RE(SOURCE)

resource

Is DePIN the Future of Shared Resources? Industry Leaders Weigh In on the Prospects

The Ultimate Directory of Apache Iceberg Resources

One of the roles of the Kubernetes controller is to monitor objects for the desired vs actual states and then send requests to change the existing state to the desired state at a controlled rate. The controller requests the Kubernetes API server to retrieve an object’s information. However, frequently retrieving data from the API server is costly and time-consuming. This is where Informer comes in, It is a client-side library that continuously watches for Kubernetes resources and keeps an in-memory copy of resources, which a given index can retrieve. But how is it different from the raw watch command? Informers and watch both use resourceVersion to keep track of the objects. If you run a command kubectl get pod <pod-name> -o yaml you will notice a field called resourceVersion. apiVersion: v1
kind: Pod
metadata:
  annotations:
    cni.projectcalico.org/podIP: 192.168.62.137/32
    kubernetes.io/psp: node-exporter
  creationTimestamp: "2021-12-12T14:35:11Z"
  labels:
    run: redis
  name: redis
  namespace: default
  resourceVersion: "39593432"
  uid: 233eaa4e-600f-4169-a617-538e5cbaa750 Informers use watch streams to get real-time updates. They relist all the objects periodically and store them in a cache while raw watch operations don't provide caching. You'd need to implement your own caching and event handling. Additionally, missing events are probable in the case of using just a raw watch. For instance, we set up a watch operation to monitor Kubernetes jobs. If a network partition occurs, temporarily disconnecting our watch from the Kubernetes API server, we might miss critical events. During this disconnection, a job could be created and then quickly deleted. Once the network connection is restored, our watch operation resumes, but it has no way of knowing about the job that was briefly created and deleted during the outage.  In this case, the informer would come in handy. SharedInformer As various controllers may be watching over a particular resource and each controller has its informer, which maintains its cache, this could lead to synchronization issues. To solve this problem, it is usually recommended to use sharedInformers as they have a shared cache that multiple controllers can use. In SharedInformers, only one watch is opened on the Kubernetes API server regardless of the number of consumers, without adding extra overhead. The implementation of informers is present in the client-go library. It is the official client library used internally by Kubernetes itself.  Now let’s take a look at the components of SharedIndexInformer which is an instantiation of SharedInformer consisting of add and get Indexers ability (Indexer is a storage interface that lets you list objects using multiple indexing functions). type SharedIndexInformer interface {
    SharedInformer
    // AddIndexers add indexers to the informer before it starts.
    AddIndexers(indexers Indexers) error
    GetIndexer() Indexer
} The recommended way to create an informer is using sharedInformerFactory. Below is an example code that will create and start a pod Informer. factory := informers.NewSharedInformerFactory(client, 10*time.Minute)
podsInformer := factory.Core().V1().Pods().Informer()

