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This paper proposes a real-time approach to detect the 2D pose of multiple people in an image. OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. Human pose estimation is focused on finding individuals and their pose from an image. This problem is quite challenging and is quite complex to determine the number of people that can appear in an image. Moreover, the interferences caused due to contact or proximity between people make the association of parts quite challenging. The current state-of-the-art techniques use a top-down strategy that estimates the pose of each person in each of the detected regions. This approach suffers from performance issues because the run time is directly proportional to the number of people in the image. The method used in the paper uses a nonparametric representation called Part Affinity Fields (PAFs). This approach uses the location and orientation of body parts to encode unstructured pairwise relationships between the body parts of the individuals in the image. In the earlier version of the same paper, they refined both the confidence maps and the part affinity fields (PAFs) at each stage using a branched multistage Convolutional neural network (CNN) and this resulted in much more computation and time at each stage. [1] This paper focused on refining the existing techniques and the architecture of the CNN [1]. They found that PAF refinement is crucial for maximising accuracy, while body part prediction refinement is not that important. In the paper, the author used the first 10 layers of the VGG-19 model as a baseline network to extract feature maps. These feature maps are input to the model to derive the PAFs that define the degree of association between the parts and the confidence maps, which are then refined using the greedy approach. The paper OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields is published by Zhe Cao, Student Member, IEEE, Gines Hidalgo, Student Member, IEEE, Tomas Simon, Shih-En Wei, and Yaser Sheikh under IEEE Transactions on Pattern Analysis and Machine Intelligence journal. Summary of the work, results, and findings In the first stage, the feature maps of the input image are processed by passing it through a baseline network. In this paper, the author uses the first 10 layers of the VGG-19 model for generating the feature maps. Then the feature maps are processed with multiple stages of CNN to generate a set of Part Confidence Maps and a set of Part Affinity Fields. The system uses a multi-stage CNN. It consists of two types of stages. The first set of stages predicts part affinity fields i.e., degree of associations between the body parts the last set of stages predicts the confidence maps. By replacing each 7x7 convolutional kernel with 3 consecutive 3x3 kernels, the current model was able to preserve the receptive field while computation expense is reduced. Using an approach like DenseNet [11] the output of the 3 convolution kernels is concatenated. This approach tripled the non-linearity layers and helped the model to keep both higher and lower-level features. A loss function is applied at the end of every step in both branches. Finally, the Confidence Maps and Part Affinity Fields are processed by a greedy algorithm where optimal partite graph matching is used to obtain the poses for each person in the image. The results and findings are presented, and the accuracy of the multi-person keypoint detection has been compared to the existing methods [2][3]. The real-time performance metric for the system has been specified by mentioning the number of frames it can process each second. All findings are visualized, and graphs are provided for better understanding. The author finds that the newly refined approach increased both the speed and accuracy by approximately 200% and 7% respectively. The paper contributed to the field of foot detection by presenting an annotated foot dataset with 15K human foot instances that have been publicly released for further study and development. The author open-sourced their work and the OpenPose system provides easy-to-use pipelines with command-line interfaces, python API, Unity plugin. It has also been included in the OpenCV library for ease of access. The system runs on CPUs and GPUs such as CUDA and OpenCL. They also provide portable executable binaries which can easily be downloaded and run with minimal setup. Pose-ShuffleNet is a lightweight multi-person pose estimation method developed by integrating the concepts of this paper [4]. This method is highly suited for resource-constrained scenarios. It is based on the building blocks of the ShuffleNet unit [5] for image analysis in place of VGG-19. An architecture of multi-stages with two branches, jointly learning parts detection and parts association has been used. Pose-ShuffleNet has a significantly lower resource (network size and FLOPs) consumption than some of the available leading methods. Using the concepts of OpenPose the research has been further extended to the 3D human pose estimation [6]. Further research on Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation has been carried out. Overall, the proposed pipeline provides promising results on the intention recognition of vulnerable road users. [7] Pros and Cons of the paper: Explanation with pictorial references clarifies the topic in a better way and was effective in clarifying relationships, simplifying complex topics, and was one of the main key factors that had me inclined toward the paper. All the edge cases have been acknowledged and future actions that can mitigate the failure cases have also been suggested. The accuracy and performance of the combined model with body and foot key points is also an important contribution to the paper. The generality of the method by applying it to the task of vehicle key point estimation and the use of the approach has contributed to the wide applications and competitive performance of the model. The paper also showcased three benchmarks performed on reputed and easily available datasets such as MPII [9], COCO [10], and a subset of COCO used as a foot dataset. And provide runtime analysis comparison against Mask R-CNN and Alpha-Pose to quantify the efficiency of the system and analyses the main failure cases. The model performs up to 3 times faster on GPUs when compared to CPUs and in the case of the body and foot model the performance difference is up to 30 times. Making the system more optimised on CPUs can help to make the model more accessible in smaller and less powerful systems and increase the use cases. [8] Potential Advancement and Future Work There have been many advancements in the field of pose estimation, and it is being used in various fields and applications, but at present, the practical applications are limited due to the challenges of crowded people and occluded body parts. A hybrid system of both top-down and bottom-up approaches along with Using anthropometric ratios for determining context can make the model more efficient [7]. Future developments can be made to generate a similar performance on CPUs using lightweight CNNs such as MobileNetV1, EfficientNet or a combination and can help the system be more efficient so that it is less reliant on GPUs and work on embedded systems. It can have various applications in security cameras, child monitors, automating warehouses, gesture control etc. Data plays a key role in improving the robustness of the network. Adding complex scenarios of human poses along with samples of human statues and paintings would boost the accuracy by avoiding the problem of occluded body parts. Improvement in the greedy approach can help to make the pose estimation more accurate. The greedy approach used in the current system suffers from limited structural context when subjects are occluded. Also, the Hungarian algorithm [8] employed is having a cubic complexity. By using a larger structural context of a subset of pre-assigned parts rather than using only the immediate predecessors as in [7], this time complexity can become linear in the number of candidates of any single part-class. Refining the algorithm to reject less likely or complex poses and adding an animal dataset to train the system can have wide applications in pest control, animal farming, analysis of the bees as shown in Honeybee Detection and Pose Estimation using Convolutional Neural Networks [10]. References [1] Cao, Zhe, et al. "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [2] Fang, Hao-Shu, et al. "Rmpe: Regional multi-person pose estimation." Proceedings of the IEEE international conference on computer vision. 2017. [3] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017. [4] Guan, Chen-zhi. "Realtime multi-person 2d pose estimation using shufflenet." 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, 2019. [5] Zhang, Xiangyu, et al. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [6] Schwarcz, Steven, and Thomas Pollard. "3d human pose estimation from deep multi-view 2d pose." 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. [7] Fang, Zhijie, and Antonio M. López. "Intention recognition of pedestrians and cyclists by 2d pose estimation." IEEE Transactions on Intelligent Transportation Systems 21.11 (2019): 4773-4783. [8] Osokin, Daniil. "Real-time 2d multi-person pose estimation on cpu: Lightweight openpose." arXiv preprint arXiv:1811.12004 (2018). [9] Andriluka, Mykhaylo, et al. "2d human pose estimation: New benchmark and state of the art analysis." Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. 2014. [10] Rodríguez, I. F., et al. "Honeybee detection and pose estimation using convolutional neural networks." Congres Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP). 2018. [11] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 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Reviewing “OpenPose - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”

Integrating North American Credit Models into Asian and Middle Eastern Educational Systems

Solos: A Dataset for Audio-Visual Music Analysis-  Conclusions and References

সোলোস: অডিও-ভিজ্যুয়াল মিউজিক অ্যানালাইসিসের জন্য একটি ডেটাসেট- উপসংহার এবং রেফারেন্স

Solos: Ein Datensatz für audiovisuelle Musikanalyse – Schlussfolgerungen und Referenzen

Solos: un conjunto de datos para el análisis de música audiovisual: conclusiones y referencias

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Conclusions et références

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट- निष्कर्ष और संदर्भ

Solos: オーディオビジュアル音楽分析のためのデータセット - 結論と参考文献

솔로: 시청각 음악 분석을 위한 데이터 세트 - 결론 및 참고 자료

Solos: um conjunto de dados para análise musical audiovisual - conclusões e referências

Соло: набор данных для аудиовизуального анализа музыки – выводы и ссылки

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - Sonuçlar ve Referanslar

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn- Kết luận và tài liệu tham khảo

Solos：视听音乐分析数据集 - 结论和参考文献

Authors:
(1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu};
(2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu};
(3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro
Related work
Dataset
Experiments
Conclusions and References III. DATASET Solos[1] was designed to have the same categories as the URMP [1] dataset, so that URMP can be used as testing dataset in a real-world scenario. This way we aim to establish a standard way of evaluating source separation algorithms’ performance avoiding the use of mix-and-separate in testing. Solos consists of 755 recordings distributed amongst 13 categories as shown in Figure 1, with an average amount of 58 recordings per category and an average duration of 5:16 min. It is interesting to highlight that, for 8 out of 13 categories, the median of resolution is HD, despite being a YouTube-gathered dataset. Per-category statistics can be found in Table I. These recordings were gathered by querying YouTube using the tags solo and auditions in several languages such as English, Spanish, French, Italian, Chinese or Russian. A. OpenPose Skeletons Solos is not only a set of recordings. Apart from the videos identificators We also provide: i) body and hand skeletons estimated by OpenPose [33] in each frame of each recording and ii) timestamps indicating useful parts. OpenPose is a system capable to predict body skeleton and hands skeletons making use of two different neural networks. To do so, they predict a confidence map of the belief that a specific body part may be located at any given pixel as well as part affinity fields which encode the degree of association between different body parts. Finally, it predicts 2D skeletons and per-joint confidence via greedy inference. In practice, the body skeleton is estimated with a first network. Then, the position of the wrists in the body skeleton are used to estimate the position of both hands. A second neural network obtains the skeleton of each hand independently. Note that since each body part is estimated independently, OpenPose makes no assumptions about the limbs to find. It just calculates the most likely skeleton given confidence maps and part affinity fields. The whole process is carried out frame-wise. This leads to a small flickering and mispredictions between frames. B. Timestamps estimation and skeleton refinement OpenPose maps mispredicted joints to the origin of coordinates. We empirically found that such a big jump in the position of a joint induces noise. Using interpolated coordinates helps to address this problem. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [1] Dataset available at https://juanfmontesinos.github.io/Solos/ Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Authors: Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro Related work Dataset Experiments Conclusions and References Abstract and Intro Abstract and Intro Related work Related work Dataset Dataset Experiments Experiments Conclusions and References Conclusions and References III. DATASET Solos[1] was designed to have the same categories as the URMP [1] dataset, so that URMP can be used as testing dataset in a real-world scenario. This way we aim to establish a standard way of evaluating source separation algorithms’ performance avoiding the use of mix-and-separate in testing. Solos consists of 755 recordings distributed amongst 13 categories as shown in Figure 1, with an average amount of 58 recordings per category and an average duration of 5:16 min. It is interesting to highlight that, for 8 out of 13 categories, the median of resolution is HD, despite being a YouTube-gathered dataset. Per-category statistics can be found in Table I. These recordings were gathered by querying YouTube using the tags solo and auditions in several languages such as English, Spanish, French, Italian, Chinese or Russian. A. OpenPose Skeletons A. OpenPose Skeletons Solos is not only a set of recordings. Apart from the videos identificators We also provide: i) body and hand skeletons estimated by OpenPose [33] in each frame of each recording and ii) timestamps indicating useful parts. OpenPose is a system capable to predict body skeleton and hands skeletons making use of two different neural networks. To do so, they predict a confidence map of the belief that a specific body part may be located at any given pixel as well as part affinity fields which encode the degree of association between different body parts. Finally, it predicts 2D skeletons and per-joint confidence via greedy inference. In practice, the body skeleton is estimated with a first network. Then, the position of the wrists in the body skeleton are used to estimate the position of both hands. A second neural network obtains the skeleton of each hand independently. Note that since each body part is estimated independently, OpenPose makes no assumptions about the limbs to find. It just calculates the most likely skeleton given confidence maps and part affinity fields. The whole process is carried out frame-wise. This leads to a small flickering and mispredictions between frames. B. Timestamps estimation and skeleton refinement B. Timestamps estimation and skeleton refinement OpenPose maps mispredicted joints to the origin of coordinates. We empirically found that such a big jump in the position of a joint induces noise. Using interpolated coordinates helps to address this problem. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [1] Dataset available at https://juanfmontesinos.github.io/Solos/

