Robot Updated at:7 Feb 2025 21:10:24 GMT

Author: yiliuyan161

docs/awesome/GoBooks.md

@@ -466,6 +466,13 @@ progressively building a blockchain from scratch in Go with gRPC. An interesting
 adventure that takes you from the foundational concepts and purpose through the technical design and
 implementation to the practical testing and usage of the proposed blockchain. Simple, yet non-trivial. Concise, yet detailed. Practical, yet well-grounded.
 
+### 2025 - [Pro Go Patterns: Advanced Function Design, Concurrency Models, and Clean Code](https://www.amazon.com/dp/B0DW9JNCVV?ref_=ast_author_mpb)
+
+<a href="https://www.amazon.com/dp/B0DW9JNCVV?ref_=ast_author_mpb"><img src="https://m.media-amazon.com/images/I/61JkL-4aXsL._SL1500_.jpg" width="120x"></a>
+
+Pro Go Patterns is your comprehensive guide to writing scalable, maintainable, and efficient Go code. Moving beyond the basics, this book dives deep into advanced patterns and practices used in real-world applications.
+
+
 ### [Go with the Domain: Building Modern Business Software in Go](https://threedots.tech/go-with-the-domain/) *Free*
 
 <a href="https://threedots.tech/go-with-the-domain/"><img src="https://threedots.tech/img/go-with-domain-cover-160-retina.jpg" width="120px"/></a>

docs/awesome/awesome-ada.md

@@ -309,11 +309,13 @@ A curated list of awesome resources related to the Ada and SPARK programming lan
 - [ahven](http://ahven.stronglytyped.org/) - Ahven is a simple unit test library and framework for the Ada programming.
 language.  It is loosely modelled after JUnit and some ideas from AUnit.
 - [aunit](https://github.com/AdaCore/aunit) - Ada unit testing framework.
+- [bbt](https://github.com/LionelDraghi/bbt) - Simple tool to black box check the behavior of an executable through the command line.
 - [gnatbdd](https://github.com/briot/gnatbdd) - Behavior Driven Development in Ada.
 - [scripted-testing](https://github.com/simonjwright/scripted_testing) - Supports functional testing using Tcl scripts.
 
 ### Logging
 - [alog](https://www.codelabs.ch/alog/) - Alog is a stackable logging framework for Ada.
+- [elogs](https://github.com/kevlar700/elogs) - Logging framework for embedded systems absent of runtime errors.
 
 ### Machine Learning
 - [fuzzyml](https://sourceforge.net/projects/fuzzyml/) - Fuzzy machine learning framework.
@@ -444,6 +446,7 @@ language.  It is loosely modelled after JUnit and some ideas from AUnit.
 - [aforth](https://github.com/samueltardieu/aforth) - Embeddable Forth interpreter written in Ada.
 - [parser-tools](https://github.com/flyx/ParserTools) - Tools for writing lexers / parsers in Ada.
 - [xia](https://github.com/simonjwright/xia) - An Ada implementation of XPath 1.0.
+- [ada-lisp-embedded](https://github.com/BrentSeidel/Ada-Lisp-Embedded) - Embeddable Lisp interpreter.
 
 ### Format Readers, Writers and Checkers
 - [ini-files](https://github.com/zertovitch/ini-files) - The Ini file manager consists of a package, Config, which can read and modify informations from various configuration files known as "ini" files.
@@ -493,7 +496,6 @@ language.  It is loosely modelled after JUnit and some ideas from AUnit.
 [template-parser]: https://github.com/AdaCore/templates-parser
 
 ### Graphics and Multimedia
-- [orka](https://github.com/onox/orka) - The OpenGL 4.6 Rendering Kernel in Ada 2012.
 - [opengl-ada](https://github.com/flyx/OpenGLAda) - Thick Ada binding for OpenGL and GLFW.
 - [adagl](https://github.com/godunko/adagl) - Multiplatform Ada/OpenGL bindings (ported to native/OpenGL, A2JS/WebGL and WASM/WebGL).
 - [sdlada](https://github.com/Lucretia/sdlada) - Ada 2012 bindings to SDL 2.

docs/awesome/awesome-agi-cocosci.md

@@ -227,7 +227,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [From information scaling of natural images to regimes of statistical models](https://www.jstor.org/stable/43638808?seq=1) - ***Quarterly of Applied Mathematics***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=17387130978932998303)]. [[Preprint](http://www.stat.ucla.edu/~sczhu/papers/Quarterly_final.pdf)].  One fundamental property of natural image data that distinguishes vision from other sensory tasks such as speech recognition is that scale plays a profound role in image formation and interpretation. Specifically, visual objects can appear at a wide range of scales in the images due to the change of viewing distance as well as camera resolution. The same objects appearing at different scales produce different image data with different statistical properties. In particular, this work shows that the entropy rate of the image data changes over scale. Moreover, the inferential uncertainty changes over scale too. The authors call these changes information scaling. They then examine both empirically and theoretically two prominent and yet largely isolated classes of image models, namely, wavelet sparse coding models and Markov random field models. The results indicate that the two classes of models are appropriate for two different entropy regimes: sparse coding targets low entropy regimes, whereas Markov random fields are appropriate for high entropy regimes. Because information scaling connects different entropy regimes, both sparse coding and Markov random fields are necessary for representing natural image data, and information scaling triggers transitions between these two regimes. 
 
-* [A Theory of Generative ConvNet](https://proceedings.mlr.press/v48/xiec16.html) - ***ICML'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11062907630625111054&hl=en&as_sdt=2005&sciodt=0,5)]. 
+* [A Theory of Generative ConvNet](https://proceedings.mlr.press/v48/xiec16.html) - ***ICML'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11062907630625111054)]. The authors show that a generative random field model, which they call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the category is a base category generated by a reference distribution. For a further assumption that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then a generative ConvNet model that is unique among energy-based models is obtained: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process. 
 
 * [Cooperative Training of Descriptor and Generator Networks](https://ieeexplore.ieee.org/abstract/document/8519332) - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=18202808849093155435)]. This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the energy. We call it the descriptor network. The second model is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed image. The maximum likelihood learning algorithms of both models involve MCMC sampling such as Langevin dynamics. This work observes that the two learning algorithms can be seamlessly interwoven into a cooperative learning algorithm that can train both models simultaneously. Specifically, within each iteration of the cooperative learning algorithm, the generator model generates initial synthesized examples to initialize a finite-step MCMC that samples and trains the energy-based descriptor model. After that, the generator model learns from how the MCMC changes its synthesized examples. That is, the descriptor model teaches the generator model by MCMC, so that the generator model accumulates the MCMC transitions and reproduces them by direct ancestral sampling.
 

docs/awesome/awesome-angular.md

@@ -605,6 +605,7 @@ become an Angular expert.
 * [ngx-barcode6](https://github.com/efgiese/ngx-barcode6) - An Angular component for Angular 9+ for creating 1-D barcodes based on [JsBarcode](https://github.com/lindell/JsBarcode).
 * [ngx-viz](https://github.com/vedph/ngx-viz) - Simple Angular [viz.js](https://viz-js.com/) wrapper to render [DOT graphs](https://graphviz.org/doc/info/lang.html).
 * [ngx-reactify](https://github.com/knackstedt/ngx-reactify) - Library to make running Angular and React applications together easy.
+* [solidgate](https://github.com/solidgate-tech/angular-sdk) - With its Angular SDK, you can add Solidgate Payment Form.
 
 #### Internationalization
 
@@ -1410,6 +1411,7 @@ to simplify usage and allow quick customization.
 * [Angular-html5qrcode](https://github.com/mohamedfakhreldin/Angular-html5qrcode) - This library provides an Angular wrapper for the [html5-qrcode](https://github.com/mebjas/html5-qrcode) library, allowing developers to easily integrate QR code and barcode scanning functionalities into their applications.
 * [ngx-kjua](https://github.com/werthdavid/ngx-kjua) - Angular QR-Code generator component using [kjua](https://github.com/lrsjng/kjua).
 * [ngx-qrcode](https://github.com/GNURub/ngx-qrcode) - A simple Angular 18+ component to generate QR codes. Based on [react-native-qrcode-skia](https://github.com/enzomanuelmangano/react-native-qrcode-skia) library.
+* [ngx-scan-detect](https://github.com/sezmars/ngx-scan-detect) - Detects barcode or QR code scanning on document and emits the scanned code.
 
 #### Scroll
 
@@ -1501,6 +1503,7 @@ to simplify usage and allow quick customization.
 * [ng-web-apis/storage](https://github.com/taiga-family/ng-web-apis/blob/main/libs/storage/README.md) - This is a library to use Web Storage API with Angular.
 * [ngx-odm](https://github.com/voznik/ngx-odm) - Angular 14+ wrapper for RxDB.
 * [signaldb](https://github.com/maxnowack/signaldb) - A local JavaScript database with a MongoDB-like interface and TypeScript support, enabling optimistic UI with signal-based reactivity. It integrates easily with Angular, Solid.js, Preact, and Vue, simplifying data management with schema-less design, in-memory storage, and fast queries.
+* [dexie](https://github.com/dexie/Dexie.js) - A Minimalistic Wrapper for IndexedDB.
 
 #### Tooltips
 

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

@@ -52,7 +52,7 @@ Reports will be classified by a header that describes their primary content or e
 - [CrowdStrike](https://www.crowdstrike.com/resources/reports/global-threat-report/) - [Global Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Crowdstrike-Global-Threat-Report-2024.pdf) (2024) - Analyzes global cyber threats, offering insights into adversary tactics, emerging attack trends, and strategies for improving cyber defense.
 - [DeepInstinct](https://www.deepinstinct.com/blog/2022-cyber-threat-landscape-report) - [Threat Landscape Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2023/Deep-Instinct-Cyber-Threat-Landscape-Report-2023.pdf) (2023) - Examines evolving cyber threats, offering insights into attack techniques, malware trends, and strategies for enhancing organizational cybersecurity.
 - [Deepwatch](https://www.deepwatch.com/2024-ati-threat-report/) - [Annual Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Deepwatch-Annual-Threat-Report-2024.pdf) (2024) - Analyzes cybersecurity trends, observations, and metrics to provide insights and forecasts for the upcoming year.
-- [Department of Homeland Security](https://www.dhs.gov/publication/homeland-threat-assessment) - [Threat Assessment](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/DHS-Threat Assessment-2025.pdf) (2025) - Outlines key threats to the U.S. homeland, including public safety, terrorism, illegal drugs, and nation-state influence, aiming to safeguard American people, homeland, and values.
+- [Department of Homeland Security](https://www.dhs.gov/publication/homeland-threat-assessment) - [Threat Assessment](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/DHS-Threat-Assessment-2025.pdf) (2025) - Outlines key threats to the U.S. homeland, including public safety, terrorism, illegal drugs, and nation-state influence, aiming to safeguard American people, homeland, and values.
 - [DNSFilter](https://explore.dnsfilter.com/2025-annual-security-report) - [Annual Security Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/DNSFilter-Annual-Security-Report-2025.pdf) (2025) - Analyzes trends in DNS-based cyber threats, highlighting phishing, malware distribution, and evasive techniques used by adversaries, along with recommendations for improving domain-layer security.
 - [ENISA](https://www.enisa.europa.eu/publications/enisa-threat-landscape-2024) - [Threat Landscape Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/ENISA-Threat-Landscape-2024.pdf) (2024) - An annual summary of key cybersecurity threats, trends, and attack techniques. It examines threat actors, motivations, impacts, and suggests mitigation strategies.
 - [Ensign](https://www.ensigninfosecurity.com/resources/threat-insights/cyber-threat-landscape-report-2024) - [Cyber Threat Landscape Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Ensign-Cyber-Threat-Landscape-Report-2024.pdf) (2024) - Analysis of key cyber threats across Asia, focusing on Singapore, Malaysia, Indonesia, South Korea, Australia, and Greater China.
@@ -191,7 +191,7 @@ Reports will be classified by a header that describes their primary content or e
 - [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.
 - [Hyperproof](https://hyperproof.io/q1-2025-readers-digest-benchmark-report/) - [IT Risk and Compliance Benchmark Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/Hyperproof-IT-Risk-and-Compliance-Benchmark-Report-2025.pdf) (2025) - Examines the state of IT risk and compliance, focusing on the maturation of GRC programs and trends in framework adoption.
-- [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.
+- [Immuta](https://www.immuta.com/resources/2025-state-of-data-security-report/) - [State of Data Security Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/Immuta-State-of-Data-Security-Report-2025.pdf) (2025) - A survey of 700+ data professionals examines the current state of data security, including challenges, trends, and best practices across various industries.
 - [ISACA](https://www.isaca.org/resources/reports/state-of-privacy-2025) - [State of Privacy](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/ISACA-State-of-Privacy-2025.pdf) (2025) - Outlines key trends in global privacy practices, including staffing needs, budget constraints, and the increasing integration of AI in privacy operations.
 - [Kiteworks](https://www.kiteworks.com/report-2025-forecast-for-managing-private-content-exposure-risk/) - [Forecast for Managing Private Content Exposure Risk](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/Kiteworks-Forecast-for-Managing-Private-Content-Exposure-Risk-2025.pdf) (2025) - Outlines 12 predictions for managing private content exposure risk, based on cybercrime, cybersecurity, and compliance trends focusing on sensitive content communications.
 - [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.

