Best AI tools for< Smoothly Loop Recordings >

Flowith is a node-based GPT-4 and Claude-3 driven AI productivity tool that helps you write better, faster, and more creatively. With Flowith, you can generate text, translate languages, write different kinds of creative content, summarize long pieces of text, answer your questions, debug and write codes, and more. Flowith is designed to be easy to use, even for beginners. With its user-friendly interface and powerful AI capabilities, Flowith can help you save time and improve your productivity.

TTAPI is an AI tool that offers affordable and smoothly integrated Midjourney API V6 solutions for websites and applications. It allows users to generate and return images based on descriptive terms within 90 seconds. Additionally, TTAPI provides services like DALL-E text-to-image generation models and SwapFace face-changing service. The platform aims to simplify the process of integrating Midjourney API into projects, offering a user-friendly experience for developers and businesses.

Ain Finance is an AI-driven platform offering professional voice AI-driven crypto operations services. It aims to simplify the crypto journey for investors, traders, developers, and enthusiasts by providing a comprehensive suite of services all in one place. Users can access various crypto services, such as sending, swapping, staking, price alerts, and more, through a mobile-friendly AI-powered application. Ainexa AI Agent assists users in performing crypto operations smoothly, providing analytics, alerts, and transaction tracking. The platform also encourages users to share their ideas for new services or features, fostering community engagement and continuous improvement.

This is a super customizable, concise, user-friendly, and efficient toolkit for training and aligning LLMs. It provides support for various methods such as SFT, Distillation, DPO, ORPO, CPO, SimPO, SMPO, Non-pair Reward Modeling, Special prompts basket format, Rejection Sampling, Scoring using RM, Effective FAISS Map-Reduce Deduplication, LLM scoring using RM, NER, CLIP, Classification, and STS. The toolkit offers key libraries like PyTorch, Transformers, TRL, Accelerate, FSDP, DeepSpeed, and tools for result logging with wandb or clearml. It allows mixing datasets, generation and logging in wandb/clearml, vLLM batched generation, and aligns models using the SMPO method.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

KaibanJS is a JavaScript-native framework for building multi-agent AI systems. It enables users to create specialized AI agents with distinct roles and goals, manage tasks, and coordinate teams efficiently. The framework supports role-based agent design, tool integration, multiple LLMs support, robust state management, observability and monitoring features, and a real-time agentic Kanban board for visualizing AI workflows. KaibanJS aims to empower JavaScript developers with a user-friendly AI framework tailored for the JavaScript ecosystem, bridging the gap in the AI race for non-Python developers.

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

AddaxAI is an application designed to streamline the work of ecologists dealing with camera trap images. It's an AI platform that allows you to analyse images with machine learning models for automatic detection, offering ecologists a way to save time and focus on conservation efforts.

FastLLM is a high-performance large model inference library implemented in pure C++ with no third-party dependencies. Models of 6-7B size can run smoothly on Android devices. Deployment communication QQ group: 831641348

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

Libre Aim is a free and open source first-person shooter (FPS) aim trainer developed using Godot. It is highly customizable, allowing users to modify all aspects of the tool easily. The focus is on providing a lightweight training tool that runs smoothly on low-end machines, offering high FPS and minimal input lag. Although still in early development stages, Libre Aim aims to provide a platform for users to enhance their aiming skills in FPS games through customizable features and simplicity.

MagicMirror is an AI-powered tool that allows users to instantly try on new faces, hairstyles, and outfits with a simple drag and drop interface. It runs smoothly on standard computers without the need for dedicated GPU hardware, ensuring privacy with completely offline processing. The tool is ultra-lightweight with a small installer size and model files, providing a fun and easy way to experiment with different looks.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

prose-polish is a tool for AI interaction through drag-and-drop cards, focusing on editing copy and manuscripts. It can recognize Markdown-formatted documents, automatically breaking them into paragraph cards. Users can create prefabricated prompt cards and quickly connect them to the manuscript for editing. The modified manuscript is still presented in card form, allowing users to drag it out as a new paragraph. To use it smoothly, users just need to remember one rule: 'Plug the plug into the socket!'

Effort is an example implementation of the bucketMul algorithm, which allows for real-time adjustment of the number of calculations performed during inference of an LLM model. At 50% effort, it performs as fast as regular matrix multiplications on Apple Silicon chips; at 25% effort, it is twice as fast while still retaining most of the quality. Additionally, users have the option to skip loading the least important weights.

RPA Framework is an open-source collection of libraries and tools for Robotic Process Automation (RPA), designed to be used with Robot Framework and Python. It offers well-documented core libraries for Software Robot Developers, optimized for Robocorp Control Room and Developer Tools, and accepts external contributions. The project includes various libraries for tasks like archiving, browser automation, date/time manipulations, cloud services integration, encryption operations, database interactions, desktop automation, document processing, email operations, Excel manipulation, file system operations, FTP interactions, web API interactions, image manipulation, AI services, and more. The development of the repository is Python-based and requires Python version 3.8+, with tooling based on poetry and invoke for compiling, building, and running the package. The project is licensed under the Apache License 2.0.

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