Embodying The Leader

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>

In this lesson, we learn how to effectively showcase leadership qualities during engineering manager interviews by understanding the most commonly assessed leadership abilities. Interviewers are assigned specific attributes to evaluate, and crafting thoughtful, real-world stories that highlight these traits is essential.

- **We prepare stories around 9 common leadership abilities**, including growing talent, delivering under pressure, and influencing stakeholders, knowing these form the backbone of most behavioral interview questions.
- **We ensure each story highlights not just outcomes, but the judgment and systems we used**, especially in situations involving conflict, ambiguity, or strategic risk.
- **We practice showcasing our resilience and decision-making**, such as dealing with underperformers, making fast-paced calls under time pressure, or navigating conflict with senior stakeholders.
- **We recognize that good answers require emotional maturity**, thoughtful communication, and the ability to reflect on learnings—not perfection or bravado.
- **We strive to embody senior leadership through our communication style**, avoiding over-explaining, exaggeration, or emotional volatility, and focusing instead on clarity, calmness, and authenticity.

There Is Nothing Wrong With You

We close by reframing layoffs as a survivable, even formative, career event rather than a personal failure. Here are the core points from the lesson:

- Being laid off is not a reflection of your ability or worth, since layoffs are driven by executives making decisions with incomplete information and often feel effectively random from an individual’s perspective.
- There is no value in self-blame or overanalyzing what you could have done differently, because once a layoff happens, the only productive move is to look forward.
- With severance as temporary financial runway, layoffs can be treated as a reset point to reflect, recover, and intentionally choose your next step, knowing this is a normal part of a long tech career.

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>

Paper Reading: AutoDev: Automated AI-Driven Development

Are you interested in tools like the recently announced Devin?\
\
Join us for an enlightening session as we delve into the cutting-edge world of agentic AI-driven software development, featuring a comprehensive exploration of the AutoDev framework, Microsoft's agentic AI-driven software development framework.

This reading event will elucidate how AutoDev will revolutionize the software development process. Through enabling autonomous planning and the execution of complex engineering tasks, AI agents such as AutoDev will be fully integrated into common tools and technologies such as VSCode and GitHub. Discover how these agents can perform multiple operations on a codebase, from file editing and testing to git operations, all within a secure Docker-based environment.

Moreover, we will examine AutoDev's impressive performance on the HumanEval dataset, achieving remarkable scores of 91.5% and 87.8% for Pass@1 in code generation and test generation tasks, respectively. We will also discuss the implications of this research for the future of AI-assisted software development.

This event is a must-attend for software developers, researchers, and tech enthusiasts eager to understand the future of agentic AI-driven software development and its potential impact on the software development landscape. Whether you're interested in improving your development workflow, exploring AI applications in software engineering, or simply curious about the latest advancements in AI-driven development tools, this paper reading offers valuable insights and discussions on the forefront of technology innovation.

Mark your calendars and join us for an exciting hour of "AutoDev: Automated AI-Driven Development" exploration!

Paper Reading Session: Simple Testing Can Prevent Most Critical Failures

Paper Discussion led by [Sudhi Awasthi](https://www.linkedin.com/in/sudhiawasthi/)

In this session, we will be discussing this paper titled "[Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf)".

Large, production quality distributed systems still fail periodically, and do so sometimes catastrophically, where most or all users experience an outage or data loss. This paper presents the result of a comprehensive study investigating 198 randomly selected, user-reported failures that occurred on Cassandra, HBase, Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Redis, with the goal of understanding how one or multiple faults eventually evolve into a user-visible failure and how simple testing can prevent majority of them.\
\
Event Link: <https://www.jointaro.com/event/paper-reading-session-simple-testing-can-prevent-most-critical-failures/>

Leadership Bar

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>

In this lesson, the instructor explains the core attributes that engineering manager (EM) interviewers, especially directors and above, look for when hiring. Unlike IC roles, EM hiring is predominantly trust-based and focused on leadership capabilities over technical depth.

