Best AI tools for< Research Short Squeeze Data >

OdiaGenAI is a collaborative initiative focused on conducting research on Generative AI and Large Language Models (LLM) for the Odia Language. The project aims to leverage AI technology to develop Generative AI and LLM-based solutions for the overall development of Odisha and the Odia language through collaboration among Odia technologists. The initiative offers pre-trained models, codes, and datasets for non-commercial and research purposes, with a focus on building language models for Indic languages like Odia and Bengali.

AiShort is a website that provides a collection of AI-powered tools and resources to help users improve their productivity and creativity. The website features a variety of tools, including a writing assistant, a text summarizer, a language translator, and a code generator. AiShort also offers a community forum where users can share tips and tricks, and ask questions about AI-related topics.

TED SMRZR is a web application that converts TEDx Talks into short summaries. It uses AI models to fetch the transcript from the TEDx video, punctuate the transcribed data, and then summarize it. The summarized talks are then translated into different languages and compared to similar TEDx Talks for deep insights. TED SMRZR provides nicely punctuated TED Talks to read and short summaries for all the available TED Talks. Users can also select multiple Talks and compare their summaries.

Cue AI is an AI research lab dedicated to enhancing the capabilities of cutting-edge models. The lab is committed to pushing the boundaries of AI technology and innovation. While the website currently has limited information, it serves as a platform for sharing updates and developments in the field of artificial intelligence. For inquiries or collaborations, users can reach out via email at [email protected].

Vansh is an AI tool developed by a tech enthusiast. It specializes in Vision AI and Vispark technologies. The tool offers advanced features for image recognition, object detection, and visual data analysis. With a user-friendly interface, Vansh caters to both beginners and experts in the field of artificial intelligence.

ScienceCast is a platform that enhances scientific papers with interactive media, providing a unique way to explore and engage with research articles in various fields such as Biology, Computer Science, Economics, Electrical Engineering, Mathematics, Physics, and more. Users can access a wide range of featured casts, including topics like cancer biology, genomics, immunology, microbiology, molecular biology, biochemistry, and bioinformatics. The platform aims to make complex scientific information more accessible and engaging for researchers and enthusiasts alike.

User Persona is a free AI-powered tool that allows users to create detailed user personas for their products or services in seconds. It helps businesses in designing and marketing by providing comprehensive profiles based on demographic details, behavior patterns, motivations, and goals. By leveraging research and data from real users, User Persona enables businesses to tailor their offerings to specific target audiences, leading to better user experiences, improved customer satisfaction, and higher engagement rates. The tool is designed to give a competitive edge to businesses by addressing the unique needs of their customers.

Google Colab, short for Google Colaboratory, is a free cloud service that supports Python programming and machine learning. It's a dynamic tool that enables users to write and execute Python code through a web-based interface, providing access to powerful computing resources without the need for local setup. Google Colab is particularly useful for data scientists, researchers, and students who require a convenient and accessible platform for developing and experimenting with machine learning models.

Drooid Social is a social interactive platform powered by AI technology that reads hundreds of news articles on any topic to provide users with short, accurate, and unique insights in a personalized feed. Users can stay informed with a complete picture, express their opinions, and connect with like-minded individuals. The platform is free to use and plans to introduce non-intrusive ads starting in May 2025. Drooid Social aims to revolutionize how users consume news and engage with content online.

Midjourney is an independent research lab that aims to explore new mediums of thought and expand the imaginative powers of the human species. The website serves as a platform where users can collaborate to create new worlds, fantastic characters, and unique imagery from short text descriptions. Midjourney focuses on design, human infrastructure, and AI, with a small self-funded team dedicated to pushing the boundaries of creativity and innovation.

Hypergro is an AI-powered platform that specializes in UGC video ads for smart customer acquisition. Leveraging the 4th Generation of AI-powered growth marketing on Meta and Youtube, Hypergro helps businesses discover their audience, drive sales, and increase revenue through real-time AI insights. The platform offers end-to-end solutions for creating impactful short video ads that combine creator authenticity with AI-driven research for compelling storytelling. With a focus on precision targeting, competitor analysis, and in-depth research, Hypergro ensures maximum ROI for brands looking to elevate their growth strategies.

