Best AI tools for< Structural Engineer >

Lavo Life Sciences is an AI-accelerated crystal structure prediction application that aims to accelerate drug development processes. The platform provides solutions for de-risking pipelines, optimizing solid-state formulations, and avoiding late-stage surprises using AI technology. Lavo Life Sciences combines the expertise of chemists and engineers in AI and computational chemistry to offer innovative solutions for drug development teams.

SpeakStruct is an AI-powered application that enables professionals, businesses, and developers to effortlessly convert voice input into structured formats using customizable templates. The platform leverages advanced AI and natural language processing to ensure high accuracy in voice transcription and data structuring, making it ideal for various industries such as sales & marketing, customer support, product & engineering, financial/mortgage advisors, and healthcare professionals. SpeakStruct's flexible template builder allows users to tailor the application to their specific needs, capturing voice input from any channel and transforming it into a consistent, structured format.

CEBRA is a machine-learning method that compresses time series data to reveal hidden structures in the variability of the data. It excels in analyzing behavioral and neural data simultaneously, allowing for the decoding of activity from the visual cortex of the mouse brain to reconstruct viewed videos. CEBRA is a novel encoding method that leverages both behavioral and neural data to produce consistent and high-performance latent spaces, enabling the mapping of space, uncovering complex kinematic features, and providing rapid, high-accuracy decoding of natural movies from the visual cortex.

GPT CLI is an all-in-one AI tool that allows users to build their own AI command-line interface tools using ChatGPT. It provides various plugins such as AI Commit, AI Command, AI Translate, and more, enabling users to streamline their workflow and automate tasks through natural language commands. With GPT CLI, users can easily generate Git commit messages, execute commands, translate text, and perform various other AI-powered tasks directly from the command line.

Aipify is a platform that allows users to build AI-powered APIs in seconds. With Aipify, users can access the latest AI models, including GPT-4, to enhance their applications' capabilities. Aipify's APIs are easy to use and affordable, making them a great choice for businesses of all sizes.

AI Toolhouse is a comprehensive AI tools catalog and directory that allows users to explore various categories of AI tools and Generative AI advancements. Users can discover the newest additions, stay updated with daily data updates, and access cutting-edge resources in areas such as General Writing, Art, Code Assistant, SQL, Human Resources, E-Commerce, Productivity, Sales, Image Editing, and Developer Tools. The platform offers a wide range of verified filters to help users find the most suitable tools for their needs.

NuMind is an AI tool designed to solve information extraction tasks efficiently. It offers high-quality lightweight models tailored to users' needs, automating classification, entity recognition, and structured extraction. The tool is powered by task-specific and domain-agnostic foundation models, outperforming GPT-4 and similar models. NuMind provides solutions for various industries such as insurance and healthcare, ensuring privacy, cost-effectiveness, and faster NLP projects.

CVBee.ai is an AI-powered online CV maker that offers a comprehensive solution for creating, optimizing, and refining professional resumes. The platform utilizes artificial intelligence to generate CVs from users' career background, enhance existing CVs with industry-specific keywords, and provide format and structure suggestions. With features like iterative refinement and keyword optimization, CVBee.ai aims to help job seekers craft job-winning resumes that stand out in Applicant Tracking Systems (ATS) and increase their chances of landing interviews.

Hyperscience is a leading enterprise AI platform that provides hyperautomation solutions for businesses. Its platform enables organizations to automate complex business processes with high accuracy and efficiency. Hyperscience offers a range of solutions across various industries and processes, leveraging technologies such as intelligent document processing, machine learning, and natural language processing. The platform is designed to help businesses transform their operations, improve decision-making, and gain a competitive advantage.

jsonAI is an AI tool that allows users to easily transform data into structured JSON format. Users can define their schema, add custom prompts, and receive AI-structured JSON responses. The tool enables users to create complex schemas with nested objects, control the response JSON on the fly, and test their JSON data in real-time. jsonAI offers a free trial plan, seamless integration with existing apps, and ensures data security by not storing user data on their servers.

Synthace is a digital experiment platform designed for R&D teams in the life science industry. It allows users to design and run powerful experiments in the lab, automatically build structured data, and gain insights without the need for coding. The platform centralizes bioprocess data, reduces human error, and enables confident protocol reproducibility.

WebDB is an open-source and efficient Database IDE that focuses on providing a secure and user-friendly platform for database management. It offers features such as automatic DBMS discovery, credential guessing, time machine for database version control, powerful queries editor with autocomplete and documentation, AI assistant integration, NoSQL structure management, intelligent data generation, and more. With a modern ERD view and support for various databases, WebDB aims to simplify database management tasks and enhance productivity for users.

Sensei AI is a real-time interview copilot application designed to provide assistance during live interviews. It offers instant answers to questions, personalized responses, and aims to help users land their dream job. The application uses advanced AI insights to understand the true intent behind interview questions, tailoring responses based on tone, word choices, keywords, timing, formality level, and context. Sensei AI also offers a hands-free experience, robust privacy features, and a personalized interview experience by tailoring answers to the user's job role, resume, and personal stories.

The ZAI is an AI application that offers a platform for generating code files and app structures with the help of artificial intelligence. It provides features such as app structure generation, multi-platform support, code optimization, and rapid prototyping. Users can quickly create functional app prototypes for testing and iteration. The application aims to enhance productivity and streamline the app development process by leveraging AI technology.

