API7.ai is an open-source software infrastructure company that empowers API management and analysis. We provide a wide range of products for microservices and real-time traffic processing, such as API Gateway, Kubernetes Ingress Controller and Service Mesh. Apache APISIX, a top-level project donated by API7.ai to the Apache Software Foundation, is the next generation of cloud-native API gateway that provides rich traffic management functions such as load balancing, dynamic upstream, canary release, circuit breaking, authentication and authorization, and observability. Built on Apache APISIX, the enterprise-grade API7 products provide more advanced capabilities to meet the core requirements of enterprise users: multi-tenancy, multi-cluster management, role-based access control (RBAC), API audits and much more. Whether your services are on-premises, on the hybrid cloud, or multi-cloud, you will get the solution best suited to your needs. Apache APISIX and API7 products are used by thousands of companies worldwide across the internet, financial, blockchain, manufacturing, retail and IoT industries. Founded in 2019, API7.ai has attracted global talents from 6 countries and 20 cities, many of whom are Apache Software Foundation members, OpenResty / Nginx core maintainers, Kubernetes members and industry experts in cloud infrastructure and security.

API7.ai

Last year, I wrote my first . I liked the experience, so I'm trying one more time. Let the future decide if it will become a trend or not. yearly retrospective Before diving into our safe technological world, my thoughts go to Ukraine, to my friends who had to flee their own country, to other friends who fought on the front to defend it from an imperial power, and to all victims of an old kleptocrat who clings to power despite the cost to others. The free world needs to support Ukraine more. I hope 2024 will be the year of Ukrainian victory. The AI revolution Last year, I kept the post focused on what I did. However, AI is pervasive in our tech world, if not the whole world, and deserves a dedicated section. Nobody even remotely connected to tech can ignore the buzz surrounding AI. Even friends and families who don't work near tech probably talk about it. So far, I didn't weave any AI-related thing into any of my talks, despite the huge incentive to do so: having AI in your Call For Paper proposal vastly increases your chances of being selected. And yet, it doesn't mean that I'm not playing with it on a personal level. Here's my experience so far. I've been playing with Dall-E as a use case to try out web development in Rust. I found the results mind-blowing. However, the limitations are enormous; it seems the size is always square, and the set of possible dimensions is limited. I've used ChatGPT in several areas. First, I tried to generate conference abstracts. I fed an entire blog post and asked ChatGPT to turn it into an abstract. Despite my lack of prompt engineering skills, I've found results severely lacking. Abstracts felt artificial, like any content created with ChatGPT, but worse, the abstract revealed too much or not enough. I also tried to use it to refactor two nested Kotlin statements, one nested in the other, to the null-safe construct. The first result didn't compile; the second one forgot a branch. It led me to the correct solution, though. if else let Finally, I recently started using GitHub Copilot as an extension inside of IntelliJ IDEA. Its behavior is fascinating: most of the time, it does nothing, but once in a while, it offers a snippet of a couple of lines, which is either entirely correct or at least very close to the target solution. Even better, the suggestions seem to be more frequent and even more relevant with time. All in all, I'm far from impressed by the current state of AI. However, I like Copilot a lot: I prefer rare advice, which is relevant, than the opposite. Technical content Next February will mark the two-year milestone that I'm working for: on . I'm still very pleased about both. It allows me to do things I like a lot, such as writing posts and giving talks. API7.ai Apache APISIX In 2023, I published fifty blog posts on : one each week on Sunday, but on Christmas and the New Year - for obvious reasons. Here are the top most viewed pages: this blog Rank Post Views Avg. time on page #1 Leverage the richness of HTTP status codes 14,848 :29 #2 A list of cache providers 12,770 1:15 #3 Calling Rust from Python 8,780 :52 #4 My final take on Gradle (vs. Maven) 8,238 2:11 #5 Learning by doing: An HTTP API with Rust 6,642 1:00 I continue to cross-post on different sites. Here are the numbers compared with last year's: Site 2023 2022 Medium 741 564 Dev.to 8156 1,838 Hashnode 89 80 Absolute numbers are not that interesting, but comparing them is. Interestingly, numbers on are growing wildly, while on Hashnode, they plateau. Note that other sites provide no precise follower count or no count. dev.to Besides, I created a script to track daily metrics across sites and social media, just as I did for Apache APISIX. It displays interesting results: As above, numbers are much less important than the trend. Growth visibly happens mainly on and Bluesky for reasons I cannot fathom (yet?). dev.to Finally, I started a weekly newsletter, unoriginally named A Java Geek weekly. So far, I've written a . I list the posts and videos I found interesting during the week. Note that they contain the same links I post on , , and , with a bit more content, either a summary or my opinion. couple of them LinkedIn Mastodon BlueSky Open Source contributions In 2023, after over twenty years in software, I finally became ! I'm both excited and impressed; it's like belonging to a group of mythical beings I'd only heard about. an Apache committer Of course, working on Apache APISIX helped a lot. Yet, the exciting bit is that all my contributions are entirely unrelated to code; they are blog posts, reviews of blog posts written by others, issues, comments on issues, etc. Here's the GitHub summary: Conclusion Last year, my resolutions were: To deepen my understanding of the Apache APISIX ecosystem Write as many blog posts as in 2022 - it's hard to do better, anyway Design at least three new talks I fulfilled the two first goals but unfortunately failed the last item. I have only created two new talks, both based on previous posts. I'll keep them secret for now; they should appear soon on my page in case they are selected. Speaking I lack imagination, so I'll keep the same goals for this year as last year's and add exploring the ecosystem as well. Let's see how it fares. Happy New Year! API7.ai Originally on on January 7th, 2024 published A Java Geek

