What do we expect : The right language will help us build the project faster, with fewer errors, and make it easier to maintain and scale in the future. Factors to consider: - Team expertise - Project requirements: Some languages are better for certain tasks. For example: -- JavaScript/Node.js is great for web applications. -- Python is popular for data analysis and AI. -- Java/PHP/Dot Net might be better for large systems or complex software. - Scalability - Performance - Community and Support Final Decision: We choose the language that fits the project needs, is comfortable for the team, and ensures long-term success.

As a software engineer, choosing the right programming language comes down to a balance between project needs and team capabilities. First, we assess the project’s core requirements whether it’s scalability, speed, or security and choose languages that align with those priorities. Next, we evaluate the team’s expertise to ensure efficiency and avoid a steep learning curve. We also look at long-term maintenance, choosing languages with strong community support and staying power. If there’s still debate, we might prototype in multiple languages to see what works best. In the end, it’s about aligning technical goals with the team’s strengths to make an informed decision.

It's more about the problem than the language. Of course maintainability must be looked at with priority. With a great understanding of the project, one can ensure to develop a implementation design that is easy to understand/change/update when needed. This principle can be applied to all language. If you are confused about the language, pick the one you like and create an MVP version on how it solved the problem (or part of the problem).

To select a programming language for a team, first assess project requirements like performance, scalability, and security. Choose languages suited to the task (e.g., C++ for speed, Java for scalability). Consider team expertise—leveraging familiar languages can speed development. Evaluate community support to ensure future-proofing and access to libraries. Ensure compatibility with existing infrastructure and long-term maintainability, avoiding technical debt. Sometimes, building a prototype can help test language suitability. Balancing project needs, team skills, and long-term viability is key to making the right decision.