SWE Internship Survival Guide [Part 15] - AI Tools

With AI tools becoming increasingly embedded in the software development workflow, it’s important for interns to use them wisely without compromising their own growth or credibility. Here are the core points from the lesson:

- Interns should embrace AI tools but avoid over-reliance, as teams are assessing *their* engineering abilities—not the tool’s
- Use of AI must comply with company policies, and interns should always understand and be able to defend the code they submit
- AI is best used for boilerplate and simple tasks, but the intern should remain the core problem solver and decision-maker

SWE Internship Survival Guide [Part 14] - Debugging

Debugging is a vital yet often underestimated skill for software engineers, especially interns who are frequently tasked with bug fixes or need to debug their own code. Here are the core points from the lesson:

- Interns often start with low-priority bugs or tackle backlog issues, making debugging a key part of ramping up and delivering impact
- Effective debugging requires identifying the relevant code, using logs or dashboards, and writing down your hypotheses to stay focused
- Reframing debugging as evidence gathering for a potential question helps you avoid tunnel vision and often leads to discovering the root issue
- Common mistakes include poor organization, incorrect environment setup, and focusing on irrelevant details—clarifying these with teammates is encouraged

Go through the debugging masterclass here: [\[Masterclass\] How To Become A Debugging Master And Fix Issues Faster](https://www.jointaro.com/lesson/ioR6WzamAIUTpySLAr7o/masterclass-how-to-become-a-debugging-master-and-fix-issues-faster/)

Ship & Grow 500k+ User Side Projects [Part 19] - Managing Your Project

If you're just starting off with side projects, especially if you're a junior engineer, it can easily get overwhelming. This lesson teaches you:

- The important thing to never forget about side projects
- The 1 project management tool you absolutely should *not* use to manage a side project
- The 1 simple, organic tool that is probably best to manage your side project and keep track of open tasks
- How to organize your side project with decomposition, shown with an example

Ship & Grow 500k+ User Side Projects [Part 7] - KISS

Engineers naturally gravitate towards complexity, and it shows in how they communicate and design things. This is a problem when it comes to side projects. This lesson teaches you:

- The beautiful, often under-appreciated value of simplicity
- Assumptions you shouldn't make about the end-user
- What it means to KISS (Keep It Simple, Stupid), explained with an in-depth example

Ship & Grow 500k+ User Side Projects [Part 4] - Planning Your Initial Launch: API Example

You have now figured out what should go into your side project MVP (minimum viable product). How do you bring it to life? This lesson teaches you:

- The exact steps to follow to ship your MVP
- What good decomposition looks like, explained with an in-depth example

You can go deeper into the mechanics of software decomposition here: <https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/break-it-down/>

Ace The ML System Design Interview [Part 9] - Common Problems

While specific interview questions vary, common themes emerge, and preparing for these can significantly improve readiness. Here are the core points from this lesson:

- Scaled recommendation systems are frequently encountered, with core components like filtering, ranking, and embedding computation remaining relevant despite variations in specific use cases
- Adapt recommendation system designs to different contexts, such as local recommendations, where rule-based systems might complement or replace certain machine learning components
- Harmful content detection is another common topic, requiring a feedback loop to generate training data, particularly from ambiguous cases in the "maybe" category
- Be prepared to explain how to handle ambiguous cases, as this demonstrates a deeper understanding of the problem
- Understanding the general concepts behind these two question types will provide a strong foundation for tackling many other interview questions

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 8] - How To Prepare

Preparing for a machine learning system design interview can feel overwhelming, but a structured approach makes it manageable. Here are the core points from this lesson:

- Identify the specific areas of machine learning likely to be covered in your interview to focus your preparation
- Understand the trade-offs between different machine learning techniques by creating comparison tables outlining their pros and cons
- Conduct a mock interview early in your preparation to establish a baseline and identify areas for improvement
- Address feedback from mock interviews and consider a second mock to gauge progress
- Avoid last-minute cramming—structured preparation over a week or month is far more effective

If you want a mock interview from Ilya, you can book time with him here: [MLEpath - Mock Interviews](https://go.mlepath.com/taro-mocks)

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 6] - Deployment & Beyond

Discussing deployment and monitoring is a crucial signal of real-world machine learning experience in an interview. Here are the core points from this lesson:

