A personal DSA mentor that builds your roadmap, fills your gaps in conversation, and adapts as you learn. No locked content. No “Day 247” guilt mechanics. No grinding for grinding’s sake.
Linear curriculums gate content behind progression — you can’t practice what you came to practice. Open problem sets do the opposite — you flounder on topics you don’t have the foundations for. Both treat you like you’re broken.
DSAlove does neither. The student never hears “no.” They hear “yes, and here’s the fastest path.”
Onboarding to meaningful learning in under three minutes. The loop runs every time you open the app — different inputs, different output.
Goal · experience · timeline · daily commitment. No typing. No self-assessment. No coding test.
The pattern graph is filtered by your goal, linearized into phases, and shown as a scrollable timeline with a clear finish line.
Follow the recommendation or jump ahead. If prerequisites aren’t met, the gate builds a bridge to where you wanted to go.
Socratic conversation. Progressive hints. And when you get stuck — the AI diagnoses whether it’s a missing foundation, and fills it inline.
Every signal feeds the map. Breeze through a pattern, it trims. Struggle, it stretches. Every change is visible — and you can veto any of it.
You hit step 05, the map updates, you tap the next pattern, and the loop starts again. Onboarding → meaningful learning in under three minutes.
Tap a pattern whose prerequisite isn’t met and the system fires a quick 3–5 question MCQ. Three outcomes — none of them “you can’t be here yet.”
Under two minutes. Card-based. Never feels like a test.
Prerequisite marked validated. The roadmap backfills your chosen topic with fewer, harder problems — no busywork on something you already own.
We diagnose exactly which sub-concepts are shaky and populate problems on those specifically. Then your chosen topic. Path-finding, not remediation.
“To get to Sliding Window, we need Two Pointers solid. Let’s start here.” Your destination doesn’t change. We just give you the shortest honest route.
The AI never spoils. It nudges harder only when nudges aren’t working.
The AI probes whether the struggle is the current problem or a missing foundation. If it’s the foundation, you get a micro-lesson inline — and then we return to where we were.
Every problem you attempt becomes a signal — hints used, time taken, whether you reached the optimal approach, whether a gap was detected and filled. The map reads those signals and adapts.
We took a thousand “nos” so the product could say one clear yes. Here are the loudest ones.
Retention mechanic, not a learning mechanic. Out of scope for Layer 1.
Curated 3–5 problems per pattern. Enough to validate the loop, not enough to drown in.
Tap any pattern from day one. The gate builds a bridge, never a wall.
“Rate your DSA skill 1–10” is unreliable. We calibrate from actual performance.
No compiler, no green-tick gaming. The AI critiques the reasoning behind your code.
Your only comparison is your own past self. Calibration data comes in later, anonymized.
Three layers. Each is a self-contained product increment. We ship and prove L1, then deepen, then expand the platform.
The whole loop: onboarding, roadmap, free navigation, MCQ gate, Socratic teaching, gap detection, performance-based recalibration.
Objective verification, longitudinal profile, long-term retention. The map becomes a portrait.
The teaching engine and roadmap architecture generalize. One platform, multiple interview surfaces.
We’re building the core loop now. Drop your email and we’ll tell you the moment the beta opens — nothing else, no newsletter, no fluff.