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Copy file name to clipboardexpand all lines: docs/awesome/awesome-agi-cocosci.md
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*[Theory-based Bayesian models of inductive learning and reasoning](https://www.cell.com/AJHG/fulltext/S1364-6613(06)00134-3) - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=6741344960992898446)]. [[Preprint](http://www.charleskemp.com/papers/TenenbaumGK06.pdf)]. Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. This paper argues that both components are necessary to explain the nature, use and acquisition of human knowledge, and the authors introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
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* [Word learning as Bayesian inference](https://psycnet.apa.org/doiLanding?doi=10.1037%2F0033-295X.114.2.245) - ***Psychological Review***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=5476233692839102256)]. [[Preprint](https://tallinzen.net/media/readings/xu_tenenbaum_2007.pdf)]. Fei Xu's review on Bayesian word learning. The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.
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* [Word learning as Bayesian inference](https://psycnet.apa.org/doiLanding?doi=10.1037%2F0033-295X.114.2.245) - ***Psychological Review***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=5476233692839102256)]. [[Preprint](https://tallinzen.net/media/readings/xu_tenenbaum_2007.pdf)]. The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.
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*[How to Grow a Mind: Statistics, Structure, and Abstraction](https://cocosci.princeton.edu/tom/papers/growamind.pdf) - ***Science***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=2667398573353002097&hl=en&as_sdt=0,5)]. Josh Tenenbaum's review on Bayesian theory induction.
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*[How to Grow a Mind: Statistics, Structure, and Abstraction](https://www.science.org/doi/full/10.1126/science.1192788) - ***Science***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=2667398573353002097)]. [[Preprint](https://cocosci.princeton.edu/tom/papers/growamind.pdf)]. This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?
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*[Human-level concept learning through probabilistic program induction.](https://ai6034.mit.edu/wiki/images/LakeDec2015.pdf) - ***Science***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=11844685101409624506&hl=en&as_sdt=0,5)]. [[Supplementary Material](https://cims.nyu.edu/~brenden/LakeEtAl2015Science_supp.pdf)]. Bayesian program induction for few-shot learning.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-angular.md
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*[ngx-cookieconsent](https://github.com/tinesoft/ngx-cookieconsent) - Cookie Consent module for Angular.
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*[cookieconsent](https://github.com/orestbida/cookieconsent) - Simple cross-browser cookie-consent plugin written in vanilla js that can be added to [Angular](https://cookieconsent.orestbida.com/essential/getting-started.html#angular).
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*[ngx-cookiebot](https://github.com/halloverden/ngx-cookiebot) - An Angular wrapper around the [Cookiebot](https://www.cookiebot.com/) SDK.
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*[ngx-cookie-ssr](https://github.com/Ask-786/ngx-cookie-ssr) - A straightforward cookie service for Angular 19 applications, inspired by ngx-cookie-service.
Copy file name to clipboardexpand all lines: docs/awesome/awesome-for-beginners.md
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## Ansible
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-[Exosphere](https://gitlab.com/exosphere/exosphere/-/issues/?label_name[]=Good%20First%20Issue)_(label: Good First Issue)_ <br> Exosphere is a user-friendly client interface for OpenStack-based cloud systems.
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-[Exosphere](https://gitlab.com/exosphere/exosphere)_(label: Good First Issue)_ <br> Exosphere is a user-friendly client interface for OpenStack-based cloud systems.
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## C
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## Elm
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-[Exosphere](https://gitlab.com/exosphere/exosphere/-/issues/?label_name[]=Good%20First%20Issue)_(label: Good First Issue)_ <br> Exosphere is a user-friendly client interface for OpenStack-based cloud systems.
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-[Exosphere](https://gitlab.com/exosphere/exosphere)_(label: Good First Issue)_ <br> Exosphere is a user-friendly client interface for OpenStack-based cloud systems.
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## Go
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-[Killgrave](https://github.com/friendsofgo/killgrave)_(label: good first issue)_ <br> Simple way to generate mock servers in Go.
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-[Kubernetes](https://github.com/kubernetes/kubernetes)_(label: good first issue)_ <br> Production-Grade Container Scheduling and Management System
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-[lxd](https://github.com/lxc/lxd)_(label: easy)_ <br> System container and virtual machine manager.
