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Copy file name to clipboardexpand all lines: docs/awesome/awesome-agi-cocosci.md
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* [Human-level concept learning through probabilistic program induction](https://www.science.org/doi/full/10.1126/science.aab3050) - ***Science***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=11844685101409624506)]. [[Preprint](https://ai6034.mit.edu/wiki/images/LakeDec2015.pdf)]. [[Supplementary Material](https://cims.nyu.edu/~brenden/LakeEtAl2015Science_supp.pdf)]. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. This work presents a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches.
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*[Building Machines That Learn and Think Like People](https://leylaroksancaglar.github.io/Caglar_Hanson_2017.pdf) - ***Behavioral and Brain Sciences***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=8504723689348856287&hl=en&as_sdt=0,5)]. Brenden Lake and Josh Tenenbaum's review on Bayesian modeling.
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* [Building Machines That Learn and Think Like People](https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/building-machines-that-learn-and-think-like-people/A9535B1D745A0377E16C590E14B94993) - ***Behavioral and Brain Sciences***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=8504723689348856287)]. [[Preprint](https://leylaroksancaglar.github.io/Caglar_Hanson_2017.pdf)]. Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. The authors review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, the authors argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. The authors suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.
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*[Building machines that learn and think with people](https://www.nature.com/articles/s41562-024-01991-9) - ***Nature Human Behavior***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=4420595706578245444)]. [[Preprint](https://arxiv.org/abs/2408.03943)]. This perspective shows how the science of collaborative cognition can be put to work to engineer systems that really can be called ‘thought partners’, systems built to meet humans' expectations and complement humans' limitations. The authors lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and they propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, this work motivates an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the constructed partners actively build and reason over models of the human and world.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-angular.md
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*[Angspire](https://github.com/tbarracha/Angspire) - Angular + .NET (monorepo) project template designed to simplify development with out-of-the-box features like (basic) user authentication and frontend themes, reducing setup time and providing a scalable, maintainable foundation for your applications.
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*[keycloakify-starter-angular-vite](https://github.com/keycloakify/keycloakify-starter-angular-vite) - Angular + Vite Starter for [Keycloakify 11](https://www.keycloakify.dev/).
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*[@rainerhahnekamp/angular-starter](https://github.com/rainerhahnekamp/angular-starter) - A starter project for a modern Angular 19 application.
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*[BoilerPlate-FirebaseAngular](https://github.com/Yewo-Devs/BoilerPlate-FirebaseAngular) - Starting point for apps built using .NET, Angular, and Firebase.
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##### Paid
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*[@TanStack/form](https://github.com/TanStack/form) - Designed with first-class TypeScript support, headless UI components, and a framework-agnostic design, it streamlines form handling and ensures a seamless experience across various front-end frameworks.
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*[@luistabotelho/angular-signal-forms](https://github.com/luistabotelho/angular-signal-forms) - Simple Angular library to implement forms with signals.
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*[ngx-form-object](https://github.com/infinum/ngx-form-object) - ngx-form-object is an abstraction on top of Angular's reactive forms. It generates a form from a given model and automatically handles creation of nested forms (HasOne and HasMany model relationships).
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*[reactive-form-extensions](https://gitlab.com/Riuen/reactive-form-extensions) - This project improves Angular's ReactiveForms with useful features like trimming inputs, showing error messages, and changing CSS classes based on validation rules, all without extra dependencies and with less code.
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##### Form Controls
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*[ngx-hover-preload](https://github.com/mgechev/ngx-hover-preload) - Preload Angular lazy-loaded routes on mouse over.
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*[skeletonizer](https://github.com/lukaVarga/skeletonizer) - A lightweight package that provides a simple way to create skeletonized views for your application. It has adapters for Vue and Angular and is highly customizable and easy to use.
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*[dz-ngx-element](https://github.com/zdigambar/dz-ngx-element) - [NgxElement](https://www.npmjs.com/package/@zdigambar/ngx-element) enables to lazy load Angular components in non-angular applications. The library will register a custom element to which you can pass an attribute to specify what component you want to load.
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*[ngx-loading-overlay](https://github.com/shaman-apprentice/ngx-loading-overlay) - An Angular directive adding a loading overlay to your HTML.
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#### Loggers
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*[voicecapture-angular](https://github.com/angular-a11y/voicecapture-angular) - The library offers customizable options for handling voice input and transcription, making it a flexible solution for enhancing user interfaces.
