DSAKarle
Interactive algorithm visualization platform for learning DSA execution.
D
DSAKarle
Case study visual placeholder
Architecture flow
2 layersProblem
Traditional DSA learning resources are static and fail to convey how algorithms execute step-by-step on real data structures.
My Role
Built the Next.js architecture, content schema, visualization state model, and search/indexing flow.
Solution
A JSON-driven stepper engine synchronizes SVG visuals, code highlights, variable state, and searchable lesson content.
Stack
Case study
Problem
Traditional DSA learning resources are static and fail to convey how algorithms execute step-by-step on real data structures.
Proof signal
25+ topics · 300+ micro-steps · Build-time search index
My Role
Built the Next.js architecture, content schema, visualization state model, and search/indexing flow.
Core product work
- Algorithm steppers
- Variable tracing
- Code highlighting
- Search
Solution
A JSON-driven stepper engine synchronizes SVG visuals, code highlights, variable state, and searchable lesson content.
Next.js app with a JSON-driven stepper engine. Each algorithm is defined as discrete validated state objects that synchronize SVG visualizations, code highlighting, and variable tracing.
Architecture Highlights
Validated JSON state model for algorithm steps
Synchronized SVG visualization and code highlighting
Build-time search index for fast content lookup
Challenges and Tradeoffs
- Keeping visual state, code state, and explanatory content synchronized.
- Designing reusable state objects across different algorithm families.
Impact / Outcome
- Built a structured learning product for visualizing algorithms step by step.
- Used schema validation to make complex educational content easier to maintain.
- Authored 25+ DSA topics with 300+ micro-steps.
Learnings
- Interactive education products benefit from treating content as typed data.
- Schema-first content makes future algorithm additions less fragile.
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