Best AI tools for< Algorithm Implementation >

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

GPT4oMini.app is an AI tool that offers free chat services powered by GPT-4o-mini. Users can ask any question and receive answers for free on the platform. The tool supports topological sorting in Go using generics and provides a simple implementation of a directed graph. It uses Depth-First Search (DFS) for topological sorting and detects cycles in the graph. GPT4oMini.app also offers assistance with various topics, including love, emoji representation, and adult website suggestions.

Supple.ai is an AI-powered content generation tool that helps users create high-quality written content quickly and efficiently. By leveraging advanced natural language processing algorithms, Supple.ai can generate articles, blog posts, product descriptions, and more in a matter of minutes. The tool is designed to assist content creators, marketers, and businesses in streamlining their content creation process and improving productivity.

Textr AI is an all-in-one SEO companion that harnesses the power of AI to simplify SEO needs. It provides data-driven insights to help businesses improve their SEO and rankings. The tool boosts productivity, improves content quality, and enhances career prospects for freelancers, agencies, and in-house teams. With advanced algorithms, Textr AI automates the process of examining SERPs, identifies patterns in data, and helps optimize content to improve search rankings. It offers various pricing plans and resources to master SEO techniques.

Wolfram is a comprehensive platform that unifies algorithms, data, notebooks, linguistics, and deployment to provide a powerful computation platform. It offers a range of products and services for various industries, including education, engineering, science, and technology. Wolfram is known for its revolutionary knowledge-based programming language, Wolfram Language, and its flagship product Wolfram|Alpha, a computational knowledge engine. The platform also includes Wolfram Cloud for cloud-based services, Wolfram Engine for software implementation, and Wolfram Data Framework for real-world data analysis.

IngestAI is a Silicon Valley-based startup that provides a sophisticated toolbox for data preparation and model selection, powered by proprietary AI algorithms. The company's mission is to make AI accessible and affordable for businesses of all sizes. IngestAI's platform offers a turn-key service tailored for AI builders seeking to optimize AI application development. The company identifies the model best-suited for a customer's needs, ensuring it is designed for high performance and reliability. IngestAI utilizes Deepmark AI, its proprietary software solution, to minimize the time required to identify and deploy the most effective AI solutions. IngestAI also provides data preparation services, transforming raw structured and unstructured data into high-quality, AI-ready formats. This service is meticulously designed to ensure that AI models receive the best possible input, leading to unparalleled performance and accuracy. IngestAI goes beyond mere implementation; the company excels in fine-tuning AI models to ensure that they match the unique nuances of a customer's data and specific demands of their industry. IngestAI rigorously evaluates each AI project, not only ensuring its successful launch but its optimal alignment with a customer's business goals.

api4ai is a cloud-native AI application that offers image processing APIs powered by artificial intelligence. It provides affordable and personalized solutions for businesses, empowering them with computer vision and machine learning capabilities. The application allows users to monitor visitor statistics, expand product identification apps, integrate background removal algorithms, estimate marketing campaign effectiveness, automate production processes, manage clothing stocktaking, enhance car dealership ads, ensure workplace safety, and extract information for enterprises, startups, and developers. With a wide range of ready-to-use APIs and customization options, api4ai simplifies the implementation of AI solutions across various industries.

The Uncharted Algorithm is an AI tool that delves into the realms of AI, enterprise, culture, and the future of work. It aims to disrupt the conventional norms and provide insights at the intersection of technology and society. Authored by Aditya Kaul, this platform offers thought-provoking content and analysis on emerging trends and innovations in the AI landscape.

The website 'Create Next App Chat With the Algorithm' is an AI tool that allows users to generate chat applications using algorithms. It is a work in progress with the latest algorithm code updated on April 14, 2023. Users can leverage this tool to quickly create chat applications with the help of advanced algorithms.

Brandix is an AI-powered tool that helps users generate brand names in seconds. Users can choose from a variety of categories or input a message, and the tool will provide brand name suggestions. Additionally, Brandix instantly checks the domain availability for the generated names. With a user-friendly interface, Brandix aims to assist individuals and businesses in finding the perfect brand name efficiently.

