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Cognicept Systems

A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable. This famous quote from, Leslie Lamport, an A.M. Turing Award Laureate, summarizes the challenges in building and maintaining a distributed system. But why is there a need for such complicated systems? With the advent of the internet and smarter devices, the amount of data that needs to be processed has exploded. Simple day-to-day activities like ordering an Uber, watching a show on Netflix, a simple Google search, online shopping, or interacting with social media, all trivial actions that we take for granted are powered by hundreds of distribution services. All these services are built on the backbone of some  foundational papers in distributed systems. While this list is definitely not comprehensive, here are some of my favorite papers that have had a massive impact on the world of distributed systems. CAP Theorem While not a traditional paper, Eric Brewer first introduced it as a conjecture in a keynote address at the 2000 ACM Symposium on Principles of Distributed Computing (PODC). The paper was later formalized and proved by Nancy Lynch and Seth Gilbert in the paper Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services Eric Brewer's CAP theorem is a fundamental concept in distributed systems theory, stating that it is impossible for a distributed data store to simultaneously provide more than two out of three guarantees: consistency, availability, and partition tolerance. All other papers mentioned here apply the above principle and make the necessary tradeoffs in their system. CAP theorem always leads to a lot of discussions based on readers’ understanding of the paper. Martin Kleppmann's " " provides a better framework to discuss the tradeoffs. A Critique of the CAP Theorem Paxos Made Simple In this seminal from 2001, Leslie Lamport presents the Paxos algorithm for achieving consensus in a distributed system in an easy and accessible way.  Paxos-based consensus protocols form the backbone of many distributed databases, storage systems, messaging platforms, and coordination services used by many technology companies. It heavily influenced other technologies like Google's Chubby, Google's Spanner, Apache ZooKeeper, Apache BookKeeper, etc. paper The Google File System introduces a scalable distributed file system for large distributed data-intensive applications on commodity hardware, which is the foundation for many distributed file systems that followed. GFS served as a major inspiration for HDFS, the distributed file system used by the Apache Hadoop framework and eventually Amazon S3 (event though s3 is fundamentally different). The Google File System (GFS) paper MapReduce: Simplified Data Processing on Large Clusters This introduces the MapReduce programming model, which demonstrates a scalable approach to processing large-scale datasets using distributed computing infrastructure. MapReduce played a pivotal role in the "big data" revolution, enabling organizations to harness the power of distributed computing to analyze and derive insights from massive datasets. You can see how combining GFS and MapReduce allowed Google to process Petabytes of data to organize data of the "internet." paper The MapReduce paper (along with GFS) inspired the development of an entire ecosystem of tools and libraries built around Apache Hadoop such as Apache Hive (data warehouse infrastructure built on Hadoop), Apache Pig (high-level data flow language for Hadoop), Apache Spark (in-memory data processing engine), Apache HBase (distributed NoSQL database), and many others. Bigtable: A Distributed Storage System for Structured Data The represents a distributed storage system for managing structured data at Google. Once MapReduce and GFS allowed Google to process data at scale in a cost-effective manner, the next step was to enable access to the data in a reliable and highly available manner. BigTable was able to provide a flexible, high-performance solution for applications like web indexing, Google Earth, and Google Finance. Bigtable paper Just like MapReduce revolutionized the "big data" era, BigTable paper was the moving force for the "NoSQL" era. Many of the design principles and architectural concepts introduced in the Bigtable paper, were used in technologies like "Apache HBase", "Cassandra", "MongoD", etc. While some of these applications might use different data models (e.g., MongoDB),  they share common principles such as horizontal scalability, fault tolerance, and automatic sharding. Dynamo: Amazon's Highly Available Key-Value Store presents the design and implementation of a highly available key-value store developed by Amazon. Dynamo addressed the need for real-time access to highly dynamic data, such as items in your shopping cart. The paper introduced the concept of "eventual consistency" as a core principle of distributed systems design, allowing for relaxed consistency guarantees to achieve high availability and performance (hi CAP theorem!). Dynamo paper From the paper itself, “Compared to Bigtable, Dynamo targets applications that require only key/value access with primary focus on high availability where updates are not rejected even in the wake of network partitions or server failures.” Similar to BigTable, the Dynamo paper highly influenced subsequent technologies like Riak, Voldemort, Cassandra, and even event streaming technologies like Apache Kafka. Cassandra—A Decentralized Structured Storage System Facebook's rapid growth necessitated a database solution capable of handling massive amounts of data and supporting a large number of concurrent users. While BigTable and Dynamo were quite influential in their own right, Cassandra was the first technology that went a step ahead of others. By releasing it as an open-source contribution under the Apache License along with publishing the , Facebook was instrumental in enabling access to such technology to the entire industry. paper Cassandra differentiated itself from the previous two by providing a tunable consistency model, allowing users to choose between strong consistency (like BigTable) and eventual consistency (like Dynamo) based on their application requirements. ZooKeeper: Wait-free coordination for Internet-scale systems This introduces Apache ZooKeeper and presents its design principles and algorithms for providing highly reliable and scalable coordination services in distributed systems. Before the introduction of ZooKeeper, software developers often had to implement their own ad-hoc solutions for distributed coordination and consensus in distributed systems. paper ZooKeeper proposed a centralized service for distributed coordination, offering primitives such as distributed locks, leader election, and configuration management This allowed to simplify the development of distributed applications by offloading complex coordination logic to ZooKeeper. One of the most common use cases of using Zookeeper is for service discovery. Apache Kafka: a Distributed Messaging System for Log Processing This introduces Apache Kafka, a distributed messaging system designed for high-throughput, fault-tolerant processing of event streams. Kafka's publication as a research paper and its open-source release as an Apache project established it as a standard messaging system for highly scalable and fault-tolerant real-time data processing and event-driven architectures. paper Kafka introduced a highly scalable and fault-tolerant messaging system designed for handling large volumes of data streams in real time. Kafka was quite influential in enabling the development of Lambda architecture, which combines batch processing and stream processing to handle large volumes of data with low latency and high throughput. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing This introduces Resilient Distributed Datasets (RDDs), the core abstraction in Apache Spark, which enables fault-tolerant, in-memory data processing across distributed clusters. Spark's in-memory execution engine  provides significantly faster performance compared to MapReduce (which has a disk-based execution model), especially for iterative algorithms, machine learning, and interactive analytics. paper These papers cover a broad range of topics in distributed systems, including storage systems, consensus algorithms, fault tolerance, and scalability. Reading them will provide a solid foundation in the principles and practices of building and managing distributed systems. If you're starting your journey in distributed systems and want to learn more, or you're already an expert and simply want to refresh your fundamentals, there is no better way to learn than reading some of these foundational papers on distributed systems.