Everything I Achieved in 2023

is an excellent key-value distributed database used internally by Kubernetes and managed by the CNCF. It's a great option, and that's the reason why Apache APISIX uses it too. Yet, it's not devoid of issues. etcd First, some mention scalability, but one can expect this from a distributed data store that values consistency. Another issue may be the need for more familiarity with etcd. It's relatively new, so your Ops team may need help operating it correctly while having decades of operating MySQL or Postgres. Finally, only a few etcd users are aware that it lacks maintainers: In the last few months, primary maintainers G.L. (Amazon, announcement) and S.B. (Red Hat) have stopped actively participating in the project. This leaves the project with only one active and two occasionally-reviewing maintainers, M.S. (Google),  P.T. (Google), both are relatively new to the project (1 month and 1 year of tenure) and S.P.Z. (IBM). Other maintainers are either dormant or have very minimal activity over the last six months. . The project is effectively unmaintained (emphasis mine) -- Google Groups of Kubernetes Steering Committee, March 2022 For all those reasons, you may prefer to use a standard SQL database with Apache APISIX. In this post, I'll show how you can use MySQL. The kine project It would be a lot of effort if each product had to introduce an abstraction layer and different adapters for both etcd and other databases. kine is a project that aims to offer a translation step between etcd calls and other implementations: Kine is an etcdshim that translates etcd API to: SQLite Postgres MySQL NATS Jetstream Features Can be ran standalone so any k8s (not just K3s) can use Kine Implements a subset of etcdAPI (not usable at all for general purpose etcd) Translates etcdTX calls into the desired API (Create, Update, Delete) -- Kine (Kine is not etcd) In essence, kine is a Go library that translates etcd calls to the datastore you want (among those implemented). Yet, using kine directly is a non-trivial effort. Fortunately, , the company that gave Apache APISIX to the Apache Software Foundation, provides a component already focused on APISIX usage. api7 ETCD adapter ETCD adapter wraps kine to be APISIX-specific: ETCD Adapter mimics the ETCD V3 APIs best effort. It incorporates the kine as the Server side implementation, and it develops a totally in-memory watchable backend. Not all features in ETCD V3 APIs supported, this is designed for Apache APISIX, so it's inherently not a generic solution. -- ETCD adapter Two things of note: At the moment of this writing, the adapter supports either local in-memory storage or MySQL It's available as an embeddable library but also as a standalone component Therefore, we can design our architecture as the following: Demo Let's implement the above architecture with an additional admin UI over MySQL. I'll use Docker Compose: version: "3"

services:
  apisix:
    image: apache/apisix:3.4.0-debian                         #1
    volumes:
      - ./config.yaml:/usr/local/apisix/conf/config.yaml:ro
    ports:
      - "9080:9080"
      - "9180:9180"
    depends_on:
      - etcd-adapter
    restart: always                                           #2
  etcd-adapter:
    build: ./etcd-adapter                                     #3
    volumes:
      - ./adapter.yml:/etcd-adapter/conf/config.yaml:ro       #4
    depends_on:
      - mysql
    restart: always                                           #2
  mysql:
    image: bitnami/mysql:8.0                                  #5
    ports:
      - "3306:3306"
    environment:
      MYSQL_ROOT_PASSWORD: root
      MYSQL_USER: etcd
      MYSQL_PASSWORD: etcd
      MYSQL_DATABASE: apisix
  adminer:
    image: adminer:standalone                                 #6
    ports:
      - "8080:8080"
    environment:
      ADMINER_DEFAULT_SERVER: mysql
    depends_on:
      - mysql Latest version of Apache APISIX, yeah! To avoid any failure with dependencies between containers, restart until it works. Kubernetes's manifests would involve health checks still needs to provide a container. We need to build from the source code api7.ai Override the default configuration file with a context-specific one The regular MySQL image didn't work for me. Let's take the one from Bitnami Adminer, formerly known as PHP myAdmin, will help to visualize the database state ETCD-adapter's configuration looks like this: server:
  host: 0.0.0.0                 #1
  port: 12379

log:
  level: info

datasource:
  type: mysql                   #2
  mysql:
    host: mysql                 #3
    port: 3306                  #3
    username: etcd              #3
    password: etcd              #3
    database: apisix Bind any IP since Docker will assign a random one Implementation type. The default is ; we need to change it. btree As configured in the file docker-compose.yml Finally, here's Apache APISIX configuration: deployment:
  admin:
    allow_admin:
      - 0.0.0.0/0
  etcd:
    host:
      - "http://etcd-adapter:12379"   #1 Use this etcd instance, which is the adapter Testing Now that we are set let's test our system by creating a route: curl -H 'X-API-KEY: edd1c9f034335f136f87ad84b625c8f1' -X PUT -d '{                                          
  "methods": ["GET"],
  "uris": ["/get"],
  "upstream": {
    "nodes": {
      "httpbin.org:80": 1
    }
  }
}' http://localhost:9180/apisix/admin/routes/1 We can now get it: curl -H 'X-API-KEY: edd1c9f034335f136f87ad84b625c8f1' http://localhost:9180/apisix/admin/routes/1 We can also check via the Adminer interface that it has been persisted via MySQL: Unfortunately, we need to stop at this point. Getting all routes doesn't work: curl -H 'X-API-KEY: edd1c9f034335f136f87ad84b625c8f1' http://localhost:9180/apisix/admin/routes {"header":{"revision":"1689689596"},"message":"Key not found"} Worse, using the route fails: curl localhost:9080/get {"error_msg":"404 Route Not Found"} Conclusion etcd is an excellent piece of infrastructure Kubernetes uses, but there might be better choices in some contexts. Worse, it might become a security threat in the future - or is already one, because of the lack of maintenance. Being able to move away from etcd is a considerable benefit. kine offers an etcd-compatible facade and multiple implementations. Using kine with Apache APISIX requires some adaptation effort, already done in ETCD-Adapter. Currently, ETCD-Adapter is not feature-complete (to say the least) and requires more love. That's why it was not donated to the Apache Foundation yet. If you're a Go developer and are interested in the project, feel free to subscribe to the to offer your help. Apache APISIX mailing list and/or join our Slack The complete source code for this post can be found on . GitHub To go further: Kine ETCD Adapter "Goodbye etcd, Hello PostgreSQL: Running Kubernetes with an SQL Database" Originally published at on July 23rd, 2023 A Java Geek

Apache APISIX Without etcd?

