01 · Roasts
Portfolio, not audience
You shipped at least six named projects, including live Spend, but totalStars is still 0.
CI is the missing teammate
scrapeverse has Actions, while spend_native, silentbug-bench, and genAI_assignment still ship without CI.
Benchmark assembly line
silentbug-bench, sycophancy-bench, and rewardhack-bench collectively define 78 benchmark tasks; now get humans to run them.
Horizontal builder
165 multi-repo recent commits and 444 yearly commits say you build broadly; sustained public adoption has not caught up.
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% weight68C
- Consistency20% weight65C
- Quality20% weight79B
- Depth15% weight58D
- Breadth10% weight80A
- Community10% weight25F
03 · Stats
365-day commit heatmap
76 active days
Language distribution
- TypeScript40%
- Python23%
- JavaScript14%
- Kotlin6%
- Java5%
- CSS4%
- Other8%
04 · Numbers
Owned repos
non-fork
30
Commits
last 12 months
444
Followers
5
Joined GitHub
Sep 2024
05 · Top repos
Json604 /
scrapeverse
Driftwatch is a substantial typed TypeScript web-monitoring system with a tested break-vs-change classifier, guarded healing saga, multi-source adapters, dashboard, CLI, and scheduled Bright Data ingestion; adoption is not yet evidenced.
Json604 /
spend_native
Spend is a substantial typed Android expense tracker with React Native/Kotlin clients, Fastify/Postgres sync, SMS parsing, self-hosted updates, and strong transactional/conflict-handling tests, but it has no stars, CI, or license.
Json604 /
silentbug-bench
A documented 18-task PyTorch benchmark with gold patches, hidden defect tests, deterministic generation, and an out-of-process verifier; strong engineering scope but no demonstrated adoption or CI.
Json604 /
genAI_project
A deployed, typed Next.js multimodal catalogue search with 400 products, Jina/Gemini enrichment, Supabase pgvector retrieval, analytics, and validation; adoption remains unproven at 0 stars.
Json604 /
genAI_assignment
A substantial coursework portfolio with four GenAI assignments, including a deployed persona chatbot, typed RAG modules, and a feature-rich Playwright/CDP web automation agent, but no tests, CI, license, or visible adoption.
Json604 /
rewardhack-bench
A documented, test-backed Python benchmark with 20 deterministic reward-hacking tasks, a JSON-only XPS interpreter, executable verifier, and six exploit-search strategies; adoption is not yet evidenced by its 0 stars and 0 forks.
Json604 /
hld_typeahead
A well-documented FastAPI typeahead service with trie indexing, consistent-hash caching, trending scores, SQLite batching, and a substantial pytest suite, but it is a same-day, zero-star project without CI or a license.
Json604 /
sycophancy-bench
A carefully engineered Python benchmark with 40 paired flaw/control tasks, deterministic synthetic experiments, framing probes, and strict scoring; adoption is not yet evidenced by its 0 stars and same-day launch.
Json604 /
devops-assignments
A well-documented DevOps coursework repository spanning seven assignments, with reproducible shell evidence and Docker experiments, but no tests, CI, license, or demonstrated external adoption; README also records a broken Python Dockerfile.
Json604 /
Json604
A 4 KB profile-style repository with a GitHub README and social links, but no fetched source files, tests, CI, license, or demonstrated software output.
06 · Timeline
- Sep 29, 2024Joined GitHub
- Mar 8, 2026Created Json604
- Apr 29, 2026Created genAI_assignment
- Jun 14, 2026Created genAI_project
- Jun 21, 2026Created hld_typeahead
- Aug 8, 2026Created spend_native — A personal expense tracker app/widget
- Aug 19, 2026Created rewardhack-bench — Predict how a policy games a reward. RL environments scored by an executable-exploit verifier.
- Aug 20, 2026Created sycophancy-bench — Do models catch flawed ML results? Flawed reports with byte-identical matched controls.
- Aug 20, 2026Created silentbug-bench — ML training defects that never crash. An agentic benchmark with patch-and-rerun, unit-test verification.
- Aug 21, 2026Created scrapeverse
- Sep 2, 2026Created devops-assignments — DevOps assignments: Linux, shell scripting, networking, Git, Docker
- Sep 2, 2026Most recent push to devops-assignments
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.