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

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?

This Facebook Engineering Director Quit To Sail The World

After more than 10 years working at Meta (Facebook), James quit to work on his boat full-time. While sailing, he also contributes to open-source! <https://github.com/tinyplex/tinybase>

Master The DSA Interview [Part 16] - Alternate Approaches

The beautiful (and sometimes frustrating) thing about software engineering is that almost every technical problem has a ton of possible solutions. This lesson teaches you:

- Why being able to come up with alternate approaches is so important as a software engineer at the top level
- How to have a proper discussion with your interviewer about alternate approaches
- What a great alternate approach discussion looks like, discussed with an in-depth example

Dive deeper into real system design here: [\[Course\] System Design Masterclass: Shipping Real Features To Production](https://www.jointaro.com/course/frontend-system-design-masterclass-building-playlists/)

Master The DSA Interview [Part 15] - Edge Cases

Too many engineers only care about the happy flow, but it is the easy thing to do. This lesson teaches you:

- Why handling edge cases is so important as an engineer, especially at FAANG
- The mindset you need to have to tease out edge cases systematically
- Common edge case scenarios for data structures and algorithms problems
- What a great edge case analysis looks like, explained with an in-depth example

Take the code quality course here: [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

Master The DSA Interview [Part 14] - Clean Code

Just because you're solving a made-up problem doesn't mean that you can write messy code during a data structures and algorithms interview. This lesson teaches you:

- What most engineers don't realize when it comes to code quality, in particular with why it's easier to achieve than they think
- The types of engineers who tend to struggle with code quality
- Common messy code patterns to avoid when coding up a data structures and algorithms solution (and in general)

Take the code quality course here: [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

Master The DSA Interview [Part 13] - You Must Be Fast

Quality is important, but quantity is too, which is mainly demonstrated with velocity. This lesson teaches you:

- Why raw speed is so important with data structures and algorithms interviews in particular
- The quantity bar you need to hit to pass a data structures and algorithms interview the vast majority of the time
- Why interviewers often ask 2 data structures and algorithms problems in a single round
- How an interviewer asking you a harder data structures and algorithms could actually mean that they're trying to save you
- Tips for getting faster at data structures and algorithms

Master The DSA Interview [Part 12] - Stuck? Here's What To Do

A defining trait of a strong software engineer is their ability to conquer hardship, and one of these hardships is getting stuck on a data structures and algorithms interview problem. This lesson teaches you:

- Why you need to communicate when you're stuck
- A proactive foundation you should set up to minimize your chances of getting stuck
- What actions you can take when you're stuck outside of coding up the optimal solution (which you obviously don't have as otherwise why would you be stuck?)

Master The DSA Interview [Part 11] - Space Analysis

**CORRECTION:** Recursive solutions can also have O(n) space for non-binary search style problems where you don't eliminate half of the problem space with each iteration. The classic example here is using recursion to calculate fibonacci numbers.

Run-time analysis is important, but it isn't everything. When it comes to FAANG, run-time on its own can actually be sorely lacking. This lesson teaches you:

- Why space complexity is also important when it comes to data structures and algorithms
- What a great space complexity analysis looks like, explained with an in-depth example
- A common mistake engineers make with space complexity concerning a very common data structures and algorithms pattern

Master The DSA Interview [Part 10] - Run-Time Analysis

After clarifying the problem and coming up with a detailed, granular plan, coding up the solution should have been trivial. Now it's time for big O. This lesson teaches you:

- How to actually learn run-time analysis instead of memorizing the big O of every individual problem
- The structure of a strong run-time analysis for a data structures and algorithms problem
- Exactly what a quality run-time analysis looks like, explained with an in-depth example

Master The DSA Interview [Part 9] - Outline Your Approach

Once you have clarified exactly what you need to deliver with a data structures and algorithms interview problem, you need to figure out how to deliver it. This lesson teaches you:

- How to come up with a data structures and algorithms solution plan
- The many benefits of coming up with a plan proactively
- What a good data structures and algorithms plan looks like, explained with an in-depth example

Master The DSA Interview [Part 8] - Clarify The Problem

Don't just start coding when you get a data structures and algorithms problem in an interview. This lesson teaches you:

- The first thing you should do when getting a data structures and algorithm problem, which is critical for setting you up for success
- Why dealing with ambiguity is so important as an engineer
- The overall question categories you should pull from when clarifying a problem
- How exactly to clarify a data structures and algorithms problem, shown with an in-depth example

Master The DSA Interview [Part 7] - Regularly Be Talking

There is far more to a data structures and algorithms question than simply being able to code up a solution. This lesson teaches you:

- Why consistent communication is critical for data structures and algorithms interviews, especially at top companies
- How much silence is too much in an data structures and algorithms interview
- How bad vs. good engineers solve technical problems in general

Master The DSA Interview [Part 6] - Why Does FAANG Do This?

