I personally always liked computers precisely because they are not like humans. Just saying.

We cooked.

interesting read. Alan Turing's "Delilah" project | spectrum.ieee.org/alan-turings-d…

a university undergrad made hash tables faster and broke 40-year-old belief 🤯 for 40 years, it was believed that we cannot have better than O(n) operation for hash table worst case inserts and searches if it uses open addressing - for the hash index, look linearly until you

"Mediocre youtube engineer" casually drops video on "killer" defence tech maybe without knowing it. Doing this with SDR and you can "see" things emitting a radio signal (all electronics) on a battlefield. Freeing information and not compartmentalized in corps/countries changes

just learned that pressing the period key `.` on any github repo opens it in gh's web editor. useful mistake lol

core ml is boring. the mathematical maturity you need to understand the beauty of such gaussians, svms, bayesian inferences etc is just overwhelmingly hard. that’s why you would see novices jumping to llms, since it’s just an api away. if you want to be a machine learning

Everyone recommends reading research papers, but few talk about how to do it effectively. Like any skill, it gets easier with time, but starting out can be daunting. It helps to know that the struggles you face are common, and that there are ways to overcome them. I wrote about

What does the CPU internally do? Check out the animation... The dance goes like - 1. Fetch 2. Decode 3. Execute 4. Memory Access 5. Write Back See it in action here: eseo-tech.github.io/emulsiV/

good time to get into cybersecurity because all these vibe coded apps are gonna make it into production soon enough

100% agree with that. Whether or not you pursue CS, I think working in the software industry is going to require deeper knowledge and systems level reasoning skills. Just knowing a framework to build a webapp may not be sufficient.

free career tip: be so competent they forget you’re unmanageable

A major mistake I made in my undergrad is that I focused way too much on mathematical lens of computing - computability, decidability, asymptotic complexity etc. And too little on physical lens - energy/heat of state change, data locality, parallelism, computer architecture. The

The article made me realize it’s okay—healthy, even—to let some things just… be. Not every broken thing is screaming for my attention, and stepping back can be just as valuable as nailing a tricky debug. notashelf.dev/posts/curse-of…

Quant at Jane Street, 3 crore per annum. Sounds too good? You’re not going to be there if you don’t understand Math. Here’s a basic question from Number Theory: > Prove that if n is odd, then n² − 1 is a multiple of 8. If you’re a true Math nerd, you’ll solve this instantly.

this is why I am constantly trying to approach teaching software engineering from multiple angles. the one that I already know works is giving people enough knowledge about how production systems work, instead of focusing too much on basic crud apps and leaving them to figure

Many people think CS is just DSA. It's not. My CS curriculum had: 0. Data Structures and Algorithms 1. Calculus 2. Linear Algebra 3. Physics 4. Mathematical Physics 5. Differential Equations 6. Theory of Functions of a Complex Variable 7. Theory of Probability and Mathematical

if you are a curious engineer, it is so difficult not to have fomo. every other domain seems interesting every other book seems worth reading every other problem seems worth solving every other project looks like the next big thing every other framework feels like something you

I'm currently starting the 'backend from first principles' playlist. I'm hoping it'd give me a pretty good grasp of the overall picture. apart from this, what else do you recommend? i wish not to be overwhelmed by resources, rather focus on implementing learnt concepts as well.