Best AI tools for< Improve Score >

SmartDispute.ai is an AI-powered credit repair application that helps users improve their credit scores by leveraging artificial intelligence technology. The application offers a patented Smart Dispute System™ technology that identifies and helps remove negative accounts from credit reports, making it one of the most effective credit repair systems available. Users can easily repair their own credit with the help of SmartDispute.ai's automated processes and fact-based dispute method. The application provides users with a simple and clearly explained process, allowing them to track their progress and see significant improvements in their credit scores over time.

PrepGenius.ai is an AI-driven test preparation platform designed to revolutionize the way students prepare for AP courses, college admission tests, and more. The platform offers personalized study plans, real-time feedback, interactive learning tools, and comprehensive resources to help students understand their strengths and weaknesses. With PrepGenius.ai, students can study smarter, receive tailored feedback, and track their progress to improve their test scores effectively.

Hubtype is an enterprise chatbot platform that offers customer service automation solutions beyond text-only AI. It provides a messaging-first helpdesk, a drag-and-drop flow builder, and a customer service analytics suite to help businesses create interactive and efficient automated customer experiences. With end-to-end encryption, secure SSL Webhooks, and seamless integration with existing technologies, Hubtype aims to enhance customer satisfaction and drive sales conversions.

Balto is a real-time guidance platform designed for contact centers, powered by AI technology. It offers features such as real-time coaching, QA copilot, notetaking automation, and integrations with various CCaaS systems. Balto aims to improve agent performance, enhance customer experience, ensure compliance, and drive sales growth through behavior change and perfect conversations on every call.

ioni.ai is an AI application that offers ChatGPT-4 solution for customer support. It is a smart chatbot based on the latest AI technology, designed to handle general inquiries, complex questions, and user-specific requests. The application streamlines workflow with immediate responses, brings CSAT scores to a new level, and ensures human-in-the-loop verification for quality control. With self-learning capabilities, ioni.ai constantly improves its responses and provides accurate solutions to customer inquiries.

Sapien ai is an AI-powered tool that helps users transform their notes into powerful quizzes. By simply uploading notes in pdf, docx, or txt format, the tool generates custom quizzes to aid in exam preparation and enhance the learning experience. With Sapien ai, studying becomes more interactive and efficient, making it easier for users to grasp and retain information.

HireFlow.net is an AI-powered platform designed to optimize resumes and enhance job prospects. The website offers a free resume checker that leverages advanced Artificial Intelligence technology to provide personalized feedback and suggestions for improving resumes. Users can also access features such as CV analysis, cover letter and resignation letter generators, and expert insights to stand out in the competitive job market.

AI Credit Repair is an AI-powered application designed to help users improve their credit score by providing tools such as credit card utilization builder, public records remover, dispute letter generator, and SMS alerts. The application assists users in managing their credit card payments, disputing collections, and staying on top of their credit-related activities. By leveraging artificial intelligence technology, AI Credit Repair aims to simplify the credit repair process and empower users to build a better financial future.

Yogger is a video analysis app and AI movement screening tool that enables users to analyze movement anytime, anywhere. The technology allows for motion capture on mobile devices, making it easy to improve performance, prevent injuries, and achieve personal bests effortlessly. With Yogger, users can perform multiple movements, gather information instantly, and receive detailed reports on movement screenings. It is a motivational tool for clients looking to improve their assessment scores and a convenient way for trainers and coaches to assess clients and communicate ways to enhance performance.

Unspam is an email spam checker and deliverability test tool that helps businesses ensure their emails land in the inbox, not spam. It offers a range of features including email spam checking, deliverability testing, email preview, AI eye-tracking heatmap, SPF, DKIM, DMARC, and more. Unspam's mission is to help businesses improve their email deliverability and engagement, and its tools and insights are designed to help users optimize their email campaigns for maximum impact.

The SAT Reading & Writing Question Generator is an AI-powered tool designed to help students practice for the SAT exam. It generates a wide variety of reading and writing questions to improve students' skills and boost their confidence. With its smart algorithms, the tool provides personalized practice questions tailored to each student's needs, making exam preparation more effective and efficient. The tool is user-friendly and accessible, offering a seamless experience for students to enhance their test-taking abilities.

How Attractive Am I is an AI-powered tool that analyzes facial features to calculate an attractiveness score. By evaluating symmetry and proportions, the tool provides personalized beauty scores. Users can upload a photo to discover their true beauty potential. The tool ensures accuracy by providing guidelines for taking photos and offers a fun and insightful way to understand facial appeal.

VMock Dashboard is an AI-powered platform designed to provide personalized feedback on resumes. It leverages machine learning algorithms to analyze resumes and offers detailed insights on areas of improvement. Users can upload their resumes, receive instant feedback on content, formatting, and overall effectiveness, and track their progress over time. VMock Dashboard aims to help individuals enhance their resumes and increase their chances of landing their desired job opportunities.

