Landmark 81 has a total of 81 floors, 461 m high. This is the tallest building in Vietnam, located in the center of Binh Thanh District, Ho Chi Minh City. The maintenance and technical maintenance of the entire building is performed daily by the management team and staff. Anh Vu – the technical manager of the Landmark 81 building, said that the technical team often had quick briefings early in the morning to determine the arising work to do during the day. At the same time, the technical status of the building, the work done in the previous day must be reported in detail. After that, the group of employees moved to the 79th floor of the building. This is the highest floor accessible by elevator. After that, people will walk up to the 80th and 81st floors (top floor) of the building to do their assigned work. Check the building’s power supply system. All electrical specifications must ensure safety conditions in accordance with regulations. Technical staff use specialized temperature gauges to check the heat at conductor structures and power-using equipment on the 80th floor. This is a routine job to promptly detect problems and limit problems. minimize the risk of accidental fire or explosion. A group of workers periodically maintain the glass. To do this, the worker must move on specialized rigs, in turn, check all the glass panes. The outer glass of the building is reinforced with special materials, ensuring safety and durability. Workers check a building’s cooling system. The staff checks the operating parameters of the elevator. “The building is tall, the elevator is an indispensable element in the operation. We understand this, so we should pay attention to the daily maintenance and technical maintenance, so that the elevator does not cause problems,” said Mr. Vu. to speak. The elevator of Landmark 81 building has a pulling force of 12,500 kg, each car has a load of 2,000 kg, a speed of 5m/s – is one of the fastest elevators in Vietnam. “Working on high-rise buildings requires us to have good health and overcome specific limitations. For example, when it comes to glass maintenance, every worker must overcome fear, focus on work and comply with all protection principles to ensure safety,” said Quang Trung, a technical staff member. Periodic inspection and technical maintenance is carried out in the early morning hours, or when people take a lunch break. Troubleshooting was also implemented urgently. This helps the building operate in a safe condition, and at the same time does not affect the activities of residents, merchants and visitors. In the photo is a visitor visiting the observatory on the 81st floor of the building on April 11. When put into operation in July 2018, the Landmark 81 building has become one of the symbols of Ho Chi Minh City. Inside the building, service-commercial activities take place quite bustling, contributing to the driving force to promote the development of the city in the direction of civilization and modernity.