stopCh := make(chan struct{})
factory.Start(stopCh) Components of sharedIndexInformer Reflector The reflector consists of the ListerWatcher interface and thus opens a stream to check for events and proactively sends requests over regular intervals. // ListerWatcher is any object that knows how to perform an initial list and start a watch on a resource.
// /tools/cache/listwatch.go
type ListerWatcher interface {
Lister
Watcher
} This ensures the controller is not only edge (event) triggered but also level triggered, so if an event is missed, it doesn’t affect the environment. You can learn more about edge and level-triggered logic from James Bowes’s article - “Level Triggering and Reconciliation in Kubernetes”. Delta FIFO The Reflector uses Delta FIFO queues to store information about state changes. It is the main channel between the Reflector and the Indexer. It contains the changed object and type of the change/Delta (Added, Updated, Deleted, Replaced, and Sync) and ensures no duplicates. Indexer Indexer is a local storage for storing resource objects with its indexing feature; it helps reduce the number of API calls made to etcd. The Indexer implements thread-safe operations, allowing concurrent reads and serialized writes. SharedProcessor SharedProcessor distributes the information to all registered event handlers Finally, here is an overview of the function calls involved after invoking SharedInformer: Feel free to reach out if you have any questions :) One of the roles of the Kubernetes controller is to monitor objects for the desired vs actual states and then send requests to change the existing state to the desired state at a controlled rate. The controller requests the Kubernetes API server to retrieve an object’s information. Kubernetes However, frequently retrieving data from the API server is costly and time-consuming. This is where Informer comes in, It is a client-side library that continuously watches for Kubernetes resources and keeps an in-memory copy of resources, which a given index can retrieve. But how is it different from the raw watch command? Informers and watch both use resourceVersion to keep track of the objects. If you run a command kubectl get pod <pod-name> -o yaml you will notice a field called resourceVersion. kubectl get pod <pod-name> -o yaml apiVersion: v1
kind: Pod
metadata:
  annotations:
    cni.projectcalico.org/podIP: 192.168.62.137/32
    kubernetes.io/psp: node-exporter
  creationTimestamp: "2021-12-12T14:35:11Z"
  labels:
    run: redis
  name: redis
  namespace: default
  resourceVersion: "39593432"
  uid: 233eaa4e-600f-4169-a617-538e5cbaa750 apiVersion: v1
kind: Pod
metadata:
  annotations:
    cni.projectcalico.org/podIP: 192.168.62.137/32
    kubernetes.io/psp: node-exporter
  creationTimestamp: "2021-12-12T14:35:11Z"
  labels:
    run: redis
  name: redis
  namespace: default
  resourceVersion: "39593432"
  uid: 233eaa4e-600f-4169-a617-538e5cbaa750 Informers use watch streams to get real-time updates. They relist all the objects periodically and store them in a cache while raw watch operations don't provide caching. You'd need to implement your own caching and event handling. Additionally, missing events are probable in the case of using just a raw watch. For instance, we set up a watch operation to monitor Kubernetes jobs. If a network partition occurs, temporarily disconnecting our watch from the Kubernetes API server, we might miss critical events. During this disconnection, a job could be created and then quickly deleted. Once the network connection is restored, our watch operation resumes, but it has no way of knowing about the job that was briefly created and deleted during the outage.  In this case, the informer would come in handy. SharedInformer As various controllers may be watching over a particular resource and each controller has its informer, which maintains its cache, this could lead to synchronization issues. To solve this problem, it is usually recommended to use sharedInformers as they have a shared cache that multiple controllers can use. In SharedInformers, only one watch is opened on the Kubernetes API server regardless of the number of consumers, without adding extra overhead. The implementation of informers is present in the client-go library. It is the official client library used internally by Kubernetes itself.  Now let’s take a look at the components of SharedIndexInformer which is an instantiation of SharedInformer consisting of add and get Indexers ability (Indexer is a storage interface that lets you list objects using multiple indexing functions). client-go type SharedIndexInformer interface {
    SharedInformer
    // AddIndexers add indexers to the informer before it starts.
    AddIndexers(indexers Indexers) error
    GetIndexer() Indexer
} type SharedIndexInformer interface {
    SharedInformer
    // AddIndexers add indexers to the informer before it starts.
    AddIndexers(indexers Indexers) error
    GetIndexer() Indexer
} The recommended way to create an informer is using sharedInformerFactory . sharedInformerFactory Below is an example code that will create and start a pod Informer. factory := informers.NewSharedInformerFactory(client, 10*time.Minute)
podsInformer := factory.Core().V1().Pods().Informer()

stopCh := make(chan struct{})
factory.Start(stopCh) factory := informers.NewSharedInformerFactory(client, 10*time.Minute)
podsInformer := factory.Core().V1().Pods().Informer()

stopCh := make(chan struct{})
factory.Start(stopCh) Components of sharedIndexInformer Reflector The reflector consists of the ListerWatcher interface and thus opens a stream to check for events and proactively sends requests over regular intervals. // ListerWatcher is any object that knows how to perform an initial list and start a watch on a resource.
// /tools/cache/listwatch.go
type ListerWatcher interface {
Lister
Watcher
} // ListerWatcher is any object that knows how to perform an initial list and start a watch on a resource.
// /tools/cache/listwatch.go
type ListerWatcher interface {
Lister
Watcher
} This ensures the controller is not only edge (event) triggered but also level triggered, so if an event is missed, it doesn’t affect the environment. You can learn more about edge and level-triggered logic from James Bowes’s article - “ Level Triggering and Reconciliation in Kubernetes ”. Level Triggering and Reconciliation in Kubernetes Delta FIFO The Reflector uses Delta FIFO queues to store information about state changes. It is the main channel between the Reflector and the Indexer. It contains the changed object and type of the change/Delta (Added, Updated, Deleted, Replaced, and Sync) and ensures no duplicates. Indexer Indexer is a local storage for storing resource objects with its indexing feature; it helps reduce the number of API calls made to etcd. The Indexer implements thread-safe operations, allowing concurrent reads and serialized writes. SharedProcessor SharedProcessor distributes the information to all registered event handlers Finally, here is an overview of the function calls involved after invoking SharedInformer: Finally, here is an overview of the function calls involved after invoking SharedInformer: Feel free to reach out if you have any questions :)

