Best AI tools for< Arbitrate Disputes >

AI Judge is an innovative online platform that utilizes artificial intelligence technology to generate verdicts and resolve disputes between two parties. It provides a convenient and efficient way to settle disagreements without the need for lengthy and costly legal proceedings. AI Judge's platform is designed to be impartial and unbiased, ensuring that both parties have an equal opportunity to present their cases and receive a fair verdict. The AI algorithms carefully analyze the arguments, evidence, and legal principles presented by each party to determine a just and reasonable outcome. AI Judge's verdicts are not legally binding but serve as an additional perspective for the contending parties to consider in their dispute resolution process. Additionally, users have the option to have a qualified legal professional review the AI-generated verdict for an extra layer of scrutiny and confidence. AI Judge is constantly striving to improve its AI algorithms and ensure that its platform adheres to ethical standards. It values user feedback and continuously works to refine its processes, ensuring that it remains a reliable tool for dispute resolution.

SmartScout is a comprehensive Amazon FBA product research software designed to help grow wholesale, arbitrage, and private label businesses. It provides valuable insights and tools for Amazon sellers to make informed decisions, increase revenue, and discover new opportunities. With features like Ad Spy, AI Listing Architect, FBA Calculator, Keyword Detective, and Seller Tools, SmartScout offers a competitive edge in the e-commerce landscape. The platform also offers resources such as industry reports, a blog, and a university to educate users on leveraging its tools effectively.

Lazy AI is a platform that enables users to build full stack web applications 10 times faster by utilizing AI technology. Users can create and modify web apps with prompts and deploy them to the cloud with just one click. The platform offers a variety of features including AI Component Builder, eCommerce store creation, Crypto Arbitrage Scraper, Text to Speech Converter, Lazy Image to Video generation, PDF Chatbot, and more. Lazy AI aims to streamline the app development process and empower users to leverage AI for various tasks.

MetaMevs is an advanced AI-powered trading solution designed to maximize profits in the fast-paced world of decentralized finance (DeFi). The platform offers cutting-edge technology and customizable solutions to help traders stay ahead of the curve and capitalize on market opportunities. With features like Sandwich Mevbot, HFT Futures and Spot Bot, FlashLoan Arbitrage Bot, and MetaMev Sniper, MetaMevs provides unmatched performance, security, and reliability for users to optimize their trading strategies.

This website provides a tool that allows users to check if an image is safe for work (SFW) or not. The tool uses Stable Diffusion's safety checker, which can be used with arbitrary images, not just AI-generated ones. Users can upload an image or drag and drop it onto the website to check if it is SFW.

EDGE is an AI-powered tool for editable dance generation from music. It utilizes a transformer-based diffusion model paired with Jukebox to create realistic and physically-plausible dances while staying faithful to input music. The tool offers powerful editing capabilities such as joint-wise conditioning, motion in-betweening, and dance continuation. EDGE has been compared to other methods like Bailando and FACT, with human raters showing a strong preference for dances generated by EDGE. The tool supports arbitrary spatial and temporal constraints, enabling users to create dances of any length with temporal continuity, joint-wise constraints, and more. EDGE also focuses on physical plausibility by avoiding unintentional foot sliding and incorporating a Contact Consistency Loss to improve realism.

The 'curriculum' repository is an open-source content repository by Enki, providing a community-driven curriculum for education. It follows a contributor covenant code of conduct to ensure a safe and engaging learning environment. The content is licensed under Creative Commons, allowing free use for non-commercial purposes with attribution to Enki and the author.

EVE is an official PyTorch implementation of Unveiling Encoder-Free Vision-Language Models. The project aims to explore the removal of vision encoders from Vision-Language Models (VLMs) and transfer LLMs to encoder-free VLMs efficiently. It also focuses on bridging the performance gap between encoder-free and encoder-based VLMs. EVE offers a superior capability with arbitrary image aspect ratio, data efficiency by utilizing publicly available data for pre-training, and training efficiency with a transparent and practical strategy for developing a pure decoder-only architecture across modalities.

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

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.

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.

Galah is an LLM-powered web honeypot designed to mimic various applications and dynamically respond to arbitrary HTTP requests. It supports multiple LLM providers, including OpenAI. Unlike traditional web honeypots, Galah dynamically crafts responses for any HTTP request, caching them to reduce repetitive generation and API costs. The honeypot's configuration is crucial, directing the LLM to produce responses in a specified JSON format. Note that Galah is a weekend project exploring LLM capabilities and not intended for production use, as it may be identifiable through network fingerprinting and non-standard responses.

