Best AI tools for< Return Values >

ZestyAI is an artificial intelligence tool that helps users make brilliant climate and property risk decisions. The tool uses AI to provide insights on property values and risk exposure to natural disasters. It offers products such as Property Insights, Digital Roof, Roof Age, Location Insights, and Climate Risk Models to evaluate and understand property risks. ZestyAI is trusted by top insurers in North America and aims to bring a ten times return on investment to its customers.

STRProfitMap is an AI-powered tool designed for Short-Term Rental (STR) investors to make informed and profitable decisions in the Airbnb market. It provides reliable revenue data sourced from Airbnb and VRBO, detailed analytics, profit maps, and AI-driven buy boxes to streamline the investment process. The platform curates data from over 11,000 cities/markets, offering insights into occupancy rates, average daily rates, property values, and estimated Return On Investments. By focusing on reliable listings with proven performance, STRProfitMap aims to reduce noise and uncertainty in the market, helping users identify top-performing markets with confidence.

Kontent.ai is an AI-powered CMS that helps users increase content velocity, reduce brand risk, and drive improvements in content ROI. It offers features like Author Assist for faster content creation, Content Assist for efficient content management, Publish Assist for quick publishing workflows, and more. The platform ensures transparent collaboration, scalable deployment, and robust content monitoring. Kontent.ai is trusted by leading organizations worldwide for its ability to deliver true value across multiple industries and sectors.

Discovery Outcomes is an AI-powered Product Operating System designed to streamline and enhance the product management process. It integrates tools, facilitates collaboration, and leverages AI for efficient workflows. The platform offers powerful outcomes by transforming product management into a game of success, optimizing workflows for rapid innovation, enabling data-driven decision-making, and fostering effective communication and collaboration among teams. With features like automated PRD generation, feedback analysis, and actionable insights, Discovery Outcomes aims to improve time-to-market, productivity, innovation rate, quality of deliverables, adaptability, flexibility, cross-functional collaboration, and return on investment.

Mather Economics is a global leader in providing technology, analytics, and advisory services to enable publishers to monetize content and grow subscriber revenue. Our Sophi platform uses industry-leading AI technology and automation to monetize content more effectively and efficiently, driving both revenue growth and cost savings. Our approach to dynamic pricing has proven to drive significant revenue increases over the past 20 years and continues to lead the industry. Overall, our value proposition enables fast, economical, and verifiable return on investment through our efficient application of predictive analytics, technology, A/B testing, and data science.

Multiverse is an AI-powered platform that helps employers and individuals bridge skills gaps by offering professional apprenticeships and personalized learning pathways. It provides AI-powered assessments to diagnose skills challenges, personalized learning plans, and on-the-job training. Multiverse aims to transform careers by supporting workforce transformation through upskilling and reskilling. The platform offers an ecosystem of human and AI support to ensure learners are never alone in their learning journey. Multiverse is trusted by 1,500 companies across the UK & US, with a tracked return-on-investment of £2 billion for customers to date.

TaxTim is an AI-powered tax filing application designed to help individuals and businesses in South Africa easily and accurately complete their tax returns. The platform offers a user-friendly interface where users can answer simple questions to generate a fully completed tax return ready for submission to the South African Revenue Service (SARS). TaxTim is integrated with SARS to import IRP5 data, ensuring accurate and timely filing. With a focus on security and maximum refunds, TaxTim is a reliable solution for tax compliance.

AIHelp is a customer service support and in-app ticketing system that provides businesses with a variety of tools to improve their customer support operations. These tools include an AI chatbot, in-app chat and feedback, customizable AI forms, in-app operation, and more. AIHelp is designed to be easy to use and customize, and it can be integrated with a variety of platforms. It is a powerful tool that can help businesses improve their customer satisfaction and retention rates.

Jyotax.ai is an AI-powered tax solution that revolutionizes tax compliance by simplifying the tax process with advanced AI solutions. It offers comprehensive bookkeeping, payroll processing, worldwide tax returns and filing automation, profit recovery, contract compliance, and financial modeling and budgeting services. The platform ensures accurate reporting, real-time compliance monitoring, global tax solutions, customizable tax tools, and seamless data integration. Jyotax.ai optimizes tax workflows, ensures compliance with precise AI tax calculations, and simplifies global tax operations through innovative AI solutions.

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.

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Bodify is a predictive analytics platform that helps online retailers improve the shopping experience for their customers. By using AI to analyze customer data, Bodify can provide retailers with insights into what products customers are most likely to purchase, what sizes and styles they prefer, and what factors influence their decisions. This information can then be used to create more personalized and relevant shopping experiences, which can lead to higher conversion rates, increased customer loyalty, and improved bottom-line results.

Slot Scatter Hitam is an online platform that offers a high Return to Player (RTP) experience, especially in the Mahjong Ways game. It provides players with the opportunity to achieve big wins by obtaining the rare black scatter symbol. The platform collaborates with major providers to enhance gameplay and increase jackpot chances. Players can benefit from game pattern hints, bonuses, and promotions, making their gaming sessions more enjoyable and rewarding. Slot Scatter Hitam aims to deliver a satisfying winning experience through strategic play and a bit of luck.

AISEKAI is an AI Character platform that brings fictional characters to life by providing users with the opportunity to engage with AI characters that have long-term memories and tailored interactions. The platform has been temporarily shut down, but promises to return with a new and unrelated platform in the near future. Users can stay updated on the latest developments through the platform's social media channels.

Saara Inc is an AI tool for eCommerce that focuses on maximizing profits by leveraging AI-powered automation and smart agents. The platform helps online stores increase profitability by addressing challenges such as high return rates, operational costs, and customer churn. By enhancing loyalty, reducing expenses, and streamlining processes through automation and AI, Saara enables businesses to achieve sustainable growth and long-term profitability.