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-[Mattermost](https://github.com/mattermost/mattermost-server/issues?utf8=✓&q=is:open+label:"Up+For+Grabs"+label:"Difficulty/1:Easy"+label:"Tech/Go")_(label: n/a)_ <br> Open source Slack-alternative in Golang and React
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-[Mattermost](https://github.com/mattermost/mattermost)_(label: Good First Issue, Difficulty/1:Easy)_ <br> Open source Slack-alternative in Golang and React<br>Look for issues labelled 'Up For Grabs'
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-[Meshery](https://github.com/layer5io/meshery)_(label: good first issue)_ <br> Meshery, the service mesh management plane.
-[PureLB](https://gitlab.com/purelb/purelb/-/issues?label_name[]=GoodFirstIssue)_(label: n/a)_ <br> Load-balancer orchestrator for Kubernetes that uses standard Linux networking and routing protocols.
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-[PureLB](https://gitlab.com/purelb/purelb)_(label: n/a)_ <br> Load-balancer orchestrator for Kubernetes that uses standard Linux networking and routing protocols.
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-[script](https://github.com/bitfield/script)_(label: good first issue)_ <br> A Go library for doing the kind of tasks that shell scripts are good at: reading files, executing subprocesses, counting lines, matching strings, and so on. Beginners are very welcome and will get detailed code review and help through the PR process.
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-[Terraform](https://github.com/hashicorp/terraform)_(label: good first issue)_ <br> A tool for building, changing, and versioning infrastructure safely and efficiently.
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-[TiDB](https://github.com/pingcap/tidb)_(label: good first issue)_ <br> A distributed scalable Hybrid Transactional and Analytical Processing (HTAP) database
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-[TEAMMATES](https://github.com/TEAMMATES/teammates)_(label: good first issue)_ <br> TEAMMATES is a free online tool for managing peer evaluations and other feedback paths of your students.
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-[Trino (formerly Presto SQL)](https://github.com/trinodb/trino)_(label: good first issue)_ <br> A distributed SQL query engine for big data. Ask for guidance on project's Slack.
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-[Wikimedia Commons Android App](https://github.com/commons-app/apps-android-commons)_(label: good first issue)_ <br> Allows users to upload pictures from their Android phone/tablet to Wikimedia Commons.
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-[XWiki](https://jira.xwiki.org/issues/?jql=labels-%3D-Onboarding)_(label: n/a)_ <br> XWiki is a free wiki software platform written in Java with a design emphasis on extensibility. Beginners should follow the onboarding wiki.
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-[XWiki](https://jira.xwiki.org/issues)_(label: onboarding)_ <br> XWiki is a free wiki software platform written in Java with a design emphasis on extensibility. Beginners should follow the [onboarding wiki](http://dev.xwiki.org/xwiki/bin/view/Onboarding/).
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-[zerocode](https://github.com/authorjapps/zerocode)_(label: good first issue)_ <br> API Automation without coding, easy JSON response assertions, Testing REST, SOAP, Kafka and Java/DB APIs, CI/Jenkins Friendly.
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## JavaScript
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-[Kinto.js](https://github.com/Kinto/kinto.js)_(label: easy-pick)_ <br> An offline-first JavaScript client leveraging the Kinto API for remote data synchronization.
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-[Leaflet](https://github.com/Leaflet/Leaflet)_(label: good first issue)_ <br> JavaScript library for mobile-friendly interactive maps.
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-[material-ui](https://github.com/mui-org/material-ui)_(label: good first issue)_ <br> React components for faster and easier web development. Build your own design system, or start with Material Design.
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-[Mattermost](https://github.com/mattermost/mattermost-server/issues?utf8=✓&q=is:open+label:"Up+For+Grabs"+label:"Difficulty/1:Easy"+label:"Tech/Go")_(label: n/a)_ <br> Open source Slack-alternative in Golang and React
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-[Mattermost](https://github.com/mattermost/mattermost)_(label: Good First Issue, Difficulty/1:Easy)_ <br> Open source Slack-alternative in Golang and React<br>Look for issues labelled 'Up For Grabs'
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-[Meteor](https://github.com/meteor/meteor)_(label: good first issue)_ <br> Meteor is an ultra-simple environment for building modern web applications.
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-[Mocha](https://github.com/mochajs/mocha)_(label: good first issue)_ <br> Javascript test framework for Node.js and the browser.
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-[Moment.js](https://github.com/moment/moment)_(label: Up-For-Grabs)_ <br> A lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.
