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Stream is an AI application developed by the Tensorplex Team to showcase the capabilities of existing Bittensor Subnets in powering consumer Web3 platforms. The application is designed to provide precise summaries and deep insights by utilizing the TPLX-LLM model. Stream offers a curated list of podcasts that are summarized using the Bittensor Network.

ChatGPT is a large language model developed by OpenAI. It is designed to understand and generate human-like text, and can be used for a variety of tasks such as answering questions, writing stories, and translating languages. ChatGPT is free to use, and can be accessed through a web interface or via an API.

Image In Words is a generative model designed for scenarios that require generating ultra-detailed text from images. It leverages cutting-edge image recognition technology to provide high-quality and natural image descriptions. The framework ensures detailed and accurate descriptions, improves model performance, reduces fictional content, enhances visual-language reasoning capabilities, and has wide applications across various fields. Image In Words supports English and has been trained using approximately 100,000 hours of English data. It has demonstrated high quality and naturalness in various tests.

CEBRA is a machine-learning method that compresses time series data to reveal hidden structures in the variability of the data. It excels in analyzing behavioral and neural data simultaneously, allowing for the decoding of activity from the visual cortex of the mouse brain to reconstruct viewed videos. CEBRA is a novel encoding method that leverages both behavioral and neural data to produce consistent and high-performance latent spaces, enabling the mapping of space, uncovering complex kinematic features, and providing rapid, high-accuracy decoding of natural movies from the visual cortex.

iLoveSong.ai is an AI music generator application that allows users to create original AI songs based on user input. It offers features like generating complete songs in minutes, demonstrating various music styles for educational purposes, creating custom music for content creators, producing soundscapes for game development, and more. Users can choose from different subscription plans to access various features and benefits. The application is designed to break barriers between users and the music they dream of making, requiring no instruments, only imagination.

Docebo is an AI-powered learning platform designed for businesses to deliver innovative and valuable learning experiences. It offers solutions for employee onboarding, compliance training, sales enablement, talent development, customer education, partner enablement, and member training. With features like AI-powered learning, content creation, embedded learning, learning intelligence, and a generative AI LMS, Docebo aims to help organizations drive engagement, productivity, advocacy, and connection with their stakeholders.

The website offers a brand new course titled 'Prompt Engineering for Everyone' to help users master the language of AI. With over 100 courses and 20+ learning paths, users can learn AI, Data Science, and other emerging technologies. The platform provides hands-on content designed by expert instructors, allowing users to gain practical, industry-relevant knowledge and skills. Users can earn certificates to showcase their expertise and build projects to demonstrate their skills. Trusted by 3 million learners globally, the platform offers a community of learners with a proven track record of success.

Identable is an all-in-one AI-powered platform for social media marketing solutions, specializing in personal branding and social media management. It offers automated scheduling, real-time performance tracking, personalized content recommendations, and intelligent content optimization. With Identable, users can streamline their social media workflow, maximize visibility and engagement across channels, and access customizable content templates. The platform also provides detailed analytics and insights to help users optimize their social media strategy and demonstrate the impact of their efforts.

Poker Bot AI+ is an advanced poker AI application that offers fully automated poker bots powered by neural networks and machine learning. The application provides a suite of products to enhance poker gameplay, including automated online poker bots, AI advisor PokerX, Poker Ecology service, poker skill development with AI-guided tips, and Android-based poker farms on emulators. It supports various poker games and rooms, ensuring optimal decision-making for players. The software guarantees secure gameplay by emulating human behavior and safeguarding user identity. Before purchasing, the effectiveness of the poker bot is demonstrated privately. Poker Bot AI+ aims to revolutionize the poker industry with cutting-edge AI technology.

Phenaki is a model capable of generating realistic videos from a sequence of textual prompts. It is particularly challenging to generate videos from text due to the computational cost, limited quantities of high-quality text-video data, and variable length of videos. To address these issues, Phenaki introduces a new causal model for learning video representation, which compresses the video to a small representation of discrete tokens. This tokenizer uses causal attention in time, which allows it to work with variable-length videos. To generate video tokens from text, Phenaki uses a bidirectional masked transformer conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to create the actual video. To address data issues, Phenaki demonstrates how joint training on a large corpus of image-text pairs as well as a smaller number of video-text examples can result in generalization beyond what is available in the video datasets. Compared to previous video generation methods, Phenaki can generate arbitrarily long videos conditioned on a sequence of prompts (i.e., time-variable text or a story) in an open domain. To the best of our knowledge, this is the first time a paper studies generating videos from time-variable prompts. In addition, the proposed video encoder-decoder outperforms all per-frame baselines currently used in the literature in terms of spatio-temporal quality and the number of tokens per video.

Devin AI, developed by Cognition Labs, is the world's first fully autonomous AI software engineer. It streamlines software development by handling complex tasks, allowing engineers to focus on more challenging aspects. Devin AI possesses advanced programming skills, can manage complex tasks, understands and learns contextually, integrates with developer tools, and offers collaborative features. It can build and deploy applications, detect and fix bugs, contribute to open-source projects, train AI models, and handle GitHub repositories. Devin AI has demonstrated strong performance in issue resolution, surpassing previous AI models. It is currently in early access, with plans for future enhancements and integration with various development tools and platforms.