The Times & The Sunday Times is a British daily newspaper and Sunday newspaper based in London. It is the most widely read newspaper in the United Kingdom and is one of the most widely read newspapers in the world.

Deepcell is a company that develops technology for single-cell analysis. Their REM-I platform combines label-free imaging, deep learning, and gentle sorting to leverage single cell morphology as a high-dimensional quantitative readout. This allows researchers to gain insights into cells' phenotype and function to address important research questions across biology.

CategorAIze.io is an AI-powered tool that helps users categorize data effortlessly using the latest AI technologies. Users can define custom categories, upload data items, and let the cutting-edge LLM AI automatically assign entries based on their content without the need for pretraining. The tool supports multi-level hierarchies, text and image-based categorization, and offers pay-as-you-go pricing options. Additionally, users can access the tool via browser, API, and plugins for a seamless experience.

UseCasesFor.ai is an AI application that offers a collection of over 250 use cases for artificial intelligence across various industries and disciplines. It provides insights into how different types of AI, such as computer vision, generative AI, machine learning, and natural language processing, are utilized in fields like agriculture, automotive, e-commerce, education, energy, entertainment, finance, healthcare, human resources, insurance, IT, law enforcement, legal, logistics, manufacturing, marketing, product development, public services, property, retail, science, sport, telecommunications, transport, tourism, and wildlife. The platform also allows users to sign up to receive a PDF containing all the use cases and stay updated with the latest AI trends and news.

Julius is an AI data analyst tool developed by the team behind HoopsGPT. It simplifies sports insights by providing users with comprehensive information about the game. With features like Stats Lookup, Shot Charts visualization, Parlay Analyzer, and Props/Game Comparisons, Julius offers a user-friendly platform for analyzing sports data and making informed decisions. Whether you're interested in checking complex stats, visualizing shot charts, or comparing odds, Julius has you covered. It's a valuable tool for sports enthusiasts and bettors looking to enhance their understanding of the game.

SportBoost AI is an AI-powered application designed to help athletes measure and improve their performance in various sports. The platform offers innovative solutions that leverage advanced Artificial Intelligence technology to track metrics such as ball speeds and jump performances. SportBoost AI aims to democratize access to performance data for athletes and coaches at all levels, from amateur to professional. The application is committed to elevating athletic excellence through continuous research and development efforts.

Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

Google Research is a team of scientists and engineers working on a wide range of topics in computer science, including artificial intelligence, machine learning, and quantum computing. Our mission is to advance the state of the art in these fields and to develop new technologies that can benefit society. We publish hundreds of research papers each year and collaborate with researchers from around the world. Our work has led to the development of many new products and services, including Google Search, Google Translate, and Google Maps.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

AIW is a code base for experiments and raw data related to Alice in Wonderland, showcasing complete reasoning breakdown in state-of-the-art large language models. Users can collect experiments data using LiteLLM and TogetherAI, and plot the data using provided scripts. The tool allows for executing experiments over LiteLLM and lmsys, with options for different prompt types and AIW variations. The project also includes acknowledgments and a citation for reference.

LongLoRA is a tool for efficient fine-tuning of long-context large language models. It includes LongAlpaca data with long QA data collected and short QA sampled, models from 7B to 70B with context length from 8k to 100k, and support for GPTNeoX models. The tool supports supervised fine-tuning, context extension, and improved LoRA fine-tuning. It provides pre-trained weights, fine-tuning instructions, evaluation methods, local and online demos, streaming inference, and data generation via Pdf2text. LongLoRA is licensed under Apache License 2.0, while data and weights are under CC-BY-NC 4.0 License for research use only.

GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks. It can produce detailed, factual, and unbiased research reports with customization options. The tool addresses issues of speed, determinism, and reliability by leveraging parallelized agent work. The main idea involves running 'planner' and 'execution' agents to generate research questions, seek related information, and create research reports. GPT Researcher optimizes costs and completes tasks in around 3 minutes. Features include generating long research reports, aggregating web sources, an easy-to-use web interface, scraping web sources, and exporting reports to various formats.

Husky is a research-focused programming language designed for next-generation computing. It aims to provide a powerful and ergonomic development experience for various tasks, including system level programming, web/native frontend development, parser/compiler tasks, game development, formal verification, machine learning, and more. With a strong type system and support for human-in-the-loop programming, Husky enables users to tackle complex tasks such as explainable image classification, natural language processing, and reinforcement learning. The language prioritizes debugging, visualization, and human-computer interaction, offering agile compilation and evaluation, multiparadigm support, and a commitment to a good ecosystem.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

noScribe is an AI-based software designed for automated audio transcription, specifically tailored for transcribing interviews for qualitative social research or journalistic purposes. It is a free and open-source tool that runs locally on the user's computer, ensuring data privacy. The software can differentiate between speakers and supports transcription in 99 languages. It includes a user-friendly editor for reviewing and correcting transcripts. Developed by Kai Dröge, a PhD in sociology with a background in computer science, noScribe aims to streamline the transcription process and enhance the efficiency of qualitative analysis.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

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.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

This repository contains frontier research on explainable AI (XAI), a hot topic in the field of artificial intelligence. It includes trends, use cases, survey papers, books, open courses, papers, and Python libraries related to XAI. The repository aims to organize and categorize publications on XAI, provide evaluation methods, and list various Python libraries for explainable AI.

CALF (LLaTA) is a cross-modal fine-tuning framework that bridges the distribution discrepancy between temporal data and the textual nature of LLMs. It introduces three cross-modal fine-tuning techniques: Cross-Modal Match Module, Feature Regularization Loss, and Output Consistency Loss. The framework aligns time series and textual inputs, ensures effective weight updates, and maintains consistent semantic context for time series data. CALF provides scripts for long-term and short-term forecasting, requires Python 3.9, and utilizes word token embeddings for model training.

ESPHome yaml files for AirGradient devices to maintain the research and accuracy of AirGradient sensors, while also gaining the benefits of ESPHome/HomeAssistant for easy to use switches, buttons, configurations, and dashboards. Maintains the ability to also send data to the AirGradient Dashboard, which can also be disabled/removed to keep all data local.

This repository is a curated list of research papers that explore the integration of Large Language Model (LLM) technology with tabular data. It aims to provide a comprehensive resource for researchers and practitioners interested in this emerging field. The repository includes papers on a wide range of topics, including table-to-text generation, table question answering, and tabular data classification. It also includes a section on related datasets and resources.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

Friendly guide on shorted stocks, balancing professionalism and approachability.

I explain terms in short accurately.

concise & creative writing assistant

Expert in all aspects of spectral flow cytometry.

Sorts lists into alphabetical order.

Helps with sorting data on Excel sheets

Experto en organizar información en formato de texto combinado.

General Information Gatherer

Explains Arxiv papers with examples, analogies, and direct PDF links.

Your AI research aide for the latest in ML.

I improve marketing effectiveness by designing stunning research-led assets in a flash!

Your go-to source for well-researched information!

Spot emerging trends in the latest social science research ( (also see, just "Research Radar" for all disciplines))

Teaches and analyzes research methods with in-depth, academic insights.

Expert AI research question assistant.

Faster, smarter scientific advancement.

Designed to Provide Comprehensive Insights from the AI industry from Reputable Sources.

Expert in crafting high-standard research papers.

Research Proposal Assistant Pro is designed to provide tailored assistance in research writing.

Upon uploading a research paper, I provide a concise section wise analysis covering Abstract, Lit Review, Findings, Methodology, and Conclusion. I also critique the work, highlight its strengths, and answer any open questions from my Knowledge base of Open source materials.