- **Behavioral Over Technical**: 80% of the EM hiring decision is based on behavioral criteria like trustworthiness, resilience, and ownership, while only 20% is focused on technical competency such as architecture and system design.
- **Understand the Decision-Maker’s World**: Directors and VPs prioritize reliability, communication skills, and the ability to handle uncertainty and people problems over deep technical expertise.
- **Subjective Evaluation**: EM interviews aren’t about getting “right” answers. Instead, they're conversational and context-driven, evaluating fit with the team, culture, and leadership expectations.
- **Demonstrate Ownership and Initiative**: Candidates must show extreme ownership, proactive problem-solving, and the ability to deliver results, even outside formal responsibilities.
- **Adapt Communication**: EMs need to adjust messaging depending on the audience, from engineers to upper leadership, showing mastery in stakeholder communication.

[Paper Reading] ROSE: Robust Caches for Amazon Product Search

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

ROSE introduces a robust caching system that tolerates misspellings and typos while maintaining the efficiency of traditional caches. The core innovation is a randomized hashing schema that allows ROSE to index and retrieve a vast set of queries with constant memory and time. This system is robust against query intent, typos, and grammatical errors, offering theoretical guarantees of its performance.

Extensive experiments on real-world datasets highlight ROSE's effectiveness and efficiency, showcasing significant improvements in Amazon's search engine performance across key business metrics.\
\
In this session we deep dive into the query caching layer at Amazon that allows for far better and robust search performance

[**https://drive.google.com/file/d/13YwkeCc1XwnRVDVkcuE7MKt2r7aqld24/view**](https://drive.google.com/file/d/13YwkeCc1XwnRVDVkcuE7MKt2r7aqld24/view)

Tailoring Your Answers

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 deliver unforgettable interview answers, we need to learn how to tailor our responses to match the company, the role, and the specific audience we’re speaking to. This session explains how to adapt our stories based on context, organization type, and our own leadership philosophy—even when we haven’t faced a specific scenario before.

- **Tailor Stories to the Team and Business**
  - We must deeply understand the hiring team and the company’s business context to highlight the most relevant parts of our story.
  - Even small adjustments—altering 10% of a story—can have a big impact in aligning with the company’s values and needs.
- **Adapt Answers Based on Company Type**
  - For example, when answering a question about influencing stakeholders, we need to highlight different dynamics depending on whether it’s a startup (external stakeholders like customers or investors) or a big org (internal cross-functional teams).
  - Similarly, questions about balancing delivery vs. quality should be approached differently for consulting roles (focus on fast delivery and milestones) vs. product roles (focus on long-term impact, maintainability, and operational excellence).
- **Responding to Questions You Haven’t Faced Before**
  - If we haven’t directly experienced a situation (e.g., managing underperformers or doing capacity planning), we should draw on adjacent stories and clearly articulate our leadership philosophy and approach.
  - It’s helpful to describe what we’d do in the first 90 days on the job—such as building a plan or upskilling—and how that reflects our values and learning mindset.
- **Use Leadership Philosophy as a Guide**
  - Our answers should reflect consistent values, such as building high-performing teams, holding people accountable, and prioritizing quality.
  - Even without a direct experience, framing our response through this philosophy helps convey credibility, ownership, and potential.

Paper Reading: Refactoring vs Refuctoring: Advancing the state of AI-automated code improvements

Paper Discussion led by [Adi Pratheek](https://www.linkedin.com/in/pratheekadi/)

"**Refactoring vs Refuctoring**" is the conclusion of a benchmark study of the most popular Large Language Models (LLMs) and their ability to generate code for refactoring tasks. This paper aims to illustrate the current standards and limitations, and seek to show new methodologies with higher confidence results.

**What's in store?**

In this paper reading session, we will be examining:

- The performance of the most popular Large Language Models (LLMs) on code refactoring
- How does an AI err?
- A novel innovation by CodeScene for fact-checking the AI-output and augmenting the proposed refactorings by measuring the confidence levels

Companies build pretty substantial codebases over a period of time (sometimes decades). As and when the companies grow, a significant amount of time and energy are constantly invested by development teams on code refactoring (also covered in this paper).

But, how well do the existing AI tools/models actually perform this task? Are we there yet to confidently say "**AI will replace software developers**"? Join this session where we dive deep into the paper to find more answers.

Link to the paper: [**Refactoring vs Refuctoring: Advancing the state of AI automated code improvements**](https://codescene.com/hubfs/whitepapers/Refactoring-vs-Refuctoring-Advancing-the-state-of-AI-automated-code-improvements.pdf)\
Event Link: <https://www.jointaro.com/event/paper-reading-refactoring-vs-refuctoring-advancing-the-state-of-ai-automated-code-improvements/>

Paper Reading: Mamba - Revolutionizing Sequence Modeling

Session led by: [Akshika Wijesundara, PhD](https://www.linkedin.com/in/akshika/overlay/about-this-profile/)

*Abstract:*

Explore the Mamba paper's groundbreaking approach to sequence modeling efficiency in this event. Uncover how Mamba addresses computational inefficiencies in transformers, especially for long sequences.

