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Baidu, Inc. offers internet search services in China. It operates through Baidu Core and iQIYI segments. The company offers Baidu App to access search, feed, and other services using mobile devices; Baidu Search to access its search and other services; Baidu Feed that provides users with personalized timeline based on their demographics and interests; and Haokan, a short video app. It also provides Baidu Knows, an online community where users can ask questions to other users; Baidu Wiki; Baidu Healthcare Wiki; Baidu Wenku; Baidu Scholar; Baidu Experience; Baidu Post; Baidu Maps, a voice-enabled mobile app that provides travel-related services; Baidu Drive; Baijiahao; and DuerOS, a smart assistant platform. In addition, it offers online marketing services, which include pay for performance, an auction-based services that allow customers to bid for priority placement of paid sponsored links and reach users who search for information related to their products or services; other marketing services that include display-based marketing services and other online marketing services based on performance criteria other than cost per click; mobile ecosystem, a portfolio of apps, including Baidu App, Haokan, and Baidu Post; various cloud services and solutions, such as platform as a service, software as a service, and infrastructure as a service; self-driving services, including maps, automated valet parking, autonomous navigation pilot, electric vehicles, and robotaxi fleets, as well as Xiaodu smart devices. Further, the company provides iQIYI, AN online entertainment service, including original and licensed content; other video content and membership; and online advertising services. Baidu, Inc. has strategic partnership with Zhejiang Geely Holding Group. The company was formerly known as Baidu.com, Inc. Baidu, Inc. was incorporated in 2000 and is headquartered in Beijing, China.

Baidu

baidu

Co-founder of China&#x27;s largest search engine, <strong>Baidu</strong>, Robin Li, has shared his perspective on the timeline for the realization of AGI. This visionary leader

Baidu CEO forecasts a decade-long journey to achieve artificial general intelligence - The National

Nvidia Promises 40x Hopper Performance in Blackwell Unveil at GTC 2025

Korea Line to open 6 crypto exchanges; Bithumb taxed $28mn; Singapore says ICO not a security To get the latest alerts on China and Asia Cryptocurrency happenings, subscribe to our newsletter below. It’ll be a quick, hassle-free update to your inbox daily. Top News is looking to open 6 crypto exchanges. Korea Line http://bit.ly/2M6l7xP with USD $28 mn. Korea taxes Bithumb http://bit.ly/2McxRD0 : ICO by nature is not a security. **Chinese Media** Singapore Monetary Authority Google translated link establishes WaltonChain Institute, following Ripple. South Korean Government https://prn.to/2MaxaKt Coin and Exchange News is now available on VeChainThor Testnet Github. confirmed issuing its first official rejection of an exchange. Japan http://bit.ly/2sFXnZw Startup Advisory Group are cooperating. ICON and Deloitte http://bit.ly/2MbKOxc Business News on India’s surging cryptocurrency crime. Quartz’s take http://bit.ly/2sG09ho launched its first blockchain native App. It’s about the universe and it’s quite pretty. Baidu http://bit.ly/2sHXIL3 has put their Reddit AMA in medium. BinanceLabs http://bit.ly/2sC9Q0f raises US$14 bn. Ant Financial https://s.nikkei.com/2sH1ioU Looking for our Asia-based company calendar? We’ve moved it to our website!

Today’s Crypto News on Asia — June 8th

The Chinese tech user base has grown at an astronomical pace over the past decade. . In addition to growing at a neck-breaking pace, the industry has been becoming increasingly transparent to western audiences. In 2016, online spending in China was greater than that of the US and UK combined Currently, the Chinese tech landscape is dominated by 3 kingpins: (Market Cap $374B), Tencent ($327B), and Baidu ($64B). Alibaba The largest of the trio is Alibaba, led by Jack Ma. Starting off initially as a B2B (business-to-business) website for connecting manufacturers and potential buyers outside of , Alibaba has transformed into the second-largest e-commerce company in the world by revenue, behind Amazon.com. For more on Alibaba, see published in . China my article HackerNoon The next largest is Tencent, which also tends to be the lesser known of the trio in the US. The company is primarily mobile-focused, with popular apps for social media and gaming, with investments in just about every other aspect of tech. Tencent’s owns WeChat, one of the largest messaging apps in the world, with over 938M monthly users. But Tencent is also the largest gaming company in the world, with its Honor of Kings being the most downloaded app in the world for Q1 of 2017. . Tencent recently announced that it will be making its most successful game (Honor of Kings) available in the US and Europe in the fall The smallest of the trio is Baidu, commonly referred to as “the Google of China” in Western media. Baidu owns the most popular search engine in China, but also has operations like “Baidu Baike” (a Chinese Wikipedia) and Baidu Music. These companies don’t operate in strict silos; they’ve all expanded into most of the technology sub-sectors. See the below graphic from The Boston Consulting Group: Source: What China Reveals About the Future of Shopping . In the last two years, . Partnering with one of the Chinese giants is almost a necessity for Western tech companies who want to penetrate the Chinese market. And their investments do not stop at China’s borders these companies (and their follower JD.com) invested $5.6 billion into US tech companies In early 2016, and in 2014, they became an investor in Lyft. Alibaba has been investing heavily into Augmented Reality startups across the globe: they have equity in , , and even the mysterious (in the form of a ~$800M investment). See: . Alibaba became the 4th largest owner of Groupon Lumus WayRay Magic Leap Top Funded AR Startups in 2017 Tencent too is growing outside of the Great Wall. The company was an early investor in Snapchat and bought Supercell, the makers of the popular mobile game Clash of Clans, for a whopping $8.6 billion. Tencent even owns Riot Games, the makers of the competitive game League of Legends, which had ~67M monthly active users as of 2014 .They’ve also invested $1.2 billion into Flipkart and $1.8 billion into Tesla. Source: The Rise of China’s Tech Firms in Five Charts Even their investments into individual companies are intertwined. All of the aforementioned companies have investments in Didi Chuxing, the Uber of China. One can see how none of these companies are comfortable simply staying inside of their specialty; much like their US counterparts, they’ve each invested heavily in other aspects of tech. The Chinese tech industry has been largely ignored in US financial/tech media, but as the companies expand and grow, the days of this trend are numbered. Further Reading: China’s Internet Giants Go Global Chinacast: Alibaba The Rise of China’s Tech Firms in Five Charts Meet the 8 Chinese Tech Giants You Need to Know The Next Disney Will Come from China and Its Name Is Tencent Full Disclosure: I am employed by Amazon.com and the viewpoints in this article are entirely my own.

The Chinese Tech Trio And Their Holdings

The State of The Market — February 15, 2019 : $3,640.40 (+0.69%) : $123.21 (+0.91%) : $0.303646 (+0.20%) BTC ETH XRP After a slow decline in the last couple of days, the crypto market posted small gains today. While Bitcoin is still struggling to move past $3,650, the total market cap added nearly $1.5 Billion in the last 24 hours. All of the top 10 cryptocurrencies are in green right now, posting single-digit gains. A clear trend could emerge over the weekend when volumes are low. Meanwhile, Ethereum managed to defend its support at $100 this week, and Ripple has bounced back above $0.30. In other news, as it continues to evolve its platform. The StellarX team says purpose behind the acquisition is to allow for the StellarX platform to fulfill its potential as Coinsquare offers regulatory experience and a full scope plan to build around the platform, as well as an established network in the United States, Europe and Canada. Also, . At the moment Bitcoin, Tether, Ethereum and Litecoin can be traded with Thai baht on the move is designed to recruit merchants. OKEx also announced that it had hosted a conference in Bangkok to “bring industry leaders together to exchange ideas on blockchain technology.” Coinsquare, one of the largest cryptocurrency exchanges in Canada, has announced the acquisition of the StellarX decentralized exchange OKEx now offers Thai baht-to-crypto trading on its client-to-client (C2C) platform It will be used to settle a small portion of its transactions between clients of its wholesale payments business in real time. The American bank moves over $6 Trillion every day as a part of that business. JP Morgan claims it could also be used to provide instant settlement for securities issuances and even replace the US Dollar held internationally by subsidiaries of major corporations using JPMorgan’s treasury services. Eventually, it could even be used for mobile payments. JP Morgan’s CEO Jamie Dimon is not a fan of cryptocurrencies. On multiple instances, he has criticized Bitcoin and called it a fraud. However, that is not stopping his bank from releasing its own cryptocurrency. ( ) 1) American bank JP Morgan is set to release a digital currency called JPM Coin. Read More , an operating system for decentralized application (Dapp) development. It is a product of Baidu’s cloud computing unit, Baidu cloud. BBE is an open source, commercialized platform, to simplify Dapp development. It provides developers with services such as multi-chain and middle-tier frameworks, as well as smart contract and DApp templates. Additionally, it comes with enhanced data security and privacy protection. BBE will be compatible with Baidu’s existing cloud services. ( ) 2) Chinese search engine giant Baidu has announced the launch of its Baidu Blockchain Engine (BBE) Read More Grayscale oversees crypto investment trusts that give investors exposure to some of the leading cryptocurrencies, including Bitcoin, Ethereum, and XRP. Of the total amount raised last year, two-thirds of the funds, or $237 million, originated from institutional investors in what turned out to be one of Grayscale’s best calendar years ever. Nonetheless, inflows slowed in the final months of 2018, as evidenced by $30.1 million coming in versus $95.4 million in the year-ago period. ( ) 3) Digital currency investment firm Grayscale published its financial report for 2018, over which time they attracted nearly $360 million to their coffers. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

JP Morgan Launches Its Own Digital Currency, And China’s Baidu Reveals Blockchain OS

