Master the Art of Receiving Quality Feedback (from an ex-Meta Staff Researcher)

In this time of uncertainty in the workplace and job market, what’s one skill you can develop that would **yield disproportionate rewards**?

It’s hard to pinpoint *the one* skill, but I believe the **ability to ask for, receive, and iterate on feedback** is incredibly important. Adaptability is what helps us not just survive, but thrive—and adaptability begins with how well we take in and work with feedback.

When you learn to process feedback effectively, **you develop a kind of likability and professional superpower** that organizations and leaders find hard to ignore.

We hear feedback all the time, so what exactly are we trying to get better at? Most of the feedback we receive is vague and hard to act on. Think about examples like “improve your communication” or “increase your coding speed”—where do you even start? The **key is in asking for feedback** proactively, clarifying and chunking it down into something actionable, working on it, and then circling back to check if you’ve understood and implemented it well.

In this talk, we’ll learn how to do exactly that—a process I call “**exhausting the feedback process**,” a phrase I often use in my coaching practice. We’ll also explore how to handle feedback that’s just plain off the mark (spoiler: it’s rarely useful to say that outright to the person giving it).

Yogi on LinkedIn: <https://www.linkedin.com/in/yogi1/>

Putting It All In Perspective

This bonus session wraps up the *Essence of Management* course by reflecting on the symbolism behind the **Management Deltas** logo and what it teaches us about effective leadership. The logo—featuring a lowercase delta—serves as a metaphor for the kind of impact we aim to create as managers.

- **We focus on small changes**—daily and weekly deltas—that compound over time into meaningful, lasting transformation for our team, our impact, and our careers.
- **We recognize the multidimensional nature of management**, much like the mathematical partial derivative (∂), where influence happens across many axes—people, processes, priorities, and culture.
- **We look for deltas in every direction**, and one of our responsibilities is to map these dimensions so we can lead more intentionally and with greater clarity.
- **We embrace the idea that leadership is not one-size-fits-all**—it's a creative, ongoing practice of identifying leverage points and applying thoughtful, sustainable change.

This session reminds us that our job isn’t to be perfect—it’s to continuously look for small, deliberate ways to move our teams and organizations forward.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Gilad’s Take On Management

In this video, we explore the core responsibility of managers and define what great management truly entails. The speaker provides a clear, actionable definition of management and unpacks each part of it to guide us toward becoming more impactful leaders.

- We learn that **the essence of management is to create a positive and sustainable change in the organization’s performance**, a definition we are encouraged to internalize and apply.
- We embrace the idea that management is a **creative, proactive discipline**, not just a reactive one—our actions should be intentional and forward-looking.
- We are reminded that our impact must be **positive** and **lasting**, focusing on growing people and systems that perform well even in our absence.
- We are taught to focus on **creating deltas**, meaning our value lies in the improvement we drive, not in maintaining the status quo.
- We expand our view beyond just our teams, recognizing that **great managers influence the broader organization**, constantly looking for opportunities to elevate overall performance.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Foundational Ideas

In this key section of the course, we dive into the essential machine learning concepts that consistently appear across all types of ML interviews. This foundation prepares us to explain not just the “what,” but the “why” behind our choices—something interviewers value highly.

- We review core **ML paradigms** like supervised, unsupervised, and reinforcement learning, along with real-world examples to explain their use cases clearly.
- We explore **machine learning tasks** such as classification and regression, and when to apply specific evaluation metrics like precision, recall, or RMSE.
- We gain a deep understanding of **model performance** concepts—bias/variance trade-off, underfitting/overfitting—and how to recognize and address them.
- We cover **validation strategies**, data leakage prevention, and the right use of metrics beyond just accuracy, such as AUC-ROC and F1 score.
- We enhance our skills in **feature engineering**, **optimization algorithms** (like SGD and Adam), **model selection reasoning**, and **dimensionality reduction** techniques like PCA and UMAP.
- We also touch on **LLM fundamentals**—such as Transformers and encoder-decoder architectures—to stay current with emerging trends.
- Most importantly, we practice explaining all of this in a way that connects to real-world problems and highlights our decision-making skills.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Conclusion

That’s it.

Not the end—just the moment you stop walking with a guide and start carrying the weight yourself.

You’ve got the tools. The stories. The bruises that come with learning anything real.

Now carry it forward.