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*[ngx-video-timeline](https://github.com/LennonReid/ngx-video-timeline) - Video playback progress bar component.
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*[ngx-cam-shoot](https://github.com/RzoDev/ngx-cam-shoot) - A streamlined Angular component that simplifies using your device's camera and accelerates image capture and saving.
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*[velt](https://velt.dev/) - Add AI powered collaboration features ridiculously fast.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-cpp.md
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*[value-category-cheatsheet](https://github.com/jeaye/value-category-cheatsheet) A PDF cheatsheet for lvalues, rvalues, and the like. [Jank copyleft]
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*[VarTypes](https://github.com/szi/vartypes) - A feature-rich, object-oriented framework for managing variables in C++ / Qt4. [LGPL]
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*[Wildcards](https://github.com/zemasoft/wildcards/) - A simple C++ header-only template library implementing matching using wildcards. [BSL-1.0]
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*[xxHash](https://github.com/Cyan4973/xxHash) - Extremely fast non-cryptographic hash algorithm. [BSD-2-Clause][website](https://xxhash.com/)
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*[xxhash_cpp](https://github.com/RedSpah/xxhash_cpp) - Port of the xxhash library to C++17. [BSD-2-Clause]
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*[ZBar](http://zbar.sourceforge.net/) - A barcode scanner library, which allows to scan photos/images/video streams for barcodes and return their value. [LGPL2]
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*[ZXing](https://github.com/zxing/zxing/) - An open-source, multi-format 1D/2D barcode image processing library implemented in Java, with ports to other languages. [Apache]
Copy file name to clipboardexpand all lines: docs/awesome/awesome-integration.md
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-[Anypoint MQ](https://www.mulesoft.com/platform/anypoint-mq-message-queue) - An enterprise-class cloud messaging service that is fully integrated with Anypoint Platform. It offers a flexible and reliable messaging infrastructure that enables you to integrate and orchestrate your applications and systems.
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-[Azure Service Bus](https://azure.microsoft.com/en-us/services/service-bus/) - A reliable cloud messaging service that offers a variety of messaging patterns, such as pub/sub and request/response, and supports a range of protocols and standards.
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-[CloudAMQP](https://www.cloudamqp.com/) - A RabbitMQ as a Service that provides a fully managed and scalable message broker service for your applications. It offers a simple and easy-to-use web-based management console, advanced monitoring and analytics, and seamless integration with other cloud services.
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-[CloudKarafka](https://www.cloudkarafka.com/) - An affordable and straightforward Kafka service that provides fully managed and scalable Kafka clusters on AWS and Google Cloud. It offers a user-friendly web-based dashboard, advanced security features, and seamless integration with other cloud services.
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-[Google Cloud Managed Service for Apache Kafka](https://cloud.google.com/products/managed-service-for-apache-kafka) - A fully managed platform that simplifies the deployment, scaling, and management of real-time data pipelines and streaming applications on Google Cloud.
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-[Google Cloud Pub/Sub](https://cloud.google.com/pubsub) - A messaging and ingestion service that enables event-driven systems and streaming analytics. It provides high throughput, low latency, and reliable message delivery for your applications. It supports flexible messaging patterns, such as pub/sub and push/pull, and integrates seamlessly with other Google Cloud services.
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-[Huawei Cloud Distributed Message Service](https://www.huaweicloud.com/intl/en-us/product/dms.html) - A fully managed and high-performance message queuing service that enables reliable, flexible, and asynchronous communication between distributed applications. It offers high scalability, security, and durability, and supports a variety of messaging patterns and protocols.
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-[Huawei Cloud Distributed Message Service for Kafka](https://www.huaweicloud.com/intl/en-us/product/dmskafka.html) - A managed Apache Kafka service that provides easy setup, scaling, and management of Kafka clusters. It offers high availability, performance, and security, and integrates seamlessly with other Huawei Cloud services.
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-[echasnovski/mini.nvim#mini.visits](https://github.com/echasnovski/mini.nvim/blob/main/readmes/mini-visits.md) - Module of `mini.nvim` to persistently track and reuse file system visits. Allows listing "recent"/"frequent"/"frecent" visits, adding/removing labels to visits and other data.
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-[LintaoAmons/cd-project.nvim](https://github.com/LintaoAmons/cd-project.nvim) - All you need is just an easier way to `cd` to another project directory.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-rest.md
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*[node-restify](https://github.com/restify/node-restify) - Framework specifically meant for REST API.
