A continuación, te explicamos cómo puedes apreciar los comentarios de tus colegas en Algoritmos.

Embracing a growth mindset involves being open to learning and continuously improving your code. Adapt the practice of code pairing, where you collaborate with others to review and enhance code together. When mistakes occur, view them as learning opportunities rather than failures. Seek continuous feedback from code reviewers to refine your skills. Additionally, don’t hesitate to contribute your ideas about effective algorithm usage—even if they’re not always correct—because sharing insights fosters collective growth

Approach feedback with a positive mindset, seeing it as an opportunity for growth. Be receptive to both positive and constructive feedback. Avoid becoming defensive; instead, listen actively to understand the feedback fully. Recognize that feedback is given to help you improve, not to criticize you personally. Encourage a culture of open communication within your team. Show appreciation for colleagues who take the time to provide feedback. Use feedback as a tool to foster collaboration and mutual respect.

When receiving feedback from colleagues in the context of algorithms or technical work: 1. Stay Open-Minded: Approach feedback as an opportunity for improvement. 2. Be Specific in Appreciation: Acknowledge specific contributions or insights. 3. Assume Good Intent: Assume colleagues want to help you grow. 4. Explain Why: Share your reasoning when making changes based on feedback. 5. Avoid Negativity: Keep your tone positive and professional. 6. Keep Code Reviews Focused: Smaller, targeted code reviews are more effective. 7. Express Gratitude: Thank colleagues for their time and insights.

When receiving feedback on your algorithms, adopt an open mindset. Put aside ego and be open to the idea that there's always room for improvement. Acknowledge that your colleagues bring diverse experiences and perspectives that can offer valuable insights into your work.

Saber dar e receber feedbacks sobre códigos é extremamente positivo em um ambiente de trabalho colaborativo, trazer perspectivas novas e ideias colaborativas para a melhoria do código de alguém faz de você uma pessoa preparada para a senioridade, e lógico saber receber e saber abordar de maneira colaborativa e produtiva as críticas te faz mais sênior ainda. Revisão de código é essencial em um ambiente que deseja ter uma boa experiência de equipe com boas metodologias agéis.

While seeking clarity is always encouraged when required. But one should also try to explain their approach as well to the reviewers if it seems that they might not be fully grasping your approach. This may even something help coming up with better solutions.

Asking questions is an important part of setting a positive impression. Most often the questions are incomplete set by the interviewer to test how far the interviewee can think about it. Also try to think about the edge cases that may occur and seek clarity on it. This not only helps to set your first impression but also helps you to clearly think about the approach.

Ask questions if the feedback is unclear or vague to ensure you fully understand it. Request specific examples to illustrate the points being made. Clarify the context and reasons behind the feedback to grasp its full significance. Summarize what you’ve heard to confirm your understanding. Avoid making assumptions about the feedback; seek confirmation when in doubt. Discuss possible solutions or improvements suggested by the feedback. Use follow-up questions to explore deeper insights and suggestions.

A tática de "divisão e conquista" tão amplamente usada na resolução de problemas algorítmicos, tem relação com a capacidade de ouvir. As vezes, um ponto de vista diferente sobre um mesmo problema, simplifica algumas etapas do algoritmo, trazendo maior desempenho, ou levando a uma melhora considerável na legibilidade e simplicidade de manutenção do código.

In my view, gaining a clear understanding of feedback on your algorithm is vital for improvement. Whenever feedback seems unclear, I make it a point to ask questions to fully grasp the suggestions and the rationale behind them. This practice not only aids in making better decisions but also deepens my understanding of important topics like time complexity and space optimization. Engaging in open dialogue ensures that I effectively utilize the feedback provided.

Evaluate the feedback objectively, considering its relevance and applicability. Reflect on how the feedback aligns with your personal and professional goals. Identify patterns in feedback received from different sources. Distinguish between constructive criticism and opinions that may not be as useful. Consider the source of the feedback and their perspective or expertise. Weigh the feedback against your own experiences and knowledge. Develop an action plan based on the analysis of the feedback.

Man sollte klar zwischen dem Review und dem Reviewer trennen. Ein Review ist ein fachlicher Hinweis, keine persönliche Kritik. Vorschläge aus dem Review sollten unvoreingenommen und ohne Ego bewertet werden. Anstatt Vorschläge kommentarlos zu ignorieren sollte es immer begründet/erläutert werden warum man von der Vorgeschlagenen Lösung abweicht. Eine Diskussion vor der Implementierung kann wertvolle Zeit sparen und Missverständnisse vermeiden

Critically Analyzing Feedback to Enhance Your Algorithms Once you have a clear understanding of the feedback on your algorithm, take the time to analyze it critically. This doesn't mean being critical of the person providing the feedback, but rather evaluating the merits of their suggestions. Consider how their advice might improve your algorithm's performance or readability. Does it make your code more efficient or maintainable? Critical analysis will help you decide which pieces of feedback to implement and which to set aside. This thoughtful approach ensures you're incorporating the most valuable insights to refine your algorithms and achieve optimal results.

Incorporating colleagues' suggestions can significantly improve an algorithm's performance and readability. Fresh perspectives, diverse expertise, and collaborative problem-solving can identify inefficiencies and optimize solutions. Formal code reviews enhance robustness and maintainability, while feedback on clarity and structure improves overall code quality. To maximize these benefits, be open to feedback, seek input, clearly explain your rationale, and iteratively refine your algorithm.

+1, One should always compare tradeoffs of incorporating the suggested changes. It may sometime happen during discussions that you are completely aligned with the suggested approach but later during the implementation you realizes that there are some cons of using the suggested approach - so it's always better to approach the feedback with tradeoffs perspective to avoid running into reiterations later.