Regulation is not a sexy topic, unless we are discussing crypto regulations. In the last few weeks, many countries have voiced their unique ways to welcome ( or not so much) the crypto community. Korea has decided to as banks, while to be traded and exchanges to be opened. Even the SEC made remarks on BTC and ETH not being securities (horay!). China certainly has voiced their opinions too, this time through their permitting of e-commerce company JD regulate crypto exchanges Thailand has permitted certain cryptocurrencies to build a blockchain-backed token. This is particularly important, for 2 reasons. For one, we now know that China doesn’t just care about blockchain, it cares about tokens too. Past announcements only suggested the former, such as President Xi’s recent speech and the country’s unfavorable stance towards ICOs and crypto exchanges. But here we are, with an asset-backed-security token being issued by JD in collaboration with Huatai Securities, a state-owned investment bank. Secondly, and more importantly, the announcement divulges a glimpse of China’s crypto strategy. Tokens can be issued, but only if it’s issued by the right party. touting the benefits of blockchain technology, TL:DR: to really play and win the crypto game in China as an investor, you should actually be investing and buying into a basket of the top established tech companies such as Tencent, Alibaba, Baidu and JD, instead of the Chinese tokens that are currently ranked in coinmarketcap.com . Very few of the current top market cap tokens can succeed in China (but that doesn’t mean they won’t succeed.) Some more details about the news per Coindesk: “According to a from the Securities Times, an outlet of the China Securities Regulatory Commission, JD Finance established the trial in partnership with Huatai Securities, a brokerage firm that will underwrite the issuance, as well as Xingye Bank, which will act as a trust. JD Finance aims to issue the securities using a consortium blockchain that will see each party act as a node, recording the transactions in a transparent manner. The experiment sets out to ascertain if the blockchain can meet the demands of the various parties involved in the asset securitization process, such as issuers, underwriters and buyers.” report Upon first glance, this partnership is quite unique. First, a Chinese bank is collaborating with a Chinese tech company to jointly develop a blockchain product. This is equivalent to the US if Amazon worked together with Morgan Stanley to develop a blockchain product. That is unheard of, and unlikely to happen given the sheer number of legal issues that would be involved. However, in China, this is not out of the ordinary. In China, for example, tech companies can take a significant ownership of banks through equity. Mr. Moritz shared a great data point in the aforementioned Financial Times article about how Tencent and Alibaba’s investments totaled in over a thousand companies. Many of those investments also include public-listed companies such as banks and state owned enterprises (or commonly known as SOEs). Tencent, for example, (China International Capital Corporation) last year, the first investment bank in China born out of a joint venture between China Construction Bank and Morgan Stanley. Such large type of stake in a company naturally fosters closer relationship and collaboration. And what to better collaborate on than blockchain? acquired 5% of CICC Now, I don’t believe JD owns significant or any equity in Huatai Securities, but with this collaboration announcement, I promise you that it won’t be the last. Other new token creations between tech companies and banks may already be in the works. Note: You may ask, why is this important? Now in the US, we have a situation where the finance companies are trying to invest and build out their technology platform and possibly crypto trading I.e Goldman Sachs and JP Morgan, and tech companies like Coinbase are fighting daily over legal battles and clearing through gray areas with the SEC. And then you have an alternative situation in China, where the tech and finance companies are working together to build new products with the support of regulators. Who do you think is going to move faster in getting the cryptocurrency recognized by the masses, US or China? I believe each of these country’s approaches will have its benefits and flaws, opportunities and problems. But the fact that China’s regulators permit such possibility of collaboration is worth learning from. Now, the Chinese securities commission granted JD the right to issue a financial token in collaboration with Huatai. They did not grant this opportunity to any of the top market cap crypto companies such as NEO, EOS nor TRON. is primarily an . Despite the company’s recent , it’s not a pure, crypto-focused company like the others that I’ve mentioned. Note a tech company has been blessed with issuing the first blockchain token, after almost a year since the country banned cryptocurrency trading and ICOs. Even the recent Ministry of Industry & Information Technology’s (MIIT) crypto ranking did not give away this clue. Despite of Ethereum and NEO in its recent technology evaluation, it still opted to let a traditional tech company to issue tokens. JD.com e-commerce company with multiple product lines including food delivery developments on blockchain MIIT’s recent high ranks I believe JD was chosen because the Chinese regulators already had a close working relationships with them, along with Tencent, Alibaba and Baidu. When it comes to working with a “foreign” technology like blockchain, the regulators trust the existing technology leaders to test out these products more than the new kids on the block. A China expert tweeted once- “the purpose of a blockchain is to decentralize, which goes completely against what the Chinese .” Indeed, decentralization undermines the , and that’s exactly the opposite of what China under . Regulators will not support companies that attempt to decentralize the party’s power or put power into people’s hands in ways they don’t them want to. So Crypto companies that have touted decentralization, even if they were Chinese and may arguably have a leg up to non-Chinese Crypto companies when doing business in China, are at direct opposition with the regulators’ primary objectives. This is something Binance knew early on, and subsequently established their businesses elsewhere (they also didn’t have a choice, but they were also strategic and established a long-term relationship with Malta). Meanwhile, with it’s main objectives to the Chinese government continued to work towards transforming China with the help of its technology companies. There is a reason why Tencent and Alibaba’s products such as WeChat and Alipay are so ingrained into people’s lives in China (if you don’t know what those apps are, you need to. Better yet, go to Beijing for your next vacation and experience first hand). These tech companies had the blessings of the federal and local government, which permitted them to build amazing tools for their citizens, with recent example such as the and ”. Nonetheless, this is also the same reason why all the and there is no privacy on these platforms. The exact reason that tech companies have collaborated so closely with the government is why these same tech companies will get to first try new shiny toys. When it comes to new token issuance, the government is going to default to working with the technology companies they have worked with before than working with the newcomers in the industry. government wants Central Party President Xi is trying to do become the world’s dominant power, mobile “metro cards” mobile “passport WeChat messages are monitored The Chinese government also has a habit of fostering healthy competition between the large tech conglomerates, so not just one has all the leverage. To me, the signs of allowing JD to work with Huatai means that not soon after we’ll see Tencent or Alibaba announcing another token. All of this bodes well for the tech companies as well as China’s crypto space. If the initial blockchain+token experiments goes well, these technology companies will potentially take over as the backbone of China’s finance infrastructure. It’ll be a win for regulators as they then would have better control and monitor of banks and all of the country’s money supply. It’ll also be a win for the tech companies, as they will be entrusted with more innovation and potential future businesses. Note: Now, once the value of tokens is validated, I believe the Chinese government may feel more comfortable to allow companies like NEO to build private chains for businesses and permit it to flourish. Nonetheless, for crypto companies that are based in China or founded by Chinese teams, but don’t have the support of the government will either have try to find a business case elsewhere or be absorbed by the large tech companies who are looking for talent. This is why, for a long time, I’m going to be very bullish on the future of the top tech companies in China — Tencent, , JD and Baidu. I don’t believe that one can just do traditional financial analysis and derive these companys’ valuations by forecasting each of their products’ line revenues. For these companies, their products are not just what you may see on the roadmap, or what’s already in their apps. Instead, their products are influenced and created by whatever the Chinese government wants them to build and whichever technological direction the Chinese government wants to turn to. So, the possibilities are limitless. And for now, it’s blockchain. Alibaba

China’s True Crypto Giants- Tencent, Baidu, Alibaba and JD

INSANE One-click MCP AI Agent Hits the Market 

China is rapidly scaling AI dominance through massive investment, government-backed innovation, and aggressive competition. But can it sustain its AI momentum? China is rapidly scaling AI dominance through massive investment, government-backed innovation, and aggressive competition. But can it sustain its AI momentum? The global artificial intelligence (AI) race isn’t just about innovation anymore—it’s a battle for dominance in efficiency, affordability, and accessibility. To take the lead, China is making serious moves. The country now aims to lead the AI revolution, and recent developments like DeepSeek R1 suggest it’s closing in on that goal. With billions pouring into AI infrastructure and regulations designed to fast-track innovation, China is set to position itself as anAI powerhouse by 2030. AI powerhouse by 2030 AI powerhouse by 2030 In 2024 alone, its private sector pumped $3.15 billion into generative AI—a staggering 500% jump from the previous year. According to a report by Bain & Company, AI is expected to contribute $600 billion annually to China’s economy by the end of the decade, the impact reaches far beyond tech. $3.15 billion into generative AI $3.15 billion into generative AI report by Bain & Company report by Bain & Company However, it's not only the country’s tech giants that drive its AI momentum—startups are shaking up the race. While industry leaders like Baidu and Alibaba spearhead large language model (LLM) innovation, emerging players like ShengShu are proving to be serious contenders against OpenAI and Meta. China’s AI Arsenal: Formidable Competitors in the Global AI Arms Race Alibaba’s latest LLM, Qwen 2.5, is making waves with its superior text, image, and audio processing capabilities. Supporting 29 languages and handling up to 128,000 tokens in a single conversation, it far exceeds the context window of previous models. The company is also teaming up with Apple to support iPhone AI services in China—a strategic move that could help Apple regain ground as it battles slipping smartphone sales in its key market. While Apple’s devices outside China rely on a blend of its own Apple Intelligence and OpenAI’s ChatGPT, it remains unclear whether the Alibaba partnership will follow a similar path. Meanwhile, Baidu is gearing up to launch Ernie 5 in late 2025, a multimodal model designed to process and generate text, video, images, and audio. Ernie 5 in late 2025 Ernie 5 in late 2025 China’s also making waves in video AI, a key battleground for U.S. tech giants. One standout player is ShengShu Technology, rivaling OpenAI’s Sora with its Vidu 2.0 model within the next six months. Backed by Baidu Ventures, Vidu 2.0 can generate AI-driven videos at just $0.0375 per second—an astonishing 52% lower than the industry average. Vidu’s one-click prompt templates allow users to easily create complex actions, props, and animations with a single command. So why is China’s AI sector suddenly booming? A big part of China’s success comes from its universities and academic institutions, where top researchers often stay in academia rather than jumping to big tech—a stark contrast to their Western counterparts, where talent is quickly snapped up by companies. “The U.S. is currently underinvesting in its academic ecosystem,” Yury Lifshits, founder and CEO of AI video tool Nim, told me. “Much of the progress in AI and technology has been driven by a strong academic foundation and an open-source culture that fosters collaboration. Innovation cannot rely solely on corporate efforts.” The country has implemented a research evaluation system that favors publications indexed by the Science Citation Index (SCI). SCI papers are mandatory for doctoral degrees, faculty hiring and promotion, funding applications and university rankings. Monetary incentives and performance bonuses are used to encourage Chinese scholars to publish SCI papers. Some universities offer cash-per-publication policies, leading to additional revenues for researchers. additional revenues additional revenues Rather than chasing AI for the sake of innovation, China is focused on building tech that’s practical and widely useful for particular use cases—one of the key reasons behind its rapid advancement. Moreover, the rise of open source culture within its tech startup ecosystem has also led to a substantial new flurry of ideas, especially for AI. There’s also a strong business and economic incentive for Chinese companies to align with government goals. The government employs various policy incentives such as grants, tax breaks and preferential treatment for companies that align with its strategic goals. “The government sets long-term targets—where they want to be in five or ten years—but companies have plenty of freedom in how they get there. That creates a landscape where multiple approaches are explored at once, even fostering competition within companies to hit the broader objective,” Itamar Friedman, CEO and co-founder of Qodo, told me. Friedman, a former Director of Machine Vision at Alibaba, believes this structured yet flexible approach helps prevent wasteful over-investment. He noted that China’s AI push isn’t a sudden, unsustainable boom—it’s the result of a long-term strategy that dates back to 2016 when the country ramped up AI investment after DeepMind’s AlphaGo victory. Can the West and Others Keep Up with China’s AI Expansion? For years, the U.S. has seemingly led the AI race, largely driven by innovations from Silicon Valley giants including OpenAI, Google DeepMind and Meta. Europe, on the other hand, though slower to build major AI companies, has played a key role in shaping AI ethics and regulation. But China’s AI ecosystem operates differently. “Chinese AI firms have unparalleled access to vast amounts of data, often collected through government-backed initiatives, which enables them to develop more accurate and effective AI models,” said Serena H. Huang, AI Advisor at Fortune 100 companies and a former big tech analytics leader. “They have been able to leverage their country's large population and mobile-first economy to develop AI solutions that are highly scalable and adaptable to real-world scenarios.” With vast data access and a culture of rapid deployment, China may soon surpass the West in certain areas of tech innovation. The U.S. government has started to recognize the heat of the global AI arms race. The Trump administration recently took steps to slow China’s progress, imposing export controls on advanced semiconductors and AI chips to curb the country’s high-performance AI computing ambitions. While such measures are aimed at protecting national interests and controlling AI’s path forward, they also hinder global innovation. “It’s possible that China will find alternative ways to boost its domestic production, accelerating innovation on its own terms,” says Friedman. “While sanctions may slow progress in the short term, they will ultimately push China to develop even more robust and independent capabilities in the chip industry.” China’s AI momentum alone won’t guarantee success. But one thing is certain: whether the West can keep pace or China stumbles under new constraints will define the next chapter of global AI innovation. “Western companies and governments should invest in AI research and development, attract and retain top talent, and prioritize ethical considerations in the development and deployment of AI technologies,” added Huang. “The global AI landscape is rapidly evolving, and China's influence is only just beginning to be realized.”