Unveiling the Architecture: Key Papers to Understand Distributed Systems!

স্থাপত্য উন্মোচন: ডিস্ট্রিবিউটেড সিস্টেম বোঝার মূল কাগজপত্র!

Enthüllung der Architektur: Wichtige Dokumente zum Verständnis verteilter Systeme!

Revelando la arquitectura: ¡Documentos clave para comprender los sistemas distribuidos!

Dévoilement de l'architecture : articles clés pour comprendre les systèmes distribués !

आर्किटेक्चर का अनावरण: वितरित सिस्टम को समझने के लिए महत्वपूर्ण दस्तावेज!

アーキテクチャの解明: 分散システムを理解するための重要な論文!

아키텍처 공개: 분산 시스템을 이해하기 위한 주요 문서!

Revelando a Arquitetura: Artigos Chave para Compreender Sistemas Distribuídos!

Раскрытие архитектуры: ключевые статьи для понимания распределенных систем!

Mimariyi Ortaya Çıkarmak: Dağıtılmış Sistemleri Anlamak İçin Temel Makaleler!

Tiết lộ Kiến trúc: Các bài viết chính để hiểu về Hệ thống Phân tán!

揭开架构的面纱：理解分布式系统的关键论文！

Moving Beyond Self-Esteem For Self-Fulfilment 

স্ব-পরিপূর্ণতার জন্য স্ব-সম্মান অতিক্রম করা

Über das Selbstwertgefühl hinausgehen und sich selbst verwirklichen

Más allá de la autoestima para la realización personal

Aller au-delà de l’estime de soi pour l’épanouissement personnel

आत्म-संतुष्टि के लिए आत्म-सम्मान से आगे बढ़ना

自己実現のために自尊心を超える

자존감을 넘어 자기 실현을 위해

Indo além da autoestima para a autorrealização

Выход за пределы самооценки ради самореализации

Kendini Gerçekleştirmek İçin Benlik Saygısının Ötesine Geçmek

Vượt lên trên lòng tự trọng để hoàn thiện bản thân

超越自尊，实现自我价值

The OpenTelemetry Collector sits at the center of the OpenTelemetry architecture but is unrelated to the W3C Trace Context. In my , I use Jaeger instead of the Collector. Yet, it's ubiquitous, as in every OpenTelemetry-related post. I wanted to explore it further. tracing demo In this post, I explore the different aspects of the Collector: The data kind: logs, metrics, and traces Push and pull models Operations: reads, transformations, and writes First steps A long time ago, as we know it didn't exist; what we had instead was . Back then, monitoring was a bunch of people looking at screens displaying dashboards. Dashboards themselves consisted of metrics and only system metrics: mainly CPU, memory, and disk usage. For this reason, we will start with metrics. observability monitoring is one of the primary monitoring solutions. It works on a pull-based model: Prometheus scrapes compatible endpoints of your application(s) and stores them internally. Prometheus We will use the OTEL Collector to scrape a Prometheus-compatible endpoint and print out the result in the console. Grafana Labs offers a that generates random metrics to play with. For simplicity's sake, I'll use Docker Compose; the setup looks like the following: project version: "3"

