Best AI tools for< Halal Food Researcher >

Salam Chat is an AI-powered chatbot application designed to help users learn about Islam more easily. It features a halal filter that provides citations to the Quran & Hadith, ensuring the accuracy of the information shared. The chatbot is volunteer-driven and open-source, with plans to implement more features in the future. It is created by the Minority Think Tank Foundation, aiming to offer a ChatGPT alternative for the Muslim community.

Theoriestudio.nl is an online platform that provides a comprehensive theory course, practice tests, and mock exams to help users pass their theory exam for driving license in the Netherlands. The platform utilizes AI (Artificial Intelligence) to enhance the learning experience and improve the chances of passing the exam in one attempt. With its user-friendly interface, accessible on any device, and a supportive community, Theoriestudio.nl aims to make the process of obtaining a driving license easier and more efficient.

AIDE (Advanced Intrusion Detection Environment) is a tool for monitoring file system changes. It can be used to detect unauthorized changes to monitored files and directories. AIDE was written to be a simple and free alternative to Tripwire. Features currently included in AIDE are as follows: o File attributes monitored: permissions, inode, user, group file size, mtime, atime, ctime, links and growing size. o Checksums and hashes supported: SHA1, MD5, RMD160, and TIGER. CRC32, HAVAL and GOST if Mhash support is compiled in. o Plain text configuration files and database for simplicity. o Rules, variables and macros that can be customized to local site or system policies. o Powerful regular expression support to selectively include or exclude files and directories to be monitored. o gzip database compression if zlib support is compiled in. o Free software licensed under the GNU General Public License v2.

Ollama-SwiftUI is a user-friendly interface for Ollama.ai created in Swift. It allows seamless chatting with local Large Language Models on Mac. Users can change models mid-conversation, restart conversations, send system prompts, and use multimodal models with image + text. The app supports managing models, including downloading, deleting, and duplicating them. It offers light and dark mode, multiple conversation tabs, and a localized interface in English and Arabic.

This repository contains course materials for T81 559: Applications of Generative Artificial Intelligence at Washington University in St. Louis. The course covers practical applications of Large Language Models (LLMs) and text-to-image networks using Python. Students learn about generative AI principles, LangChain, Retrieval-Augmented Generation (RAG) model, image generation techniques, fine-tuning neural networks, and prompt engineering. Ideal for students, researchers, and professionals in computer science, the course offers a transformative learning experience in the realm of Generative AI.

UNav-Sim is an open-source underwater robotics simulator tool that leverages the power of Unreal Engine 5 (UE5) and AirSim to provide highly detailed rendering and simulation capabilities. With UNav-Sim, you can explore underwater terrains, design and test autonomous underwater vehicles (AUVs), and accelerate your learning and experimentation process in the field of underwater robotics.

Airgeddon is a versatile bash script designed for Linux systems to conduct wireless network audits. It provides a comprehensive set of features and tools for auditing and securing wireless networks. The script is user-friendly and offers functionalities such as scanning, capturing handshakes, deauth attacks, and more. Airgeddon is regularly updated and supported, making it a valuable tool for both security professionals and enthusiasts.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

Habitat-Sim is a high-performance physics-enabled 3D simulator with support for 3D scans of indoor/outdoor spaces, CAD models of spaces and piecewise-rigid objects, configurable sensors, robots described via URDF, and rigid-body mechanics. It prioritizes simulation speed over the breadth of simulation capabilities, achieving several thousand frames per second (FPS) running single-threaded and over 10,000 FPS multi-process on a single GPU when rendering a scene from the Matterport3D dataset. Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each ‘step’ involves rendering 1 RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec.

This repository contains a curated list of resources on generative information retrieval, including research papers, datasets, tools, and applications. Generative information retrieval is a subfield of information retrieval that uses generative models to generate new documents or passages of text that are relevant to a given query. This can be useful for a variety of tasks, such as question answering, summarization, and document generation. The resources in this repository are intended to help researchers and practitioners stay up-to-date on the latest advances in generative information retrieval.

Laravel Slower is a powerful package designed for Laravel developers to optimize the performance of their applications by identifying slow database queries and providing AI-driven suggestions for optimal indexing strategies and performance improvements. It offers actionable insights for debugging and monitoring database interactions, enhancing efficiency and scalability.

Kumo search is an end-to-end search engine framework that supports full-text search, inverted index, forward index, sorting, caching, hierarchical indexing, intervention system, feature collection, offline computation, storage system, and more. It runs on the EA (Elastic automic infrastructure architecture) platform, enabling engineering automation, service governance, real-time data, service degradation, and disaster recovery across multiple data centers and clusters. The framework aims to provide a ready-to-use search engine framework to help users quickly build their own search engines. Users can write business logic in Python using the AOT compiler in the project, which generates C++ code and binary dynamic libraries for rapid iteration of the search engine.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

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Professional, supportive Sharia-compliant forex guide.

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