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What is Twigg?

Twigg is a cutting-edge tool designed to manage context, transforming conventional linear chat threads into a dynamic, visually branching tree of conversation nodes that enables users to control the context presented to the model. This unique approach allows users to navigate various conversation routes by creating new branches from any node, while also optimizing their prompts through actions like cutting, copying, deleting, or relocating content, ultimately improving performance and reducing complexity. The platform boasts an intuitive dashboard that tracks token usage for each model, offering valuable insights into credit consumption. By granting control at the node level, Twigg reduces unnecessary token expenditure and supports simultaneous idea development without complicating the main workflow. It seamlessly integrates with leading models via a Bring Your Own Key (BYOK) system and is specifically designed for projects that demand prolonged engagement, thus earning the title of the “Git for LLMs” by providing version control, branching, and detailed context management for conversational AI tasks. Consequently, Twigg not only boosts the efficiency of user interactions but also equips users to enhance their conversational strategies more effectively. Additionally, its innovative features position Twigg as a transformative solution in the realm of conversational AI, fostering deeper and more meaningful exchanges.

What is LTM-2-mini?

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

Media

Media

Integrations Supported

ChatGPT
Claude Opus 4.1
Claude Opus 4.5
Claude Opus 4.6
Claude Opus 4.7
GPT-5 mini
Gemini 2.0 Flash

Integrations Supported

ChatGPT
Claude Opus 4.1
Claude Opus 4.5
Claude Opus 4.6
Claude Opus 4.7
GPT-5 mini
Gemini 2.0 Flash

API Availability

Has API

API Availability

Has API

Pricing Information

$6.66 per month
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Twigg

Date Founded

2025

Company Location

United States

Company Website

twigg.ai/

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

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