How Do I Know If I'm Making Progress With Leetcode?

It's demoralizing to get stuck on similar problems repeatedly. How do you know if you're making progress?

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

Homework: Define Your Engineering Management System

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

To answer engineering manager (EM) behavioral interview questions effectively, it’s essential to clearly articulate your **leadership style and decision-making frameworks**. Interviewers expect thoughtful, structured responses that show how you operate as a leader across the core responsibilities of the role.

- **Three Core EM Responsibilities**: Every EM must deliver results, lead the team, and manage stakeholders. You must show systems and habits that support all three.
- **Delivery Systems**: Explain how you ensure *sustainable delivery*—using tools like KPIs, OKRs, sprint metrics, dashboards, and demolocation strategies.
- **Team Leadership**: Be ready to share your approach to building *high-performance cultures*, setting team structures, conducting 1:1s, growing individuals, and setting vision and expectations.
- **Stakeholder Management**: Detail how you *influence and align* stakeholders—through communication systems, relationship building, regular reporting, and rituals like syncs and buy-ins.

Writing down these frameworks helps reinforce your leadership identity and provides a solid foundation for compelling interview storytelling. Let me know if you’d like help turning this into a worksheet or practice guide.

Machine Learning System Design Ask & Learn

**Event description**

Join us remotely for an interactive Q&A with Ilya Reznik—Founder of MLEPath, veteran ML engineer with nearly 15 years at Meta, Twitter, and Adobe, and interviewer of hundreds of real-world ML system design (MLSD) interviews (plus nearly 1,000 mock sessions in the last two years). Whether you’re tackling your first MLSD interview or leveling up for senior roles.

- **Core frameworks & approaches**: how to structure your end-to-end design, balance scalability, latency, cost, and reliability
- **Key components & trade-offs**: data pipelines, model serving, feature stores, monitoring, and A/B testing
- **Real-world insights**: pitfalls Ilya’s seen candidates fall into—and what top performers do differently
- **Live feedback**: get tailored advice on designing everything from recommendation systems to real-time anomaly detection

Perfect for ML engineers aiming to land roles at FAANG, startups, or anywhere that demands rock-solid system design chops. Come ready with your toughest questions—and leave with a clear, actionable roadmap for your next MLSD interview.

Team Dynamics

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

To stand out in EM interviews, it’s not enough to understand the company—you also need to empathize with the **team’s specific challenges**. By researching and anticipating what the team needs, you can tailor your answers to resonate with the hiring manager and demonstrate high potential fit.

- **Put Yourself in the Hiring Manager’s Shoes**: Use empathy to analyze what the team is responsible for and what challenges might be preventing sustainable delivery.
- **Anticipate Common Team Challenges**: These may include stakeholder alignment in large orgs, speed in startups, product robustness, scaling issues, or even interpersonal conflicts like team infighting.
- **Showcase Relevant Experience**: Prepare stories from your own background that reflect how you’ve solved similar problems or would approach the challenges facing this specific team.
- **Turn the Interview into a Conversation**: Ask insightful questions to learn more about the team’s current pain points. This shows curiosity, initiative, and models what working with you would be like.

Why There Will Be Fewer Product Managers In Tech

The trend is clear: engineers will have increasingly more power in the workplace, and the number of PMs needed for 'alignment' will diminish.

The Plan To Make Taro Q&A Suck Less - 9/13/2022

- **Update:** Literally a few hours after recording this video, the ability to like answers on Taro web was shipped.
- The #1 priority with the Q&A platform is adding notifications. Here are some important ones:
  - Being notified when someone answers a question that you have asked
  - Being notified when someone likes an answer that you have shared, as you should be able to feel good if you have shared your wisdom in a high-quality answer
- Overall strategy: Make the process of asking a question and getting answers delightful and silky smooth, as you can't have answers without questions. From there, make it a great experience and rewarding to answer questions.
- Ideas:
  - The ability to write a private "Thank You" note to someone who left a really great answer on your question.
  - Question askers can choose and highlight an answer on each of their questions, which helped them the most. This is another way to let question answerers know the impact they're having.

Why Big Tech Companies Do Open Source (From A Director at Meta)

Companies like Meta, Google, Microsoft, and Hugging Face have made enormous contributions to open source. These companies often allocate tens or hundreds of millions of dollars to creating or maintaining open source software. Why? 

- These companies often rely on the software for their production code
- Companies can use popular open-source projects as a tool to recruit top engineers
- Engineers familiar with a company's software will onboard faster

Paper Reading: The Future of Large Language Model Pre-training is Federated

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

Is the future of model pre-training decentralized?

Join us for a captivating session as we explore the cutting-edge breakthrough of federated learning in language model pre-training, featuring an in-depth discussion of the "The Future of Large Language Model Pre-training is Federated" paper!

This paper reading event will shed light on how federated learning (FL) could be set to revolutionize the training of large language models (LLMs). Discover how FL could mobilize vast, underutilized data and computational resources from across the globe, enabling large-scale collaboration and democratizing the pre-training of LLMs. Learn about the innovative approach that allows organizations, and hopefully, soon individuals, to collectively train LLMs using limited resources while matching or even surpassing the performance of traditional centralized models!