Solos: A Dataset for Audio-Visual Music Analysis -  Dataset

সোলোস: অডিও-ভিজ্যুয়াল সঙ্গীত বিশ্লেষণের জন্য একটি ডেটাসেট - ডেটাসেট

Solos: Ein Datensatz für die audiovisuelle Musikanalyse - Datensatz

Solos: un conjunto de datos para el análisis de música audiovisual - Conjunto de datos

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Ensemble de données

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट - डेटासेट

Solos: オーディオビジュアル音楽分析のためのデータセット - データセット

Solos: 시청각 음악 분석을 위한 데이터세트 - 데이터세트

Solos: um conjunto de dados para análise musical audiovisual - conjunto de dados

Соло: набор данных для аудиовизуального анализа музыки - Набор данных

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - Veri Kümesi

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn - Bộ dữ liệu

Solos：用于视听音乐分析的数据集 - 数据集

Plunging Into Data: Unraveling Time Series Patterns

How Understanding CPU Caches Can Supercharge Your Code: 461% Faster MatMul Case Study

The world of work is changing––rapidly. Thanks to the advent and rapid adoption of generative AI, tech stocks had a bumper 2023, with the share price of the Big Five ( , , , , and ) leaping by 60% from the start of that year. 2023 was also a , which saw its shares triple in price. Alphabet Amazon Apple Meta Microsoft smash success for chipmaker, Nvidia And while adoption isn’t even across the board yet, corporate America is sitting up and taking notice. At JPMorgan Chase, for example, the company said it had 600 machine-learning engineers, with AI working on more than 300 different internal applications. A recent found that 71% of C-suite leaders said that advancing their use of AI is either a high or medium priority for their organization, with 62% of executives noting that they are worried they’re not moving fast enough with AI. study from UKG That has led to some companies doing “AI washing”, aka exaggerating the amount of AI technology they use in their products or services. 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HackerNoon Job Board DataAnnotation is seeking a to train its AI chatbots to code. This flexible, part-time or full-time role allows you to choose the projects you want to work on, involves coming up with diverse problems and solutions for a coding chatbot, writing high-quality answers and code snippets, and evaluating the code quality produced by the AI model. You will need to be proficient in at least one programming language and able to solve coding problems (LeetCode, HackerRank). Software Developer Adobe is looking for a in San Jose. The salary range for this position is $135,200 - $250,900, and you will have either research or industry experience in building scalable data and ML platforms. In this role, you will have the opportunity to build a new GenAI foundation to change the creative landscape and will collaborate with data platform engineers and architects to seamlessly integrate low latency data pipelines into the ML platform for model training, as well as writing high quality, product level code that is easy to maintain and test following standard methodologies. Other tasks include designing and implementing reusable and scalable data-loading framework to support video and audio foundational model training in large-scale and distributed environments. Machine Learning Engineer Python programmers looking for a new role can consider this role at SAIC in Chantilly. You’ll be experienced, results-oriented, and mission-driven, with a specialized focus on data engineering in order to perform data model design, data formatting, and ETL development optimized for efficient storage, access, and computation in support of national security objectives. A solid understanding and experience with developing, automating, and enhancing all parts of the data pipeline to include ingestion, processing, storage, and exposing data for consumption is required, along with a focus on improving inefficient tooling and adopting new transformative technologies while maintaining operational continuity. Python Developer Want a great new tech role? Discover it now on the HackerNoon Job Board right By Kirstie McDermott

MIT Has Released a Bunch of Free Courses: Discover 5 To Boost Your Tech Career

এমআইটি একগুচ্ছ বিনামূল্যের কোর্স প্রকাশ করেছে: আপনার কারিগরি ক্যারিয়ারকে উন্নত করতে 5 আবিষ্কার করুন

Das MIT hat eine Reihe kostenloser Kurse veröffentlicht: Entdecken Sie 5, um Ihre Tech-Karriere voranzutreiben

El MIT ha lanzado una serie de cursos gratuitos: descubra 5 para impulsar su carrera tecnológica

Le MIT a publié un certain nombre de cours gratuits : découvrez-en 5 pour booster votre carrière technologique

एमआईटी ने कई मुफ्त पाठ्यक्रम जारी किए हैं: अपने तकनीकी करियर को बढ़ावा देने के लिए 5 पाठ्यक्रमों के बारे में जानें

MIT が無料コースを多数公開: テクノロジーのキャリアを伸ばす 5 つのコースをご紹介します

MIT가 다양한 무료 강좌를 출시했습니다: 기술 경력을 향상하는 5가지를 찾아보세요

O MIT lançou vários cursos gratuitos: descubra 5 para impulsionar sua carreira tecnológica

MIT выпустил несколько бесплатных курсов: откройте для себя 5, которые помогут улучшить вашу техническую карьеру

MIT Bir Çok Ücretsiz Kurs Yayımladı: Teknoloji Kariyerinizi Yükseltecek 5 Kursu Keşfedin

MIT đã phát hành một loạt khóa học miễn phí: Khám phá 5 khóa học để thúc đẩy sự nghiệp công nghệ của bạn

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MinIO is frequently used to store data from logging, metrics and trace data whether it be ElasticSearch, OpenTelemetry, OpenSearch, OpenObserve or any of the other dozen or so great monitoring solutions. MinIO is more efficient when used with storage tiering, which decreases total cost of ownership for the data stored, plus you get the added benefits of writing data to MinIO that is , and protected by . In addition, saving data to MinIO object storage makes it available to other cloud native machine learning and analytics applications. immutable versioned erasure coding Quickwit and MinIO share a lot of the same principles. Quickwit is designed for sub-second search straight from object storage allowing true decoupled compute and storage. This means you can store your data on cheap commodity hardware, while MinIO handles the and Integrity of the data. As your needs and requirements change, you scale out your cluster as needed. Quickwit has concepts of Tenants similar to MinIO that are easily isolated and can manage their individual usage. Replication In today’s post we’ll show you how to setup MinIO and Quickwit with a specific focus on Configuring MinIO as a storage provider for Quickwit Set up MinIO as a metadata store for Quickwit Installing MinIO In a previous blog we discussed how to configure service. We’ll use the same principles here except instead of a binary it will be installed as an OS package. MinIO as a SystemD Install the MinIO .deb package. If you are using another OS family you can find other packages here root@aj-test-1:~# wget https://dl.min.io/server/minio/release/linux-amd64/archive/minio_20231120224007.0.0_amd64.deb -O minio.deb

root@aj-test-1:~# dpkg -i minio.deb Create a user and group and , respectively minio-user minio-user root@aj-test-1:~# groupadd -r minio-user
root@aj-test-1:~# useradd -M -r -g minio-user minio-user Create the data directory for MinIO and set the permissions with the user and group created in the previous step root@aj-test-1:~# mkdir /opt/minio

root@aj-test-1:~# chown minio-user:minio-user /opt/minio Enable and Start MinIO service root@aj-test-1:~# systemctl enable minio

root@aj-test-1:~# systemctl start minio You can verify MinIO is running either through the console by going to or through mc admin http://localhost:9001 root@aj-test-1:~# wget https://dl.min.io/client/mc/release/linux-amd64/mc

root@aj-test-1:~# chmod +x mc

root@aj-test-1:~# mv mc /usr/local/bin/mc

root@aj-test-1:~# mc alias set local http://127.0.0.1:9000 minioadmin minioadmin

root@aj-test-1:~# mc admin info local
●  127.0.0.1:9000
   Uptime: 5 minutes
   Version: 2023-11-25T07:17:05Z
   Network: 1/1 OK
   Drives: 1/1 OK
   Pool: 1

Pools:
   1st, Erasure sets: 1, Disks per erasure set: 1

1 drive online, 0 drives offline If you see messages similar to these, you can be assured that MinIO has started. Now we’ll create a bucket and later some objects using Quickwit. root@aj-test-1:~# mc mb local/quickwit

Bucket created successfully `local/quickwit`. Now we are ready to install Quickwit and configure it with MinIO as the backend. Configure Quickwit The Quickwit installer automatically picks the correct binary archive for your environment and then downloads and unpacks it in your working directory. In this case since we are running Ubuntu it will install packages related to that OS but it supports all the popular distributions. curl -L https://install.quickwit.io | sh

cd ./quickwit-v*/
./quickwit --version Curl the configuration file and let's modify it to add the MinIO bits. curl -o quickwit.yaml
https://github.com/quickwit-oss/quickwit/blob/main/config/quickwit.yaml Open the yaml and first add the credentials to configure MinIO storage:
   s3:
     flavor: minio
     access_key_id: minioadmin
     secret_access_key: minioadmin
     endpoint: http://127.0.0.1:9000 Next we’ll add the Storage and Metadata store configurations default_index_root_uri: s3://quickwit/indexes metastore_uri: s3://quickwit/indexes Once the above configurations are set in the YAML, save it and close. In order to use it set it as an environment variable and run the service export QW_CONFIG=./quickwit.yaml ./quickwit run We can check if its working by browsing the UI at or doing a GET http://localhost:7280 curl http://localhost:7280/api/v1/version Let's create an index configured to receive Stackoverflow posts. You need to create an index configured with a YAML to map your input documents to your index fields and whether these fields should be stored and indexed. curl -o stackoverflow-index-config.yaml 
https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.6.4/config/tutorials/stackoverflow/index-config.yaml Once the index is downloaded create it ./quickwit index create --index-config ./stackoverflow-index-config.yaml To hydrate the index we just created, we’ll download a sample of the first 10,000 Stackoverflow posts and then feed this data into Quickwit which will store it on MinIO in the backend. curl -O https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json ./quickwit index ingest --index stackoverflow --input-path stackoverflow.posts.transformed-10000.json --force As soon as the ingest command finishes you can start querying data by using the command search ./quickwit index search --index stackoverflow --query "search AND engine" You can use more advanced features such as aggregations like the following query to find the most popular tags used on the questions in this dataset curl -XPOST "http://localhost:7280/api/v1/stackoverflow/search" -H 'Content-Type: application/json' -d '{
    "query": "type:question",
    "max_hits": 0,
    "aggs": {
        "foo": {
            "terms":{
                "field":"tags",
                "size": 10
            }
        }
    }
}' Final Thoughts MinIO is the right choice for Quickwit because of its industry-leading performance and scalability. MinIO’s combination of scalability and high-performance puts every data-intensive workload, not just Quickwit, within reach. MinIO is capable of tremendous performance - a achieved 325 GiB/s (349 GB/s) on GETs and 165 GiB/s (177 GB/s) on PUTs with just 32 nodes of off-the-shelf NVMe SSDs. This makes managing Quickwit with MinIO seamless for log management, distributed tracing, and immutable data such as conversational data, event-based analytics among others. recent benchmark By storing the data in MinIO, Quickwit can be used as a Grafana datasource for achieving fast visibility into the operations of your application. You can see patterns and set alerts in Grafana's graphical interface that would allow you to run historical analysis and act on anomalies based on certain thresholds. For example, you might want to check for trends or bottlenecks and try to identify patterns in workload type during a specific time of the day. Got questions? Want to get started? Reach out to us on . Slack Also published . here

Setting Up MinIO With Quickwit

Quickwit এর সাথে MinIO সেট আপ করা হচ্ছে

MinIO mit Quickwit einrichten

Configurar MinIO con Quickwit

Configuration de MinIO avec Quickwit

क्विकविट के साथ मिनियो की स्थापना

Quickwit を使用した MinIO のセットアップ

Quickwit으로 MinIO 설정하기

Configurando MinIO com Quickwit

Настройка MinIO с помощью Quickwit

Quickwit ile MinIO'yu Kurma

Thiết lập MinIO bằng Quickwit

使用 Quickwit 设置 MinIO

242 Stories To Learn About Ml

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In 2016, when I began advocating for a switch from WhatsApp and Telegram to Signal in conversations with colleagues, friends, and family, I made a startling discovery: the vast majority of people, especially those outside the cybersecurity field, seemed largely indifferent to the crucial role of cryptography in our everyday lives. This apathy was particularly pronounced during discussions about Facebook's plans to integrate its messaging platforms with WhatsApp. The concern here stems from the different approaches to privacy and security adopted by these platforms despite them utilizing the same end-to-end encryption protocol developed by . WhatsApp, operating under Facebook, has been scrutinized for its potential use of metadata, raising questions about user privacy. On the other hand, Signal's open-source nature and minimal data collection practices are generally perceived as more secure and private, underscoring its commitment to user privacy. Open Whisper Systems Telegram, another popular messaging app, offers end-to-end encryption only in its 'Secret Chats' feature and not in regular chats or group chats. This critical distinction is important for users to understand when selecting a messaging platform based on their privacy needs. This scenario underscores a broader issue: the public's lack of awareness about the critical importance of robust cryptographic practices in safeguarding our digital interactions. It seems that many fail to recognize that cryptography forms the bedrock of our online communications, whether it's for personal conversations, financial transactions, or sensitive data sharing. As I engaged more in these discussions, it became clear that a fundamental understanding of how our data is protected—or possibly exposed—is vital due to our increasing reliance on digital platforms. If you are contemplating a switch from WhatsApp to Signal or wondering about Telegram's position in this context, a comprehensive comparison of these platforms is available in . This piece thoughtfully explains the nuanced differences in how these apps manage end-to-end encryption and user data, highlighting the intricacies of cryptography in everyday use. this article on CyberNews Cryptography ignorance is a bliss for Government regulators In the halls of the European Union, a bill has sparked significant debate with its aim to weaken end-to-end encryption, ostensibly to combat child sexual abuse. Despite some good intentions, this legislative effort is mired in controversy due to its potential impact on the privacy rights of EU citizens. A detailed analysis by Riana Pfefferkorn at Stanford's Center for Internet and Society, available , delves into the complexities of this issue. The EU's draft Child Sex Abuse Regulation suggests that online service providers proactively scan their services, including private communications, for abusive material, a major shift from current laws that allow for voluntary detection. here This proposal has triggered dissent among EU member states, underscoring the tension between child protection and the rights of over 447 million EU citizens. The bill's approach to end-to-end encryption, a cornerstone of digital privacy and security, has raised significant legal and technical concerns. End-to-end encryption safeguards communications from external surveillance, but poses challenges for law enforcement in detecting illegal activities. The ongoing debate in the EU highlights how a lack of public understanding of cryptography can be exploited by regulators. Policies lacking a comprehensive grasp of encryption and its implications risk inadvertently undermining the foundational principles of privacy and security. An informed discussion, considering both the technical realities of encryption and the legal frameworks governing digital rights, is essential. In Australia, the government has passed an unprecedented law that poses a significant threat to global privacy. Known as the TOLA Act, this 2018 legislation compels tech companies to provide Australian authorities with access to encrypted messages, essentially mandating the creation of backdoors. Despite a recent review proposing additional safeguards and oversight mechanisms, the law's core mandate, particularly regarding access to encrypted communications, remains largely intact. This move has serious implications, as it not only compromises the security of communications within Australia but also sets a dangerous precedent globally. The law's broad scope and potential for misuse raise significant concerns among privacy advocates and security experts. More details about this concerning development, including the recent review's findings, can be found in this article by the . Foundation for Economic Education Similarly, the United Kingdom is on the cusp of adopting policies that could significantly weaken encryption worldwide. The Online Safety Bill, ostensibly designed to enhance online safety, paradoxically risks making individuals less secure online. The recently passed bill mandates that tech companies weaken their encryption services, potentially exposing users to increased cyberattacks and privacy violations. The far-reaching implications of these legislative actions are explored in greater depth by the and . Electronic Frontier Foundation The Conversation Panama Papers and the Value of Cryptography in Journalism and Advocacy The Panama Papers were a groundbreaking revelation in the world of investigative journalism, showcasing the transformative power of encryption in the fight against global corruption. In 2016, over 11.5 million documents were leaked from Mossack Fonseca, the world's fourth-largest offshore law firm, uncovering the intricate financial dealings of the elite. The leak revealed the identities of individuals and entities involved in potentially illicit activities across more than 214,000 offshore companies. Encryption was crucial in this process, as it protected the identity of the whistleblower, known as "John Doe," and ensured the secure and confidential transmission of this sensitive data. This case demonstrated how strong encryption is not only a technical tool but a fundamental enabler of privacy, democratic principles, and journalistic integrity. In an era where digital security and privacy are increasingly under threat, the story of the Panama Papers serves as a potent reminder of the essential role of encryption. Bastian Obermayer, the investigative journalist who brought the Panama Papers to light, emphasizes this in for Euroviews. He recounts how the insistence on strictly encrypted communication was a lifeline for the whistleblowers, enabling them to safely share information that would shake the world. this article As Obermayer points out, without strong encryption, the risks to both the source and the journalists would have been exponentially greater. The investigation into the Panama Papers not only brought to light financial crimes and global corruption but also highlighted how encryption empowers individuals to challenge powerful entities and expose injustice. However, this crucial tool for maintaining privacy and enabling transparency is facing legislative challenges globally, from the UK's Online Safety Bill to the US's EARN IT Act and the EU's Child Sexual Abuse Regulation proposals. Encryption, as Obermayer asserts, is a massive asset for democracy. It allows for private conversations, which are essential for a healthy public discourse and investigative journalism. The Panama Papers are a testament to the power of encryption in giving a voice to the voiceless and standing up against injustice. It's a narrative that emphasizes the need to not only understand but also to defend strong encryption – for it is in this technology that the strength of our democracies and the protection of our fundamental freedoms lie. Cryptography 101: Public key cryptography (PKC) and the RSA algorithm In the following paragraph, I will share a series of resources for people of different backgrounds interested in learning more about cryptography. However, the world would already be in a better place if most people had a basic understanding of two of the pillars of modern cryptography: public key cryptography (PKC) and the RSA Algorithm. In 1976, Whitfield Diffie, Martin Hellman, and Ralph Merkle revolutionized electronic communication security by developing public key cryptography (PKC). This innovative method forms the cornerstone of modern secure communication, ensuring the confidentiality and integrity of our digital interactions. : The Essence of PKC At the core of cryptographic security are two types of keys: private and public. These keys are essential to asymmetric encryption, a method where different keys are used for encrypting and decrypting messages. Public and Private Keys This key is confidential and exclusively known to the user. It's never shared and is used primarily for two purposes: decrypting data encrypted with the corresponding public key and signing digital documents to authenticate the sender's identity. Private Key: Contrasting with the private key, the public key is openly shared and available to anyone. It is utilized to encrypt data that can only be decrypted by the matched private key, thus ensuring that only the intended recipient can access the transmitted information. Public Key: Building on PKC Principles The RSA algorithm, developed by Rivest, Shamir, and Adleman, is one of the first and most widely used public-key cryptographic algorithms. It builds upon the principles of PKC established by Diffie, Hellman, and Merkle. This algorithm is a fundamental technology behind many secure systems we depend on today, from HTTPS protocols on the internet to secure email communication. The RSA Algorithm: RSA functions by generating two large prime numbers in secret and multiplying them to produce a public number. The security of RSA hinges on the difficulty of factoring this large public number back into its prime components. Functionality: RSA is integral in various applications, including digital signatures, secure data transmission, and encryption of sensitive information. Its extensive use is a strong indicator of its effectiveness and security. Use Cases: RSA, while robust, confronts challenges like increasing computational capabilities and the potential impact of quantum computing, underscoring the need for continuous development in cryptographic methods. Challenges and Limitations: The Significance of PKC and RSA Understanding the basics of PKC and the RSA algorithm is crucial for anyone involved in cybersecurity or digital privacy. These concepts are practical tools safeguarding our digital lives, not merely theoretical ideas. Their comprehension clarifies much of the encryption we encounter daily and empowers us to make more informed decisions about our digital security. Resources to Become Cryptography-Savvy The journey to becoming a cryptography-savvy user is a path of continuous learning and engagement. Whether you're a novice with no technical background or someone looking to deepen existing knowledge, the world of cryptography offers an array of resources to explore. Here's a guide to help you navigate this journey, categorized for different levels of expertise. For Beginners (No Technical Skills Required): A comprehensive introduction to cryptography's history and principles. " " by Simon Singh: The Code Book A user-friendly online course covering the basics of cryptography. Khan Academy's : Cryptography Course Engaging talks that make complex concepts in cryptography accessible. TED Talks on Cryptography: Intermediate Level (Some Technical Background Helpful): Delves deeper into cryptographic principles and digital applications. " " by Keith Martin: Cryptography: The Key to Digital Security, How It Works, and Why It Matters Intermediate-level courses offering deeper insights into cryptography. : Coursera's Cryptography Courses Advanced Level (Technical Skills Required): A classic text for those with a solid technical background. " " by Bruce Schneier: Applied Cryptography Find it on Amazon Challenging courses for an in-depth understanding of advanced topics. MIT : OpenCourseWare's Cryptography and Cryptanalysis Courses Build real-world cryptography knowledge and explore modern algorithms. This book is ideal for deepening your understanding of advanced cryptography concepts and applications. “ " by Massimo Bertaccini: Cryptography Algorithms: A guide to algorithms in blockchain, quantum cryptography, zero-knowledge protocols, and homomorphic encryption Online Forums and Communities: Engage with communities on Reddit for insights and discussions. : Cryptology Subreddits and Online Forums For practical understanding, explore projects related to cryptography on GitHub. : GitHub Repositories Staying Updated: Blogs and Newsletters: Keep up with the latest developments by following leading cryptography blogs ( and subscribing to newsletters. ) Bruce Schneier's Blog Remember, becoming knowledgeable in cryptography is about more than just learning; it's about understanding its societal impact, advocating for digital rights, and ensuring security. Share your insights and learnings with your network to contribute to a more informed community. How I Protect My Data In my pursuit of digital privacy and security, I carefully select platforms renowned for their robust encryption and commitment to user privacy. However, I am acutely aware that in the dynamic world of technology, no solution is ever permanent. Changes in company ownership or philosophy can alter the landscape, requiring constant vigilance and readiness to adapt. My primary choice for instant messaging is . Praised for its end-to-end encryption, Signal ensures that my communications remain confidential. As an open-source platform, it undergoes regular scrutiny by the cybersecurity community, enhancing its trustworthiness. Signal's commitment to privacy is not just about robust technology; it's also about a philosophy that values user privacy above all else. Signal for Instant Messaging: Signal For email, I rely on . Based in Switzerland, a country known for its strong privacy laws, ProtonMail offers end-to-end encrypted email services. This means that not even ProtonMail can access the contents of my emails, ensuring complete privacy. Their use of open-source encryption and their transparent operational policies align with my personal security requirements. ProtonMail for Email Communications: ProtonMail Beyond these platforms, I am also exploring decentralized solutions, particularly blockchain-based communication tools. Decentralization offers a unique advantage by removing the single point of control or failure, which is a vulnerability in centralized systems. Exploring Decentralized Solutions: One such example is , a blockchain-based chat platform. Status operates on the Ethereum blockchain, providing not only messaging services but also a crypto wallet and a decentralized web browser. Its open-source and peer-to-peer protocol offers transparency and security, resonating with my approach to digital privacy. The platform's commitment to user sovereignty and data protection, free from corporate oversight or governmental interference, presents a promising alternative in the ever-changing digital landscape. Status In conclusion, while platforms like Signal and ProtonMail currently meet my privacy and security needs, I remain open and attentive to the developments in decentralized solutions like Status. The digital world is in constant flux, and staying informed and flexible is key to ensuring the enduring safety and privacy of my data. Understanding Cryptography: Protecting Your Privacy Rights In this rapidly evolving digital age, the significance of cryptography extends far beyond the realms of specialized cybersecurity professionals. It touches the lives of every individual who interacts with the digital world. While becoming an expert in cryptography may not be feasible or necessary for everyone, having a fundamental understanding of its principles and implications is crucial. This knowledge empowers us to make informed decisions about our digital privacy and security and to critically assess the policies and technologies that govern our online interactions. As we have explored, cryptography is not just a tool for safeguarding data; it's a cornerstone of our digital rights and freedoms. Whether it’s choosing secure communication platforms, understanding the legislative landscape, or simply staying informed about the latest developments in encryption and privacy, every step you take toward understanding cryptography strengthens your position in the digital world. Remember, in the fight for digital privacy and freedom, awareness is your greatest ally. You don't need to be an expert, but understanding the basics of cryptography and staying informed about how it impacts your life is a step towards a more secure and private digital future.