- Familiarize yourself with deployment patterns like shadow mode, A/B testing, and blue-green deployments, understanding their trade-offs and when to apply them
- Monitor both offline (ML engineer-focused) and online (business-focused) metrics, paying close attention to the distribution of inputs and outputs at each stage of a multi-stage system
- Recognize that significant shifts in input or output distributions should trigger immediate attention, as they often indicate real-world changes affecting model performance
- Avoid suggesting online learning unless you have deep expertise, as it can lead to complex follow-up questions
- Be prepared to discuss retraining strategies, as models inevitably need updates over time

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 7] - Questions For Your Interviewers

Interviews are a two-way street, providing an opportunity to assess whether a company and team are a good fit. Here are the core points from this lesson:

- Prepare thoughtful questions that focus on factors directly impacting your work experience, such as on-call responsibilities and decision-making processes
- Avoid vague questions like "What's the work-life balance like?" and instead probe for root causes, such as the frequency and intensity of on-call duties or whether decisions are made top-down or bottom-up
- Understand the company's decision-making culture to gain insights into potential work-life balance challenges
- Ask targeted questions to get a clearer picture of the team's dynamics and the day-to-day realities of the role
- Take advantage of engineers’ straightforward answers to learn what it’s really like to work at the company

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 5] - Modeling

Modeling plays a critical role in machine learning system design, with classification and regression being the most frequently encountered problem types in interviews. Here are the core points from this lesson:

- Understand various evaluation metrics, such as precision, recall, F1 score, NDCG, and loss metrics like cross-entropy and focal loss, and know when to apply each
- Be prepared to discuss trade-offs between metrics, particularly precision and recall, and avoid over-optimizing for a single metric
- Recognize the significance of loss metrics, memorizing the equations for cross-entropy and focal loss, and understanding their applications in balanced and imbalanced datasets
- Be ready to discuss model selection, including architecture choices like two-tower models or matrix factorization, and anticipate follow-up questions on optimization techniques and troubleshooting issues like overfitting and vanishing gradients
- Consider a baseline model for comparison if no current system exists, and if there is an existing system, use it as a reference point

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 4] - Data

Data is the foundation of machine learning, and a thorough discussion of data is essential in any machine learning system design interview. Here are the core points from this lesson:

- Discuss labels, features, and data set splitting, understanding the trade-offs involved in each area
- Explore various methods for generating data labels, such as human annotation, synthetic data, and LLMs, while considering the pros and cons of each approach
- Focus on high-level feature sources, providing relevant examples and selecting predictive features while avoiding sensitive topics
- Understand feature encoding techniques, such as one-hot encoding and embeddings, and their impact on model performance
- Weigh the trade-offs between cross-validation and train-validate-test splits, while considering issues like data leakage and imbalances in the data set

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Taro Book Club: The Pragmatic Programmer - Session 6

Session Led by [Bikram Kashyap](https://www.linkedin.com/in/kashyapbikram/)

In this #taro-bookclub discussion, we'll go over [**"The Pragmatic Programmer"**](https://www.amazon.com/Pragmatic-Programmer-Journeyman-Master/dp/020161622X) by Andrew Hund & David Thomas

Here's the agenda:

- Design by Contract
- Dead Programs Tell No Lies
- Assertive Programming
- How to Balance Resources
- Don't Outrun Your Headlights

Please send your questions ahead of time to [**Bikram Kashyap**](https://www.linkedin.com/in/kashyapbikram/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion. *If you don't have the book or want more details on how this all works, check out the #taro-bookclub channel in the Taro Premium Slack as well!*

Get ready for another engaging and informative session!

Part 8 [RBP] Next Steps With React

Follow Florian on various social media platforms like [YouTube](https://www.youtube.com/c/CodinginFlow), [Instagram](https://www.instagram.com/codinginflow/), [TikTok](https://www.tiktok.com/@codinginflow), and [X/Twitter](https://x.com/codinginflow) for more web development content and updates. Mailing list: [CodingInFlow.com/newsletter](http://CodingInFlow.com/newsletter)

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/branches/stale>

Part 6 [RBP] Create Hooks For Your Context Providers

This lesson presents a best practice for working with context providers in React, specifically addressing situations where a meaningful default value isn't available.

- **The Problem with Undefined Context:** Understand the challenges of using context providers without a proper default value, leading to potential silent failures and difficulties in debugging.
- **Custom Hooks for Context Providers:** Learn how to create custom hooks that encapsulate the logic for accessing and validating context values, ensuring that errors are caught early in the development process.
- **Improving Code Reliability and Reusability:** Discover how this approach improves code readability, promotes reusability of context-related logic, and provides more informative error messages, making your React applications more robust.

