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#688 — Top 42.4%

parthak314

Partha K

D

README enthusiast

Overall

0.0

/ 100

01 · Roasts

Coursework Cosplaying as a Portfolio

Your most impressive repo — RISCV-Team5 — is Imperial College graded coursework that lived for exactly 3.5 weeks before going dark. That's not a portfolio, that's a deadline.

24 Commits in a Year? My Grandma's Blog Has More Traffic

totalCommitsYear = 24. With 52 weeks on the calendar, you averaged less than one commit every two weeks. The heatmap looks like a Morse code SOS signal — burst, silence, burst, silence.

The Profile README That Promises Everything

You have a repo named after yourself with 12 commits and 23 KB of text describing skills and 'planned projects.' The most committed thing in your portfolio is the word 'planned.'

TypeScript at 45% But Where's the App?

Nearly half your code bytes are TypeScript — yet no TypeScript repo appears in your top projects. Either that code is buried somewhere private, or it's all CSS and HTML inside .ts files.

1 PR All Year

totalPRsYear = 1, totalIssuesYear = 0. You opened exactly one pull request across all of GitHub in 12 months. Even Stack Overflow lurkers upvote things occasionally.

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
    40D
  • Consistency
    20% weight
    20F
  • Quality
    20% weight
    57D
  • Depth
    15% weight
    55D
  • Breadth
    10% weight
    65C
  • Community
    10% weight
    25F

03 · Stats

365-day commit heatmap

54 active days

Less
More

Language distribution

7 langs
  • TypeScript45%
  • Python14%
  • C++13%
  • CSS8%
  • HTML6%
  • JavaScript6%
  • Other8%

04 · Numbers

Owned repos

non-fork

9

Commits

last 12 months

24

Followers

11

Joined GitHub

Jan 2022

05 · Top repos

06 · Timeline

  1. Jan 9, 2022
    Joined GitHub
  2. Nov 7, 2024
    Created parthak314
  3. Nov 21, 2024
    Created RISCV-Team5 — RISC-V 32I CPU designed as part of the instruction set architecture (ISA) module for 2nd year EIE at Imperial College London.
  4. Nov 12, 2025
    Created embeddings-accuracy — A library for evaluating the accuracy of embeddings through clustering and dimensionality reduction.
  5. Dec 1, 2025
    Most recent push to parthak314

07 · Compare

github.com/
parthak314 · 6dmedian coder

08 · Rubric

How this score was produced

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

CategoryWeightScoreContrib.
Raw total42.6
Top-end curve+1.3
Final overall43.9

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.
parthak314 · 43.9/100 — Rate My GitHub