Most People Don't Care About Cryptography, and That's a Problem

অধিকাংশ মানুষ ক্রিপ্টোগ্রাফি সম্পর্কে চিন্তা করে না, এবং এটি একটি সমস্যা

Die meisten Menschen interessieren sich nicht für Kryptographie, und das ist ein Problem

A la mayoría de la gente no le importa la criptografía, y eso es un problema

La plupart des gens ne se soucient pas de la cryptographie, et c'est un problème

अधिकांश लोग क्रिप्टोग्राफी की परवाह नहीं करते, और यह एक समस्या है

ほとんどの人は暗号化について関心がありませんが、それが問題です

대부분의 사람들은 암호화에 관심이 없으며 이것이 문제입니다.

A maioria das pessoas não se importa com criptografia, e isso é um problema

Большинству людей плевать на криптографию, и это проблема

Çoğu İnsan Kriptografiyi Önemsemiyor ve Bu Bir Sorun

Hầu hết mọi người không quan tâm đến mật mã và đó là một vấn đề

大多数人不关心密码学，这是一个问题

Introduction Developer Relations/Advocacy, or “DevRel,” is a relatively recent formalization of an activity that has been going on for decades now. When I had my own Linux from about 1999 to 2010 that was heavy on KDE, I would engage in this all the time. It was harder back then to find the watering holes where the interested parties would hang out. You’re in the IRC channels, message boards, or mail lists, talking with people, explaining what you’re doing, showing how to do it, answering questions, etc.  We didn’t have convenient ways to create and publish training videos or any of that kind of thing. I had my version of OpenCore back then before it was more formalized as a business strategy. We were blazing a new trail on how to monetize and be respectful members of the community. software company Today, there is a glut of options, but the challenge is the same: find the watering holes and become a natural part of that community that is liked and trusted. Don’t just drop in to announce your own thing; be involved on an ongoing basis. What that has also given rise to is the “social media influencer” type of DevRel person. DevRel fundamentals When I had my , two basic things had to be done with a new release. record label Awareness Availability Part of awareness was . Getting out there and talking about it, getting it reviewed, getting press releases, reviews, all that jazz. So, for me, the first step of DevRel is spreading awareness. If I’m doing this on behalf of a company, then the focus should be the product and the company, not me as a “personality,” IMO. I also want to be very mindful of your time. I get paid to create content to engage and educate, but you don’t get paid to consume it. Your life doesn’t revolve around what I am talking about, so I can’t just assume you have enough information on top of your mind without me providing some context. very similar to DevRel That fits into an empathy component that I believe is core to everything. I want to be empathetic of your time. I want to be empathetic of what you are trying to learn and the problem you are trying to solve. I want to grow my knowledge base around what YOU are asking, not what I think is going to be fun or interesting for me. I want to always put myself in the position of the consumer of the resource. The idea is to reduce the friction between you and solve your problem with the technology I’m talking about. That said, I also want to point you in a different direction that might be the better answer for you than what I’m working with. Content can take many forms, and people absorb content in different ways. I like short videos, interactive tutorials, blogs, and articles. I differentiate between a blog and an article, with the blog being the deep dive, more of a tutorial, perhaps. The article is a more surface-level piece where we pose a problem and show a solution with the product. We don’t have to do a deep dive; it’s meant to almost be a teaser to show what is possible and then provide that deeper dive through technical documentation, tutorials, and blogs. The article, short video, and interactive tutorials are baiting the hook, if you will. By providing quick bites of information, it allows people to quickly ascertain if something might fit into their ecosystem, thus prompting them to dive deeper. If all they can find is deep dives or cotton candy pieces, then they will typically just move on. ChatGPT-style Being a Personality I’m seeing growth in the technology space of a wannabe influencer type. People are trying to be whacky characters and treating it more like being on . The videos they put out for the company are filled with links to their personal sites and content. They tend to overshare about themselves. While it is nice to feel you know something about these people and develop a connection, we seem to have erred on the side of oversharing now. Again, this is all in my opinion. Maybe I’m a bit old-fashioned. TikTok Summary For me personally, I am paid to promote a brand or product, not myself. Your time is precious, so you don’t have the time or inclination for me to squeeze in my self-promotion. We’re all in tech, and we’re not wanting TikTok types of content, as far as I can tell. Sure, be fun, be approachable, be informative, and be engaging, but again, be respectful of people's time. Meet people where they are as much as possible. Make it easy; reduce all the friction you possibly can. Be a trusted voice and resource. Have a personality, but don’t just a personality. be

Developer Relations: To Be, Or Not To Be, A Personality?