Ownership doesn’t show up in the big speeches. It shows up in the quiet. In what you do when no one’s clapping. In what happens after the meeting ends.

**Workbook:** [Creator’s Orientation](https://drive.google.com/file/d/1sysZuPhlrLZbU5stxlJBqg3LAcGTy_S7/view?usp=sharing)

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

What sounds like help can feel like control.

At some point, support starts to sound like hovering.\
And it’s hard to see when you’ve crossed that line.

This section takes a closer look at that moment—when helping quietly turns into handling.\
It’s more common than you think. Especially when you care.

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

You tried to be clear. They heard threat.

The words were fine.\
The logic was solid.\
The delivery? Unclear. At least to them.

This is the story of feedback that missed—badly—and why it had nothing to do with the message.\
You’ll walk into a moment that cost more than expected… and learn why it’s so common.

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these. 

- **Taro members get 30% off** my flagship course [**Top Tech Leader**](https://bit.ly/tarotopleader)*. Discount available later in the course.*

Become A Debugging Master [Part 18] - Build The Infra

Building debugging infrastructure is a powerful way to level up your team and career. Here are the core points from the lesson:

- Invest in infrastructure like logging, dashboards, session recordings, bisect tools, and experiment tracking to make debugging easier for everyone

- Plan ahead by adding useful logging into products before bugs occur to speed up investigations and root cause analysis

- Funnel logging is especially valuable because it formalizes the end-to-end data flow, making it easier to spot where issues happen

- Building internal tools, even simple ones like the Log Insight tool at Meta, can lead to major team-wide adoption and career milestones

- Internal tooling projects are great side projects because they serve engineers directly, have less red tape, and can be very high impact

Rahul's internal tool case study: [\[Case Study\] Building A Meta Internal Tool To Empower An Entire Org: Staff Promotion Story](https://www.jointaro.com/lesson/qvp2quodqAEWSkN7sayt/case-study-building-a-meta-internal-tool-to-empower-an-entire-org-staff-promotion-story/)

Become A Debugging Master [Part 16] - Retrospect

One of the best ways to turn a bug fix into long-term engineering growth is to reflect on what happened and how to prevent it next time. Here are the core points from the lesson:

- After a fix, run a retrospective to figure out how to prevent or better handle similar issues in the future
- Writing a clean, detailed doc about the incident helps future engineers quickly learn without digging through scattered updates
- Big tech companies like Meta normalize failure and use formal SEV reviews to systematically improve after incidents

Meta's SEV culture: <https://atscaleconference.com/videos/metas-sev-culture-how-todays-sevs-create-tomorrows-reliability/>

Build Deep Relationships Quickly In Tech [Part 11] - Identifying Fit

Some people simply aren't meant to get along, and you will run into a lot of these people across your career. This lesson teaches you:

- Why finding right fit connections is so important for networking, particularly with your efficiency
- How professional networking really isn't that different from making friends "in real life" (IRL)
- Solidarity angles you can leverage to find good fit connections

SWE Internship Survival Guide [Part 16] - Diversify Your Mentors

As you approach the end of your internship, one of the most important factors in getting a return offer is how you’re evaluated during your performance review. This lesson focuses on how to set yourself up for success by building strong relationships across your team. Here are the core points from the lesson:

- Don’t rely solely on your assigned intern mentor—build relationships with multiple teammates to avoid single points of failure
- You’ll receive 360 feedback at the end of your internship, so proactively engage with others who can speak positively about your work
- Identify 2–3 potential advocates by week two and keep them updated through code reviews, one-on-ones, or casual check-ins

Learn how to build relationships here: [\[Masterclass\] How To Build Deep Relationships Quickly In Tech](https://www.jointaro.com/lesson/tyaPFMXPoz2pmzB7OVzt/masterclass-how-to-build-deep-relationships-quickly-in-tech/)

SWE Internship Survival Guide [Part 11] - Code Velocity

Your success as a software engineering intern hinges on one key metric: your ability to ship code consistently. Here are the core points from the lesson:

- Code velocity is the primary way interns are evaluated, so focus your time on writing and shipping impactful code
- Every company has a different pace, but you should ask your manager for benchmarks and observe full-time engineers to calibrate your own output
- Structure your day around long blocks of uninterrupted coding time, and avoid distractions like intern socials or long-term architecture meetings
- Make small, frequent code changes instead of large ones to make code review easier, allow quicker course correction, and prevent painful merge conflicts
- Communicate your approach with your team so they understand your intent to seek faster feedback through smaller pull requests

