Dashboard / Repos / Velaro

Velaro

D 54 completed
Other
desktop_app / json · small
83
Files
11,931
LOC
4
Frameworks
10
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#1535511
Phase
done
Progress
0%
Started
2026-04-16 16:14:46
Finished
2026-04-16 16:14:46
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
64.20
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:34:51
Deduplication group #53535
Member of a group with 6 similar repo(s) — canonical #1570966 view group →
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🧪 Code Distillation

Browse all specs →
Function bodies →
137
Real LLM specs →
105
76.6% coverage
All specs →
105
LLM insights →
0
Sample distilled functions (click for full spec)
formatParams
Formats a given numerical count into a human-readable string representation using appropriate magnitude suffixes. It accepts a single number as input and returns a string. The function scales the number to show Billions (B), Millions (M), or Thousands (K) if the count meets those thresholds, otherwi
estimateParams
Calculates an estimated total parameter count based on various model configuration parameters. It accepts an object containing the model's hidden size, number of layers, intermediate size, vocabulary size, and context length. The function returns a single number representing the sum of parameters fr
ConfigRow
Renders a structured display component for configuration settings. It accepts two string inputs: a label and a corresponding value. The function outputs a React element structured as a div containing two spans, one for the label and one for the value. There are no observable side effects beyond rend

AI Prompt

I want to build a desktop application called Velaro using Tauri and React. The core functionality should be a tool designed to create LLM models. The project structure should include a backend using FastAPI and a frontend built with Vite. I'll be using TypeScript for the main logic, and I need to ensure the project structure supports both JSON data handling and standard web technologies like HTML and CSS.
tauri react fastapi typescript desktop-app llm vite json python frontend backend
Generated by gemma4:latest

Catalog Information

I want to build a desktop application called Velaro using Tauri and React. The core functionality should be a tool designed to create LLM models. The project structure should include a backend using FastAPI and a frontend built with Vite. I'll be using TypeScript for the main logic, and I need to ensure the project structure supports both JSON data handling and standard web technologies like HTML and CSS.

Tags

tauri react fastapi typescript desktop-app llm vite json python frontend backend

Quality Score

D
53.5/100
Structure
46
Code Quality
84
Documentation
40
Testing
0
Practices
67
Security
72
Dependencies
90

Strengths

  • Code linting configured (eslint)
  • Properly licensed project

Weaknesses

  • No tests found — high risk of regressions
  • No CI/CD configuration — manual testing and deployment
  • 2 files with critical complexity need refactoring
  • 319 duplicate lines detected — consider DRY refactoring

Recommendations

  • Add a test suite — start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment

Languages

json
33.6%
typescript
24.4%
python
14.3%
html
14.1%
css
12.5%
markdown
0.4%
javascript
0.2%
rust
0.2%
toml
0.2%
text
0.1%

Frameworks

FastAPI React Tauri Vite

Symbols

variable247
function106
method29
interface19
class18
type_alias9
constant9

API Endpoints (27)

Open data · scored by Repobility · https://repobility.com
MethodPathHandlerFramework
Repobility · code-quality intelligence platform · https://repobility.com
GET/activeget_active_trainingsPython
POST/docker/{model_name}generate_dockerPython
POST/exportexport_modelPython
GET/export/{model_name}/fileslist_export_filesPython
POST/generategenerate_endpointPython
GET/healthhealth_checkFastAPI/Flask
GET/healthhealthFastAPI/Flask
POST/load/{model_name}load_model_endpointPython
POST/model-card/{model_name}create_model_cardPython
GET/{model_name}/metricsget_metricsPython
POST/{model_name}/pausepause_trainingPython
POST/{model_name}/resumeresume_trainingPython
POST/{model_name}/stopstop_trainingPython
GET/modelsget_available_modelsPython
POST/modelscreate_modelPython
GET/modelslist_modelsPython
POST/server/startstart_serverPython
GET/server/statusserver_statusPython
POST/server/stopstop_serverPython
POST/startstart_trainingPython
GET/statsget_system_statsPython
POST/unload/{model_name}unload_model_endpointPython
POST/v1/chat/completionschat_completionsFastAPI/Flask
POST/v1/completionscompletionsFastAPI/Flask
GET/v1/modelslist_modelsFastAPI/Flask
WEBSOCKET/ws/generategenerate_websocketPython
WEBSOCKET/ws/{model_name}training_websocketPython

Quality Timeline

1 quality score recorded.

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BinComp Dependency Hardening

All packages →
9 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.
Ftorch2.11.0 · 1,257 gadgets · risk 5116.6Nasyncio4.0.0 · 0 gadgets · risk 0.0Ndatasets4.8.4 · 0 gadgets · risk 0.0Nfastapi0.135.3 · 0 gadgets · risk 0.0Fpsutil7.2.2 · 19 gadgets · risk 0.0Npydantic2.12.5 · 0 gadgets · risk 0.0Dsafetensors0.7.0 · 421 gadgets · risk 0.0Dtiktoken0.12.0 · 763 gadgets · risk 0.0Nuvicorn0.44.0 · 0 gadgets · risk 0.0