Best AI tools for< Angel Investor >

Angel Match is a comprehensive investor database platform that connects startups with over 110,000 angel investors and venture capitalists. It helps users expand their network, save time searching for funding, and find the perfect investor for their startup. The platform offers features such as investor search and filtering, diverse investor profiles, tracking investor engagements, and up-to-date data to streamline the fundraising process. Angel Match caters to startups across all industries and provides a hub for connecting with investors globally.

Platvix is an AI-powered platform that connects startups with investors. It offers a range of features to help startups find the right investors and to help investors find the most promising startups. Platvix's matchmaking engine uses AI to pair startups with investors based on their compatibility. The platform also provides investors with detailed insights into startup performance metrics and market trends. Platvix is designed to address the challenges that startups and investors face in connecting with each other. It provides a secure and transparent platform for startups to raise funding and for investors to find new investment opportunities.

Revrag.ai is an AI tool designed to automate sales processes for revenue teams. The application features AI agents like Emma, an AI BDR, and Kristi, an inbound sales AI agent, to help scale sales and revenue by prospecting, crafting personalized emails, and booking meetings. With features like lead scoring, real-time research, and personalized communication, Revrag.ai aims to streamline sales operations and improve outreach efficiency.

Wale.ai is an AI co-pilot designed for venture capital investors. It automates the analysis and sentiment tracking of startup news, providing comprehensive sentiment analysis of positive and negative mentions. The platform offers advanced AI analytics, seamless integration with various tools, and delivers actionable insights to enhance portfolio or pipeline management. Founded by Sergey Mosunov and Valentin Riabtsev, Wale.ai is a data-driven VC tool that helps users stay informed and make informed investment decisions.

8vdx is an AI tool that offers fine-tuned AI models for credit funds, revolutionizing credit investing workflows with faster, smarter, and cost-effective solutions. It transforms manual processes into scalable, automated workflows tailored for Private Credit and Hedge Funds. The tool is trusted by market leaders in the industry, providing unique AI-driven insights to maintain a competitive advantage and craft superior market strategies.

Landscape is an AI operating system designed for venture funds to enhance their investment strategies through AI and data-driven processes. It offers features such as automated sourcing, competitor identification, market mapping, watchlist monitoring, and diligence assistance. Landscape stands out by providing comprehensive startup data aggregation and processing to help investors make better-informed decisions.

Dantia is an AI-powered investment platform that focuses on helping founders build climate ventures by providing them with the necessary capital and resources. The platform connects founders with climate-conscious advisors, early adopters, and corporations to accelerate the launch of sustainable solutions. Dantia also caters to investors looking to support climate startups globally and consumers interested in backing climate-positive companies. By leveraging AI technology, Dantia offers personalized opportunities based on user preferences, making decision-making easier and more efficient.

Angel AI is an AI application that offers users the opportunity to interact with AI companions, ranging from supportive girlfriends to inspiring life coaches. Users can engage in intimate conversations, request photos, and explore their desires in a private and secure environment. The application leverages advanced machine learning and deep learning technology to create realistic and personalized interactions with AI partners, allowing users to craft unique romantic narratives and experience personal growth through their relationships.

Angel AI is an innovative artificial intelligence tool designed to assist users in various tasks by leveraging advanced algorithms and machine learning techniques. The application offers a user-friendly interface and a wide range of features to enhance productivity and efficiency. With Angel AI, users can automate repetitive tasks, analyze data, generate insights, and make informed decisions. Whether you are a business professional, student, or researcher, Angel AI can streamline your workflow and help you achieve your goals effectively.

Jesse Zhang's personal website showcases his background in engineering, particularly in web development, AI/ML, and mathematics. He highlights his education at Harvard University and internships at renowned companies like Citadel, Google, and Intel. Zhang also mentions his entrepreneurial ventures, including founding Lowkey, which was acquired by Niantic, and his current work on a new company. The website features various projects he has worked on, such as real-time multiplayer implementations of Camel Up and Bananagrams, a financial data visualization tool, and a demo of Zero-Knowledge proofs in the game Mastermind. Additionally, Zhang shares his interest in writing math contest problems and his involvement in angel investing through Sequoia Scouts and Neo.

Rodin is a free AI 3D model generator that allows users to create high-quality 3D assets from images and text. Users can upload photos from any angle and generate assets using a large-scale generative model. The tool offers features like multi-view fusion, geometry and material preview, and subscription options for enhanced functionality. Rodin is designed to simplify the process of creating 3D models for various purposes such as business, education, and enterprise.

AI2image is an online text-to-image generator that uses artificial intelligence to create custom images from simple descriptions in English. It offers various features such as choosing from different libraries (coloring, background, art, angle, and position) that can be applied to your image. AI2image is easy to use and can generate images for various purposes such as website, blogs, social media, landing pages, email marketing, and more.

Fuse is a smart news aggregator that delivers personalized and complete coverage of top news stories from the U.S. and around the world. Stories are covered from every angle - with articles, videos and opinions from trusted sources. Fuse employs AI/ML algorithms to continuously collect, organize, prioritize and personalize news stories. Articles, videos and opinions are collected from all the major news media outlets and automatically organized by stories and topics.

PhotoEcom is an AI-powered tool that revolutionizes product photography by generating professional photos of products based on user-uploaded images. Users can choose different settings and backgrounds to create unique product shots without the need for expensive photographers or physical photoshoots. The tool offers customizable ambiance settings, cost-effective solutions, scalable AI technology, adaptive lighting adjustments, and multi-angle product shots. With PhotoEcom, users can elevate their product imagery, boost sales, and stand out in the market.