86 Stories To Learn About Customer Service

Artificial intelligence is a technology that surprised the world by evolving from a fictional tale in movies to now having a ubiquitous influence in every sphere of human life. But was this an unpredictable change? Not really. The algorithms and experiments started back in the 1950s. It built the foundation of the AI we know today, like business automation software, the infamous Alexa and one of the biggest accomplishments of AI this year - . ChatGPT This level of evolution is possible only if the right minds put on their thinking caps and begin execution in the right direction. Lets See The List of 10 AI Development Companies in USA 1. Google Cloud Google Cloud offers an array of machine learning and artificial intelligence tools for developers and data scientists. These tools can be used at every stage of the development pipeline, i.e. preparation, building, validation and deployment of AI platforms. Google Cloud’s services primarily include virtual resources and physical assets. Some tools available on the platform include , Vertex AI Workbench, Cloud Natural Language, Video AI, Vision AI, and AutoMl, to name a few. Its services extend to infrastructure development tools for building prototypes, scaling, and deploying projects. Vertex AI For instance, its Deep Learning Containers have pre-configured deep learning environments which can assist in building prototypes. And the high-performing GPUs help with scientific computing and ease specific workloads. For its remarkable vision and resources, in 2022, in Magic Quadrant for Cloud AI Developer Services. Some noteworthy clients of Google Cloud include PayPal, Gojek, 20th Century Fox, P&G and Twitter. Gartner named Google Cloud the leader Launched: April 7, 2008 Employees: 32000+ Stock Price: USD 96.94 (as of 16th February 2023) 2.  Inc. RisingMax Inc. RisingMax is one of the leading AI company, acts as a digital transformation catalyst for startups and FORTUNE-500 companies. They are obliged to assist businesses worldwide in reducing their carbon footprint and creating a sustainable future. With the strong eco-community, they support green tech initiatives and reward businesses working to build a better tomorrow. They celebrate the project's success by planting 100+ trees each time. By rewarding or compensating the plating cost, they also encourage our clients to plant at least 50 trees. 3. OpenAI Open AI is a non-profit AI research and deployment company based in San Francisco, California. Open AI’s hall of fame includes Chat GPT, DALL·E 2 and Whisper. Chat GPT is a masterpiece of conversational AI with the ability to produce unique dialogues based on inputs. DALL·E 2 can create original and realistic image compositions based on descriptions. Whisper is an automatic speech recognition tool that can go through any quality of audio in any language and transcribe it accurately. Additionally, Open AI has other development tools to offer, like GPT3 and Codex. Users can also leverage Open AI’s free content filtering, end-user monitoring and scoping of API usage services. Pacific Northwest National Laboratory, Scale Media Inc., and Stellar Science Ltd. currently use OpenAI services. As per , OpenAI aims to achieve revenue of $1 billion by 2024. With investors like Microsoft, Khosla Ventures and Reid Hoffman’s charitable foundation, the future of  AI innovation in OpenAI looks bright. Reuters Launched: December 10, 2015 Employees: 375+ Stock Price: Open AI is a private company as of February 16, 2023 4. IBM Cloud IBM is leading the AI sector with its AI technologies. IBM cloud features a list of tools, including AI and Machine learning tools, specifically . It was released in April 2010 and is still actively used to build machine learning models. Watson AI IBM Watson Assistant, Discovery, Knowledge Catalog, Language Translator, Knowledge Studio and Natural Language Classifier are some useful AI tools available on IBM Cloud. In 2020 AI market share report with the largest market share of 13.7%. IBM received the #1 ranking in the IDC IBM Cloud’s Watson AI is used by 70% of banking institutions and by some of the top companies like Panasonic, Covéa, DB Dialog & DB Systel and Humana Inc. Its use cases in multiple industries spread across 21 verticals, including insurance, aerospace, defense, banking and transportation. Launched: April 2010 Employees: 300k+ Stock Price: USD 136.4 (as of 16th February 2023) 4. Amazon Web Services (AWS) Amazon Web Services has a comprehensive set of AI and machine learning tools targeting business analysts, data scientists, and engineers, allowing them to build, train, and deploy their ML models even without ML experience. Users can engage with ML experts and utilize AWS’s consulting services to complete their projects. The platform has a list of free tier tools like Amazon Polly, , Amazon Comprehend, Amazon Translate, Amazon Lex and Amazon Transcribe. AWS boasts a partnership with industry leaders like Intel, Accenture, Deloitte and C3 AI. Amazon SageMaker AWS AI has been employed by many startups and enterprises, such as Amazon, Discovery, Intuit, Formula 1 and Cambia Health Solutions. For its successful operation, Gartner recognized it as a leader in the Cloud AI Developer services' Magic Quadrant. Launched: November 2016 Employees: 40,000+ Stock Price: USD 101.16 (as of 16th February 2023) 6. Alibaba Cloud Alibaba Cloud is one of