Endia is a dynamic Array library for Scientific Computing, offering automatic differentiation of arbitrary order, complex number support, dual API with PyTorch-like imperative or JAX-like functional interface, and JIT Compilation for speeding up training and inference. It can handle complex valued functions, perform both forward and reverse-mode automatic differentiation, and has a builtin JIT compiler. Endia aims to advance AI & Scientific Computing by pushing boundaries with clear algorithms, providing high-performance open-source code that remains readable and pythonic, and prioritizing clarity and educational value over exhaustive features.

AiiDA (www.aiida.net) is a workflow manager for computational science with a strong focus on provenance, performance and extensibility. **Features** * **Workflows:** Write complex, auto-documenting workflows in python, linked to arbitrary executables on local and remote computers. The event-based workflow engine supports tens of thousands of processes per hour with full checkpointing. * **Data provenance:** Automatically track inputs, outputs & metadata of all calculations in a provenance graph for full reproducibility. Perform fast queries on graphs containing millions of nodes. * **HPC interface:** Move your calculations to a different computer by changing one line of code. AiiDA is compatible with schedulers like SLURM, PBS Pro, torque, SGE or LSF out of the box. * **Plugin interface:** Extend AiiDA with plugins for new simulation codes (input generation & parsing), data types, schedulers, transport modes and more. * **Open Science:** Export subsets of your provenance graph and share them with peers or make them available online for everyone on the Materials Cloud. * **Open source:** AiiDA is released under the MIT open source license

Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

EZKL is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). It enables the following workflow: 1. Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow. 2. Export the final graph of operations as an .onnx file and some sample inputs to a .json file. 3. Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as: > "I ran this publicly available neural network on some private data and it produced this output" > "I ran my private neural network on some public data and it produced this output" > "I correctly ran this publicly available neural network on some public data and it produced this output" In the backend we use the collaboratively-developed Halo2 as a proof system. The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device.

This tool utilizes the OpenAI ChatGPT API to translate text, with a focus on line-based translation, particularly for SRT subtitles. It optimizes token usage by removing SRT overhead and grouping text into batches, allowing for arbitrary length translations without excessive token consumption while maintaining a one-to-one match between line input and output.

FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) is a tool designed for uniform quantization and lookup table quantization of weights in lower-precision intervals. It offers flexibility in mapping intervals to arbitrary values through a lookup table. FLUTE supports various quantization formats such as int4, int3, int2, fp4, fp3, fp2, nf4, nf3, nf2, and even custom tables. The tool also introduces new quantization algorithms like Learned Normal Float (NFL) for improved performance and calibration data learning. FLUTE provides benchmarks, model zoo, and integration with frameworks like vLLM and HuggingFace for easy deployment and usage.

MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities: * Emotional speech rhythm and tone in English. * Zero-shot cloning for American & British voices, with 30s reference audio. * Support for (cross-lingual) voice cloning with finetuning. * We have had success with as little as 1 minute training data for Indian speakers. * Synthesis of arbitrary length text

db-ally is a library for creating natural language interfaces to data sources. It allows developers to outline specific use cases for a large language model (LLM) to handle, detailing the desired data format and the possible operations to fetch this data. db-ally effectively shields the complexity of the underlying data source from the model, presenting only the essential information needed for solving the specific use cases. Instead of generating arbitrary SQL, the model is asked to generate responses in a simplified query language.

The 'ollama-ebook-summary' repository is a Python project that creates bulleted notes summaries of books and long texts, particularly in epub and pdf formats with ToC metadata. It automates the extraction of chapters, splits them into ~2000 token chunks, and allows for asking arbitrary questions to parts of the text for improved granularity of response. The tool aims to provide summaries for each page of a book rather than a one-page summary of the entire document, enhancing content curation and knowledge sharing capabilities.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

DeepBI is an AI-native data analysis platform that leverages the power of large language models to explore, query, visualize, and share data from any data source. Users can use DeepBI to gain data insight and make data-driven decisions.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

Quanta is a new kind of Content Management platform, with powerful features including: Wikis & micro-blogging, ChatGPT Question Answering, Document collaboration and publishing, PDF Generation, Secure messaging with (E2E Encryption), Video/audio recording & sharing, File sharing, Podcatcher (RSS Reader), and many other features related to managing hierarchical content.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

JUEZ GPT es una herramienta diseñada para arbitrar conflictos y tomar decisiones de manera pragmática, justa y lógica, proporcionando veredictos claros

Your go-to guy for tenancy law. Got a landlord or tenant issue? Better Call Saul

Sports betting arbitrage assistant for horse racing and other sports.

A savvy guide for retail arbitrage.

Revisa las cotizaciones el BTC, USDT y USDC en distintas apps.

任意のイラストを作成するGPT／GPTs to create arbitrary illustrations.