Become An Engineer Influencer [Part 19] - What If Nothing Works?

Become An Engineer Influencer [Part 17] - Frequency

Become An Engineer Influencer [Part 16] - Editing

Taro Book Club: Designing Data Intensive Applications - Chapter 8 (Distributed Systems)

Topic: Chapter 8 - The Trouble with Distributed Systems

In this #taro-bookclub discussion, we'll go over Chapter 8 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 8.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Rohit Katyal**](https://www.linkedin.com/in/rohitkatyal/) 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!

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.\
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paper link: \[[**https://arxiv.org/abs/2312.00752**](https://arxiv.org/abs/2312.00752)\]

Taro Book Club: Designing Data Intensive Applications - Chapter 7 (Transactions)

Topic: Chapter 7 - Transactions

Session lead by [**Raj Shah**](https://www.linkedin.com/in/therajshah/), [**Raghu Kalyan**](https://www.linkedin.com/in/graghukalyan/), and [**Kevin Wu**](https://www.linkedin.com/in/wkevn/) 

In this #taro-bookclub discussion, we're having a session about Chapter 7 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 7
- There will also be time for any questions and answers you might have

Get ready for another engaging and informative session!

Paper Reading: AutoDev: Automated AI-Driven Development

Are you interested in tools like the recently announced Devin?\
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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!

Q&A: Acing Tech Interviews In A Bad Job Market

Check out the full 4.5+ hour course diving even deeper into this session's concepts: [\[Course\] Ace Your Tech Interview And Get A Job As A Software Engineer](https://www.jointaro.com/course/ace-your-tech-interview-and-get-a-job-as-a-software-engineer/)

Get Taro Premium at a discount with our special link here: <https://www.jointaro.com/r/achiou15/>

As a Taro Premium community member, you can get personalized advice from proven tech leads like Alex and Rahul at any time by starting career discussions in our forum and joining [Group Office Hours](https://www.jointaro.com/event/group-office-hours-with-alex-career-direction-company-offer-evaluation-jan19/). 

This is the Q&A portion of the 1-hour session we did about finding interview success in a bad job market. You can watch that session here: [Acing Tech Interviews In A Bad Job Market](https://www.jointaro.com/lesson/Vz4jXfFIeQSSSzonpvH0/acing-tech-interviews-in-a-bad-job-market/)

Related resources for the questions in this video:

- [\[Masterclass\] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/)
- ["How can I get more internships?"](https://www.jointaro.com/question/k8TNvbobgU1R7ICDWKus/how-can-i-get-more-internships/)
- [\[Masterclass\] How To Choose A Good Company And Team As A Software Engineer](https://www.jointaro.com/lesson/0WEmTNZOMIh8xAdfO3JH/masterclass-how-to-choose-a-good-company-and-team-as-a-software-engineer/)

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.\
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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.\
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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/>

Taro Book Club: The Fundamentals of Data Engineering - Chapter 3 (Part 2)

Session led by [Gideon Blinick](https://www.linkedin.com/in/gideonblinick/)

Chapter 3 - Part 2: Designing Good Data Architecture

In our first session on Chapter 3, we learned the 9 principles of Good Data Architecture, which are largely based on Google's Principles of Cloud-Native Architecture and Amazon's Well-Architectured Framework.

Now we get less abstract and cover Major Architecture Concepts and Examples, including Domains and Services, Distributed Systems, Data Warehouses and Lakes, the Modern Data Stack, and so much more!

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.\
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Event Link: <https://www.jointaro.com/event/paper-reading-session-simple-testing-can-prevent-most-critical-failures/>

Live AMA with Casey Dai - Former Head of Engineering & Founder of Expanded Skills

[Casey Dai](https://www.linkedin.com/in/caseydai/) is a career switcher, Head of Engineering, and entrepreneur based in Canada. After getting a finance degree, he worked in consulting before switching to engineering and quickly climbed the ranks to become the Head of Engineering at Capgemini, a multi-billion dollar IT company. 

Topics covered in this Ask Me Anything (AMA):

- What are the most underrated skills for SWEs and EMs?
- How do I future-proof my skills to stay competitive in the tech industry when things are constantly changing?
- Why are there a lot of bad managers? Is there anything that can be done to prevent that?
- How do I identify opportunities to scale my impact?
- How do I become a better decision-maker when faced with uncertainty?
- What are some ways to grow aside from the company career ladder?

Casey will go **in-depth on these topics in [his upcoming course](https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/)** starting Jan 16th.

Enroll in Casey's cohort-based course Operate Like An Engineering Leader and receive a free 1:1 coaching session: [**https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/**](https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/)

This Is Why You Should Work For Big Tech

So many engineers want to work for FAANG and for good reason: There are lot of career benefits that Big Tech is exceptionally good at providing.