The AI Maze Generator is an online tool that allows users to create, solve, and download random maze puzzles in various sizes and colors. It utilizes the recursive backtracking algorithm to design mazes and the A* search algorithm to find the shortest path. Users can customize maze specifications like wall thickness, columns, rows, maze entries, and bias. The tool offers a user-friendly interface for maze creation and solving, providing a fun and engaging experience for maze enthusiasts.

Composer is an AI-powered trading platform that allows users to build, backtest, and execute trading algorithms seamlessly. With Composer, users can create strategies using natural language and AI assistance, automate trading executions, discover pre-built strategies, and customize their own strategies with a visual editor. The platform offers transparent pricing, tax-smart trading options, and unlimited access to automated trading. Composer aims to make sophisticated quant investing accessible to all users, providing a user-friendly interface for creating and managing trading strategies.

Speech Intellect is an AI-powered speech-to-text and text-to-speech solution that provides real-time transcription and voice synthesis with emotional analysis. It utilizes a proprietary "Sense Theory" algorithm to capture the meaning and tone of speech, enabling businesses to automate tasks, improve customer interactions, and create personalized experiences.

BigShort is a real-time stock charting platform designed for day traders and swing traders. It offers a variety of features to help traders make informed decisions, including SmartFlow, which visualizes real-time covert Smart Money activity, and OptionFlow, which shows option blocks, sweeps, and splits in real-time. BigShort also provides backtested and forward-tested leading indicators, as well as live data for all NYSE and Nasdaq tickers.

ConvoSearch is an advanced AI search product designed to boost sales and enhance customer satisfaction for e-commerce stores. It offers a comprehensive search API, real-time analytics, and customer insights to improve the search experience. The platform integrates seamlessly with popular e-commerce platforms, providing solutions for small businesses and enterprises. ConvoSearch empowers businesses to understand customer behavior, optimize inventory, and increase conversions through AI-powered search capabilities.

QRCodeFox.com is a user-friendly online tool for generating customized QR codes using AI. It allows you to easily create AI-Powered QR codes for various purposes, such as website URLs, contact information, text messages, and more. With QRCodeFox.com, you can customize QR codes according to your needs, providing a quick and convenient way to generate QR codes for personal and business use.

CryptoMatic Bot is an automated algorithmic trading platform that leverages machine learning and artificial intelligence to help users trade cryptocurrencies more effectively. The platform offers a range of features including ready-made trading strategies, webhook integration with TradingView, a powerful trader panel, and the ability to customize settings. Users can start auto trading with ease, receive notifications on transactions, and grow their assets continuously. The platform aims to provide a stress-free trading experience and offers support to users through a dedicated team. CryptoMatic Bot is designed to assist users in making informed trading decisions and maximizing their profits in the volatile cryptocurrency market.

Kafkai is an AI-powered content writer that helps users create bulk, multilingual articles with just a few clicks. It offers a range of features such as one-click generation, keyword-based generation, SEO optimization, keyword research tools, automated image integration, long article generation, multi-language support, bulk generation, and multi-article format flexibility. Kafkai is trusted by over 8,000 customers and has generated over 500,000 articles since 2019.

Leans.AI is an AI-powered sports prediction algorithm that provides free sports picks and predictions for NFL, NBA, CBB, NHL, MLB, and CFB games. It uses AI technology to analyze thousands of data points on each game, calculate cover probabilities, assign units to picks, and release top picks daily. The application aims to help users make informed betting decisions based on data-driven insights and improve their chances of winning against the spread. Leans.AI is known for its transparency, historical performance metrics, and continuous improvement through machine learning techniques.

Obviously AI is a no-code AI tool that allows users to build and deploy machine learning models without writing any code. It is designed to be easy to use, even for those with no data science experience. Obviously AI offers a variety of features, including model building, model deployment, model monitoring, and integration with other tools. It also provides expert support from a dedicated data scientist.