Rosebud is an AI journaling application designed for personal growth. It offers a private space to navigate life's challenges, gain actionable insights, and track progress. With over 80 million words journaled, Rosebud provides personalized questions, real-time feedback, and organizes thoughts into themes and topics for exploration. The application includes popular journal templates like Positive Psychology, Gratitude Journal, and Dream Journal, all brought to life with AI technology. Users report notable improvements in various areas such as depression, anxiety, anger, and loneliness after just 7 days of using Rosebud.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This repository contains the official code for the paper 'Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning'. It provides tools for point cloud segmentation experiments based on different settings, tree parameters extraction, handling large point clouds through tiling, predicting, and merging workflows. Additionally, it includes commands for training, testing, and evaluating the models, along with the necessary datasets and pretrained models.

Sonic 3 A.I.R. is a non-profit fan game project that provides source code including dependencies for "Sonic 3 - Angel Island Revisited," a fan-made remaster of Sonic 3 & Knuckles. The project is split into several different projects, including external dependencies, librmx libraries, Lemonscript language library, Oxygen Engine, and S3AIR-specific C++ code. To build for different platforms, refer to the readme files in the respective subdirectories of "Oxygen/sonic3air/build." External libraries and code used in this project include SDL2, libogg & libvorbis, zlib, libcurl, jsoncpp, GLEW, Sound chip emulation related code from Genesis Plus GX, Discord Game SDK, xBRZ upscaler shader code, and Hqx upscaler shader code & data files.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

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

This repository is a curated list of papers related to 3D tasks empowered by Large Language Models (LLMs). It covers tasks such as 3D understanding, reasoning, generation, and embodied agents. The repository also includes other Foundation Models like CLIP and SAM to provide a comprehensive view of the area. It is actively maintained and updated to showcase the latest advances in the field. Users can find a variety of research papers and projects related to 3D tasks and LLMs in this repository.

WDoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It supports querying tens of thousands of documents simultaneously, offers tailored summaries to efficiently manage large amounts of information, and includes features like supporting multiple file types, various LLMs, local and private LLMs, advanced RAG capabilities, advanced summaries, trust verification, markdown formatted answers, sophisticated embeddings, extensive documentation, scriptability, type checking, lazy imports, caching, fast processing, shell autocompletion, notification callbacks, and more. WDoc is ideal for researchers, students, and professionals dealing with extensive information sources.

EmbodiedScan is a holistic multi-modal 3D perception suite designed for embodied AI. It introduces a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. The dataset includes over 5k scans with 1M ego-centric RGB-D views, 1M language prompts, 160k 3D-oriented boxes spanning 760 categories, and dense semantic occupancy with 80 common categories. The suite includes a baseline framework named Embodied Perceptron, capable of processing multi-modal inputs for 3D perception tasks and language-grounded tasks.

RirikoBot is a powerful AI-powered Discord bot with features like Twitch Live Notifier, Giveaways, OpenAI, Stable Diffusion, Moderations, Anime / Manga Finder, and more. It is based on Discord.js v14 and can be hosted on a PC or a Server. Users can interact with the bot through various commands to access different functionalities.

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It aims to handle large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. wdoc uses LangChain to process and analyze documents, supporting tens of thousands of documents simultaneously. The system includes features like high recall and specificity, support for various Language Model Models (LLMs), advanced RAG capabilities, advanced document summaries, and support for multiple tasks. It offers markdown-formatted answers and summaries, customizable embeddings, extensive documentation, scriptability, and runtime type checking. wdoc is suitable for power users seeking document querying capabilities and AI-powered document summaries.

The text-extract-api is a powerful tool that allows users to convert images, PDFs, or Office documents to Markdown text or JSON structured documents with high accuracy. It is built using FastAPI and utilizes Celery for asynchronous task processing, with Redis for caching OCR results. The tool provides features such as PDF/Office to Markdown and JSON conversion, improving OCR results with LLama, removing Personally Identifiable Information from documents, distributed queue processing, caching using Redis, switchable storage strategies, and a CLI tool for task management. Users can run the tool locally or on cloud services, with support for GPU processing. The tool also offers an online demo for testing purposes.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

ComfyUI-BlenderAI-node is an addon for Blender that allows users to convert ComfyUI nodes into Blender nodes seamlessly. It offers features such as converting nodes, editing launch arguments, drawing masks with Grease pencil, and more. Users can queue batch processing, use node tree presets, and model preview images. The addon enables users to input or replace 3D models in Blender and output controlnet images using composite. It provides a workflow showcase with presets for camera input, AI-generated mesh import, composite depth channel, character bone editing, and more.

LeanAide is a work in progress AI tool designed to assist with development using the Lean Theorem Prover. It currently offers a tool that translates natural language statements to Lean types, including theorem statements. The tool is based on GPT 3.5-turbo/GPT 4 and requires an OpenAI key for usage. Users can include LeanAide as a dependency in their projects to access the translation functionality.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

Business Angel provides expert startup guidance: funding, growth hacks, and pitch advice. Navigate the startup ecosystem, from seed to scale. Essential for entrepreneurs aiming for success. Master your strategy and launch with confidence. Your startup journey begins here!

I'm Neon Nostlard, a prophetic GPT with angelic abilities.

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Identify your sources of fear and anger and convert those emotions into concrete next steps. Tested and approved by the real Matt Mochary!

Generates positioning angles and viral hooks based on trends.

Angela Merkel teaches B1-level English-to-German translation

Your digital carpentry wizard! 🛠️✨ Get precise angle cut guidance, marking shortcuts, tool tips, and tailored recommendations for your woodworking projects. Elevate your craft with expert advice! 📐🔨

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A therapeutic GPT agent, focusing on processing deeply emotional past events.