services:
  fake-metrics:
    build: ./fake-metrics-generator                                         #1
  collector:
    image: otel/opentelemetry-collector:0.87.0                              #2
    environment:                                                            #3
      - METRICS_HOST=fake-metrics
      - METRICS_PORT=5000
    volumes:
      - ./config/collector/config.yml:/etc/otelcol/config.yaml:ro           #4 No Docker image is available for the fake metrics project; hence, we need to build it Latest version of the OTEL Collector at the time of this writing Parameterize the following configuration file Everything happens here As I mentioned above, the OTEL Collector can do a lot. Hence, configuration is everything. receivers:                                                                  #1
  prometheus:                                                               #2
    config:
      scrape_configs:                                                       #3
        - job_name: fake-metrics                                            #4
          scrape_interval: 3s
          static_configs:
            - targets: [ "${env:METRICS_HOST}:${env:METRICS_PORT}" ]

exporters:                                                                  #5
  logging:                                                                  #6
    loglevel: debug

service:
  pipelines:                                                                #7
    metrics:                                                                #8
      receivers: [ "prometheus" ]                                           #9
      exporters: [ "logging" ]                                              #9 List of receivers. A receiver reads data; it can be either push-based or pull-based. We use the pre-defined receiver prometheus Define pull jobs Job's configuration List of exporters. In contrast to receivers, an exporter writes data. The simplest exporter is to write data on the standard out Pipelines assemble receivers and exporters Define a metric-related pipeline The pipeline gets data from the previously-defined receiver and sends it to the exporter, , prints them prometheus logging i.e. Here's a sample of the result: 2023-11-11 08:28:54 otel-collector-collector-1     | StartTimestamp: 1970-01-01 00:00:00 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Timestamp: 2023-11-11 07:28:54.14 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Value: 83.090000
2023-11-11 08:28:54 otel-collector-collector-1     | NumberDataPoints #1
2023-11-11 08:28:54 otel-collector-collector-1     | Data point attributes:
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__embrace_world_class_systems: Str(concept)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__exploit_magnetic_applications: Str(concept)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__facilitate_wireless_architectures: Str(extranet)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__grow_magnetic_communities: Str(challenge)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__reinvent_revolutionary_applications: Str(support)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__strategize_strategic_initiatives: Str(internet_solution)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__target_customized_eyeballs: Str(concept)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__transform_turn_key_technologies: Str(framework)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__whiteboard_innovative_partnerships: Str(matrices)
2023-11-11 08:28:54 otel-collector-collector-1     | StartTimestamp: 1970-01-01 00:00:00 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Timestamp: 2023-11-11 07:28:54.14 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Value: 53.090000
2023-11-11 08:28:54 otel-collector-collector-1     | NumberDataPoints #2
2023-11-11 08:28:54 otel-collector-collector-1     | Data point attributes:
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__expedite_distributed_partnerships: Str(approach)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__facilitate_wireless_architectures: Str(graphical_user_interface)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__grow_magnetic_communities: Str(policy)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__reinvent_revolutionary_applications: Str(algorithm)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__transform_turn_key_technologies: Str(framework)
2023-11-11 08:28:54 otel-collector-collector-1     | StartTimestamp: 1970-01-01 00:00:00 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Timestamp: 2023-11-11 07:28:54.14 +0000 UTC
2023-11-11 08:28:54 otel-collector-collector-1     | Value: 16.440000
2023-11-11 08:28:54 otel-collector-collector-1     | NumberDataPoints #3
2023-11-11 08:28:54 otel-collector-collector-1     | Data point attributes:
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__exploit_magnetic_applications: Str(concept)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__grow_magnetic_communities: Str(graphical_user_interface)
2023-11-11 08:28:54 otel-collector-collector-1     |      -> fake__target_customized_eyeballs: Str(extranet) Beyond printing The above is an excellent first step, but there's more than printing to the console. We will expose the metrics to be scraped by a regular Prometheus instance; we can add a to visualize them. While it may seem pointless, bear with it, as it's only a stepstone. Grafana dashboard To achieve the above, we only change the OTEL Collector configuration: exporters:
  prometheus:                                                               #1
    endpoint: ":${env:PROMETHEUS_PORT}"                                     #2

service:
  pipelines:
    metrics:
      receivers: [ "prometheus" ]
      exporters: [ "prometheus" ]                                           #3 Add a exporter prometheus Expose a Prometheus-compliant endpoint Replace printing with exposing That's it. The OTEL Collector is very flexible. Note that the Collector is multi-input, multi-output. To both print data and expose them via the endpoint, we add them to the pipeline: exporters:
  prometheus:                                                               #1
    endpoint: ":${env:PROMETHEUS_PORT}"
  logging:                                                                  #2
    loglevel: debug