HireList.io is a Next-Gen AI Recruitment tool designed for fast-growing startups. The platform offers Intelligent Recruitment Software powered by AI to streamline the hiring process, target the right talent, and connect with suitable candidates. HireList helps in building dream teams efficiently by providing a job board, AI-powered candidate filtering, and a collaborative hiring process. The tool simplifies recruitment with features like a direct application portal, efficient hiring pipeline, applicant tracking system, communication automation, and structured interviews.

Docugami is an AI-powered document engineering platform that enables business users to extract, analyze, and automate data from various types of documents. It empowers users with immediate impact without the need for extensive machine learning investments or IT development. Docugami's proprietary Business Document Foundation Model leverages Generative AI to transform unstructured text into structured information, allowing users to unlock insights and drive business processes efficiently.

Google DeepMind is an AI research company that aims to develop artificial intelligence technologies to benefit the world. They focus on creating next-generation AI systems to solve complex scientific and engineering challenges. Their models like Gemini, Veo, Imagen 3, SynthID, and AlphaFold are at the forefront of AI innovation. DeepMind also emphasizes responsibility, safety, education, and career opportunities in the field of AI.

Teste.ai is an AI-powered platform for creating software test scenarios and cases using top-notch artificial intelligence technology. It offers a comprehensive set of tools based on AI to accelerate the software quality testing journey. With Teste.ai, testers can cover a wide range of requirements with a variety of test scenarios efficiently, ultimately increasing test coverage while reducing the time spent on test creation and specification. The platform provides intelligent features to enhance productivity in test creation, execution, and management, leveraging AI to generate test plans, scenarios, step-by-step guides, and structured data effortlessly.

HUAWEI Cloud Pangu is an AI tool designed for accelerating drug discovery by optimizing drug molecules. It offers features such as Molecule Search, Molecule Optimizer, and Pocket Molecule Design. Users can submit molecules for optimization and view historical optimization results. The tool is based on the MindSpore framework and has been visited over 300,000 times since August 23, 2021.

Glass is an AI copilot designed for React and Next.js developers. It allows users to edit code straight from the browser using AI technology. Glass's AI capabilities include creating components, modifying props, and generating Tailwind CSS. The tool helps developers visualize component structures and easily navigate to source code. Glass is precise and efficient, making React coding faster and more streamlined. It is currently in open beta for startups, with ongoing improvements to its AI functionality.

AirSLAM is an efficient visual SLAM system designed to tackle short-term and long-term illumination challenges. It combines deep learning techniques with traditional optimization methods, featuring a unified CNN for keypoint and structural line extraction. The system includes a relocalization pipeline for map reuse, accelerated using C++ and NVIDIA TensorRT. Outperforming other SLAM systems in challenging environments, it runs at 73Hz on PC and 40Hz on embedded platforms.

This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

ActionWeaver is an AI application framework designed for simplicity, relying on OpenAI and Pydantic. It supports both OpenAI API and Azure OpenAI service. The framework allows for function calling as a core feature, extensibility to integrate any Python code, function orchestration for building complex call hierarchies, and telemetry and observability integration. Users can easily install ActionWeaver using pip and leverage its capabilities to create, invoke, and orchestrate actions with the language model. The framework also provides structured extraction using Pydantic models and allows for exception handling customization. Contributions to the project are welcome, and users are encouraged to cite ActionWeaver if found useful.

This repository is a collection of papers on leveraging Large Language Models (LLMs) in Graph Tasks. It provides a comprehensive overview of how LLMs can enhance graph-related tasks by combining them with traditional Graph Neural Networks (GNNs). The integration of LLMs with GNNs allows for capturing both structural and contextual aspects of nodes in graph data, leading to more powerful graph learning. The repository includes summaries of various models that leverage LLMs to assist in graph-related tasks, along with links to papers and code repositories for further exploration.

The collection is actively updated with the help of an internal literature search engine.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

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.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

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.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

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.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

Civil Engineering expert

Expert in architectural and structural design principles.

Engineering-savvy assistant for creative solutions, accurate calculations, and detailed blueprints.

Especialista em teses e dissertações de Engenharia Civil

Tu guía interactiva en geotecnia y cimentaciones, con respuestas basadas en textos de referencia.

Expert in GH-Python scripting & Grasshopper components

Providing the most comprehensive, cutting-edge, and detailed technical guidance on the latest international CAE simulation technology（HyperMesh、THESEUS-FE、ANSA、STAR-CCM+、Amesim、Ncode、Adams、Abaqus）

Golang, algorithms, data structures & HTMX expert

Create soccer stadiums variations

Gives structure to messy GitHub issues.

RISEN Prompt Engineer GPT streamlines AI interactions by applying the RISEN framework, focusing on specific, structured prompts for targeted, creative responses. This method improves the precision of AI-generated answers, ensuring they meet user expectations effectively.

Describe the AI you want to build and what kind of tasks you need assistance with, get a structured, focused and well prompt engineered blueprint to paste into GPT-Builder.

Tutor on purely functional data structures and functional programming

Guides data structure design with tailored, step-by-step insights.

Irritating Northern advisor on UK building regs for structure. Based on Oliver Rigg and Approved Document A

Assistant for all data structures and algorithms related questions

Expert in algorithms and data structures, providing clear and concise explanations.

Get into FAANG. Practice with an AI expert in algorithms, data structures, and system design. Do a mock interview and improve.

Tutor for data structures, algorithms, LeetCode, and SQL.

Premier expert in engineering, architecture, design, construction, and project management, powered by OpenAI