How to Create and Publish a GitHub Action in Typescript - ULTIMATE 2023 GUIDE

GM, GN, WAGMI, HODL, DYOR, probably nothing, ser – do these terms sound familiar? They are part of an ever-growing thesaurus of crypto-speak. A lexicon that the says is reflective of a cult. And not in a good way. Like Jonestown. skeptic’s guide to crypto If you think about it though, all cultures and subcultures develop their own lingo. They create their own customs and rituals. Social behaviors. In-groups and out-groups. Sub-communities. Idiosyncrasies. And so on. These subcultures are born out of political, technological, art, or other contemporary movements. Influences which also shape their vocabulary. For example, a combination of politics, economics, cryptography, computer science, and current affairs influenced Bitcoin’s creation. As a result, its culture and diction are also influenced by these. Subcultures can be niche like Goth or Steampunk. Or more popular cultural phenomena like Hip Hop. The Furry fandom falls somewhere between. And so does cryptocurrencies, or crypto – not niche anymore, not a mass movement yet. But . getting there People more competent than me have written at length trying to explain the who, what, why, and how of this movement. The most recently popular of them being a by finance columnist Matt Levine. 40,000-word treatise In spite of all the ink spent on crypto, its culture remains one of the lesser-explored aspects in popular media. With this guide, I hope to take a stab at it in fewer words than Levine. “Crypto” Explained For semantic clarity, “crypto” was originally used to refer to cryptography only. Cryptocurrencies use applications of cryptography. With their growing popularity, “crypto” is also now common parlance for cryptocurrency-related stuff. Like Bitcoin, Ethereum, public blockchains, NFTs, and more. I will be referring to the newer connotation when using “crypto” here. Now coming back to the original question, do the terms mentioned above sound familiar? If you are into crypto, it is a resounding yes. If you dislike crypto, these phrases may invoke negative sentiments. But if you are unaware or indifferent to them, you may be a nocoiner. Or a precoiner, to be more specific. Nocoiner and Precoiner Explained What is a nocoiner? Someone who doesn’t own any cryptocurrency or related assets like NFTs. As with many things in crypto, “nocoiner” has turned out to be contentious. Even among crypto advocates ( , ). 1 2 Urban Dictionary currently five explanations. All have polarized votes. Two of those are disparaging of people who like cryptocurrencies. The other three down-talk critics. has two definitions of which the first one says: lists Wiktionary Elsewhere, there is resentment added towards crypto in the description of the term. From to to to Wiktionary’s second definition. What of those who are unconcerned one way or the other? VICE CoinDesk CoinMarketCap Entrepreneur and investor Balaji Srinivasan make this distinction. He “precoiner” i.e. a nocoiner who has no grudges against crypto. suggests As Matt Levine’s “Crypto Story” points out, “disciples” (coiners) and “skeptics” (anti-coiners) are at loggerheads. “Normies” (precoiners) are undecided. Hence, this guide may find a more receptive audience among precoiners. Nonetheless, it is addressed to all nocoiners. All who wish to peek into the layered and esoteric world of crypto culture. Part I – Bitcoin v/s Ethereum (and Other Crypto) Culture To understand the overall culture, let’s list some of the shared principles among Bitcoin/Ethereum/crypto/public blockchain/Web3 proponents to begin with: · trustlessness, decentralization, disintermediation · self-sovereignty, ownership rights, be-your-own-bank · individual privacy, data protection, strong cryptography · censorship-resistance, freedom to transact, immutability · triple entry accounting, public accountability of systems, FOSS Many of these values can be attributed to libertarian thought and anti-establishment sentiment born out of socio-political developments. The earliest supporters of some of these values were cypherpunks – a privacy-focused subculture. Naturally, crypto found acceptance from whistleblowers. Like Chelsea Manning, Edward Snowden, and Frances Haugen. Thereby starting its life as a niche internet counterculture. Over time, some chains/projects/platforms in crypto have made concessions for one value in exchange for other benefits/value systems hoping to gain more mass appeal. Trade-offs such as compromising on decentralization for achieving speed. Or shelving open source to avoid exploits. Or even ditching immutability to curry favors with governments. Nevertheless, some crypto entities have held on to their zealously. cypherpunk roots Bitcoin’s Origins and Culture Bitcoin was born out of the 2008 housing bubble collapse. Its pseudonymous creator Satoshi Nakamoto’s goal was to make a system that no longer needs a trusted intermediary to transact. A truly peer-to-peer financial paradigm. Publicly auditable. Run on computers by independent parties distributed around the world. Cannot be corrupted by colluding actors. In the words of Satoshi: ( ) Image Source This is why Bitcoin culture focuses so much on the separation of money from state. The ethos of resonates strongly. The memetic themes often revolve around sound money, borderless finance, and economic theory. Fix The Money, Fix The World Throwing shade at censorship, central banking, fiat money, and government-issued diktat is usual. This mission-driven doggedness is one of the reasons for Bitcoin’s resilience through “crypto winters”. In spite of its being written over and over again. obituaries ( ) Image Source Many years later, the vibes are still cypherpunk AF. There’s vigorous advocacy for free speech, privacy, anti-surveillance, and cybersecurity. Self-custody of assets is sacrosanct. Keeping crypto coins on centralized/custodial platforms is a no-no. Many prominent Bitcoiners are generally skeptical of the overall (non-Bitcoin) crypto space. But staying true to their roots, they the EU’s plan to ban privacy coins. And come to the of Tornado Cash when it is sanctioned by the US Treasury and one of its developers is arrested. protest defense OPSEC (operations security) is second nature. And it comes from years of experience. Sometimes learning the hard way like a Solana dev did last year . As security expert Jameson Lopp can also . in Bogotá confirm While glitzy events like the Miami Bitcoin Conference or Bitcoin Amsterdam attract people from across the cryptocurrency spectrum and beyond, the true essence of Bitcoin culture shows in smaller developer-focused events: ( ) Image Source Products, research, activism, and other developmental work on Bitcoin center around , , , , , , , , , , , etc. custody scalability anti-censorship uptime privacy accessibility circular economies nation-state adoption power grids renewable energy human rights , offline payments Popular crypto developments like (Decentralised Finance), (Non-Fungible Tokens), , (Initial Coin Offerings), (Decentralised Applications), (Decentralized Autonomous Organizations), etc. are nearly non-existent on Bitcoin. DeFi NFTs Yield Farms ICOs DApps DAOs , tokens Even things like (Decentralised Exchanges) are few and far between. People who invest in these “altcoin” projects or associate with them may face criticism from Bitcoin purists. Dex’es The idea that public blockchains can be viable means to build anything beyond money rails is not well received in these circles (see Oracle Problem in Part V). So there is disinterest in “ ” and “ ”. Web3 metaverse There are at ideations and implementations of tokens, DApps, NFTs, and more on or around Bitcoin too (see , , / , , , , Mastercoin/ , , , / , etc.), but they have never really taken off. attempts Colored Coins Counterparty Inscriptions Ordinals Bitcoin Stamps OpenOrdex Rollkit Omni EthSide Raretoshi BitDNS Namecoin , VeriBlock This indifference, and occasional disdain ( , , , , , , , , , )*, makes Bitcoin culture feel puritanical and over-serious. Yet, at the same time, it has paved the way for what is considered as crypto culture today. In times of crisis, the of Bitcoin gains greater appreciation ( , , , , ) from altcoiners/multicoiners too. 1 2 3 4 5 6 7 8 9 10 single-minded pursuit 1 2 3 4 5 *Despite the disdain, Bitcoiners have overwhelmingly rejected calls for censorship and the throttling of (see Bitcoin-not-crypto in Part VII). undesirables ( ) Image Source PS. Eric Wall is a vocal critic of “maximalism” in Bitcoin spaces. In the dorky v/s dogmatic image composite above, the left one is from Ethereum’s developer conference . The right one is from Bitcoin’s community-focused conference . To be fair, the Bitcoin event had other non-ominous-looking settings too (see and ). in Bogotá in Amsterdam here here The Story of Ethereum One of the original proposers of Colored Coins was Vitalik Buterin. A co-founder of Bitcoin Magazine. He wanted to build apps and blockchain-based assets on top of Bitcoin. A decentralized world computer using Bitcoin. That did not generate enough interest from other Bitcoin devs. So he ventured out to create a separate blockchain called Ethereum. A permissionless platform to deploy software applications and digital assets. Apart from the proceeds of a Thiel Fellowship, an initial sale of pre-mined coins was arranged to fund early development. This led to differences with the Bitcoin community since Satoshi’s approach was more bootstrapped. Another point of contention was Bitcoin had no premine, and mining was egalitarian. No added advantage was afforded to Satoshi or other early miners. To play devil’s advocate though, it could be argued that Satoshi’s was a high privilege that few had access to. Bitcoin mailing list Notwithstanding, the first cracks began to appear between the two communities. From to to to , Ethereum developers and researchers have their hands full. Ethereum is more “degen” (degenerate) though in the sense that it houses all those endeavors mentioned before that don’t catch on in Bitcoin. cryptography research scaling data protection improving decentralization Apart from , it is fertile grounds for experimenting with non-money implementations in , , , , , , , , , , , , , , , , etc. finance art governance grants arbitration community management fundraising revenue sharing treasury administration co-working fan clubs insurance VR/metaverse publishing gaming royalties identities , ticketing Even are not exempt. As a result, Ethereum’s culture, memes, and lexicon capture these various pursuits and their hits and misses. The themes go beyond money and state. cloud nations ( ) Image Source The ballooning thesaurus of crypto-speak reflects the pace of development on Ethereum. As of Jan’23, ~1M different tokens have been issued on its blockchain. Of these, are fungible tokens (ERC20 tokens), are NFT collections (ERC721 tokens), and are other kinds of tokens (ERC1155 tokens). ~760k ~205k ~35k And we have not even included the 1:1 NFTs such as single pieces of work created by artists. On top of this, more than smart contracts have been deployed (as of Aug’22). 44M This high level of enterprise is a driver of the “Web3” movement. A loosely held term for an internet running on public blockchain and crypto token rails to maintain data provenance. On the flip side, where there’s money to be made, there is sketchy activity too. Apart from projects that fail, there are also scams perpetrated by bad actors. From vaporware to snake oil to rug pulls to hacks to malicious contracts. Many of these get flagged quickly (see Bane of Scams in Crypto in Part VIII). Their claims get called out ( , , , , , , , , , , , ). Or clowned on ( , , , , , , ). But many slip through. For every Uniswap, Aave, Compound, and MakerDAO’s of the world, there are many grifts that happen. 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 A large part of criticism directed towards crypto arises from these misadventures. One of the reasons why Bitcoin purists insist on distancing from “crypto”. Notwithstanding “degen” behavior on Ethereum, cypherpunk tendencies are alive and well. There are tools to . NFTs to Silk Road creator Ross Ulbricht. And DAOs seeking the release of WikiLeaks founder . subvert authority free Julian Assange So Bitcoin now has a partner in Ethereum when it comes to driving crypto culture. Other public blockchains like Solana, Dogecoin, Polygon, Avalanche, Cosmos, Binance, Monero, Zcash, Litecoin, Polkadot, Stacks, Tron, Near, Cardano, etc. have a comparatively smaller footprint on cultural phenomena for now. Because of its dominant influence, many Ethereum developments (NFTs, Dex’es, DAOs, etc.) become part of the crypto zeitgeist before other chains adopt them. This is why a lot of the lexicon and memetic themes carry over from Ethereum as well. , stablecoins Up Next – Part II: Key People/Opinion Makers/Cultural Influencers NOTE: Also published . here Disclaimer: None of the information mentioned above should be construed as financial advice.

