01 · Roasts
60% Jupyter, 0% Tests
Your language split is 60% Jupyter Notebook yet you've written zero tests across every single repo. Data science without reproducibility isn't science — it's a very confident guess.
One Commit Wonder Weeks
Your heatmap is a dot-to-dot puzzle: 71 commits across the whole year, with stretches of 5–7 consecutive empty weeks. The heatmap looks like a sparse Morse code transmission.
The CI Desert
5 repos scored, 5 repos with HAS_CI=no. Not a single workflow file, not a single badge. You built a full pipelined RISC-V processor but haven't automated so much as a lint check.
Reconfigurable Logic Unit (Empty Edition)
You pushed a repo called Reconfigurable-Logic-Unit, created and abandoned it on the same day, and left it with 20 KB of mystery. It scored a 5. That's not reconfigurable — that's a placeholder with delusions of grandeur.
1 PR All Year
totalPRsYear = 1, totalIssuesYear = 0. You built a pipelined processor from scratch but haven't opened a single issue on anyone else's code. The open-source ecosystem doesn't know you exist yet.
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% weight40D
- Consistency20% weight55D
- Quality20% weight57D
- Depth15% weight50D
- Breadth10% weight65C
- Community10% weight40D
03 · Stats
365-day commit heatmap
38 active days
Language distribution
- Jupyter Notebook60%
- TypeScript16%
- HTML8%
- Python8%
- Verilog5%
- C++3%
04 · Numbers
Owned repos
non-fork
19
Commits
last 12 months
71
Followers
11
Joined GitHub
May 2021
05 · Top repos
RockingAayush /
Pipelined-RISCV
Complete 5-stage pipelined RISC-V RV32I processor in Verilog with hazard handling, forwarding, and 813 KB codebase. Well-structured educational project with README, testbench, and FPGA support, but limited external adoption.
RockingAayush /
Neural-network-Cpp
Educational neural network implementation in C++ approximating sine function with custom matrix ops and backpropagation. README documents learning outcomes, typed code with structured layout, Python visualizer includes comprehensive metrics, but no tests, CI, or license.
RockingAayush /
Single-Cycle-Processor-RISCV
Educational single-cycle RISC-V processor in Verilog supporting all 37 RV32I instructions. Well-documented with block diagrams and demo programs (bubble sort), clean modular architecture, and FPGA deployment path. However, no tests, CI, or license; created 2 days ago with minimal commit history.
RockingAayush /
OS-Assignments
Student assignment repo with 4 basic C OS programming exercises (fork, pipes, signals) completed in ~3 days; no README, no tests, minimal documentation; typical one-off coursework submission.
RockingAayush /
Reconfigurable-Logic-Unit
Empty scaffold: 20KB repo with 0 stars, no files sampled, created and pushed same day, lacks README, tests, CI, and documentation. Unsubstantiated claim of hardware reconfiguration capability.
06 · Timeline
- May 23, 2021Joined GitHub
- May 23, 2025Created Neural-network-Cpp — A neural network design made from scratch in C++.
- Jan 31, 2026Created OS-Assignments
- Mar 7, 2026Created Single-Cycle-Processor-RISCV — This is a simple implementation of the RISC V processor consisting of a single cycle core. It supports all the 37 RV32I Base Integer instructions compliant to the core instruction
- Mar 20, 2026Created Pipelined-RISCV — This is a simple pipelined implementation of the RISC V processor. It supports all the 37 RV32I Base Integer instructions compliant to the core instruction formats. It also handles
- Apr 5, 2026Created Reconfigurable-Logic-Unit — This is a dataflow graph execution unit that can be combined with a processor to reconfigure hardware on the fly using memory. It also includes a webapp that lets you generate the
- Apr 29, 2026Most recent push to Neural-network-Cpp
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.