01 · Roasts
Burst Coder, Not a Marathon Runner
Your heatmap is a tale of two accounts: 18 weeks of ghost-town zeros followed by nuclear sprints. entroscope went from zero to PyPI in 3 days — impressive, but your consistency score is being held hostage by weeks 6–29.
The Profile Repo That Codes Nothing
Par-python weighs in at 41 KB, zero source files, and a README that's basically a LinkedIn bio with badges. Your most-committed-to repo in some weeks is the one that does the least.
CI Allergy
Of 9 repos, only 2 have CI (entroscope, bigfiles). You clearly know how to write a GitHub Actions workflow — you just apparently can't bring yourself to copy it into s1napse, nextonmenu, or the portfolio.
4 Followers Despite 5 Shipped Products
You've published to PyPI, crates.io, and Cloudflare Workers, built a real desktop app, and still have the GitHub footprint of someone who joined yesterday. 24 PRs/year but 4 followers — the internet hasn't found you yet.
cv.html — The Abandoned Origin Story
1 HTML file, 1 commit in 30 days, no README, no license. Whatever this was meant to become, it didn't. At least give it a proper burial or a redirect to jjpardo.com.
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% weight56D
- Consistency20% weight65C
- Quality20% weight72B
- Depth15% weight65C
- Breadth10% weight72B
- Community10% weight30F
03 · Stats
365-day commit heatmap
108 active days
Language distribution
- JavaScript54%
- Jupyter Notebook19%
- Python11%
- Makefile8%
- TypeScript5%
- Rust2%
- Other1%
04 · Numbers
Owned repos
non-fork
9
Commits
last 12 months
528
Followers
4
Joined GitHub
Sep 2020
05 · Top repos
Par-python /
entroscope
Specialized entropy toolkit with 7 measures, consistent API for pandas/numpy, published to PyPI. Well-architected, tested (90% coverage gate), and documented with design + architecture guides.
Par-python /
bigfiles
Well-crafted Rust CLI tool for disk analysis with parallel walking, duplicate detection, interactive TUI, and comprehensive category breakdown. Typed, tested, CI-validated, shipped on crates.io.
Par-python /
pardo-portfolio
Retro-styled personal portfolio site built with Next.js 16, TypeScript, and Tailwind. Features draggable modal windows, terminal-based search, and live content loading. Clean architecture with good component composition, but lacks tests, CI, and production deployment signals.
Par-python /
nextonmenu
Jupyter-hosted early-warning system for food trend breakouts using logistic regression on Google Trends entropy/growth features. Achieves 70% LOO accuracy with shipping Gradio demo, structured src/ layout, and comprehensive tests, but minimal external adoption (3 stars, 0 forks, <1 week old).
Par-python /
s1napse-web
Marketing site for s1napse sim racing app built with Next.js 15, React 19, Tailwind v4, and TypeScript. Deployed to Cloudflare Workers via OpenNext. No stars/adoption yet, but well-structured with clear documentation and modern tooling.
Par-python /
s1napse
Real-time sim racing telemetry dashboard with lap coaching, strategy engine, and OBD-II support. Python-based PyQt6 app with documented architecture, typed coaching modules, and comprehensive test coverage for core engines.
Par-python /
Par-python
README-only portfolio repo with no source code, untyped language detection, no tests/CI/license. Personal project scaffold listing the author's work and tech stack.
Par-python /
pdfv
Single-day terminal PDF viewer for iTerm2 written in Rust. Minimal viable tool with clear functionality but explicitly marked as personal experimental project with no polish or support.
Par-python /
cv
Minimal HTML scaffold with no documentation, tests, or CI. 175 KB repo with 1 commit in 30 days—appears to be an early-stage or abandoned personal project.
06 · Timeline
- Sep 4, 2020Joined GitHub
- Sep 3, 2024Created Par-python
- Nov 9, 2025Created s1napse — real time raw data telemetry app
- Feb 3, 2026Created cv
- Mar 17, 2026Created s1napse-web
- Apr 21, 2026Created pardo-portfolio
- May 10, 2026Created bigfiles — program to find stale and duplicate files in the depths of your computer
- May 22, 2026Created pdfv
- May 30, 2026Created nextonmenu — detects food ingredients already in the early viral curve (think matcha in 2015) before they go mainstream.
- Jun 1, 2026Created entroscope — every entropy measure for time series data, in one consistent API
- Jun 24, 2026Most recent push to pardo-portfolio
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.