the leaders in the cloud computing and AI industry, with tools and services for developers and enterprises. With its end-to-end solutions, such as data processing, model training, prediction and evaluation, it aims to make AI more accessible to all. Alibaba Cloud’s AI tools include technologies like Vision AI, Intelligent Speech, NLP, Interactive AI and Intelligent Marketing. Its services are spread across many verticals like finance, education, transportation and new retail. The recognized Alibaba Cloud as one of the top 3 public cloud enterprise container platforms. Companies like Newegg, Blackboard, PicsArt and Align Technology leverage the AI services of Alibaba Cloud. Forrester report Launched: September 2009 Employees: 3000+ Stock Price: USD 103.08 (As of February 16, 2023) 7. Microsoft Azure Microsoft Azure AI services create a new way to access breakthrough research in the industry. The platform’s easy API calls can help data scientists and data engineers access language, vision, speech and decision-making AI. Broadly, the Azure services include Azure Applied AI, Azure Machine Learning, Azure Infrastructure and Azure Cognitive Services, each of which can enhance business processes, accelerate performance and add an extra layer of security. Specifically, Azure offers machine learning tools like Jupyter, Notebooks and Visual Studio Code and open-source frameworks like TensorFlow and PyTorch. Besides its low-code and no-code tools, services give users access to powerful AI tools like GitHub Copilot and GPT3. Azure’s Open AI Microsoft Azure was recognized as a leader in the ‘2021 IDC MarketScape for worldwide general-purpose conversational AI platforms.’ GigaOm also positioned . Azure’s active clients include industry leaders like BBC, NHS, Twitter and NBA. Azure among the leaders in the MLOps Radar Launched: February 2010 Employees: 200,000+ Stock Price: USD 269.32(As of February 16, 2023) 8. Salesforce Headquartered in San Francisco, California, Salesforce presents a large infrastructure of AI tools for admins and developers to build, train and deploy AI projects. Einstein is the most well-known platform in Salesforce, with no-code pre-built solutions and custom AI features. It’s well known for laying a rich set of AI tools for smart apps, such as Einstein Prediction Builder, Einstein Bots, Einstein Vision, Einstein Language and Einstein Language Discovery. Salesforce has been recognized as a leader, for the 16th consecutive year, in the 2022 Quadrant™ for Sales Force Automation Platforms. Some of the well-known clients of Salesforce include notable names like Ford, Loreal, Hologic and Engie. Gartner® Magic Launched: 2016 Employees: 73000+ Stock Price: USD 171.10 (As of February 16, 2023) 9. Deepmind Started in 2010, Deepmind is a research laboratory with a team of scientists, engineers and ethicists innovating AI. It’s headquartered in London with offices in other parts of the world like Canada, France and the United States. , The company achieved success early on when its AlphaGo program became the first to beat the professional Go Player. After partnering with Google in 2014, Deepmind was able to develop programs that detect eye diseases. According to BBC Its innovative AI is used to personalize recommendations in Google Play and predict patient deterioration in the U.S. Department of Veterans Affairs. Launched: 2010 Employees: 1000+ Stock Price: USD 96.94 (As of February 16, 2023) 10. Samsung Samsung is bringing forth its research in AI to build innovative solutions and smart devices. Samsung’s AI is currently focused on AI Fundamentals, Language and Voice AI and Vision AI. The company has expanded to five countries and established 7 AI centers in Korea, the UK, Canada, Russia and the USA. At , the company unveiled its vision of innovating technology with AI-based semiconductors. Samsung became one of the first to create AI chips using Synopsys AI software. These AI chips, called the , are used in most of the Samsung smartphones built today, like the Galaxy S20 and the Note 10 Lite. Samsung AI Forum 2022 Exynos Today, the company’s list of smart devices also includes the AI EcoBubble washer, which analyzes the patterns and determines the best washing cycle for its users. Launched: March 1938 Employees: 266,000+ Stock Price: 63,700 KRW (As of February 16, 2023) 11. Neuralink Neuralink is a neurotechnology company based in the US and owned by Elon Musk. The company's purpose is to find the brain-computer connection and build a chip that can be implanted surgically in the skull. The chip will be called the N1 chip with an 8mm diameter and electrodes and insulation for the wires. In 2021, Neuralink made a showing a monkey with a brain chip playing a video game. According to , the chip may help bring back someone’s eyesight and fix any damage to the brain. video Musk Founded: 2016 Employees: 300+ Stock Price: USD 0.000008668 (As of February 16, 2023) Final Words: What Is the Future of AI? From AI software writing an entire novel to a chip that can control humans using an app, AI innovations are not easy to comprehend. But these companies have the resources, the skills and the determination to build something big in this direction. Our list of the Top AI development companies includes the leaders that are commanding the AI industry today. Soon, AI will take over every sphere of our lives and become an indispensable aspect of every business.

