Robot Updated at:31 Jan 2025 21:10:38 GMT

Author: yiliuyan161

docs/awesome/awesome-agi-cocosci.md

@@ -243,7 +243,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [Parameter Expansion for Data Augmentation](https://www.tandfonline.com/doi/abs/10.1080/01621459.1999.10473879) - ***Journal of the American Statistical Association***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=15342818142955984734)]. [[Preprint](http://www.stat.ucla.edu/~ywu/research/papers/PXDA.pdf)]. Viewing the observed data of a statistical model as incomplete and augmenting its missing parts are useful for clarifying concepts and central to the invention of two well-known statistical algorithms: expectation-maximization (EM) and data augmentation. Recently, the authors demonstrated that expanding the parameter space along with augmenting the missing data is useful for accelerating iterative computation in an EM algorithm. The main purpose of this article is to rigorously define a parameter expanded data augmentation (PX-DA) algorithm and to study its theoretical properties. The PX-DA is a special way of using auxiliary variables to accelerate Gibbs sampling algorithms and is closely related to reparameterization techniques.
 
-* [Image segmentation by data-driven markov chain monte carlo](http://www.stat.ucla.edu/~sczhu/papers/DDMCMC_reprint.pdf) - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=3461400072144667491)]. Classic method for image segmentation via generative modeling.
+* [Image segmentation by data-driven markov chain monte carlo](https://ieeexplore.ieee.org/abstract/document/1000239) - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=3461400072144667491)]. [[Preprint](http://www.stat.ucla.edu/~sczhu/papers/DDMCMC_reprint.pdf)]. This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspects. First, it designs efficient and well-balanced Markov Chain dynamics to explore the complex solution space and, thus, achieves a nearly global optimal solution independent of initial segmentations. Second, it presents a mathematical principle and a K-adventurers algorithm for computing multiple distinct solutions from the Markov chain sequence and, thus, it incorporates intrinsic ambiguities in image segmentation. Third, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which drive the Markov chain dynamics and achieve tremendous speedup in comparison to the traditional jump-diffusion methods. Fourth, the DDMCMC paradigm provides a unifying framework in which the role of many existing segmentation algorithms, such as, edge detection, clustering, region growing, split-merge, snake/balloon, and region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. Thus, the DDMCMC paradigm combines and generalizes these segmentation methods in a principled way.
 
 * [Efficient Learning of Sparse Representations with an Energy-Based Model](https://proceedings.neurips.cc/paper/2006/file/87f4d79e36d68c3031ccf6c55e9bbd39-Paper.pdf) - ***NeurIPS'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=2247668190782691760)].
 

docs/awesome/awesome-angular.md

@@ -949,6 +949,7 @@ become an Angular expert.
 * [sequential-workflow-designer](https://github.com/nocode-js/sequential-workflow-designer) - Customizable no-code component for building flow-based programming applications or workflow automation. Zero external dependencies.
 * [ngx-hierarchy](https://github.com/rushik1992/ngx-hierarchy) - Angular Component Module for Vertical or Horizontal Hierarchy/Tree View with flexible dynamic template design and controls.
 * [ngx-relationship-visualiser](https://github.com/Rudgey84/ngx-relationship-visualiser) - A D3 force-directed-graph, implemented in Typescript for Angular, generates a visualisation graph with customisable link lengths and multiple labels between nodes. The graph can handle new data that will update lines, nodes, links, and path labels.
+* [railz-visualizations](https://github.com/railz-ai/railz-visualizations) - A collection of reusable web components that help you build a dashboard using normalized financial transactions and analytics from the FIS Accounting Data as a Service API.
 
 #### Cookies
 
@@ -1670,6 +1671,7 @@ for the creation of web applications developed with Angular.
 * [ngs-json-utils](https://github.com/andrei-shpileuski/ngs-json-utils) - A lightweight utility library for Angular applications that provides easy-to-use functions for working with JSON objects. It includes methods for deep cloning, serialization, and deserialization of JSON data, designed specifically for Angular projects with TypeScript support.
 * [lbx-change-sets](https://github.com/Service-Soft/lbx-change-sets) - This package helps you to track changes made on your entities automatically using a base repository class to extend from.
 * [ngememoize](https://github.com/akbarsaputrait/ngememoize) - Easily boost the performance of your Angular applications by memoizing functions and getters with this lightweight and simple-to-use library.
+* [ngx-gooey](https://github.com/wadie/ngx-gooey) - The gooey effect for Angular, used for shape blobbing / metaballs.
 