SWE Internship Survival Guide [Part 8] - Ask Great Questions

As an intern, asking high-quality questions is one of the most important ways to learn quickly, make progress on your project, and build strong relationships with your team. But not all questions are equal—there’s a right way to do this that can significantly increase your chances of getting a return offer. Here are the core points from the lesson:

- There’s a sweet spot between asking too few questions (and struggling in silence) and asking too many low-effort ones (which can burden your team). Most struggling interns fall on the “not asking enough” side
- Every question is a chance to build trust and relationships. They help others evaluate your growth and give you valuable feedback—so don’t underestimate their importance
- High-quality questions include just enough context (like screenshots or code snippets), are well-structured, and are ideally asked in public forums like Slack channels to benefit others too
- Avoid “leading hellos” (e.g. *“Hey, got a sec?”*). Instead, ask your question directly in the first message to reduce unnecessary back-and-forth and show respect for others’ time

Take the full question asking course here: [Ask Great Questions That Get Great Answers Quickly](https://www.jointaro.com/course/ask-great-questions-that-get-great-answers-quickly/)

SWE Internship Survival Guide [Part 4] - How Internships Work At Top Companies

Top-tier internships at companies like Meta or Google are structured, high-impact opportunities that can transform you into a confident, job-ready new grad. Here are the core points from this lesson:

- Legitimate internships at top companies have clear structure—interns are given managers, mentors, well-scoped projects, and timelines
- Interns are treated like junior engineers, often contributing to real production work and collaborating closely with full-time teammates
- Larger companies offer more formal evaluation processes for return offers, which helps reduce ambiguity and stress compared to smaller, less organized internships
- You’ll likely focus on one or two substantial projects to avoid context switching and to enable deeper, more meaningful contributions
- Adopt a collaborative mindset with fellow interns—success isn’t zero-sum, and sharing knowledge will help everyone grow and build stronger networks

Ship & Grow 500k+ User Side Projects [Part 9] - Leverage Existing Patterns

Just because you're an engineer doesn't mean that you can't be a solid product designer. This lesson teaches you:

- The easiest way to bootstrap a product design from scratch
- What the pinnacle of front-end UI/UX looks like and the company behind it
- A company you can use as inspiration for a back-end project
- A company you can use as inspiration for an ML project

Ship & Grow 500k+ User Side Projects [Part 8] - Easy Design Wins

Creating a smooth, functional user experience isn't too hard. This lesson teaches you:

- How you can make features easier to access for users, explained with an in-depth example
- How to take care of users in trickier situations, explained with an in-depth example
- What you can do to lower the cognitive burden of using your product, explained with an in-depth example

Ace The ML System Design Interview [Part 3] - Big Picture Design

A strong big-picture system design is crucial for success in machine learning system design interviews. It's not about drawing a perfect diagram immediately, but about demonstrating an understanding of the system's components and their interactions. Here are the core points from this lesson:

- Identify the inputs and outputs of the system during the alignment phase to establish a foundation for building the diagram
- Start with the user and the service, then progressively add components, especially those involving machine learning, to illustrate the flow of data and processes
- Break down machine learning components beyond a single "ML system" box to show the different stages and types of models involved
- Include essential infrastructure elements, such as feature stores, to demonstrate a comprehensive understanding of the system
- Iterate on the diagram, adding or modifying components as a deeper understanding of the problem and its requirements develops

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)

Paper Reading: DeepSeek R1 - Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Session led by [Mike A](https://www.linkedin.com/in/malik-mike-a-32b63818b/) 

Join us for an insightful session on the groundbreaking paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. In this session, we will explore DeepSeek-R1, a state-of-the-art reasoning model that pushes the boundaries of reinforcement learning (RL) applied to large language models (LLMs). 

This paper introduces two novel models, DeepSeek-R1-Zero and DeepSeek-R1, showcasing advancements in reasoning capabilities through RL-driven self-evolution. Unlike traditional models relying heavily on supervised fine-tuning (SFT), DeepSeek-R1-Zero develops its reasoning abilities purely through RL, while DeepSeek-R1 combines RL with a multi-stage training pipeline for enhanced performance. 