Enhancing Kubernetes Resource Management with SharedInformers

In recent weeks, webmasters have raised urgent concerns regarding Facebook's aggressive crawling practices, particularly highlighting the behavior of its user agent, “facebookexternalhit.” Many site owners report that these bots are creating significant strain on their web hosting servers, leading to alarming spikes in traffic that threaten site reliability. Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. One webmaster recounted their situation, said, “Our website gets hammered every 45 to 60 minutes with spikes of approximately 400 requests per second from 20 to 30 different IP addresses within Facebook’s netblocks. Between these spikes, the traffic is manageable, but the sudden load is risky.” This sentiment resonates with many, as webmasters are advocating for a more balanced distribution of requests from Facebook’s bots, akin to the behavior exhibited by Googlebot and other reputable search engine crawlers. The consequences of these excessive requests extend beyond mere inconvenience; they disrupt the user experience and lead to costly resource consumption for site owners. Smaller websites, in particular, have found themselves severely impacted. In response to the relentless onslaught, some webmasters have taken proactive measures by implementing stricter rules in their robots.txt files to shield their servers from the overwhelming traffic. However, because Facebook’s bot functions as a scraper rather than a traditional crawler, it disregards these instructions, further complicating the situation. This growing issue has sparked widespread discussions within the web development community, with experts urging Facebook to reconsider its crawling strategies. The collective voice of these webmasters underscores a critical need for a more sustainable approach to web scraping and crawling practices. In a bid to manage the excessive requests, many webmasters are turning to tools like Cloudflare, which provides robust features for managing traffic and implementing rate limiting. By configuring a rate-limiting WAF rule, webmasters can effectively throttle the number of requests originating from Facebook’s bots, alleviating server strain during peak traffic periods. One webmaster expressed their perspective on the necessity of a balanced approach, stating, “I don’t want to block the bot entirely, but the current pattern is unsustainable. Using Cloudflare’s rate limiting has allowed us to protect our site while still enabling Facebook to access our content for link previews.” Concerns regarding Facebook’s crawling practices have been echoed on various platforms. In a post on Cloudflare, one user articulated their frustrations: “I am writing to express my concern about the excessive crawling activity of Facebook’s crawler. “This excessive crawling is causing significant performance issues and potential downtime for our website.” They went on to detail, “Our web server logs indicate that Facebook’s crawler (facebookexternalhit/1.1 – 2a03:2880:22ff:7::face is making multiple requests to our WordPress website every second, even during off-peak hours,” “During peak hours, the crawler’s activity spikes to tens of thousands of requests per minute. This excessive crawling is overwhelming our servers and causing them to slow down or even crash.” While webmasters recognize the necessity of Facebook’s crawler for indexing purposes and making their content accessible to users, they firmly believe that the current level of crawling is excessive and unreasonable. As a result, many remain vigilant, closely monitoring their server performance and adjusting settings to mitigate the challenges posed by Facebook’s bots. The unfolding situation highlights a critical juncture for the web development community, with potential implications for how major tech companies manage web scraping and crawling practices in the future. As webmasters advocate for a more equitable solution, the outcome of this discussion could set important precedents in the industry, influencing the relationship between webmasters and tech giants moving forward. In recent weeks, webmasters have raised urgent concerns regarding Facebook's aggressive crawling practices , particularly highlighting the behavior of its user agent, “facebookexternalhit.” Many site owners report that these bots are creating significant strain on their web hosting servers, leading to alarming spikes in traffic that threaten site reliability. Facebook's aggressive crawling practices Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. Several site owners have come forward with their experiences, describing the overwhelming impact of Facebook’s crawling activities on their websites. One webmaster recounted their situation, said, “Our website gets hammered every 45 to 60 minutes with spikes of approximately 400 requests per second from 20 to 30 different IP addresses within Facebook’s netblocks. Between these spikes, the traffic is manageable, but the sudden load is risky.” This sentiment resonates with many, as webmasters are advocating for a more balanced distribution of requests from Facebook’s bots , akin to the behavior exhibited by Googlebot and other reputable search engine crawlers. Facebook’s bots The consequences of these excessive requests extend beyond mere inconvenience; they disrupt the user experience and lead to costly resource consumption for site owners. Smaller websites, in particular, have found themselves severely impacted. In response to the relentless onslaught, some webmasters have taken proactive measures by implementing stricter rules in their robots.txt files to shield their servers from the overwhelming traffic. However, because Facebook’s bot functions as a scraper rather than a traditional crawler, it disregards these instructions, further complicating the situation. This growing issue has sparked widespread discussions within the web development community, with experts urging Facebook to reconsider its crawling strategies. The collective voice of these webmasters underscores a critical need for a more sustainable approach to web scraping and crawling practices. In a bid to manage the excessive requests, many webmasters are turning to tools like Cloudflare, which provides robust features for managing traffic and implementing rate limiting. By configuring a rate-limiting WAF rule, webmasters can effectively throttle the number of requests originating from Facebook’s bots, alleviating server strain during peak traffic periods. One webmaster expressed their perspective on the necessity of a balanced approach, stating, “I don’t want to block the bot entirely, but the current pattern is unsustainable. Using Cloudflare’s rate limiting has allowed us to protect our site while still enabling Facebook to access our content for link previews.” Concerns regarding Facebook’s crawling practices have been echoed on various platforms. In a post on Cloudflare, one user articulated their frustrations: “I am writing to express my concern about the excessive crawling activity of Facebook’s crawler. “This excessive crawling is causing significant performance issues and potential downtime for our website.” They went on to detail, “Our web server logs indicate that Facebook’s crawler (facebookexternalhit/1.1 – 2a03:2880:22ff:7::face is making multiple requests to our WordPress website every second, even during off-peak hours,” “During peak hours, the crawler’s activity spikes to tens of thousands of requests per minute. This excessive crawling is overwhelming our servers and causing them to slow down or even crash.” While webmasters recognize the necessity of Facebook’s crawler for indexing purposes and making their content accessible to users, they firmly believe that the current level of crawling is excessive and unreasonable. As a result, many remain vigilant, closely monitoring their server performance and adjusting settings to mitigate the challenges posed by Facebook’s bots. The unfolding situation highlights a critical juncture for the web development community, with potential implications for how major tech companies manage web scraping and crawling practices in the future. As webmasters advocate for a more equitable solution, the outcome of this discussion could set important precedents in the industry, influencing the relationship between webmasters and tech giants moving forward.