Here are the core scenarios where Big Tech is a slam dunk career choice for you:
- **You are earlier in career** - Big Tech will give you a rock solid foundation across all levels: Learning, compensation, job prospects - You name it. When you're younger, you will generally be more flexible with work-life balance (i.e. you're more physically able to work longer hours), so you will be better equipped to deal with the stress of Big Tech.
- **You need money** - More money is almost always useful, and if you live in a very high cost-of-living area (e.g. SF Bay Area), it becomes necessary. Big Tech gives a consistent, incredibly high income.
- **You value stability and are risk-averse** - Even with the layoffs, these companies are some of the safest places to be. Compared to peer companies, they have done far fewer layoffs across their lifespan and they have rock solid, wildly profitable business models.

Related resources:
- ["I want to land a job in a big tech as a SWE - How to do it?"](https://www.jointaro.com/question/MWJfUBnnUr8CKYFCO8bp/i-want-to-land-a-job-in-a-big-tech-as-a-swe-how-to-do-it/)
- ["What's the difference between Big Tech and startups in terms of learning and growth?"](https://www.jointaro.com/question/KWXIrFIR1EIJsvdOdo7u/whats-the-difference-between-big-tech-and-startups-in-terms-of-learning-and-growth/)

Advice to Your Younger Self

In this lesson we wrap up with the two pieces of advice Ethan would give himself at the start of his career. We also look at how one of them is something he did and the other is something he wishes he had done sooner.

- Prefer high-growth companies, because his career was an escalator that rose even when he wasn’t climbing.
- Jobs are still with other humans, so build relationships and a reputation early.
- You don’t have to be an extrovert to do this, since tools like LinkedIn let you build connections from behind your keyboard.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

Can a Manager Fire Anyone?

In this lesson we discuss why performance improvement plans rarely succeed. We also look at what employees should do if they find themselves on one.

- Once a manager writes a PIP, they have mentally decided you’re leaving, and that bias becomes self-fulfilling.
- Ethan’s coaching clients, and others he knew of, who were put on a PIP were nearly all fired anyway.
- His advice is to use the PIP period to look for your next job.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

Stack Ranking and Unregretted Attrition

In this lesson we examine how Amazon pushes managers to remove low performers each year. We also look at why a system that sounds harsh exists in the first place.

- Each year there is a target for “unregretted attrition,” typically a few percent, which in practice means firing the bottom performers.
- When Amazon dropped the target as an experiment, managers stopped having hard conversations, so it came back the next year.
- The system forces action but can also put people on the list just to meet a quota.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

Hitting Goals vs. Delivering the Vision

In this lesson we discuss how a leader can receive good reviews while the business they run fails to reach its vision. We also look at the tension between goals that protect your pay and goals that actually matter.

- Prime Gaming and the Amazon App Store hit their interim goals but never came close to the bigger vision.
- Big companies often confuse hitting goals with doing something valuable, which leaves “zombie products” that nobody wants to shut down.
- For your own review you want goals you know you can hit, but for the company you want goals you don’t yet know how to reach, and that tension is real.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

Working With Bezos vs. Jassy

In this lesson we compare the leadership styles of Jeff Bezos and Andy Jassy, based on Ethan’s roughly 50 meetings with each. We also look at how being a founder changes the way a leader makes decisions.

- As the founder, Bezos could take bets others wouldn’t, such as telling his CFO that his job was to count the pennies and Bezos’s job was to allocate them.
- Jassy is a more classic business leader who partners with his CFO, because he answers to a board and to Bezos.
- Ethan found Bezos more inspiring and Jassy more probing and likely to leave him “on the hook,” and he stresses that this is his personal experience.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

What Young Founders Get Wrong

In this lesson we look at the gaps Ethan saw in a fast-growing startup after its acquisition. We also discuss how he handled the pressure of sitting between the founder’s team and Amazon’s leadership.

- Twitch leadership didn’t see profitability as a goal because they had always been able to raise venture money, while Amazon leadership wanted profit soon.
- Ethan was caught between those two views.
- Founders often have to replace or level early friends and employees who can’t scale with the company.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

Influence Without Authority

In this lesson we discuss why Ethan gave up a team of 800 to become an advisor at Twitch. We also look at how he approached leading someone he had no power to give orders to.

- Amazon typically assigns a VP as an integration liaison after an acquisition, while the founder stays in charge.
- He deliberately took a role with a goal but no authority to practice his soft skills.
- The challenge was influencing a much younger founder in a business that served a different generation.

Original podcast link:[ https://www.youtube.com/watch?v=40-ENZmqcz0](https://www.youtube.com/watch?v=40-ENZmqcz0)

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.