Top 10 AI Development Companies in USA

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2023 年の AI イノベーションをリードする AI 開発企業 10 社

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2023年引领人工智能创新的10家人工智能开发公司

Once in a while, I write non-technical blog posts when I've something worth sharing. Today, I'd like to write about my North America "Tour" across several conferences and user groups. The first leg of my journey started in Oakland, California, with . Developer Week is an established conference with different editions in several locations and online during the year. Though I'm on their advisory board, this is only the second time I've spoken at one of their events. Pro-tip: Avoid being on any board of a conference where you speak. It's bad taste and casts doubt on whether you validated yourself. Developer Week I flew from Geneva the day before my talk and crashed into my hotel bed. Of course, I woke up very early in the morning and decided to check the demo of a talk planned for the end of the tour. It didn't work, so I tried to remove the stopped containers. Tired as I was, I deleted my Docker images, including the ones I'd need a few hours later for my talk on ! all Open Telemetry https://twitter.com/stmcallister/status/1760397694282121559?embedable=true The talk is heavily based on a demo. When I tried to start the latter, I noticed the issue immediately and realized my mistake, but it was too late. Even though I had a Docker Compose file with statements, one of the components is in Rust—there was no time to compile it. build Long story short, it was an epic fail. I apologize again for this to the attendees if any of them read this post; I hope the explanations and slides were enough for them to play with the GitHub repository. Afterward, my friend drove me to San Francisco for lunch and a lovely walk along the piers. Josh The next day, I woke early to fly to Montréal, Canada. It was a pretty long flight; the day after, I had to talks at , one of my favorite conferences in North America. ConFoo started as a PHP conference, hence the elephant mascot, but has now widened its horizon . ConFoo a lot I had two talks there: Open Telemetry (again) and . I had rebuilt my images, and both talks went flawlessly this time. Chopping the Monolith https://twitter.com/spencerbgibb/status/1761046658303877615?embedable=true Over the weekend, my friend invited me to ski in Sutton. The temperature was very low compared to what I'm used to, around-10°C. Fortunately, Anthony was prepared and gave me self-heating thingies for my hands; unfortunately, he only had one - but it was enough nonetheless. Anthony also connected me with all the meetups I have the pleasure of presenting at in Canada, so I'm fortunate to count him as a friend. Anthony Having survived the Canadian cold, I ran one of my favorite runs on Monday: from the Bonaventure Hotel to the top of the Mount Royal. The slope is pretty steep at the foot of the mount, so you either choose to use the twisty path to the top or the multiple stairs that cut a more direct route. I managed to use all the stairs but the last (and longest) one and caught my breath running along the regular path. In the evening of the same day, I talked at the meetup. It's interesting because though I've been a developer for a long time, I never belonged to the "crafter" movement, though it resonates. The talk chosen was . The room was packed, and I believe it was pretty well received. Software Crafters Montréal Evolving your APIs The next step in my journey was the . I spent most of my developer years on the JVM, so my network is quite developed among JUGs. The organizer is : he's been accommodating and has driven me from my hotel and back again. He's trying to rebuild the Ottawa JUG back to its pre-COVID attendance. If you're a speaker and plan to be around Ottawa, please get in touch with him: his organizational skills are second to none. Ottawa Java User Group Sebastien Pelletier https://twitter.com/t_mammarella/status/1762628620193775717?embedable=true Ottawa is located between Montréal and Toronto, so the was a logical step in my tour. I stayed for a couple of days, including the weekend, so I had time to explore the city, including the CN Tower, as it was my first time there. is the organizer there, and I'm sure she'll be happy to host you. Toronto JUG Therese Mammarella You may have noticed she liked my talks so much that she drove to Ottawa on purpose the week before to attend the one I did at the JUG. The talk was well-attended but less than I expected for a city of this size. Anyway, I had a lot of fun presenting Evolving Your APIs - I hope the attendees had too. https://twitter.com/shaunmsmith/status/1764812180992426409?embedable=true Toronto is quite close to Niagara Falls. It would have been a shame not to go there, but I felt sick the weekend, so I decided to skip it. Yet, some things are just bound to happen. After the talk, a couple of us went to have dinner. There, I met a Ukrainian guy who had moved to Toronto years before the war and knew about me and my support for Ukraine. After talking together, we realized we had friends in common. He offered to drive me there as he was not working the next day. I happily took a day off myself and didn't regret it one bit! Thanks, Ihor, for the drive and the conversation. Afterward, I returned to the USA, namely Chicago, Illinois, to speak at . I have known the JUG leader, , for over a decade. She took the time to organize the meetup despite her busy schedule. Chicago JUG Mary Grygleski https://twitter.com/mgrygles/status/1765903150668517579?embedable=true is a familiar face in the Java community - and beyond. He's also the leader of the Denver Java User Group. I was lucky to know him, as he also arranged a double hit: , then . Even better, , whom I don't need to introduce, lives close to Boulder was there to invite me for a hike. But before that, I spend my weekend hiking according to his suggestion. Matt Raible Boulder Denver Venkat Subramaniam and First, I went to Boulder Moutain Park, and then, the day after, I went to Lake Bernard. The not-so-fun part about the second hike: for a reason unknown, mid-way, my head started to hurt. The headache lasted for the whole day. I checked online, and since I had my water bag and kept drinking, it might have been mountain sickness. It's weird since I live close to the mountains and go on top regularly, but it's the only explanation I could find. Fortunately, it went away the next day, and the talks went well. https://twitter.com/GregOstravich/status/1768070218855612800?embedable=true It was time for me to leave for the last leg of my journey, the in Pasadena. Before that, life took an interesting turn of events: the forecast warned about a snowstorm in the area. The airline rebooked me twice: from 6 AM to 7 AM, then from 7 AM to 11 AM. I was lucky enough to get a seat, and though spraying the plane with unfreezing liquid took a bit of time, it managed to leave anyway. I left Denver under the snow and landed a handful of hours later in Los Angeles under the sun. Southern California Linux Expo It was my second time at SCaLE, , SoCalLinux; the first time was the year of Covid. I need to explain why speaking at SCaLE during this journey was necessary. At the time, I was to speak at two different meetups in San Francisco, then SCaLe, fly to Romania, then Istanbul, get back home on Saturday, and leave on Monday for Australia. Granted, it was not terrific planning, but I like to think that I lived and learned since then. aka Anyway, one of the meetups was canceled, and I did the other online from my hotel room. At SCaLe, the venue was pretty empty for an event this size. Some people were wearing masks, and antiseptic gel dispensers were everywhere. I had around ten people in my room, which was my record at the time - I've done worse since then. Later, the Romanian conference announced they would cancel the event. I called the Istanbul one, but they confirmed the event would occur. I rebooked to Istanbul, then one day later, they canceled as well. When life gives you lemons, you make lemonade; I decided to keep it that way to avoid more rebooking fees and spend the days in Istanbul anyway. For the record, on Sunday, the whole world stopped. The Australian conference was also canceled, and I had no chance to go there since. Thus, that was what went in my head by preparing for my talk at SCaLE: I wanted to exorcise my previous experience. I'm happy to say it worked! https://twitter.com/cantbewong/status/1768725626306150526?embedable=true Before leaving for home, though, I met with my friends from Yugabites: and . We had lunch, then enjoyed an hour or so walking on the shore of Venice Beach. Here, you can see them counting on their fingers: Denis Magda Franck Pachot Did you notice that you count on your fingers differently depending on where you were raised? Hint: find out how the English spies unwillingly reveal themselves in the Inglorious Basterds movie, despite speaking flawless German. It was time to get home after this last pause on American soil. Many hours later, I was at home, tired but happy from all those events. Many thanks to all the organizers who made them possible, especially Anthony, who worked as my agent for Canada. I also want to thank the people who came to my talks: speakers are nobody if there's no audience to listen to them. Finally, I want to thank my employer , who made it all possible. api7.ai See you soon ! somewhere PS: I tried to document my journey via #APISIXNorthAmericaTour. Find more pictures on , , and . Twitter LinkedIn Mastodon BlueSky Also published here

