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UX Mantra I received from Mantra Labs

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4 minutes read

My learnings and experiences as a UI/UX intern at Mantra Labs.

UX Mantra I received from Mantra Labs

“Design creates culture. Culture shapes values. Values determine the future.” — Robert L. Peters, designer and author

In this blog, I will share my learnings and experiences working alongside the design team at Mantra Labs.

The past three months working at Mantra Labs as UI/UX intern have been one of the most memorable times of my life, from meeting some amazing folks to learning some super cool tips and tricks. It was undoubtedly an amazing experience. Most people believe that interns only work on dummy tasks and never make any impact on the company despite their hard work. At Mantra Labs, this aspect is absolutely untrue. In a very short interval of time, I worked on multiple projects from different domains. As an intern, I was given the opportunity to lead a project’s UI/UX design from start to launch. I had to take all the design decisions, interact with stakeholders, collaborate with developers, and manage even the simplest of tasks involved.

Here are some of the key learnings from my experience at the company:

1. Ask Questions

The best way to accomplish something is to ask lots of questions to be sure what exactly needs to be accomplished. To be honest, in the beginning, I didn’t know the exact way to do lots of things. But, as an overthinker, I was always concerned about not being annoying. My manager and colleagues showed humility and taught me every little thing with utter patience. There were times when my manager got into some other work which left no time for him to answer my questions. Even then, I had my lovely teammates who stood there to guide me. I learned the most from my internship by asking questions and clarifying all my doubts.

2. Keep an open mind and apply a positive approach.

UI/UX Designing is incomplete without solving problems. The client’s requirements must be met by all means while keeping accessibility, conversational and humanized approach, and all the other things in mind. Therefore, for such a task, the ability to hear all the reviews and perspectives with an open mind and apply a positive approach to it is the only key.

Working with different design minds at Mantra Labs made me understand that not everyone is going to agree with your designs and ideas — even people in your own team! One has to keep iterating, once, twice, and sometimes even ten times. No matter how many changes happen, they should not be taken personally because a majority of the time the changes are only going to improve the product in the end.

3. Stop over-evaluating!

I have always been someone who at every step has over-evaluated myself. Thriving to achieve the best of me has been overwhelming all my life. Here, at Mantra Labs, I learned how to trust my instincts as far as designing was concerned. I was corrected wherever I made wrong decisions. It all made sense when I saw the outcome. It was during my internship that I learned how important it is to always check all decisions, but never question yourself to the point where you lose interest in your own judgment.

4. Try something new, and explore different domains.

Ever since I started my career, I was mainly working with Ed-tech companies but at Mantra Labs, I got the opportunity to work in multiple domains like Health Tech, and Solar Tech in a very short duration of time. Obviously, these fields were quite different as these domains were very new to me but as a UI/UX Designer, you’ll have to be ready to solve any problems irrespective of any domain.

5. Show gratitude

An entire team is involved to complete a project. You win only when everyone in the team applies equal effort to make it happen (it’s the teamwork that counts). The work culture in Mantra Labs is great, from cool colleagues to a cooler manager. All of them work and coordinate with each other in a way that ultimately leads to the completion of the project to the satisfaction of the client. Having such people around me at work was no less than a blessing during my internship.

Better Communication skills

Communication requires a language common to the speaker and the listener. Fortunately or unfortunately that language is English. Honestly, this language has not been a very good friend of mine. I was quite good with one-on-one conversations but public speaking had mostly been a blunder. During the course of my internship, I led some client meetings and also demonstrated my work to a group of people. Talking to clients and my teammates have helped me brush my communication skills and instilled in me a sense of confidence.

Any sort of work can become boring if one stops taking fun-filled breaks from it. UI/UX Designing is a creative field and creativity comes only with the freshness of mind. I am someone who is a workaholic, I skip my meals and sleep until the work assigned to me is completed. There were instances during my internship when I would get so involved in the project that I used to forget to get myself engaged with my fellow teammates and colleagues. At Mantra Labs, the environment was so cool and friendly that we played numerous games (treasure hunt being at the top of my list) between work. We even celebrated each other’s birthdays and partied after the office. All these were a sort of my recreation to get back to work the next day with the same zeal and zest.

Before wrapping this up, let me tell you something very important:-

Design overthinking is now extremely common among designers. A deep design thinking approach is not always necessary when solving problems, the solution to some issues can be as simple as drawing rectangles.

Draw rectangles, Don’t overthink 🚀

About the author:  

Shashi Kumar is a pre-final year journalism student at Chandigarh University, who worked with Mantra Labs as a UI/UX design intern. He loves to talk about geopolitics and entrepreneurship.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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