RRepoGEO

REPOGEO REPORT · LITE

thunderbird/thunderbolt

Default branch main · commit ed194c79 · scanned 5/9/2026, 7:41:08 AM

GitHub: 4,529 stars · 303 forks

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 thunderbird/thunderbolt, 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
    Clarify project identity to avoid confusion with Thunderbird email client

    Why:

    CURRENT
    **AI You Control: Choose your models. Own your data. Eliminate vendor lock-in.**
    COPY-PASTE FIX
    **AI You Control: Choose your models. Own your data. Eliminate vendor lock-in.** This project is an AI client and is not related to the Mozilla Thunderbird email client.
  • highreadme#2
    Emphasize 'AI client' and 'on-premise LLM/agent deployment' in the README intro

    Why:

    CURRENT
    Thunderbolt is an open-source, cross-platform AI client that can be deployed on-prem anywhere.
    COPY-PASTE FIX
    Thunderbolt is an open-source, cross-platform AI client designed for secure, on-premise deployment of various LLM agents and models. It empowers enterprises to manage their AI infrastructure, ensuring data ownership and eliminating vendor lock-in.
  • mediumtopics#3
    Add more specific topics for AI client and on-premise deployment

    Why:

    CURRENT
    ai, ai-agents, llms, on-device-ai
    COPY-PASTE FIX
    ai, ai-agents, llms, on-device-ai, local-llm, on-premise-ai, ai-platform, llm-ops, enterprise-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 thunderbird/thunderbolt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 1×
  2. ollama/ollama · recommended 1×
  3. janhq/jan · recommended 1×
  4. go-skynet/LocalAI · recommended 1×
  5. nomic-ai/gpt4all · recommended 1×
  • CATEGORY QUERY
    What open-source AI client allows local model inference and data ownership?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama (ollama/ollama)
    3. Jan (janhq/jan)
    4. LocalAI (go-skynet/LocalAI)
    5. GPT4All (nomic-ai/gpt4all)
    6. KoboldAI (KoboldAI/KoboldAI-Client)

    AI recommended 6 alternatives but never named thunderbird/thunderbolt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a cross-platform solution to deploy and manage various LLM agents on-premise.
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. Kubeflow (kubeflow/kubeflow)
    3. OpenShift AI
    4. Hugging Face TGI (huggingface/text-generation-inference)
    5. Docker
    6. Podman (containers/podman)
    7. NVIDIA Triton Inference Server (triton-inference-server/server)
    8. MLflow (mlflow/mlflow)
    9. Ray Serve (ray-project/ray)
    10. OpenVINO Model Server (openvinotoolkit/model_server)

    AI recommended 10 alternatives but never named thunderbird/thunderbolt. 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 thunderbird/thunderbolt?
    pass
    AI did not name thunderbird/thunderbolt — 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 thunderbird/thunderbolt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thunderbird/thunderbolt 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 thunderbird/thunderbolt solve, and who is the primary audience?
    pass
    AI named thunderbird/thunderbolt 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 thunderbird/thunderbolt. 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/thunderbird/thunderbolt.svg)](https://repogeo.com/en/r/thunderbird/thunderbolt)
HTML
<a href="https://repogeo.com/en/r/thunderbird/thunderbolt"><img src="https://repogeo.com/badge/thunderbird/thunderbolt.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

thunderbird/thunderbolt — 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