service:
  pipelines:
    metrics:
      receivers: [ "prometheus" ]
      exporters: [ "prometheus", "logging" ]                                #3 Expose data Print data The pipeline will both print data and expose them With the Prometheus exporter configured, we can visualize metrics in Grafana. Note that receivers and exporters specify their type every one of them must be unique. To comply with the last requirement, we can append a qualifier to distinguish between them, , and and i.e. prometheus/foo prometheus/bar. Intermediary data processing A valid question would be why the OTEL Collector is set between the source and Prometheus, as it makes the overall design more fragile. At this stage, we can leverage the true power of the OTEL Collector: data processing. So far, we have ingested raw metrics, but the source format may not be adapted to how we want to visualize data. For example, in our setup, metrics come from our fake generator, "business", and the underlying NodeJS platform, "technical." It is reflected in the metrics' name. We could add a dedicated source label and remove the unnecessary prefix to filter more efficiently. You declare data processors in the section of the configuration file. The collector executes them in the order they are declared. Let's implement the above transformation. processors The first step toward our goal is to understand that the collector has two flavours: a "bare" one and a contrib one that builds upon it. Processors included in the former are limited, both in number and in capabilities; hence, we need to switch the contrib version. collector:
  image: otel/opentelemetry-collector-contrib:0.87.0                        #1
  environment:
    - METRICS_HOST=fake-metrics
    - METRICS_PORT=5000
    - PROMETHEUS_PORT=8889
  volumes:
    - ./config/collector/config.yml:/etc/otelcol-contrib/config.yaml:ro     #2 Use the flavour contrib For added fun, the configuration file is on another path At this point, we can add the processor itself: processors:
  metricstransform:                                                         #1
    transforms:                                                             #2
      - include: ^fake_(.*)$                                                #3
        match_type: regexp                                                  #3
        action: update
        operations:                                                         #4
          - action: add_label                                               #5
            new_label: origin
            new_value: fake
      - include: ^fake_(.*)$
        match_type: regexp
        action: update                                                      #6
        new_name: $${1}                                                     #6-7
# Do the same with metrics generated by NodeJS Invoke the metrics transform processor List of transforms applied in order Matches all metrics with the defined regexp List of operations applied in order Add the label Rename the metric by removing the regexp group prefix Fun stuff: syntax is $${x} Finally, we add the defined processor to the pipeline: service:
  pipelines:
    metrics:
      receivers: [ "prometheus" ]
      processors: [ "metricstransform" ]
      exporters: [ "prometheus" ] Here are the results: Connecting receivers and exporters A connector is both a receiver an exporter and connects two pipelines. The example from the documentation receives the number of spans (tracing) and exports the count, which has a metric. I tried to achieve the same with 500 errors - spoiler: it doesn't work as intended. and Let's first add a log receiver: receivers:
  filelog:
    include: [ "/var/logs/generated.log" ] Then, we add a connector: connectors:
  count:
    requests.errors:
      description: Number of 500 errors
      condition: [ "status == 500 " ] Lastly, we connect the log receiver and the metrics exporter: service:
   pipelines:
     logs:
       receivers: [ "filelog" ]
       exporters: [ "count" ]
     metrics:
       receivers: [ "prometheus", "count" ] The metric is named , but its value stays at 1. log_record_count_total Logs manipulation Processors allow data manipulation; operators are specialized processors that work on logs. If you're familiar with the Elasticsearch Logstash Kibana stack, they are the equivalent of Logstash. As of now, the log timestamp is the ingestion timestamp. We shall change it to the timestamp of its creation. receivers:
  filelog:
    include: [ "/var/logs/generated.log" ]
    operators:
      - type: json_parser                                                   #1
        timestamp:                                                          #2
          parse_from: attributes.datetime                                   #3
          layout: "%d/%b/%Y:%H:%M:%S %z"                                    #4
        severity:                                                           #2
          parse_from: attributes.status                                     #3
          mapping:                                                          #5
            error: 5xx                                                      #6
            warn: 4xx
            info: 3xx
            debug: 2xx
      - id: remove_body                                                     #7
        type: remove
        field: body
      - id: remove_datetime                                                 #7
        type: remove
        field: attributes.datetime
      - id: remove_status                                                   #7
        type: remove
        field: attributes.status The log is in JSON format; we can use the provided JSON parser Metadata attributes to set Fields to read from Parsing pattern Mapping table Accept a range, , . The operator has a special interpreted value (and similar) for HTTP statuses. e.g. 501-599 5xx Remove duplicated data Logs At this point, we can send the logs to any log aggregation component. We shall stay in the Grafana Labs sphere and use Loki. exporters:
  loki:
    endpoint: "http://loki:3100/loki/api/v1/push" We can also use logs from the collector itself: service:
  telemetry:
    logs: Finally, let's add another pipeline: service:
  pipelines:
    logs:
      receivers: [ "filelog" ]
      exporters: [ "loki" ] Grafana can also visualize the logs. Choose Loki as a datasource. Conclusion In this post, we delved into the OpenTelemetry collector. While it's not a mandatory part of the OTEL architecture, it's a useful Swiss knife for all your data processing needs. In case you're not stuck to a specific stack or don't want to, it's a tremendous help. The complete source code for this post can be found on . GitHub To go further: OpenTelemetry Collector OpenTelemetry Operators Originally at on November 12th, 2023 published A Java Geek