China’s AI Playbook: Reshaping Strategy to Outpace the West

On February 20, 2025, luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024, with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024. Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Electrification and Affordability Driven by a mixture of EU CO₂ emission standards and Germany’s own desire for increased EV usage, Germany has emerged as the EU’s largest market for EV technology, with domestic luxury vehicle manufacturers competing with Chinese EVs for market share. So far, Chinese EVs have been able to make a sizable dent in the German EV market, with nearly 44,000 Chinese EVs being sold in Germany in 2024, comprising 11.5% of the total 381,000 EVs registered last year, with Chinese EVs comprising over 40% of EV import market share in Germany in Q1 2024. Price competition has enabled Chinese-manufactured EVs to dominate not only Germany’s domestic market but also the world’s. Vehicles like Nio’s ET9, set to compete directly with BMW’s 7-series and Audi’s A8, and Xiaomi’s SU7 Ultra, competing with Porsche's Taycan Turbo S, compete directly with German luxury manufacturers at a fraction of the cost. Other Chinese EVs also compete head-to-head with German auto manufacturers, to the point where Chinese-made EVs’ affordability resulted in an EU-led anti-subsidy investigation in late 2023. Price competition has drastically affected German manufacturers, to the point where German auto supplier Bosch, the world’s largest auto supplier, made significant cuts to its workforce in 2024. Consumer preference, driven primarily by cost and affordability, has taken hold of the German domestic market, with a survey from 2024 revealing that almost two-thirds of Germans can now imagine purchasing a car from a Chinese manufacturer. In fact, this number among survey respondents shot up to 80% if the respondent was specifically interested in an EV, highlighting lower prices as a primary consumer motivator for domestic German consumers. Seamless Technology Integration as a Consumer Priority Technology integration in Chinese EVs has become a strong selling point for consumers. China, which has become a major, if not the largest, market for German luxury vehicles, has drawn the ire of Chinese consumers due to its hesitancy to integrate technology into its vehicles. In contrast, Chinese EVs, with features including advanced heads-up displays and information systems, enable a more pleasant driving experience. The utilization of connected Internet of Things (IoT) and cellular technologies to control everything within the vehicle’s purview and extenuating smart devices throughout the home and the workplace have resulted in China’s leadership in global EV manufacturing. These incorporations of software and convenience have been a primary factor that has resulted in a decrease in German automakers’ share of China’s market, controlling just 15% in the present day (compared to 25% just five years ago). In fact, the combined electric market share of all German manufacturers, including Audi, BMW, Mercedes, and Porsche, was a mere five percent in 2024. Chinese EV manufacturers’ strength in technology integration has even resulted in the exportation of these technologies. This was particularly evident in Auto China 2024, which saw the announcement of Japanese car brands integrating Chinese technologies, with agreements between Nissan and Baidu, Toyota and Tencent, and Honda and Huawei dominating the conversation at the show. Loss of Brand Power and Reputation The reputational power, brand, and “made in Germany” tagline have also evolved in recent history. However, recent reports indicate that consumers are indexing less on these factors in favor of electrification, technology integration, and price, resulting in reputational loss for German luxury auto manufacturers. To this extent, German manufacturers’ reputation of complex, industrious, and over-engineered vehicles has resulted in a corresponding backlash against these companies, with many consumers pointing to decreased quality, and subsequently, loss of brand perception. The perceived strength of German-manufactured vehicles and parts has received substantial criticism in recent history, with countless auto enthusiasts on forums such as Reddit, Bimmerpost, and MBWorld discussing the increasing decline of German luxury automobiles. Inversely, the perception and reputation of Chinese EVs have increased dramatically in recent years, with Chinese EVs performing well in many side-by-side comparisons against other electric vehicles, including German luxury cars. This can be seen in the dominance of Chinese EVs around the world, with Chinese EVs comprising over 60% of worldwide EV sales in 2024. Conclusion All in all, consumers are increasingly drawn to Chinese EVs due to their lower prices, advanced technology features, and increasing brand prestige as well as awareness. This shift is particularly noticeable in the share of Chinese EVs in the EU’s largest EV market, Germany, as well as in China, where German luxury auto manufacturers were anticipating as their primary revenue source for the foreseeable future. As a result, Chinese EVs have reshaped the global automobile ecosystem and displaced German luxury car manufacturers. While the legacy prestige of German automobiles and their desirability remains, it is clear that consumers are increasingly drawn to Chinese EVs, particularly given their affordability, technology integrations, and rising luxury features. Image Credit: Matthias Balk (source) On February 20, 2025, luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024, with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024. On February 20, 2025 , luxury car manufacturer Mercedes-Benz announced plans to cut production costs by 10% by 2027 in light of expected lower earnings in 2025. This news follows a general downturn in German luxury car manufacturing on a global level. The BMW Group, which includes the Mini and Rolls-Royce brands, announced a global decrease in sales for all of 2024 , with Volkswagen’s Porsche announcing a near-25% downturn in sales in both North America and China earlier in 2024 . February 20, 2025 February 20, 2025 2024 2024 2024 2024 Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Consumers point to increased Chinese electric vehicle (EV) affordability, increased technology integration, and a decrease in brand reputation as key factors driving their decision-making. This can be especially seen in the growth of Chinese EVs in the German market, which is the EU’s largest market for EVs, and in China, which is a significant source of revenue for German luxury car manufacturers. Electrification and Affordability Electrification and Affordability Driven by a mixture of EU CO₂ emission standards and Germany’s own desire for increased EV usage, Germany has emerged as the EU’s largest market for EV technology, with domestic luxury vehicle manufacturers competing with Chinese EVs for market share. So far, Chinese EVs have been able to make a sizable dent in the German EV market, with nearly 44,000 Chinese EVs being sold in Germany in 2024, comprising 11.5% of the total 381,000 EVs registered last year, with Chinese EVs comprising over 40% of EV import market share in Germany in Q1 2024 . EU CO₂ emission standards EU CO₂ emission standards desire desire emerged emerged 44,000 44,000 381,000 381,000 Q1 2024 Q1 2024 Price competition has enabled Chinese-manufactured EVs to dominate not only Germany’s domestic market but also the world’s. Vehicles like Nio’s ET9, set to compete directly with BMW’s 7-series and Audi’s A8, and Xiaomi’s SU7 Ultra, competing with Porsche's Taycan Turbo S, compete directly with German luxury manufacturers at a fraction of the cost. compete compete competing competing Other Chinese EVs also compete head-to-head with German auto manufacturers, to the point where Chinese-made EVs’ affordability resulted in an EU-led anti-subsidy investigation in late 2023 . Price competition has drastically affected German manufacturers, to the point where late 2023 late 2023 German auto supplier Bosch, the world’s largest auto supplier, made significant cuts to its workforce in 2024 . 2024 2024 Consumer preference, driven primarily by cost and affordability, has taken hold of the German domestic market, with a survey from 2024 revealing that almost two-thirds of Germans can now imagine purchasing a car from a Chinese manufacturer. In fact, this number among survey respondents shot up to 80% if the respondent was specifically interested in an EV, highlighting lower prices as a primary consumer motivator for domestic German consumers. 2024 2024 Seamless Technology Integration as a Consumer Priority Seamless Technology Integration as a Consumer Priority Technology integration in Chinese EVs has become a strong selling point for consumers. China, which has become a major, if not the largest, market for German luxury vehicles, has drawn the ire of Chinese consumers due to its hesitancy to integrate technology into its vehicles. In contrast, Chinese EVs, with features including advanced heads-up displays and information systems, enable a more pleasant driving experience. hesitancy hesitancy features features The utilization of connected Internet of Things (IoT) and cellular technologies to control everything within the vehicle’s purview and extenuating smart devices throughout the home and the workplace have resulted in China’s leadership in global EV manufacturing. These incorporations of software and convenience have been a primary factor that has resulted in a decrease in German automakers’ share of China’s market, controlling just 15% in the present day (compared to 25% just five years ago). In fact, the combined electric market share of all German manufacturers, including Audi, BMW, Mercedes, and Porsche, was a mere five percent in 2024 . decrease decrease 2024 2024 Chinese EV manufacturers’ strength in technology integration has even resulted in the exportation of these technologies. This was particularly evident in Auto China 2024, which saw the announcement of Japanese car brands integrating Chinese technologies, with agreements between Nissan and Baidu, Toyota and Tencent, and Honda and Huawei dominating the conversation at the show. announcement announcement Loss of Brand Power and Reputation The reputational power, brand, and “made in Germany” tagline have also evolved in recent history. However, recent reports indicate that consumers are indexing less on these factors in favor of electrification, technology integration, and price, resulting in reputational loss for German luxury auto manufacturers. recent reports recent reports To this extent, German manufacturers’ reputation of complex, industrious, and over-engineered vehicles has resulted in a corresponding backlash against these companies, with many consumers pointing to decreased quality, and subsequently, loss of brand perception. The perceived strength of German-manufactured vehicles and parts has received substantial criticism in recent history, with countless auto enthusiasts on forums such as Reddit , Bimmerpost , and MBWorld discussing the increasing decline of German luxury automobiles. pointing pointing Reddit Reddit Bimmerpost Bimmerpost MBWorld MBWorld Inversely, the perception and reputation of Chinese EVs have increased dramatically in recent years, with Chinese EVs performing well in many side-by-side comparisons against other electric vehicles, including German luxury cars. This can be seen in the dominance of Chinese EVs around the world, with Chinese EVs comprising over 60% of worldwide EV sales in 2024 . side-by-side comparisons side-by-side comparisons includ ing includ 2024 2024 Conclusion Conclusion All in all, consumers are increasingly drawn to Chinese EVs due to their lower prices, advanced technology features, and increasing brand prestige as well as awareness. This shift is particularly noticeable in the share of Chinese EVs in the EU’s largest EV market, Germany, as well as in China, where German luxury auto manufacturers were anticipating as their primary revenue source for the foreseeable future. As a result, Chinese EVs have reshaped the global automobile ecosystem and displaced German luxury car manufacturers. While the legacy prestige of German automobiles and their desirability remains, it is clear that consumers are increasingly drawn to Chinese EVs, particularly given their affordability, technology integrations, and rising luxury features. Image Credit: Matthias Balk ( source ) source

