Best AI tools for< Application Architect >

Archie is an AI-driven product architect platform that helps users transform their ideas into actionable specifications, designs, and architectural plans. It leverages the power of multiple large language models (LLMs) to provide users with insights, suggestions, and assistance throughout the product development process. With Archie, users can quickly generate a free Digital Blueprint of their software application's design, cure writer's block, rapidly conceptualize their ideas, and get data-driven assessments and enhanced creativity suggestions. Archie also allows users to socialize their ideas with early stakeholders and leverage AI to expand their ideas into detailed functional and technical plans.

Convex is a fullstack TypeScript development platform that serves as an open-source backend for application builders. It offers a comprehensive set of APIs and tools to build, launch, and scale applications efficiently. With features like real-time collaboration, optimized transactions, and over 80 OAuth integrations, Convex simplifies backend operations and allows developers to focus on delivering value to customers. The platform enables developers to write backend logic in TypeScript, perform database operations with strong consistency, and integrate with various third-party services seamlessly. Convex is praised for its reliability, simplicity, and developer experience, making it a popular choice for modern software development projects.

Architecture Helper is an AI-based application that allows users to analyze real-world buildings, explore architectural influences, and generate new structures with customizable styles. Users can submit images for instant design analysis, mix and match different architectural styles, and create stunning architectural and interior images. The application provides unlimited access for $5 per month, with the flexibility to cancel anytime. Named as a 'Top AI Tool' in Real Estate by CRE Software, Architecture Helper offers a powerful and playful tool for architecture enthusiasts to explore, learn, and create.

Genera is a website that provides users with access to a variety of generative AI applications, including an interior designer, an architectural designer, and a text-to-image generator. The website is easy to use and allows users to create stunning designs and images with just a few clicks. Genera is a great resource for anyone who is looking to use AI to improve their creativity.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Supple.ai is an AI-powered content generation tool that helps users create high-quality written content quickly and efficiently. By leveraging advanced natural language processing algorithms, Supple.ai can generate articles, blog posts, product descriptions, and more in a matter of minutes. The tool is designed to assist content creators, marketers, and businesses in streamlining their content creation process and improving productivity.

Co-Founder AI is an AI-powered tool that accelerates startup success by providing well-structured business plans and actionable insights. It offers in-depth reports on essential aspects like viability, revenue opportunities, risks, user insights, and strategic frameworks. The tool utilizes data-driven insights from metrics, client feedback, and operational efficiencies to guide growth. It supports multiple languages and provides detailed technical specifications, market trends, sales techniques, and frameworks like Lean Canvas and AARRR for business planning and analysis.

Rendair is an AI application that offers a range of tools and tutorials for architectural visualization and real estate professionals. The platform provides AI-powered solutions for tasks such as upscaling images, removing objects, placing 3D models in real-life locations, and designing over real locations with sketches. Rendair aims to streamline workflows, enhance visual communication, and boost efficiency in the architectural and real estate industries.

IACrea is an online platform that allows users to virtually stage and decorate their homes using artificial intelligence. With IACrea, users can upload photos of their rooms and choose from a variety of furniture and décor options to create realistic and visually appealing images. IACrea's AI technology automatically generates the staged images, making it quick and easy for users to see how their homes would look with different furnishings and décor.

Visoid is an AI-powered architectural visualization tool that allows architects and industry professionals to create stunning visualizations in minutes. It offers a simple web-based application for fast rendering, enabling users to iterate on images, explore variations, and communicate concepts effectively. With Visoid, users can transform ideas into powerful renderings, elevate early concept phase communication, and visualize projects in different lighting or seasons. The tool is designed to save time, improve communication, and provide different license options to match users' needs.

Indesignify is an innovative platform that utilizes artificial intelligence to revolutionize interior design. It offers users the ability to decorate rooms, generate design ideas, and visualize spaces quickly and effortlessly. With Indesignify, users can build prototypes of interior designs in seconds, transform empty spaces into fully furnished rooms, explore a wide range of interior design styles, customize design rules, and generate diverse and stunning visualizations to boost conversion rates for real estate agents or secure more clients for interior designers.

InQI.AI is an AI design software that offers smart design solutions for modern architects and builders. It leverages data at the core of architecture and construction excellence, reimagining design and construction through data. InQI.AI provides streamlined site planning and enhanced collaboration for designers, architects, home & ADU builders, pool builders, and land developers. With features like AI-generated site plans, high-resolution aerial imagery, and tools for designing ADUs, pools, and more, InQI.AI aims to empower professionals in the industry with precision and creativity. The platform offers three editions tailored to different needs, crafted by industry experts to provide comprehensive solutions for architecture and construction projects.