Delving Into OpenTelemetry Collector

ওপেনটেলিমেট্রি সংগ্রাহকের মধ্যে যোগ করা

Eintauchen in OpenTelemetry Collector

Profundizando en OpenTelemetry Collector

Plonger dans le collecteur OpenTelemetry

ओपनटेलीमेट्री कलेक्टर में गहराई से जाना

OpenTelemetry コレクターの詳細を調べる

OpenTelemetry Collector 살펴보기

Aprofundando-se no OpenTelemetry Collector

Углубляемся в сборщик OpenTelemetry

OpenTelemetry Collector'ı İncelemek

Đi sâu vào Bộ thu thập từ xa mở

深入研究 OpenTelemetry Collector

Decentralization happens when the power is distributed among several parties. The more parties in control, the more decentralization —and the higher security, since there’s no single point of failure. Most cryptocurrencies at least try to apply this concept in their internal systems, but you can apply it to yourself too with a multi-signature crypto wallet or address. Imagine having a cryptocurrency wallet that's not just secure but also That's where multi-signature crypto wallets come into play. They require multiple private keys to authorize transactions, making them incredibly safe. It's like having multiple keys to unlock a treasure chest. allows you to share control (and funds) with trusted individuals, almost like a digital joint bank account. To create one, you'll typically need with this function available. Then, you’ll specify the number of signatures required (approvals to use the funds; e.g., 2 out of 3), designate the co-signers, and set up the address. It's like creating a financial trust circle where no one person has all the power, offering peace of mind and added security for your digital assets. a wallet app Multi-Sig Addresses Potential Use Cases Whether you're looking for added security, sharing financial responsibilities, or planning for the future, a multi-signature wallet is a versatile and secure tool for managing your digital assets. Let’s see some use cases. Multi-signature wallets may require multiple private keys to authorize transactions. This means that even if one key is compromised, your funds remain safe. It's like a digital fortress guarding your assets! Enhanced Security: If you're running a business and want to ensure no single person has complete control over the company's funds, a multi-signature wallet is perfect. It requires multiple people to approve transactions, making it ideal for partnerships. Business Partnerships: Managing family finances? A multi-signature wallet allows you and your family members to have shared control over funds while still requiring a consensus to spend money. It's like a digital piggy bank everyone can access, but only with permission. Family Finances: Planning for the future? You can set up a multi-signature wallet with trusted family members, friends, or even some lawyers. This way, your loved ones can access your assets in case of unforeseen events, but not without a consensus. Secure Inheritance: Multi-signature wallets are used for escrow services in various transactions. It ensures that the seller gets paid only when the buyer confirms the product or service has been delivered as promised. It's like having a digital arbiter in your transactions. In Obyte, you can also use the __ __to do this and get human help in case of any issue with your trade. Escrow Services: ArbStore You can also be “all the signers”, by using different devices. This would be done to increase your wallet security because no one (not even yourself) would be able to spend your funds without approval from several “parties” (for instance, your laptop and mobile phone). This feature is also available in the . 2FA Authentication: Obyte wallet How to Create a Multi-Sig Address in Obyte First of all, you’ll need to download the Obyte wallet app. This one is available for desktop (Windows, Mac, and Linux), and mobile (Android and iOS). If you want to be “all the signers” of your multi-sig address(es), you’ll need to download the app to every device involved and pair all of them. and back up Then, you can either send an invitation yourself (in the form of a QR code) or accept an invitation received by the other party. Once paired, the name of the new device should appear in your Contact list. To pair a new device, either yours or controlled by another co-signer, you need to go to the Chat tab – Contacts – Add new device. Next, in the Main Menu of the wallet (three parallel lines in the upper left corner), you’ll find the option to and set up your co-signers. They need their own Obyte wallets/devices previously paired with yours to appear in the list of options. Click on “Create account,” and that’s it! “Add account/Create New account.” Select “Multidevice account,” The multidevice account will have its own Obyte address to send and receive funds and will appear separately in the main menu. Now, if configured that way, every time one of the signers tries to move funds from there, most of the other signers will receive a notification on their own devices asking for approval. This way, the coins will be spent only if there’s consensus among signers. On the other hand, The required co-signers would be 1-of-2 or 1-of-3, assuring that one device approving transactions is enough. If you configure it this way, no approval from other devices is required. you could also use your multidevice account only to have access to your funds from multiple devices at the same time (e.g., laptop and phone). simplifies multi-signature security, ensuring that your crypto assets are both easy to manage and exceptionally secure. It's the smart choice for those seeking peace of mind in the world of digital currencies. Obyte Featured Vector Image by vector4stock / Freepik

Educational Byte: What Is a Multi-Signature Wallet and What Can You Do With It?

শিক্ষাগত বাইট: একটি মাল্টি-সিগনেচার ওয়ালেট কী এবং আপনি এটি দিয়ে কী করতে পারেন?