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-[Iron](https://github.com/iron/iron)_(label: easy)_ <br> An extensible, concurrent web framework for Rust
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-[nushell](https://github.com/nushell/nushell)_(label: good first issue)_ <br> A modern shell for the GitHub era written in Rust.
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-[Ockam](https://github.com/ockam-network/ockam)_(label: good first issue)_ <br> End-to-end encryption and mutual authentication for distributed applications.
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-[Readest](https://github.com/readest/readest)_(label: good first issue)_ <br> A modern, feature-rich ebook reader designed for avid readers offering seamless cross-platform access, powerful tools, and an intuitive interface.
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-[Rust-Clippy](https://github.com/rust-lang/rust-clippy)_(label: good first issue)_ <br> A bunch of lints to catch common mistakes and improve Rust code
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-[Rustfmt](https://github.com/rust-lang-nursery/rustfmt)_(label: good first issue)_ <br> A tool for formatting Rust code according to style guidelines.
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-[Servo](https://github.com/servo/servo)_(label: E-easy)_ <br> A browser engine designed for applications including embedded use.
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-[Sniffnet](https://github.com/GyulyVGC/sniffnet)_(label: good first issue)_ <br> Application to comfortably monitor network traffic.
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-[TensorZero](https://github.com/tensorzero/tensorzero)_(label: good-first-issue)_ <br> TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.
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-[TiKV](https://github.com/tikv/tikv)_(label: difficulty/easy)_ <br> A distributed transactional key-value database
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-[Veloren](https://gitlab.com/veloren/veloren/-/issues?label_name[]=beginner)_(label: n/a)_ <br> Veloren is a multiplayer voxel RPG written in Rust.
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-[Veloren](https://gitlab.com/veloren/veloren)_(label: n/a)_ <br> Veloren is a multiplayer voxel RPG written in Rust.
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-[zoom-rs](https://github.com/security-union/zoom-rs)_(label: good first issue)_ <br> Teleconference system with a web based user interface written in Rust
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## Scala
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-[Node Efficientnet](https://github.com/ntedgi/node-efficientnet)_(label: good first issue)_ <br> EfficientNet Image Recognition model for Node JS ( written with tensorflow.js ).
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-[OpenMetadata](https://github.com/open-metadata/OpenMetadata)_(label: good first issue)_ <br> OpenMetadata is an all-in-one platform for data discovery, data quality, observability, governance, data lineage, and team collaboration.
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-[Oppia](https://github.com/oppia/oppia)_(label: good first issue)_ <br> Oppia is an open-source project whose aim is to empower learners across the globe by providing access to high-quality, engaging education. We envision a society in which access to high-quality education is a human right rather than a privilege.
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-[Readest](https://github.com/readest/readest)_(label: good first issue)_ <br> A modern, feature-rich ebook reader designed for avid readers offering seamless cross-platform access, powerful tools, and an intuitive interface.
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-[reatom](https://github.com/artalar/reatom)_(label: good first issue)_ <br> Reatom is declarative and reactive state manager, designed for both simple and complex applications.
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-[Storybook JS](https://github.com/storybookjs/storybook)_(label: good first issue)_ <br> Storybook is a frontend workshop for building UI components and pages in isolation.
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-[tinyhttp](https://github.com/talentlessguy/tinyhttp)_(label: good first issue)_ <br> A 0-legacy, tiny & fast web framework as a replacement of Express.
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-[gfile](https://github.com/Antonito/gfile) - Securely transfer files between two computers, without any third party, over WebRTC.
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-[Go Package Store](https://github.com/shurcooL/Go-Package-Store) - App that displays updates for the Go packages in your GOPATH.
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-[go-peerflix](https://github.com/Sioro-Neoku/go-peerflix) - Video streaming torrent client.
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-[goblin](https://goblin.reaper.im) - Golang binaries in a curl, built by goblins.
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-[goblin](https://goblin.run) - Cloud builder for CLI's written in go lang
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-[GoBoy](https://github.com/Humpheh/goboy) - Nintendo Game Boy Color emulator written in Go.
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-[gocc](https://github.com/goccmack/gocc) - Gocc is a compiler kit for Go written in Go.
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-[GoDocTooltip](https://github.com/diankong/GoDocTooltip) - Chrome extension for Go Doc sites, which shows function description as tooltip at function list.
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