I don't start with a general design; usually, I just roll out something rough. Once the rough version starts to work, I take a step back and try to identify the beautiful components I can isolate. Then, I (re-)invent these wheels and put the project back together using the improved components I've built. I strive to maintain a clear separation between the generic core components and the mundane, income-generating projects. If a change naturally belongs to the core component, I'll make it to strengthen the core. Otherwise, I'll implement a hack in the wrapping layers to meet immediate needs. This kind of separation allows both my codebase and myself to maintain a strong backbone that isn't easily influenced by the trivialities of the world.

I think most of the time algorithm which was written will meet needs. It has two basic areas to improve 1. Removing unused code or algo: Most of the time when we design an algo or in course of development, algo starts containing some code which is not required and maybe slowing down your system. 2. Handling scale: It might happen that your algo is performing best in start, but after an increase in load it becomes slow How to solve this: 1. Testing on multiple payloads and how its performing on large dataset. Also don't forget to test concurrency . 2. Regular code review: It helps us to use best practices and also to remove any unused code.

To keep algorithms aligned with evolving project needs, I focus on a proactive, multi-layered approach. I regularly monitor performance metrics like accuracy and latency to detect issues such as data or concept drift. Benchmarking against baselines and top industry models helps ensure competitiveness, while feedback from stakeholders provides valuable real-world insights. I also prioritize retraining with updated data and stress testing to improve resilience. By designing adaptable systems and staying updated on advancements in machine learning, I ensure my algorithms remain effective and ready to meet the challenges of a constantly changing environment.

In dynamic projects, selecting optimal algorithms involves evaluating key metrics: time complexity, space complexity, adaptability, resilience, and scalability. This ensures efficiency, robustness, and scalability. Regular reviews, feedback mechanisms, and data-driven insights are essential to refine choices, address evolving needs, and maintain alignment with project goals. Such a structured approach ensures the algorithms remain efficient, reliable, and future-ready.

Depends on the scenario and workload our logic should be work then if they wrote in wrong manner feel free to redesign his/her logics