REPOGEO REPORT · LITE

xtreme1-io/xtreme1

Default branch main · commit 20cffa07 · scanned 6/19/2026, 11:26:11 PM

GitHub: 1,208 stars · 209 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 xtreme1-io/xtreme1, 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 intro to emphasize "multimodal data labeling platform"

Why:

CURRENT

Xtreme1 is an all-in-one open-source platform for multimodal training data.

COPY-PASTE FIX

Xtreme1 is an all-in-one open-source **data labeling and annotation platform** for multimodal training data, supporting 3D LiDAR point cloud, image, and LLM.

mediumtopics#2

Add specific topics to reinforce 'platform' and 'data-centric AI'

Why:

CURRENT

3d-annotation, annotation, annotation-tool, computer-vision, image-annotation, image-classification, image-labelling-tool, labeling-tool, lidar-annotation, lidar-camera-fusion, lidar-object-detection, lidar-object-tracking, multimodal, point-cloud, rlhf

COPY-PASTE FIX

3d-annotation, annotation, annotation-tool, computer-vision, image-annotation, image-classification, image-labelling-tool, labeling-tool, lidar-annotation, lidar-camera-fusion, lidar-object-detection, lidar-object-tracking, multimodal, point-cloud, rlhf, data-labeling-platform, annotation-platform, mlops-platform, data-centric-ai

lowcomparison#3

Add a comparison/alternatives section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, e.g., '## Why Xtreme1? (Compared to Alternatives)' or '## Xtreme1 vs. Other Platforms', and briefly highlight its open-source nature, multimodal support (especially 3D LiDAR), and all-in-one capabilities compared to common alternatives.

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 xtreme1-io/xtreme1

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Scale AI Platform

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Scale AI Platform · recommended 1×
Superb AI Suite · recommended 1×
CVAT · recommended 1×
Labelbox · recommended 1×
V7 · recommended 1×

CATEGORY QUERY
How can I efficiently label multimodal datasets for 3D LiDAR, images, and LLM training?
you: not recommended
AI recommended (in order):
1. Scale AI Platform
2. Superb AI Suite
3. CVAT
4. Labelbox
5. V7
6. AWS Ground Truth
7. Roboflow
AI recommended 7 alternatives but never named xtreme1-io/xtreme1. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What open-source tools assist with 2D/3D object detection and segmentation for computer vision projects?
you: not recommended
AI recommended (in order):
1. OpenCV
2. MMDetection
3. Detectron2
4. Ultralytics YOLOv5/YOLOv8
5. Darknet
6. PyTorch Geometric (PyG)
7. Open3D
8. PointRCNN / VoteNet / 3DETR
AI recommended 8 alternatives but never named xtreme1-io/xtreme1. 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 xtreme1-io/xtreme1?
pass
AI named xtreme1-io/xtreme1 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts xtreme1-io/xtreme1 in production, what risks or prerequisites should they evaluate first?
pass
AI named xtreme1-io/xtreme1 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 xtreme1-io/xtreme1 solve, and who is the primary audience?
pass
AI named xtreme1-io/xtreme1 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite