Robot Updated at:30 Oct 2023 21:07:32 GMT

docs/awesome/awesome-board-games.md

@@ -633,6 +633,16 @@
 
 ## Strategy
 
+### [Ashtapada](https://en.wikipedia.org/wiki/Ashtapada)
+
+> Ashtapada, an ancient game originating in India, is played on an 8x8 grid featuring marked spaces. The game can be played by two to four players, with the objective being to enter and exit both pieces strategically. Players use cowrie shells to determine movement. If a player lands on an opponent's piece, it's sent off the board for re-entry, except on 'crosscut' spaces, where pieces remain safe.
+
+![Ashtapada game image](https://cf.geekdo-images.com/kg1eP5Vdj6LoFqQ2E2Kxew__opengraph/img/lykEixPPGFGql26yrwEYvhj3XMo=/fit-in/1200x630/filters:strip_icc()/pic55539.jpg)
+
+| Players | Min. Age | Time   |
+| ------: | -------: | -----: |
+|   2–4 |          8 | 30–60 Min |
+
 ### [Blood Rage](https://boardgamegeek.com/boardgame/170216/blood-rage)
 
 > "Life is Battle; Battle is Glory; Glory is ALL"
@@ -873,6 +883,16 @@
 | ------: | -------: | -----:  |
 |       2 |        6 |     30m |
 
+### [Chaupar](https://en.wikipedia.org/wiki/Chaupar)
+
+> Chaupar, chopad or chaupad is a cross and circle board game very similar to pachisi, played in Pakistan & Ancient India. The board is made of wool or cloth, with wooden pawns and seven cowry shells to be used to determine each player's move, although others distinguish chaupur from pachisi by the use of three four-sided long dice. Variations are played throughout India. It is similar in some ways to Pachisi, Parcheesi and Ludo. In most of the villages of India, this game is played by old people.
+
+![Chaupar image](https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Chopat.jpg/330px-Chopat.jpg)
+
+| Players | Min. Age |  Time   |
+| ------: | -------: | -----:  |
+|       4 |       15 |     30m |
+
 ### [Lotería](https://boardgamegeek.com/boardgame/5878/loteria)
 
 > Lotería (Spanish word meaning "lottery") is a traditional game of chance, similar to bingo, but using images on a deck of cards instead of numbered ping pong balls. Every image has a name and an assigned number, but the number is usually ignored. Each player has at least one tabla, a board with a randomly created 4 x 4 grid of pictures with their corresponding name and number. Players choose a tabla (Spanish word for "board") to play with, from a variety of previously created tablas, each with a different selection of images.

docs/awesome/awesome-datascience.md

@@ -220,7 +220,7 @@ These are some Machine Learning and Data Mining algorithms and models help you t
   - [Density-based clustering](https://scikit-learn.org/stable/modules/clustering.html#dbscan)
   - [Fuzzy clustering](https://en.wikipedia.org/wiki/Fuzzy_clustering)
   - [Mixture models](https://en.wikipedia.org/wiki/Mixture_model)
-- Dimension Reduction
+- [Dimension Reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction)
   - [Principal Component Analysis (PCA)](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
   - [t-SNE; t-distributed Stochastic Neighbor Embedding](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
   - [Factor Analysis](https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)
@@ -249,7 +249,7 @@ These are some Machine Learning and Data Mining algorithms and models help you t
 
 - C4.5
 - k-Means
-- SVM
+- [SVM](https://en.wikipedia.org/wiki/Support_vector_machine)
 - Apriori
 - EM
 - PageRank

docs/awesome/awesome-directus.md

@@ -77,6 +77,10 @@
 - [Classified Group](https://github.com/formfcw/directus-extension-classified-group) - A group to which a class can be assigned for custom styling.
 - [Tokenized Preview](https://github.com/formfcw/directus-extension-tokenized-preview) - An endpoint that adds an active auth token to your preview URL.
 
+### Extension scripts
+
+- [Directus Hook Library](https://github.com/formfcw/directus-hook-library) - A collection of customizable hooks for Directus.
+
 ## Articles
 
 ### Educational

docs/awesome/awesome-go.md

@@ -496,6 +496,7 @@ _Data stores with expiring records, in-memory distributed data stores, or in-mem
 - [cache2go](https://github.com/muesli/cache2go) - In-memory key:value cache which supports automatic invalidation based on timeouts.
 - [cachego](https://github.com/faabiosr/cachego) - Golang Cache component for multiple drivers.
 - [clusteredBigCache](https://github.com/oaStuff/clusteredBigCache) - BigCache with clustering support and individual item expiration.
+- [coherence-go-client](https://github.com/oracle/coherence-go-client) - Full implementation of Oracle Coherence cache API for Go applications using gRPC as network transport.
 - [couchcache](https://github.com/codingsince1985/couchcache) - RESTful caching micro-service backed by Couchbase server.
 - [fastcache](https://github.com/VictoriaMetrics/fastcache) - fast thread-safe inmemory cache for big number of entries. Minimizes GC overhead.
 - [GCache](https://github.com/bluele/gcache) - Cache library with support for expirable Cache, LFU, LRU and ARC.
@@ -1943,6 +1944,7 @@ _Libraries that are used to help make your application more secure._
 - [argon2pw](https://github.com/raja/argon2pw) - Argon2 password hash generation with constant-time password comparison.
 - [autocert](https://godoc.org/golang.org/x/crypto/acme/autocert) - Auto provision Let's Encrypt certificates and start a TLS server.
 - [BadActor](https://github.com/jaredfolkins/badactor) - In-memory, application-driven jailer built in the spirit of fail2ban.
+- [beelzebub](https://github.com/mariocandela/beelzebub) - A secure low code honeypot framework, leveraging AI for System Virtualization.
 - [booster](https://github.com/anatol/booster) - Fast initramfs generator with full-disk encryption support.
 - [Cameradar](https://github.com/Ullaakut/cameradar) - Tool and library to remotely hack RTSP streams from surveillance cameras.
 - [certificates](https://github.com/mvmaasakkers/certificates) - An opinionated tool for generating tls certificates.

docs/awesome/awesome-machine-learning.md

@@ -118,6 +118,7 @@ Further resources:
 * [Polyaxon](https://github.com/polyaxon/polyaxon) - A platform for reproducible and scalable machine learning and deep learning.
 * [QuestDB](https://questdb.io/) A relational column-oriented database designed for real-time analytics on time series and event data.
 * [Phoenix](https://phoenix.arize.com) Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
+* [XAD](https://github.com/auto-differentiation/XAD) Comprehensive backpropagation tool for C++.
 
 <a name="cpp-natural-language-processing"></a>
 #### Natural Language Processing
@@ -900,6 +901,7 @@ be
 * [neural-dream](https://github.com/ProGamerGov/neural-dream) - A PyTorch implementation of DeepDream.
 * [Openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) - A real-time multi-person keypoint detection library for body, face, hands, and foot estimation
 * [Deep High-Resolution-Net](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) - A PyTorch implementation of CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
+* [TF-GAN](https://github.com/tensorflow/gan) - TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs).
 * [dream-creator](https://github.com/ProGamerGov/dream-creator) - A PyTorch implementation of DeepDream. Allows individuals to quickly and easily train their own custom GoogleNet models with custom datasets for DeepDream.
 * [Lucent](https://github.com/greentfrapp/lucent) - Tensorflow and OpenAI Clarity's Lucid adapted for PyTorch.
 * [lightly](https://github.com/lightly-ai/lightly) - Lightly is a computer vision framework for self-supervised learning.
@@ -909,6 +911,8 @@ be
 * [Exadel CompreFace](https://github.com/exadel-inc/CompreFace) - face recognition system that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition and is easily deployed with docker.
 * [computer-vision-in-action](https://github.com/Charmve/computer-vision-in-action) - as known as ``L0CV``, is a new generation of computer vision open source online learning media, a cross-platform interactive learning framework integrating graphics, source code and HTML. the L0CV ecosystem — Notebook, Datasets, Source Code, and from Diving-in to Advanced — as well as the L0CV Hub.
 * [timm](https://github.com/rwightman/pytorch-image-models) - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
+* [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) - A PyTorch-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images.
+* [segmentation_models](https://github.com/qubvel/segmentation_models) - A TensorFlow Keras-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images.
 
 <a name="python-natural-language-processing"></a>
 #### Natural Language Processing
@@ -1306,7 +1310,7 @@ be
 <a name="python-reinforcement-learning"></a>
 #### Reinforcement Learning
 * [DeepMind Lab](https://github.com/deepmind/lab) - DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.
-* [Gym](https://github.com/openai/gym) - OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
+* [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - A library for developing and comparing reinforcement learning algorithms (successor of [gym](https://github.com/openai/gym).
 * [Serpent.AI](https://github.com/SerpentAI/SerpentAI) - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
 * [ViZDoom](https://github.com/mwydmuch/ViZDoom) - ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
 * [Roboschool](https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.

