Robot Updated at:17 Dec 2024 21:09:58 GMT

Author: yiliuyan161

docs/awesome/Colorful.md

@@ -66,6 +66,8 @@ Inspired by the __[awesome](https://github.com/sindresorhus/awesome)__ list. Ple
 with AI-Powered Palette Generator.
 - [Colorca](https://colorca.org/) - Design an accessible color palette for digital products in HSLuv color space
 - [ColorBeta](https://colorbeta.com/) - Advanced CSS Gradient Generator.
+- [Color Wheel](https://colorwheel.co/) - A color wheel based on the drawings by Goethe, Johann Wolfgang von from the year 1810.
+- [RGB HEX Code](https://rgbhexcode.com/) - HTML/CSS Color Picker & Converter.
 
 ## Color Palettes
 - [ColorHunt](http://colorhunt.co/) - Color palettes with quick preview feature.
@@ -89,6 +91,7 @@ with AI-Powered Palette Generator.
 - [Color Name List](https://github.com/meodai/color-names/) - Huge curated list of unique color names.
 - [Realtime Colors](https://www.realtimecolors.com) - Visualize Your Colors & Fonts On a Real Site.
 - [InclusiveColors](https://www.inclusivecolors.com/) - Create custom accessible Tailwind-style color palettes, with WCAG/APCA contrast checks and live previews on a mockup.
+- [ColorMagic](https://colormagic.app) - Generate color palettes with AI. Enter any keyword and generate a matching color palette.
 
 ## Articles
 - [Trendy Web Color Palettes and Material Design Color Schemes & Tools](http://www.awwwards.com/trendy-web-color-palettes-and-material-design-color-schemes-tools.html?utm_source=Twitter&utm_medium=Social&utm_campaign=Twitter-Blog-Color&utm_content=Twitter)

docs/awesome/awesome-agi-cocosci.md

@@ -537,7 +537,7 @@ organized into intricate patterns that seem to be consistent across individuals.
 
 * [Domain-specific languages: an annotated bibliography](https://dl.acm.org/doi/abs/10.1145/352029.352035) - ***ACM SIGPLAN Notices***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=8845429548327315750)]. A survey on the topic of domain-specific languages as used for the construction and maintenance of software systems. The survey lists a selection of 75 key publications in the area, and provides a summary for each of the papers. Moreover, the survey discusses terminology, risks and benefits, example domain-specific languages, design methodologies, and implementation techniques.
 
-* [Usability Evaluation of Domain-Specific Languages](http://www-ctp.di.fct.unl.pt/QUASAR/Resources/Papers/2012/Barisic2012SEDES.pdf) - ***ICQICT'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=3047215455890195199)]. An initiative arguing that a systematic approach based on User Interface experimental validation techniques should be used to assess the impact of new DSLs.
+* [Usability Evaluation of Domain-Specific Languages](https://ieeexplore.ieee.org/abstract/document/6511840) - ***ICQICT'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=3047215455890195199)]. [[Preprint](http://www-ctp.di.fct.unl.pt/QUASAR/Resources/Papers/2012/Barisic2012SEDES.pdf)]. The purpose of this proposal is to contribute to the systematic activity of Software Language Engineering by focusing on the issue of the Usability evaluation of DSLs. Usability evaluation is often skipped, relaxed, or at least omitted from papers reporting development of DSLs. The authors argue that a systematic approach based on User Interface experimental validation techniques should be used to assess the impact of new DSLs. For that purpose, the authors propose to merge common Usability evaluation processes with the DSL development process.
 
 
 #### Design Practises

docs/awesome/awesome-annual-security-reports.md

@@ -166,6 +166,8 @@ Reports will be classified by a header that describes their primary content or e
 
 ## Privacy and Data Protection
 - [Cisco](https://www.cisco.com/c/en/us/about/trust-center/data-privacy-benchmark-study.html) - [Data Privacy Benchmark Study](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Cisco-Privacy-Benchmark-Study-2024.pdf) (2024) - Provides insights into data privacy trends, challenges, and breaches across various industries.
+- [Code42](https://www.code42.com/content/2024-data-exposure) - [Annual Data Exposure Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Code42-Annual-Data-Exposure-Report-2024.pdf) (2024)  
+  Highlights insider threat risks and trends based on insights from over 700 security professionals.
 - [Immuta](https://www.immuta.com/resources/2024-trendbook/) - [State of Data Security Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Immuta-State-of-Data-Security-Report-2024.pdf) (2024) - Examines the current state of data security, including challenges, trends, and best practices across various industries.
 - [Proofpoint](https://www.proofpoint.com/us/resources/threat-reports/data-loss-landscape) - [Data Loss Landscape](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Proofpoint-Data-Loss-Landscape-2024.pdf) (2024) - Provides an overview of the data loss landscape, including trends and challenges faced by organizations across various industries.
 - [Proofpoint](https://go.proofpoint.com/2024-Frost-Radar-for-Email-Security.html) - [Global Email Security Market Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Proofpoint-Global-Email-Security-Market-Report-2024.pdf) (2024) - Benchmarks 21 top email security vendors, highlighting growth opportunities and market trends.

