01 · Roasts
Portfolio, not pull
Eight named projects earned the prolific-shipper bump, but the whole portfolio has 3 stars: the launch button works harder than the discoverability plan.
Test-suite famine
accura brings Playwright tests, then most of the portfolio watches from the sidelines: seven scored repos report no tests.
One-weekend architecture
version-db and typeahead pack serious systems work into June 21–22 windows; impressive scope, but sustained maintenance has not clocked in yet.
CI is a choose-your-own-adventure
accura, doc-buddy, lurkr, and github-stats automate checks, while typeahead, version-db, and SQL Optimizer Environment leave the pipeline uninvited.
Built using
Zoral
Shadows one worker for a week, then takes over their job with zero extra setup. Behaves exactly like the original.
zoral.ai
02 · Category breakdown
- Impact25% weight62C
- Consistency20% weight80A
- Quality20% weight75B
- Depth15% weight58D
- Breadth10% weight80A
- Community10% weight80A
03 · Stats
365-day commit heatmap
234 active days
Language distribution
- Java38%
- TypeScript35%
- Rust10%
- JavaScript6%
- HTML5%
- Python5%
- Other1%
04 · Numbers
Owned repos
non-fork
59
Commits
last 12 months
1,376
Followers
11
Joined GitHub
Jul 2024
05 · Top repos
vimalyad /
doc-buddy
A documented, typed full-stack RAG application with hybrid Qdrant retrieval, CRAG, optional Cohere reranking, and a polished React UI; technically substantial but has 0 stars and no stated external adoption.
vimalyad /
typeahead
A documented Java 21/Spring Boot typeahead system with trie-based serving, consistent-hash Redis routing, Kafka batching, PostgreSQL persistence, React UI, Docker orchestration, and reported k6 performance results.
vimalyad /
lurkr
Lurkr is a documented, multi-tier React/Express market-intelligence app with AI-agent orchestration, live Tavily/Google News grounding, Neon persistence, auth, scheduled refresh, and Android packaging; it is an early one-day project with no tests, license, or typed code.
vimalyad /
accura
Accura is a thoughtfully structured TypeScript/Playwright browser-agent monorepo with strong validation, recovery, evaluation, and testing infrastructure, but it is a newly shipped 0-star project without demonstrated external adoption or a license.
vimalyad /
version-db
Ambitious Java 17 single-node database capstone with disk storage, ARIES WAL recovery, MVCC, SQL parsing, B+Tree indexing, cost-based planning, and Volcano execution; well documented and modular, but newly shipped with no visible adoption signals.
vimalyad /
sql_optimizer_environment
A documented, multi-module Python/OpenEnv environment with Dockerized PostgreSQL, SQLGlot rewrites, typed Pydantic contracts, and real EXPLAIN ANALYZE rewards, but no tests, CI, license, or demonstrated adoption.
vimalyad /
github-stats
A documented Go utility that generates SVG/JSON GitHub statistics through GraphQL and a daily Actions workflow, but it has no visible adoption, tests, or license and shows a Go-version workflow mismatch.
vimalyad /
vimalyad
A personal GitHub profile repository centered on a styled README and one scheduled contribution-snake workflow; it has no stars, tests, license, or substantive application source.
06 · Timeline
- Jul 7, 2024Joined GitHub
- Jun 2, 2025Created vimalyad — Full-Stack Developer • System Design Learner • Backend in Java ☕
- Dec 3, 2025Created github-stats — 📊 Automatically generate beautiful GitHub statistics SVG badges with language breakdowns, contribution counts, and more. Updates daily via GitHub Actions.
- Apr 6, 2026Created sql_optimizer_environment — An OpenEnv reinforcement-learning environment that teaches agents to rewrite slow SQL into fast SQL
- May 6, 2026Created doc-buddy — DocBuddy is a full-stack, production-ready Retrieval-Augmented Generation (RAG) application. Designed as a personalized version of Google's NotebookLM, it allows users to upload th
- Jun 13, 2026Created lurkr — Lurkr is a multi-agent market intelligence tool. You describe your own startup or idea; Lurkr finds the real competitors in that space, gathers live data on them, and a team of AI
- Jun 21, 2026Created version-db — A single-node relational database engine built from scratch in Java 17. VersionDB implements the core of a real database — a disk-backed storage engine, write-ahead logging with AR
- Jun 21, 2026Created typeahead — Low-latency search autocomplete: prefix suggestions ranked by popularity and recency, with every submitted search recorded so rankings stay current.
- Jun 22, 2026Created accura — An accuracy-first browser agent. TypeScript, Playwright, model-agnostic — develop on free models, run on Claude.
- Sep 3, 2026Most recent push to vimalyad
07 · Compare
08 · Rubric
How this score was produced
Overall = Σ (category × weight) + gentle top-end curve
Tier thresholds
▸ How the pipeline works
- 01Scrape.Pull every non-fork repo pushed in the last 90 days, plus your contribution calendar, followers, and language byte counts — straight from GitHub's REST & GraphQL APIs.
- 02Triage.A small model reads every repo's file tree + README and picks the 20 files per repo that actually reveal how you code.
- 03Grade each repo. All repos run in parallel through a fast scoring model that reads the picked files and rates each one independently on Impact, Quality, and Depth — with evidence citations.
- 04Aggregate. A larger reasoning model combines the per-repo scores with server-computed stats (heatmap, commit cadence, language entropy, follower count) to produce the 6-dimension profile score + roasts.
- 05Correct.Deterministic server-side checks enforce anchor-scale floors (e.g. a profile with 2,000+ public commits can't score 30 Consistency) and recompute the final verdict.
~90 seconds per profile, ~$0.25 in compute. Total of ~240 files read across your top-12 repos. One rating per GitHub account per day.
▸ Data sources & caveats
- Heatmap & commit totals: GitHub GraphQL
contributionsCollection— covers the last 365 days, includes private repos when the user has opted in (default). - Language %: byte totals across the top 30 owned non-fork repos.
- Curve: a small upward nudge centered on raw score ≈ 70, capping at 100. Prevents specialists from being unfairly penalised for narrow breadth.
- Anchor corrections: when server-measured signals (e.g. privateWorkLikely, multiRepoVolume, follower count) mandate a minimum category score, the aggregation step enforces it. These are signal-conditional, not identity-based floors.