docs/awesome/awesome-neovim.md

@@ -61,7 +61,7 @@
 - [roobert/hoversplit.nvim](https://github.com/roobert/hoversplit.nvim) - Automatically updated documentation and information about code symbols in a split window.
 - [mfussenegger/nvim-jdtls](https://github.com/mfussenegger/nvim-jdtls) - Extensions for the built-in LSP support for eclipse.jdt.ls.
 - [Kasama/nvim-custom-diagnostic-highlight](https://github.com/Kasama/nvim-custom-diagnostic-highlight) - Inline diagnostics popup-highlight much like coc-nvim but based on `vim.diagnostic`.
-- [MrcJkb/haskell-tools.nvim](https://github.com/MrcJkb/haskell-tools.nvim) - Seamless integration of Neovim with Haskell development tools like haskell-language-server and Hoogle.
+- [mrcjkb/haskell-tools.nvim](https://github.com/mrcjkb/haskell-tools.nvim) - Seamless integration of Neovim with Haskell development tools like haskell-language-server and Hoogle.
 - [ranjithshegde/ccls.nvim](https://github.com/ranjithshegde/ccls.nvim) - Use off-spec extensions of ccls LSP and browse AST.
 - [idanarye/nvim-buffls](https://github.com/idanarye/nvim-buffls) - Add LSP functionality to specific Neovim buffers.
 - [DNLHC/glance.nvim](https://github.com/DNLHC/glance.nvim) - A pretty window for previewing, navigating and editing your LSP locations.
@@ -192,6 +192,7 @@
 - [dcampos/nvim-snippy](https://github.com/dcampos/nvim-snippy) - Snippet plugin written in Lua with support for [vim-snippets](https://github.com/honza/vim-snippets).
 - [ellisonleao/carbon-now.nvim](https://github.com/ellisonleao/carbon-now.nvim) - Create beautiful code snippets directly from Neovim.
 - [TobinPalmer/rayso.nvim](https://github.com/TobinPalmer/rayso.nvim) - Create code snippets in Neovim using [ray.so](https://ray.so).
+- [mrcjkb/haskell-snippets.nvim](https://github.com/mrcjkb/haskell-snippets.nvim) - Haskell snippets for LuaSnip, powered by Tree-sitter and LSP.
 
 ## Register
 

docs/awesome/awesome-nodejs.md

@@ -771,6 +771,7 @@
 - [npm](https://docs.npmjs.com/about-npm) - The default package manager.
 - [pnpm](https://pnpm.io) - Disk space efficient package manager.
 - [yarn](https://yarnpkg.com) - Alternative package manager.
+- [bun](https://bun.sh) - All-in-one toolkit for JavaScript and TypeScript apps. 
 
 ## Resources
 

docs/awesome/awesome-python-data-science.md

@@ -39,9 +39,6 @@
 ### Kernel Methods
 * [liquidSVM](https://github.com/liquidSVM/liquidSVM) - An implementation of SVMs.
 
-### Extreme Learning Machine
-* [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks.
-
 ## Deep Learning
 
 ### PyTorch
@@ -65,20 +62,6 @@
 * [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
 * [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
 
-## Time Series
-* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
-* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
-* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
-* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
-* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
-* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
-* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.
-* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
-* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
-* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
-* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
-* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
-
 ## Natural Language Processing
 * [spaCy](https://spacy.io/) - Industrial-Strength Natural Language Processing.
 * [NLTK](https://github.com/nltk/nltk) -  Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
@@ -108,6 +91,20 @@
 * [albumentations](https://github.com/albu/albumentations) - Fast image augmentation library and easy-to-use wrapper around other libraries.
 * [LAVIS](https://github.com/salesforce/LAVIS) - A One-stop Library for Language-Vision Intelligence.
 
+## Time Series
+* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
+* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
+* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
+* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
+* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
+* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
+* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.
+* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
+* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
+* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
+* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
+* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
+
 ## Reinforcement Learning
 * [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
 * [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.

docs/awesome/awesome-rust.md

@@ -1272,7 +1272,7 @@ See also [Are we game yet?](https://arewegameyet.rs)
 
 * [DaveKram/coord_transforms](https://github.com/DaveKram/coord_transforms) [[coord_transforms](https://crates.io/crates/coord_transforms)] — coordinate transformations (2-d, 3-d, and geospatial)
 * [Georust](https://github.com/georust) — geospatial tools and libraries written in Rust
-* [MapLibre/Martin](https://github.com/maplibre/martin) — Map tile server with PostGIS, MBTiles, PMTiles, and sprites support. [![CI build](https://github.com/maplibre/martin/workflows/CI/badge.svg)](https://github.com/maplibre/martin/actions)[![crates.io version](https://img.shields.io/crates/v/martin.svg)](https://crates.io/crates/martin)[![Book](https://img.shields.io/badge/docs-Book-informational)](https://maplibre.org/martin/)
+* [MapLibre/Martin](https://github.com/maplibre/martin) — Map tile server with PostGIS, MBTiles, PMTiles, and sprites support. [![CI build](https://github.com/maplibre/martin/actions/workflows/ci.yml/badge.svg)](https://github.com/maplibre/martin/actions)[![crates.io version](https://img.shields.io/crates/v/martin.svg)](https://crates.io/crates/martin)[![Book](https://img.shields.io/badge/docs-Book-informational)](https://maplibre.org/martin/)
 * [rust-reverse-geocoder](https://github.com/gx0r/rrgeo) — A fast, offline reverse geocoder in Rust, inspired by [thampiman/reverse-geocoder](https://github.com/thampiman/reverse-geocoder)
 * [vlopes11/geomorph](https://github.com/vlopes11/geomorph) [[geomorph](https://crates.io/crates/geomorph)] — conversion between UTM, LatLon and MGRS coordinates