HugstonOne 1.0.7 Enterprise Edition

The free AI in A Box for all Standalone with installer (exe, msi) and portable for windows.

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A Workflow/Software for AI inference.

• Image to text / Text to text
• Offline/Online Mode for model and data isolation
• Load LLM Models in CLI or SERVER (both with image processing mode integrated
• Local API with CORS option
• GPU (Nvidia) / CPU support
• Instant Preview of the code
• Code Editior also with preview
• GGUF support 10000+ LLM Models
• Session and minisessions with persistent memory
• Offline/Online Button for completely offline (cutting all connections)
• Terminal for advanced use (with edit mode active)
• Upload files (only TXT and JPEG/PNG) for now
• Export tables in CSV and save sessions/tabs/preview
• Easy to create Webpages/Graphic interfaces
• Great for creating games, tables, maps with data, charts, plots
• Good coding assistant (hopping from tab to tab, conserving memory, solving the ctx size and memory issue)
• Live news feed on the app while working (can retrieve all news/radio/podcast feeds)
• Ideal for Medical Research (with free medical models available from HuggingFace or Hugston.com)
• AI-powered diagnostics for X-rays, MRI, drawings, 3D modeling, etc. (via Image-to-Text)
• Multitasking like a browser/app, now equipped with AI—no subscription or worry about costs

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Our Strongest Points:

• We don’t collect user data
• No forced updates included in the app
• No telemetry
• No HTTP involved (load with local CLI)
• No internet required unless explicitly chosen by the user
• No API key required
• Supports long lines of code Input/Output (50000+ tokens tested/tab)
• No refusal Using the right LLM model (potential for 200,000+ code lines, divided into 1000-5000-20000 lines at once)
• Completely free

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Getting Started

First-time users need to request the activation code via email to:

• [email protected] (Check spam if the email is delayed, as we are a new service.)

🔗 Download the App: Hugston Website

Enjoy!

Some examples: Of the previous versions

Future goals:

• 1 To make it more accessible to users
• 2 Connecting to Hugston Repository for curated models
• 3 Adding chat between HugstonOne Users for real time talk
• 4 Integrating better memory options like Memos Framework
• 5 Build for Linux, MacOs, Mobile Platforms.

Credits

Many thanks for the great job to:

• Co-authored-by: Leyden Fernandez Vidal https://github.com/microbioinformatic

Acknowledgments

This project bundles the following third-party libraries:

• llama.cpp by Georgi Gerganov et al.
• Electron