Educational Byte: Was ist ein Multi-Signatur-Wallet und was kann man damit machen?

Byte educativo: ¿Qué es una billetera multifirma y qué se puede hacer con ella?

Octet éducatif : qu'est-ce qu'un portefeuille multi-signatures et que pouvez-vous en faire ?

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In this era, we’re exploring cyberspace almost as much as the physical world, so we need to pay attention to our security online as well. A lot of potential threats, including malware and exploits, are hiding around our favorite digital spaces and applications. Luckily, there are also a lot of cybersecurity tools available, and we can use some of them for free. Numerous open-source developers have taken the time (and often their own funds) to build these tools for everyone, so, they’d surely appreciate a contribution —in the form of time if you’re another developer or writer, or in the form of donations for everybody else. , an Obyte-based cascading-donation platform, can make your crypto gifts flow across several repositories and reduce any kind of censorship or high fees. Kivach This application only asks for the GitHub profile to send some cryptocurrencies, even if the recipient doesn’t have a wallet yet. Therefore, if you want to donate to an open-source project available on GitHub, you can use Kivach as a decentralized, globally available, unstoppable, as well as faster and cheaper option. In this new episode of our Kivach series, we’ll discover some handy cybersecurity tools available on GitHub to download and use for free. Let’s start! OWASP ModSecurity ModSecurity, born out of Trustwave's SpiderLabs in 2002, is a powerful application firewall (WAF) engine that provides robust protection against a spectrum of web-based attacks. Initially developed as an Apache HTTP Server module, it has since expanded to offer filtering capabilities across diverse platforms like Microsoft IIS and Nginx. Its event-based programming language facilitates HTTP traffic monitoring, logging, and real-time analysis, ensuring heightened security for web applications. Website owners can use it to keep their online spaces safe and secure at every moment, since the app is always on the lookout, analyzing all the traffic. If it spots anything fishy, it jumps into action, blocking those harmful attacks in real time and keeping a record of everything that happens. In other words, is like a digital guardian for websites, protecting them from harmful attacks that could otherwise wreak havoc. this software In February 2024, Trustwave the custodianship of the software to the Open Worldwide Application Security Project (OWASP) —an NGO dedicated to improving cybersecurity through open-source development. This way, ModSecurity is now fully in the hands of its community. It’s possible to contribute to its development via GitHub or donate through its website with fiat currencies. To send them some crypto coins, you can use Kivach, where they appear as . entrusted spiderlabs/owasp-modsecurity-crs Qubes OS Have you ever thought that your commercial operating system (OS), like Windows or MacOS, couldn’t be entirely secure or private by itself? The team of Qubes certainly did it, and that’s why they launched this security-focused OS in 2012, led by Marek Marczykowski-Górecki. Over the years, the project has garnered support from a wide range of users, including journalists, activists, whistleblowers, researchers, and renowned security experts like Edward Snowden and Micah Lee. Unlike traditional operating systems, relies on a concept called compartmentalization, where different parts of the system are isolated from each other like rooms in a building. Qubes OS This isolation helps prevent cyberattacks by limiting the damage that can be caused if one part of the system is compromised. Other key features of Qubes OS include its support for multiple operating systems simultaneously, disposable virtual machines that self-destruct when shut down, a specialized proxy, device isolation for secure handling of network cards and USB controllers, and integration with Whonix for anonymous browsing using Tor. Funding for Qubes OS primarily comes from community support and donations. This ensures that the software remains free, secure, and open-source. Besides credit cards or PayPal, they accept Bitcoin, Ethereum, and Monero. You can also donate to them . via Kivach Nikto Launched in 2001 by Chris Sullo, Nikto is a popular open-source web server scanner. among other tasks. Its comprehensive testing extends to scrutinizing server configurations, detecting the presence of multiple index files, and endeavoring to identify installed web servers and software. It works to conduct thorough examinations of web servers, scrutinizing over 6,700 potentially perilous files/programs and identifying outdated versions of over 1,250 servers, In simpler terms, is to carefully examine every nook and cranny of these web servers to uncover any potential risks or vulnerabilities. It's like scanning the entire house to make sure there are no unlocked doors or windows that hackers could exploit. However, it's worth noting that not every finding uncovered by Nikto is cause for alarm. Some checks serve to provide informative insights, alerting users to elements that might not necessarily pose a security threat but could still benefit from attention. Nikto's job The project is currently maintained by Sullo and David Lodge, besides a community of volunteers on GitHub. Sullo accepts donations via Patreon, which could be kind of expensive and non-private for contributors —not to mention the fees taken from Sullo by the platform. You can donate some coins via Kivach instead, where the project appears as . sullo/nikto Sanesecurity (& ClamAV) Sanesecurity is an organization that has been actively involved in enhancing the effectiveness of ClamAV, a free and open-source antivirus toolkit (owned by Cisco). Since its inception in 2006, particularly concerning macro malware, JavaScript malware, phishing attempts, spam, and other forms of emailed ransomware. Sanesecurity has been providing with hourly updated malware signatures aimed at improving its detection capabilities, ClamAV These signatures are unique identifiers or characteristics that help the antivirus software recognize and detect specific instances of malware. They’re meticulously crafted by the dedicated team at , are of professional quality, and undergo regular updates throughout the day to ensure their relevance and efficacy. This way, Sanesecurity plays a pivotal role in enriching ClamAV's native capabilities. Sanesecurity Unlike commercial entities, Sanesecurity operates as a community-driven initiative, with the sole objective of enhancing ClamAV's functionality (often criticized without this kind of add-on) and bolstering web community security. The team behind Sanesecurity is comprised of volunteers who contribute their time and expertise to produce and maintain these signatures in their spare time. To sustain their efforts, Sanesecurity relies on donations from users who benefit from their signatures. Donations can be made in fiat money through PayPal or credit card, with options for one-time contributions or monthly/yearly subscriptions. On the other hand, Kivach is a decentralized and inexpensive option to send them funds. They appear there as . Sanesecurity/bad3cx x64dbg This one is aimed at programmers and researchers, but it’s equally useful and free in the realm of cybersecurity. In case you don’t know, a debugger is a software tool used by developers and analysts to examine and manipulate the behavior of computer programs during execution. It allows users to step through the code line by line, inspect variables and memory contents, set breakpoints to pause execution at specific points and analyze program state to identify bugs or understand how the program operates. is an open-source binary debugger specifically designed for Windows operating systems. It’s tailored for malware analysis and reverse engineering tasks, particularly for analyzing executable files for which the source code isn’t available. With an intuitive and user-friendly interface, x64dbg offers a comprehensive set of features, including full-featured debugging of both DLL and EXE files, a plugin system, disassembly of code, memory mapping, symbol and thread views, and a graph view to visualize program flow. x64dbg As an open-source project under active development, x64dbg encourages community contributions and donations through GitHub Sponsors. Of course, it’s also available on Kivach as . x64dbg/x64dbg Tell them you donated! Likely, the recipients of your donations via Kivach don’t know about it until you tell them, so don’t forget to communicate with them. with a simple attestation in the same app. Then, they should be able to receive and transfer any cryptocurrency sent in the name of that profile. To claim a donation on this platform, they’ll need to download the and verify their GitHub profile Obyte wallet If you want to discover other interesting projects to use for free and optionally donate to, please check our previous chapters in this series. Kivach Donations: 5 GitHub projects it has helped so far How You Can Support Key Open-Source Initiatives With Kivach 5 Open-source Projects You Can Support With Kivach — and Cryptocurrencies 5 Open-source Projects You Can Support with Kivach and Cryptos, Episode III 5 Open-Source Projects You Can Donate to via Kivach, Episode IV: Privacy Tools 5 Open-source Blogging and Writing Tools to Donate to via Kivach (Ep V) 5 Open-Source Tools You Can Donate to via Kivach, Episode VI: Decentralized Services 5 Open-Source Projects to Donate via Kivach, Episode VII: Games to Play for Free! Featured Vector Image by Freepik

