The CryptoToken-Sender-Airdrop-Staking-Liquidity repository provides an ultimate tool for efficient and automated token distribution across blockchain wallets. It is designed for projects, DAOs, and blockchain-based organizations that need to distribute tokens to thousands of wallet addresses with ease. The platform offers advanced integrations with DeFi protocols for staking, liquidity farming, and automated payments. Users can send tokens in bulk, distribute tokens to multiple wallets instantly, optimize gas fees, integrate with DeFi protocols for liquidity provision and staking, set up recurring payments, automate liquidity farming strategies, support multi-chain operations, monitor transactions in real-time, and work with various token standards. The repository includes features for connecting to blockchains, importing and managing wallets, customizing mailing parameters, monitoring transaction status, logging transactions, and providing a user-friendly interface for configuration and operation.

This repository extends the LLVM framework to generate code for use with AMD/Xilinx AI Engine processors. AI Engine processors are in-order, exposed-pipeline VLIW processors focused on application acceleration for AI, Machine Learning, and DSP applications. The repository adds LLVM support for specific features like non-power of 2 pointers, operand latencies, resource conflicts, negative operand latencies, slot assignment, relocations, code alignment restrictions, and register allocation. It includes support for Clang, LLD, binutils, Compiler-RT, and LLVM-LIBC.

Geoweaver is an in-browser software that enables users to easily compose and execute full-stack data processing workflows using online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides server management, code repository, workflow orchestration software, and history recording capabilities. Users can run it from both local and remote machines. Geoweaver aims to make data processing workflows manageable for non-coder scientists and preserve model run history. It offers features like progress storage, organization, SSH connection to external servers, and a web UI with Python support.

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

AI.Labs is an open-source project that integrates advanced artificial intelligence technologies to create a powerful AI platform. It focuses on integrating AI services like large language models, speech recognition, and speech synthesis for functionalities such as dialogue, voice interaction, and meeting transcription. The project also includes features like a large language model dialogue system, speech recognition for meeting transcription, speech-to-text voice synthesis, integration of translation and chat, and uses technologies like C#, .Net, SQLite database, XAF, OpenAI API, TTS, and STT.

SuperKnowa is a fast framework to build Enterprise RAG (Retriever Augmented Generation) Pipelines at Scale, powered by watsonx. It accelerates Enterprise Generative AI applications to get prod-ready solutions quickly on private data. The framework provides pluggable components for tackling various Generative AI use cases using Large Language Models (LLMs), allowing users to assemble building blocks to address challenges in AI-driven text generation. SuperKnowa is battle-tested from 1M to 200M private knowledge base & scaled to billions of retriever tokens.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.

DevOpsGPT is an AI-driven software development automation solution that combines Large Language Models (LLM) with DevOps tools to convert natural language requirements into working software. It improves development efficiency by eliminating the need for tedious requirement documentation, shortens development cycles, reduces communication costs, and ensures high-quality deliverables. The Enterprise Edition offers features like existing project analysis, professional model selection, and support for more DevOps platforms. The tool automates requirement development, generates interface documentation, provides pseudocode based on existing projects, facilitates code refinement, enables continuous integration, and supports software version release. Users can run DevOpsGPT with source code or Docker, and the tool comes with limitations in precise documentation generation and understanding existing project code. The product roadmap includes accurate requirement decomposition, rapid import of development requirements, and integration of more software engineering and professional tools for efficient software development tasks under AI planning and execution.

The ASReview project implements active learning for systematic reviews, utilizing AI-aided pipelines to assist in finding relevant texts for search tasks. It accelerates the screening of textual data with minimal human input, saving time and increasing output quality. The software offers three modes: Oracle for interactive screening, Exploration for teaching purposes, and Simulation for evaluating active learning models. ASReview LAB is designed to support decision-making in any discipline or industry by improving efficiency and transparency in screening large amounts of textual data.