Illustration. Source: Internet. Implementing an effective Mobile Money pilot requires readiness in terms of technical infrastructure, technology, human resources, finance and moreover, the security issue cannot be taken lightly. Ready to “enter” Mobile Money Mobile Money service helps people, especially in remote and isolated areas, have more means of non-cash payment, especially for small expenditures under 100,000 VND. People will be able to withdraw and top up money at telecommunications transaction points or business transaction points authorized by DNVT as a legal entity. Sharing about Viettel’s preparation to deploy Mobile Money service, Deputy General Director of Viettel Digital Services Corporation, Major Truong Quang Viet said: “Viettel has successfully tested Mobile Money for 40,000 customers. internally, ready to provide services to 100% of Viettel customers In addition, we have a set of knowledge ready to train for Mobile Money transaction points, so as soon as it is approved by the authorities , all transaction or payment acceptance points of Viettel have a certain understanding of Mobile Money to support users.Besides, Viettel also prepares a 24/7 customer care system for advice and answers. all customer inquiries. Preparing to enter a completely new field, Deputy General Director of MobiFone Telecommunications Corporation Bui Son Nam informed: MobiFone established MobiFone Digital Service Center and prepared the entire technical infrastructure to ensure deploy Mobile Money as soon as the Government allows. Commenting on the advantages of operators when implementing Mobile Money, Deputy Director of the Department of Telecommunication (Ministry of Information and Communications) Tran Duy Hai said: “Thanks to taking advantage of the telecommunications network infrastructure and transaction points on the Internet. Nationwide, DNVT has many advantages when deploying this service, however, the implementation process also faces certain difficulties such as: Changing user habits or DNVT previously only stopped at deploying e-wallets, now the network will be expanded and deployed to remote areas, accompanied by requirements for risk prevention and control, ensuring the safety of money in the account, and anti-money laundering … “. Security comes from both sides As a non-cash payment activity, one of the biggest concerns of users when using Mobile Money is information security and safety. Regarding this issue, according to Deputy Director Tran Duy Hai: In the Prime Minister’s decision to allow pilot service, the pilot enterprise must have a system that meets the requirements of ensuring system safety. Level 3 information in accordance with current law. In addition, there must be a system to store the arising Mobile Money transaction history; at the same time, there must be a backup copy of archival information in service of inspection, examination and information provision upon request from competent state management agencies. For accounting documents, enterprises conducting the pilot must store them in accordance with the provisions of the Law on Accounting,… From the point of view of a cybersecurity expert, Deputy Chief Technology Officer of Vietnam Cybersecurity Technology Joint Stock Company Ngo Quoc Vinh also gave warnings about security when people in remote areas approach Mobile. Money. The reason is that people in this area do not have knowledge about network security as well as have no conditions to equip measures to protect equipment or personal data. Cybercriminals will take advantage of that and be well-prepared to target financial theft. Specifically, making the most of fake websites, messages, and information to reach users. Mr. Ngo Quoc Vinh also said that in order to be licensed, service providers must comply with and equip a series of security architectural solutions at the request of the State. Therefore, what businesses need to do is to continuously strictly maintain the state of the system to ensure information security to the highest level. At the same time, continuously improve the security level for users’ terminals when using the service. Meanwhile, users themselves must raise awareness that digital assets are targets for cybercriminals to exploit personal data and steal assets. Users need to follow instructions and directions from authorities in the field of information security. When they comply with that regulation and raise their awareness, the ability to prevent and combat risks will increase. Major Truong Quang Viet said: Viettel has experience in deploying telecommunications services to 70 million users, deploying e-wallets in 6 markets around the world. ViettelPay digital ecosystem is the infrastructure for Viettel to deploy Mobile Money. ViettelPay has ensured that security related regulations meet the world’s leading standards. Besides, Viettel has a network security company that protects users from attacks on the system or stealing accounts. Referring to measures to prevent money laundering, Viettel’s representative said that this enterprise applies big data technology and artificial intelligence to ensure the detection of all unusual transactions. Responding to users’ concerns when the Mobile Money service only transfers within one provider, Deputy Director Tran Duy Hai explained: “Mobile Money accounts can only pay with internal accounts in telecommunications carriers. registration, there is no cross-linking between carriers to help limit risks that may arise during the testing process.After a period of implementation of technical solutions along with evaluation of the results of the test implementation process. point, internetwork can be suggested”.