5 Cybersecurity Tools to Use for Free and Donate to Via Kivach

বিনামূল্যে ব্যবহার করার জন্য 5টি সাইবার নিরাপত্তা সরঞ্জাম এবং কিভাচের মাধ্যমে দান করুন৷

5 Cybersicherheitstools, die Sie kostenlos nutzen und an Via Kivach spenden können

Cinco herramientas de ciberseguridad para usar de forma gratuita y donar a Via Kivach

5 outils de cybersécurité à utiliser gratuitement et à faire un don à Via Kivach

5 साइबर सुरक्षा उपकरण जिन्हें आप निःशुल्क उपयोग कर सकते हैं और वाया किवाच को दान कर सकते हैं

無料で使用でき、Kivach に寄付できる 5 つのサイバーセキュリティ ツール

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5 ferramentas de segurança cibernética para usar gratuitamente e doar para Via Kivach

5 инструментов кибербезопасности, которые можно использовать бесплатно и делать пожертвования через Kivach

Ücretsiz Olarak Kullanabileceğiniz ve Via Kivach'a Bağış Yapabileceğiniz 5 Siber Güvenlik Aracı

5 công cụ an ninh mạng có thể sử dụng miễn phí và quyên góp cho Via Kivach

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Computer-aided design (CAD) software revolutionized design, replacing drafting tables with sophisticated technology that streamlines design processes for engineers and designers. But the story doesn't end there. Can new advancements in AI and blockchain technology push the boundaries of CAD even further? In this interview, CEO and Co-Founder of CADAICO explored the intersection between CAD and such frontline technologies as AI and blockchain. Pedram Shahid, Mr. Shahid, can you briefly tell us about yourself and your route to web3? Absolutely, I'm a 43-year-old mechanical engineer with a rich 16-year experience in engineering firms across Germany. In my recent role as the Head of Industrial Engineering at Festo SE's sizable production facility, I observed firsthand the considerable amount of time my design engineers dedicated to manual tasks in CAD software. It was three years ago that this observation sparked the concept of integrating AI within CAD systems to significantly enhance engineering productivity. As I couldn't find a solution on the market, I departed from my position at Festo SE last year to establish CADAICO. In the initial months, I aligned with my first collaborator, the blockchain enterprise ONINO, gaining substantial insights into blockchain technology and its potential applications. This period was crucial for crystallizing the comprehensive vision for the CADAI platform, inclusive of its proprietary blockchain. What is CAD, and what makes it a sophisticated tool? Computer-aided design (CAD) is a pivotal tool across numerous industries, facilitating the creation of 3D models for objects. Widely utilized in fields such as mechanical engineering, architecture, and automotive manufacturing, CAD software enables precision and innovation in design. Each CAD software developer offers unique features specific to its product, leading to a lack of compatibility across different CAD systems. This disparity necessitates that designers invest considerable time and effort in learning and mastering these complex software programs, each distinct in its operation and capabilities. What advantages does using CAD software provide over traditional practices? Before the advent of CAD, engineers and designers relied heavily on manual drafting to sketch everything by hand, a process that was both time-intensive and prone to errors. The emergence of CAD revolutionized the design process, significantly enhancing productivity by automating and streamlining the creation of digital 3D models. Today, CAD has become the foundational tool across the engineering spectrum, indispensable in sectors including automotive, mechanical engineering, architecture, medical engineering, and electronics, setting a new standard for precision and efficiency in design. What are the challenges that designers often face with CAD software? Despite advancements in CAD technology, the design process remains predominantly manual and time-consuming. Remarkably, only about 25% of an engineer's design work leverages their creativity, with the bulk of their tasks being repetitive and routine. Mechanical engineering companies typically possess vast repositories of existing CAD data that could be repurposed for new designs. However, the challenge lies in efficiently locating the appropriate files within extensive archives containing thousands of entries. This situation underscores a significant opportunity for innovation in streamlining access to and utilization of existing CAD resources. How does CAD intersect with AI and blockchain? Leveraging the extensive CAD data for AI training, in conjunction with the high standards of security and transparency provided by blockchain technology, offers an ideal solution to the productivity challenges in the CAD design process. This combination not only promises to streamline workflow but also ensures data integrity and enhances collaborative possibilities, marking a significant leap forward in engineering design efficiency. This innovative approach is about augmenting the capabilities of engineers, not replacing them. By automating routine tasks, AI enables engineers to dedicate more time to creative and complex problem-solving tasks. Thus, this fusion of AI and blockchain doesn't diminish the role of engineers; rather, it elevates their work, freeing up valuable time for innovation and design excellence. Can you share the story behind the founding of CADAICO? Do you know what the most important thing is when you start a new business from scratch? Everything at the same time. The cornerstone of such an endeavor encompasses a clear vision, a dedicated team, sufficient funding, and strategic partnerships. My journey with CADAICO commenced solely with a vision. Progressively, I embarked on forging partnerships, securing funding, and assembling a team, one step at a time. My highlight was meeting Daniel by chance at a startup event in Karlsruhe. I overheard him talking to someone about his background in machine learning and approached him. We talked for hours and completely forgot about the event. He is now co-founder and CTO of CADAICO. From my findings, you recently received a backing from the German state of Baden Wurttemberg. How does this translate to progress? For me personally, it's not just about the financial support. What really matters is the nod we got from the tough judges. Being chosen as one of the few startups is a big deal and tells me we're doing the right thing. Plus, this kind of support is going to help us get noticed more and find good partners to work with. CADAICO claims to be the first to combine AI and blockchain for a collaborative CAD development platform. What does this entail? I have been looking around the market for the past three years to see who our competitors might be. I found a few startups trying to use AI to make CAD better in ways that are somewhat similar to ours. However, none of them are working on creating an AI platform that lets developers build many AI-driven apps and plugins specifically for CAD. Also, none of them are using blockchain to make sure these platform features are secure and well-managed. Right now, CADAICO stands out as the only company that's merging CAD with the latest in AI technology and blockchain, all on a platform made for developers. On the other hand, I genuinely hope to see more activity in this field. Collaboratively, we could significantly extend the limits of what's achievable, pushing the boundaries of innovation and efficiency in CAD development and application even further. Any Parting Words? Absolutely, I'm excited to share our vision with you. CADAICO is set on ushering in the next leap in the design process, a significant evolution since the inception of CAD. Looking ahead, we envision a future where engineers won't be tied down to weeks of meticulous drawing in CAD. Instead, they'll be interacting with AI assistants, swiftly bringing their 3D designs to life in mere minutes. Furthermore, I want to extend my deepest gratitude to our supportive community, which has been our backbone from the start. A special thank you goes out for the overwhelming support we received during our public sale.

AI And Blockchain In Computer Aided Design: Exclusive Interview With CADAICO CEO Pedram Shahid

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