How China’s EVs and Their Integrated Technologies Are Displacing German Luxury Car Manufacturers

The race between China and the United States to dominate artificial intelligence (AI) is not just about technological supremacy or global power projection – it threatens national security and constitutes an existential risk. Both nations are aggressively developing an AI-driven military, with heightened capabilities. Today, the competition is fast approaching what experts fear may become an AI-driven global crisis. It is becoming abundantly clear that AI is the battleground of the future. Both China and the U.S. recognize that whoever leads in AI will not only dominate commercially but will also set the geopolitical terms for the future world order. AI will be the primary driver of economic productivity and military activity in the coming decades. The country that can automate industries, optimize supply chains, and create new technological paradigms will control the global economy. AI-powered autonomous weapons, cyberwarfare, and intelligence gathering, is redefining military strategy. The nation that harnesses AI first will have the advantage in future conflicts. AI’s ability to monitor, predict, and influence human behavior is already being used to tighten control over populations. China has pioneered AI-driven surveillance, while the U.S. is integrating AI into cyber and intelligence operations at an unprecedented scale. China has placed AI at the core of its national development strategy. In 2017, Beijing announced a plan to become the world’s AI leader by 2030, pouring billions into research, fostering AI entrepreneurship, and leveraging massive datasets generated by its vast surveillance apparatus. China has an advantage. Unlike the U.S., where AI is largely driven by private companies, China’s AI push is state-backed, with direct funding and strategic support to companies like Baidu, Alibaba, Tencent, and Huawei. Relying on the the darker side of AI, China has also built the world’s most advanced AI-powered surveillance state, using facial recognition, predictive policing, and social credit scoring to manage its population. In an effort to ensure regional dominance, China’s AI research is heavily integrated into military development through its strategy of “civil-military fusion,” ensuring that AI breakthroughs in the civilian sector rapidly benefit the People’s Liberation Army (PLA). In contrast, America is falling short. Its AI strategy is competitive, but chaotic.The U.S. has historically been the global leader in AI innovation, thanks to its world-class universities, leading tech companies (Google, Microsoft, OpenAI), and a culture of entrepreneurship. However, unlike China, the U.S. faces challenges in coordinating its AI efforts due to its decentralized economic system and regulatory constraints. While China’s AI race is managed by the government, in the U.S., AI is primarily driven by tech giants like OpenAI, Google DeepMind, and Meta. These companies prioritize commercial applications but have increasingly worked with the Pentagon through projects like the Joint Artificial Intelligence Center (JAIC). The U.S. is still grappling with bureaucracy and government oversight. It is hampered, forced to balance innovation with ethical and security concerns. China, by contrast, imposes AI policies with top-down enforcement. Of course, the Pentagon recognizes the urgency of AI warfare, but bureaucratic inefficiencies and ethical debates about autonomous weapons have slowed progress compared to China’s rapid integration. For this reason, we can expect an AI arms race with no rules. With China and the U.S. accelerating AI development, the world is heading into a dangerous era where AI-powered conflict could emerge with little oversight or global regulation. AI-driven weapons systems that can make lethal decisions without human intervention raise the risk of accidental escalation and warfare beyond human control. The ever-advancing technology is increasingly being used in cyberattacks, deepfakes, and information warfare. Both nations are engaged in covert AI-driven operations to manipulate public opinion and disrupt adversaries. The push to develop ever more sophisticated AI, including artificial general intelligence (AGI), will most certainly lead to unintended consequences, from economic instability to AI systems operating beyond human comprehension. What experts are asking is, can AI be controlled? The current trajectory is unsustainable. The absence of global AI regulations, combined with increasing geopolitical tensions, will result in a catastrophic AI conflict. But there are solutions. Just as nuclear arms treaties helped prevent a Cold War catastrophe, China and the U.S. must establish AI agreements to limit the use of autonomous weapons and prevent an uncontrolled AI arms race. The international community must work together to establish ethical AI guidelines and pressure China to prevent misuse in warfare, surveillance, and disinformation campaigns. Globally, nations must be willing to invest in AI safety systems to ensure that it does not spiral out of control. The clock is ticking. The AI race between China and the U.S. is heading down a dangerous path. The world risks descending into an era of AI-driven geopolitical instability if leaders do not take the initiative. The time to act is now – before it’s too late. The race between China and the United States to dominate artificial intelligence (AI) is not just about technological supremacy or global power projection – it threatens national security and constitutes an existential risk. Both nations are aggressively developing an AI-driven military, with heightened capabilities. Today, the competition is fast approaching what experts fear may become an AI-driven global crisis . AI-driven global crisis It is becoming abundantly clear that AI is the battleground of the future. Both China and the U.S. recognize that whoever leads in AI will not only dominate commercially but will also set the geopolitical terms for the future world order. AI will be the primary driver of economic productivity and military activity in the coming decades. The country that can automate industries, optimize supply chains, and create new technological paradigms will control the global economy. AI-powered autonomous weapons, cyberwarfare, and intelligence gathering, is redefining military strategy. The nation that harnesses AI first will have the advantage in future conflicts. AI’s ability to monitor, predict, and influence human behavior is already being used to tighten control over populations. China has pioneered AI-driven surveillance, while the U.S. is integrating AI into cyber and intelligence operations at an unprecedented scale. China has placed AI at the core of its national development strategy. In 2017, Beijing announced a plan to become the world’s AI leader by 2030, pouring billions into research, fostering AI entrepreneurship, and leveraging massive datasets generated by its vast surveillance apparatus. announced a plan China has an advantage. Unlike the U.S., where AI is largely driven by private companies, China’s AI push is state-backed, with direct funding and strategic support to companies like Baidu, Alibaba, Tencent, and Huawei. Relying on the the darker side of AI, China has also built the world’s most advanced AI-powered surveillance state, using facial recognition, predictive policing, and social credit scoring to manage its population. In an effort to ensure regional dominance, China’s AI research is heavily integrated into military development through its strategy of “civil-military fusion,” ensuring that AI breakthroughs in the civilian sector rapidly benefit the People’s Liberation Army (PLA). In contrast, America is falling short. Its AI strategy is competitive, but chaotic.The U.S. has historically been the global leader in AI innovation, thanks to its world-class universities, leading tech companies (Google, Microsoft, OpenAI), and a culture of entrepreneurship. However, unlike China, the U.S. faces challenges in coordinating its AI efforts due to its decentralized economic system and regulatory constraints. While China’s AI race is managed by the government, in the U.S., AI is primarily driven by tech giants like OpenAI, Google DeepMind, and Meta. These companies prioritize commercial applications but have increasingly worked with the Pentagon through projects like the Joint Artificial Intelligence Center (JAIC). The U.S. is still grappling with bureaucracy and government oversight. It is hampered, forced to balance innovation with ethical and security concerns. China, by contrast, imposes AI policies with top-down enforcement. Of course, the Pentagon recognizes the urgency of AI warfare, but bureaucratic inefficiencies and ethical debates about autonomous weapons have slowed progress compared to China’s rapid integration. For this reason, we can expect an AI arms race with no rules. With China and the U.S. accelerating AI development, the world is heading into a dangerous era where AI-powered conflict could emerge with little oversight or global regulation. AI-driven weapons systems that can make lethal decisions without human intervention raise the risk of accidental escalation and warfare beyond human control. The ever-advancing technology is increasingly being used in cyberattacks, deepfakes, and information warfare. Both nations are engaged in covert AI-driven operations to manipulate public opinion and disrupt adversaries. The push to develop ever more sophisticated AI, including artificial general intelligence (AGI), will most certainly lead to unintended consequences, from economic instability to AI systems operating beyond human comprehension. What experts are asking is, can AI be controlled ? can AI be controlled The current trajectory is unsustainable. The absence of global AI regulations, combined with increasing geopolitical tensions, will result in a catastrophic AI conflict. But there are solutions. Just as nuclear arms treaties helped prevent a Cold War catastrophe, China and the U.S. must establish AI agreements to limit the use of autonomous weapons and prevent an uncontrolled AI arms race. The international community must work together to establish ethical AI guidelines and pressure China to prevent misuse in warfare, surveillance, and disinformation campaigns. Globally, nations must be willing to invest in AI safety systems to ensure that it does not spiral out of control. The clock is ticking. The AI race between China and the U.S. is heading down a dangerous path. The world risks descending into an era of AI-driven geopolitical instability if leaders do not take the initiative. The time to act is now – before it’s too late.

Are China and the U.S. Engaged in a Race Toward AI Armageddon?

ቻይና እና ዩኤስ ወደ AI አርማጌዶን በሚደረገው ውድድር ውስጥ ገብተዋል?

هل تنخرط الصين والولايات المتحدة في سباق نحو نهاية الذكاء الاصطناعي؟

চীন এবং মার্কিন যুক্তরাষ্ট্র কি কৃত্রিম বুদ্ধিমত্তা (এআই) আর্মাগেডনের দিকে দৌড়ে লিপ্ত?

La Xina i els Estats Units estan involucrats en una cursa cap a l'Armageddon de l'IA?

Befinden sich China und die USA in einem Wettlauf hin zum KI-Armageddon?

¿Están China y Estados Unidos inmersos en una carrera hacia el Armagedón de la IA?

中国と米国は AI による終末への競争に突入しているのか?

Қытай мен АҚШ AI Армагеддонға қарай жарысуда ма?

Adakah China dan A.S. Terlibat dalam Perlumbaan Menuju AI Armageddon?

Участвуют ли Китай и США в гонке за Армагеддон ИИ?

චීනය සහ ඇමරිකා එක්සත් ජනපදය AI ආමගෙදොන් දෙසට තරඟයක නිරත වී සිටිනවාද?

Оё Чин ва ИМА дар мубориза ба сӯи Армагеддони AI машғуланд?