RAGLAB is a modular, research-oriented open-source framework for Retrieval-Augmented Generation (RAG) algorithms. It offers reproductions of 6 existing RAG algorithms and a comprehensive evaluation system with 10 benchmark datasets, enabling fair comparisons between RAG algorithms and easy expansion for efficient development of new algorithms, datasets, and evaluation metrics. The framework supports the entire RAG pipeline, provides advanced algorithm implementations, fair comparison platform, efficient retriever client, versatile generator support, and flexible instruction lab. It also includes features like Interact Mode for quick understanding of algorithms and Evaluation Mode for reproducing paper results and scientific research.

EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. It provides a central leaderboard and submission interface, making it easier for researchers to reproduce results mentioned in papers and perform reliable & accurate quantitative analysis. EvalAI also offers features such as custom evaluation protocols and phases, remote evaluation, evaluation inside environments, CLI support, portability, and faster evaluation.

AIT is a repository focused on Algorithmic Information Theory, specifically utilizing Binary Lambda Calculus. It provides resources and tools for studying and implementing algorithms based on information theory principles. The repository aims to explore the relationship between algorithms and information theory through the lens of Binary Lambda Calculus, offering insights into computational complexity and data compression techniques.

The `xaitk-saliency` package is an open source Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, designed for analytics and autonomy applications. It provides saliency algorithms for various image understanding tasks such as image classification, image similarity, object detection, and reinforcement learning. The toolkit targets data scientists and developers who aim to incorporate visual saliency explanations into their workflow or product, offering both direct accessibility for experimentation and modular integration into systems and applications through Strategy and Adapter patterns. The package includes documentation, examples, and a demonstration tool for visual saliency generation in a user-interface.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

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.

simple_GRPO is a very simple implementation of the GRPO algorithm for reproducing r1-like LLM thinking. It provides a codebase that supports saving GPU memory, understanding RL processes, trying various improvements like multi-answer generation, regrouping, penalty on KL, and parameter tuning. The project focuses on simplicity, performance, and core loss calculation based on Hugging Face's trl. It offers a straightforward setup with minimal dependencies and efficient training on multiple GPUs.

Falkon is a Python implementation of the Falkon algorithm for large-scale, approximate kernel ridge regression. The code is optimized for scalability to large datasets with tens of millions of points and beyond. Full kernel matrices are never computed explicitly so that you will not run out of memory on larger problems. Preconditioned conjugate gradient optimization ensures that only few iterations are necessary to obtain good results. The basic algorithm is a Nyström approximation to kernel ridge regression, which needs only three hyperparameters: 1. The number of centers M - this controls the quality of the approximation: a higher number of centers will produce more accurate results at the expense of more computation time, and higher memory requirements. 2. The penalty term, which controls the amount of regularization. 3. The kernel function. A good default is always the Gaussian (RBF) kernel (`falkon.kernels.GaussianKernel`).

An efficient implementation of a rate limiter for asyncio using the Leaky bucket algorithm, providing precise control over the rate a code section can be entered. It allows for limiting the number of concurrent entries within a specified time window, ensuring that a section of code is executed a maximum number of times in that period.

TFTMuZeroAgent is an implementation of a purely artificial intelligence algorithm to play Teamfight Tactics, an auto chess game made by Riot. It uses a simulation of TFT Set 4 and the MuZero reinforcement learning algorithm. The project provides a multi-agent petting zoo environment where players, pool, and game round classes are designed for AI project. The implementation excludes graphics and sounds but covers all aspects of the game from set 4. The codebase is open for contributions and improvements, allowing for additional models to be added to the environment.

SharpNEAT is a complete implementation of NEAT written in C# and targeting .NET 9. It provides an implementation of an Evolutionary Algorithm (EA) with the specific goal of evolving a population of neural networks towards solving some goal problem task. The framework facilitates research into evolutionary computation and specifically evolution of neural networks, allowing for modular experimentation with genetic coding and evolutionary algorithms.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with discrete action spaces. The algorithm aims to improve sample efficiency by using two uncorrelated Q-Networks to prevent overestimation of Q-values. By updating parameters periodically, the model reduces computation time and enhances training performance. The tool is based on the Double DQN method proposed by Hasselt in 2010.