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/tree/context-hook/src/app>

Part 5 [RBP] Create Custom Hooks

This lesson explains how to improve the organization and reusability of React components by extracting complex logic, particularly related to data fetching, into custom hooks.

- **Creating Custom Hooks:** Learn the process of creating a custom hook to encapsulate state management and side effects, such as those involved in fetching data and handling loading and error states.
- **Organizing and Reusing Logic:** Understand the benefits of separating concerns by moving complex logic into custom hooks, leading to cleaner and more maintainable components.
- **Improving Code Readability:** Discover how custom hooks can make your code more declarative and easier to understand by abstracting away implementation details and providing a clear interface for interacting with data.

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/tree/custom-hook/src>

Amazon Behavioral Interview [Part 32] - Missing A Commitment (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they failed to meet a commitment.

- **Strengths:**

  - **Relevant and Engaging Story:** The interviewee provided a compelling and relevant example by describing a critical incident during the Super Bowl, a high-pressure situation.
  - **Honest Acknowledgement of Failure:** The interviewee openly acknowledged the failure and took responsibility for the issue.
  - **Detailed Explanation:** The interviewee provided a detailed explanation of the situation, including the specific challenges encountered (e.g., software bug, communication issues), the impact of the failure, and the steps taken to address the issue.
  - **Focus on Learning and Improvement:** The interviewee highlighted the key learnings from the experience, such as the importance of thorough testing, robust contingency plans, and clear escalation procedures.
  - **Positive Outcome:** The interviewee emphasized that the team ultimately resolved the issue and learned valuable lessons, demonstrating their ability to learn and adapt.

- **Areas for Potential Improvement:**

  - **Deeper Dive into Root Cause Analysis:** The interviewee could have provided more detail on the specific steps taken to identify the root cause of the software bug and how the team prevented similar issues from occurring in the future.
  - **Highlighting Leadership:** The interviewee could have further emphasized their leadership role in addressing the situation, such as:
    - Taking ownership of the problem and communicating proactively with stakeholders.
    - Motivating the team to work together to find a solution.
    - Effectively communicating the resolution to all stakeholders.

- **Overall:**

  - This was a strong response that effectively demonstrated the interviewee's ability to:
    - Acknowledge and learn from mistakes.
    - Take responsibility for their actions.
    - Demonstrate problem-solving and critical thinking skills.
    - Highlight their ability to work effectively under pressure and as part of a team.
    - Communicate their experiences clearly and concisely.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 31] - Missing A Commitment (Answer)

This video segment describes a situation where the interviewee failed to meet a commitment during their time at Google.

- **The Commitment:** The interviewee committed to flawless execution of a major advertising campaign during the Super Bowl, ensuring all ads ran smoothly across all platforms.

- **The Obstacle:** Despite thorough planning and preparation, a critical ad failed to run on Mac computers due to a software bug.

- **The Impact:** This failure resulted in significant disruption, including:

  - **Lost revenue:** The ad in question was a high-value advertisement.
  - **Damaged reputation:** The incident could have negatively impacted Google's reputation with advertisers.
  - **Increased stress:** The interviewee experienced significant stress due to the unexpected issue and the pressure to resolve it quickly.

- **Corrective Actions:**

  - **Root Cause Analysis:** The team conducted a post-mortem analysis to identify the root causes of the failure, including inadequate testing on all devices and insufficient escalation procedures for technical issues.
  - **Process Improvements:**
    - Implemented a more comprehensive testing process to ensure compatibility across all devices and browsers.
    - Established a clear escalation path for technical issues, including multiple levels of support and on-call engineers.
  - **Improved Communication:** Enhanced communication protocols to ensure timely identification and resolution of technical issues.

- **Key Learnings:**

  - The importance of thorough planning and rigorous testing.
  - The need for robust contingency plans to address unexpected challenges.
  - The importance of effective communication and collaboration within and across teams.
  - The value of learning from mistakes and using them to improve future performance.

This example demonstrates the interviewee's ability to: *Acknowledge and take responsibility for their mistakes.* Conduct a thorough root cause analysis to identify areas for improvement. *Implement corrective actions to prevent similar issues from occurring in the future.* Demonstrate resilience and the ability to learn from challenging situations.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 30] - Building Trust With Stakeholders (Evaluation)

This video segment discusses the quality of an interview conducted by a person named Alan.

