics that don’t exist in a standard dashboard.

Instead of relying on standard website metrics or sales reports, they may need to measure customer behavior specific to their business, combine data from multiple systems, or track operational metrics that no commercial platform was built to calculate.

Those workarounds are often a sign that the software has reached its limits. The challenge is no longer collecting data. It’s organizing that data in a way that reflects how the business actually operates.

When Existing Platforms Hit Their Limit

Choosing custom software doesn’t mean replacing every application an organization already uses. Most businesses continue using commercial software where it makes sense, and expand to custom solutions when existing platforms no longer hit the mark.

That was the challenge Creative2 encountered when evaluating analytics platforms for a unique client project.

The objective wasn’t to replace established products, but to find a platform that could remain within the client’s infrastructure, provide ownership of analytics data, and remove barriers that kept employees from using the reporting.

Several platforms addressed part of the problem.

Google Analytics and Adobe Analytics offered mature reporting but required data to leave the client’s environment. Umami solved the self-hosting requirement, yet its privacy-first design limited some of the visitor insights clients needed. A proprietary tool provided detailed reporting but required users to install a desktop application and request access before viewing analytics.

Adoption suffered because the process added unnecessary friction. None of these platforms was deficient; each was built for a different set of priorities.

For Creative2, those evaluations reinforced a broader point. Commercial software can solve most problems. When it can’t, the answer is software designed around the business instead.

Building Around the Business

Starting with a clean slate made it possible to design analytics around how clients actually work, rather than asking them to adapt to someone else’s software. It also created opportunities that commercial products couldn’t offer.

One example involved measuring how long visitors watched a live stream. Traditional analytics platforms often lose visibility when browsers fail to report the exact time a session ends. For organizations that depend on audience engagement metrics, that gap can significantly affect reporting.

Instead of relying solely on browser events, Creative2 built a timed heartbeat system that periodically pings the server to confirm that a visitor is still active. The result is a more accurate measurement of viewing duration and engagement.

That’s where custom software creates value. Not by recreating features that already exist, but by solving problems commercial platforms were never designed to address.

The Decision Isn’t Build vs. Buy

Off-the-shelf analytics platforms remain the right solution for many organizations. They quickly and efficiently address common reporting needs.

The decision depends on whether the business has reached a point where generic software is creating workarounds rather than delivering value.

But every business reaches a different point in its growth. When standard reports no longer provide the information needed to make decisions, custom software becomes less about adding new technology and more about removing obstacles.

Those are signs that the conversation should change.

Instead of asking which subscription to purchase next, it may be time to ask whether software built around the organization’s workflows would deliver better long-term value.

AI in Web Apps: It’s Not Just Plug-and-Play

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Organizations across industries are exploring how AI can improve the digital experiences they deliver to customers.

Often, the conversation starts the same way. A team experiments with an AI tool, sees promising results, and asks if similar capabilities can be incorporated into their existing web application.

The idea sounds straightforward, but the reality is more complex.

Building a reliable AI-powered experience requires more than connecting an application to a language model. While every project is different, organizations should understand three key realities:

  • AI operates differently than traditional software.
  • Reliable AI experiences require planning beyond the model itself.
  • AI features require ongoing monitoring and refinement after launch.

AI Operates Differently Than Traditional Software

Traditional software is built around predictable outcomes. A user takes an action and the system returns an expected result because it follows a defined set of rules.

AI introduces a different model.

With traditional software, a specific input produces a predictable output. AI systems are fluid by nature. The same request may generate multiple valid responses depending on context, data, and model behavior.

That flexibility is part of what makes AI so powerful. It can evaluate information, generate content, and identify patterns in ways that traditional software cannot.

To make the most of these capabilities, developers must carefully guide how AI interprets requests and delivers responses.

The Engineering Navigator, a children’s STEM learning platform developed by Creative2 in partnership with DiscoverE, illustrates this challenge in practice.

