Why do big-tech interviews focus so much on assessing problem-solving abilities? It turns out, problem-solving skills correlate well with pattern matching. Most of software engineering involves recognizing and applying patterns. For example: - Read about decentralization? Use

Finding a skilled engineering mentor or manager in a tech company is rare. Many managers can be supportive, but not all are skilled engineers themselves. In tech, where many just coast along, getting guidance from someone who is truly exceptional in engineering is crucial for

Unlike software engineering or software development, the entry barrier for a machine learning engineer is still very high and it hasn’t changed much over the last 5 years Being an ML engineer at FAANG still requires one to have a master's or PhD. So how can you enter this

"I don’t want to pursue this field because there’s no big market for it." Whatever career path you choose, you're going to be in it for the next few decades. Software engineering, or any career for that matter, is a long journey. I’ve seen people choose a career just because it

I loved doing proofs in my information theory classes. Sometimes, my proofs were a bit unconventional. They felt intuitive, but they had small gaps or errors here and there. Still, I really enjoyed coming up with ideas from first principles. It made me love math even more.

Career in tech is a multi-variable problem. You can be from a very mediocre college but still be an exceptional coder, proving yourself through competitive programming. Or, you might be from a mediocre college and be an intermediate coder, but your persistence in learning,

When it comes to compute available for doing cutting edge research IITs lag way behind western institutions and way way behind private research institutions. They need to write big proposals for small funds and then wait for months for it to get approved. The only way out of

We need more engineering docs from OnlyFans

> 5AM > Green Tea > VS code > Bugs > Infinite stackoverflow tabs The ultimate combination

Tech moves fast—what you know today could be outdated tomorrow. Flutter today is way different than it was two years ago. TensorFlow is completely different than four years ago. Same with React—before 2019, there were no hooks, and people had to rely on class-based components.

Building quality software required 1000s of iterations. Even best software engineers don’t end up writing the final code in one shot, they take many iterations. So don’t feel bad if your code didn’t run on the first trial or if you had to make many changes to get to the final

This was so so informative to read

This approach feels really intuitive and human like It’s like how I would respond to varying natural language queries Every query has to be processed differently. Some need reasonings(like this one), some are just knowledge based questions and some are in between. How we

13 seconds of thinking just for this o1 can't solve the legendary question (might as well consider it as a turning test by now)

So freaking goood!

Build. Build. Build.

Whenever OpenAI makes a new release which i expected from Google I’m reminded of this

Don’t over optimize your code prematurely. You don’t need it. Just focus on getting the MVP done as fast as you can and then iterate When working within a large codebase, readability significantly matters and sometimes supersedes the need for efficiency. For example,