See the algorithm. Solve the problem. Land the interview.
A next-generation platform for mastering algorithms through step-by-step trace visualizations, rigorous practice, and AI-powered mentorship.
AlgoVisuals is a learning platform built around a simple belief:
Algorithms click when you can see them run.
We pair the rigor of a competitive-coding judge with the clarity of a great teacher — every problem ships with animated, step-by-step traces of every solution approach, so learners don't just submit code, they understand it.
| Pillar | What it does |
|---|---|
| Problem Library | A curated catalogue of interview-grade problems — Easy, Medium, Hard — with crisp statements, edge cases, and starter code in multiple languages. |
| Trial Run & Submission | An in-editor Run button for fast iteration plus a graded Submit path that updates streaks and acceptance rate. |
| Trace Visualizer | Each accepted approach has a hand-built or AI-generated Trace — a step-by-step replay of the algorithm executing on real input. |
| Daily Challenge | One Easy, one Medium, one Hard — refreshed at UTC midnight, shared globally, never repeating within 7 days. |
| Streaks | Consecutive-day momentum, with Restoration Tickets so a single missed day doesn't undo a month of work. |
| Editorials | Beginner-friendly, worked-example walkthroughs for every optimal solution — not just a wall of code. |
LeetCode-style platforms test you. Textbooks explain to you. Almost nothing shows you what a heap rebalances into, or how a sliding window actually slides. That gap is where most learners stall. AlgoVisuals closes it.
A polyglot workspace of focused services, each in its own repo:
algovisuals/
├── service/ FastAPI backend — problems, submissions, streaks
├── judge-service/ Sandboxed code execution and grading
├── ui/ Next.js frontend
├── algovisuals-problems/ Problem definitions, test cases, trace generators
├── algovisuals-deploy/ AWS CDK infra (api gw + lambda + RDS + judge VM)
└── docs/ Workspace-level documentation
Deployed on AWS (ap-south-1) with API Gateway, Lambda, RDS Postgres, a dedicated judge VM, and GitHub Actions CI/CD via OIDC.
We're heads-down on three tracks for the next quarter:
- Visualizer overhaul — smoother animations, better edge-case rendering, side-by-side compare across approaches.
- Editorial redesign — every optimal solution gets the teach-a-beginner treatment.
- Problem audit — quality pass across the full catalogue.
- Public profiles, shareable trace links, and a lightweight discussion layer.
- Mobile-first reading experience for editorials and traces.
These are the moves we believe will define the next chapter of AlgoVisuals.
A real-time, conversational tutor you can talk to while you solve.
- Think aloud, get nudged. Walk through your approach verbally — the tutor listens, asks clarifying questions, and points out gaps without spoiling the answer.
- Socratic by default. Hints are progressive: a gentle nudge first, then a stronger one, then a worked partial step — never the full solution unless you ask.
- Code-aware. The tutor sees your editor state, your test results, and the problem's trace data, so its guidance is grounded in your code, not generic advice.
- Interview simulation. A "mock interview" mode where the tutor plays the role of a senior engineer — timed, probing, with a written debrief at the end.
The goal: every learner gets a patient, infinitely-available mentor — the kind that, until now, only the luckiest among us had.
Personalized, time-boxed roadmaps that turn "I have an interview in 6 weeks" into a concrete daily plan.
- Target the role. Pick your target company tier and role (frontend, backend, systems, ML) — the plan adapts to the patterns those interviews actually test.
- Calibrated difficulty. A short diagnostic gauges your current level; the plan starts where you are, not at problem #1.
- Pattern-first sequencing. Topics are ordered the way they build on each other — two pointers → sliding window → monotonic deque — not alphabetically.
- Daily rhythm. Each day mixes a new concept, a guided problem, a fresh problem, and spaced-repetition review of past stumbles.
- Progress that means something. Mastery is measured by problems solved without hints on first attempt, not raw submission count.
The goal: replace the anxiety of "what do I study tonight?" with a plan you can trust.
AlgoVisuals is built in the open across this workspace. If you're a learner, an educator, or just curious — explore the repos, file issues, and tell us what's missing.
- Product: algovizuals.com (replace with live URL)
- Issues & feedback: open a GitHub issue on any of the service repos
- Contact: vashiraj2000@gmail.com
Built with rigor. Designed for clarity. Made for the next generation of engineers.
AlgoVisuals