This event is a must-attend for AI researchers, software developers, data scientists, and tech enthusiasts eager to grasp the future of LLM pre-training and its implications for AI advancements. Whether you're interested in scaling AI models, leveraging federated learning for collaborative training, or simply curious about the next big thing in AI technology, this paper reading offers invaluable insights and discussions on some of the latest AI research.

Mark your calendars and join us for an exciting hour of "Federated Learning: The Future of Large Language Model Pre-training" exploration!\
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**Reference Links:**\
[https://flower.ai/\
https://github.com/adap/flower](https://flower.ai/￼https://github.com/adap/flower)

Why You Should Be Racist Toward European VCs (An Argument For YC)

James lists his location as San Francisco Bay Area simply to avoid the horrible European VCs (Venture Capitalists).

Competitive Pay

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.

- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FANG companies to experience dramatic increases in pay—often as much as 50–100%. Even the speaker was surprised by the magnitude of the raise upon joining Meta.

- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.

- **Historical Offers Data**: In 2014, new grad offers ranged from $164K to $270K annually from Big Tech firms—Google, Facebook, and others. Salaries have only increased since then, due to ongoing talent wars and inflation.

- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Lessons From Crossing $10M ARR (Entirely Inbound)

How did James achieve viral growth as a product analytics company? How does the open-core model of the company help them grow? How deliberate were they with growth?

Taro Book Club: The Pragmatic Programmer - Session 5

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:

- Decoupling
- Juggling the Real World
- Transforming Programming
- Inheritance Tax
- Configuration

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!

Decoding the One Big Beautiful Bill Act for Tech Workers

New tax law, same tax code for tech workers… mostly.\
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President Trump signed the One Big Beautiful Bill Act into law on July 4, 2025.\
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In terms of the impact to your taxes, this bill largely maintains the status quo by making permanent most of the tax changes introduced on a temporary basis through the Tax Cuts and Jobs Act in 2017.\
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It does, however, add a few new wrinkles to the tax code.\
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I’m hosting a live webinar next week to break it all down for tech professionals like you.\
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🎙️ Webinar: Decoding the One Big Beautiful Bill Act for Tech Workers\
🗓️ Date & Time: July 24, 2025 from 2-2:45 PM Pacific Time\
📍 Where: Live on Zoom\
💡 Especially relevant for:\
👉 Anyone in a high-income bracket that pays a lot in state & local tax\
👉 Folk planning to make Roth conversions in the next few years\
👉Those considering retiring at 65+ in the next few years\
👉 Anyone considering taking a career break or on an ACA plan\
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We’ll cover:\
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✅ Changes to the deductibility of state and local taxes\
✅ How the new bill affects ISO exercises and your likelihood of paying AMT\
✅ What's up with Section 174?\
✅ Changes to QSBS\
✅ "No tax on tips/overtime"\
✅ Deductibility of new U.S. car loan interest\
✅ The new "Trump account"\
✅ Impacts to Medicaid\
✅ The new bonus deduction for seniors\
✅ Updates to clean energy and EV credits\
✅ How the new rules impact ACA health insurance subsidies

Paper Reading: Enhancing Reasoning Abilities in LLMs through Geometrical Understanding

Session Led by [Lucia Mocz](https://www.linkedin.com/in/lucia-mocz-ph-d/)

The advancement of large language models (LLMs) for real-world applications hinges critically on enhancing their reasoning capabilities. In this paper read, we explore the reasoning abilities of LLMs through a geometrical framework. We establish a connection between the expressive power of LLMs and the density of their self-attention graphs.

The analysis performed in this work demonstrates that the density of these self-attention graphs defines the intrinsic dimension of the inputs to the MLP blocks. In particular, it shows, through theoretical analysis and toy examples, that a higher intrinsic dimension implies a greater expressive capacity of the LLM. Additionally, it provides empirical evidence linking this geometric framework to recent advancements in methods aimed at enhancing the reasoning capabilities of LLMs.

Don't miss this opportunity to deepen your understanding of how geometrical insights can drive improvements in AI reasoning. Whether you're an AI researcher, practitioner, or enthusiast, this talk promises to offer valuable perspectives and groundbreaking ideas.\
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Paper: [**https://arxiv.org/abs/2407.02678**\
(Accompanying](https://arxiv.org/abs/2407.02678￼\(Accompanying) paper: [**https://arxiv.org/abs/2312.01648**](https://arxiv.org/abs/2312.01648))

Paper Reading: Adaptive Radix Tree - ARTful Indexing for Main-Memory Databases

**Session Led by:[ Dharin Shah](https://www.linkedin.com/in/dharinshah/)**

**Link to Paper:[ ](https://db.in.tum.de/%5C~leis/papers/ART.pdf)[https://db.in.tum.de/\\\~leis/papers/ART.pdf](https://db.in.tum.de/~leis/papers/ART.pdf)**

This paper reading session, we will deep dive into a in-memory optimized index structure, that leverages modern CPU caches to make IOPS faster, also allowing range queries.