FaceBook Bots, Crawlers And User Agents Causing Resource Drains On Websites And Hosting Accounts

ZUG, Switzerland, September 19th, 2024/Chainwire/--Agile Coretime Paves the Way for Polkadot 2.0 Improving the Network’s Scalability, Cost, Speed, and Flexibility. The Polkadot community celebrates the release of its latest major product, “Agile Coretime”, as part of the ongoing Polkadot 2.0 upgrade. This new feature represents a significant advancement in how computational resources are allocated and managed within the Polkadot ecosystem, delivering unprecedented efficiency, scalability, and accessibility for projects of all sizes. Agile Coretime is not just a technical upgrade; it is the most important product launch this year for Polkadot as it evolves, through seamless on-chain governance, into a network ready to onboard the Web3 masses. It also serves as a primary catalyst for the rebirth of the entire ecosystem. By making it easier for projects to build and scale on Polkadot, this feature is designed to attract a new wave of innovative applications and use cases. As the second crucial component on the path to Polkadot 2.0, which previously saw Asynchronous Backing go live and will later allow for Elastic Scaling, Agile Coretime redefines how blockchain resources are allocated by offering a dynamic, on-demand blockspace model. This new approach replaces the previous auction system, in which single cores were leased for two years at a time. The new approach makes it easier and more cost-effective for projects to access the resources they need, when they need them. Eskimor, lead developer at Parity Technologies, Polkadot’s leading technical contributor, said: “Agile Coretime is a huge milestone in making the high quality blockspace Polkadot offers more accessible. With this and other features we have in the pipeline, I expect more experimentation and awesome projects to be launched on Polkadot, showcasing its amazing capabilities. Let's wake our sleeping giant!” One of the key advantages of Agile Coretime is its ability to align resource availability with actual network demand. By dynamically allocating computational resources, Polkadot ensures that no resources are wasted during low activity periods, while also preventing congestion during peak times. This adaptability is crucial for projects with varying needs, enabling them to scale and operate more efficiently without the burden of high upfront costs. "Devs have historically faced a binary choice: deploy a smart contract and compete with other protocols for limited blockspace, or deploy a blockchain and pay for a large amount of dedicated blockspace," says Derek Yoo, CEO of Moonsong Labs. "Agile Coretime addresses this challenge by offering a flexible approach. For projects starting out, you can harness the power and customizability of a blockchain while paying only for the blockspace you need. For mature projects with product-market fit, Agile Coretime allows scaling to meet high levels of demand without the need for sharding." For new developers and smaller projects, Agile Coretime lowers the barrier of entry by providing access to Polkadot’s robust infrastructure without the need for significant DOT collateral. This democratizes access, fostering greater innovation and participation within the ecosystem. Projects can purchase coretime either on-demand or in bulk, providing flexibility or predictability depending on their specific needs. On-demand purchases are ideal for projects with fluctuating demands, while bulk purchases offer stable and reliable resource allocation for teams requiring a steady flow of blockspace. For media inquiries, please contact Jonathan Duran at Jonathan(at)Distractive(dot)xyz About Polkadot Polkadot is the powerful, secure core of Web3, providing a shared foundation that unites some of the world’s most transformative apps and blockchains. Polkadot offers advanced modular architecture that allows devs to easily design and build their own specialized blockchain projects, pooled security that ensures the same high standard for secure block production across all connected chains and apps connected to it, and robust governance that ensures a transparent system where everyone has say in shaping the blockchain ecosystem for growth and sustainability. With Polkadot, you're not just a participant, you're a co-creator with the power to shape its future. Contact Marketing Communications Director Jen Wheatley Distractive jen@distractive.