The platform uses AI to help students and educators discover STEM learning opportunities in their local community. The system reviews a wide range of programs, and helps users find activities that fit their interests and age group.

As new opportunities are added, the AI must consistently determine what is relevant, how it should be categorized, and whether it is age-appropriate. Maintaining that level of accuracy requires continuous refinement from developers so the system can make better decisions over time.

Building AI for Reliability and Scale

Much of the public conversation around AI focuses on what the technology can do.

For development teams, the focus is on delivering those capabilities consistently across thousands of interactions.

That means designing systems that can grow without sacrificing performance or reliability.

Scalability becomes a critical consideration when AI is introduced into an application. A feature that performs well for a handful of users may encounter significant challenges when thousands of people attempt to use it simultaneously.

Unlike traditional applications, AI interactions often require more processing power and infrastructure planning. Teams must think beyond the model itself and consider how increased usage will affect infrastructure, performance, and response times.

Fast performance is another important consideration. Users have come to expect websites and applications to respond almost instantly. AI-powered features can take considerably longer to process requests, particularly when large datasets or complex prompts are involved.

To create a smoother experience, development teams often rely on background processing and caching. This helps to reduce delays and keep applications responsive.

The challenge is no longer determining whether AI can perform a task; it’s ensuring it can do so reliably, consistently, and at scale for every user who depends on it.

AI Delivers the Most Value Through Continuous Improvement

One of the advantages of AI is that it continues to evolve.

New models, new capabilities, and new use cases are emerging at a remarkable pace. Organizations that embrace AI are finding opportunities to continually improve how their applications serve users.

While traditional software features may remain largely unchanged for years, AI-powered systems require ongoing attention. Models are updated frequently, and organizations must determine when new options can deliver better results, lower costs, or improved performance.

As adoption grows, businesses gain insight into how customers interact with AI-powered features. Administrative dashboards provide visibility into AI usage, including input and output tokens, average costs, and total spending. That data helps organizations understand usage patterns and establish guardrails such as token limits or subscription controls as adoption grows.

Cost management is a critical part of any AI implementation. AI operating costs can grow quickly as every request consumes resources. Some organizations have even reported exhausting annual AI budgets within the first quarter after adoption outpaced expectations.

What appears affordable during testing can become much more expensive once hundreds or thousands of users begin interacting with an AI-powered feature. Development teams address this challenge by testing common user scenarios and measuring the cost of individual AI interactions before launch. The resulting data helps organizations estimate operating expenses, set appropriate usage limits, and scale AI capabilities more predictably.

Building an AI feature is only the beginning. Long-term success depends on managing the technology, costs, and customer experience as the system evolves.

 

Start With the Business Need, Not the Technology

AI can create real value when applied to a specific business challenge.

The organizations seeing the best results are not asking how to add AI for its own sake. They are identifying a problem first and then determining whether AI is the right tool to solve it. When companies implement their AI effectively, it becomes one of their most powerful tools.

The technology is improving quickly. Performance is getting faster. Accuracy continues to improve. New use cases are emerging every month.

AI capabilities are here. The challenge lies in applying them to the right needs.

Success depends on more than the model itself. Organizations need the right strategy, infrastructure, and implementation approach to turn AI’s potential into measurable business value.

For businesses willing to invest in that foundation, AI represents one of the most significant opportunities for web application advancement.

The Evolution of Front-End Animation

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Front-end web development has evolved dramatically over the past two decades, but the core foundations of the web, including HTML, CSS, and JavaScript, have remained surprisingly unchanged. What has evolved is how creatively and efficiently developers use those tools to shape user experience. For leadership evaluating digital partners, understanding the evolution of front-end animation and interaction design is critical.

Today, web users expect polished, intuitive, and fast-loading websites that feel alive and attention-catching without becoming distracting. The challenge for modern development teams is balancing mobile experience, visual engagement, and performance, while keeping pace with rapidly changing design trends. Agencies and technical partners that succeed in this user landscape are those that understand not only the latest tools, but also when and how to use them strategically.