*Overview:*

Discover the motivation behind Mamba, highlighting its improvements over subquadratic-time architectures struggling with language tasks. Learn about Mamba's content-based reasoning and enhancements for superior performance.

*Key Features:*

1\. **Selective Structured State Space Models (SSMs):** Mamba introduces adaptive SSM parameters for content-based reasoning, allowing selective information handling for discrete modalities.

2\. **Hardware-Aware Parallel Algorithm:** Despite forgoing efficient convolutions, Mamba's hardware-aware parallel algorithm maintains fast inference and linear scaling in sequence length.

3\. **Architecture Overview:** Dive into Mamba's streamlined architecture without attention or MLP blocks, achieving efficiency without sacrificing effectiveness.

4\. **Performance Metrics:** Witness Mamba's 5× higher throughput than Transformers, with linear scaling, showcasing its real-world improvements in language, audio, and genomics tasks.

5\. **Comparison with Transformers:** See Mamba outperforms Transformers of the same size and match larger Transformers, especially in language modeling.

Join us in unraveling the secrets of Mamba, poised to redefine sequence modeling standards. Anticipate a paradigm shift in efficiency and performance in deep learning.\
\
paper link: \[[**https://arxiv.org/abs/2312.00752**](https://arxiv.org/abs/2312.00752)\]

[Office Hours] Doubling Your Impact As An Engineer By Doubling Your Empathy

Session led by [Ryan Kuck](https://www.linkedin.com/in/ryan-kuck/)

A core theme on Taro is that by increasing empathy you will build better, stronger relationships with your teammates. Additionally by having empathy for your manager, you will simplify your communication with them and make it easy for them to see your impact. And while it is tempting to put up a pull request as soon as the code is done, taking extra time to divide it into small, thoughtfully crafted pull requests that make OTHER people's life easier will make you a joy to work with. All of this is *slightly* counterintuitively about taking time to understand someone else better to make their life better which actually is a practice that makes your life a lot better.

Let's get together and swap stories about this! If you landed a promotion from thinking about the user's perspective and building a new feature for them, or you built an internal tool that saved other folks from having to do a repetitive task, please join and share! Also welcome is anyone who has a challenging work situation and is wondering if increasing empathy could help improve it.

Business 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>

Many engineers struggle with engineering manager (EM) interviews because they underestimate how different they are from individual contributor (IC) roles. This course aims to prepare candidates for the unique expectations, mindset, and evaluation criteria involved in EM interviews.

- **Shift in Role and Expectations**: Transitioning from IC to EM is not just a promotion but a full career change, requiring a shift from deep technical implementation to broad people and strategy responsibilities.
- **Interview Criteria Differences**: For EM roles, **80% of the evaluation is behavioral** (trust, leadership, communication) and only 20% is technical (system design, operational excellence), the reverse of IC interviews.
- **Understanding the Hiring Manager's World**: EM interviewers, typically directors or above, prioritize reliability, trust, emotional maturity, and ownership. Candidates must show they can simplify complexity and manage uncertainty.
- **Research and Business Alignment**: Successful candidates understand the business context of the role (e.g., quality for physical products, speed for startups, security for regulated industries) and tailor their answers accordingly.
- **Empowerment Over Expertise**: EMs don’t need to be the most technical on the team. Their primary responsibility is to ensure the team delivers by empowering others and focusing on strategic growth and impact.

Verify Your Severance Package

We explain what to do immediately after a layoff to protect yourself financially and legally, and to reduce unnecessary stress. Here are the core points from the lesson:

- Most large tech companies provide severance due to legal requirements and long-term reputational incentives, but layoffs are messy, so you must carefully review all paperwork and verify dates, vesting, and severance amounts.
- Never sign termination documents on the spot; take time to review them, ask questions, and involve a lawyer if possible, especially for severance disputes or visa-related considerations.
- After a layoff, it’s critical to understand your personal burn rate and runway by reviewing your finances, since clarity around how long you can sustain yourself reduces anxiety and helps you plan your next steps.