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here ZUG, Switzerland, September 19th, 2024/Chainwire/--Agile Coretime Paves the Way for Polkadot 2.0 Improving the Network’s Scalability, Cost, Speed, and Flexibility. The Polkadot community celebrates the release of its latest major product, “Agile Coretime”, as part of the ongoing Polkadot 2.0 upgrade. This new feature represents a significant advancement in how computational resources are allocated and managed within the Polkadot ecosystem, delivering unprecedented efficiency, scalability, and accessibility for projects of all sizes. Polkadot Polkadot Agile Coretime is not just a technical upgrade; it is the most important product launch this year for Polkadot as it evolves, through seamless on-chain governance, into a network ready to onboard the Web3 masses. It also serves as a primary catalyst for the rebirth of the entire ecosystem . the rebirth of the entire ecosystem the rebirth of the entire ecosystem By making it easier for projects to build and scale on Polkadot, this feature is designed to attract a new wave of innovative applications and use cases. As the second crucial component on the path to Polkadot 2.0, which previously saw Asynchronous Backing go live and will later allow for Elastic Scaling , Agile Coretime redefines how blockchain resources are allocated by offering a dynamic, on-demand blockspace model. Asynchronous Backing Asynchronous Backing Elastic Scaling Elastic Scaling This new approach replaces the previous auction system, in which single cores were leased for two years at a time. The new approach makes it easier and more cost-effective for projects to access the resources they need, when they need them. Eskimor, lead developer at Parity Technologies, Polkadot’s leading technical contributor, said: “Agile Coretime is a huge milestone in making the high quality blockspace Polkadot offers more accessible. With this and other features we have in the pipeline, I expect more experimentation and awesome projects to be launched on Polkadot, showcasing its amazing capabilities. Let's wake our sleeping giant!” Eskimor, lead developer at Parity Technologies, Polkadot’s leading technical contributor, said: “Agile Coretime is a huge milestone in making the high quality blockspace Polkadot offers more accessible. With this and other features we have in the pipeline, I expect more experimentation and awesome projects to be launched on Polkadot, showcasing its amazing capabilities. Let's wake our sleeping giant!” One of the key advantages of Agile Coretime is its ability to align resource availability with actual network demand. By dynamically allocating computational resources, Polkadot ensures that no resources are wasted during low activity periods, while also preventing congestion during peak times. This adaptability is crucial for projects with varying needs, enabling them to scale and operate more efficiently without the burden of high upfront costs. "Devs have historically faced a binary choice: deploy a smart contract and compete with other protocols for limited blockspace, or deploy a blockchain and pay for a large amount of dedicated blockspace," says Derek Yoo, CEO of Moonsong Labs. "Devs have historically faced a binary choice: deploy a smart contract and compete with other protocols for limited blockspace, or deploy a blockchain and pay for a large amount of dedicated blockspace," says Derek Yoo, CEO of Moonsong Labs . Moonsong Labs Moonsong Labs "Agile Coretime addresses this challenge by offering a flexible approach. For projects starting out, you can harness the power and customizability of a blockchain while paying only for the blockspace you need. For mature projects with product-market fit, Agile Coretime allows scaling to meet high levels of demand without the need for sharding." "Agile Coretime addresses this challenge by offering a flexible approach. For projects starting out, you can harness the power and customizability of a blockchain while paying only for the blockspace you need. For mature projects with product-market fit, Agile Coretime allows scaling to meet high levels of demand without the need for sharding." For new developers and smaller projects, Agile Coretime lowers the barrier of entry by providing access to Polkadot’s robust infrastructure without the need for significant DOT collateral. This democratizes access, fostering greater innovation and participation within the ecosystem. Projects can purchase coretime either on-demand or in bulk, providing flexibility or predictability depending on their specific needs. On-demand purchases are ideal for projects with fluctuating demands, while bulk purchases offer stable and reliable resource allocation for teams requiring a steady flow of blockspace. For media inquiries, please contact Jonathan Duran at Jonathan(at)Distractive(dot)xyz About Polkadot Polkadot is the powerful, secure core of Web3, providing a shared foundation that unites some of the world’s most transformative apps and blockchains. Polkadot Polkadot Polkadot offers advanced modular architecture that allows devs to easily design and build their own specialized blockchain projects, pooled security that ensures the same high standard for secure block production across all connected chains and apps connected to it, and robust governance that ensures a transparent system where everyone has say in shaping the blockchain ecosystem for growth and sustainability. With Polkadot, you're not just a participant, you're a co-creator with the power to shape its future. Contact Marketing Communications Director Jen Wheatley Distractive jen@distractive.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Polkadot Upgrades With Agile Coretime For Resource Allocation, Unlocking Efficiency And Scalability