Edit | Tina Technology Radar is a technology trend report that ThoughtWorks releases every six months. It keeps track of how interesting technologies are developed, which we call items. The technology radar uses quadrants and rings to classify it. Different quadrants represent different types of technologies, and the rings represent our maturity assessment. After six months of tracking and precipitation, ThoughtWorks TAB (ThoughtWorks Technical Advisory Committee) produced the 24th technical radar for technicians based on our practice cases in multiple industries. Analyze more than one hundred technical items, explain their current maturity, and provide corresponding technical selection suggestions. 1 Four themes in this issue Platform team that accelerates product launch More and more organizations are adopting the concept of platform team to develop products, that is, setting up a dedicated team to create and support internal platform functions, and use these functions to accelerate application development, reduce the complexity of operation and maintenance, and shorten the time to market time. This increasingly mature approach is welcome, so as early as 2017, we introduced this technology to technology radar. But as maturity increases, we find that organizations should avoid using some anti-patterns when applying this technology. For example, “using one platform to rule everything” may not be the best choice. “Building a big platform in one step” may take several years to deliver value. In line with the original intention of “Once it is built, there will be people using it”, it may be a huge waste in the end. On the contrary, using product thinking helps to understand the services that each internal platform should provide based on the needs of product customers. But if the platform team is allowed to only solve the problems submitted in the technical support ticket system, then this approach will produce the old-fashioned operation and maintenance island team, and the corresponding disadvantages of demand priority imbalance, such as slow feedback and response, and Competing for scarce resources and other issues. In addition, we have also seen some new tools and integration models emerge to effectively divide teams and technologies. The convenience of integration overwhelms the single best solution We have seen corresponding developer-oriented tool integrations on many platforms (especially in the cloud field). For example, artifact libraries, source code control, CI/CD pipeline, wiki, and similar tools. Development teams usually manually select these tools and stitch them together as needed. Having a combined tool stack can bring more convenience and less work to developers, but this tool set rarely represents the best solution. Too complicated to get into the radar In the nomenclature of technical radar, the final state of the discussion of many complex topics will be “TCTB-too complex to enter the radar (too complex to blip)”. This means that those items cannot be categorized because they exhibit too many pros and cons, as well as a large number of nuances regarding suggestions, applicability of tools, and other reasons, which prevents us from summarizing them in just a few sentences. . These topics usually appear every year, including monorepos, distributed architecture orchestration guidelines, branch models, and so on. Like many topics in software development, there are too many trade-offs to provide clear advice. Identify the architectural coupling context In software architecture, how to determine an appropriate level of coupling between microservices, components, API gateways, integration centers, front-ends, etc. is a topic that is discussed in almost every meeting. It can be seen everywhere that when two pieces of software need to be connected together, architects and developers are struggling to find the right level of coupling. We need to spend time and energy to understand these factors, and then make these decisions according to local conditions, instead of hoping to find a general but inappropriate solution. 2 First look at some of the quadrant highlights Platform Engineering Product Team (Adopted) As pointed out in one of the topics of this issue of Radar, the industry has accumulated more and more experience in the “platform engineering product team” that creates and supports internal platforms. Teams in the organization use these platforms to accelerate application development, reduce operational complexity, and shorten time to market. As the adoption rate increases, we are becoming more and more aware of the good and bad patterns of this approach. When creating a platform, it is important to clearly define the customers and products that can benefit from it, rather than building it out of thin air. We not only have to beware of the layered platform team, it retains the existing technology islands, but is just labeled as the “platform team”, and we must also be careful about the work order-driven platform operation model. When considering how to best organize the platform team, we are still loyal supporters of the concept of team topology. We believe that the platform engineering product team is a standard method and an important enabler of high-performance IT. Cloud sandbox (experimental) As cloud services become more and more common, and creating cloud sandboxes becomes easier and can be applied on a large scale, our team is therefore more inclined to use a completely cloud-based (relatively local) development environment. Reduce maintenance complexity. We found that the tools used to simulate cloud-native services locally limited developers’ confidence in the build and test cycle, so we focused on standardized cloud sandboxes instead of running cloud-native components on development machines. This approach can better enforce the application of infrastructure as code practices, and provide developers with a good adaptation process in the sandbox environment. Of course, this shift is also risky, because it assumes that developers will be completely dependent on the availability of the cloud environment and may slow down the speed at which developers can get feedback. We strongly recommend that you adopt some lean governance practices to manage the standardization of these sandbox environments, especially in terms of security, access control, and regional deployment. Homomorphic encryption (evaluation) Complete homomorphic encryption (Homomorphic encryption) refers to a type of encryption method that allows direct calculation operations (such as search and arithmetic operations) on encrypted data. At the same time, the calculated result still exists in encrypted form, and can be decrypted and displayed later. Although the homomorphic encryption problem was raised as early as 1978, the solution did not appear until 2009. With the improvement of computer computing power and the emergence of easy-to-use open source libraries such as SEAL, Lattigo, HElib and partial homomorphic encryption in Python, the application of homomorphic encryption in real-world applications has truly become feasible. Those exciting application scenarios include privacy protection when outsourcing computing to an untrusted third party, such as computing encrypted data in the cloud, or enabling a third party to aggregate the intermediate results of homomorphic encrypted federal machine learning . In addition, most homomorphic encryption schemes are considered safe for quantum