docs/awesome/awesome-cpp.md

@@ -387,6 +387,7 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny
 ## Data visualization
 *Data visualization Libraries*
 
+* [gplot++](https://github.com/ziotom78/gplotpp) - Cross-platform header-only C++ plotting library that interfaces with Gnuplot. [MIT]
 * [matplotplusplus](https://github.com/alandefreitas/matplotplusplus) - C++ Graphics Library for Data Visualization. [MIT] [website](https://alandefreitas.github.io/matplotplusplus/)
 
 ## Debug

docs/awesome/awesome-crystal.md

@@ -238,7 +238,7 @@ described in [RFC 8032]
 
 ## HTML/XML Parsing
  * [docx_cr_converter](https://github.com/aristotelesbr/docx_cr_converter) - parse DOCX Word
- * [myhtml](https://github.com/kostya/myhtml) - Fast HTML5 Parser that includes CSS selectors
+ * [lexbor](https://github.com/kostya/lexbor) - Fast HTML5 Parser that includes CSS selectors
 
 ## HTTP
  * [Cable](https://github.com/cable-cr/cable) - An ActionCable "port" to Crystal, framework agnostic, 100% compatible with the ActionCable JS Client

docs/awesome/awesome-db-tools.md

@@ -256,7 +256,7 @@ Low-code and no-code platforms for application building
 - [pglookout](https://github.com/aiven/pglookout) - PostgreSQL replication monitoring and failover daemon.
 - [pgslice](https://github.com/ankane/pgslice) - Postgres partitioning as easy as pie.
 - [PostgreSQL Automatic Failover](https://github.com/ClusterLabs/PAF) - High-Availibility for Postgres, based on industry references Pacemaker and Corosync.
-- [postgresql_cluster](https://github.com/vitabaks/postgresql_cluster) - PostgreSQL High-Availability Cluster (based on "Patroni" and "DCS(etcd)"). Automating deployment with Ansible.
+- [autobase](https://github.com/vitabaks/autobase) - Autobase for PostgreSQL® is an open-source DBaaS that automates the deployment and management of highly available PostgreSQL clusters.
 - [Vitess](https://github.com/vitessio/vitess) - Database clustering system for horizontal scaling of MySQL through generalized sharding.