Apache APISIX: How My North America Tour Went

How I Made My Own Screenshot-To-Code Project 

This blog post will help Getting start to Machine Learning Journey to Deep Learning . I have tried to keep it short and clean . is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Essentially, it is a method of teaching computers to make and improve predictions based on some data. Machine learning In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. is a subset of AI. Machine learning The theory is simple, machines take data and ‘learn’ for themselves. is a state of machine when it can take decisions just like a human.In today’s modern world a machine fully capable to take decisions just like human brain even in the most complex and difficult environment can be said to have achieved Artificial Intelligence. It is not necessary for the machine to have arms and shape of a human. A simple computer box can also be artificially intelligent. Artificial intelligence Today tiny fragments of AI are all around us. Siri is AI, Alexa is AI, automatic turning off of the light based in number of people inside the room is AI. But the full and final state of AI would be reached when the human mind can be implemented or emulated completely. That emulated human mind computer would behave exactly like human and may be even more efficient. It would provide exactly same responses. Absolute Beginner : If you start with hardest stuff first,It will be way easier to get discouraged and give up so create small achievable goals during the learning process to stay motivated . ~ Before we start learning or deep dive into ML : ~ Choose Programming Language . _By Christina Voskoglou_medium.com What is the best programming language for Machine Learning? “Python is a Good Choice” scientific and numeric computing (with the help of libraries such as NumPy, SciPy, etc.), Support’s wide range of Libraries for various algorithms and have large community in ML . 2. (Optional: This is not must, Having some basic knowledge about it would be good, Since we can take the advantage of Python Scientific libraries like ,because while learning different algorithms you need to make visualization about the data & use it’s properties in algorithm’s using algebra,calculus concept’s) Basics Maths Knowledge about Algebra,Calculus,Probability & Statistics: Numpy & Scipy _Learn for free about math, art, computer programming, economics, physics, chemistry, biology, medicine, finance…_www.khanacademy.org Linear Algebra | Khan Academy _Learn for free about math, art, computer programming, economics, physics, chemistry, biology, medicine, finance…_www.khanacademy.org Calculus (all content, first year) | Khan Academy _Learn for free about math, art, computer programming, economics, physics, chemistry, biology, medicine, finance…_www.khanacademy.org Statistics and Probability | Khan Academy 3. : There are of machine learning libraries already written for Python. Just Learn it one by one . Learn Python Libraries: tons OpenCV : Helpful Analyzing Images/Videos and Applying Cascade’s and More etc, Numpy : Helpful performing Mathematical operations Matplotlib: Helps in Plotting Mathematically operations Visually in Dimensions Pandas : Helps in Gathering,Preparing the Data to feed into our Algorithms. After Learning above 3 Powerful Libraries . Jump right into . 4. : There is a Excellent and Highly Recommended Free Course by at coursera, course is a very good starting point for you to get your understanding about algorithms in Theory and different concept’s to Machine Learning . Andrew-Ng Course Andrew Ng _About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In…_www.coursera.org Machine Learning | Coursera After Finishing Andrew-Ng Course, Grabbing Lot of Theoretical Knowledge about Algorithms . 5. (one of the most powerful API with different Algorithms,Powerful Data Encoders etc) Learn Scikit-Learn Library : ~ Now it’s time to Apply Theoretical Knowledge into Practical ~ 6. I Would Highly Suggest you to read Python Machine Learning Edition 2 by Sebastian Raschka _Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source…_www.packtpub.com Python Machine Learning - Second Edition | PACKT Books From theory(Mathematical explanations) of different machine learning algorithms & optimization methods to practical code , covers large varieties of Practical Algorithms with Python, aswell as Using it with Scikit-Learn API, replaying it with Tensorflow API . 7. CheatSheet !!!!!!!! _Learning machine learning and deep learning is difficult for newbies. As well as deep learning libraries are difficult…_startupsventurecapital.com Essential Cheat Sheets for Machine Learning and Deep Learning Engineers 8. Develop CNN(Convolution Neural Network) to identify Dogs VS Cats Using TensorFlow 9. Simulating Self Driving Car with Keras : 10. Neural Network Applied Series on GTA-5 : This is a big, big journey. Very tiring, very irritating and exceptionally time consuming. If you can make your way through this list, by the end you should at least be familiar with the field of machine learning, and be prepared to figure out what you want to learn next. Good luck! ~Let’s Spread this AI Power ! ~ Note : Whatever i have shared totally based on the thing’s i have tried out during the learning process . ~Feedback’s and Edit’s are Welcome ~

