turbovec
Rust rocket
turbovec appears to have skipped the warm-up lap and launched straight into being the portfolio flagship: a Rust project with enough visible traction to make the other repositories look like its supporting cast.
B
Solid engineer
bronze medal
Overall
0.0/ 100
Roasts
gemma-emotional-probes
Feelings, but make it empirical
gemma-emotional-probes is an admirably specific premise: instead of asking whether a model works, it asks what kind of inner weather might be hiding in the weights.
llm-essentials
The textbook arc
llm-essentials sounds like the sensible opening chapter of the portfolio, patiently explaining the basics while the rest of the lineup experiments with considerably stranger questions.
intuitive-ml
Intuition with a notebook kernel
intuitive-ml promises that machine learning can be approachable, then chooses Jupyter as its stage—an appropriately hands-on format for turning intuition into something reproducible enough to poke at.
sourced
Mysterious by design
sourced is a wonderfully compact name for a project whose metadata leaves the premise just out of reach: concise branding, recent activity, and enough mystery to make the portfolio feel like it has a secret passage.
Category breakdown
Impact
25% weight
The portfolio combines a highly prominent Rust project with focused machine-learning and language-model projects, suggesting substantial ambition and usefulness across systems and applied research tooling.
Consistency
15% weight
All five repositories are original, non-forked projects, with four showing activity in 2025 or 2026 and one updated in 2023, indicating a generally sustained recent pattern.
Quality
25% weight
Metadata supports positive signals from original ownership, active status, project differentiation, and continued updates, while the available metadata does not establish additional project attributes.
Depth
20% weight
A large Rust project alongside multiple specialized ML projects indicates meaningful sustained ownership and scope; repeated activity across several years strengthens the signal, though implementation depth cannot be directly verified.
Breadth
10% weight
The portfolio spans Rust, Python, and Jupyter Notebook work, covering systems software, educational material, and machine-learning research-oriented projects.
Community
5% weight
Follower and star counts provide a modest positive signal for community visibility, especially for turbovec, but community evidence is treated only as a weak tie-breaker.
Stats
52-week commit heatmap
166 active daysLanguage distribution
3 langs
- Jupyter Notebook40%
- Python40%
- Rust20%
Based on the bounded repository sample saved with this analysis.
Numbers
Owned repos
Non-fork sample
5
Commits
Last 12 months
602
Followers
Accepted fact
408
Joined GitHub
Profile date
Feb 2015
Top repos
Scores marked Profile rating use bounded public repository metadata. Full repo analysis appears only when a separate accepted repository analysis exists.
ryancodrai /
turbovec
91/100
A non-fork, non-archived Rust project with substantial apparent community interest and a very recent update; its prominence makes it the portfolio's clearest flagship.
OwnedActiveProfile ratingRust
★ 14699 · 1d agoOpen on GitHub ↗
ryancodrai /
gemma-emotional-probes
73/100
An original Python project centered on emotional probes for Gemma, combining a distinctive language-model research premise with recent activity and moderate community interest.
OwnedActiveProfile ratingPython
★ 59 · 4mo agoOpen on GitHub ↗
ryancodrai /
sourced
67/100
An original Python project with a concise name and recent activity, suggesting a maintained applied-ML or tooling effort, though its exact scope is not available from metadata.
OwnedActiveProfile ratingPython
★ 27 · 5mo agoOpen on GitHub ↗
ryancodrai /
llm-essentials
48/100
An original Jupyter Notebook project focused on LLM essentials, with an older update and limited visible community signal; credible but comparatively thin evidence of ongoing development.
OwnedActiveProfile ratingJupyter Notebook
★ 5 · 3y agoOpen on GitHub ↗
ryancodrai /
intuitive-ml
47/100
An original Jupyter Notebook project with an ML-learning orientation and a recent update, but limited metadata evidence of scale or sustained impact.
OwnedActiveProfile ratingJupyter Notebook
★ 4 · 10mo agoOpen on GitHub ↗
Timeline
- Joined GitHub
- Created ryancodrai/intuitive-ml - Machine learning methods implemented in NumPy with an emphasis on simple explanations and an abundance of well commented code.