The Simple Guide to Crypto Culture for Nocoiners

Nocoiners জন্য ক্রিপ্টো সংস্কৃতির সহজ গাইড

Der einfache Leitfaden zur Kryptokultur für Nocoiner

Nocoiners를 위한 암호화폐 문화에 대한 간단한 가이드

Nocoiners için Basit Kripto Kültürü Rehberi

The previous post was about Phantom Metrics: Why Your Monitoring Dashboard May Be Lying to You . On this post we’ll look at Expensive Metrics and how our bill quickly gets out of control. We all collect logs, metrics, traces, and possibly other telemetry data. Yet, this can get expensive pretty quickly, especially in today’s cloud-native and microservices-based systems. Why is that? How is it related to the “cardinality problem”? And how can we proactively manage this data surge and keep the associated costs under control? I decided to delve into this intricate cost challenge in a recent episode of , with the help of my guest Ben Sigelman, co-founder and the GM of Lightstep. Ben architected Google’s own planet-scale metrics and distributed tracing systems (still in production today), and went on to co-create the open-source OpenTracing and OpenTelemetry projects, both part of the (CNCF). OpenObservability Talks Cloud Native Computing Foundation Cost factor #1: A multitude of microservices Regardless of whether you’re using a commercial vendor or deploying open source tools, the cost for observability ends up being dominated by the collection and long term storage of the raw data itself, that’s used for observability. It is quite reasonable to expect that the amount of telemetry data being generated from your application will be proportional to the number of business transactions, which ultimately translates to the amount of revenue that an application is generating. If you were to increase the load on an application by 10X, it’s fair to assume that the amount of data it generates will increase by roughly 10X, broadly speaking. In microservices based systems, however, there is another significant factor on your data volume, one that people often don’t take into consideration in their preliminary cost assessment. That is the number of services deployed. We need to monitor each service, as well as the interaction between the services, and adding new services inevitably results in extra telemetry data. The reason that cost has gotten so unwieldy for cloud native is that as the application grows, it increases the top line in terms of application use, and then also increases the number of services that are involved, which drives a surge in the data volumes. Cost factor #2: High cardinality In monolith systems life was simpler, as these systems failed in pretty predictable ways, which helped us determine what we need to monitor, and we built dashboards to capture these known failure modes. The introduction of cloud native systems complicated things. Now I may need to investigate my microservice’s performance per pod, per node, per namespace, per deployment version, and so on. These additional dimensions increase the cardinality of my infrastructure monitoring. Moreover, unlike infrastructure monitoring, such as CPU, memory and disk utilization, application monitoring is very open-ended, which causes a surge in failure modes. In order to ensure your application’s availability and reliability to your customers, you need to monitor business related metrics, such as the amount of time it takes a user to log into the system or check out with his shopping cart, or similar . With each incident you investigate, you discover a new failure mode you need to track, and after the postmortem developers add metrics or tags to increase observability coverage for these cases. product observability This is a really slippery slope and a difficult thing for developers to estimate. Developers oftentimes aren’t aware that this well-intentioned action of adding a tag in fact means adding a dimension to the metric, one that each and every time series will be impacted by. This is how dimensions keep building up, which quickly becomes a high cardinality challenge, which ends up extraordinarily expensive. As Ben shared on the podcast: I talked to economic buyers of metrics tools, where they’ve had single developers add in one line of code, that’s costing them $200,000 a year, steady state of. And that’s totally typical. Cost factor #3: Wide-range dimensions It’s not just about the number of dimensions but the size of these dimensions. If the dimension is your number of endpoints, it makes sense. But what if you choose as a dimension your user ID? You can have millions of these in B2C applications. Choosing a dimension with such a wide range of values, or maybe even an open-ended range of values, is an anti-pattern, and can drive your data volume up very quickly. Remember that this is combinatorics of all the different dimensions that determines the total cardinality. Ben said that back in his days at Google they had a few core latency metric for all Gmail use which ended up with 300 million time series values! “That’s why I remember it so clearly, because we had to do a lot of work to support that. We had situations where a single metric wouldn’t even fit in memory in one of the shards of the Monarch system.” Cost factor #4: Collecting but not using Interestingly, in many production deployments, many of these tags and labels aren’t actually put to use. In my company Logz.io, when we help our users analyze their metric data costs, we often find between 60%–80% of the anywhere, not in dashboards nor in alerts. Time and time again I see users amazed to discover metric they’ve been collecting but but not using, metrics with dozens of labels that take up storage that eats up their bill. Here's a screenshot of a typical system with collected but unused metrics  (source: Data Optimization Hub): metrics that aren’t used Logz.io Ben shared a similar experience from Lightstep’s users, where the discovered that only is ever queried for any purpose over its entire lifetime. That means 90% of the data is literally never accessed. one in 10 metrics Endnote We collect metrics for our monitoring, but it quickly gets unwieldy. It’s the multiplication factor, the combinatorics of many dimensions and high cardinality, that quickly causes data to explode, and the metrics bill to get out of control. It’s not easy for the developer to see the combinatorial impact of adding that one line. You might have several dimensions that seem independently okay, with just a hundred or a thousand values each. But if you add all of those attributes up, the combinatorics of those values can quickly get you way up into the hundreds of thousands, or millions of distinct time series. Liked it? Check out the OpenObservability Talks episode: on , or . Expensive Observability: The Cardinality Challenge Spotify Apple Podcasts other podcast apps

The Ballooning Costs of Metrics: Why Your Monitoring Data and Bill Get Out Of Hand

Computer vision (CV) is a sub-domain of the field of Artificial Intelligence that is aimed at helping machines to identify and understand content in digital video or images. Simply put, CV enabled machines to “see” the world like we humans do and use that knowledge to augment human efforts. This technology uses cameras and computers instead of the human eye to identify, track and measure targets for further image processing. The field of computer vision has progressed tremendously in the last decade thanks to: exponential growth in the ability to complex machine learning or deep learning models through higher computation efficiency in the form of GPUs or purpose-built hardware accelerators, decreasing hardware costs, rapid advancement of new technologies (better algorithms, automation, and easy-to-use tooling), and advancement of camera technology that allows higher quality images to be captured and used for CV tasks. How does Computer Vision work CV tasks can be performed by using either traditional image processing techniques or modern deep learning (DL) based techniques. Traditional image processing techniques used statistical methods and did not involve learning from patterns (i.e., did not need training data). OpenCV is a popular image-processing library and is widely used today to pre-process images. However, the performance of such models has been restricted by the scene, lighting, and illumination. It also doesn’t scale well with new use cases. Deep Learning based techniques offer much higher performance across a wide variety of scenes and lighting conditions but rely on large training data as well as powerful hardware to run the computations. DL-based techniques constitute the state-of-the-art for CV and we will focus on DL-based CV techniques only for the rest of the article. On a very high level, computer vision can be described as a 3-step workflow: Capture an image from a camera or video (video is nothing but a series of images) Analyze the image for the presence of objects or features by passing the image through special deep learning (DL) models If needed, pass original images along with the output of the DL model into another DL model to identify activities and complex higher-order traits In theory, any image from a camera can be used for CV tasks. However, even with the most light-weight algorithms, CV tasks are computationally intensive and require massive data sets to train the models on (it could cost hundreds of thousands of dollars to train a state-of-the-art DL model) so in many cases where real-time results are needed, the image or video’s resolution (thereby size) is reduced along with other pre-processing steps such as contrast enhancement, noise reduction, etc. to speed up computation. Modern CV technologies rely heavily on deep learning models, specifically, deep Convolutional Neural Network (CNN) models, that have been trained on hundreds of millions of images and offer considerably good precision and recall (more on this later in the article). A CNN model helps understand the contents of the image by breaking it down into objects with labels that describe what the object is. The labels used are part of the dataset used to train the model. Today in many tasks, CV algorithms are surpassing human performance. Some state-of-the-art deep learning models for CV are discussed later in the article. Key Computer Vision tasks There are dozens of tasks that can be performed with Computer Vision. Some major tasks are described below, with the most important task being object detection. Object detection includes 3 sub-tasks: Object Detection: a) detecting an arbitrary number of objects in images or videos, locating them within the image with a bounding box, classifying which classes they belong to (humans, animals, vehicles etc.) and labeling them as such. Object detection tasks help answer the question of What type of objects are present and where they are located. Object detection is a super-set of tasks like person detection, vehicle detection, animal detection, plant detection, etc. An object detection model can detect many different object types, but detection performance might be different for different objects based on the data used to train the model. Object detection is the primitive used for many other analytics tasks such as person counting, loitering, measuring queue length, average dwell times, etc. a type of object detection focused on detecting human faces within an image/video Face detection: people present in the image/video are recognized by their names and by matching their faces with a database containing names of people and their faces. Facial recognition: estimate the position and orientation of humans in the image/video. Pose detection involves detecting different “parts” of the human body (such as right elbow, left knees, right shoulders, etc.) and their pose relative to other parts to estimate the overall pose of the human body in order to determine the actions or activities (such as sitting, walking, running, hitting, etc.) that are being done by the humans in the image/video and get more in-depth understanding of the human body language to estimate complex higher order tasks such as determining human behavior, estimating stress, demeanor, mood, etc. Pose estimate is used in the world of online and remote fitness classes to determine if the exercises and activities are being performed in the optimal way to prevent injuries. Pose estimation also has other interesting applications such as training robots, motion tracking in augmented reality and virtual reality, fall detection, etc. Pose estimation: Identification of characters (alphanumeric numbers and symbols) in an image. Automatic License Plate Recognition is an application of optical character recognition techniques Optical character recognition: Key Computer Vision Models to know There are many different CV model architectures with new versions coming out every few months. But each model has its own pros and cons and is good for certain use cases and tradeoffs between speed, detection accuracy, and efficiency. YOLO stands for You Only Look Once is one of the most popular detection algorithms that can detect objects in real-time at the expense of slightly reduced detection accuracy. It is widely used across the industry from large corporations to startups. The first generation of YOLO (Yolo v1) was launched in 2016 and it was the fastest object detector of its time. Since then, there have been multiple iterations with every new release improving the speed, accuracy, and efficiency of object detection. YOLOv7 was released in July 2022 and is the fastest and most accurate object detection model as of writing this article. YOLO also has many optimized versions for special use cases, such as YOLOv7-tiny which is a lightweight version of the main YOLOv7 and optimized for edge AI and efficient yet performant enough to run on edge GPU devices, and YOLOv7-pose that delivers state-of-the-art pose estimation performance. YOLOv7: stands for cascade-mask region-based convolutional neural network and is state-of-the-art in terms of image segmentation. Image segmentation is the task of clustering parts of an image together that belong to the same object class by annotating pixel by pixel. Image segmentation goes deeper than just object detection and gives a richer partitioning of the elements in the image. Mask R-CNN is the state of the art in terms of image segmentation and instance segmentation. Image segmentation has broad applications such as detecting tumors, road sign detection from different angles, autonomous driving assistance systems, precision agriculture, and use for sensing through drones and UAVs. MASK R-CNN is optimized for detection accuracy at the expense of speed of detection. However, YOLOv7 achieves slightly better accuracy (2% higher than Cascade-Mask R-CNN) at a significantly increased detection speed (509% faster). Cascade-Mask R-CNN: is lightweight and designed for mobile CV applications that are restricted by available power, available computing, and hardware to run. MobileNet was a popular model but recent advances with YOLO architecture, especially YOLOv7 significantly improved detection speed even in processing power-constrained environments while still providing good accuracy. MobileNet-SSD V2: detects human body, hand, facial, and foot key points (in total 135 key points) in multi-person images in real-time and is a must-have for pose estimation. Knowing pose and orientation has a lot of applications, especially when tracking the movement of the human body is involved. For example, in fitness, pose estimation is used to identify if the athlete is following the right movements and if the pose will prevent injuries. OpenPose detects multiple people so with decent accuracy and in real-time. OpenPose also enables single-person tracking that can be used to speed up detection and visually smooth images and video by predicting where the movement will be. OpenPose: Edge computing for high-performance CV applications Edge computing is when computing happens near the source of data rather than in data centers optimized for large-scale compute workloads. For example, when you are typing in your mobile phone and it tries to predict the next word, that prediction is happening on the mobile phone itself close to the data (your previous chats or conversation) and is a type of edge computing. Other examples of edge computation devices are laptops, TVs, special edge computing hardware, Raspberry-pi, etc. Edge computing has tremendous value for computer vision due to bandwidth reduction, increased speed, and privacy as well as other security benefits. Edge computing is also more efficient than cloud computing (since data is not passed between the edge device and the cloud). Because computation happens so close to the user, edge computation allows for real-time and ultra-low latency performance. Imagine in a self-driving car example, if the car had to evaluate the scene by sending each frame to the cloud and waiting for analysis, it would never be able to respond in milliseconds to avoid accidents. Also, edge computation is resilient to network downtime and outages (imagine the car going through an area where the cellular network dropped, and not being able to process and stop in time for a pedestrian crossing the street. As a corollary, since data doesn’t go to the cloud for processing, edge computation minimizes internet bandwidth usage and reduces the cost of data transmission back and forth from the cloud servers. Also, because computation is more efficient, edge computing consumes less energy. Lastly, edge computing allows for techniques such as federated learning to happen that give best-of-breed machine learning solutions while protecting privacy. This is because data never leaves the end devices and thereby is less prone to being intercepted or leaked from central servers. This allows for high-accuracy computation to happen on customers’ personal and confidential data to give highly personalized experiences and best-in-class UX without the threats and risks of data being intercepted or breached or hijacked. The disadvantages of edge computing have been the complexity of working with various edge devices and compatibility with the device classes. The distributed nature makes edge computing hard to standardize and requires heavy lifting for a lot of different architectures and systems. However, in this world of diverse classes of devices, there are a few edge devices that stand out in terms of acceleration of CV inference. Note that model training is still ideally done in the cloud and even with the most powerful GPUs, training a modern deep neural network can take anywhere from hours to a few days. NVIDIA Jetson NVIDIA Jetson is a family of edge computers that is optimized for running AI / ML / CV applications on the edge. It is ideal for students, tech enthusiasts, DIYers as well as professionals to quickly build and deploy edge AI applications. It comes with its own SDK and a complete ecosystem, comprehensive tutorials, training modules, and getting-started guides. Due to the strong ecosystem support as well as being high performance and power efficient, it is one of the most popular platforms for edge AI applications today. Additionally, the platform has been designed for flexibility so modularity is built into each component. The SDK comes with all the required libraries, debuggers, and APIs, and supports popular Machine Learning frameworks like Kera, Caffe, and TensorFlow that just makes development on NVIDIA Jetson very easy and fast. There are different modules available in the market, starting with Jetson Nano which is optimized for building and deploying small AI systems and is great for beginners and students. Don’t mistake the nano in its name to mean small - it is power packed to run parallel neural network workloads, processing data from high-resolution sensors and running hefty workloads, and is ideal for building proof of concepts. The TX2 raises the performance bar and is designed for beefy workloads with high efficiency and speed. The module carries NVIDIA Pascal GPUs and is 2.5x more powerful than the Nano, allowing running deep neural networks with higher accuracy. Jetson Xavier is said to be the smallest supercomputer in the world for edge computation workloads and is designed to run parallel neural networks and process data from multiple high-resolution sensors simultaneously. Jetson AGX Xavier is developed with fully autonomous applications in mind and 20x the performance of the TX2 and is the highest-performing module in the Jetson series. Jetson AGX is ideal for autonomous mobility applications, warehouse robotics, and large surveillance drone use cases. Google Coral Google Coral is a Tensor Processing Unit processor-powered edge computing device that is ideal for fast neural network inferencing. Google Coral is basically a purpose-built inference engine for Tensor workloads. Google Coral has 2 components - a compute device that is an edge compute device sized slightly larger than a credit card, and also has a USB accessory that is an AI accelerator. Google Coral is a high-performance, extremely low power-consuming edge computer and has a pretty good cost-per-FPS processing ratio. Intel Neural Compute Stick 2 Intel Neural Compute Stick 2 is Intel’s answer to edge computing for AI workloads and is powered by the Intel Movidius Myriad X VPU (vision processing unit) chip that is optimized for CV applications. Highlights of this device are high performance and ultra-low power and can be used for a wide variety of applications including VR/AR headsets and security solutions. It comes in a tiny, USB thumb drive form factor and is an accelerator for speeding up neural network inference on the edge in a cost-effective way. Essentially, they can be thought of as purpose-built co-processors that take the burden of some special computation from the CPU that is required for CV applications. As with NVIDIA Jetson, Intel Neural Compute Stick 2 comes with its own SDK (called the Myriad Development Kit) that contains the necessary libraries, tools, and APIs to quickly implement solutions. Because of the USB interface, it can be plugged into any device that has USB ports and is easy to get started. There are extensive tutorials and guides and a strong community that has tons of getting-started guides and examples to help anybody get started quickly. The Neural Compute Stick can take in multiple high-resolution video streams at the same time and with high frame rates.