Lunar New Year Travel is a Weird Experiment to See How Much Humans Will Endure for Love

চন্দ্র নববর্ষ ভ্রমণ হল একটি অদ্ভুত পরীক্ষা যা দেখার জন্য মানুষ কতটা ভালবাসার জন্য সহ্য করবে

Putovanje po lunarnoj novoj godini je čudan eksperiment da vidimo koliko će ljudi izdržati zbog ljubavi

Reisen zum chinesischen Neujahr sind ein seltsames Experiment, um herauszufinden, wie viel Menschen für die Liebe ertragen können

El viaje del Año Nuevo Lunar es un experimento extraño para ver cuánto aguantan los humanos por amor

Ilargi Urte Berriko bidaiak Esperimentu bitxi bat da gizakiek maitasunagatik zenbat jasango duten ikusteko

سفر سال نو قمری آزمایشی عجیب است برای اینکه ببینیم انسان ها چقدر برای عشق تحمل خواهند کرد

Voyager à l'occasion du Nouvel An lunaire est une expérience étrange pour voir jusqu'où les humains peuvent aller par amour

चंद्र नववर्ष यात्रा एक अजीब प्रयोग है, जिससे पता चलता है कि मनुष्य प्रेम के लिए कितना कुछ सह सकता है

旧正月の旅行は、人間が愛のためにどれだけ耐えられるかを見る奇妙な実験である

음력설 여행은 인간이 사랑을 위해 얼마나 오래 버틸 수 있는지 알아보는 이상한 실험이다

Reizen tijdens het Chinese Nieuwjaar is een vreemd experiment om te zien hoeveel mensen zullen doorstaan voor de liefde

A viagem do Ano Novo Lunar é um experimento estranho para ver o quanto os humanos suportarão por amor

Killa musuq wata puriyqa huk extraño experimentom qawanapaq maykamam runakuna munakuywan aguantanqaku

Путешествие на Лунный Новый год — это странный эксперимент, чтобы увидеть, на что способны люди ради любви

Ukwezi kwumwaka mushya ni ukwezi kudasanzwe kugirango turebe uko abantu bazihanganira urukundo

Kufamba Kwegore Idzva Kwemwedzi Chiyedzo Chisinganzwisisike Kuona Kuti Vanhu Vachatsungirira Zvakadii Nerudo

ወርሓዊ ሓድሽ ዓመት ጉዕዞ ደቂ ሰባት ንፍቕሪ ክሳብ ክንደይ ክጻወሩ ምዃኖም ንምርኣይ ዝግበር ጓና ፈተነ እዩ።

Ay Yeni Yılı Seyahati, İnsanların Aşk İçin Ne Kadar Dayanabileceğini Görmek İçin Garip Bir Deneyimdir

Du lịch Tết Nguyên Đán là một thử nghiệm kỳ lạ để xem con người sẽ chịu đựng được bao nhiêu vì tình yêu

农历新年旅行是一次奇怪的实验，看看人类能为爱情忍受多少

After months, actually years of waiting, Tesla finally unveiled its robotaxi last October. Much analysis went into the new self driving car. The company unveiled its long-awaited robotaxi with two gull-wing doors and no steering wheel or pedals. The “Cybercab” is a two-seater and low slung, a design far from the idea of a taxi right now, causing experts to criticize it, since a taxi usually has space for more than two passengers. “When you think of a cab, you think of something that’s going to carry more than two people,” Reuters quoted Jonathan Elfalan, vehicle testing director for the automotive website Edmunds.com. “Making this a two-seat-only car is very perplexing.” The design caused Tesla’s stocks to tumble by 9%. But buyers were already lining up. Palo Alto, home to Tesla’s engineering headquarters, is in talks with the carmaker to deploy a fleet of robotaxis in the city. It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars Elon Musk says the Cybercab, built without a steering wheel or pedals, will start production by 2026 or 2027. But we know Tesla and deadlines aren’t often in sync. The fact that the design is an FSD (Full Self Driving), is also raising concerns about regulations. It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars. A design like Tesla’s Cybercab, which is an FSD, means a tectonic shift in how the consumer perceives an automobile. Research is ongoing into the influence of anthropomorphism and social responsiveness in the transition from human to automated driving. Research also says that drivers are more likely to engage in non-driving activities, such as checking their phones or eating a sandwich, when using partial automation systems, with some easily skirting rules set to limit distractions. Tesla is not the only one to offer driverless rides. As of September, Uber and Waymo have begun to offer driverless ride-hailing trips in Austin and Atlanta. Other concerns at another level. The US is cautious about Chinese software in US autonomous and connected vehicles. The automated vehicle is a piece of formidable machine that can accomplish much damage if hacked. Meanwhile, Artificial Intelligence (AI) is entering the automobile space as well. Japan’s Toyota announced tying up with China’s Tencent while Nissan will team up with Baidu. The separate announcements from two of Japan’s biggest automakers at the Beijing auto show highlight the importance of AI for carmakers. What Today’s Drivers Want: AI As per Continental Mobility Study 2024, the majority (54%) of younger drivers in countries like Germany China, France, Japan and the US, cars are a status symbol. They’re particularly enthusiastic about technological advances in cars. They look forward to the benefits self-driving cars will offer in terms of being able to read, play video games or work (51% of 25 to 34-year-olds). In addition to autonomous driving, Artificial Intelligence (AI) in the form of digital voice assistants is very popular with this group. “The findings show that the response to new technologies such as automated driving, large displays and AI in cars varies greatly between generations and also between countries. That’s why we aim to provide customized solutions – market-specific, tailor-made and modular,” says Philipp von Hirschheydt, Continental Executive Board member responsible for the Automotive group sector, commenting on the survey findings. Around two-thirds of younger people up to the age of 34 also believe that state-of-the-art technologies should be mandatory in newly registered cars to make traffic safer, a perception that signals approval of the EU directive requiring certain advanced driver assistance systems in new cars, which has been in force since July 2024. Another future technology that is particularly popular with younger people is AI assistants in cars. Almost three-quarters of respondents (74 %) between the ages of 18 and 34 would welcome an AI voice as a service that, like a virtual travel companion, that provides useful information about sights and restaurants along the route, finds the nearest gas or charging station, searches for free parking spaces or even compiles personal messages. *“Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences.” *— Philipp von Hirschheydt “Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences,” explains Philipp von Hirschheydt, adding, “At Continental, we’re already equipping cars with AI. Together with our partner Google Cloud, we have developed a virtual companion for drivers. We are particularly proud to be one of the first automotive suppliers worldwide to integrate Google Cloud applications directly into our vehicle computers.” AI-Powered Virtual Travel Companion, Large Displays The comparison between countries reveals a widespread openness to highly automated and autonomous driving in Asia across all age groups. In China, nine out of 10 respondents (90 %) view the relevant technologies as a useful development, while in Japan, almost three- quarters (72 %) share this sentiment. In France (60 %) and the USA (56 %), more than half of those surveyed have a positive attitude. In Germany, around one in two respondents (49 %) feel the same. An AI-powered virtual travel companion is particularly popular in China, where nine out of 10 respondents (91 %) say they would like to have such a service. In the US (66 %) and Japan (63 %), around two-thirds express this wish, while in France (58 %) and Germany (57 %) more than half would be happy to have the technology. There is broad agreement across all countries on the ideal size of a car display for infotainment content. Most people prefer larger displays (China 90%, Germany 81%, France 79%, and the US 80%, and Japan 69%). However, preferences differ significantly when it comes to technological details. While the Japanese and German prefer a simpler display on car screens, a slight majority in the US favor more colors. Voice control is particularly popular in Japan and China, while the number is significantly lower in Germany. Navanwita Bora Sachdev, Editor, The Tech Panda After months, actually years of waiting, Tesla finally unveiled its robotaxi last October. Much analysis went into the new self driving car. Tesla The company unveiled its long-awaited robotaxi with two gull-wing doors and no steering wheel or pedals. The “Cybercab” is a two-seater and low slung, a design far from the idea of a taxi right now, causing experts to criticize it , since a taxi usually has space for more than two passengers. long-awaited robotaxi “Cybercab” experts to criticize it “When you think of a cab, you think of something that’s going to carry more than two people,” Reuters quoted Jonathan Elfalan, vehicle testing director for the automotive website Edmunds.com. “Making this a two-seat-only car is very perplexing.” Reuters The design caused Tesla’s stocks to tumble by 9%. But buyers were already lining up. Palo Alto, home to Tesla’s engineering headquarters, is in talks with the carmaker to deploy a fleet of robotaxis in the city. Tesla’s stocks to tumble is in talks with It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars Elon Musk says the Cybercab, built without a steering wheel or pedals, will start production by 2026 or 2027. But we know Tesla and deadlines aren’t often in sync. The fact that the design is an FSD (Full Self Driving), is also raising concerns about regulations. It’s interesting to see the change in the auto consumer as the world is being pushed towards automated cars. A design like Tesla’s Cybercab, which is an FSD, means a tectonic shift in how the consumer perceives an automobile. Research is ongoing into the influence of anthropomorphism and social responsiveness in the transition from human to automated driving. Research also says that drivers are more likely to engage in non-driving activities , such as checking their phones or eating a sandwich, when using partial automation systems, with some easily skirting rules set to limit distractions. influence of anthropomorphism likely to engage in non-driving activities Tesla is not the only one to offer driverless rides. As of September, Uber and Waymo have begun to offer driverless ride-hailing trips in Austin and Atlanta. Uber Waymo driverless Other concerns at another level. The US is cautious about Chinese software in US autonomous and connected vehicles. The automated vehicle is a piece of formidable machine that can accomplish much damage if hacked. Chinese software in US autonomous Meanwhile, Artificial Intelligence (AI) is entering the automobile space as well. Japan’s Toyota announced tying up with China’s Tencent while Nissan will team up with Baidu . The separate announcements from two of Japan’s biggest automakers at the Beijing auto show highlight the importance of AI for carmakers . Toyota Tencent Nissan Baidu the importance of AI for carmakers What Today’s Drivers Want: AI What Today’s Drivers Want: AI What Today’s Drivers Want: AI As per Continental Mobility Study 2024 , the majority (54%) of younger drivers in countries like Germany China, France, Japan and the US, cars are a status symbol. They’re particularly enthusiastic about technological advances in cars. They look forward to the benefits self-driving cars will offer in terms of being able to read, play video games or work (51% of 25 to 34-year-olds). In addition to autonomous driving, Artificial Intelligence (AI) in the form of digital voice assistants is very popular with this group. Continental Mobility Study 2024 “The findings show that the response to new technologies such as automated driving, large displays and AI in cars varies greatly between generations and also between countries. That’s why we aim to provide customized solutions – market-specific, tailor-made and modular,” says Philipp von Hirschheydt, Continental Executive Board member responsible for the Automotive group sector, commenting on the survey findings. Around two-thirds of younger people up to the age of 34 also believe that state-of-the-art technologies should be mandatory in newly registered cars to make traffic safer, a perception that signals approval of the EU directive requiring certain advanced driver assistance systems in new cars, which has been in force since July 2024. Another future technology that is particularly popular with younger people is AI assistants in cars. Almost three-quarters of respondents (74 %) between the ages of 18 and 34 would welcome an AI voice as a service that, like a virtual travel companion, that provides useful information about sights and restaurants along the route, finds the nearest gas or charging station, searches for free parking spaces or even compiles personal messages. *“Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences.” *— Philipp von Hirschheydt *“Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences.” * — Philipp von Hirschheydt *“Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences.” * *“Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences.” * “Younger people in particular have changing expectations of cars. These are closely linked to pioneering technologies such as automated driving, which deliver new user experiences,” explains Philipp von Hirschheydt, adding, “At Continental, we’re already equipping cars with AI. Together with our partner Google Cloud, we have developed a virtual companion for drivers. We are particularly proud to be one of the first automotive suppliers worldwide to integrate Google Cloud applications directly into our vehicle computers.” AI-Powered Virtual Travel Companion, Large Displays AI-Powered Virtual Travel Companion, Large Displays AI-Powered Virtual Travel Companion, Large Displays The comparison between countries reveals a widespread openness to highly automated and autonomous driving in Asia across all age groups. In China, nine out of 10 respondents (90 %) view the relevant technologies as a useful development, while in Japan, almost three- quarters (72 %) share this sentiment. In France (60 %) and the USA (56 %), more than half of those surveyed have a positive attitude. In Germany, around one in two respondents (49 %) feel the same. An AI-powered virtual travel companion is particularly popular in China, where nine out of 10 respondents (91 %) say they would like to have such a service. In the US (66 %) and Japan (63 %), around two-thirds express this wish, while in France (58 %) and Germany (57 %) more than half would be happy to have the technology. There is broad agreement across all countries on the ideal size of a car display for infotainment content. Most people prefer larger displays (China 90%, Germany 81%, France 79%, and the US 80%, and Japan 69%). However, preferences differ significantly when it comes to technological details. While the Japanese and German prefer a simpler display on car screens, a slight majority in the US favor more colors. Voice control is particularly popular in Japan and China, while the number is significantly lower in Germany. Navanwita Bora Sachdev, Editor, The Tech Panda Navanwita Bora Sachdev, Editor, The Tech Panda Navanwita Bora Sachdev, Editor, The Tech Panda

Is Tesla's Robotaxi a Bold Step or a Blunder? 

