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Average Ratings 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

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Description

Confer is a privacy-first AI chatbot developed by Signal founder Moxie Marlinspike to challenge the data practices of popular AI assistants. It mirrors the conversational experience of tools like ChatGPT while eliminating the risk of user data collection. Confer is built so that conversations cannot be accessed, stored, or monetized by the service itself. Messages are protected using WebAuthn-based encryption, ensuring secure communication between users and the AI. On the backend, all processing occurs inside a Trusted Execution Environment with remote attestation safeguards. The system uses open-weight AI models to handle a wide range of user queries. This approach makes Confer far more complex than traditional AI setups but significantly more secure. Users can safely discuss highly personal topics without worrying about surveillance or profiling. Confer offers a limited free tier for casual use. A paid plan unlocks unlimited access and more advanced capabilities for privacy-conscious users.

Description

LFM3 represents Liquid AI's suite of streamlined, open-weight foundational models designed for a variety of applications, including text, vision, audio, and multimodal tasks. This lineup features variants for text generation, vision-language processing, audio, omni tasks, and decision-making models. Developers have the option to download the model weights, customize the supported models via fine-tuning, and deploy them on local edge hardware, or alternatively, utilize Liquid AI’s hosted API for access. Potential applications encompass conversational agents, tool invocation, speech transcription and synthesis, understanding of images and videos, document extraction, classification, routing, and robotics functionalities. LFM3 aims to cater to developers, researchers, and organizations focused on creating tailored AI solutions that require adaptable deployment options, including the ability for local inference. Furthermore, the open-weight models operate under Liquid AI’s LFM Open License, where the eligibility for commercial use and its associated terms are contingent upon the annual revenue of the organization. This flexibility allows users to tailor their AI implementations precisely to their needs, enhancing the overall utility of the models.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Hugging Face No 
Hugging Face Transformers No 
LM Studio No 
Ollama No 
OpenRouter No 
Unsloth No 
Vercel AI Gateway No 
vLLM No 

Integrations

Hugging Face Yes 
Hugging Face Transformers Yes 
LM Studio Yes 
Ollama Yes 
OpenRouter Yes 
Unsloth Yes 
Vercel AI Gateway Yes 
vLLM Yes 

Pricing Details

$35/month
Free Trial Yes 
Free Version Yes 

Pricing Details

$0.08/MTok
LFM3-1.7B API usage is $0.08 per million input tokens, $0.02 per million cached input tokens, and $0.20 per million output tokens. New organizations receive $2 in API credit. Open-weight models can be used in production free by developers, researchers, nonprofits, and businesses with less than $10 million in annual revenue; businesses above that threshold require a paid license.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Confer

Website

confer.to/

Vendor Details

Company Name

Liquid AI

Founded

2023

Country

United States

Website

www.liquid.ai/blog/lfm3

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