If data structures and algorithms aren't actually used on the job, why do Big Tech companies use these problems as a cornerstone of their interview process? This lesson teaches you:

- Why Big Tech isn't just being evil and stupid when they use data structures and algorithms problems on their interviews
- How data structures and algorithms problems can have a surprising amount of depth
- What most engineers don't understand when it comes to how FAANG rejects candidates
- How Google and Meta in particular differ from almost all other companies when it comes to hiring, which affects how their interview processes work

If you're interested in working at early-stage startups that don't ask LeetCode problems: ["How to find and connect with scrappy startups?"](https://www.jointaro.com/question/9jFuRj3UymeHsaKIzXia/how-to-find-and-connect-with-scrappy-startups/)

Master The DSA Interview [Part 5] - It's A Parasite

While data structures and algorithms are pivotal when it comes to cracking interviews, you need to be careful about how you interact with them. If you don't, they can easily tank your job search. This lesson teaches you:

- Why data structures and algorithms are parasitic
- How data structures and algorithms are actually the most shallow interview type, especially for non-junior engineers
- Other activities you can do on the job search to achieve more well-rounded results
- The decision tree you should use to figure out if you should go hard on data structures and algorithms studying

To learn how to build up your network: [\[Masterclass\] How To Build Deep Relationships Quickly In Tech](https://www.jointaro.com/lesson/tyaPFMXPoz2pmzB7OVzt/masterclass-how-to-build-deep-relationships-quickly-in-tech/)

To learn how to build effective side projects: [\[Taro Top 10\] Building Impressive Side Projects](https://www.jointaro.com/playlist/vKUWMgmmaEvzcYUXZs4B/)

Master The DSA Interview [Part 4] - DSA Prep Is Insanely High ROI

It's easy to look at data structures and algorithms and see them as an annoying waste of time. However, there's more to them than you think. This lesson teaches you:

- A healthy perspective to have when it comes to studying data structures and algorithms
- Why data structures and algorithms are uniquely positioned when it comes to career return on investment for your time
- How to transform your mindset to actually be excited when grinding LeetCode

Master The DSA Interview [Part 3] - Struggling? That's Completely Normal

You are smarter than you think. Yes, this is even true if you're struggling with LeetCode easy problems. This lesson teaches you:

- Why engineers often underestimate their ability when it comes to data structures and algorithms
- How the learning progression curve for data structures and algorithms works for 90%+ of engineers
- How to set up your mentality and goals when it comes to studying data structures and algorithms

Master The DSA Interview [Part 2] - The Key To Truly Learning DSA

One of the biggest mistakes software engineers make is memorizing content instead of actually understanding the concepts. This lesson teaches you:

- How to learn data structures and algorithms properly instead of merely doing rote memorizations
- What it looks like to truly master a data structures and algorithms problem, shown with an in-depth example
- Common patterns to remember with data structures and algorithms

Master The DSA Interview [Part 1] - How This Course Works

Hate data structures and algorithms? We do too, which is why we're here to help! 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 with rapid career advancement, ending up as a high-performing tech lead making $750k at Robinhood. More importantly, he goes through how he gave \~100 data structures and algorithms interviews at Meta (codenamed the "Ninja" round) and rejected 80% of candidates

- You'll understand the high-level reasons why engineers struggle with data structures and algorithms problems, particularly with top-level interviews

- We walk you through the course objectives. By the end of the course, you will:

  - 🔥 Be **motivated** to grind DSA

  - 💡 Understand how to **truly learn** DSA concepts

  - 🗣️ Know how to navigate DSA in a **live** interview

  - 🏔️ See how DSA is proctored at **top companies** (FAANG)

  - 🧠 Set up a proper DSA **studying regimen**

If you haven't already, we recommend taking the overall job searching course here: [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/)

[Paper Reading] Deploy ML Models from Lab to Production

Session led by: [Akshika Wijesundara PhD](https://www.linkedin.com/in/akshika/)

If you don't have a Taro Premium membership, you can get it with a discount with Akshika's special link here: [**https://www.jointaro.com/r/akshikw802/**](https://www.jointaro.com/r/akshikw802/)

**WHAT YOU'LL LEARN**

- **Discover the benefits of CI/CD pipelines:** Learn how CI/CD pipelines streamline the model building, testing, and deployment processes, enabling rapid iteration and continuous improvement.
- **Containerize your ML code:** Understand the steps and advantages of containerizing your machine learning code to ensure consistency, portability, and efficient resource management.
- **Leverage the AWS stack for ML workloads:** Explore how to effectively utilize AWS as your cloud infrastructure provider to run and scale your machine learning workloads with ease and reliability.

**WHY THIS TOPIC MATTERS**

MLOps isn't just about fancy models - it's about getting them working for real! Learn how to deploy, maintain, and collaborate on ML projects, making you a highly-demand expert who can turn machine learning into real-world impact.

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.