Ace Your Tech Interview As A Software Engineer [Part 5] - Dynamic State vs. Prepared State

The overwhelming majority of software engineers went to college and grew up in a very academically-inclined environment. Bringing the mentality cultivated from that experience to the job search often unfortunately has bad consequences. This lesson teaches you:

- Why traditional learning methods don't translate well to software engineering
- The difference between *prepared state* (how you grew up learning) and *dynamic state* (how you should actually learn)
- A defining trait of the most successful interviewers, which most candidates don't have
- How to train yourself properly when it comes to your interview preparation and overall learning

Ace Your Tech Interview As A Software Engineer [Part 4] - The #1 Trait For Interview Success

Software engineers overwhelmingly blame the wrong thing when it comes to why they aren't landing job offers, particularly their own lack of skill. This lesson teaches you:

- What the actual #1 trait among successful job searchers is
- How to build up your personal grit
- Why most software engineers have the wrong perspective post-interview and are unable to fully learn from their failures
- The success metric you should be weighing yourself against as you progress through the job search, viewing everything as more of a spectrum instead of a binary state

To go deeper into developing your grit and boosting your productivity, watch this video: [2 Ways To 100x Your Productivity That Nobody Does](https://www.jointaro.com/lesson/RXEca9zfyZG0pl015att/2-ways-to-100x-your-productivity-that-nobody-does/?playlistId=JNCZ1vbRF3NwsOQCqblt)

Ace Your Tech Interview As A Software Engineer [Part 3] - Interviewing: Why It Sucks

A lot of software engineers don't really know what happens on the other side (i.e. the interviewer giving you the questions), especially for top companies like FAANG. This lesson teaches you:

- Why interview processes are much messier than job candidates expect
- Why tech interviews are inherently unpredictable and random
- How incredibly hard it is to create a quality interview process that both only lets in good talent while not being too torturous for the candidates
- Why it's near impossible for this dark reality of terrible interviewers and interview processes to ever change

If you want to dive even deeper into the interviewer's perspective, check this out: [\[Masterclass\] How To Be An Amazing Interviewer In Tech](https://www.jointaro.com/lesson/OYKgGZ7syfymlvIJKzuf/masterclass-how-to-be-an-amazing-interviewer-in-tech/)

Ace Your Tech Interview As A Software Engineer [Part 2] - Organizing Your Job Search

A lot of software engineers struggle on the job search because they're just randomly flailing around, drifting from interview failure to interview failure without learning from the overall process. This lesson teaches you:

- The surprisingly basic master plan every successful job search must follow
- How to fix a job search where you are failing to get interviews to begin with
- Actions you can take when you are failing to pass interviews
- What a healthy time spent looks like depending on whether you are trying to *get* interviews vs. trying to *pass* interviews
- How you can organize your job search and turn it into a well-oiled machine where you are constantly overcoming obstacles and improving

Ace Your Tech Interview As A Software Engineer [Part 1] - How This Course Works

Let's not mince words: Looking for a job as a software engineer is *brutal*. It is so easy to lose heart on the journey and stop believing in yourself. This video (and broader course) will solve that for you by giving you the tools to overcome any job search adversity and stand out from the hordes of other candidates. Here are the core parts of the lesson:

- Your instructor [Alex Chiou](https://www.linkedin.com/in/alexander-chiou/) walks you through his extensive 10+ year Silicon Valley background across top companies like PayPal, Course Hero, Meta, and Robinhood, explaining how he increased his TC (total compensation) by a whopping +800% across just 7 years and led thousands of engineers to interview success at top companies like Meta, Google, and Amazon.
- You'll understand why the existing landscape about interview preparation resources fails software engineers so miserably, particularly when it comes to data structures and algorithms (DSA) and LeetCode.
- We walk you through the course objectives. By the end of the course, you will understand how to organize your job search, learn interview skills properly, get job opportunities effectively, follow a framework to come prepared into any interview, and understand all the common software engineer interview round types.