Textbuddy is an online writing editor that uses AI to analyze and improve your writing. It highlights potential flaws in your text, such as long sentences, complex words, formatting errors, passive voice, adverbs, filler and vague words. The tool also provides synonyms, CEFR language levels, its own readability score, text statistics, and a keyword list for your text. Textbuddy's AI writing assistant can help you with grammar, spelling, and punctuation, and can even (re-)write text in plain language. You can use the AI assistant to overcome writer's block, create headlines and summaries, paraphrase text, analyze text sentiment, or just let it write for you as a copywriting AI.

AlphaRank is an AI SEO optimizer tool designed to boost website traffic by providing advanced SEO solutions. It offers a comprehensive suite of features to audit and optimize websites, leveraging AI algorithms and a vast database of keywords and merchant insights. Trusted by top-tier partners, AlphaRank aims to enhance content relevance, user engagement, and SEO precision for businesses, bloggers, and freelancers. The tool streamlines SEO operations, provides market insights, and offers expert recommendations for technical and content improvements.

Zonka Feedback is a powerful Customer Feedback and Survey Platform that offers User Segmentation for precise targeting, AI capabilities for smarter surveys, and a wide range of features to measure and improve Customer Experience. It provides solutions for various industries and use cases, integrates with popular tools, and offers in-depth reporting and analytics. Zonka Feedback is known for its modern-looking surveys, ease of use, and extensive integrations, making it a versatile tool for collecting feedback from customers, users, visitors, patients, and employees.

IELTSWritingPro is an AI-powered platform designed to help users improve their IELTS writing skills. It offers detailed feedback, band estimation, practice questions, and correction services. The platform utilizes advanced AI technology to analyze grammar, coherence, task response, and lexical resource. Users can receive personalized improvement suggestions and insights to enhance their writing abilities. IELTSWritingPro aims to assist individuals in preparing effectively for the IELTS exam by providing comprehensive evaluation and valuable feedback.

Mailivery is an AI-powered email warm-up tool designed to improve email deliverability by engaging in real conversations within your inbox. By leveraging AI technology and a peer-to-peer network, Mailivery enhances your sender reputation, ensuring your emails land in the primary inbox rather than being flagged as spam. The tool offers features such as data-driven spam and reputation analytics, customizable sending behavior, peer-to-peer interactions, scalable team management solutions, and a reputation checker. With Mailivery, users can optimize their email campaigns and increase the chances of their emails being delivered successfully.

Pitchleague.ai is an AI-powered pitch deck coach that provides slide-by-slide feedback on how to improve your startup's pitch deck. It also scores your deck's investability and allows you to compare your deck to other startups. Pitchleague.ai is used by over 3000 founders and has helped many startups to improve their fundraising success.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository focuses on utilizing generative AI to bridge the gap between natural language questions and SQL queries, aiming to improve data consumption in enterprise data warehouses. It addresses challenges in SQL query generation, such as foreign key relationships and table joins, and highlights the importance of accuracy metrics like Execution Accuracy (EX) and Exact Set Match Accuracy (EM). The workshop content covers advanced prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning models, and security measures against prompt and SQL injections.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

Akagi is a project designed to help users understand and improve their performance in Majsoul game matches in real-time. It provides educational insights and tools for analyzing gameplay. Users can install Akagi on Windows or Mac systems and follow the setup instructions to enhance their gaming experience. The project aims to offer features like Autoplay, Auto Ron, and integration with MajsoulUnlocker. It also focuses on enhancing user safety by providing guidelines to minimize the risk of account suspension. Akagi is a tool that combines MITM interception, AI decision-making, and user interaction to optimize gameplay strategies and performance.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

UltraSinger is a tool under development that automatically creates UltraStar.txt, midi, and notes from music. It pitches UltraStar files, adds text and tapping, creates separate UltraStar karaoke files, re-pitches current UltraStar files, and calculates in-game score. It uses multiple AI models to extract text from voice and determine pitch. Users should mention UltraSinger in UltraStar.txt files and only use it on Creative Commons licensed songs.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

Examor is a website application that allows you to take exams based on your knowledge notes. It helps you to remember what you have learned and written. The application generates a set of questions from the documents you upload, and you can answer them to test your knowledge. Examor also uses GPT to score and validate your answers, and provides you with feedback. The application is still in its early stages of development, but it has the potential to be a valuable tool for learners.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

DriveLM is a multimodal AI model that enables autonomous driving by combining computer vision and natural language processing. It is designed to understand and respond to complex driving scenarios using visual and textual information. DriveLM can perform various tasks related to driving, such as object detection, lane keeping, and decision-making. It is trained on a massive dataset of images and text, which allows it to learn the relationships between visual cues and driving actions. DriveLM is a powerful tool that can help to improve the safety and efficiency of autonomous vehicles.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

ICE-Score is a tool designed to instruct large language models to evaluate code. It provides a minimum viable product (MVP) for evaluating generated code snippets using inputs such as problem, output, task, aspect, and model. Users can also evaluate with reference code and enable zero-shot chain-of-thought evaluation. The tool is built on codegen-metrics and code-bert-score repositories and includes datasets like CoNaLa and HumanEval. ICE-Score has been accepted to EACL 2024.

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