computers, and efforts to standardize homomorphic encryption are also underway. Although homomorphic encryption currently has many limitations in terms of performance and supported computing types, it is still a technology worthy of our attention. Sentry (adopted) Sentry has become the default option for many teams. Sentry provides some convenient functions, such as error grouping and using appropriate parameters to define error filtering rules, which can greatly help deal with a large number of errors from end-user devices. By integrating Sentry into the continuous delivery pipeline, you can upload source code mapping files to debug errors more efficiently, and it is easy to trace which version of the software produced these errors. We appreciate that although Sentry is a SaaS product, its source code is public, so it can be used for free in some smaller use cases and self-hosting. imgcook (evaluation) imgcook is a software as a service product under Alibaba. It can generate front-end code from different types of visual drafts (Sketch/PSD/static pictures) with one click through intelligent technology. imgcook can generate static code. If you define a domain-specific language, it can also generate data binding module code. The technology has not yet reached the perfect level. Designers need to refer to certain specifications to improve the accuracy of code generation (hereafter The adjustment of the developer is still needed). We have always been very cautious about magic code generation, because in the long run, the generated code is usually difficult to maintain, and imgcook is no exception. But if you limit it to a specific context, such as a one-time event advertising page, this technique is worth a try. AWS CodePipeline (suspended) Based on the experience of multiple ThoughtWorks teams, we recommend that you use AWS CodePipeline with caution. Specifically, we found that once the team’s needs go beyond the scope of a simple pipeline, this tool becomes difficult to use. Although the first time you use AWS, it seems like you won a “quick victory”, but we suggest you take a step back and evaluate whether AWS CodePipeline can meet your long-term needs, such as the fan-out and fan-in of the pipeline, or more complicated Deployment, and test scenarios with special dependencies and trigger conditions. Snowflake (experimental) Since Snowflake was mentioned on the radar last time, we have gained more experience in its use and data mesh as an alternative to data warehouses and data lakes. Snowflake continues to impress people in terms of time travel, zero-copy cloning, data sharing and its application market. Snowflake has not appeared in any place that we dislike, so our consultants prefer to use it compared to other options. Amazon’s data warehouse product Redshift is moving in the direction of separating storage and computing, and this has always been Snowflake’s strengths. If you use the Spectrum feature in the Redshift product for data analysis, you will feel that it is not so convenient and flexible, partly because it is constrained by Postgres (although we also like to use Postgres). Federated queries may be the reason for using Redshift. In terms of operation, Snowflake’s operation will be simpler. Although BigQuery is another option and very easy to operate, Snowflake is a better choice in a multi-cloud scenario. We have successfully used Snowflake on GCP, AWS and Azure. Feature Store (evaluation) Feature Store is a data platform for machine learning, which can solve some of the key problems we currently encounter in feature engineering. It provides three basic functions: (1) Use managed data pipelines to eliminate conflicts between new data and data pipelines; (2) Catalog and store feature data to promote the discoverability of features across models And synergy; (3) Continuously provide characteristic data during model training and interference. Since Uber disclosed the Michelangelo platform, many organizations and start-ups have established their own signature libraries; such as Hopsworks, Feast, and Tecton. We see the potential of Feature Store and recommend careful evaluation. Self-developed infrastructure as code (IaC) products (suspended) Those products supported by the company or the community (at least in the industry) are constantly evolving. But sometimes organizations tend to build frameworks or abstractions on top of existing external products to meet very specific needs within the organization, and believe that this adaptation will have more benefits than their existing external products. We found that some organizations tried to create self-developed infrastructure as code (IaC) products based on existing external products. They underestimated the amount of work required to continuously evolve these self-developed solutions based on their needs. Soon they will realize that the original version of the external product on which it is based is much better than their own product. In some cases, the abstraction built on the external product even weakens the original function of the external product. Although we have witnessed some successful cases of organizing self-developed products, we still recommend that this approach be carefully considered. Because this will bring a workload that cannot be ignored, and a long-term product vision needs to be established in order to achieve the desired results. Combine (adopted) We moved ReactiveX (a series of reactive programming open source frameworks) a few years ago to the “adoption” ring of the technology radar. In 2017, we mentioned RxSwift, which can apply reactive programming to Swift-based iOS development. Since then, Apple has launched its own reactive programming framework in the form of Combine. For apps that only support iOS 13 and later, Combine has become the default choice. It is easier to learn than RxSwift and integrates well with SwiftUI. If you want to convert an existing project framework from RxSwift to Combine, or use both in a project, you can learn about RxCombine. Kotlin Flow (experimental) The introduction of Kotlin coroutines opened a door to Kotlin’s innovation-Kotlin Flow, which is directly integrated into the coroutine library, is one of them. Kotlin Flow is an implementation of responsive flow based on coroutines. Unlike RxJava, stream is a Kotlin native API, similar to the familiar sequence API, including map and filter methods. Like sequences, streams are “cold”, which means that the values ​​of the sequence are constructed only when needed. All these features make the writing of multi-threaded code easier and easier to understand than other methods. It is foreseeable that converting a stream into a list through the toList method will become a common pattern in testing. River (evaluation) The core of many machine learning methods is to create a model from a set of training data. Once the model is created, it can be used repeatedly. However, the world is not static, and usually models need to change with the emergence of new data. Simply retraining the model can be very slow and expensive. Incremental learning solves this problem, it makes it possible to learn incrementally from the data stream, thereby reacting to changes more quickly. As an added benefit, computing and memory requirements are lower and predictable. We have accumulated good experience in the implementation based on the River framework, but so far, we need to add verification after the model is updated, sometimes manually.

