REPOGEO REPORT · LITE

rentruewang/aioway

Default branch main · commit eb4dab81 · scanned 5/22/2026, 1:08:09 AM

GitHub: 1,820 stars · 64 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface rentruewang/aioway, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README's opening to clearly state aioway's purpose

Why:

CURRENT

The README starts with "## 🚧 Notice" and discusses `koila` and `FakeTensor`.

COPY-PASTE FIX

Add a concise introductory paragraph at the very top of the README, before the "Notice" section, like: "AioWay is a declarative, RDBMS-inspired framework for building explainable, scalable, and optimizable deep learning algorithms. It allows you to specify *what* to do in ML, rather than *how*, by compiling algorithms based on context and data."

mediumabout#2

Refine the repository description for clarity and uniqueness

Why:

CURRENT

AI on the way. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.

COPY-PASTE FIX

AioWay: A declarative, RDBMS-inspired framework for building explainable, scalable, and optimizable deep learning algorithms. Focus on *what* to do in ML, not *how*.

mediumtopics#3

Add more specific topics to highlight the unique approach

Why:

CURRENT

compiler, deep-learning, gradient-accumulation, lazy-evaluation, machine-learning, memory-management, neural-architecture-search, neural-network, out-of-memory, python, pytorch, relational-algebra

COPY-PASTE FIX

declarative-ml, rdbms-for-ml, ml-compilation, explainable-ai

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface rentruewang/aioway

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

TensorFlow

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

TensorFlow · recommended 2×
PyTorch · recommended 2×
Keras · recommended 1×
TensorFlow Extended (TFX) · recommended 1×
What-If Tool (WIT) · recommended 1×

CATEGORY QUERY
How to build explainable and scalable deep learning models with a declarative approach?
you: not recommended
AI recommended (in order):
1. TensorFlow
2. Keras
3. TensorFlow Extended (TFX)
4. What-If Tool (WIT)
5. SHAP (SHapley Additive exPlanations)
6. Captum
7. TensorFlow Privacy
8. PyTorch
9. PyTorch Lightning
10. LIME
11. Kubeflow Pipelines
12. Kubernetes
13. BentoML
14. MLflow
15. scikit-learn
16. Ray
17. Ray Train
18. Ray Core
19. Hugging Face Transformers
20. Accelerate
21. InterpretML
AI recommended 21 alternatives but never named rentruewang/aioway. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a high-level framework for building complex deep learning algorithms easily and efficiently.
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. JAX
4. Keras (standalone)
5. Fast.ai
AI recommended 5 alternatives but never named rentruewang/aioway. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of rentruewang/aioway?
pass
AI named rentruewang/aioway explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts rentruewang/aioway in production, what risks or prerequisites should they evaluate first?
pass
AI named rentruewang/aioway explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo rentruewang/aioway solve, and who is the primary audience?
pass
AI named rentruewang/aioway explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of rentruewang/aioway. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/rentruewang/aioway.svg)](https://repogeo.com/en/r/rentruewang/aioway)

HTML

<a href="https://repogeo.com/en/r/rentruewang/aioway"><img src="https://repogeo.com/badge/rentruewang/aioway.svg" alt="RepoGEO" /></a>

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rentruewang/aioway — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite