How To Be An Effective Tech Lead [Part 13] - Leverage The Power Of Feedback

Feedback is the main fuel you need to improve as a software engineer. It's especially important for tech leads as they are responsible for the well-being and productivity of many other people. This lesson teaches you:

- The proper mentality to have when it comes to feedback
- The right medium to deliver feedback based on its contents
- How to deliver feedback in a healthy way that doesn't hurt people
- What to do after receiving/delivering feedback to guarantee that it actually creates value

To dive deeper into how feedback is a gift, read through our excellent explainer here: ["Can someone elaborate on "always treating feedback as a gift"?"](https://www.jointaro.com/question/4DoEnMIq2ISadfAJuwp9/can-someone-elaborate-on-always-treating-feedback-as-a-gift/)

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.

[Timeless Career Advice] Promos, Time, and Scope

Become An Engineer Influencer [Part 25] - Digital Products

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/)

What Are Advanced Concepts I Should Know?

In this lesson we talk about how to effectively prepare for AI-assisted coding interviews, with a focus on shifting your study habits to match what these interviews actually test.

- **Learn the "why" before the "how"** — rather than drilling implementations, focus on understanding the canonical problem each data structure solves and practice explaining it in one sentence out loud; with AI handling the code, being unable to independently identify the right approach is now much harder to hide or compensate for.
- **Know the common gotchas for each pattern** — every classic structure has a specific edge case trap (e.g. the `is_end` flag in a trie, recency updates in an LRU cache, off-by-one errors in sliding windows); getting familiar with these upfront dramatically speeds up your ability to verify AI-generated output during the interview.
- **Practice reading code, not just writing it** — since interviews now involve more time spent reading starter code and verifying AI output than typing from scratch, deliberately train yourself to mentally trace logic quickly by reading existing solutions online or having AI generate code for problems you already understand, then tracing through them by hand.

How Should I Practice For These Interviews?

In this lesson we talk about how AI is making previously time-prohibitive advanced data structures and algorithms newly relevant in coding interviews, and what that means for how you should prepare.

- **AI unlocks harder concepts by handling the boilerplate** — complex structures like tries, heaps, and LRU caches were rarely testable in a 45–60 minute interview because implementation alone could eat up 15–20 minutes; with AI generating that scaffolding in seconds, interviewers can now reasonably include them, making them worth studying once you've solidified the fundamentals.
- **What you actually need to demonstrate has shifted** — since AI handles much of the implementation, your value lies in recognizing when a given data structure is the right tool, clearly articulating *why* it helps (e.g. "a trie eliminates the factor of M by checking all patterns simultaneously through shared prefix traversal"), and writing precise prompts that reflect that understanding.
- **Master the basics first, and don't over-rotate on this** — the vast majority of interviews (roughly 95%) will not require these advanced concepts, so treat this as the final 5–10% polish layer, not the foundation; verification of AI output remains just as critical here as everywhere else in the interview.

How Do I Talk Without Sounding Chaotic?

In this lesson we talk about how to communicate effectively during an AI-assisted coding interview without sounding scattered, frantic, or going completely silent.

- **Avoid the two extremes** — narrating every single keystroke comes across as junior, while going silent for several minutes leaves the interviewer in the dark; the goal is to sound controlled and intentional somewhere in between.
- **Choose the communication model that fits your thinking style** — Model A is light, continuous commentary as you work ("I'm pulling the date out of each event to populate the per-day set"), while Model B is stating your high-level intent upfront, working silently for 60–90 seconds, then summarizing what you did before repeating the cycle.
- **Practice talking while you work before the interview** — like touch typing, verbalizing your thought process while solving a problem is a skill that needs to be rehearsed, not figured out on the spot; a few mock sessions with a friend or mentor can make a significant difference.

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.