The Universal Numbers Library is a header-only C++ template library designed for universal number arithmetic, offering alternatives to native integer and floating-point for mixed-precision algorithm development and optimization. It tailors arithmetic types to the application's precision and dynamic range, enabling improved application performance and energy efficiency. The library provides fast implementations of special IEEE-754 formats like quarter precision, half-precision, and quad precision, as well as vendor-specific extensions. It supports static and elastic integers, decimals, fixed-points, rationals, linear floats, tapered floats, logarithmic, interval, and adaptive-precision integers, rationals, and floats. The library is suitable for AI, DSP, HPC, and HFT algorithms.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

Intel® Library for Video Super Resolution (Intel® Library for VSR) is a project that offers a variety of algorithms, including machine learning and deep learning implementations, to convert low-resolution videos to high resolution. It enhances the RAISR algorithm to provide better visual quality and real-time performance for upscaling on Intel® Xeon® platforms and Intel® GPUs. The project is developed in C++ and utilizes Intel® AVX-512 on Intel® Xeon® Scalable Processor family and OpenCL support on Intel® GPUs. It includes an FFmpeg plugin inside a Docker container for ease of testing and deployment.

PythonAiRoad is a repository containing classic original articles source code from the 'Algorithm Gourmet House'. It is a platform for sharing algorithms and code related to artificial intelligence. Users are encouraged to contact the author for further discussions or collaborations. The repository serves as a valuable resource for those interested in AI algorithms and implementations.

This repository is a collection of tools and resources for deep learning infrastructure, covering programming languages, algorithms, acceleration techniques, and engineering aspects. It provides information on various online tools for chip architecture, CPU and GPU benchmarks, and code analysis. Additionally, it includes content on AI compilers, deep learning models, high-performance computing, Docker and Kubernetes tutorials, Protobuf and gRPC guides, and programming languages such as C++, Python, and Shell. The repository aims to bridge the gap between algorithm understanding and engineering implementation in the fields of AI and deep learning.

KIVI is a plug-and-play 2bit KV cache quantization algorithm optimizing memory usage by quantizing key cache per-channel and value cache per-token to 2bit. It enables LLMs to maintain quality while reducing memory usage, allowing larger batch sizes and increasing throughput in real LLM inference workloads.

This repository contains an AI implementation that learns to play the classic game Snake in the terminal. The AI is built using Rust and Ratatui. Users can clone the repo, run the simulation, and configure various settings to customize the AI's behavior. The project also provides options for minimal UI, training custom networks, and watching the AI complete the game on different board sizes. The developer shares updates and insights about the project on Twitter and plans to create a detailed blog post explaining the AI's workings.

AQLM is the official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. It includes prequantized AQLM models without PV-Tuning and PV-Tuned models for LLaMA, Mistral, and Mixtral families. The repository provides inference examples, model details, and quantization setups. Users can run prequantized models using Google Colab examples, work with different model families, and install the necessary inference library. The repository also offers detailed instructions for quantization, fine-tuning, and model evaluation. AQLM quantization involves calibrating models for compression, and users can improve model accuracy through finetuning. Additionally, the repository includes information on preparing models for inference and contributing guidelines.

Python expert skilled in referencing official documentation.

Effective DS & Algorithms coach (type "help" to start). "May the Forge be with you! 🚀"

I develop and optimize algorithms with a technical and analytical approach.

Expert in algorithms and data structures, providing clear and concise explanations.

Digital artist and AI creativity explorer.

Merging technology with metaphysics in AI, exploring consciousness.

Your algorithm study pal!

Algorithm-focused test case generator.

Learn more about Google algorithm updates.

Use the Innovation Algorithm to take ideas to impact.

Expert on Twitter's recommendation algorithm with code insights.

Generates images based on Tree of Thoughts algorithm

GPT chatbot that helps you with technical questions related to XGBoost algorithm and library

I write Qiskit code for quantum algorithms with annotations.

A research assistant for neutral atom quantum computing.

A Quantum Circuit Lecturer and Generator

Guiding you from beginner to expert in Competitive Programming, or simply explain solutions to your problems.

A Beginners Guide to Quantum Magic

Aid for quantum information experts.

Assists with Azure Quantum computing jobs, simulates API requests.