An Intro to Edge Computer Vision: Technologies, Applications, Use Cases and Key Models

Whether you’re a DevOps, SRE, or just a data-driven individual, you’re probably addicted to dashboards and metrics. We look at our metrics to see how our system is doing, whether on the infrastructure, the application, or the business level. We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? You’d be surprised how often it’s not the case. In this post I will look into the math and mechanics behind metrics, some common misconceptions, what it takes to have accurate metrics, and if there even is such a thing. Metrics essentials Metrics are essentially roll-ups of raw events. During this roll-up process, the events are translated into numerical data points. A simple example is errors occurring in the system, with a simple metric to count the errors. Metrics can also involve multiple variables, such as a count of requests with a response time higher than 1 second. When measured over time, these data points form a . time series Metrics can be of various types, such as Counters, Gauges, and Histograms. are used for the cumulative counting of events, as we saw in the above examples. typically represent the latest value of measurement. And then there are more elaborate types such as that can sample the of metric values, by counting events in configurable “buckets” or “bins”. For example, you may want to understand the memory usage percent segmented by pods across your cluster in given points in time. Counters Gauges Histograms distribution The mechanics of metrics In an ideal world, we would ingest and store all the raw events, and then calculate the metrics on query time. This would allow us to slice and dice the events in any way we need, and ask any ad-hoc questions we desire. In the real world, however, keeping all the raw events for extended periods of time can be prohibitively expensive, due to the high volumes of data. To overcome this, events are frequently rolled up into metrics in the collection pipeline, while discarding the raw events or retaining them for short periods only. This is oftentimes a matter of a simple configuration in your metrics collector agent. In addition to reducing cost, aggregation upon collection can improve the performance of real-time analytics with higher metric transmission and ingestion rates at a higher frequency, and by avoiding heavy aggregations and calculations on query time. The math of metrics in a nutshell This rolling-up process involves some math. We might want to calculate the mean or median of the response times, or maybe a percentile, or an aggregation over a time window. We might also want to roll up multiple events into one composite metric. For example, I may want to calculate the 95th percentile (commonly known as P95) of all the pods of a specific service across my cluster. Even if you don’t like math, you cannot avoid it with metrics. You need to understand the different aggregation functions, and the relation between the question you wish to ask and the metric and aggregate you need in order to answer it. Let’s look at the Average function as an example, as many tend to start there. Averages, by definition, smoothen things up and will be less suitable for flushing out anomalous behavior and outliers. When investigating latency problems, for example, it will be quite useless to look at average metric values, and you’d be better off looking at percentiles. Determine your questions, design your metrics accordingly In a way, you can think about these metrics as a lossy compression, during which we lose data and context from the raw events. If we don’t keep the raw events, then we need to determine upfront what’s important for us. For example, if I only calculate the average value over the data, I will not be able to ask about the P95 (95th percentile) later over the pre-aggregated data. You need to determine what questions you want to answer, what’s important for you, and design your metrics and aggregations accordingly. A common mistake is that people avoid this design phase, and just use the preset metrics and default values provided out of the box with their metrics collector of choice. While you may think these defaults represent some industry standard, these are oftentimes quite legacy, and in most cases won’t be in tune with your specific needs. https://youtu.be/t9hpWv7fVSk?t=555?embedable=true The measurement problem Just like in physics, the measurement problem occurs when we measure a (seemingly) continuous property at discrete intervals, often called the , which determines the This creates a distorted representation, where the metric may not actually reflect the originally measured property. For example, if we measure the CPU utilization every 60 seconds, then any CPU outlier happening between these sampling points will be invisible to us. sampling interval sampling rate. Moreover, in order to draw a consecutive line, visualization tools oftentimes average over consecutive data points, which gives the misleading appearance of a smooth line. On some occasions the opposite can occur, where you can get artifacts in your metrics that aren’t real, like peaks in your metrics that don’t really exist. This can happen when running aggregations within the storage backend, due to the in which the calculation is being made. Mean time to detection The sampling period also influences how fast a change in the system will be visible in the metrics. Most algorithms require five data points to detect a trend. If the sampling interval is 60 sec, then the simple math determines that it will take five minutes (that is, 60 sec X 5 data points) before we see something is wrong. Could you afford to wait 5 minutes to know that your system crashed? Using shorter sampling intervals (i.e. higher sampling rates) will shorten this period and enable us to detect and react faster. Of course, higher sampling rates incur overhead in CPU and storage, so we need to find the configuration that strikes the right balance for our needs. Varying resolution and downscaling A common practice is to save metrics in different resolutions in a tiered approach, to reduce cost. For example, you may want to save the metric every 10 seconds for the first day, but then every 5 minutes for the next week, and perhaps every 1 hour for the month or more ahead. This practice assumes that we need the finest granularity for the near real-time period, which we may need it if there’s an issue in the system, while investigations over longer periods require larger-scale trends. The different granularities can be achieved by downscaling the metrics, namely calculating the less granular metric off of the higher granularity one. While this sounds perfectly reasonable, math can interfere here, as some aggregation functions are not compatible with certain computations, and can therefore not be aggregated later. For example, percentiles are not additive and cannot be summed up. So, following the above example, if you have a P99 percentile sampled with 10 seconds resolution, you can’t roll them up to a 5-minute resolution. It’s important to be cognizant of the compatibility of the aggregation functions, and when using non-compatible functions such as percentiles, to make design decisions about which resolutions we require, and calculate these time series upfront. The varying resolution is not limited only to the time factor. Another example is saving per-pod data, and then wishing to “group by” nodes or clusters. The same constraint applies here, meaning that if we expect to be interested in slicing and dicing a percentile-based metric per node, per region, per namespace, or across the entire cluster, we need to pre-aggregate accordingly. Another approach is to give up the accuracy of measurements to gain compatibility in computation, by using histograms. You can take histograms of a few servers and sum them up, or histograms of several time windows and sum them up, and then to downscale. The problem is that in this case, percentiles will be estimates rather than accurate. It’s also important to note that histograms are more time-consuming in storage and throughput, as every sample is not just a single number but a few samples (one per bucket). Summary Metrics are a powerful way to monitor our applications. But they are not necessarily representative of the actual system’s state. It requires an understanding of the math and nature of metrics, as well as careful design, to make sure our metrics are indeed useful to answer the questions we need. Having access to the raw data in addition to the metrics is always good, as this is ultimately the source of truth. Want to learn more? Check out the OpenObservability Talks episode: on , or . All Metrics Are Wrong, Some Are Useful Spotify Apple Podcasts other podcast apps This article was originally . here