Abstract—Main memory capacities have grown up to a point where most databases fit into RAM. For main-memory database systems, index structure performance is a critical bottleneck. Traditional in-memory data structures like balanced binary search trees are not efficient on modern hardware, because they do not optimally utilize on-CPU caches. Hash tables, also often used for main-memory indexes, are fast but only support point queries.

Leaving Facebook

In this lesson, Philip explains why he chose to leave Facebook despite having a successful career at the company. Part of his decision came from wanting to focus his energy on broader societal challenges, including income inequality and his early concerns about technology-driven unemployment. 

- **Your value to an organization can change as the company grows.** Philip’s generalist skill set allowed him to make an outsized contribution when Facebook was smaller, but as the company scaled to thousands of engineers, he felt his particular strengths were becoming less differentiated.
- **Generalists can have an advantage in smaller companies.** Startups frequently have work that doesn't justify a dedicated full-time role, making people who can move between engineering, product, hiring, operations, and other responsibilities particularly valuable.
- **Scale creates greater demand for specialization.** As organizations grow, previously fractional responsibilities become full-time jobs. At that point, deep specialists can provide more value in a particular domain, while some of a generalist’s broader skill set may go unused.

Management vs IC Transitions

In this lesson, Philip reflects on his experience moving between individual contributor (IC) and management roles throughout his career. He explains why he initially pursued management, why he ultimately prefers being an IC, and how he has managed to switch between the two tracks multiple times—even when doing so meant willingly stepping down in level.

- **Choose management because you want to lead—not simply because it feels like the next promotion.** Before becoming a manager, consider whether your team would genuinely want you to lead them and whether you would want to work for yourself.
- **Be careful of turning management into a one-way door.** Moving away from hands-on technical work can make returning to the IC track difficult, particularly if maintaining your title, level, or compensation becomes more important than making the right career move.
- **IC and management skills can converge at senior levels.** Senior ICs often need many of the same capabilities as strong leaders—including communication, vision, coaching, business understanding, and the ability to lead complex initiatives—although highly specialized technical roles may follow a different path.

Original podcast link: [**https://www.youtube.com/watch?v=v2JxdjTi_1I**](https://www.youtube.com/watch?v=v2JxdjTi_1I)

Working With Zuck And Other Impressive ICs

In this lesson, Philip reflects on what he learned from working alongside some of Meta’s most influential leaders and senior engineers. He shares what stood out about leaders like Mark Zuckerberg and Andrew “Boz” Bosworth—particularly their willingness to seek feedback, continually develop their skills, and make deliberate improvements even after reaching highly influential positions.

- **Exceptional leaders remain committed to their own growth.** Philip observed that leaders like Zuckerberg and Bosworth continued to seek coaching, respond to feedback, and deliberately improve their skills rather than assuming they had finished developing once they reached senior positions.
- **The best engineers can create impact far beyond their immediate responsibilities.** Philip recalls John Carmack quickly identifying technical and performance improvements in a product he hadn't worked on for months, demonstrating the depth of insight that can distinguish exceptional technical talent.
- **The highest-impact ICs are often force multipliers.** Scott Renfro stood out not only for his ability to deliver large, complex initiatives, but for making the engineers around him better through thoughtful feedback, supportive code reviews, and a willingness to invest time in others’ success.

Becoming Senior Staff IC7 at Microsoft

In this lesson, Philip reflects on his rapid career progression at Microsoft and the factors he believes contributed most to his growth as an engineer. He discusses the value of working alongside exceptionally talented colleagues and mentors, deliberately accumulating experience early in your career, and the role that hard work can play in accelerating professional development.

- **Career growth is a combination of luck, talent, and hard work.** While some factors are outside your control, putting yourself in strong environments and learning from exceptional people can accelerate your development.
- **More experience can compress the learning curve.** Taking on a high volume of meaningful work early in your career can expose you to situations and problems that might otherwise take years to encounter.
- **Hard work has diminishing returns—and real trade-offs.** Longer hours may increase total output and experience, but each additional hour can become less productive, and career acceleration can come at the expense of health, relationships, and balance.

Original podcast link: [**https://www.youtube.com/watch?v=v2JxdjTi_1I**](https://www.youtube.com/watch?v=v2JxdjTi_1I)

Gratitude & Paying It Forward

In this lesson, Philip shares the story behind spending more than $23,000 to buy coffee for everyone at Facebook on his final day at the company. What began years earlier as a simple Christmas gesture became a meaningful way to close out a chapter of his career and express gratitude to a company that had given him extraordinary opportunities, mentorship, and financial success.

- **Recognize the role luck plays in success.** Philip credits hard work for parts of his career, but also acknowledges circumstances outside his control—from when and where he was born to joining Facebook at an unexpectedly transformative moment.
- **Gratitude can be expressed by paying success forward.** After Facebook’s IPO, Philip donated his proceeds to charity, and when he eventually left the company, buying coffee across several offices became another way to give something back.
- **Small gestures can create an outsized impact.** What began as buying coworkers coffee for a day created genuine joy and became a memorable tradition—showing that generosity doesn't always need to be complicated to be meaningful.

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.