From Minimal Layouts to Dynamic Movement

Because of its technical limitations, the early web was rigid and highly structured. Developers relied heavily on table-based layouts to position content, creating sites that were functional but visually limited. As CSS matured, layouts shifted toward floats, then flexbox, and eventually modern grid systems that gave developers significantly more control over responsive design.

At the same time, JavaScript emerged as the engine behind nearly every major front-end trend. Parallax scrolling became one of the first major animation movement trends to dominate the web. Elements moved independently while users scrolled, creating exciting possibilities for depth and storytelling. Then came scroll-triggered animations, dynamic transitions, hover effects, and motion-based navigation systems.

Brands wanted websites that felt clean, interactive, and memorable. But as with any trend cycle, what begins as innovative and exciting can quickly become overwhelming, and many sites became overloaded with animation. Too much movement slowed performance, hurt usability, and distracted users from core conversion goals. Today’s web is a balance between movement and accessibility.

The Shift To Subtlety

In a response to this trend of overwhelming movement, today’s best front-end experiences prioritize restraint. A clean fade-in animation, for example, creates a significantly more polished experience than hard text appearing instantly on screen. The effect may seem small, but collectively these details influence how users perceive a brand’s professionalism and credibility.

The industry has also learned an important lesson: animation should enhance usability, not compete with it. Modern front-end teams are increasingly focusing on what developers sometimes call “Easter egg” interactions: small, delightful moments users discover naturally while navigating a site. These experiences feel intentional and rewarding, but don’t overwhelm the user experience.

Most users now make engagement decisions within three to ten seconds of landing on a website, and during that short window, the experience must communicate professionalism, speed, clarity, and trust. Overwhelming motion or resource-heavy animation often works against those goals.

Mobile-First Is Best

One of the biggest strategic shifts in front-end development has been the transition to mobile-first thinking. Historically, websites were designed for desktop screens and later adapted for mobile devices. But today, the process is often reversed. The majority of users access many websites with their phones, which means mobile-first development is crucial to reach these users. Astute developers begin with the phone and tablet experience first, then expand upward for desktop environments.

Consequently, user engagement and conversion rates are directly tied to mobile experience quality, meaning development architecture must prioritize responsive performance from the start.

Animation strategies must adapt accordingly. Heavy desktop-style effects that may appear beautiful on high-powered machines often perform poorly on mobile devices. Large background animations, excessive scroll-triggered events, or resource-intensive visual layers can quickly degrade performance and negatively impact SEO metrics. Therefore, many modern development teams focus instead on lightweight, hardware-accelerated animations optimized for mobile rendering. SVG-based animation has become especially valuable in this environment.

Why SVG Animation Has Become the Preferred Standard

In earlier eras of web animation, GIFs were widely used to create movement and visual flair. GIFs are still in use today but the problem is that GIFs are inefficient, difficult to scale cleanly, and often resource-heavy. SVG animations solve many of these problems.

Because SVGs are vector-based, they remain crisp across all screen sizes and resolutions. More importantly, browsers can animate SVG elements natively and efficiently, creating smoother interactions with lower performance overhead. This use of SVGs has opened the door for highly sophisticated motion design without sacrificing performance.

Brands can now implement animated logos, icon transitions, interactive illustrations, and path-based drawing effects that feel clean and exciting. A logo might appear to “draw itself” during page load, or a navigation icon may subtly morph during interaction. These effects create memorable moments while remaining technically efficient, but the key is moderation. Users rarely want constant movement across an entire interface. Instead, strategic animation applied to specific components with buttons, forms, navigation systems, or logos creates a cleaner and more refined experience.

The Role of GreenSock and Modern Animation

Among professional front-end developers, one animation platform has become particularly influential: GreenSock Animation Platform. GreenSock has emerged as a leader because it offers highly performative, browser-consistent animation control while remaining flexible enough for advanced interaction design.

  • For example, a product grid can reorganize dynamically while maintaining smooth visual transitions.
  • Search bars can expand fluidly from compact icons into fully interactive input fields.
  • Interface components can resize and reposition naturally without jarring jumps.