If you want to learn how Alex cracked the FAANG interview, you can check out his story here: [How Alex Got Into Meta With 0 Prior LeetCode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

Taro Book Club - The Fundamentals of Data Engineering - Chapter 3: Designing Good Data Architecture

Discussion led by [Gideon Blinick](https://www.linkedin.com/in/gideonblinick/)\
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In this session, we'll discuss Chapter 3 (Designing Good Data Architecture) of [**"The Fundamentals Of Data Engineering"**](https://drive.google.com/file/d/1ZM6Wf-uyFjO5TXFGF8vIaVKi4XEhAeYf/view?usp=drive_link).

How do you do good Data Architecture? Wouldn’t it be great if there were principles you could follow? Well, there are! Google has 5 in their Principles for Cloud-Native Architecture. Amazon has 6 in their AWS Well-Architected Framework. But I’m here to tell you that there are 9.

This is a great chapter for those interested in System Design!

Check out #taro-bookclub in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack for more details.\
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Event link: <https://www.jointaro.com/event/taro-book-club-the-fundamentals-of-data-engineering-chapter-3-designing-good-data-architecture/>

Taro Book Club: Designing Data Intensive Applications - Chapter 1

Discussion led by: [**Ankur Agrawal**](https://www.linkedin.com/in/erankuragr/) & [**Raj Shah**](https://www.linkedin.com/in/therajshah/)\
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In this #taro-bookclub discussion, we'll go over Chapter 1 of the book [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann. This chapter is about Reliable, Scalable, and Maintainable Applications and will provide good mental models to think about reliability, scalability and maintainability of Distributed Data Systems.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 1.
- There will also be time for any questions and answers you might have.

Get ready for our first session on this engaging and informative book!

We hope to see you for Chapter 2 as well: [**Taro Book Club: Designing Data Intensive Applications - Chapter 2 (Data Models and Query Languages)**](https://www.jointaro.com/event/taro-book-club-designing-data-intensive-applications-chapter-2-data-models-and-query-languages/)

If you don't have a Taro Premium membership, you can get it at a discount with Ankur's special link here: [**https://www.jointaro.com/r/eventu507/**](https://www.jointaro.com/r/eventu507/)

Becoming A Staff Engineer In Big Tech (Panel Discussion)

The journey to Staff Engineer is unique for everybody. Let’s talk about all the ways we got there! Senior Engineer is a terminal level at most tech companies.

Staff Engineer is the next level up. This is referred to as Staff at Meta/Google, or Principal Engineer at Amazon/Microsoft. ([Rahul's promotion story](https://www.jointaro.com/lesson/qvp2quodqAEWSkN7sayt/case-study-building-a-meta-internal-tool-to-empower-an-entire-org-staff-promotion-story/).)

Partial list of questions discussed:

- How do you know which problems are worth solving?
- How do you communicate to your manager that you want Staff promo? And what do you look for in a manager?
- What type of teams do you think best support growth into the Staff level?

Panelists:

- [**Zach Wilson** - Airbnb](https://www.linkedin.com/in/eczachly/)
- [**Rahul Pandey - Meta**](https://www.linkedin.com/in/rpandey1234/)
- [**Carly Taylor - Activision**](https://www.linkedin.com/in/carly-taylor-data/)
- [**Lee McKeeman - Google**](https://www.linkedin.com/in/lmckeeman/)
- [**Ryan Peterman - Meta**](https://www.linkedin.com/in/ryanlpeterman/)

See discussions about Staff Engineers on Taro: <https://www.jointaro.com/topic/staff-engineer/>

Watch the full [Senior to Staff course on Taro](https://www.jointaro.com/lesson/NE3J4vWP7BoWlNgbRtCw/becoming-a-staff-engineer-in-big-tech-panel-discussion/).

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: AlphaGeometry: Advancing Automated Reasoning in Olympiad-Level Geometry

Paper Discussion led by [Lucia Mocz](https://www.linkedin.com/in/lucia-mocz-ph-d-35a98a1b6/)

Solving mathematical problems at the Olympiad level is incredibly difficult, requiring advanced problem-solving skills, a deep understanding of mathematical principles, and the ability to think critically and creatively under pressure. Those who excel in Olympiad-level problem solving usually have exceptional talent, undergo extensive preparation, and possess a unique problem-solving mindset. Achieving human-level automated reasoning in mathematical theorem proving, especially when competing with top performers, is a significant milestone in artificial intelligence research.

