RRepoGEO

REPOGEO REPORT · LITE

Kedreamix/Linly-Talker

Default branch main · commit dc831b3e · scanned 6/24/2026, 5:26:35 AM

GitHub: 3,367 stars · 532 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 Kedreamix/Linly-Talker, 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

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

OVERALL DIRECTION
  • highreadme#1
    Add a clarifying sentence after the main README heading

    Why:

    CURRENT
    # Digital Human Intelligent Dialogue System - Linly-Talker — 'Interactive Dialogue with Your Virtual Self'

<div align="center">
<h1>Linly-Talker WebUI</h1>
    COPY-PASTE FIX
    # Digital Human Intelligent Dialogue System - Linly-Talker — 'Interactive Dialogue with Your Virtual Self'

Linly-Talker is an open-source framework for building intelligent conversational AI systems that combine large language models (LLMs) with visual models for dynamic talking head generation.

<div align="center">
<h1>Linly-Talker WebUI</h1>
  • mediumreadme#2
    Add a 'What is Linly-Talker?' section to the README

    Why:

    COPY-PASTE FIX
    ## What is Linly-Talker?
Linly-Talker is an intelligent AI system designed for developers and researchers in AI and multimedia. It provides a comprehensive open-source framework to create digital avatars capable of engaging in interactive conversations by integrating large language models (LLMs) with advanced visual models for talking head generation. It's ideal for prototyping, research, and building custom conversational AI applications.

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 Kedreamix/Linly-Talker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Synthesia
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Synthesia · recommended 2×
  2. Unreal Engine · recommended 1×
  3. MetaHuman Creator · recommended 1×
  4. Unity · recommended 1×
  5. Ready Player Me · recommended 1×
  • CATEGORY QUERY
    How to create an interactive digital avatar for conversational AI applications?
    you: not recommended
    AI recommended (in order):
    1. Unreal Engine
    2. MetaHuman Creator
    3. Unity
    4. Ready Player Me
    5. Character Creator 4
    6. DeepMotion
    7. Animate 3D
    8. Blender
    9. Rokoko Studio
    10. Adobe Mixamo
    11. Synthesia
    12. D-ID
    13. VRChat

    AI recommended 13 alternatives but never named Kedreamix/Linly-Talker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for building AI systems that combine large language models with talking head generation?
    you: not recommended
    AI recommended (in order):
    1. D-ID Creative Reality Studio
    2. Synthesia
    3. HeyGen
    4. RunwayML
    5. DeepMotion (Animate 3D)
    6. OpenAI (GPT models)
    7. ElevenLabs

    AI recommended 7 alternatives but never named Kedreamix/Linly-Talker. 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 Kedreamix/Linly-Talker?
    pass
    AI named Kedreamix/Linly-Talker explicitly

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

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

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

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Kedreamix/Linly-Talker — 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