GenInnov is a generative innovation fund that provides a platform for investors seeking to be at the forefront of technological advancement. The fund invests in companies driving transformative change across multiple sectors and geographies, prioritizing material innovations with demonstrable profitability and global reach. GenInnov operates with a research-driven approach, focusing on investing in material innovations that are monetizable, profitable, and transformative, rather than incremental. The fund looks at various domains such as technology, robotics, consumer electronics, biotech, healthcare, mobility, and clean tech, aiming to amplify human creativity through machine intelligence.

Sample notebooks for IBM Watsonx.ai for IBM Cloud and IBM Watsonx.ai software product. The notebooks demonstrate capabilities such as running experiments on model building using AutoAI or Deep Learning, deploying third-party models as web services or batch jobs, monitoring deployments with OpenScale, managing model lifecycles, inferencing Watsonx.ai foundation models, and integrating LangChain with Watsonx.ai. Notebooks with Python code and the Python SDK can be found in the `python_sdk` folder. The REST API examples are organized in the `rest_api` folder.

LLM Agentic Tool Mesh is a platform by HPE Athonet that democratizes Generative Artificial Intelligence (Gen AI) by enabling users to create tools and web applications using Gen AI with Low or No Coding. The platform simplifies the integration process, focuses on key user needs, and abstracts complex libraries into easy-to-understand services. It empowers both technical and non-technical teams to develop tools related to their expertise and provides orchestration capabilities through an agentic Reasoning Engine based on Large Language Models (LLMs) to ensure seamless tool integration and enhance organizational functionality and efficiency.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

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.

AI4Animation is a comprehensive framework for data-driven character animation, including data processing, neural network training, and runtime control, developed in Unity3D/PyTorch. It explores deep learning opportunities for character animation, covering biped and quadruped locomotion, character-scene interactions, sports and fighting games, and embodied avatar motions in AR/VR. The research focuses on generative frameworks, codebook matching, periodic autoencoders, animation layering, local motion phases, and neural state machines for character control and animation.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.

MarkLLM is an open-source toolkit designed for watermarking technologies within large language models (LLMs). It simplifies access, understanding, and assessment of watermarking technologies, supporting various algorithms, visualization tools, and evaluation modules. The toolkit aids researchers and the community in ensuring the authenticity and origin of machine-generated text.

`npcsh` is a python-based command-line tool designed to integrate Large Language Models (LLMs) and Agents into one's daily workflow by making them available and easily configurable through the command line shell. It leverages the power of LLMs to understand natural language commands and questions, execute tasks, answer queries, and provide relevant information from local files and the web. Users can also build their own tools and call them like macros from the shell. `npcsh` allows users to take advantage of agents (i.e. NPCs) through a managed system, tailoring NPCs to specific tasks and workflows. The tool is extensible with Python, providing useful functions for interacting with LLMs, including explicit coverage for popular providers like ollama, anthropic, openai, gemini, deepseek, and openai-like providers. Users can set up a flask server to expose their NPC team for use as a backend service, run SQL models defined in their project, execute assembly lines, and verify the integrity of their NPC team's interrelations. Users can execute bash commands directly, use favorite command-line tools like VIM, Emacs, ipython, sqlite3, git, pipe the output of these commands to LLMs, or pass LLM results to bash commands.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

CosyVoice is a tool designed for speech synthesis, offering pretrained models for zero-shot, sft, instruct inference. It provides a web demo for easy usage and supports advanced users with train and inference scripts. The tool can be deployed using grpc for service deployment. Users can download pretrained models and resources for immediate use or train their own models from scratch. CosyVoice is suitable for researchers, developers, linguists, AI engineers, and speech technology enthusiasts.

Macaw-LLM is a pioneering multi-modal language modeling tool that seamlessly integrates image, audio, video, and text data. It builds upon CLIP, Whisper, and LLaMA models to process and analyze multi-modal information effectively. The tool boasts features like simple and fast alignment, one-stage instruction fine-tuning, and a new multi-modal instruction dataset. It enables users to align multi-modal features efficiently, encode instructions, and generate responses across different data types.

APOLLO is a memory-efficient optimizer designed for large language model (LLM) pre-training and full-parameter fine-tuning. It offers SGD-like memory cost with AdamW-level performance. The optimizer integrates low-rank approximation and optimizer state redundancy reduction to achieve significant memory savings while maintaining or surpassing the performance of Adam(W). Key contributions include structured learning rate updates for LLM training, approximated channel-wise gradient scaling in a low-rank auxiliary space, and minimal-rank tensor-wise gradient scaling. APOLLO aims to optimize memory efficiency during training large language models.

The ChatBotKit Node SDK is a JavaScript-based platform for building conversational AI bots and agents. It offers easy setup, serverless compatibility, modern framework support, customizability, and multi-platform deployment. With capabilities like multi-modal and multi-language support, conversation management, chat history review, custom datasets, and various integrations, this SDK enables users to create advanced chatbots for websites, mobile apps, and messaging platforms.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

The Turing Test, first named the imitation game by Alan Turing in 1950, is a measure of a machine's capacity to demonstrate intelligence that's either equal to or indistinguishable from human intelligence.