AI is the key to enhance the smart home experience

Since the end of last year, Yunmi has conducted a national user survey. In 4 months, across more than 20 provinces and more than 60 cities, Yunmi had an in-depth communication with users. In this process, Yunmi clearly felt that in the current era of the rise of smart technology, the deepest confusion among users about smart home appliances is: Is smart really useful?

How to make users feel that smart home appliances are useful? Let the device perceive, analyze, and judge like a human, and actively help users solve problems? The answer given by Yunmi is AI. Yunmi believes that AI intelligence will become the key to enhancing the smart home experience.

Just like Tesla (Tesla) cars do AI, through the collection and analysis of various data such as people/long/road/city, etc., comprehensive decisions are made, so as to realize the equipment one step ahead, think what people think, and help users save time and effort. . In Yunmi’s view, AI can also empower home appliances, making home appliances highly active and intelligent, and achieving “know you better than you”. Yunmi collects and analyzes Home Data such as the environment, equipment, and user behavior of the home appliance, and performs self-adjustment and analysis of parameters and the global status of networked devices, so that home appliances have the “super power” of autonomous learning and automatic evolution, and finally realized The home of AI.

In fact, as early as 2016, Yunmi established an artificial intelligence laboratory AI lab, focusing on exploring application technologies including voice control, face/image recognition, gesture control, and algorithmic big data. In addition, Yunmi also released the first AI bionic chip “Wukong”, and early applied AI to smart home appliances. The sensational Yunmi AI range hood Air Bot can recognize the size of the smoke with AI, and the wind moves with the smoke; the large-screen refrigerator can follow the drama while cooking, while controlling the whole house appliances; the water heater can automatically adjust the water temperature according to the user’s daily use behavior; high-end AI air conditioner creates a whole-house temperature field sensing system, and feels AI smart wind with one key.