Why Your Monitoring Dashboard May Be Feeding You Phantom Metrics

Por qué su tablero de monitoreo puede estar brindándole métricas fantasma

Pourquoi votre tableau de bord de surveillance peut-il vous fournir des métriques fantômes

आपका मॉनिटरिंग डैशबोर्ड आपको फैंटम मेट्रिक्स क्यों खिला सकता है

モニタリング ダッシュボードがファントム メトリックを提供している理由

Por que seu painel de monitoramento pode estar alimentando você com métricas fantasmas

Tại sao Bảng điều khiển giám sát của bạn có thể cung cấp cho bạn các số liệu ảo

为什么您的监控仪表板可能会为您提供幻象指标

Computer science has become a popular discipline among students in the US and other developed nations. It is also one of the most preferred courses by international students due to its marketability in a modern job market where technological advancements are driving business. However, completing the course requires students to handle numerous lengthy programming assignments to make sure they have the skills demanded in the job market. Studying and applying different programming languages requires time, research, and resilience. Most times, students spend sleepless nights writing codes, creating Apps, and developing software. The assignments can make it difficult for students to maintain a good balance between academics and personal life, which results in burnout, anxiety, stress, and poor grades. That is where companies that help with programming assignments come in. When facing challenges with a programming assignment, do not compromise your grades. Instead, you can enlist a reputable academic writing website to help you with projects, exams, and assignments. Most programming help sites have experts in each field of computer science ready to fulfill your assignment order within the agreed deadline. Other useful sites have discussion forums where you can interact directly with experts and ask any questions related to your programming assignment. Still, with many websites to choose from, students often have difficulties identifying reliable and legitimate sites to get help. There are certain factors to consider when choosing a legit homework help service. In the following post, we have shortlisted the 7 best websites where you can get assistance with your assignment. The List of the 8 Best Programming Assignment Help Sites Among Students in the USA We have scoured the internet for the best assignment help websites for students and identified the ones with the best features so that you don’t have to. –The best programming homework help site Stemhave – High-speed delivery Domymathhomeworks.com – Fast reaction to customer questions Livepaperhelp.org - Coding help by Experts Programming Assignment Help – World-class IT experts The Python Tutorial – Undisputed leader in solving programming tasks Stackoverflow – Top-quality programming educational materials MIT OpenCourseWare – Best programming forum and community for beginners CodeRanch Let’s review the features that make these sites the best for programming assignment help. Affordability. Cost is a crucial factor to consider when seeking online assistance. Always chooses a reputable site that charges reasonable and flexible prices. While it’s ok to be economical, don’t ignore the outcomes of settling for the cheapest programming assignment help site. All the sites shortlisted in this post are either free or affordable to students working on a budget. The Reliability of Customer Support. There is nothing as awful as paying for a programming task and not getting answers to your queries when you need them. The best sites have a support team on standby around the clock. Ability to choose your writer. You should be at liberty to choose the expert you want to handle your programming assignment. Most of the sites and forums in this post have samples written by experts and ratings by other customers to help you hire the most suited writer. That way, you can get quality online assistance. Custom Content. Plagiarism is a serious academic mistake with harsh consequences. The website you enlist to help with an assignment should be trustworthy to ensure you achieve good results in your coursework. The programming assignment websites in this list guarantee custom-written codes along with clear instructions and comments. Some of the sites give additional advice and tips that you can use in future assignments. Quality. One of the best ways to determine if a site offers good quality help with programming assignments is to check reviews on independent forums and websites. All the platforms featured in this article have top ratings on customer review platforms, such as Google, Trustpilot, and Sitejabber. The sites also have quality control teams that ensure all content adheres to high-quality standards. Guarantees. All the sites in this shortlist have policies guaranteeing customers quality services and data protection. The assurances include free revisions, confidentiality of private information, the safety of payment details, custom content, and a money-back guarantee. Customers can cancel orders and request refunds in case of late deliveries or poor quality. Detailed Overview of the Companies – One the best websites to seek high-quality professional programming assignment help. The service has experts who offer high deliverables, including short practice questions such as shortcodes, theoretical questions, and output screenshots. Still, the experts are known for handling complex tasks in different STEM fields. The site has numerous positive reviews online from customers who use it regularly. Stemhave The most popular services on the site are Java homework assistance, Python homework help, Mobile App Development, Algorithms, 3D CAD Modeling, data analysis, C++ programming assistance, C#, and Delphi. Thus, instead of wasting time trying to identify an error in the code or solving a task, let the coding gurus at Stemhave solve your problem at an affordable price. – When you need help with coding assignments, this site is a reliable place to get it. The complexity of programming assignments leaves some students strapped for time. Other times, you have multiple projects with similar deadlines. Domymathhomeworks.com The site provides programming assignment help to papers with a deadline of at least four hours. The company has a wide range of services, from beginner-level assignments in C and C++ to complex tasks in Java, Machine Learning, Python, Web Development, and Matlab. In addition, the prices of computer science assignments are flexible. The price depends on the order size, deadline, and discipline. You can lower the cost by extending the deadline. In case of any difficulties or questions relating to an order, the site has 24/7 customer service that is reachable across multiple platforms. The service also has a money-back guarantee for any dissatisfied customers. – Here you get world-class IT specialists to work on your homework. Students from undergraduate to doctoral levels seek help on the site for all types of programming problems, projects, and assignments. Livepaperhelp.org All the writers undergo a rigorous recruitment and testing process to confirm that they are adept at writing clean and efficient codes. The writers are competent in various programming languages, such as Python, Swift, Java, C++, and R. Regardless of the complexity of your programming assignment, you can rely on the company to link you with the most experienced expert in the subject area. The customer support at Livepaperhelp.org is the best part. The support staff pays close attention to each query and works fast to resolve it. If you are looking for a programming assignment help site without any compromises in quality, then you are at the right place. – If you are a beginner who is passionate about programming, The Python Tutorial is a great place to find help with basic concepts and features of the Python language. The site has great examples and hands-on projects that you can use as a guide for completing your programming assignment. The Python Tutorial You can use the content as a reference when reviewing a new topic or assignment. The expert guide will get you comfortable with basic and intermediate python and programming concepts. The tutorial also makes it easy for students coming from another programming language. The popular assignment help you can get from the tutorial includes training on data types and creative variables, installing python, decision making and repetition, input and output, iterations, and functions. The site also provides students with a guide on object-oriented programming, data structures, and exception handling. – This is an online question-and-answer forum for IT developers and programmers. The site serves millions of people by helping them find answers to challenging assignments when they need them. Stackoverflow has different sections: Java, JavaScript, PHP, C#, jQuery, Android, HTML, and Python. Students who need programming assignment help can join groups of professionals in different sections and request help with problematic programming tasks. Stackoverflow There is a high chance that your programming assignment has already been answered by an expert on the platform. If not, the group has professionals who can guide you on how to solve the programming problem. Professionals on the site also give advice, tips, and answers to help students with programming homework. – You will no longer have trouble getting programming course materials for students and educators. The free-access manuals on this site include content from undergraduate to graduate-level MIT courses, such as lecture notes, exams, problem sets, video lectures, and a reading list. The institution has published the materials in five languages. MIT OpenCourseWare MIT OpenCourseWare has a high consumer rating on Sitejabber and Google, with many students praising the quality of the video lectures. If you can design programs but don’t consider yourself an expert, the site has helpful resources where you can find gaps in your knowledge and fill them with knowledge from video lectures. – The site has many success stories and positive testimonials from beginners. It’s a friendly forum for programmers where students can post questions and get answers from experts. You can ask programming questions for different languages, such as JavaScript, HTML, CSS Groovy, and Python. CodeRanch Experts on CodeRanch also post tips and advice that students can apply when completing programming assignments. What’s more, the site is not just a question-and-answer forum because you get a chance to have deep discussions. So if you are a student preparing for a certification course and want advice related to your tests, go hang out in this forum. Questions that Need Answers Find out more information about the popular questions asked by students who are looking for programming assignment help online. Maybe, you will find the answer to yours as well. What are the risks of getting online assistance? The risk is that you might stumble on an untrustworthy site. Ensure you research the company you select to confirm that it has reputable quality standards and guarantees the safety of your information and payment details. You can choose among the 7 sites listed in this review to avoid an unpleasant experience. How can these sites be legit? Yes, you can use programming assignment help sites without worry because they are legal. Students are at liberty to use the content obtained from the website in the way they desire. For example, you can choose to use the content as a reference tool or for inspiration. Who will write my programming assignment? What is my writer's degree? All experts employed by the sites have academic degrees and many years of experience in programming. The writers undergo a strict recruitment process that ensures only the most qualified experts are selected. You can check the rating and qualifications of an expert in a particular field to help you choose the best fit for your assignment. How fast can I receive my order? Several factors determine the time required to complete a paper, such as the volume and complexity of the assignment. Still, some websites, such as Domymathhomeworks.com and Stemhave.com deliver programming homework help within a deadline of at least 4 hours. However, it’s always good to order the paper early to give the writer enough time to produce a top-notch paper. Conclusion Identifying an online platform where you can get quality programming assignment help is not easy, especially if you don’t give it maximum attention. Some of the critical factors to consider include price, services offered, on-time delivery, paper quality, and writer’s qualifications. Always go for a site with positive customer reviews on independent websites. We derived the shortlist featured in this post by researching reviews from numerous American students who have used online assistance. Use this guide to make informed decisions and avoid negative experiences. Take a break sometimes by enlisting help from one of the websites so that you can regroup and start again. Looks good!

7 Best Programming Assignment Help Sites

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