 ---
 

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

@@ -51,6 +51,7 @@ 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.
+- [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.
 - [Expel](https://expel.com/annual-threat-report/) - [Annual Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Expel-Annual-Threat-Report-2024.pdf) (2024) - Provides an overview of cyber threats and attack trends observed by Expel's security operations team throughout the year.

docs/awesome/awesome-ansible.md

@@ -59,6 +59,7 @@ For more information about communication, see the [Ansible communication guide](
 - [Ansible for DevOps](https://www.ansiblefordevops.com/) - This book helps to start using Ansible to provision and manage anywhere from one to thousands of servers. Free sample can be read [here](https://leanpub.com/ansible-for-devops/read_sample).
 - [Ansible for Kubernetes](https://www.ansibleforkubernetes.com/) - Deploy and maintain real-world massively-scalable and high-available applications with Ansible.
 - [How To Manage Remote Servers with Ansible eBook](https://www.digitalocean.com/community/books/how-to-manage-remote-servers-with-ansible-ebook) - This book is based on the "How To Manage Remote Servers with Ansible" tutorial series.
+- [The Tao of Ansible: Mastering Automation with Simplicity and Grace](https://www.amazon.co.uk/Tao-Ansible-Mastering-Automation-Simplicity/dp/B0DTTTM3XG) - A guide to Ansible that emphasizes simplicity, elegance, and the philosophy of automation without complexity.
 
 ## Videos
 

docs/awesome/awesome-cpp.md

@@ -1143,6 +1143,7 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny
 * [jwt-cpp](https://github.com/Thalhammer/jwt-cpp) - A header only library for creating and validating JSON web tokens in C++. [MIT]
 * [Kangaru](https://github.com/gracicot/kangaru) - A dependency injection container for C++11 and C++14. [MIT]
 * [Klib](https://github.com/attractivechaos/klib) - Small and lightweight implementations of common algorithms and data structures. [MIT]
+* [KOMIHASH](https://github.com/avaneev/komihash) - Very fast, high-quality hash function, discrete-incremental and streamed hashing-capable. [MIT]
 * [libcpuid](https://github.com/anrieff/libcpuid) - A small C library for x86 CPU detection and feature extraction. [BSD]
 * [libenvpp](https://github.com/ph3at/libenvpp) - A modern C++ library for type-safe environment variable parsing. [Apache-2.0]
 * [libevil](https://github.com/avati/libevil) - The Evil License Manager. [GPLv3]

docs/awesome/awesome-fastapi.md

@@ -62,7 +62,7 @@
 - [asyncpgsa](https://github.com/CanopyTax/asyncpgsa) - A wrapper around [asyncpg](https://github.com/MagicStack/asyncpg) for use with [SQLAlchemy Core](https://docs.sqlalchemy.org/en/latest/core/).
 - [Databases](https://github.com/encode/databases) - Async SQL query builder that works on top of the [SQLAlchemy Core](https://docs.sqlalchemy.org/en/latest/core/) expression language.
 - [PyPika](https://github.com/kayak/pypika) - A SQL query builder that exposes the full richness of the SQL language.
-  
+
 #### ODMs
 
 - [Beanie](https://github.com/BeanieODM/beanie) - Asynchronous Python ODM for MongoDB, based on [Motor](https://motor.readthedocs.io/en/stable/) and [Pydantic](https://docs.pydantic.dev/latest/), which supports data and schema migrations out of the box.
@@ -108,6 +108,7 @@
 - [FastAPI Jinja](https://github.com/AGeekInside/fastapi-jinja) - Adds integration of the Jinja template language to FastAPI.
 - [FastAPI Lazy](https://github.com/yezz123/fastango) - Lazy package to start your project using FastAPI.
 - [FastAPI Limiter](https://github.com/long2ice/fastapi-limiter) - A request rate limiter for FastAPI.
+- [FastAPI Listing](https://github.com/danielhasan1/fastapi-listing) - A library to design/build listing APIs using component-based architecture, inbuilt query paginator, sorter, django-admin like filters & much more.
 - [FastAPI MQTT](https://github.com/sabuhish/fastapi-mqtt) - An extension for the MQTT protocol.
 - [FastAPI Opentracing](https://github.com/wesdu/fastapi-opentracing) - Opentracing middleware and database tracing support for FastAPI.
 - [FastAPI Pagination](https://github.com/uriyyo/fastapi-pagination) - Pagination for FastAPI.
@@ -152,7 +153,7 @@
 
 ### Tutorials
 
-- [Async SQLAlchemy with FastAPI](https://stribny.name/blog/fastapi-asyncalchemy/) - Learn how to use SQLAlchemy asynchronously.
+- [Async SQLAlchemy with FastAPI](https://stribny.name/posts/fastapi-asyncalchemy/) - Learn how to use SQLAlchemy asynchronously.
 - [Build and Secure an API in Python with FastAPI](https://blog.yezz.me/blog/Build-and-Secure-an-API-in-Python-with-FastAPI) - Secure and maintain an API based on FastAPI and SQLAlchemy.
 - [Deploy a Dockerized FastAPI App to Google Cloud Platform](https://towardsdatascience.com/deploy-a-dockerized-fastapi-app-to-google-cloud-platform-24f72266c7ef) - A short guide to deploying a Dockerized Python app to Google Cloud Platform using Cloud Run and a SQL instance.
 - [Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker](https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece)
@@ -164,10 +165,10 @@
 - [Introducing FARM Stack - FastAPI, React, and MongoDB](https://www.mongodb.com/developer/languages/python/farm-stack-fastapi-react-mongodb/) - Getting started with a complete FastAPI web application stack.
 - [Multitenancy with FastAPI, SQLAlchemy and PostgreSQL](https://mergeboard.com/blog/6-multitenancy-fastapi-sqlalchemy-postgresql/) - Learn how to make FastAPI applications multi-tenant ready.
 - [Porting Flask to FastAPI for ML Model Serving](https://www.pluralsight.com/tech-blog/porting-flask-to-fastapi-for-ml-model-serving/) - Comparison of Flask vs FastAPI.
-- [Real-time data streaming using FastAPI and WebSockets](https://stribny.name/blog/2020/07/real-time-data-streaming-using-fastapi-and-websockets/) - Learn how to stream data from FastAPI directly into a real-time chart.
-- [Running FastAPI applications in production](https://stribny.name/blog/fastapi-production/) - Use Gunicorn with systemd for production deployments.
+- [Real-time data streaming using FastAPI and WebSockets](https://stribny.name/posts/real-time-data-streaming-using-fastapi-and-websockets/) - Learn how to stream data from FastAPI directly into a real-time chart.
+- [Running FastAPI applications in production](https://stribny.name/posts/fastapi-production/) - Use Gunicorn with systemd for production deployments.
 - [Serving Machine Learning Models with FastAPI in Python](https://medium.com/@8B_EC/tutorial-serving-machine-learning-models-with-fastapi-in-python-c1a27319c459) - Use FastAPI to quickly and easily deploy and serve machine learning models in Python as a RESTful API.
-- [Streaming video with FastAPI](https://stribny.name/blog/fastapi-video/) - Learn how to serve video streams.
+- [Streaming video with FastAPI](https://stribny.name/posts/fastapi-video/) - Learn how to serve video streams.
 - [Using Hypothesis and Schemathesis to Test FastAPI](https://testdriven.io/blog/fastapi-hypothesis/) - Apply property-based testing to FastAPI.
 
 ### Talks
@@ -203,7 +204,7 @@
 - [AWS Elastic Beanstalk](https://aws.amazon.com/elasticbeanstalk/)
 - [Deta](https://www.deta.sh/) ([example](https://dev.to/athulcajay/fastapi-deta-ni5))
 - [Fly](https://fly.io) ([tutorial](https://fly.io/docs/python/frameworks/fastapi/), [Deploy from a Git repo](https://github.com/fly-apps/hello-fastapi))
-- [Google App Engine](https://cloud.google.com/appengine/)
+- [Google App Engine](https://cloud.google.com/appengine)
 - [Heroku](https://www.heroku.com/) ([Step-by-step tutorial](https://tutlinks.com/create-and-deploy-fastapi-app-to-heroku/), [ML model on Heroku tutorial](https://testdriven.io/blog/fastapi-machine-learning/))
 - [Microsoft Azure App Service](https://azure.microsoft.com/en-us/products/app-service/)
 
@@ -212,7 +213,7 @@
 (Infrastructure-as-a-Service)
 
 - [AWS EC2](https://aws.amazon.com/ec2/)
-- [Google Compute Engine](https://cloud.google.com/compute/)
+- [Google Compute Engine](https://cloud.google.com/compute)
 - [Digital Ocean](https://www.digitalocean.com/)
 - [Linode](https://www.linode.com/)
 
@@ -227,7 +228,7 @@ Frameworks:
 Compute:
 
 - [AWS Lambda](https://aws.amazon.com/lambda/) ([example](https://github.com/iwpnd/fastapi-aws-lambda-example))
-- [Google Cloud Functions](https://cloud.google.com/functions/)
+- [Google Cloud Functions](https://cloud.google.com/functions)
 - [Azure Functions](https://azure.microsoft.com/en-us/products/functions/)
 - [Google Cloud Run](https://cloud.google.com/run) ([example](https://github.com/anthonycorletti/cloudrun-fastapi))
 

docs/awesome/awesome-generative-ai.md

@@ -123,6 +123,7 @@ Contributions to this list are welcome. Before submitting your suggestions, plea
 - [Sybill](https://www.sybill.ai/) - Sybill generates summaries of sales calls, including next steps, pain points and areas of interest, by combining transcript and emotion-based insights.
 - [Loopin AI](https://www.loopinhq.com/) - Loopin is a collaborative meeting workspace that not only enables you to record, transcribe & summaries meetings using AI, but also enables you to auto-organise meeting notes on top of your calendar.
 - [Fireflies.ai](https://fireflies.ai/) - Fireflies.ai helps your team transcribe, summarize, search, and analyze voice conversations.
+- [Read AI](https://www.read.ai/) - An AI copilot for wherever you work, making your meetings, emails, and messages more productive with summaries, content discovery, and recommendations.
 
 ### Academia
 
@@ -270,17 +271,21 @@ Contributions to this list are welcome. Before submitting your suggestions, plea
 - [Synthesia](https://www.synthesia.io/) - Create videos from plain text in minutes.
 - [Rephrase AI](https://www.rephrase.ai/) - Rephrase's technology enables hyper-personalized video creation at scale that drive engagement and business efficiencies.
 - [Hour One](https://hourone.ai/) - Turn text into video, featuring virtual presenters, automatically.
-- [D-ID](https://www.d-id.com/) - Create and interact with talking avatars at the touch of a button.
 - [Colossyan](https://www.colossyan.com/) - Learning & Development focused video creator. Use AI avatars to create educational videos in multiple languages.
 - [Fliki](https://fliki.ai/) - Create text to video and text to speech content with ai powered voices in minutes.
 - [Pictory](https://pictory.ai/) - Pictory's powerful AI enables you to create and edit professional quality videos using text.
 - [Pika](https://pika.art/) - An idea-to-video platform that brings your creativity to motion.
-- [HeyGen](https://app.heygen.com/) - Turn scripts into talking videos with customizable AI avatars in minutes.
 - [Sora](https://openai.com/sora) - An AI model that can create realistic and imaginative scenes from text instructions.
 - [Luma Dream Machine](https://lumalabs.ai/dream-machine) - An AI model that makes high quality, realistic videos fast from text and images.
 - [Infinity AI](https://infinity.ai/) - Infinity is a video foundation model that allows you to craft your characters and then bring them to life.
 - [KLING AI](https://klingai.com/) - Tools for creating imaginative images and videos.
 
+### Avatars
+
+- [D-ID](https://www.d-id.com/) - Create and interact with talking avatars at the touch of a button.
+- [HeyGen](https://app.heygen.com/) - Turn scripts into talking videos with customizable AI avatars in minutes.
+- [RenderNet](https://rendernet.ai/) - RenderNet AI is a tool for generating images and videos, providing control over character design, composition, and style.
+
 ### Animation
 
 - [Wonder Dynamics](https://wonderdynamics.com/) -  Effortlessly animate, light, and compose CG characters into live scenes.