NeuReality is an AI-centric solution designed to democratize AI adoption by providing purpose-built tools for deploying and scaling inference workflows. Their innovative AI-centric architecture combines hardware and software components to optimize performance and scalability. The platform offers a one-stop shop for AI inference, addressing barriers to AI adoption and streamlining computational processes. NeuReality's tools enable users to deploy, afford, use, and manage AI more efficiently, making AI easy and accessible for a wide range of applications.

Eventual is a platform that simplifies the process of building and operating resilient event-driven applications. It offers code-first APIs, Events, and Workflows to create durable, scalable systems with end-to-end type safety. The platform enables the creation of composable microservices that are fully serverless, easily evolve, and have minimal operational complexity. Eventual runs in the user's cloud environment, adhering to their security and privacy policies, and integrates with their preferred Infrastructure as Code (IaC) framework.

DryRun Security is a contextual security analysis tool designed to help organizations uncover risks in their codebase that traditional pattern-matching tools may miss. By providing real-time feedback, clear guidance, and customizable code policies, DryRun Security empowers security leaders, AppSec engineers, and developers to enhance code security, streamline compliance, and increase developer engagement. The tool supports various languages and frameworks, offers natural language code policies, and integrates with GitHub and Slack for notifications and reporting.

Heroku is a cloud platform that lets companies build, deliver, monitor, and scale apps. It simplifies the process of deploying applications by providing a platform as a service (PaaS) that supports various programming languages. With Heroku, developers can focus on coding without worrying about infrastructure management.

Cartesia Sonic Team Blog Research Playground is an AI application that offers real-time multimodal intelligence for every device. The application aims to build the next generation of AI by providing ubiquitous, interactive intelligence that can run on any device. It features the fastest, ultra-realistic generative voice API and is backed by research on simple linear attention language models and state-space models. The founding team, who met at the Stanford AI Lab, has invented State Space Models (SSMs) and scaled it up to achieve state-of-the-art results in various modalities such as text, audio, video, images, and time-series data.

Visualizee.ai is the #1 AI rendering tool that transforms sketches, images, 3D models, or text prompts into realistic renders in just 10 seconds. It offers tools to enhance creativity, such as converting low-detail models into high-resolution renders, inpainting missing parts of images, and upscaling image resolution without compromising detail. With pricing plans starting at $9, Visualizee.ai is trusted by over 2100 architects and designers for its ease of use and efficiency in generating renders. The application allows users to create unlimited renders, work directly on 3D models, and iterate quickly to achieve perfect results, making it an essential tool for architects and designers.

Cognee is an AI application that helps users build deterministic AI memory by perfecting exceptional AI apps with intelligent data management. It acts as a semantic memory layer, uncovering hidden connections within data and infusing it with company-specific language and principles. Cognee offers data ingestion and enrichment services, resulting in relevant data retrievals and lower infrastructure costs. The application is suitable for various industries, including customer engagement, EduTech, company onboarding, recruitment, marketing, and tourism.

X Headshot is a generative AI photo company that offers professional business headshots for customers. It allows users to turn selfies into business AI headshots, build trust and credibility, and make a powerful first impression. The AI technology creates high-definition headshots in just 1 hour, starting at $29. Users can upload their photos, and the AI model generates stunning headshots without the need for a physical photoshoot. X Headshot is trusted by professionals from various renowned companies and offers different packages to cater to users' needs.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

Ask Astro is an open-source reference implementation of Andreessen Horowitz's LLM Application Architecture built by Astronomer. It provides an end-to-end example of a Q&A LLM application used to answer questions about Apache Airflow® and Astronomer. Ask Astro includes Airflow DAGs for data ingestion, an API for business logic, a Slack bot, a public UI, and DAGs for processing user feedback. The tool is divided into data retrieval & embedding, prompt orchestration, and feedback loops.

The 'llm-dev' repository contains source code and resources for the book 'Practical Projects of Large Models: Multi-Domain Intelligent Application Development'. It covers topics such as language model basics, application architecture, working modes, environment setup, model installation, fine-tuning, quantization, multi-modal model applications, chat applications, programming large model applications, VS Code plugin development, enhanced generation applications, translation applications, intelligent agent applications, speech model applications, digital human applications, model training applications, and AI town applications.

GenAIComps is an initiative aimed at building enterprise-grade Generative AI applications using a microservice architecture. It simplifies the scaling and deployment process for production, abstracting away infrastructure complexities. GenAIComps provides a suite of containerized microservices that can be assembled into a mega-service tailored for real-world Enterprise AI applications. The modular approach of microservices allows for independent development, deployment, and scaling of individual components, promoting modularity, flexibility, and scalability. The mega-service orchestrates multiple microservices to deliver comprehensive solutions, encapsulating complex business logic and workflow orchestration. The gateway serves as the interface for users to access the mega-service, providing customized access based on user requirements.