Absolute Beginning into Machine Learning

Authors:
(1) Mohammadreza Hazhirpasand, University of Bern, Bern, Switzerland;
(2) Oscar Nierstrasz, University of Bern, Bern, Switzerland;
(3) Mohammad Ghafari, University of Auckland, Auckland, New Zealand. Table of Links Abstract and I. Introduction
II. Methodology
III. Results and Discussion
IV. Threats to Validity
V. Related Work
VI. Conclusions
VII. Acknowledgments and References II. METHODOLOGY In the following, we explain the objectives of this study as well as the methodology used for data collection and analysis. 1) Objective: In this study, we pose the following research question “what technical difficulties are prevalent among crypto libraries?” to tackle the underlying reasons why developers’ performance varies in using crypto APIs. The objectives of this research are listed in the following: • Finding prevalent themes of technical challenges in using crypto libraries helps library designers to improve the design of APIs. • Underlying factors can assist team leaders to be aware of areas where the developers might encounter difficulties in using cryptography. 2) Selecting crypto libraries: We aim at studying posts associated with popular crypto libraries on Stack Overflow. We assumed that discussions related to crypto libraries contain the name of the library as a tag. Hence, we selected the “cryptography” tag, i.e., base tag, to observe what other tags were used together with the base tag. We used Stack Exchange Data Explorer to run a query in order to fetch tags that appeared together with cryptography.[1] We realized that there are 2 184 tags, i.e., candidate tags. The two authors of this paper separately checked each of the candidate tags. Each of the reviewers selected the ones that are crypto libraries. They used the internet to explore a tag in which they had a lack of certainty. Then, they cross-checked the choices and discussed the tags. They arrived at the conclusion that tags that do not represent a crypto library or only provide a particular, limited service in cryptography (e.g., hashing) should not be considered. As a result, there were 6 tags that were eliminated from the list, namely rsacryptoserviceprovider, aescryptoserviceprovider, rijndaelmanaged, bcrypt, javax.crypto, and hashlib. The aforementioned tags are either a crypto class, namespace, or a dedicated module only for hashing. Ultimately, they agreed on a list of 20 crypto libraries, illustrated in Table I. 3) Crypto libraries: The selected crypto libraries are all widely used in practice and have been examined in research projects. For instance, six of the selected libraries, i.e., OpenSSL, libsodium, Bouncy Castle, SJCL, Crypto-JS, and PyCrypto, were studied for finding usability issues [6]. MCrypt is the successor to the Unix crypt command, which supports modern encryption algorithms. [2] The phpseclib library offers pure-PHP implementations of SSH2, SFTP, RSA, DSA, and many other algorithms. [3] Crypto++ and Botan are both C++ crypto libraries that support a wide range of crypto algorithms and security protocols. [4] [5] The Microsoft CryptoAPI interface enables developers to employ authentication, encoding, and encryption to Windows-based applications. [6] Jasypt and Java Cryptography Architecture (JCA) are both intended for Java developers, and the latter is part of the Java security API. [7] [8] The Web Crypto AP is intended to present basic cryptographic operations for web applications and defines cryptographic primitives in a native JavaScript API. [9] The wolfSSL TLS library is a lightweight, C-language-based library designed for IoT, embedded systems, and smart grids. [10] There are also popular OpenSSL wrappers in languages such as nodecrypto in Node.js and pyOpenSSL in Python. There have been numerous studies to investigate the security point of view of aforementioned crypto libraries and their strengths and weaknesses were examined [7] [8] [9]. However, the security evaluation of these crypto libraries falls outside the scope of this paper. 4) Manual investigation: In total, there are 24 648 posts that contained the selected crypto libraries’ tags. We computed the required sample size for the population with a confidence level of 95% and a margin of error of 4.34%, which results in sampling 500 posts. We then equally selected 25 posts from each tag (i.e., a crypto library). We queried the posts containing a crypto library tag,e.g., OpenSSL, and set the search criteria to “recent activity”, so that Stack Overflow returns the recent active discussions. Since we observed questions that are either unanswered or received negative votes, we decided to choose the posts for which the question received at least one upvote and at least one answer. The list of the selected questions are available online.[11] Thereafter, we employed thematic analysis, a qualitative research method for finding themes in texts [10], to deduce the frequent topics from the chosen posts. Since our study is of an exploratory nature, we did not devise a list of themes prior to studying the posts. Hence, in order to link each post to a suitable theme, two authors of the paper were responsible to separately study the posts and deduce the main issue (i.e., theme) of the post. The reviewers carefully reviewed the title, question body, and answer body of each post. Despite the fact that each post may entail several crypto concepts, the reviewers’ objective was to find the key issue of each post. They employed open coding in which a short explanation label was assigned to each post [11]. Each author reiterated the coding phase three times to improve their deduced list of themes. To evaluate the inter-rater agreement between the two reviewers, we employed Cohen’s kappa to assess the agreement level [12]. Deducing the themes from the posts, the reviewers received 68% Cohen’s Kappa score, which indicates a substantial agreement between the two reviewers. Finally, the two reviewers compared the two lists and discussed any disagreements. The two reviewers used different wording for building the list of themes and the total number of themes was not identical. They re-analyzed the particular posts in multiple sessions where they had different views. In some scenarios, they realized that one of the reviewers broke down one theme into several sub-themes, which they then merged if necessary. Ultimately, they agreed on 10 themes for the analyzed posts. This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://data.stackexchange.com/ [2] http://mcrypt.sourceforge.net [3] https://github.com/phpseclib/phpseclib [4] https://www.cryptopp.com [5] https://botan.randombit.net [6] https://docs.microsoft.com/en-us/windows/win32/seccrypto/cryptography-portal [7] http://www.jasypt.org/ [8] https://www.oracle.com/java/technologies/javase/javase-tech-security.html [9] https://www.w3.org/TR/WebCryptoAPI/ [10] https://www.wolfssl.com/ [11] http://crypto-explorer.com/crypto_libs/ Authors: (1) Mohammadreza Hazhirpasand, University of Bern, Bern, Switzerland; (2) Oscar Nierstrasz, University of Bern, Bern, Switzerland; (3) Mohammad Ghafari, University of Auckland, Auckland, New Zealand. Authors: Authors: (1) Mohammadreza Hazhirpasand, University of Bern, Bern, Switzerland; (2) Oscar Nierstrasz, University of Bern, Bern, Switzerland; (3) Mohammad Ghafari, University of Auckland, Auckland, New Zealand. Table of Links Abstract and I. Introduction II. Methodology III. Results and Discussion IV. Threats to Validity V. Related Work VI. Conclusions VII. Acknowledgments and References Abstract and I. Introduction Abstract and I. Introduction II. Methodology II. Methodology III. Results and Discussion III. Results and Discussion IV. Threats to Validity IV. Threats to Validity V. Related Work V. Related Work VI. Conclusions VI. Conclusions VII. Acknowledgments and References VII. Acknowledgments and References II. METHODOLOGY In the following, we explain the objectives of this study as well as the methodology used for data collection and analysis. 