You're torn between new programming languages and stable systems. How do you find the perfect balance?

When you take on a new programming language, you are making a commitment either on your, or your client's behalf, to the lifespan of the project. Consider carefully whether choosing shiny-new is going to burden either yourself or someone else with a maintenance chore down the line. If you commit to a technology that has not got a well established bench of practitioners, you're setting a timer on a potentially costly rebuild effort down the line. I regularly create projects in new programming languages in order to understand new conceptual models and innovations which I can take to my daily practice. I am exceedingly careful however about the long term burden and costs introducing a new language to production may impose.

To find the perfect balance between new programming languages and stable systems, consider the following suggestions: Assess Needs: Evaluate the specific requirements of your project or organization. Determine the features and performance metrics essential for your application. Research and Test: Explore new programming languages and their capabilities. Conduct thorough testing and benchmarking to understand their performance and reliability. Compatibility: Ensure the new language or system integrates well with existing infrastructure. Check for compatibility with tools, libraries, and frameworks already in use. Community and Support: Look into the community support and documentation available for the new language.

When faced with the choice between a new programming language and a stable, well-established system, it's crucial to carefully evaluate the needs of the project. The presence of a new technology in the market doesn’t automatically mean it should be adopted. Instead, the decision should be based on which option best meets the project's requirements and delivers the most efficient results. Just because something can be achieved with a new language doesn't mean it's the optimal choice. The focus should always be on selecting the technology that aligns with the project’s goals and ensures long-term efficiency and stability.

Find the perfect balance by integrating new programming languages into stable systems gradually, prioritizing ongoing learning, and evaluating the practical benefits and risks of each new technology.

Building a stable system and managing it is easy nowadays. There are lots of tools and techniques. However for new programming languages follow the below steps: 1- get a clear understanding of memory management and concurrency techniques 2- Use AI tools to learn techniques that can be helpful in writing efficient codes 3- repeat the first two steps and practice ??.

Road-mapping your system is a cost of doing business, particularly in these days of "cyber-security insecurity". Major components and frameworks go end-of-support. With no new security updates, you're now vulnerable. Sometimes there are whole paradigm shifts, too. Pick a time frame (3, 5, 7, & 9 years all have pros and cons) for a cycle where you update your OS, DBMS, back-end language, front-end language and framework, and even your coding / collaboration tools. When you do, you WILL break your code as the library calls don't always match syntax or semantics, despite best intentions. Expect to spend a small but mighty fraction of your R&D budget on this. If you don't, you're welcoming the technical debt.

If circumstances necessitate switching to a different programming language, the best approach is to use microservices, gradually migrating the most critical parts of the system first. With various options available for transitioning between languages today, careful planning and a structured process are essential for a successful migration to a new development stack.

Evaluating the risks associated with new technologies versus stable systems is essential. New languages can offer cutting-edge features but may come with stability and community support concerns. Conduct a thorough risk assessment to identify potential challenges such as compatibility issues, learning curve for the team, and long-term support

Every programming language comes with trade-offs. Speed, memory safety, ease of adoption, community involvement, complexity, coding style, the list goes on. First, understand the needs of the project. Are you processing millions of transactions per second? Are you building a dashboard for a handful of internal users? Understanding the project needs will narrow the list of potential languages, then you can evaluate those in terms of suitability for the organization and the abilities of your developers.

Depending on the circumstances, if there is a need to switch to another programming language, the ideal approach is to do so using microservices, gradually migrating the core parts of the system that require the most urgent attention. Today, there are many options for transitioning between languages. It requires a lot of planning and orderly steps to migrate to a new development stack.

Consider the long-term viability of both the new language and the stable systems you’re using. Evaluate trends in technology and the likelihood that the language will be supported and developed over time. Ensure that your choice aligns with future needs and that you’re not investing in something that might become obsolete.