AI is one of the most game-changing tech innovations today improving business operations across different industries. Bill Gates stated in his blog that the emergence of AI was as significant as the establishment of the Internet. Businesses worldwide are increasingly embracing innovation through the power of AI. Siemens, a global technology powerhouse, leverages AI to boost operational efficiency by advancing coordination and time management, refining malfunction identification, and stabilizing production procedures. Rolls-Royce, an engineering company specializing in automobiles and aerospace technologies, together with Singapore’s DSTA, developed aviation upkeep systems (digital twins) that use machine vision to reach hard-to-access engine parts and detect probable issues before they lead to costly repairs or disruptions. KFC, in partnership with Baidu, introduced a regional facial recognition system that evaluates age, gender, and facial expressions and recommends menu items that align with the customer’s mood and tastes. So, it should be no surprise that QA and software testing are no exception. According to the World Quality Report 2024-25 (WQR), 34% of respondents are already actively utilizing Gen AI in their quality engineering processes. Even with the complexities and considerable concerns surrounding AI implementation, its combination with test automation can potentially bring various advantages for businesses. In this article, let’s see what prospective advantages companies can derive from AI-driven test automation, what hurdles they can confront, and the use cases of applying this technology. Unlocking the Full Potential of AI-driven Test Automation The WQR interviewees mentioned that they expect test automation to minimize various risks (54%), decrease the number of live issues (51%), and improve customer experience (50%). By strengthening it with AI, businesses can: Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Analyzing Two Sides of a Coin: Hurdles to Overcome Although AI can enhance the capabilities of automated testing, organizations should remember that its implementation isn’t always smooth sailing, meaning the following roadblocks can occur: Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly.
Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities.
Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives).
Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Top 5 AI Use Cases for Enhancing Traditional Test Automation Let’s analyze some potential cases of harnessing AI to facilitate more efficient operations. Improved regression testing Regression testing ensures that newly introduced software features do not disrupt existing functionality. As the project evolves and the codebase grows, it becomes increasingly challenging for QA automation engineers to maintain comprehensive test coverage and ensure the reliability of the IT product. Fortunately, AI has the capabilities to swiftly examine previous code alterations and testing outcomes to identify areas most prone to defects and suggest what verifications should be executed first. This helps overcome lackluster productivity and speed up software rollout. More effective GUI verifications First impressions do matter. Studies show that it takes only 50 ms for the target audience to decide whether they like your IT solution or not, while other research shows that 94% of reasons for rejecting a website were related to issues with design. QA automation engineers can enhance their user interface testing capabilities with the help of AI. It allows them to automate the process of verifying UI elements with the help of image-based testing, which improves overall velocity and accuracy. Next-generation scripts AI can automatically and quickly generate intelligent scripts, ensuring a more effective quality control process. AI gathers data on how users work with the IT product and builds predictions on the expected outcomes. In case of any abnormal situations, AI highlights probable issues. Then, QA specialists analyze these messages to identify further actions - opening a defect or continuing testing. Automated recovery opportunities Due to AI’s self-healing possibilities, QA automation engineers can have less work with maintaining and updating scripts after code changes in the application. AI-driven automated scripts can adapt leveraged locators to alterations in the UI to ensure continuous test execution. This helps enhance the robustness of scripts, ease maintenance, prevent disruptions in the testing process, and dedicate more effort to performing other vital QA activities. Better approach for working with issues When AI supplements test automation, it can review previous application-related data and consider software intricacy. This helps to forecast areas where potential issues may arise, allowing QA specialists to focus more on risky software aspects. In addition, companies can leverage test automation frameworks with AI elements. They serve to better understand the reasons for diverse glitches, which makes it easier and faster for QA and development teams to mitigate them. All Things Considered By leveraging AI-empowered test automation, QA teams can potentially boost the productivity, precision, and velocity of testing workflows. Although AI’s introduction is associated with hurdles, this technology can facilitate regression and GUI testing, as well as provide effective self-learning, self-healing, predictive, and cause analysis opportunities. AI is one of the most game-changing tech innovations today improving business operations across different industries. Bill Gates stated in his blog that the emergence of AI was as significant as the establishment of the Internet. blog Businesses worldwide are increasingly embracing innovation through the power of AI . Siemens, a global technology powerhouse, leverages AI to boost operational efficiency by advancing coordination and time management, refining malfunction identification, and stabilizing production procedures. AI leverages AI Rolls-Royce, an engineering company specializing in automobiles and aerospace technologies, together with Singapore’s DSTA, developed aviation upkeep systems (digital twins) that use machine vision to reach hard-to-access engine parts and detect probable issues before they lead to costly repairs or disruptions. machine vision KFC, in partnership with Baidu, introduced a regional facial recognition system that evaluates age, gender, and facial expressions and recommends menu items that align with the customer’s mood and tastes. system So, it should be no surprise that QA and software testing are no exception. According to the World Quality Report 2024-25 (WQR), 34% of respondents are already actively utilizing Gen AI in their quality engineering processes. Even with the complexities and considerable concerns surrounding AI implementation, its combination with test automation can potentially bring various advantages for businesses. In this article, let’s see what prospective advantages companies can derive from AI-driven test automation, what hurdles they can confront, and the use cases of applying this technology. Unlocking the Full Potential of AI-driven Test Automation The WQR interviewees mentioned that they expect test automation to minimize various risks (54%), decrease the number of live issues (51%), and improve customer experience (50%). By strengthening it with AI, businesses can: Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality. Analyzing Two Sides of a Coin: Hurdles to Overcome Although AI can enhance the capabilities of automated testing, organizations should remember that its implementation isn’t always smooth sailing, meaning the following roadblocks can occur: Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly. Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities. Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives). Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly. Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities. Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives). Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits. Top 5 AI Use Cases for Enhancing Traditional Test Automation Let’s analyze some potential cases of harnessing AI to facilitate more efficient operations. Improved regression testing Improved regression testing Regression testing ensures that newly introduced software features do not disrupt existing functionality. As the project evolves and the codebase grows, it becomes increasingly challenging for QA automation engineers to maintain comprehensive test coverage and ensure the reliability of the IT product. Fortunately, AI has the capabilities to swiftly examine previous code alterations and testing outcomes to identify areas most prone to defects and suggest what verifications should be executed first. This helps overcome lackluster productivity and speed up software rollout. More effective GUI verifications More effective GUI verifications First impressions do matter. Studies show that it takes only 50 ms for the target audience to decide whether they like your IT solution or not, while other research shows that 94% of reasons for rejecting a website were related to issues with design. 50 ms 94% QA automation engineers can enhance their user interface testing capabilities with the help of AI. It allows them to automate the process of verifying UI elements with the help of image-based testing, which improves overall velocity and accuracy. Next-generation scripts Next-generation scripts AI can automatically and quickly generate intelligent scripts, ensuring a more effective quality control process. AI gathers data on how users work with the IT product and builds predictions on the expected outcomes. In case of any abnormal situations, AI highlights probable issues. Then, QA specialists analyze these messages to identify further actions - opening a defect or continuing testing. Automated recovery opportunities Automated recovery opportunities Due to AI’s self-healing possibilities, QA automation engineers can have less work with maintaining and updating scripts after code changes in the application. AI-driven automated scripts can adapt leveraged locators to alterations in the UI to ensure continuous test execution. This helps enhance the robustness of scripts, ease maintenance, prevent disruptions in the testing process, and dedicate more effort to performing other vital QA activities. Better approach for working with issues Better approach for working with issues When AI supplements test automation, it can review previous application-related data and consider software intricacy. This helps to forecast areas where potential issues may arise, allowing QA specialists to focus more on risky software aspects. In addition, companies can leverage test automation frameworks with AI elements. They serve to better understand the reasons for diverse glitches, which makes it easier and faster for QA and development teams to mitigate them. All Things Considered By leveraging AI-empowered test automation, QA teams can potentially boost the productivity, precision, and velocity of testing workflows. Although AI’s introduction is associated with hurdles, this technology can facilitate regression and GUI testing, as well as provide effective self-learning, self-healing, predictive, and cause analysis opportunities.