- Created ryancodrai/llm-essentials - Large language model cookbooks
- Created ryancodrai/sourced - Maps packages to source code. Allows coding agents to search any dependency with grep.
- Created ryancodrai/turbovec - A vector index built on TurboQuant, written in Rust with Python bindings
- Created ryancodrai/gemma-emotional-probes - Emotional probes for Gemma 4 E4B
- Most recent push to ryancodrai/turbovec
Compare
RYANCODRAI · 6DMEDIAN CODER
Rubric
How this score was produced
Overall = Σ(category × weight) + deterministic top-end curve
| Category | Weight | Score | Contribution |
|---|---|---|---|
| Impact | 25% | 88 | 22.00 |
| Quality | 25% | 70 | 17.50 |
| Depth | 20% | 82 | 16.40 |
| Consistency | 15% | 78 | 11.70 |
| Breadth | 10% | 68 | 6.80 |
| Community | 5% | 58 | 2.90 |
Impact · 88/100
What it measures. Project usefulness, ambition, originality, and coherence. Popularity is not impact.
Evidence for ryancodrai. The portfolio combines a highly prominent Rust project with focused machine-learning and language-model projects, suggesting substantial ambition and usefulness across systems and applied research tooling.
Quality · 70/100
What it measures. Engineering and project-maturity signals supported by the available public metadata.
Evidence for ryancodrai. Metadata supports positive signals from original ownership, active status, project differentiation, and continued updates, while the available metadata does not establish additional project attributes.
Depth · 82/100
What it measures. Sustained ownership, meaningful scope, and continued maintenance rather than one-shots.
Evidence for ryancodrai. A large Rust project alongside multiple specialized ML projects indicates meaningful sustained ownership and scope; repeated activity across several years strengthens the signal, though implementation depth cannot be directly verified.
Consistency · 78/100
What it measures. Recency and contribution patterns across the supplied activity window.
Evidence for ryancodrai. All five repositories are original, non-forked projects, with four showing activity in 2025 or 2026 and one updated in 2023, indicating a generally sustained recent pattern.
Breadth · 68/100
What it measures. Language entropy and project-type diversity across owned repos.
Evidence for ryancodrai. The portfolio spans Rust, Python, and Jupyter Notebook work, covering systems software, educational material, and machine-learning research-oriented projects.
Community · 58/100
What it measures. A weak positive tie-breaker for supplied community signals, never a popularity penalty.
Evidence for ryancodrai. Follower and star counts provide a modest positive signal for community visibility, especially for turbovec, but community evidence is treated only as a weak tie-breaker.
- Raw total
- 77.30
- Top-end curve
- +0.00
- Final overall
- 77.3
Tier thresholds
S 90-100 Mass-producing humansA 80-89 Ship machineB 70-79 Solid engineerC 60-69 Getting thereD 40-59 README enthusiastF 0-39 GitHub tourist
I. How this saved profile rating was produced
- 01
Validate. The server validates the login, signed browser session, attempts, cooldown, daily ceiling, and the single provider permit.
- 02
Collect. The VPS collector reads bounded public profile metadata and up to 12 recent repository metadata rows in volatile memory.
- 03
Rate. One pinned provider returned a closed profile-rating object with no tools, browsing, shell, or repository-content access.
- 04
Verify. Server checks bound the subject, rejected unsafe or echoed text, validated the schema, and recomputed rubric-v3 final arithmetic.
- 05
Save. Only a valid completion and allowlisted post-completion facts commit atomically; the public projection is then rebuilt.
This saved profile analysis is metadata-only and did not read repository trees, README files, or source code. Availability of new analysis is separate from this historical result.
Rated 2026-08-10 · rating-rubric/3
II. Data sources & caveats
- Scores summarize one accepted, subject-bound analysis; they are not a security audit or endorsement.
- Rank, percentile, and median use 1095 currently accepted public profiles and can change as the leaderboard changes.
- Repository and language details reflect the bounded analyzed sample, not necessarily every repository.
- Missing data is shown as unavailable or not evaluated instead of being inferred as zero.
- Analysis completed Aug 10, 2026, 1:24 AM UTC and is marked aging.