With the advancement of the integration of 5G+IoT+AI technology, Yunmi’s understanding and application of AI intelligence is also deepening. The ultimate meaning of AI is to solve various problems for users. This is the useful intelligence and the intelligence that users want. In order to implement the usefulness, Yunmi released the “Helpful” strategy and a series of new AI strategy products to actively and intelligently solve the various problems in users’ home life, and use the product to speak, allowing users to truly experience the benefits and convenience brought by smart home life.

Working with Nobel Prize winners, Yunmi builds a top AI technical team

AI intelligence landing in thousands of households in China does not mean that it is achieved through the user’s purchase or use of one or two smart home appliances. Instead, on the basis of the whole house interconnection, AI intelligence is integrated into various home life scenarios to build a smart home environment for users, and solve problems for users anytime, anywhere. In order to better achieve this goal, Yunmi will also heavily increase its AI technology and talent reserves in 2021 to provide a long-term guarantee for the home smart AI upgrade campaign.

In order to further expand the underlying research of AI and the in-depth application of AI in smart homes, in the future, Yunmi AI Lab will start research from four aspects: intelligent perception, AI brain, natural language interaction, and active intelligence. Intelligent perception can better capture and collect all kinds of behavioral data and body of users, provide a basis for the comprehensive decision-making of the AI ​​brain, and provide data support for the development of more new Yunmi AI intelligent products in the future; research on the AI ​​brain can Provide users with intelligent personalized services, such as habit memory, environmental analysis, intelligent recommendation and other functions, will promote AI smart home appliances to better meet the different needs of different users; natural voice interaction is essential to improve user experience The indispensable section, natural and unrestricted human-computer interaction, is the inevitable trend of smart homes in the future; the realization of active intelligence will provide users with a more comfortable smart living environment and solve various life problems for users from the source.

At present, research on the four dimensions of intelligent perception, AI brain, natural language interaction, and active intelligence has shown results in the new products of Yunmi’s 2021 strategy. For example: EyeBot AI health detection toilet can AI human feeling, no need to use hands, just sit down Physical examination, automatic flushing when you get up and leaving + disinfection in the bucket; EROx Yun⽶·Quanxian AI mineral water purifier, stop when the cup is full, AI adjusts the mineral content in the water; Yunmi AI inverter air conditioner, real-time monitoring of human body temperature/ Attitude/position and automatically adjust wind speed…

At the same time, in order to further consolidate the underlying technical architecture, Yunmi also specially invited the world’s top scientist, the 2006 Nobel Prize winner in physics, and the father of cosmological embryology—George Smoot, to serve as the Yun AI seat. the scientist. Professor George Smoot is currently engaged in research on the application of AI in the field of life and health. His main artificial intelligence medical technology has been listed as a national priority development project by the United States. The strong joining of George Smoot not only brings unlimited possibilities for the research and development of Yunmi AI technology, but also injects new vitality and vitality into the future development of the smart home industry.

In order to deepen the company’s AI technology moat, Yunmi has also built an international team of AI scientists. Team members include various AI experts. Their research fields involve computer vision, voice interaction, big data mining, business intelligence, etc., covering almost the entire field of AI from the underlying foundation to cognitive and perceptual technologies. The international and diversified R&D team helps Yunmi to be full of innovation and vitality in the research and development and practice of new AI technology products, and also gives consumers more new expectations for the application of AI intelligence in Yunmi home appliances.

As the pioneer of smart home appliances in the whole house, Yunmi has always been at the forefront of the smart home industry. Relying on deep insights into future technology development trends and user needs, Yunmi has joined hands with top scientists and formed a strong AI R&D team to deepen the research and development of AI smart products and realize the upgrade of 5GIoT whole-house interconnection. Yunmi uses AI to redefine the home of the future, allowing the home to evolve continuously, full of “wisdom” like human beings. Yunmi’s series of strategic moves have not only realized the re-upgrading of the brand itself, but also explored a new path for future development in the smart home industry.