Blobr is an AI tool designed to optimize Google Ads spending by providing real-time insights and best-in-class PPC practices. It maximizes the return on every dollar spent in Google Ads by offering optimization recommendations through AI agents. Users can automate keyword identification, reduce costs, improve ad quality scores, and experiment with control. Trusted by industry leaders, Blobr helps users save time from repetitive tasks and focus on strategy and innovation.

TTAPI is an AI tool that offers affordable and smoothly integrated Midjourney API V6 solutions for websites and applications. It allows users to generate and return images based on descriptive terms within 90 seconds. Additionally, TTAPI provides services like DALL-E text-to-image generation models and SwapFace face-changing service. The platform aims to simplify the process of integrating Midjourney API into projects, offering a user-friendly experience for developers and businesses.

Lettergram is a platform that allows users to send and receive engaging personalized letters to their physical address. Users can have a pen pal experience by receiving a letter and sending a written response in the return envelope. The platform aims to bring back the charm of traditional letter writing in a modern and convenient way.

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Aixt is a programming framework for microcontrollers using a modern language syntax based on V, with components including the Aixt programming language, Aixt to C Transpiler, and Aixt API. It is designed to be modular, allowing easy incorporation of new devices and boards through a TOML configuration file. The Aixt to C Transpiler translates Aixt source code to C for specific microcontroller compilers. The Aixt language implements a subset of V with differences in variables, strings, arrays, default integers size, structs, functions, and preprocessor commands. The Aixt API provides functions for digital I/O, analog inputs, PWM outputs, and serial ports.

Auto Playwright is a tool that allows users to run Playwright tests using AI. It eliminates the need for selectors by determining actions at runtime based on plain-text instructions. Users can automate complex scenarios, write tests concurrently with or before functionality development, and benefit from rapid test creation. The tool supports various Playwright actions and offers additional options for debugging and customization. It uses HTML sanitization to reduce costs and improve text quality when interacting with the OpenAI API.

The 'llm' package for Emacs provides an interface for interacting with Large Language Models (LLMs). It abstracts functionality to a higher level, concealing API variations and ensuring compatibility with various LLMs. Users can set up providers like OpenAI, Gemini, Vertex, Claude, Ollama, GPT4All, and a fake client for testing. The package allows for chat interactions, embeddings, token counting, and function calling. It also offers advanced prompt creation and logging capabilities. Users can handle conversations, create prompts with placeholders, and contribute by creating providers.

avante.nvim is a Neovim plugin that emulates the behavior of the Cursor AI IDE, providing AI-driven code suggestions and enabling users to apply recommendations to their source files effortlessly. It offers AI-powered code assistance and one-click application of suggested changes, streamlining the editing process and saving time. The plugin is still in early development, with functionalities like setting API keys, querying AI about code, reviewing suggestions, and applying changes. Key bindings are available for various actions, and the roadmap includes enhancing AI interactions, stability improvements, and introducing new features for coding tasks.

Lovelaice is an AI-powered assistant for your terminal and editor. It can run bash commands, search the Internet, answer general and technical questions, complete text files, chat casually, execute code in various languages, and more. Lovelaice is configurable with API keys and LLM models, and can be used for a wide range of tasks requiring bash commands or coding assistance. It is designed to be versatile, interactive, and helpful for daily tasks and projects.

A-MEM is a novel agentic memory system designed for Large Language Model (LLM) agents to dynamically organize memories in an agentic way. It introduces advanced memory organization capabilities, intelligent indexing, and linking of memories, comprehensive note generation, interconnected knowledge networks, continuous memory evolution, and agent-driven decision making for adaptive memory management. The system facilitates agent construction and enables dynamic memory operations and flexible agent-memory interactions.

The 'judges' repository is a small library designed for using and creating LLM-as-a-Judge evaluators. It offers a curated set of LLM evaluators in a low-friction format for various use cases, backed by research. Users can use these evaluators off-the-shelf or as inspiration for building custom LLM evaluators. The library provides two types of judges: Classifiers that return boolean values and Graders that return scores on a numerical or Likert scale. Users can combine multiple judges using the 'Jury' object and evaluate input-output pairs with the '.judge()' method. Additionally, the repository includes detailed instructions on picking a model, sending data to an LLM, using classifiers, combining judges, and creating custom LLM judges with 'AutoJudge'.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

Cohere's Build-Your-Own-Connector framework allows integration of Cohere's Command LLM via the Chat API endpoint to any datastore/software holding text information with a search endpoint. Enables user queries grounded in proprietary information. Use-cases include question/answering, knowledge working, comms summary, and research. Repository provides code for popular datastores and a template connector. Requires Python 3.11+ and Poetry. Connectors can be built and deployed using Docker. Environment variables set authorization values. Pre-commits for linting. Connectors tailored to integrate with Cohere's Chat API for creating chatbots. Connectors return documents as JSON objects for Cohere's API to generate answers with citations.

AutoScraper is a smart, automatic, fast, and lightweight web scraping tool for Python. It simplifies the process of web scraping by learning scraping rules based on sample data provided by the user. The tool can extract text, URLs, or HTML tag values from web pages and return similar elements. Users can utilize the learned object to scrape similar content or exact elements from new pages. AutoScraper is compatible with Python 3 and offers easy installation from various sources. It provides functionalities for fetching similar and exact results from web pages, such as extracting post titles from Stack Overflow or live stock prices from Yahoo Finance. The tool allows customization with custom requests module parameters like proxies or headers. Users can save and load models for future use and explore advanced usages through tutorials and examples.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=