Profiles, repos, scores & roasts
Solid engineer
bronze medal
Overall
0.0/ 100
Repository binding unavailable
You made machine learning look so organized that the repository name practically arrives with office hours, prerequisites, and a midterm it expects us to grade ourselves.
Historical result · no repository binding was retained
Repository binding unavailable
The transformer repository has the energy of someone arriving at the AI frontier with a clipboard, a reading list, and absolutely no intention of letting the hype cycle finish first.
Historical result · no repository binding was retained
Repository binding unavailable
A repository about diffusion and large vision models is a wonderfully ambitious choice: even the project premise sounds like it is still spreading into new territory.
Historical result · no repository binding was retained
keras-data-generator
After surveying the grand landscape of AI, you also kept a dedicated Keras data generator around to remind every glamorous model that someone still has to feed it.
Impact
25% weight
The projects center on structured educational material for deep learning, machine learning, artificial intelligence, transformers, and diffusion models, plus a practical Keras utility. This is a coherent and potentially highly useful body
Consistency
15% weight
The portfolio shows multiple substantial repositories, with activity spanning 2018 through 2026 and recent updates on advanced machine-learning topics. The supplied dates do not establish continuous maintenance, so this is strong but not at
Quality
25% weight
Repository scope and focused subject areas provide positive maturity signals, but the evidence omits source, tests, documentation, architecture, licensing, and implementation details. The score therefore reflects credible project framing
Depth
20% weight
Six non-fork, non-archived repositories include several sizable course-oriented subjects and specialized modern-model topics. The sustained thematic ownership is strong, though implementation depth cannot be verified from metadata alone.
Breadth
10% weight
The portfolio covers several major AI and machine-learning areas and includes Python in one repository. Language metadata is unavailable for the other repositories, and the projects remain concentrated within one technical domain.
Community
5% weight
The repositories show substantial public interest and the profile has many followers, which is a weak positive signal for community reach. Popularity is not used to raise the other category scores, and community evidence remains incomplete.
Based on the bounded repository sample saved with this analysis.
Non-fork sample
6
Last 12 months
2
Accepted fact
4618
Profile date
Mar 2017
Scores marked Profile rating use bounded public repository metadata. Full repo analysis appears only when a separate accepted repository analysis exists.
afshinea /
84/100
A substantial machine-learning course repository with a clear, broad educational scope and strong public reach; the available evidence does not reveal its internal completeness or engineering practices.
afshinea /
82/100
A specialized repository centered on transformers and large language models, indicating timely subject focus and ambitious scope; recent activity is visible, but project maturity beyond metadata is unknown.
afshinea /
79/100
A focused Stanford CS 230 deep-learning resource with sustained public interest and a clear educational purpose; implementation and maintenance quality cannot be assessed from the supplied metadata.
afshinea /
77/100
An artificial-intelligence course repository with a coherent educational scope and meaningful public reach; details of contents and implementation are unavailable.
afshinea /
68/100
A focused repository on diffusion and large vision models with recent activity and a specialized premise; its relatively limited visible history leaves completion and depth uncertain.
afshinea /
55/100
A practical Python Keras data-generator project with a concrete utility premise; it is older and narrower than the course repositories, while its public metadata indicates a focused, narrow scope.
How this score was produced
Overall = Σ(category × weight) + deterministic top-end curve
| Category | Weight | Score | Contribution |
|---|---|---|---|
| Impact | 25% | 88 | 22.00 |
| Quality | 25% | 62 | 15.50 |
| Depth | 20% | 82 | 16.40 |
| Consistency | 15% | 76 | 11.40 |
| Breadth | 10% | 57 | 5.70 |
| Community | 5% | 74 | 3.70 |
What it measures. Project usefulness, ambition, originality, and coherence. Popularity is not impact.
Evidence for afshinea. The projects center on structured educational material for deep learning, machine learning, artificial intelligence, transformers, and diffusion models, plus a practical Keras utility. This is a coherent and potentially highly useful body
What it measures. Engineering and project-maturity signals supported by the available public metadata.
Evidence for afshinea. Repository scope and focused subject areas provide positive maturity signals, but the evidence omits source, tests, documentation, architecture, licensing, and implementation details. The score therefore reflects credible project framing
What it measures. Sustained ownership, meaningful scope, and continued maintenance rather than one-shots.
Evidence for afshinea. Six non-fork, non-archived repositories include several sizable course-oriented subjects and specialized modern-model topics. The sustained thematic ownership is strong, though implementation depth cannot be verified from metadata alone.
What it measures. Recency and contribution patterns across the supplied activity window.
Evidence for afshinea. The portfolio shows multiple substantial repositories, with activity spanning 2018 through 2026 and recent updates on advanced machine-learning topics. The supplied dates do not establish continuous maintenance, so this is strong but not at
What it measures. Language entropy and project-type diversity across owned repos.
Evidence for afshinea. The portfolio covers several major AI and machine-learning areas and includes Python in one repository. Language metadata is unavailable for the other repositories, and the projects remain concentrated within one technical domain.
What it measures. A weak positive tie-breaker for supplied community signals, never a popularity penalty.
Evidence for afshinea. The repositories show substantial public interest and the profile has many followers, which is a weak positive signal for community reach. Popularity is not used to raise the other category scores, and community evidence remains incomplete.
Validate. The server validates the login, signed browser session, attempts, cooldown, daily ceiling, and the single provider permit.
Collect. The VPS collector reads bounded public profile metadata and up to 12 recent repository metadata rows in volatile memory.
Rate. One pinned provider returned a closed profile-rating object with no tools, browsing, shell, or repository-content access.
Verify. Server checks bound the subject, rejected unsafe or echoed text, validated the schema, and recomputed rubric-v3 final arithmetic.
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 2026-08-09 · rating-rubric/3