These details significantly improve perceived usability. Modern animation systems are no longer about impressive visuals alone, but are increasingly based on user psychology and interaction flow. Smooth transitions reduce cognitive friction and help users understand what changed, why it changed, and where their attention should move next. That subtle guidance can directly impact engagement and conversion performance.

What Businesses Should Expect From Modern Front-End Partners

For those evaluating agencies or technical partners, the key question is no longer whether a team can create animations. Nearly every agency can add movement to a page; the more important question now is what movement serves a strategic purpose.

The strongest front-end teams approach animation as part of a larger user experience system. They prioritize performance, accessibility, responsiveness, and long-term maintainability alongside visual polish. They also understand that modern design trends evolve quickly. Parallax-heavy websites that once felt cutting-edge often now appear dated or excessive. In contrast, tomorrow’s best practices will continue to emphasize speed, clarity, subtle interaction, and thoughtful motion design.

Ultimately, successful front-end development is about creating experiences that feel intuitive, trustworthy, and frictionless. The technologies may continue evolving, but the objective remains the same: helping users engage with digital experiences naturally and efficiently while reinforcing brand credibility at each interaction point.

Developer Workflow Optimization

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In a landscape filled with new tools, frameworks, AI applications, and competing approaches to productivity, one principle remains constant: developers perform best when their concentration is unbroken and their time is maximized.

For one of Creative2’s senior developers, that realization led to a complete shift—not toward more tools, but toward fewer, more intentional ones. The result? A faster, more focused, and ultimately more enjoyable development experience built around Vim and the terminal.

Rethinking Workflow: Less Friction, More Flow

At its core, workflow optimization is about reducing friction.

Modern development environments often rely on multiple applications, tabs, and constant context switching. While powerful, this approach can quietly introduce inefficiencies:

  • Jumping between windows
  • Reaching for the mouse
  • Breaking focus to navigate interfaces

By contrast, a terminal-first workflow consolidates everything into a single environment—keeping developers anchored, focused, and in control.

How Vim Changes the Game

To a developer, switching from traditional editors like VS Code or PHPStorm to Vim/Neovim can feel like a complete reset.

And that’s exactly the point.

Vim isn’t designed around menus or mouse input—it’s built for movement, precision, and speed through the keyboard. Once learned, it allows developers to:

  • Navigate codes rapidly
  • Execute edits in seconds
  • Stay fully immersed without breaking concentration

There’s always a faster way to move, edit, or execute—and Vim is built with the goal of increasing efficiency as a developer.

The Reality: It’s Not Instant

This transition to Vim from traditional editors comes with a learning curve.

Expectations matter here. Vim is not something you install and immediately master. 

Expect to:

  • Be slower than your current setup
  • Forget commands
  • Want to quit (and likely actually quit, temporarily)

Learning Vim is much more similar to building a physical skill rather than installing a software. Just like any other physical skill, it requires repetition, patience, and time. Expect the learning process to take six months—often switching back and forth—before operating in Vim begins to feel natural.

For developers wanting to jump into this process and increase their efficiency:

  • Enable Vim mode in your current editor as “training wheels”
  • Practice motions in low-pressure situations
  • Gradually increase usage as comfort improves

Over time, something will shift: you’ll stop thinking about commands and start thinking in Vim motions.

The Power of Macros

One of Vim’s most impactful features is its ability to eliminate repetitive work.

Vim does this via macros, which allow developers to record a sequence of actions and replay them instantly. This allows developers to:

  • Repeat edits across multiple lines
  • Automate formatting or restructuring
  • Execute multi-step changes with a single command

The result is a workflow where repetition is minimized and execution is extremely efficient.

Going All-In on the Terminal

For many developers, Vim is just the entry point. The deeper transformation happens when they fully embrace a terminal ecosystem.