In this paper reading session, we introduce AlphaGeometry, a groundbreaking theorem prover tailored for Euclidean plane geometry. Unlike traditional methods that rely on human demonstrations, AlphaGeometry autonomously synthesizes millions of theorems and proofs across various complexities without human intervention. By combining a neural language model trained on vast synthetic data with a symbolic deduction engine, AlphaGeometry employs a neuro-symbolic approach to navigate intricate problem spaces. Notably, AlphaGeometry generates human-readable proofs and has successfully tackled challenging problems from previous International Mathematical Olympiads, even discovering new generalizations of established theorems.

Whether you're an AI researcher, engineer, or simply curious about this achievement in AI research, join us as we explore the intricacies of AlphaGeometry and its potential implications for automated reasoning in mathematics.\
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Paper: [**https://www.nature.com/articles/s41586-023-06747-5**](https://www.nature.com/articles/s41586-023-06747-5)\
Event Link: <https://www.jointaro.com/event/paper-reading-alphageometry-advancing-automated-reasoning-in-olympiad-level-geometry/>

Blog: [**https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/**](https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/)

GitHub: [**https://github.com/google-deepmind/alphageometry**](https://github.com/google-deepmind/alphageometry)

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/>

Building A Career Without Applying To Jobs (From An Eng Director)

[**Daniel O'Shea**](https://www.linkedin.com/in/danieloshea22/) is a Director of Engineering at X (formerly Twitter) and a 3x exited founder. He founded Laskie in 2020, a platform to help people understand the job market, find full-time work, and take on moonlight gigs. Daniel built a robust job-matching platform with tens of thousands of candidates. Laskie was the first acquisition by Elon Musk at X (Twitter) in 2023.

Throughout his professional career, **Daniel has never applied to a job**. Instead, he leveraged his network to find opportunities where he had an unfair advantage, and then continued to seek opportunities from his jobs and professional network. Almost all engineers suck at this: sending connection requests on LinkedIn is not enough.

In this talk, Daniel will share tactical advice to better leverage your own network, along with lessons from his professional journey. He'll also share data from seeing 1000s of engineers looking for jobs as part of running Laskie.

- Daniel on LinkedIn: [**https://www.linkedin.com/in/danieloshea22/**](https://www.linkedin.com/in/danieloshea22/)
- Daniel on X: [**https://twitter.com/oshea**](https://twitter.com/oshea)

Additional expert networking resources:

- [**"How to network effectively in a tech event?"**](https://www.jointaro.com/question/wx6HewLcKbxNRfMBiAAI/how-to-network-effectively-in-a-tech-event/)
- [**"How do you network effectively within a company?"**](https://www.jointaro.com/question/DwT9GtdYK9nwFV3a7jrU/how-do-you-network-effectively-within-a-company/)
- [**\[Masterclass\] How To Build Deep Relationships Quickly In Tech**](https://www.jointaro.com/lesson/tyaPFMXPoz2pmzB7OVzt/masterclass-how-to-build-deep-relationships-quickly-in-tech/)

Level Up Your Code Quality As A Software Engineer [Part 27] - Continue The Learning Journey

Watching one course is not going to automatically make you master of code quality (or anything really). To really learn these concepts and burn them into your mind, you need to continue the learning journey. What's next:

- If you have mastered code quality, then here's a great skill for you to move on to: [\[Masterclass\] How To Become A Debugging Master And Fix Issues Faster](https://www.jointaro.com/lesson/ioR6WzamAIUTpySLAr7o/masterclass-how-to-become-a-debugging-master-and-fix-issues-faster/)
- Apply your coding skills to open source! [Become An Open Source Master](https://www.jointaro.com/course/become-an-open-source-master/)

Level Up Your Code Quality As A Software Engineer [Part 26] - Side Projects & Open-Source