docs/awesome/awesome-executable-packing.md

@@ -432,6 +432,7 @@
 - [ASPack](http://www.aspack.com/aspack.html) - Advanced solution created to provide Win32 EXE file packing and to protect them against non-professional reverse engineering.
 - [ASProtect 32](http://www.aspack.com/asprotect32.html) - Multifunctional EXE packing tool designed for software developers to protect 32-bit applications with in-built application copy protection system.
 - [ASProtect 64](http://www.aspack.com/asprotect64.html) - Tool for protecting 64-bit applications and .NET applications for Windows against unauthorized use, industrial and home copying, professional hacking and analysis of software products distributed over the Internet and on any physical media.
+- [ASM Guard](https://github.com/DosX-dev/ASM-Guard) - Free packer utility for compressing and complicating reversing compiled native code (native files), protecting resources, adding DRM, and packing into an optimized loader.
 - [AutoIT](https://www.autoitscript.com/site) - Legitimate executable encryption service.
 - [AxProtector](https://www.wibu.com/us/products/protection-suite/axprotector.html) - Encrypts the complete software you aim to protect, and shields it with a security shell, AxEngine, best-of-breed anti-debugging and anti-disassembly methods are then injected into your software.
 - [BangCle](https://github.com/woxihuannisja/Bangcle) - Protection tool using the second generation Android Hardening Protection, loading the encrypted DEX file from memory dynamically.
@@ -467,6 +468,7 @@
 - [.netshrink](https://www.pelock.com/products/netshrink) - Executable compressor for your Windows or Linux .NET application executable file using LZMA.
 - [NPack](http://www.nsdsn.com/english/nspack.zip) - Can compress 32bits and 64bits exe, dll, ocx, scr Windows program.
 - [Obsidium](http://www.obsidium.de/show.php?home) - Feature-rich professional software protection and licensing system designed as a cost effective and easy to implement, yet reliable and non-invasive way to protect your 32- and 64-bit Windows software applications and games from reverse engineering.
+- [obfus.h](https://github.com/DosX-dev/obfus.h) - Macro-header for compile-time C obfuscation/virtualization (tcc, win x86/x64)
 - [Origami](https://github.com/dr4k0nia/Origami) - Packer compressing .net assemblies, (ab)using the PE format for data storage.
 - [OSX_Packer](https://github.com/AlysonBee/OSX_Packer) - Binary packer for the Mach-O file format.
 - [Pakkero](https://github.com/89luca89/pakkero) - Pakkero is a binary packer written in Go made for fun and educational purpose.

docs/awesome/awesome-go.md

@@ -819,6 +819,7 @@ _Packages that help with building Distributed Systems._
 - [glow](https://github.com/chrislusf/glow) - Easy-to-Use scalable distributed big data processing, Map-Reduce, DAG execution, all in pure Go.
 - [gmsec](https://github.com/gmsec/micro) - A Go distributed systems development framework.
 - [go-doudou](https://github.com/unionj-cloud/go-doudou) - A gossip protocol and OpenAPI 3.0 spec based decentralized microservice framework. Built-in go-doudou cli focusing on low-code and rapid dev can power up your productivity.
+- [go-eagle](https://github.com/go-eagle/eagle) - A Go framework for the API or Microservice with handy scaffolding tools.
 - [go-health](https://github.com/InVisionApp/go-health) - Library for enabling asynchronous dependency health checks in your service.
 - [go-jump](https://github.com/dgryski/go-jump) - Port of Google's "Jump" Consistent Hash function.
 - [go-kit](https://github.com/go-kit/kit) - Microservice toolkit with support for service discovery, load balancing, pluggable transports, request tracking, etc.

docs/awesome/awesome-machine-learning.md

@@ -24,7 +24,7 @@ Further resources:
 
 ### Frameworks and Libraries
 <!-- MarkdownTOC depth=4 -->
-
+<!-- Contents-->
 - [Awesome Machine Learning ![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](#awesome-machine-learning-)
 