Discover the journey of building a generative AI application using LangChain.js and Azure. This demo explores the development process from idea to production, using a RAG-based approach for a Q&A system based on YouTube video transcripts. The application allows to ask text-based questions about a YouTube video and uses the transcript of the video to generate responses. The code comes in two versions: local prototype using FAISS and Ollama with LLaMa3 model for completion and all-minilm-l6-v2 for embeddings, and Azure cloud version using Azure AI Search and GPT-4 Turbo model for completion and text-embedding-3-large for embeddings. Either version can be run as an API using the Azure Functions runtime.

This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

Pluto is a development tool dedicated to helping developers **build cloud and AI applications more conveniently** , resolving issues such as the challenging deployment of AI applications and open-source models. Developers are able to write applications in familiar programming languages like **Python and TypeScript** , **directly defining and utilizing the cloud resources necessary for the application within their code base** , such as AWS SageMaker, DynamoDB, and more. Pluto automatically deduces the infrastructure resource needs of the app through **static program analysis** and proceeds to create these resources on the specified cloud platform, **simplifying the resources creation and application deployment process**.

The 'local-talking-llm' repository provides a tutorial on building a voice assistant similar to Jarvis or Friday from Iron Man movies, capable of offline operation on a computer. The tutorial covers setting up a Python environment, installing necessary libraries like rich, openai-whisper, suno-bark, langchain, sounddevice, pyaudio, and speechrecognition. It utilizes Ollama for Large Language Model (LLM) serving and includes components for speech recognition, conversational chain, and speech synthesis. The implementation involves creating a TextToSpeechService class for Bark, defining functions for audio recording, transcription, LLM response generation, and audio playback. The main application loop guides users through interactive voice-based conversations with the assistant.

Ultralytics YOLO for Flutter is a Flutter plugin that allows you to integrate Ultralytics YOLO computer vision models into your mobile apps. It supports both Android and iOS platforms, providing APIs for object detection and image classification. The plugin leverages Flutter Platform Channels for seamless communication between the client and host, handling all processing natively. Before using the plugin, you need to export the required models in `.tflite` and `.mlmodel` formats. The plugin provides support for tasks like detection and classification, with specific instructions for Android and iOS platforms. It also includes features like camera preview and methods for object detection and image classification on images. Ultralytics YOLO thrives on community collaboration and offers different licensing paths for open-source and commercial use cases.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

Vespa documentation is served using GitHub Project pages with Jekyll. To edit documentation, check out and work off the master branch in this repository. Documentation is written in HTML or Markdown. Use a single Jekyll template _layouts/default.html to add header, footer and layout. Install bundler, then $ bundle install $ bundle exec jekyll serve --incremental --drafts --trace to set up a local server at localhost:4000 to see the pages as they will look when served. If you get strange errors on bundle install try $ export PATH=“/usr/local/opt/[email protected]/bin:$PATH” $ export LDFLAGS=“-L/usr/local/opt/[email protected]/lib” $ export CPPFLAGS=“-I/usr/local/opt/[email protected]/include” $ export PKG_CONFIG_PATH=“/usr/local/opt/[email protected]/lib/pkgconfig” The output will highlight rendering/other problems when starting serving. Alternatively, use the docker image `jekyll/jekyll` to run the local server on Mac $ docker run -ti --rm --name doc \ --publish 4000:4000 -e JEKYLL_UID=$UID -v $(pwd):/srv/jekyll \ jekyll/jekyll jekyll serve or RHEL 8 $ podman run -it --rm --name doc -p 4000:4000 -e JEKYLL_ROOTLESS=true \ -v "$PWD":/srv/jekyll:Z docker.io/jekyll/jekyll jekyll serve The layout is written in denali.design, see _layouts/default.html for usage. Please do not add custom style sheets, as it is harder to maintain.

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

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.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Feeds Fun is a self-hosted news reader tool that automatically assigns tags to news entries. Users can create rules to score news based on tags, filter and sort news as needed, and track read news. The tool offers multi/single-user support, feeds management, and various features for personalized news consumption. Users can access the tool's backend as the ffun package on PyPI and the frontend as the feeds-fun package on NPM. Feeds Fun requires setting up OpenAI or Gemini API keys for full tag generation capabilities. The tool uses tag processors to detect tags for news entries, with options for simple and complex processors. Feeds Fun primarily relies on LLM tag processors from OpenAI and Google for tag generation.

eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

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