Upgrady Polkadot s agilním Coretime pro alokaci zdrojů, uvolnění efektivity a škálovatelnosti

Polkadot-opgraderinger med agil Coretime til ressourceallokering, frigørelse af effektivitet og skalerbarhed

Polkadot se actualiza con Agile Coretime para la asignación de recursos, lo que permite mayor eficiencia y escalabilidad

Polkadot はリソース割り当てのための Agile Coretime でアップグレードし、効率性と拡張性を実現

Polkadot обновляется с помощью Agile Coretime для распределения ресурсов, повышения эффективности и масштабируемости

Përmirësimet e Polkadot me Coretime të shkathët për shpërndarjen e burimeve, zhbllokimin e efikasitetit dhe shkallëzueshmërisë

வள ஒதுக்கீடு, திறத்தல் திறன் மற்றும் அளவிடுதல் ஆகியவற்றிற்காக போல்கடாட் சுறுசுறுப்பான கோர்டைமுடன் மேம்படுத்துகிறது

Polkadot Resurslary paýlamak, netijeliligi açmak we ulalmak üçin çalt wagt bilen täzelenýär

Nag-upgrade ang Polkadot Gamit ang Agile Coretime Para sa Paglalaan ng Resource, Pag-unlock ng Efficiency At Scalability

وسائل کی تقسیم، غیر مقفل کرنے کی کارکردگی اور توسیع پذیری کے لیے فرتیلی کور ٹائم کے ساتھ Polkadot اپ گریڈ کرتا ہے۔

Uphuculo lwePolkadot nge-Agile Coretime yoLwabiwo lweZincedisi, ukuvula ngokuSebenzayo kunye nokuSebenza

Polkadot 通过 Agile Coretime 进行升级，以实现资源分配、提高效率和可扩展性

The TechBeat: Top Resources for Learning About AI in Finance  (9/17/2024)