Work Smarter, Not Harder: Using AI to Supercharge Test Automation

Edit: Previously I wrote about the "6 Most Popular Vue.js UI Libraries (Vue 3) in 2023". As we are now in 2024, I have revisited the different libraries and updated them for the current year 🎉 Vue.js, a JavaScript framework, has been gaining a lot of attention in recent years due to its simplicity, flexibility, and performance. Also, User Interface (UI) component libraries have become an essential aspect of web development, providing a fast and convenient way to build beautiful and responsive user interfaces. Vue 3 has become the default choice to build modern applications since Vue 2 reached its end of life on December 31, 2023. The need for UI component libraries designed specifically for Vue 3 has risen. In this article, we will take a look at the six most popular Vue.js UI component libraries for 2024, categorized based on their popularity within English and Chinese-speaking communities. I decided to categorize this way because of the massive popularity of Vue.js in China according to an interview with Evan You, the creator of Vue.js, who was born in China and grew up in a city near Shanghai. Moreover, Vue has well-written documents in Chinese. As teams from big companies in China such as Alibaba, Tencent, and Baidu started using Vue.js, all these factors may have contributed to its popularity in the country. Vue-PDF-Viewer: Flexible and Powerful Vue.js PDF Component Just a quick background about what I’m working on. Vue-PDF-Viewer renders the PDF viewer on your Vue or Nuxt websites so that your users can interact with your PDF document without leaving your sites. The component has over 20 features including theme customization, built-in localization, web responsiveness, and more. I would be grateful if you could check Vue-PDF-Viewer out. It will encourage me to make even more content. ❤️‍🔥 English Focused Community 1. Vuetify Vuetify is a popular Vue.js UI component library based on Material Design specifications. It provides over 100 customizable components for creating beautiful and responsive user interfaces. With its modular design, developers can selectively import components, which keeps the bundle size small and improves performance. Additionally, Vuetify integrates seamlessly with Nuxt 3 and features powerful theming capabilities where developers can customize their application style and match it with their brand. As of February 2024, Vuetify has over 38,800 stars (from 37,900 stars in 2023) on GitHub and an average weekly download count of close to 500,000. (The trend line seems to be growing steadily over the years) Learn more at https://vuetifyjs.com Remark: There are still a couple of Vuetify 2 components including calendar, overflow-btn, speed-dial, time-picker, and treeview that are not yet available in Vuetify 3. According to the official Vuetify guide, they will be released by Vuetify Labs when development is completed. 2. Quasar Quasar's UI Components feature 70 high-performance customizable Material Design components and icons of various styles (bootstrap, material, fontawesome, and many more). The documentation is detailed and well thought-out. The library also has pre-built features including animations and functions to handle dates and times. What makes Quasar, founded in 2015, different and stand out from other UI component libraries is that Quasar is not only a UI component library but a dynamic Vue framework. You can develop Vue.js desktop, web, and mobile applications with a single codebase. As the saying goes, write once and use it everywhere. As of February 2024, Quasar has over 25,000 stars (from 24,200 stars in 2023) on GitHub and an average weekly download count of over 100,000. Learn more on https://quasar.dev/ 3. PrimeVue PrimeVue is a sleek Vue.js component library that offers over 90 components and 200+ icons making it one of the most comprehensive libraries in the Vue.js community. It's a lightweight library with exclusive Tailwind CSS integration, enabling developers to build complex enterprise-level applications with ease. PrimeVue is powered by PrimeTek, which serves millions of developers of Fortune 500 companies such as Intel, Nvidia, and American Express, lending it massive credibility in the enterprise space. PrimeVue also features an intuitive API, allowing developers to quickly customize the components to achieve their desired designs. As of February 2024, PrimeVue has over 6,800 stars (from 4,300 stars in 2023) on GitHub and an average weekly download count of over 170,000 (A huge increase from around 100,000 downloads in September 2023). Learn more on https://primevue.org/ Chinese Focused Community 1. Element Plus Element Plus is a community-developed project born from Element UI which supports only Vue 2.X. Due to its predecessor's popularity, it has naturally gained a lot of attention from developers, especially those in China. A TypeScript-based library with a complete type definition, Element Plus is not based on Material Design and has its own distinct UI style which can modified quite easily. Element Plus simplifies component utilization, making the code more maintainable and readable with Vue 3's Composition API. As of February 2024, Element Plus has over 22,600 stars (from 21,300 stars in 2023) on GitHub and an average weekly download count of over 200,000. Learn more at https://element-plus.org/en-US/ 2. Ant Design Vue Ant Design Vue (Antdv) is a popular TypeScript-based UI component library based on Ant Design, a library that was developed for React and was originally created by a team from Alibaba. But it has gained traction in the Vue.js community for its ease of use and its rich feature set. It offers a comprehensive range of components like tree structures, forms, and data visualization components. Similar to Element Plus, Antdv has its own unique UI style. Although it may initially seem daunting to explore its huge sets of UI components and features, once you get the hang of it, Antdv can be a very powerful UI component library for projects of all sizes. As of February 2024, Ant Design Vue has over 19,300 stars (from 18,600 stars in 2023) on GitHub and an average weekly download count of over 80,000. Learn more on https://antdv.com/ 3. Naive UI Naive UI is a TypeScript-based UI component library developed by TuSimple, a Chinese autonomous truck company based in California. It was mentioned by Evan You, the creator of Vue.js, in his Weibo blog back in 2021. Naive UI consists of over 90 components that can be imported to use in your project. Most impressively, all 90+ components can be treeshaken. This means that the component can be imported individually into your application without waste. Documentation of Naive UI is also well-documented and easy to follow. As of February 2024, Naive UI has over 14,800 stars on GitHub and an average weekly download count of over 30,000. Learn more at https://github.com/tusen-ai/naive-ui Special Mention: Nut UI Nut UI is a mobile UI component library that is developed by the Chinese e-commerce giant, JD.com (also known as Jingdong). It provides a JD-style mobile UI component library that is used by JD in production. Nut UI features high-quality UI components that are well-tested (unit test coverage more than 80%). It has clear documentation and supports TypeScript. Furthermore, it provides Sketch design resources for designers to easily create design screens. As of February 2024, Nut UI has over 5,700 stars on GitHub and an average weekly download count of over 3,000. Learn more at https://github.com/jdf2e/nutui/blob/v4/README_EN.md Conclusion In conclusion, Vue.js has gained popularity due to its flexibility, simplicity, and performance. The availability of different UI component libraries has made it easier for developers to build complex and elegant applications at speed with ease. Whether you're building large complex applications or small, highly performant ones, there is always the right Vue.js UI component library suitable for your needs. Vue-PDF-Viewer: Flexible and Powerful Vue.js PDF Component If you feel like this article helped you, please check out Vue-PDF-Viewer. You can customize the PDF viewer component to your theme, add locales, configure panels, and more. Each function has TS and JS codes, unit tests, and interactive demos. With our starter kits, you can start rendering the Vue-PDF-Viewer in minutes. It would encourage me to continue creating even more content. Thank you in advance! 🙏 Edit: Previously I wrote about the "6 Most Popular Vue.js UI Libraries (Vue 3) in 2023". As we are now in 2024, I have revisited the different libraries and updated them for the current year 🎉 Edit: Previously I wrote about the " 6 Most Popular Vue.js UI Libraries (Vue 3) in 2023 ". As we are now in 2024, I have revisited the different libraries and updated them for the current year 🎉 6 Most Popular Vue.js UI Libraries (Vue 3) in 2023 Vue.js, a JavaScript framework, has been gaining a lot of attention in recent years due to its simplicity, flexibility, and performance. Also, User Interface (UI) component libraries have become an essential aspect of web development, providing a fast and convenient way to build beautiful and responsive user interfaces. Vue 3 has become the default choice to build modern applications since Vue 2 reached its end of life on December 31, 2023. The need for UI component libraries designed specifically for Vue 3 has risen. In this article, we will take a look at the six most popular Vue.js UI component libraries for 2024, categorized based on their popularity within English and Chinese-speaking communities. I decided to categorize this way because of the massive popularity of Vue.js in China according to an interview with Evan You, the creator of Vue.js, who was born in China and grew up in a city near Shanghai. an interview Moreover, Vue has well-written documents in Chinese. As teams from big companies in China such as Alibaba, Tencent, and Baidu started using Vue.js, all these factors may have contributed to its popularity in the country. Vue-PDF-Viewer: Flexible and Powerful Vue.js PDF Component Just a quick background about what I’m working on. Vue-PDF-Viewer renders the PDF viewer on your Vue or Nuxt websites so that your users can interact with your PDF document without leaving your sites. The component has over 20 features including theme customization, built-in localization, web responsiveness, and more. Vue-PDF-Viewer I would be grateful if you could check Vue-PDF-Viewer out. It will encourage me to make even more content. ❤️‍🔥 Vue-PDF-Viewer English Focused Community 1. Vuetify Vuetify is a popular Vue.js UI component library based on Material Design specifications. It provides over 100 customizable components for creating beautiful and responsive user interfaces. With its modular design, developers can selectively import components, which keeps the bundle size small and improves performance. Additionally, Vuetify integrates seamlessly with Nuxt 3 and features powerful theming capabilities where developers can customize their application style and match it with their brand. As of February 2024, Vuetify has over 38,800 stars (from 37,900 stars in 2023) on GitHub and an average weekly download count of close to 500,000. (The trend line seems to be growing steadily over the years) trend line Learn more at https://vuetifyjs.com https://vuetifyjs.com Remark: There are still a couple of Vuetify 2 components including calendar, overflow-btn, speed-dial, time-picker, and treeview that are not yet available in Vuetify 3. According to the official Vuetify guide, they will be released by Vuetify Labs when development is completed. Remark: There are still a couple of Vuetify 2 components including calendar, overflow-btn, speed-dial, time-picker, and treeview that are not yet available in Vuetify 3. According to the official Vuetify guide, they will be released by Vuetify Labs when development is completed. 2. Quasar Quasar's UI Components feature 70 high-performance customizable Material Design components and icons of various styles (bootstrap, material, fontawesome, and many more). The documentation is detailed and well thought-out. The library also has pre-built features including animations and functions to handle dates and times. What makes Quasar, founded in 2015, different and stand out from other UI component libraries is that Quasar is not only a UI component library but a dynamic Vue framework. You can develop Vue.js desktop, web, and mobile applications with a single codebase. As the saying goes, write once and use it everywhere. As of February 2024, Quasar has over 25,000 stars (from 24,200 stars in 2023) on GitHub and an average weekly download count of over 100,000. Learn more on https://quasar.dev/ https://quasar.dev/ 3. PrimeVue PrimeVue is a sleek Vue.js component library that offers over 90 components and 200+ icons making it one of the most comprehensive libraries in the Vue.js community. It's a lightweight library with exclusive Tailwind CSS integration, enabling developers to build complex enterprise-level applications with ease. PrimeVue is powered by PrimeTek, which serves millions of developers of Fortune 500 companies such as Intel, Nvidia, and American Express, lending it massive credibility in the enterprise space. PrimeVue also features an intuitive API, allowing developers to quickly customize the components to achieve their desired designs. As of February 2024, PrimeVue has over 6,800 stars (from 4,300 stars in 2023) on GitHub and an average weekly download count of over 170,000 ( A huge increase from around 100,000 downloads in September 2023). A huge increase Learn more on https://primevue.org/ https://primevue.org/ Chinese Focused Community 1. Element Plus Element Plus is a community-developed project born from Element UI which supports only Vue 2.X. Due to its predecessor's popularity, it has naturally gained a lot of attention from developers, especially those in China. A TypeScript-based library with a complete type definition, Element Plus is not based on Material Design and has its own distinct UI style which can modified quite easily. Element Plus simplifies component utilization, making the code more maintainable and readable with Vue 3's Composition API. As of February 2024, Element Plus has over 22,600 stars (from 21,300 stars in 2023) on GitHub and an average weekly download count of over 200,000. Learn more at https://element-plus.org/en-US/ https://element-plus.org/en-US/ 2. Ant Design Vue Ant Design Vue (Antdv) is a popular TypeScript-based UI component library based on Ant Design, a library that was developed for React and was originally created by a team from Alibaba. But it has gained traction in the Vue.js community for its ease of use and its rich feature set. It offers a comprehensive range of components like tree structures, forms, and data visualization components. Similar to Element Plus, Antdv has its own unique UI style. Although it may initially seem daunting to explore its huge sets of UI components and features, once you get the hang of it, Antdv can be a very powerful UI component library for projects of all sizes. As of February 2024, Ant Design Vue has over 19,300 stars (from 18,600 stars in 2023) on GitHub and an average weekly download count of over 80,000. Learn more on https://antdv.com/ https://antdv.com/ 3. Naive UI Naive UI is a TypeScript-based UI component library developed by TuSimple, a Chinese autonomous truck company based in California. It was mentioned by Evan You, the creator of Vue.js, in his Weibo blog back in 2021. Naive UI consists of over 90 components that can be imported to use in your project. Most impressively, all 90+ components can be treeshaken . This means that the component can be imported individually into your application without waste. Documentation of Naive UI is also well-documented and easy to follow. treeshaken As of February 2024, Naive UI has over 14,800 stars on GitHub and an average weekly download count of over 30,000. Learn more at https://github.com/tusen-ai/naive-ui https://github.com/tusen-ai/naive-ui Special Mention: Nut UI Nut UI is a mobile UI component library that is developed by the Chinese e-commerce giant, JD.com (also known as Jingdong). It provides a JD-style mobile UI component library that is used by JD in production. JD.com Nut UI features high-quality UI components that are well-tested (unit test coverage more than 80%). It has clear documentation and supports TypeScript. Furthermore, it provides Sketch design resources for designers to easily create design screens. As of February 2024, Nut UI has over 5,700 stars on GitHub and an average weekly download count of over 3,000. Learn more at https://github.com/jdf2e/nutui/blob/v4/README_EN.md https://github.com/jdf2e/nutui/blob/v4/README_EN.md Conclusion In conclusion, Vue.js has gained popularity due to its flexibility, simplicity, and performance. The availability of different UI component libraries has made it easier for developers to build complex and elegant applications at speed with ease. Whether you're building large complex applications or small, highly performant ones, there is always the right Vue.js UI component library suitable for your needs. Vue-PDF-Viewer: Flexible and Powerful Vue.js PDF Component If you feel like this article helped you, please check out Vue-PDF-Viewer . You can customize the PDF viewer component to your theme, add locales, configure panels, and more. Each function has TS and JS codes, unit tests, and interactive demos. With our starter kits, you can start rendering the Vue-PDF-Viewer in minutes. Vue-PDF-Viewer Vue-PDF-Viewer It would encourage me to continue creating even more content. Thank you in advance! 🙏

