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Building a layered approach with AI

We have made significant strides in the evolution of design. It is crucial for us to recognize that disruptive technologies have always been the catalyst for change, shaping the way we design and communicate. In an era before the printing press, people painstakingly wrote books by hand. Similarly, the emergence of mobile phones, computers, and the internet has changed everything.

Embracing these changes and adapting to newer technologies is essential for staying relevant in the field of design. There are designers who use Adobe Dreamweaver and Photoshop to create website UI. It feels like a different era since the introduction of more recent software such as Figma, and Adobe XD. However, the core principle remains the same: staying pertinent requires adaptability as the times change.

While there’s an ongoing discussion about AI potentially replacing design jobs, my personal perspective is that we should not fear it but rather leverage it to our advantage since AI is an execution tool. AI is poised to simplify our lives. When you consider the broader picture, your value as a designer in the upcoming years will be determined by your ability to synthesize innovative ideas to solve problems. Let me elaborate on how, as designers, we must strengthen our problem-solving muscles.

How the layered approach works:

Tools like Midjourney are designed for image-specific tasks, enabling the creation of visually appealing images. Chat GPT on the other hand is a text-based AI model, although now with the incorporation of DALL-E inside Chat GPT, it gives image outputs too.

I want you to imagine for a second, what if you used Chat GPT to create nuanced prompts for Midjourney.

This is layering software on top of another software to receive your desired output. For this to work, you have to first train your Chat GPT agent on Midjourney’s functionality and then prompt using a basic framework.

The framework works like this:

First, you inform Chat GPT about your desired role or objective and define your approach or task. For example, ‘I am a skilled graphic designer who designs illustrations for top brands in India. You need to provide me with ideas for designing a coffee shop logo, along with some links for inspiration.’

Then, you specify your timeframe or any restrictions. For instance, ‘I can’t spend more than 30 minutes looking at inspiration, so please only provide the best ones you find that you believe would be most helpful.’

Finally, after training your agent, request Chat GPT to provide a prompt for Midjourney.

The key thing to remember while implementing this approach is to churn out a nuanced prompt that will serve as a base for your creative design process later. That’s it—two layers of software, and the pivotal layer comes into play once you’ve received the output: your creativity. You make AI as Robin to your Batman. Consider this as no longer needing to begin from a starting point. With AI tools such as these, people who have the potential to think clearly to formulate ideas would flourish. I encourage you to use these AI tools in your daily life to learn and become the best in your respective fields. 

There are many more tools that have come into the market such as for UI, a single line prompt can generate a whole app’s UI screens with tools like Uizard. Builder.io is a tool that uses AI to convert Figma design files into code. Recent developments also suggest that AI can help with user research for large organizations, potentially marking a paradigm shift in the UX industry, as these AI models have been trained on human behaviors, this has been put forth by fantasy.co. There is so much more to come, the least we can do is be prepared for it. 

I hope you had a good read and this article gave you a sense of what is waiting in our future.

About the Author: 

Shivani Shukla is a Senior UI & UX designer at Mantra Labs. Updating her knowledge and staying up to date with the current trends has always been her priority.

Further Readings:

Design and Technology Fusion Shaping the Future of Innovation

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Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

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Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform. 

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Source: getport.io

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization:  Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

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