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*[Sails.js](https://sailsjs.org/) - Node.js Web framework embedding a command to generate automatically a REST API.
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*[mers](https://github.com/jspears/mers) - Express service exposing Mongoose finders as RESTful API.
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*[Baucis](https://git.kun.io/wprl/baucis) - Build scalable REST API based on your Mongoose entities.
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*[flatiron/resourceful](https://github.com/flatiron/resourceful) - Isomorphic Resource engine for JavaScript.
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*[loopback](https://loopback.io/) - Powerful Node.js framework for creating APIs and easily connecting to backend data sources.
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*[Feathers](https://feathersjs.com/) - is a real-time, micro-service web framework that gives you control over your data via RESTful resources, sockets and flexible plug-ins.
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*[RequestBin](https://requestbin.com/) - Inspect and debug webhook requests sent by your clients or third-party APIs.
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*[httpbin](https://httpbin.org) - HTTP request and response service - a/k/a Swiss Army Knife for HTTP.
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*[FakeRest](https://github.com/marmelab/FakeRest) - Patch XMLHttpRequest to fake a REST API client-side.
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*[FakeRest](https://github.com/marmelab/FakeRest) - Redirect fetch() calls to a client-side fake REST API.
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*[json-server](https://github.com/typicode/json-server) - Serve a REST API from fixture files using quick prototyping.
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*[Mocky.io](https://www.mocky.io/) - Free online service to create fake HTTP responses.
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*[MockServer](https://www.mock-server.com/) - Easy mocking of any system you integrate with via HTTP or HTTPS.
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*[Assertible](https://assertible.com) - Continuously test and monitor your APIs after deployments and across environments.
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*[Moesif](https://www.moesif.com) - API Analytics for Debugging, Monitoring, and Usage Tracking for RESTful and GraphQL.
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*[Beeceptor](https://beeceptor.com/) - An HTTP inspecting, mocking and proxing service. Gives named endpoints for creating mock API endpoints and simulate responses.
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*[Apitally](https://apitally.io) - Analytics, request logging and monitoring for REST APIs with a focus on simplicity and data privacy.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-selfhosted.md
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- [Mayan EDMS](https://www.mayan-edms.com) - Free Open Source Electronic Document Management System. An electronic vault for your documents with preview generation, OCR, and automatic categorization among other features. ([Source Code](https://gitlab.com/mayan-edms/mayan-edms)) `Apache-2.0` `Python`
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- [OpenSign](https://www.opensignlabs.com) `⚠` - Free, open source & self-hosted document signing software (alternative to DocuSign). ([Source Code](https://github.com/opensignlabs/opensign)) `AGPL-3.0` `Nodejs/Docker`
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- [Paperless-ngx](https://docs.paperless-ngx.com/) - Scan, index, and archive all of your paper documents with an improved interface (fork of Paperless). ([Demo](https://demo.paperless-ngx.com/), [Source Code](https://github.com/paperless-ngx/paperless-ngx)) `GPL-3.0` `Python/Docker`
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- [Papermerge](https://papermerge.com) - Open Source Document Management System focused on scanned documents (electronic archives). Features file browsing in similar way to dropbox/google drive. OCR, full text search, text overlay/selection. ([Source Code](https://github.com/ciur/papermerge)) `Apache-2.0` `Python/Docker/K8S`
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- [Papermerge](https://papermerge.com) - Document management system focused on scanned documents (electronic archives). Features file browsing in similar way to dropbox/google drive. OCR, full text search, text overlay/selection. ([Source Code](https://github.com/papermerge/papermerge-core)) `Apache-2.0` `Docker/K8S`
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- [PdfDing](https://github.com/mrmn2/PdfDing) - PDF manager, viewer and editor offering a seamless user experience on multiple devices. It's designed to be minimal, fast, and easy to set up using Docker. ([Demo](https://demo.pdfding.com)) `GPL-3.0` `Docker`
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- [SeedDMS](https://www.seeddms.org) - Document Management System with workflows, access rights, fulltext search, and more. ([Demo](https://www.seeddms.org/about/), [Source Code](https://sourceforge.net/p/seeddms/code/ci/master/tree/)) `GPL-2.0` `PHP`
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- [Stirling-PDF](https://github.com/Frooodle/Stirling-PDF) - Local hosted web application that allows you to perform various operations on PDF files, such as merging, splitting, file conversions and OCR. `Apache-2.0` `Docker/Java`
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