Go through the changelog of new versions of the technologies used by your system. Remove deprecated features and see how can new features be used to enhance your system(both functionally and non-functionally)

Future proofing your technology stack involves considering the longevity and evolution of the language or system. Opt for languages and frameworks with strong development roadmaps and active communities. For example, Java and JavaScript have proven track records and vibrant ecosystems, ensuring they stay relevant and well-supported over time

Selecting a language for the future is a challenging task. While some languages have solid foundations and good support, having a project with a well-designed infrastructure is what will enable future integration with new technologies. This will allow us to make a technological leap when the project requires it.

The skills and dynamics of your team play a significant role in choosing the right technology. Assess the existing expertise within your team and their openness to learning new languages. Providing training and encouraging a collaborative learning environment can ease the transition to newer technologies while leveraging your team's strengths.

Understand the team's strengths, expertise, and willingness to learn. Adopting a new language requires training and adaptation, so assess whether your team can effectively transition without compromising productivity. Conversely, consider whether sticking with a stable system could hinder innovation or engagement within the team.

With the incorporation of AI and a solid team, changes can be considered, and incorporating automation tools for migration with AI developed by the same team can be an option. For example, in a development team that switched from PHP to NestJS, prior to the migration, AI automations were developed to generate the base APIs for the new project. While we are talking about tools that generate the base code for CRUD operations, the team was able to focus on more complex problems rather than the initial basics that AI handled seamlessly

La capacidad del equipo de desarrollo para aprender y trabajar con un nuevo lenguaje es importante para que la integración sea exitosa. Se debe evaluar la experiencia actual de los colaboradores con tecnologías similares y su disposición para aprender algo nuevo. Hay que buscar y poner a disposición los recursos necesarios y documentación apropiada para facilitar el aprendizaje mediante la discusión.

Is the community currently utilizing and contributing to the language? Will this stays the same in future? When onboarding your project to a new language or framework, these are the key questions you should ask. Additionally, consider the learning curve requirements and the availability of relevant documentation.

As a computer vision engineer, I can't stress enough how crucial the "Evaluate Ecosystem" point is in our field. When choosing between new and stable tech: - Library Support: Check for robust CV libraries (e.g., OpenCV). They're game-changers for efficiency. - GPU Acceleration: Ensure good support for GPU computing frameworks like CUDA. Critical for performance. - Research Implementation: Look for ecosystems where cutting-edge CV papers are quickly implemented. Keeps you at the forefront. A rich ecosystem can make or break a CV project, often outweighing the raw features of a language itself.

The surrounding ecosystem of a language or framework greatly impacts its usability. Consider the availability of libraries, frameworks, tools, and community support. A robust ecosystem can accelerate development, reduce bugs, and provide reliable solutions. For instance, the rich ecosystem of Node.js or the extensive libraries available for Python can significantly enhance productivity.

Examine the ecosystem around the new language, including libraries, frameworks, tooling, and community support. Compare this with what is available for the stable systems. A robust ecosystem can greatly enhance productivity and reduce the time needed to solve problems.

Al introducir un nuevo lenguaje, también estamos introduciendo su ecosistema que incluye herramientas, bibliotecas y frameworks asociados. Hay que asegurarse que el ecosistema sea robusto y que esté bien integrado con el stack existente. Es importante que existan y estén disponibles buenas soluciones de interoperabilidad entre el nuevo lenguaje y los lenguajes que actualmente usamos. Un ecosistema sólido facilita la integración y reduce el esfuerzo necesario para hacer que ambos lenjuajes trabajen juntos de manera eficiente.

In the fast-evolving tech landscape, continuous learning is key to staying competitive. Encourage your team to engage in ongoing education, whether through formal training, online courses, or community participation. Keeping abreast of new developments ensures your team remains versatile and capable of integrating both new and stable technologies effectively.

Foster a culture of continuous learning within the team. Even if you choose to stick with stable systems for now, encourage exploration and experimentation with new languages and technologies. This keeps the team adaptable and ready to pivot when the time is right.

Encourage the team to read, learn, and share knowledge about the programming language. Foster a collaborative environment where they can teach one another and create proof of concepts (PoCs) for different parts of the application. This will help them assess the effort involved, identify potential problems, and find solutions. Ultimately, this process will build their confidence and trust in their abilities.