When incorporating feedback into your codebase, it's crucial to do so thoughtfully. Each line of code you add impacts memory and time complexity. Often, we overlook minor redundancies or add extra lines that we fail to optimize, which might not have an immediate effect but can become significant when dealing with large datasets or high-complexity operations. As the size of 'n' grows, even an O(n) complexity can lead to noticeable slowdowns. I recently encountered a similar situation where, after implementing code and infrastructure-level optimizations, I was able to improve performance by eight times. This experience underscored the importance of optimization considering the long-term performance implications of your changes.

Prioritize actionable feedback that can lead to meaningful improvements. Set realistic goals and timelines for implementing changes. Integrate feedback into your workflow and processes gradually. Monitor the impact of the changes you make based on the feedback. Be open to adjusting your approach if initial implementations don’t yield the desired results. Collaborate with colleagues to brainstorm and execute the best strategies. Document your progress and share updates with those who provided feedback.

Effectively integrating feedback into algorithms involves a thoughtful, iterative process. Key strategies include categorizing and prioritizing feedback based on impact and feasibility, experimenting and testing changes in a sandbox environment, documenting modifications with version control and changelogs, and iterating for continuous improvement. Additional tips include encouraging healthy debate, acknowledging contributions, and learning from mistakes. These practices enhance algorithm performance, readability, and maintainability.

Decido quais partes do feedback são mais importantes e devem ser implementadas primeiro. Faço alterações de forma iterativa, testando o impacto de cada mudança para garantir que ela realmente melhora o algoritmo.

A importância de ouvir, refletir sobre o que foi dito e avaliar de forma aberta (mas crítica), é um passo para a evolução e resolução do problema. A construção de uma solução pode surgir de partes de ideias dadas, e pode gerar condições muito favoráveis.

Regularly review the feedback you’ve received and the changes you’ve made. Reflect on how the feedback has helped you grow and improve. Assess the long-term impact of the feedback on your performance and work quality. Use reflection as a tool to identify further areas for development. Keep a feedback journal to track progress and insights over time. Engage in self-assessment to complement external feedback. Encourage ongoing feedback to maintain continuous improvement.

The thing about algorithms is that the first one that you think of is usually inefficient. For example sorting algorithms can be easily implemented in O(n^2). But it takes a bit of work to implement one in O(nlogn). But again there are several algorithms which are efficient. But some are more suited to datasets that are likely to be presented by the user. So coming up with the most efficient algorithm requires that you are reflecting on your approach and thinking of constantly improving your solution.

Continuous reflection is a key tool for tracking progress and identifying feedback patterns in algorithm development. It involves journaling, benchmarking, and using version control to monitor development over time, categorizing feedback, performing root cause analysis, and trend analysis to detect recurring issues. The benefits include increased self-awareness, learning opportunities, adaptability, and motivation. Incorporating reflection into your workflow can lead to improved algorithms and better outcomes.

Reflito sobre como o feedback recebido influenciou meu trabalho e meu processo de pensamento. Estou disposto a ajustar minha abordagem com base nas lições aprendidas com o feedback.

Ouvir com atenção, cautela, cuidado, mas sem dispensar o que você já conhece, e o que você já idealizou/construiu para a resolução do problema, pode levar a obtenção de excelentes resultados. Então é importante refletir sobre os conselhos propostos, mas sem perder as características importantes que você já tem como certas. Refletir sempre é a chave.

I would say, Thank colleagues for their feedback, acknowledging their effort and time. Highlight specific ways their feedback has helped you improve. Show appreciation publicly when appropriate, fostering a positive feedback culture. Return the favor by offering constructive feedback to others. Celebrate successes and improvements that resulted from feedback. Use gratitude to build stronger professional relationships and trust. Recognize the value of feedback as a critical component of your growth.

Expresso gratidão aos colegas que forneceram feedback, reconhecendo o tempo e o esforço que eles dedicaram. Dou crédito às ideias e sugestões que ajudaram a melhorar meu trabalho.

Expressing gratitude for colleagues' feedback is crucial for a positive work environment. Effective methods include verbal thanks, public recognition, written notes, and implementing suggestions. Be sincere, timely, and specific, and adapt to individual communication styles. This not only shows appreciation but also strengthens relationships and fosters collaboration.

Discuss Tradeoffs: Appreciating feedback from colleagues on algorithms involves understanding the trade-offs between different approaches. When evaluating feedback, consider that it might offer a better or complementary solution. Discuss code extendibility, considering how easily the new approach can adapt to future changes. This might favor a more modular and maintainable codebase. Assess performance by comparing time and space efficiency, as the alternative could scale better with larger datasets. Examine complexity by analyzing the algorithmic complexity (Big O notation), balancing simplicity and efficiency. Open discussions on these factors can lead to a more robust and efficient solution, leveraging feedback for better algorithms.

Ein Review muss nicht nur (konstruktive) Kritik enthalten sondern gute Lösungen können auch positiv erwähnt/hervorgehoben werden

An algorithmic development team can greatly benefit from colleague feedback through structured code reviews, collaborative brainstorming, extensive testing and validation, knowledge sharing, and fostering continuous improvement. Regular feedback, diverse perspectives, and effective communication tools enhance code quality, innovation, and overall team performance.

Though receiving feedback and improving over It is ongoing process we have to have define certain milestones to stop and say yes this much improvement is enough on certain feature. As we go for improvement then their is always the scope. But wisely considering the feedback and get it understood is the habit of good listener.