- **The Interviewer's Approach:**

  - **Unclear Questioning:** Alan initially asked a general question about building trust, which is not a typical behavioral interview question.
  - **Unnecessary Detour:** This initial question led to a lengthy discussion that was not directly relevant to assessing the interviewee's skills or experiences.
  - **Awkward Transition:** Alan then abruptly shifted to a more relevant question about a time the interviewee failed to meet a commitment, but the transition felt awkward and disjointed.

- **Concerns about Interview Quality:**

  - **Lack of Clear Focus:** The interviewer's approach seemed unstructured and lacked a clear focus on assessing the interviewee's skills and experiences.
  - **Potential for Bias:** The interviewer's personal style and communication approach could potentially bias the interview outcome, particularly for interviewees who are less experienced or less comfortable with unexpected questioning.
  - **Limited Quality Control:** The video highlights a concern about the lack of quality control for interviewer performance and the potential impact on interview outcomes.

- **Recommendations:**

  - **Focus on Clear and Relevant Questions:** Interviewers should focus on asking clear, concise, and relevant behavioral questions that directly assess the candidate's skills and experiences.
  - **Maintain a Structured Approach:** A structured interview approach helps ensure that all candidates are evaluated fairly and consistently.
  - **Improve Interviewer Training:** Investing in interviewer training programs can help to improve the quality of interviews and ensure that all interviewers are equipped with the necessary skills and knowledge to conduct effective and unbiased interviews.

This analysis highlights the importance of conducting high-quality interviews to ensure fair and accurate assessments of candidate qualifications.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 29] - Building Trust With Stakeholders (Answer)

This video segment focuses on how to build trust relationships with colleagues and stakeholders.

- **Key Strategies:**

  - **Proactive Relationship Building:**
    - The interviewee emphasizes the importance of proactive relationship building by reaching out to stakeholders early on, even before needing to rely on them. This includes conducting preliminary research, scheduling introductory meetings, and taking the time to understand their individual goals and priorities.
  - **Demonstrating Integrity and Consistency:**
    - The interviewee highlights the importance of acting with integrity, following through on commitments, and being transparent about their goals and priorities. This builds trust and credibility with colleagues.
  - **Assuming Trust:**
    - While acknowledging the importance of earning trust, the interviewee believes in starting with a foundation of trust and assuming positive intent from others.
  - **Open and Honest Communication:**
    - The interviewee emphasizes the importance of open and honest communication, including being transparent about priorities and setting clear expectations.

- **Practical Tips:**

  - **Leverage tools like "The First 90 Days" to effectively identify and map stakeholders.**
  - **Conduct introductory meetings with key stakeholders to build rapport and understand their perspectives.**
  - **Focus on building relationships based on mutual respect and trust.**
  - **Prioritize consistency and follow-through on commitments.**
  - **Communicate openly and honestly about goals and priorities.**

This approach to building trust relationships emphasizes proactiveness, integrity, open communication, and a focus on understanding and meeting the needs of stakeholders.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 27] - Recovering From A Bad Decision (Answer)

This video segment describes a situation where the interviewee made a mistake in their role at American Express.

- **Identified the Mistake:** The interviewee implemented a new incentive system for customer care professionals that rewarded them for saving high-value customers while neglecting lower-value customers. This led to:

  - **Cherry-picking of customers:** Customer care professionals prioritized high-value customers, leading to long wait times for lower-value customers.
  - **Negative customer experience:** Long wait times resulted in poor customer satisfaction and potentially damaged brand reputation.
  - **Missed revenue opportunities:** Neglecting lower-value customers, particularly early-tenure customers, resulted in missed opportunities for future revenue growth.

- **Learned from the Mistake:** The interviewee learned the importance of:

  - **Considering all stakeholders:** Involving customer care professionals in the decision-making process.
  - **Thinking long-term:** Considering the long-term value of customers, not just their immediate spending.
  - **Gathering diverse perspectives:** Seeking input from former team members to identify potential pitfalls.

- **Implemented Corrective Actions:**

  - Revised the incentive system to account for customer lifetime value.
  - Implemented measures to improve call center response times and reduce wait times.
  - Developed a system to identify and address cases of long wait times for customers.

This example demonstrates the interviewee's: 

- ***Self-awareness:*** *Acknowledging and taking responsibility for their mistake.* 
- **Problem-solving skills:** Identifying the root cause of the issue and implementing corrective actions. 
- ***Learning agility:*** *Learning from the mistake and applying those learnings to future projects.* 
- **Focus on customer experience:** Prioritizing customer satisfaction and long-term customer relationships. 
- **Continuous improvement:** Continuously refining processes and seeking ways to improve performance.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.