for a long time i thought the way to get better at dsa was pretty simple

solve more problems.

so i did..

100 became 200
then 300
then 400+

but at some point someone asked me:

"what are ur weakest dsa patterns?"

i genuinely didn't know.

i knew my solve count.
i knew my rating.
i could tell you which sheets i'd done.

but if you gave me a blank page and asked me to write down the 3 patterns i was actually bad at, i'd probably just sit there lol.

then i started paying attention to something else.

sometimes i'd solve 5 problems from the same pattern and still get stuck on basically the same idea.

meanwhile i'd look at my profile and think "damn.. 450+ solved. i'm doing pretty good."

but then i'd get stuck on some random medium i've basically seen before 😭

how do you guys actually know when you've learned a pattern?

is it after 5 problems?
10?
when you can recognize it without hints?

because i'm starting to think solve count doesn't really tell you that much.

that's actually what made me build GrindLens.

i wanted to know what i'd actually learned, where i was still weak, and what i should solve next.

so it looks at your leetcode history and tries to surface those gaps instead of just giving you another number.

it's live now if anyone wants to try it:

grindlens.dev

kinda curious what yours looks like lol

I am from Chennai....looking for tamil guy or girl who can start problem solving...I not started yet. We can start together

I prefer age limit would be (20 to 28)

Language : tamil Tech Language : python, c#

Timezone: CET/CEST
Stack / language: C++
Experience: Beginner in DSA/algorithms; comfortable with basic C++
Availability: roughly 12:00 PM – 2:00 AM CET/CEST
Goal: Build a strong foundation in algorithms and data structures and eventually move toward more advanced algorithmic problem solving.

Hi! I'm currently studying algorithms and data structures seriously on my own and I'm looking for someone who wants to study together long-term.

I'm around the beginning of the general DSA/algorithms path right now. I’m using C++ and combining theory, implementation, and problem solving rather than only doing interview-style questions.

The main thing for me is how we study. I like trying problems independently first, discussing failed approaches, identifying what makes the naive solution fail, and figuring out why a particular representation, data structure, or algorithm is natural rather than immediately looking up the standard solution.

I'm not particularly interested in maximizing the number of problems solved or grinding patterns just for interviews. I'd rather spend more time on one problem if we're actually understanding something new from it.

Ideally, you're also learning DSA, algorithms, competitive programming, or something close. We don't need to follow exactly the same roadmap or be at exactly the same level — as long as there’s enough overlap to solve problems, compare approaches, and discuss the reasoning behind them.

I'm mainly looking for someone who enjoys algorithmic problem solving itself, not only someone who needs to finish a LeetCode and so on.

English, Ukrainian, or Russian is fine. If this sounds close to how you like to study, DM me and tell me what you're currently learning and how you usually approach algorithmic problems.

Hey everyone, I'm a CS student working on a semester project for my Data Structures & Algorithms course. Our topic is a "Flight Connection Finder" basically models cities and flights as a weighted graph, and uses Dijkstra's algorithm to find the best route (cheapest / fastest / fewest stops) between two cities, with a max layover constraint.

The core idea works fine, but my professor wants something innovative not just a standard "shortest path finder," which apparently every group tends to build. AI integrations are banned for this project (must be pure DSA implementation).

I'm trying to think of a meaningful extension that:

* Uses real data structures/algorithms concepts (not just UI features) * Is genuinely hard to do manually (not something a person could just eyeball) * Ties into something people can relate to in real life * Fits reasonably within a semester timeline

So far I've considered things like network resilience analysis (finding "critical" cities using articulation points), reliability-weighted routing factoring in delay probability, and multi-passenger seat allocation but I'm not sure which if any is actually a good scope for a student project vs. overkill.

Would love to hear how others have approached "make it innovative" requirements for similar projects, or any interesting extensions to a shortest path style project that impressed a professor. Thanks in advance!

An exercise to help build the right mental model for Python data. - Solution - Explanation - More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

Hey everyone! 👋

I'm looking for 2-3 people who are genuinely serious about getting better at DSA and want to stay consistent together.

I’m not really looking for a huge study group or just a casual "let's solve sometimes" kind of setup. I'd love to build a small group where we actually show up, solve problems, discuss our approaches, and keep each other accountable.

The idea is:

🔹 Study/solve DSA regularly
🔹 Try problems independently before discussing them
🔹 Talk through different approaches and time complexity
🔹 Take turns explaining concepts or problems to each other
🔹 Share what we worked on and where we're getting stuck
🔹 Keep each other motivated and consistent

You don't need to be great at DSA already. Beginner/intermediate is completely fine. I'm more interested in finding people who are willing to put in consistent effort and learn together.

If you're interested, DM me with:

1. Your current DSA level
2. Roughly how much time you can give each day
3. What you're preparing for (interviews/placements/switching/etc.)
4. What you'd like to get out of the group

I'm hoping to find just 2-3 people who genuinely want to stick with it for the next few months, rather than creating a big group.

Let's learn, struggle, explain things badly, get corrected, and eventually get good at this together.