Best AI tools for< inspect pavements >

Maintain-AI is an automated pavement assessment tool that uses artificial intelligence to identify and classify pavement defects. The tool is designed to help road maintenance crews identify and prioritize repairs, and to track the condition of pavements over time. Maintain-AI can be used on any type of pavement, and it can be used to assess both large and small areas.

Ascento is a robotics and AI-powered security solution that provides businesses with a cost-effective and efficient way to secure their premises. The solution includes an autonomous all-terrain robot that can patrol a site, detect threats, and provide real-time data to a central command center. Ascento's AI-powered software analyzes the data collected by the robot to identify potential security risks and provide actionable insights to security personnel. The solution is provided as a Robotics-as-a-Service (RaaS) model, which means that businesses only pay for the time that the robot is used.

Neurala is a company that provides visual quality inspection software powered by AI. Their software is designed to help manufacturers improve their inspection process by reducing product defects, increasing inspection rates, and preventing production downtime. Neurala's software is flexible and can be easily retrofitted into existing production line infrastructure, without the need for AI experts or expensive capital expenditures. The company's software is used by a variety of manufacturers, including Sony, AITRIOS, and CB Insights.

UpCodes is a comprehensive platform that accelerates delivery from design to construction. It provides access to building codes, assemblies, and building products, making it a valuable resource for architects, engineers, contractors, and building owners. UpCodes helps users stay up-to-date on the latest building codes and standards, and it provides tools for designing and constructing buildings that meet code requirements.

The website offers an AI-powered editor widget that allows users to make real-time edits directly on their website. It leverages advanced AI technology from OpenAI to streamline content editing and enhance productivity. Users can choose between GPT-3 and GPT-4 models for editing, and the tool also provides manual editing capabilities for correcting errors in AI-generated content. Additionally, users can effortlessly edit multiple elements, evaluate content before publishing, and inspect deeper structures of webpages for layout insights.

People.ai's SalesAI is a fully integrated generative AI sales solution that automates manual tasks, inspects account and deal health, and enables prescriptive coaching recommendations. It combines the power of automated activity capture with account and opportunity management to create a complete and unified GTM motion. People.ai's patented AI technology delivers insights to unlock, unify, and enrich all revenue activity. It fuels CRM with the facts needed to accurately diagnose problems and prescribe recommended next steps to achieve revenue targets.

Inspect AI is a framework developed by the UK AI Safety Institute for evaluating large language models. It offers various built-in components for prompt engineering, tool usage, multi-turn dialog, and model graded evaluations. Users can extend Inspect by adding new elicitation and scoring techniques through additional Python packages. The tool aims to provide a comprehensive solution for assessing the performance and safety of language models.

Gollama is a tool designed for managing Ollama models through a Text User Interface (TUI). Users can list, inspect, delete, copy, and push Ollama models, as well as link them to LM Studio. The application offers interactive model selection, sorting by various criteria, and actions using hotkeys. It provides features like sorting and filtering capabilities, displaying model metadata, model linking, copying, pushing, and more. Gollama aims to be user-friendly and useful for managing models, especially for cleaning up old models.

GGUF tools is a library designed to manipulate GGUF files commonly used in machine learning projects. The main goal of this library is to provide accessible code that documents GGUF files for the llama.cpp project. The utility implements subcommands to show detailed info about GGUF files, compare two LLMs, inspect tensor weights, and extract models from Mixtral 7B MoE. The library is under active development with well-commented code and a simple API. However, it has limitations in handling quantization formats.

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.

llm-swarm is a tool designed to manage scalable open LLM inference endpoints in Slurm clusters. It allows users to generate synthetic datasets for pretraining or fine-tuning using local LLMs or Inference Endpoints on the Hugging Face Hub. The tool integrates with huggingface/text-generation-inference and vLLM to generate text at scale. It manages inference endpoint lifetime by automatically spinning up instances via `sbatch`, checking if they are created or connected, performing the generation job, and auto-terminating the inference endpoints to prevent idling. Additionally, it provides load balancing between multiple endpoints using a simple nginx docker for scalability. Users can create slurm files based on default configurations and inspect logs for further analysis. For users without a Slurm cluster, hosted inference endpoints are available for testing with usage limits based on registration status.

A Rust based tool to evaluate LLM models, prompts and model params. It automates the process of selecting the best model parameters, given an LLM model and a prompt, iterating over the possible combinations and letting the user visually inspect the results. The tool assumes the user has Ollama installed and serving endpoints, either in `localhost` or in a remote server. Key features include: * Automatically fetches models from local or remote Ollama servers * Iterates over different models and params to generate inferences * A/B test prompts on different models simultaneously * Allows multiple iterations for each combination of parameters * Makes synchronous inference calls to avoid spamming servers * Optionally outputs inference parameters and response metadata (inference time, tokens and tokens/s) * Refetching of individual inference calls * Model selection can be filtered by name * List experiments which can be downloaded in JSON format * Configurable inference timeout * Custom default parameters and system prompts can be defined in settings

