RRepoGEO

REPOGEO REPORT · LITE

mosaicml/streaming

Default branch main · commit 6a4e12f6 · scanned 5/19/2026, 11:26:47 PM

GitHub: 1,507 stars · 192 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 mosaicml/streaming, 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 the README's main heading to include key problem/solution terms

    Why:

    CURRENT
    <h2><p align="center">Fast, accurate streaming of training data from cloud storage</p></h2>
    COPY-PASTE FIX
    <h2><p align="center">Accelerate Deep Learning Training: High-Performance Data Streaming from Cloud Storage</p></h2>
  • mediumtopics#2
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    dataset, deep-learning, machine-learning, neural-network, pytorch, streaming
    COPY-PASTE FIX
    dataset, deep-learning, machine-learning, neural-network, pytorch, streaming, data-loading, cloud-data, ml-training, distributed-ml
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison to Alternatives' or 'Why StreamingDataset?' that briefly outlines its advantages over common tools like WebDataset, DALI, or torch.utils.data.DataLoader for cloud-based, large-scale deep learning.

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 mosaicml/streaming
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
WebDataset
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. WebDataset · recommended 2×
  2. NVIDIA DALI · recommended 1×
  3. Petastorm · recommended 1×
  4. TFRecord · recommended 1×
  5. S3 Select · recommended 1×
  • CATEGORY QUERY
    How to speed up data loading for large deep learning models from cloud storage?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA DALI
    2. Petastorm
    3. TFRecord
    4. WebDataset
    5. S3 Select
    6. Google Cloud Storage Select
    7. Google Cloud Storage FUSE
    8. S3 FUSE
    9. Apache Arrow
    10. Parquet
    11. Redis
    12. Zarr

    AI recommended 12 alternatives but never named mosaicml/streaming. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best data streaming libraries for PyTorch deep learning workloads?
    you: not recommended
    AI recommended (in order):
    1. torch.utils.data.DataLoader
    2. WebDataset
    3. Hugging Face datasets
    4. DALI
    5. fsspec
    6. tf.data
    7. torchdata

    AI recommended 7 alternatives but never named mosaicml/streaming. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 mosaicml/streaming?
    pass
    AI named mosaicml/streaming explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts mosaicml/streaming in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mosaicml/streaming 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 mosaicml/streaming solve, and who is the primary audience?
    pass
    AI named mosaicml/streaming 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 mosaicml/streaming. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/mosaicml/streaming.svg)](https://repogeo.com/en/r/mosaicml/streaming)
HTML
<a href="https://repogeo.com/en/r/mosaicml/streaming"><img src="https://repogeo.com/badge/mosaicml/streaming.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

mosaicml/streaming — 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