A terminal-first setup often includes tools like:

  • Neovim for editing
  • tmux for session management
  • LazyGit for version control

These tools have been built by developers solving their own problems—meaning they are optimized for speed, clarity, and real-world use.

Using the terminal instead of desktop applications allows developers to preserve focus and save time by:

  • Running multiple sessions without restarting workflows
  • Avoiding opening additional apps or browser tabs
  • Customizing shortcuts and layouts to match how they think

Use Customization to Shorten the Learning Curve

Becoming comfortable with terminal tools will take time, but customizing these tools can make the learning process easier.

Pre-determined keyboard shortcuts don’t always click in a developer’s mind. Use customizations to set the shortcuts to something memorable and intuitive. 

For example:

  • Remap tmux prefixes to something intuitive
  • Create shortcuts for frequent actions
  • Tailor layouts to reduce cognitive load

When tools align with how a developer naturally thinks, they become easier to remember—and faster to use.

Keyboard-First: A Fundamental Shift

At the heart of this workflow is a simple philosophy: stay on the keyboard.

Relying less on the mouse has several advantages:

  • Faster execution of commands
  • Fewer interruptions to focus
  • Reduced physical strain from constant movement

Comfort and fluency on the keyboard are foundational. Improving typing ability can increase speed as much as learning Vim can.

Preserving Focus and Flow State

One of the most underrated benefits of this approach is its impact on concentration.

Frequent context switching—between apps, tabs, and tools—breaks flow. Each interruption adds friction and takes a developer’s focus away from his code.

The result is a more consistent flow state, where developers can stay engaged longer and produce higher-quality work.

Why Using Vim & The Terminal is Worth the Effort

The biggest barrier to adopting this approach is willingness.

Many developers avoid it because it slows them down in the short term. And it will.

But over time, the following benefits emerge:

  • Faster execution
  • Continued control over the environment
  • Reduced dependency on multiple tools
  • A more enjoyable development experience

For our senior developer, the goal was simple: preserve focus and get faster. The terminal and Vim merely provided the means to achieve that goal.

Vim and the terminal take time and effort to learn, but they enable developers to:

  • Stay focused
  • Stay fast
  • Stay in control

For developers willing to invest the time, the payoff isn’t just efficiency—it’s a workflow that feels deliberate, streamlined, and tailored to them.

AI in 2026 & Beyond

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The Creative2 editorial team interviewed Chris Murray, C2’s Senior Technical Producer, on the topic of AI. He shared with us where he thinks it may be headed, as well as his experience and recommendations regarding the tool. Read his contribution to the ongoing conversation about AI.

Will AI replace the need for human talent?

The arrival and lightning-fast iteration of Generative AI products like ChatGPT, Claude, and others has entire industries reconsidering how work gets done and companies racing to keep up. We’ve repeatedly seen the narrative that executives will look to replace human workers with automation using these large language models (LLMs).

It’s important not to lose ourselves in the excitement of all these new possibilities. Salesforce recently learned a painful lesson after firing 4,000 employees, instantly regretting their decision, and frantically seeking to hire replacements.

As business leaders, it’s critical to understand these technologies and their capabilities directly, or to have trusted partners who do. With that knowledge, it becomes easier to balance hype with reality and see how we can use these tools to augment existing workflows while also imagining entirely new ones. AI is a powerful and disruptive technology, but it’s still just a tool. It is most impressive as a force multiplier with skilled, creative humans behind it. It may seem counterintuitive, but to really get the most from AI, companies should be re-investing in their workforces with training on how to make these tools, not divesting.

Human stories and experiences are what shape us all. They motivate us to climb higher and search for more. If we fail to tap that infinite well of human experience, we risk making things just for the purpose of making them. That may be creation, but it is not creative. We’ve seen what happens when the people at Creative2 take the seed of an idea and grow it into something new and powerful. AI helps us do more of that work, freeing us to focus on making impact for our partners rather than pulling levers.

How do you use AI on a daily basis at Creative2?