 
@@ -957,7 +957,7 @@ be
 * [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python) - Python wrapper for [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP) **[Deprecated]**
 * [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkit.
 * [Rasa](https://github.com/RasaHQ/rasa) - A "machine learning framework to automate text-and voice-based conversations."
-* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector .
+* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector.
 * [Polyglot](https://github.com/aboSamoor/polyglot) - Multilingual text (NLP) processing toolkit.
 * [DrQA](https://github.com/facebookresearch/DrQA) - Reading Wikipedia to answer open-domain questions.
 * [Dedupe](https://github.com/dedupeio/dedupe) - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
@@ -1401,6 +1401,7 @@ be
 * [RusticSOM](https://github.com/avinashshenoy97/RusticSOM) - A Rust library for Self Organising Maps (SOM).
 * [candle](https://github.com/huggingface/candle) - Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use.
 * [linfa](https://github.com/rust-ml/linfa) - `linfa` aims to provide a comprehensive toolkit to build Machine Learning applications with Rust
+* [delta](https://github.com/delta-rs/delta) - An open source machine learning framework in Rust Δ
 
 #### Deep Learning
 
@@ -1508,6 +1509,7 @@ be
 <a name="r-data-analysis--data-visualization"></a>
 #### Data Manipulation | Data Analysis | Data Visualization
 
+* [data.table](https://rdatatable.gitlab.io/data.table/) - `data.table` provides a high-performance version of base R’s `data.frame` with syntax and feature enhancements for ease of use, convenience and programming speed.
 * [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) - A data manipulation package that helps to solve the most common data manipulation problems.
 * [ggplot2](https://ggplot2.tidyverse.org/) - A data visualization package based on the grammar of graphics.
 * [tmap](https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html) for visualizing geospatial data with static maps and [leaflet](https://rstudio.github.io/leaflet/) for interactive maps
@@ -1595,7 +1597,8 @@ be
 * [SwiftLearner](https://github.com/valdanylchuk/swiftlearner/) - Simply written algorithms to help study ML or write your own implementations.
 * [Smile](https://haifengl.github.io/) - Statistical Machine Intelligence and Learning Engine.
 * [doddle-model](https://github.com/picnicml/doddle-model) - An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
-* [TensorFlow Scala](https://github.com/eaplatanios/tensorflow_scala) -   Strongly-typed Scala API for TensorFlow.
+* [TensorFlow Scala](https://github.com/eaplatanios/tensorflow_scala) - Strongly-typed Scala API for TensorFlow.
+* [isolation-forest](https://github.com/linkedin/isolation-forest) - A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
 
 <a name="scheme"></a>
 ## Scheme
@@ -1642,7 +1645,7 @@ be
 <a name="tools-misc"></a>
 #### Misc
 
-* [Wallaroo.AI](https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any envirorment from cloud to edge. Let's you go from python notebook to inferencing in minutes. 
+* [Wallaroo.AI](https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any environment from cloud to edge. Let's go from python notebook to inferencing in minutes. 
 * [Infinity](https://github.com/infiniflow/infinity) - The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
 * [Synthical](https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
 * [Humanloop](https://humanloop.com) – Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
@@ -1660,13 +1663,14 @@ be
 * [DVClive](https://github.com/iterative/dvclive) - Python library for experiment metrics logging into simply formatted local files.
 * [VDP](https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
 * [Kedro](https://github.com/quantumblacklabs/kedro/) - Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
+* [Hamilton](https://github.com/dagworks-inc/hamilton) - a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
 * [guild.ai](https://guild.ai/) - Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
 * [Sacred](https://github.com/IDSIA/sacred) - Python tool to help  you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
 * [Comet](https://www.comet.com/) -  ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor their models in production.
 * [MLFlow](https://mlflow.org/) - platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
 * [Weights & Biases](https://www.wandb.com/) - Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
 * More tools to improve the ML lifecycle: [Catalyst](https://github.com/catalyst-team/catalyst), [PachydermIO](https://www.pachyderm.io/). The following are GitHub-alike and targeting teams [Weights & Biases](https://www.wandb.com/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/), [Valohai.ai](https://valohai.com/), [DAGsHub](https://DAGsHub.com/).
-* [Arize AI](https://www.arize.com) - Model validaiton and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
+* [Arize AI](https://www.arize.com) - Model validation and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
 * [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
 * [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
 * [CML](https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.

docs/awesome/awesome-nodejs.md

@@ -856,7 +856,7 @@
 - [RunKit](https://runkit.com) - Embed a Node.js environment on any website.
 - [github-npm-stats](https://chrome.google.com/webstore/detail/github-npm-stats/oomfflokggoffaiagenekchfnpighcef) - Chrome extension that displays npm download stats on GitHub.
 - [npm semver calculator](https://semver.npmjs.com) - Visually explore what versions of a package a semver range matches.
-- [CodeSandbox](https://codesandbox.io/s/node-http-server-node) - Online IDE and prototyping.
+- [CodeSandbox](https://codesandbox.io/templates/node-http-server) - Online IDE and prototyping.
 - [Amplication](https://github.com/amplication/amplication) - Auto-generates fully functional apps.
 - [RunJS](https://runjs.app) - Desktop JavaScript playground.