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

ChainForge is a visual programming environment for battle-testing prompts to LLMs. It is geared towards early-stage, quick-and-dirty exploration of prompts, chat responses, and response quality that goes beyond ad-hoc chatting with individual LLMs. With ChainForge, you can: * Query multiple LLMs at once to test prompt ideas and variations quickly and effectively. * Compare response quality across prompt permutations, across models, and across model settings to choose the best prompt and model for your use case. * Setup evaluation metrics (scoring function) and immediately visualize results across prompts, prompt parameters, models, and model settings. * Hold multiple conversations at once across template parameters and chat models. Template not just prompts, but follow-up chat messages, and inspect and evaluate outputs at each turn of a chat conversation. ChainForge comes with a number of example evaluation flows to give you a sense of what's possible, including 188 example flows generated from benchmarks in OpenAI evals. This is an open beta of Chainforge. We support model providers OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and Dalai-hosted models Alpaca and Llama. You can change the exact model and individual model settings. Visualization nodes support numeric and boolean evaluation metrics. ChainForge is built on ReactFlow and Flask.

HybridAGI is the first Programmable LLM-based Autonomous Agent that lets you program its behavior using a **graph-based prompt programming** approach. This state-of-the-art feature allows the AGI to efficiently use any tool while controlling the long-term behavior of the agent. Become the _first Prompt Programmers in history_ ; be a part of the AI revolution one node at a time! **Disclaimer: We are currently in the process of upgrading the codebase to integrate DSPy**

This repository provides a Python implementation of a method that leverages pre-trained large language models to construct and utilize world models for model-based task planning. It includes scripts to generate domain models using natural language descriptions, correct domain models based on feedback, and support plan generation for tasks in different domains. The code has been refactored for better readability and includes tools for validating PDDL syntax and handling corrective feedback.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

LearnHouse is an open-source platform that allows anyone to easily provide world-class educational content. It supports various content types, including dynamic pages, videos, and documents. The platform is still in early development and should not be used in production environments. However, it offers several features, such as dynamic Notion-like pages, ease of use, multi-organization support, support for uploading videos and documents, course collections, user management, quizzes, course progress tracking, and an AI-powered assistant for teachers and students. LearnHouse is built using various open-source projects, including Next.js, TailwindCSS, Radix UI, Tiptap, FastAPI, YJS, PostgreSQL, LangChain, and React.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

DownEdit is a powerful program that allows you to download videos from various social media platforms such as TikTok, Douyin, Kuaishou, and more. With DownEdit, you can easily download videos from user profiles and edit them in bulk. You have the option to flip the videos horizontally or vertically throughout the entire directory with just a single click. Stay tuned for more exciting features coming soon!

The 'bots' repository is a collection of guides, tools, and example bots for programming bots to play video games. It provides resources on running bots live, installing the BotLab client, debugging bots, testing bots in simulated environments, and more. The repository also includes example bots for games like EVE Online, Tribal Wars 2, and Elvenar. Users can learn about developing bots for specific games, syntax of the Elm programming language, and tools for memory reading development. Additionally, there are guides on bot programming, contributing to BotLab, and exploring Elm syntax and core library.

Deaddit is a project showcasing an AI-filled internet platform similar to Reddit. All content, including subdeaddits, posts, and comments, is generated by AI algorithms. Users can interact with AI-generated content and explore a simulated social media experience. The project provides a demonstration of how AI can be used to create online content and simulate user interactions in a virtual community.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

This repository contains Refact WebUI for fine-tuning and self-hosting of code models, which can be used inside Refact plugins for code completion and chat. Users can fine-tune open-source code models, self-host them, download and upload Lloras, use models for code completion and chat inside Refact plugins, shard models, host multiple small models on one GPU, and connect GPT-models for chat using OpenAI and Anthropic keys. The repository provides a Docker container for running the self-hosted server and supports various models for completion, chat, and fine-tuning. Refact is free for individuals and small teams under the BSD-3-Clause license, with custom installation options available for GPU support. The community and support include contributing guidelines, GitHub issues for bugs, a community forum, Discord for chatting, and Twitter for product news and updates.

Inspect and rate futuristic AI conceived VTOL aircraft.

Upload a picture of your home wall, floor, window, driveway, roof, HVAC, and get an instant opinion.

Fully-equipped .har file inspector with image extraction

Food quality inspection expert for analyzing food images.

Roofing expert offering advice on repairs, maintenance, and inspections.

Identifies hotdogs in images.

Assess a potential home's quality using your own photos and property inspection reports

Expert on Personal Protective Equipment

Expert in testing, inspection, certification, compliant with OpenAI policies, developed on OpenAI.

Is this a bed bug?

Transform Your Home Search into an Epic Journey with Choose Your Own Adventure Housing – Where Every Click is a New Path!

The buddy that helps you with all things Drones. While I'm nicer than most humans, I'm still a bot, so double check my answers

What would you say if I told you there is an app on the market that can tell you if you have a hot dog or not a hot dog.

Expert in NFPA 99-2018 for medical gas system compliance and guidance.

An expert in home buying

Informative guide on road construction, maintenance, and traffic disruptions.

I interpret drone pseudo code into funny stories.

Expert in local real estate, housing, and homelessness advice

Expert in authenticating luxury items, providing detailed evaluations and ratings.

Expert in Maritime Conservation & Drone Technology.