REPOGEO REPORT · LITE

thunderbird/thunderbolt

Default branch main · commit a4cfa113 · scanned 6/19/2026, 2:41:10 AM

GitHub: 4,718 stars · 310 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 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 in README H1 and opening sentence

Why:

CURRENT

# Thunderbolt

**AI You Control: Choose your models. Own your data. Eliminate vendor lock-in.**

COPY-PASTE FIX

# Thunderbolt: An Open-Source AI Client

**Thunderbolt is an open-source, cross-platform AI client that empowers you to choose your models, own your data, and eliminate vendor lock-in.**

mediumabout#2

Add 'AI Client' to the repository description

Why:

CURRENT

AI You Control: Choose your models. Own your data. Eliminate vendor lock-in.

COPY-PASTE FIX

Thunderbolt is an open-source AI client: Choose your models. Own your data. Eliminate vendor lock-in.

mediumreadme#3

Add a 'Thunderbolt vs. Other Local AI Tools' section to the README

Why:

COPY-PASTE FIX

### Thunderbolt vs. Other Local AI Tools (e.g., Ollama, LM Studio)

While tools like Ollama and LM Studio excel at providing local inference engines, Thunderbolt is a full-featured, cross-platform AI client designed for on-prem deployment, offering a complete application experience with a focus on enterprise features, data ownership, and eliminating vendor lock-in. Thunderbolt can integrate with these inference engines, providing the user interface and management layer.

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

Of all named tools, what % are you?

Top rival

LM Studio

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LM Studio · recommended 2×
Ollama · recommended 1×
LocalAI · recommended 1×
Hugging Face Transformers · recommended 1×
TensorFlow Lite · recommended 1×

CATEGORY QUERY
How can I deploy a self-hosted AI client to maintain data privacy and model control?
you: not recommended
AI recommended (in order):
1. Ollama
2. LM Studio
3. LocalAI
4. Hugging Face Transformers
5. TensorFlow Lite
6. ONNX Runtime
7. OpenVINO
AI recommended 7 alternatives but never named thunderbird/thunderbolt. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What open-source, cross-platform AI tools support local LLM inference without vendor lock-in?
you: not recommended
AI recommended (in order):
1. Ollama (ollama/ollama)
2. LM Studio
3. llama.cpp (ggerganov/llama.cpp)
4. Text Generation WebUI (oobabooga/text-generation-webui)
5. Jan (janhq/jan)
6. LocalAI (go-skynet/LocalAI)
AI recommended 6 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 named thunderbird/thunderbolt explicitly
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

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/thunderbird/thunderbolt"><img src="https://repogeo.com/badge/thunderbird/thunderbolt.svg" alt="RepoGEO" /></a>

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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