1) Objective : In this study, we pose the following research question “what technical difficulties are prevalent among crypto libraries?” to tackle the underlying reasons why developers’ performance varies in using crypto APIs. 1) Objective The objectives of this research are listed in the following: • Finding prevalent themes of technical challenges in using crypto libraries helps library designers to improve the design of APIs. • Underlying factors can assist team leaders to be aware of areas where the developers might encounter difficulties in using cryptography. 2) Selecting crypto libraries: We aim at studying posts associated with popular crypto libraries on Stack Overflow. We assumed that discussions related to crypto libraries contain 2) Selecting crypto libraries: the name of the library as a tag. Hence, we selected the “cryptography” tag, i.e., base tag, to observe what other tags were used together with the base tag. We used Stack Exchange Data Explorer to run a query in order to fetch tags that appeared together with cryptography.[1] We realized that there are 2 184 tags, i.e., candidate tags. The two authors of this paper separately checked each of the candidate tags. Each of the reviewers selected the ones that are crypto libraries. They used the internet to explore a tag in which they had a lack of certainty. Then, they cross-checked the choices and discussed the tags. They arrived at the conclusion that tags that do not represent a crypto library or only provide a particular, limited service in cryptography (e.g., hashing) should not be considered. As a result, there were 6 tags that were eliminated from the list, namely rsacryptoserviceprovider, aescryptoserviceprovider, rijndaelmanaged, bcrypt, javax.crypto, and hashlib. The aforementioned tags are either a crypto class, namespace, or a dedicated module only for hashing. Ultimately, they agreed on a list of 20 crypto libraries, illustrated in Table I. 3) Crypto libraries: The selected crypto libraries are all widely used in practice and have been examined in research projects. For instance, six of the selected libraries, i.e., OpenSSL, libsodium, Bouncy Castle, SJCL, Crypto-JS, and PyCrypto, were studied for finding usability issues [6]. MCrypt is the successor to the Unix crypt command, which supports modern encryption algorithms. [2] The phpseclib library offers pure-PHP implementations of SSH2, SFTP, RSA, DSA, and many other algorithms. [3] Crypto++ and Botan are both C++ crypto libraries that support a wide range of crypto algorithms and security protocols. [4] [5] The Microsoft CryptoAPI interface enables developers to employ authentication, encoding, and encryption to Windows-based applications. [6] Jasypt and Java Cryptography Architecture (JCA) are both intended for Java developers, and the latter is part of the Java security API. [7] [8] The Web Crypto AP is intended to present basic cryptographic operations for web applications and defines cryptographic primitives in a native JavaScript API. [9] The wolfSSL TLS library is a lightweight, C-language-based library designed for IoT, embedded systems, and smart grids. [10] There are also popular OpenSSL wrappers in languages such as nodecrypto in Node.js and pyOpenSSL in Python. There have been numerous studies to investigate the security point of view of aforementioned crypto libraries and their strengths and weaknesses were examined [7] [8] [9]. However, the security evaluation of these crypto libraries falls outside the scope of this paper. 4) Manual investigation: In total, there are 24 648 posts that contained the selected crypto libraries’ tags. We computed the required sample size for the population with a confidence level of 95% and a margin of error of 4.34%, which results in sampling 500 posts. We then equally selected 25 posts from each tag (i.e., a crypto library). We queried the posts containing a crypto library tag,e.g., OpenSSL, and set the search criteria to “recent activity”, so that Stack Overflow returns the recent active discussions. Since we observed questions that are either unanswered or received negative votes, we decided to choose the posts for which the question received at least one upvote and at least one answer. The list of the selected questions are available online.[11] 4) Manual investigation: Thereafter, we employed thematic analysis, a qualitative research method for finding themes in texts [10], to deduce the frequent topics from the chosen posts. Since our study is of an exploratory nature, we did not devise a list of themes prior to studying the posts. Hence, in order to link each post to a suitable theme, two authors of the paper were responsible to separately study the posts and deduce the main issue (i.e., theme) of the post. The reviewers carefully reviewed the title, question body, and answer body of each post. Despite the fact that each post may entail several crypto concepts, the reviewers’ objective was to find the key issue of each post. They employed open coding in which a short explanation label was assigned to each post [11]. Each author reiterated the coding phase three times to improve their deduced list of themes. To evaluate the inter-rater agreement between the two reviewers, we employed Cohen’s kappa to assess the agreement level [12]. Deducing the themes from the posts, the reviewers received 68% Cohen’s Kappa score, which indicates a substantial agreement between the two reviewers. Finally, the two reviewers compared the two lists and discussed any disagreements. The two reviewers used different wording for building the list of themes and the total number of themes was not identical. They re-analyzed the particular posts in multiple sessions where they had different views. In some scenarios, they realized that one of the reviewers broke down one theme into several sub-themes, which they then merged if necessary. Ultimately, they agreed on 10 themes for the analyzed posts. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://data.stackexchange.com/ [2] http://mcrypt.sourceforge.net [3] https://github.com/phpseclib/phpseclib [4] https://www.cryptopp.com [5] https://botan.randombit.net [6] https://docs.microsoft.com/en-us/windows/win32/seccrypto/cryptography-portal [7] http://www.jasypt.org/ [8] https://www.oracle.com/java/technologies/javase/javase-tech-security.html [9] https://www.w3.org/TR/WebCryptoAPI/ [10] https://www.wolfssl.com/ [11] http://crypto-explorer.com/crypto_libs/

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