Bisheng is a leading open-source **large model application development platform** that empowers and accelerates the development and deployment of large model applications, helping users enter the next generation of application development with the best possible experience.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

Llumnix is a cross-instance request scheduling layer built on top of LLM inference engines such as vLLM, providing optimized multi-instance serving performance with low latency, reduced time-to-first-token (TTFT) and queuing delays, reduced time-between-tokens (TBT) and preemption stalls, and high throughput. It achieves this through dynamic, fine-grained, KV-cache-aware scheduling, continuous rescheduling across instances, KV cache migration mechanism, and seamless integration with existing multi-instance deployment platforms. Llumnix is easy to use, fault-tolerant, elastic, and extensible to more inference engines and scheduling policies.

mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky (yorko). The course offers a perfect balance between theory and practice, with math formulae in lectures and practical assignments including Kaggle Inclass competitions. It is currently in a self-paced mode, guiding users through 10 weeks of content covering topics from Pandas to Gradient Boosting. The course provides articles, lectures, and assignments to enhance understanding and application of machine learning concepts.

Long-Context-Attention (YunChang) is a unified sequence parallel approach that combines the strengths of DeepSpeed-Ulysses-Attention and Ring-Attention to provide a versatile and high-performance solution for long context LLM model training and inference. It addresses the limitations of both methods by offering no limitation on the number of heads, compatibility with advanced parallel strategies, and enhanced performance benchmarks. The tool is verified in Megatron-LM and offers best practices for 4D parallelism, making it suitable for various attention mechanisms and parallel computing advancements.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

The CryptoToken-Sender-Airdrop-Staking-Liquidity repository provides an ultimate tool for efficient and automated token distribution across blockchain wallets. It is designed for projects, DAOs, and blockchain-based organizations that need to distribute tokens to thousands of wallet addresses with ease. The platform offers advanced integrations with DeFi protocols for staking, liquidity farming, and automated payments. Users can send tokens in bulk, distribute tokens to multiple wallets instantly, optimize gas fees, integrate with DeFi protocols for liquidity provision and staking, set up recurring payments, automate liquidity farming strategies, support multi-chain operations, monitor transactions in real-time, and work with various token standards. The repository includes features for connecting to blockchains, importing and managing wallets, customizing mailing parameters, monitoring transaction status, logging transactions, and providing a user-friendly interface for configuration and operation.

This repository contains Airflow DAGs for extracting, transforming, and loading (ETL) data from the Ethereum blockchain into BigQuery. The DAGs use the Google Cloud Platform (GCP) services, including BigQuery, Cloud Storage, and Cloud Composer, to automate the ETL process. The repository also includes scripts for setting up the GCP environment and running the DAGs locally.

Airnode is a fully-serverless oracle node that is designed specifically for API providers to operate their own oracles.

The 𝓜𝓔𝓥-𝓑𝓞𝓣 is a revolutionary tool that empowers users to maximize their ETH earnings through advanced slippage techniques within the Ethereum ecosystem. Its user-centric design, optimized earning mechanism, and comprehensive security measures make it an indispensable tool for traders seeking to enhance their crypto trading strategies. With its current free access, there's no better time to explore the 𝓜𝓔𝓥-𝓑𝓞𝓣's capabilities and witness the transformative impact it can have on your crypto trading journey.

CortexTheseus is a full node implementation of the Cortex blockchain, written in C++. It provides a complete set of features for interacting with the Cortex network, including the ability to create and manage accounts, send and receive transactions, and participate in consensus. CortexTheseus is designed to be scalable, secure, and easy to use, making it an ideal choice for developers building applications on the Cortex blockchain.

This tool is a bot that monitors the performance of MEV transactions on the Ethereum blockchain. It provides real-time data on MEV profitability, transaction volume, and network congestion. The bot can be used to identify profitable MEV opportunities and to track the performance of MEV strategies.

Airdrop-checker is a tool that helps you to check if you are eligible for any airdrops. It supports multiple airdrops, including Altlayer, Rabby points, Zetachain, Frame, Anoma, Dymension, and MEME. To use the tool, you need to install it using npm and then fill the addresses files in the addresses folder with your wallet addresses. Once you have done this, you can run the tool using npm start.

Cyber is a superintelligence protocol that aims to create a decentralized and censorship-resistant internet. It uses a novel consensus mechanism called CometBFT and a knowledge graph to store and process information. Cyber is designed to be scalable, secure, and efficient, and it has the potential to revolutionize the way we interact with the internet.

Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.