My workflow has evolved over the past several years. The general progression shifted from copying code snippets and editing piecemeal, to using IDEs with integrated AI chat windows, to the most recent evolution where AI runs in the terminal through specialized tools like Claude Code that can be directed to understand context around thousands of files.

These tools don’t just help with technical work. A lot of what we do at Creative2 involves planning and problem-solving that spans many disciplines, and AI is great for keeping those threads organized. It’s also changed how we work with data. Tasks that used to mean manually combing through thousands of lines to find a single error can now be handed off to an LLM with a good level of accuracy. Someone with strong development skills can reach further into areas like data science (for example) with far less friction than ever before.

What AI tools do you recommend?

I use a variety of tools, but Anthropic’s Claude (Code) has the edge currently for my workflows. I recently switched back to VSCode for my main editor after using Cursor for a little over a year. The indexing and context management that Claude Code does with codebases made it quickly stand above Cursor’s integrated chat flow. I rarely need to direct it to review specific files or codebase references and I can quickly iterate without as much manual friction.

MCPs and Plug-ins are great ways to expand upon Claude Code. I’ve used these tools to help debug stubborn items, giving Claude direct browser access. There are so many interesting applications, but knowing when to reach for these tools, or knowing when to stop an agent going down a bad path and redirect, is an essential skill that developers build intuition for with years of experience. While selecting a model is important, the human skill behind the model is the ultimate key.

The old adage that “we don’t know what we don’t know” rings very true in this field. As these tools become more advanced, clients need partners they can trust to help them distinguish what’s real from what looks real.

How has Creative2 used AI to solve problems for clients?

We’re using these tools for all kinds of problems. One interesting case is a client of ours in the furniture restoration business. We recently started supercharging their SaaS with AI tools, and it’s been so rewarding to see their reactions when we do something they would have never dreamed was possible.

One of the main functions of their software is building detailed proposals that document restoration jobs. With our recent additions, we’ve dramatically reduced the amount of time they spend filling out paperwork, enabling them to take on more work and improve the quality and consistency of their existing output. They can now document individual pieces by simply telling our software which photos to analyze. It categorizes the furniture, infers the type of damage and restoration work needed, and even suggests pricing and valuations for insurance. The descriptions and data generated from AI are so good that with a little human oversight, they’re saving hours on each and every proposal.

We’re even experimenting with tools like Google Gemini’s generative image tooling that allows them to visualize furniture in different finishes. It can take an image of a couch, regardless of conditions or lighting, and “re-imagine” it with a new fabric pattern or a different wood stain. We see this as the beginning of reshaping how people interact with software, augmenting real user experiences while abstracting away the AI layer into something familiar.

How would Creative2 advise a client when it comes to implementing AI effectively in 2026?

We are strong advocates for our clients and see their success as our success. While we’re very AI-forward, we don’t see it as a solution for every problem. Many problems are still best solved with traditional development. We always want to ensure that we’re solving issues at their core, rather than just selling shovels in a gold rush.

That said, there’s no shortage of interesting ways to augment with AI. The key question we always come back to with our clients is: what are you actually trying to accomplish, and does AI enhance that direction? Use of these tools should be framed around solving real problems. We encourage a calm, focused approach over chasing hype.

In summary, what is Creative2’s outlook on where AI is headed this year?

Change is the only constant in this rapidly developing area of technology. It’s exciting and full of opportunities to rethink traditional problems. We’re seeing both tech behemoths and open source communities creating more advanced ways of interacting with AI while also improving efficiency so these tools require less raw computing power to run. We expect the leading models to continue getting smarter and more reliable, and the gap between what requires a massive data center and what you can run on hardware you already own to keep closing. One of the most interesting shifts we’re watching is AI moving beyond simple question-and-answer into tools that can actually carry out multi-step tasks on your behalf, like a capable coworker who can take a set of instructions and run with them. We think this will reshape how businesses solve problems over the next few years, and Creative2 is already building with these capabilities in mind. Whatever direction things go, we’re here to bring our partners’ visions to life.

 

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