The paper highlights exceptional benchmarks achieved by DeepSeek-R1 on math, coding, and STEM-related reasoning tasks, where its performance rivals that of leading closed-source models such as OpenAI's o1-1217. Additionally, we’ll discuss distilling these capabilities into smaller, more efficient models to make advanced reasoning accessible for diverse applications. 

Whether you're a researcher, developer, or enthusiast in AI and LLMs, this paper reading will provide an in-depth understanding of the novel reinforcement learning techniques driving DeepSeek-R1 and its implications for the future of AI-driven reasoning systems. Don't miss this opportunity to engage with cutting-edge advancements in the field! \[2501.12948\] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning 

GitHub - deepseek-ai/DeepSeek-R1 

Mark Chen X Post: <https://x.com/markchen90/status/1884303237186216272?s=12&t=BSlMYtugAr8LLWXLnMLLrQ> 

GRPO Diagram: <https://www.reddit.com/r/LocalLLaMA/comments/1i78sfs/deepseek_r1_grpo_code_open_sourced/#lightbox> 

The Bitter Lesson: <http://www.incompleteideas.net/IncIdeas/BitterLesson.html> 

Gradient Descent Image: <https://miro.medium.com/v2/format:webp/1\\\*f9a162GhpMbiTVTAua_lLQ.png> 

V3: <https://arxiv.org/abs/2412.19437> 

DeepSeek Math: <https://arxiv.org/abs/2402.03300> 

rstar-math: <https://arxiv.org/abs/2501.04519> 

Verify Step-by-Step: <https://arxiv.org/abs/2305.20050> 

DeepSeek catch-up chart: <https://media.licdn.com/dms/image/v2/D4E22AQG552O63UNJWg/feedshare-shrink_800/B4EZTN\\\[…\\\]41824000&v=beta&t=ubyKQniaCJTL37PzIOJi9YZRo1AF8yipuauSioyn59U> 

UC Berkeley Student $30 replication of Aha: <https://x.com/jiayi_pirate/status/1882839370505621655> 

Deep Agent R1-V replication: <https://x.com/liangchen5518/status/1886171667522842856> 

s1: <https://arxiv.org/abs/2501.19393> 

DeepSeek-r1 cost breakdown: <https://semianalysis.com/2025/01/31/deepseek-debates/> 

R1 Deep Dive: <https://fireworks.ai/blog/deepseek-r1-deepdive> 

All in Podcast: <https://podcasts.apple.com/us/podcast/all-in-with-chamath-jason-sacks-friedberg/id1502871393?i=1000687638652> 

Peter Gostev LinkedIn: <https://www.linkedin.com/in/peter-gostev/> 

LLM Agents Learning: <https://llmagents-learning.org/sp25>

Engineer Your Ideal Tech Career [Part 14] - Defining Near-Term Goals

Moving into your last section and with inspiration from the engineers you just heard from, you can expect to start defining near-term goals that you can implement within the next week, month, or quarter, to start charging towards your ideal long-term career.

This section will include tangible advice you can implement on the job or if you are preparing for interviews.

Looking for mentorship from tech’s top engineers + personalized job hunt assistance and interview prep? [Formation.dev](http://Formation.dev) can provide you unlimited career coaching, technical mentorship, personalized practice, and mock interviews. Join us here: <https://formation.dev/partners/taro>

Worksheet: [Formation - Coding Your Career](https://firebasestorage.googleapis.com/v0/b/tech-career-growth.appspot.com/o/courses%2Fslides%2FFormation%20Career%20Direction%20Worksheet.pdf?alt=media&token=f6344b78-529a-411e-a449-7a34a8d55fc8)

15 SWE Years In 1.5 Hours [Part 18] - Negotiation

You did it - You finally made it through the interview gauntlet and gotten an offer. However, you're not done yet. This lesson teaches you:

- Why you should always negotiate
- Why tech companies are not your friend (even FAANG when they give you an initial offer that has more money than you've ever seen)
- The #1 rule of negotiation that engineers constantly violate
- How tech compensation is more than just salary, especially at top companies

Take Rahul's full negotiation course here: [The Insider's Guide To Negotiating Your Tech Salary And Compensation](https://www.jointaro.com/course/the-insiders-guide-to-negotiating-your-tech-salary-and-compensation/)

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.