The TechBeat: Top Resources for Learning About AI in Finance  (9/15/2024)

Leveraging AI can help improve operational efficiency and decision-making. AI can help with risk assessment, sentiment analysis of real-time news streams, optimizing asset allocation based on risk tolerance, and many more use cases. But where does anyone start to learn about all this?!? This is one of the most common questions I get. So, I'm curating a list of resources that folks, new or experienced, might find useful in the journey. Since finance is a very broad field, let's focus our tools on trading, which is one of the most popular use cases. Theory/Research Papers For the curious who want to understand why some of these techniques work and the prior art related to the topics. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance: Introduces a deep reinforcement learning (DRL) library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Machine learning for trading: With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case. Financial Trading as a Game: The effectiveness of applying deep reinforcement learning algorithms to the financial trading domain. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem: Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine-learning solution to the portfolio management problem. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy: An ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. Data Sets Because you really can't learn to explore much without proper data! NASDAQ: Access millions of financial and economic data points from hundreds of publishers via the Nasdaq Data Link suite of APIs. Yahoo Finance: One of the most popular data sources, the link has a Python module to get stock data from Yahoo! Finance. Existing Libraries/Strategies stockpredictionai: This is a course in itself! A complete process for predicting stock price movements. Personae: Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. Ensemble-Strategy: Deep Reinforcement Learning for Automated Stock Trading. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. If you haven’t already, check out the linked paper in the theory/research section to understand the FinRL architecture. Asset News Sentiment Analyzer - Sentiment analysis and report generation for financial assets and securities utilizing GPT models. Courses If the above list has piqued your interest, there are also some good online courses that you can follow. Coursera: Machine Learning and Reinforcement Learning in Finance Specialization:  Extend your expertise in algorithms and tools needed to predict financial markets.


Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance.



Udacity: AI for trading: Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Quantopian: - Webinars about Machine Learning for trading. QuantInsti Quantitative Learning - Videos in Quantitative finance & Algorithmic trading. AI in finance: Understand the foundations and applications of AI technology as well as the opportunities and risks associated with it. Community Financial Machine Learning and Data Science: A curated list of practical financial machine learning (FinML) tools and applications LLMs Since LLMs are a hot topic, we obviously have to mention some material related to it! Financial Statement Analysis with Large Language Models - Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. FinGPT: Open-Source Financial Large Language Models: A lot of resources, including papers, analysis and open source hugging face models for LLM in finance! I would categorize this as a community! Hands-on LLMs: Train and Deploy a Real-time Financial Advisor: Hands on LLMs course to Train and Deploy a Real-Time Financial Advisor. Disclaimer: All investments and trading involve risk. The above list is for educational purpose only.  Users should perform their own due diligence before making any trading decisions. Leveraging AI can help improve operational efficiency and decision-making. AI can help with risk assessment, sentiment analysis of real-time news streams, optimizing asset allocation based on risk tolerance, and many more use cases. But where does anyone start to learn about all this?!? This is one of the most common questions I get. So, I'm curating a list of resources that folks, new or experienced, might find useful in the journey. Since finance is a very broad field, let's focus our tools on trading, which is one of the most popular use cases. Theory/Research Papers Theory/Research Papers For the curious who want to understand why some of these techniques work and the prior art related to the topics. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance: Introduces a deep reinforcement learning (DRL) library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance : Introduces a deep reinforcement learning (DRL) library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance Machine learning for trading: With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case. Machine learning for trading : With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case. Machine learning for trading Machine learning for trading Financial Trading as a Game: The effectiveness of applying deep reinforcement learning algorithms to the financial trading domain. Financial Trading as a Game : The effectiveness of applying deep reinforcement learning algorithms to the financial trading domain. Financial Trading as a Game Financial Trading as a Game A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem: Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine-learning solution to the portfolio management problem. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem: Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine-learning solution to the portfolio management problem. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem : A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy: An ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy : An ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy Data Sets Because you really can't learn to explore much without proper data! NASDAQ: Access millions of financial and economic data points from hundreds of publishers via the Nasdaq Data Link suite of APIs. NASDAQ : Access millions of financial and economic data points from hundreds of publishers via the Nasdaq Data Link suite of APIs. NASDAQ NASDAQ Yahoo Finance: One of the most popular data sources, the link has a Python module to get stock data from Yahoo! Finance. Yahoo Finance : One of the most popular data sources, the link has a Python module to get stock data from Yahoo! Finance. Yahoo Finance Yahoo Finance Existing Libraries/Strategies stockpredictionai: This is a course in itself! A complete process for predicting stock price movements. stockpredictionai : This is a course in itself! A complete process for predicting stock price movements. stockpredictionai stockpredictionai Personae: Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. Personae : Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. Personae Personae Ensemble-Strategy: Deep Reinforcement Learning for Automated Stock Trading. Ensemble-Strategy : Deep Reinforcement Learning for Automated Stock Trading. Ensemble-Strategy Ensemble-Strategy FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. If you haven’t already, check out the linked paper in the theory/research section to understand the FinRL architecture. FinRL : A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. If you haven’t already, check out the linked paper in the theory/research section to understand the FinRL architecture. FinRL FinRL Asset News Sentiment Analyzer - Sentiment analysis and report generation for financial assets and securities utilizing GPT models. Asset News Sentiment Analyzer - Sentiment analysis and report generation for financial assets and securities utilizing GPT models. Asset News Sentiment Analyzer Asset News Sentiment Analyzer Asset News Sentiment Analyzer Courses If the above list has piqued your interest, there are also some good online courses that you can follow. Coursera: Machine Learning and Reinforcement Learning in Finance Specialization:  Extend your expertise in algorithms and tools needed to predict financial markets.


Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance. Udacity: AI for trading: Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Coursera: Machine Learning and Reinforcement Learning in Finance Specialization :  Extend your expertise in algorithms and tools needed to predict financial markets. Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance. Coursera: Coursera: Machine Learning and Reinforcement Learning in Finance Specialization Machine Learning and Reinforcement Learning in Finance Specialization Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance. Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance. Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance . Overview of Advanced Methods of Reinforcement Learning in Finance Udacity: AI for trading : Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Udacity: AI for trading Udacity: AI for trading Quantopian: - Webinars about Machine Learning for trading. Quantopian : - Webinars about Machine Learning for trading. Quantopian Quantopian QuantInsti Quantitative Learning - Videos in Quantitative finance & Algorithmic trading. QuantInsti Quantitative Learning - Videos in Quantitative finance & Algorithmic trading. QuantInsti Quantitative Learning QuantInsti Quantitative Learning AI in finance: Understand the foundations and applications of AI technology as well as the opportunities and risks associated with it. AI in finance: Understand the foundations and applications of AI technology as well as the opportunities and risks associated with it. AI in finance : AI in finance Community Financial Machine Learning and Data Science: A curated list of practical financial machine learning (FinML) tools and applications Financial Machine Learning and Data Science: A curated list of practical financial machine learning (FinML) tools and applications Financial Machine Learning and Data Science : Financial Machine Learning and Data Science LLMs Since LLMs are a hot topic, we obviously have to mention some material related to it! Financial Statement Analysis with Large Language Models - Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. Financial Statement Analysis with Large Language Models - Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. Financial Statement Analysis with Large Language Models Financial Statement Analysis with Large Language Models FinGPT: Open-Source Financial Large Language Models: A lot of resources, including papers, analysis and open source hugging face models for LLM in finance! I would categorize this as a community! FinGPT: Open-Source Financial Large Language Models : A lot of resources, including papers, analysis and open source hugging face models for LLM in finance! I would categorize this as a community! FinGPT: Open-Source Financial Large Language Models FinGPT: Open-Source Financial Large Language Models Hands-on LLMs: Train and Deploy a Real-time Financial Advisor: Hands on LLMs course to Train and Deploy a Real-Time Financial Advisor. Hands-on LLMs: Train and Deploy a Real-time Financial Advisor : Hands on LLMs course to Train and Deploy a Real-Time Financial Advisor. Hands-on LLMs: Train and Deploy a Real-time Financial Advisor Hands-on LLMs: Train and Deploy a Real-time Financial Advisor Disclaimer: All investments and trading involve risk. The above list is for educational purpose only.  Users should perform their own due diligence before making any trading decisions. Disclaimer: All investments and trading involve risk. The above list is for educational purpose only. Users should perform their own due diligence before making any trading decisions. Disclaimer: All investments and trading involve risk. The above Disclaimer: list is for educational purpose only. Users should perform their own due diligence before making any trading decisions.

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