The BIG Difference Between The Mindset of American And European Tech Workers

Belief in growth is the primary difference between Software Engineers in the USA vs Europe. Many American tech workers understand that their equity options can be huge if their company succeeds. However, European tech workers are often more geeky and "pure" as engineers.

Build Support Groups

We explain why building community after a layoff can dramatically improve both emotional resilience and job-search outcomes. Here are the core points from the lesson:

- Layoffs create a natural sense of solidarity, and bringing laid-off peers together helps transform an isolating experience into a shared support system.
- Being proactive about forming or joining support groups—through spreadsheets, LinkedIn, or communities like Taro—creates powerful networks of talented people who can help one another.
- Regular check-ins, shared encouragement, and activities like mock interviews provide structure, accountability, and momentum during the job search.

Paper Reading: Self-Discover - Empowering Large Language Models with Autonomous Reasoning Structures

In this talk, we delve into Self-Discover, an innovative framework revolutionizing Large Language Models (LLMs) by enabling them to autonomously discern task-specific reasoning structures, thereby empowering them to tackle complex problems without explicit prompting. 

At the heart of Self-Discover lies a self-discovery process, wherein LLMs independently select and amalgamate various atomic reasoning modules, such as critical thinking and step-by-step reasoning, into explicit structures to guide their decoding process. The broad applicability of these self-discovered structures across diverse model families—from PaLM2-L to GPT-4 and from GPT-4 to Llama2—underscores the framework's versatility and adaptability, suggesting seamless integration with an even wider range of advanced AI models. This holds promise for a unified approach to autonomous reasoning in artificial intelligence.\
\
Whether you're an AI researcher, engineer, or simply curious about this achievement in AI research, join us as we explore the intricacies of Self-Discover and its potential implications for prompt engineering and advancing the capabilities of Large Language Models in complex reasoning tasks.\
\
Paper: [**https://arxiv.org/pdf/2402.03620.pdf**](https://arxiv.org/pdf/2402.03620.pdf)

Lucia on LinkedIn: <https://www.linkedin.com/in/lucia-mocz-ph-d-35a98a1b6/>

Paper Reading: Llama 2: Open Foundation and Fine-Tuned Chat Models

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

Join us for the second installment of our three-part paper reading series on the LLaMA series of Large Language Models (LLMs).

In this session, we will dive into **Llama 2**, the second iteration in the LLaMA family of models, introduced by Meta AI in mid-2023. This follow-up to the original LLaMA models brings significant advancements, offering foundational and fine-tuned versions with parameter counts ranging from 7B to 70B. Llama 2 models set new benchmarks in helpfulness and safety, showcasing impressive capabilities in dialogue-based tasks and outperforming many open-source alternatives.

We will explore the innovations behind the **Llama 2** models, focusing on their **pretraining methodology**, the **fine-tuning process** that incorporates Reinforcement Learning with Human Feedback (RLHF), and the novel techniques Meta AI used to enhance model safety and alignment. Particular emphasis will be placed on the **Llama 2-Chat models**, optimized for multi-turn conversations, along with references to their performance on real-world safety benchmarks.

This session will be an insightful discussion of how the Llama 2 models have helped to close the gap between open-source and closed-source systems like ChatGPT, examining their architecture, training data, and the steps taken to ensure responsible release and deployment. We'll highlight key takeaways from Meta AI's comprehensive evaluation of these models and discuss how they are still reshaping the landscape of open-source AI.

Whether you're a researcher, developer, or AI enthusiast, this paper reading will offer you an in-depth understanding of the **Llama 2** models and their contributions to the ongoing development of LLMs. This is a unique opportunity to review cutting-edge advancements in **LLM safety**, **dialogue optimization**, and their impact on the future of open-source AI systems.

Mark your calendars for an informative hour of "Llama 2: Open Foundation and Fine-Tuned Chat Models," as we continue to chart the progress of AI language models and explore the innovations that continue to shape the future of the field!

[https://lmarena.ai/\
https://github.com/meta-llama/llama](https://lmarena.ai/￼https://github.com/meta-llama/llama)

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!

Why Don't More Big Tech Engineers Contribute To Open Source?

The modern software industry runs on open source software. Should more software engineers (especially those in Big Tech) contribute back to open source?

The Mistake Engineers Make During The Reverse Interview

Too many engineers don't think critcally about the impact of the last 5-10 minutes of an interview.

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.