You don't need to work at some fancy FAANG company to learn how to write truly good, high-quality code. Code quality is something that can be improved upon outside of your job and even if you don't have a job! This lesson will teach you:

- The power of **side projects** and **open-source contributions** when it comes to leveling up your technical skills
- How to build side projects that get a ton of users so you can maximize your learning (and your resume strength 💪)
- How to make meaningful open-source contributions that can even get the attention of Big Tech companies

To dig deeper into this topic, go through this thread: ["How to Learn/Practice Clean Code, particularly by oneself?"](https://www.jointaro.com/question/6DcTkiS9UJhDkQz1HIAL/how-to-learnpractice-clean-code-particularly-by-oneself/)

Level Up Your Code Quality As A Software Engineer [Part 25] - Seas Of Newlines

Newlines are necessary everywhere (including this video description), but they're very much something that can get annoying fast in large quantities. This includes code. This lesson will teach you:

- The "Seas Of Newlines" messy code anti-pattern
- How to fix code that's like this and write better code instead

Level Up Your Code Quality As A Software Engineer [Part 24] - Infinite Layers Of Nesting

Nesting is great for birds, but terrible for code. Having too much nesting is one of the easiest ways to make code terrible. This lesson will teach you:

- Why deeply nested code is literally hard to read
- The early-return pattern to unfurl nested code
- How to compress conditionals properly (this includes how not to mess them up)

Level Up Your Code Quality As A Software Engineer [Part 23] - Everything In 1 Place

Monoliths and god-classes are 2 of the most common code anti-patterns, especially in Big Tech. They're extremely easy to fall into, and monolithic infrastructure is actually the right call for the vast majority of software as 99% of software is nowhere near the scale of FAANG. This lesson will teach you:

- Why the "Everything In 1 Place" anti-pattern is so damaging, not just for readability but developer productivity as well
- One of the most powerful clean code techniques: **The delegate pattern** (this is how Meta referred to it). It can go by other names, but the core concept is to understand how an interface works. Once you master this pattern, you'll be able to break up any large codebase into readable, focused, modular components. Delegates were easily the most commonly used clean code pattern back during Alex's time at Instagram. Alex goes through an in-depth example from the Taro app itself to walk you through exactly how this technique works.

If you want to learn more about Instagram's stellar engineering culture around code quality, check out this in-depth discussion: ["What do mobile testing strategies look like at top tech companies?"](https://www.jointaro.com/question/fpySzIO02aQ0sCz5N6dP/what-do-mobile-testing-strategies-look-like-at-top-tech-companies/)

Level Up Your Code Quality As A Software Engineer [Part 22] - Too Many Comments

Comments is a coding technique everyone learns particularly early, which makes sense as newbies need the extra hand-holding. However, there's a weird phenomenon where code comments go from a good thing to a bad thing very rapidly in your career. This lesson will teach you:

- Why comments are so prolific across code
- How comments end up hurting rather than helping, especially at scale and with large codebases
- What you should do instead of adding a bunch of code comments
- The fundamental mentality around writing good, clean, readable code, which Alex breaks down with a concrete refactoring example

Level Up Your Code Quality As A Software Engineer [Part 21] - Non-Descriptive Names

Naming: It's important for your children but quite important for your code as well. When it comes to your variables, methods, and classes, you should put in the effort to name them properly. This lesson will teach you:

- Why descriptive, high-effort names can add up to add a huge amount of quality to a codebase
- How to identify a poor name in code and the common patterns leading to it
- How much time you should spend on each type of name and how to prioritize accordingly, particularly in code review feedback
- The mentality to convert bad names to good names in code with an in-depth example walking through a classic utility method

Level Up Your Code Quality As A Software Engineer [Part 20] - Code Comment Caches

Code comments can be immensely helpful, but they can be incredibly damaging to a codebase if used incorrectly (which this course will cover even more later on down the line). This lesson will teach you:

- Why engineers will try the very weird "code comment cache" anti-pattern
- The importance of Git, GitHub, and version control platforms in general
- How to not be lazy and properly deal with code that isn't necessary now but may be useful later

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.