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#354 — Top 71.1%

puranjayh

Puranjay

D

README enthusiast

Overall

0.0

/ 100

01 · Roasts

Sprint Athlete, Not a Marathon Runner

Every single repo in your portfolio was built in under 10 days. hmm-world-cup is your magnum opus at 24 days. The heatmap has 40 completely empty weeks before you suddenly woke up in April 2026.

The License Abolitionist

11 repos, zero licenses. Not one. You've built an entire portfolio of code that legally nobody can use, fork, or contribute to. Even your stub repos couldn't spare an MIT license.

ibcognito: 10,000 Users, 3KB of Repo

You claim 10K monthly users and 1M+ page visits in a 3KB README with 2 commits and 0 stars. The audacity-to-evidence ratio here is astronomical.

makemore README Says 'micrograd'

Your makemore repo's README is titled 'micrograd.' You also have a separate micrograd repo. Copy-paste discipline: not found.

0 Stars Across 13 Repos

The entire portfolio has accumulated exactly zero community stars. Even jarv — a repo with one commit and a README containing only the word 'jarv' — sits at zero. The floor has no basement.

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

  • Impact
    25% weight
    48D
  • Consistency
    20% weight
    65C
  • Quality
    20% weight
    57D
  • Depth
    15% weight
    58D
  • Breadth
    10% weight
    55D
  • Community
    10% weight
    25F

03 · Stats

365-day commit heatmap

39 active days

Less
More

Language distribution

6 langs
  • Python57%
  • HTML17%
  • R15%
  • CSS7%
  • JavaScript3%
  • Other1%

04 · Numbers

Owned repos

non-fork

12

Commits

last 12 months

248

Followers

1

Joined GitHub

Apr 2024

05 · Top repos

puranjayh /

ctc-mechanism

48/100

Single-cell RNA-seq re-analysis of breast cancer circulating tumor cells integrating two public datasets. Demonstrates de-confounding methodology and validates hypoxia survival program with GSEA. Well-documented research project with structured pipeline and honest correction of false positives.

I25Q60D50
README
R025d ago

puranjayh /

hmm-world-cup

45/100

Research-grade Hidden Markov Model for World Cup 2026 football predictions with comprehensive feature engineering, multi-model benchmarking (RF, XGBoost), and typed Python codebase (~5MB). No external adoption signals yet (0 stars).

I25Q60D50
READMETests
Python01mo ago

puranjayh /

black-scholes-pricer

33/100

Educational Black-Scholes pricer built from scratch with analytical pricing, all 5 Greeks, and Monte Carlo. Clean math but new repo (2 days old, 6 commits), untyped Python, no tests/CI/license, minimal sustained output signals.

I25Q45D30
README
Python03mo ago

puranjayh /

hypoxia-genomics-ai

27/100

Educational ML pipeline for hypoxia gene expression classification with clear methodology (PCA, differential expression, Random Forest on 9,908 genes). Well-documented for academic context but minimal scope: single script, no tests, CI, or package structure. Built in one day (4 commits, 2026-04-18).

I15Q45D20
README
Python03mo ago

puranjayh /

rsa-decryption-time-complexity

23/100

Educational RSA implementation with empirical time-complexity analysis via exponential modeling; pure Python educational project with no dependencies, minimal commits, no tests or CI.

I15Q45D10
README
Python03mo ago

puranjayh /

makemore

20/100

Educational single-file Python script demonstrating character-level bigram language modeling with PyTorch. Minimal scope, no tests, CI, or license; README title mismatches repo purpose (mentions "micrograd" not "makemore").

I15Q25D20
README
Python01mo ago

puranjayh /

puranjayh

20/100

Personal portfolio/resume repo with README describing ML research interests (CTCs, HMMs, sports prediction). No source code, tests, CI, or structural artifacts—essentially a CV document in a GitHub profile.

I15Q20D25
README
Unknown01mo ago

puranjayh /

micrograd

20/100

Learning implementation of Andrej Karpathy's micrograd autograd library. Implements core backpropagation mechanics (Value class with operator overloading, topological sort, chain rule) and simple neural network abstractions (Neuron, Layer, MLP), but is minimal, undocumented, and has only 4 commits in a single day.

I15Q35D10
README
Python01mo ago

puranjayh /

ibcognito

10/100

Claimed learning platform with 10K users but 3KB repo size, 2 recent commits, no source code visible, no tests/CI/license, and closed-source full codebase. Claims without verifiable substance.

I15Q15D5
README
Unknown02mo ago

puranjayh /

jarv

7/100

Empty scaffold with only a stub README containing just the project name "jarv". No source files, tests, CI, license, or meaningful documentation. Created and pushed on same day with 0 stars.

I5Q10D5
README
Unknown02mo ago

puranjayh /

me

5/100

Empty personal scaffolding project with 0 stars, 147KB HTML content, no README, tests, CI, or documentation. Created and last pushed within 24 hours with no meaningful commits sampled.

I5Q10D5
HTML01mo ago

06 · Timeline

  1. Apr 17, 2024
    Joined GitHub
  2. Apr 18, 2026
    Created rsa-decryption-time-complexity — Empirical modelling of RSA decryption time vs key length using Naïve Trial Division and Pollard's Rho - extrapolated to 308-digit production keys
  3. Apr 18, 2026
    Created black-scholes-pricer — European options pricer built from scratch - analytical BS solution, all 5 Greeks, Monte Carlo via GBM. No financial libraries used.
  4. Apr 18, 2026
    Created hypoxia-genomics-ai — ML pipeline for classifying hypoxic vs normoxic cancer cell states from 60,000+ gene expression profiles - PCA, differential expression, volcano plots, Random Forest classifier. Bu
  5. May 1, 2026
    Created ctc-mechanism — R-script powered research on CTC evasion mechanism sc-RNA sq data
  6. May 10, 2026
    Created ibcognito — Global IB learning platform - 10K+ monthly users, 108 countries, 1M+ page visits.
  7. May 11, 2026
    Created puranjayh
  8. May 13, 2026
    Created jarv
  9. May 23, 2026
    Created hmm-world-cup — Hidden Markov Chains predictions on World Cup 2026 with benchmarking against RF and XGBoost
  10. May 24, 2026
    Created me
  11. May 28, 2026
    Created micrograd — Andrej Karpathy's Micrograd
  12. May 28, 2026
    Created makemore — Andrej Karpathy's makemore
  13. Jun 26, 2026
    Most recent push to ctc-mechanism

07 · Compare

github.com/
puranjayh · 6dmedian coder

08 · Rubric

How this score was produced

Overall = Σ (category × weight) + gentle top-end curve

CategoryWeightScoreContrib.
Raw total53.1
Top-end curve+3.3
Final overall56.4

Tier thresholds

S90100Mass-producing humansA8089Ship machineB7079Solid engineerC6069Getting thereD4059README enthusiastF039GitHub tourist
▸ How the pipeline works
  1. 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.
  2. 02Triage.A small model reads every repo's file tree + README and picks the 20 files per repo that actually reveal how you code.
  3. 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.
  4. 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.
  5. 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.
puranjayh · 56.4/100 — Rate My GitHub