RRepoGEO

REPOGEO REPORT · LITE

NVlabs/LongLive

Default branch main · commit 536d1b9a · scanned 5/23/2026, 8:27:44 PM

GitHub: 1,770 stars · 166 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
33 /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
2 / 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 NVlabs/LongLive, 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
  • highabout#1
    Clarify the repository description to emphasize Generative AI

    Why:

    CURRENT
    LongLive 2.0: Infra - Long Video Gen
    COPY-PASTE FIX
    LongLive 2.0: Generative AI Infrastructure for Long Video Generation with NVFP4 and Parallelism.
  • highreadme#2
    Add a concise, category-defining opening sentence to the README

    Why:

    CURRENT
    The README currently has badges and links immediately following the H1, before the "TLDR" section.
    COPY-PASTE FIX
    Add a sentence immediately after the H1 (or initial badges) like: 'This repository provides a cutting-edge generative AI infrastructure for creating long videos, leveraging NVFP4 and parallel processing for high-performance training and inference.'
  • mediumreadme#3
    Add a 'Comparison' or 'Why LongLive?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why LongLive? Key Differentiators' or '## Comparison to Other Video Generation Frameworks', that highlights its unique technical approach (NVFP4, parallelism, real-time performance, specific optimizations like TriAttention/RoPE) and positions it against other ML/AI video generation solutions.

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 NVlabs/LongLive
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AWS Elemental MediaConvert
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS Elemental MediaConvert · recommended 1×
  2. Google Cloud MediaPipe · recommended 1×
  3. Azure Media Services · recommended 1×
  4. FFmpeg · recommended 1×
  5. kubernetes/kubernetes · recommended 1×
  • CATEGORY QUERY
    How to efficiently generate very long videos using parallel processing for high performance?
    you: not recommended
    AI recommended (in order):
    1. AWS Elemental MediaConvert
    2. Google Cloud MediaPipe
    3. Azure Media Services
    4. FFmpeg
    5. Kubernetes (kubernetes/kubernetes)
    6. Slurm (SchedMD/slurm)
    7. AWS Batch
    8. Blackmagic DaVinci Resolve Studio
    9. Adobe Media Encoder
    10. Adobe Premiere Pro
    11. After Effects
    12. Blender (blender/blender)
    13. SheepIt Render Farm
    14. Nuke
    15. Thinkbox Deadline
    16. Pixar Tractor

    AI recommended 16 alternatives but never named NVlabs/LongLive. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What infrastructure enables real-time video content creation with accelerated training and inference?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA DGX Systems
    2. NVIDIA A100/H100 GPUs
    3. NVIDIA AI Enterprise
    4. NVIDIA CUDA
    5. NVIDIA cuDNN
    6. NVIDIA TensorRT
    7. Dell PowerEdge
    8. HPE ProLiant
    9. Supermicro
    10. AMD EPYC
    11. Intel Xeon Scalable
    12. NVIDIA CUDA Toolkit
    13. Google Cloud TPUs
    14. AWS Inferentia/Trainium Instances
    15. Azure ND/NC Series Virtual Machines
    16. NVIDIA NVLink
    17. NVIDIA Jetson AGX Orin/Orin Nano
    18. AMD Instinct MI250/MI300 Series Accelerators
    19. AMD ROCm

    AI recommended 19 alternatives but never named NVlabs/LongLive. 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 NVlabs/LongLive?
    pass
    AI did not name NVlabs/LongLive — likely talking about a different project

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

  • If a team adopts NVlabs/LongLive in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVlabs/LongLive 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 NVlabs/LongLive solve, and who is the primary audience?
    pass
    AI named NVlabs/LongLive explicitly

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

Embed your GEO score

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MARKDOWN (README)
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NVlabs/LongLive — 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