⚡️ 6 Vue.js UI Libraries (Vue 3) That Are Trending in 2024 🔥

Recently, I haven’t been updating my open-source articles as frequently — not because I’ve stopped writing, but because the progress on open-source commercialization has been great, and the endless task of drafting proposals has consumed my time. As a native open-source commercial company, WhaleOps employs mostly engineers. Asking these open-source contributors to write proposals wastes their development time, and their proposals don’t always meet the quality standard. Unlike managing in a big company, being a startup CEO means stepping into any role the company needs. After every strategic meeting, I’m the first to roll up my sleeves and tackle the most urgent tasks. As a result, I haven’t had time to write articles, as my limited time is mostly taken up with creating proposals that follow the formal template style. Especially recently, with one bid after another, I’ve found myself questioning my sanity. The existing large models can’t solve my problem, so I’ll build my own As a tech person, I always look to tools to solve my problems. Naturally, I thought of large models, but they can’t fully grasp the nuances of our products and often produce unreliable outputs. Plus, you’re the one delivering the work, not the model. So, I decided to develop a tool for proposal generation based on large models using Python and a compatible large model (compatible with ChatGPT). This tool automatically generates a proposal from your product documentation, breaking down product manuals into feature points. Based on a human-created mapping of these points to the requirements, it generates a Word version of the proposal and an Excel deviation table. The model can condense or expand content or simply copy relevant functionality as needed. Features of the open-source proposal tool The functionality of this tool is simple, with the most challenging part being the Word formatting (formatting in Word is always a pain). I experimented with several methods to make sure it follows the correct Title 1, Title 2, Title 3, body text, table, and image formats in the Word template. Staying true to the open-source spirit, I’ve uploaded the tool to my personal GitHub under an Apache License, so feel free to use it if you need. Here’s what it does: Get it here: https://github.com/William-GuoWei/ProposalLLM Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”).
Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”).
For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed).
Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. With this tool, you can quickly modify and generate proposals at will. Proposal generation process Start by running Extract_Word.py to generate your product feature points as a Word document, then run Generate.py. If a feature point is missing, you can enter “X” in the Excel file, and the model will generate content that meets the requirements. However, I strongly recommend a manual review. After generation, you’ll see the proposal formatted with a table of contents, body text, images, tables, and bullet points, all automatically organized. The entire proposal is structured according to the client’s requirements in sequential format, with subheadings, content, images, and point-to-point responses. This takes care of all the repetitive work. The automatically generated deviation table includes everything, whether the content was generated by the model or not, along with the corresponding section numbers. You only need to finalize the deviation table with “&” symbols based on the final requirements — the model has written it all for you. For me, this tool has reduced what used to be 8 hours of work to around 30 minutes, and even our business team can generate the business proposal sections using the template. In total, this has cut down our time for a week-long proposal to 1–2 days, reducing the manpower required by 80%. How to use it? First, download all the code to a single directory from GitHub at https://github.com/William-GuoWei/ProposalLLM: Proposal Large Model (Chinese version). Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx.
Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section.
Copy your product manual to Template.docx. Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues.
Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly.
Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically.
Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors.
Adjust the parameters in Generate.py: API_KEY and SECRET_KEY: Baidu Cloud large model keys.
MAX_WIDTH_CM: Maximum image width; images larger than this will be resized automatically.
The prompts for generating content have been customized for large data scenarios, but feel free to modify them.
MoreSection=1 will read Column C to generate detailed tertiary headings (default is on).
ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off).
DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on).
key_flag=1 adds the importance level of each requirement to the proposal headings (default is on).
last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” 8. Run Generate.py. Summary With this tool, you only need to check the product features against the proposal requirements, and most of the proposal content is generated automatically. The business proposal section can also be generated similarly, so creating a 1,000-page proposal now only takes a few minutes. Recently, I haven’t been updating my open-source articles as frequently — not because I’ve stopped writing, but because the progress on open-source commercialization has been great, and the endless task of drafting proposals has consumed my time. As a native open-source commercial company, WhaleOps employs mostly engineers. Asking these open-source contributors to write proposals wastes their development time, and their proposals don’t always meet the quality standard. Unlike managing in a big company, being a startup CEO means stepping into any role the company needs. After every strategic meeting, I’m the first to roll up my sleeves and tackle the most urgent tasks. As a result, I haven’t had time to write articles, as my limited time is mostly taken up with creating proposals that follow the formal template style. Especially recently, with one bid after another, I’ve found myself questioning my sanity. The existing large models can’t solve my problem, so I’ll build my own As a tech person, I always look to tools to solve my problems. Naturally, I thought of large models, but they can’t fully grasp the nuances of our products and often produce unreliable outputs. Plus, you’re the one delivering the work, not the model. So, I decided to develop a tool for proposal generation based on large models using Python and a compatible large model (compatible with ChatGPT). This tool automatically generates a proposal from your product documentation, breaking down product manuals into feature points. Based on a human-created mapping of these points to the requirements, it generates a Word version of the proposal and an Excel deviation table. The model can condense or expand content or simply copy relevant functionality as needed. Features of the open-source proposal tool The functionality of this tool is simple, with the most challenging part being the Word formatting (formatting in Word is always a pain). I experimented with several methods to make sure it follows the correct Title 1, Title 2, Title 3, body text, table, and image formats in the Word template. Staying true to the open-source spirit, I’ve uploaded the tool to my personal GitHub under an Apache License, so feel free to use it if you need. Here’s what it does: Get it here: https://github.com/William-GuoWei/ProposalLLM Get it here: https://github.com/William-GuoWei/ProposalLLM https://github.com/William-GuoWei/ProposalLLM Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”). Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”). For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed). Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. Breaks down your product manual into a set of reusable detail documents, reducing the need to repeatedly reference the source document when drafting a proposal. You can also customize specific functionality (the default file name is “Template.docx”). Based on the requirements table filled in by a person, it automatically generates a proposal in a point-to-point response format, including all headings and content, with the correct Title 1, 2, 3 formatting, and automatically organizes body text, images, and bullet points (the default requirements table is “requirements_table.xlsx,” and the generated content is in “proposal_content.docx”). For any product requirements in the corresponding functionality section, it automatically copies the product manual content into the point-to-point response section, retaining images, tables, and bullet points. You can also rewrite the product description to suit different proposal needs. If there’s no matching functionality, the model automatically generates relevant content (review and modify as needed). Completes the technical requirements deviation table by automatically filling in responses in the “requirements_table.xlsx,” with responses formatted as “Answer: Fully supports, {Model-generated text based on project requirements}” and includes the section number corresponding to the proposal. With this tool, you can quickly modify and generate proposals at will. Proposal generation process Start by running Extract_Word.py to generate your product feature points as a Word document, then run Generate.py . If a feature point is missing, you can enter “X” in the Excel file, and the model will generate content that meets the requirements. However, I strongly recommend a manual review. After generation, you’ll see the proposal formatted with a table of contents, body text, images, tables, and bullet points, all automatically organized. Extract_Word.py Generate.py The entire proposal is structured according to the client’s requirements in sequential format, with subheadings, content, images, and point-to-point responses. This takes care of all the repetitive work. The automatically generated deviation table includes everything, whether the content was generated by the model or not, along with the corresponding section numbers. You only need to finalize the deviation table with “&” symbols based on the final requirements — the model has written it all for you. For me, this tool has reduced what used to be 8 hours of work to around 30 minutes, and even our business team can generate the business proposal sections using the template. In total, this has cut down our time for a week-long proposal to 1–2 days, reducing the manpower required by 80%. How to use it? First, download all the code to a single directory from GitHub at https://github.com/William-GuoWei/ProposalLLM : Proposal Large Model (Chinese version). https://github.com/William-GuoWei/ProposalLLM Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx. Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section. Copy your product manual to Template.docx. Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues. Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly. Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically. Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors. Adjust the parameters in Generate.py: Install the Python environment and packages: pip install openpyxl, docx, openai, requests, docx, python-docx . pip install openpyxl, docx, openai, requests, docx, python-docx Apply for a ChatGPT or Baidu Qianfan large model key (I used ERNIE-Speed-8K, which is free), record the token, and place it in the relevant code section. Copy your product manual to Template.docx . Be sure to use the body text, Title 1, Title 2, and Title 3 styles provided; other formats may cause issues. Template.docx Run Extract_Word.py to generate the feature point document from the product manual (supports up to 3 heading levels). If the list formatting appears off, don’t worry; the final format will align properly. Extract_Word.py Fill in Columns B and C (which will generate secondary and tertiary headings automatically) and Column G (the corresponding product manual chapter). If a chapter is missing, enter “X.” Note that if there is no corresponding chapter or an “X” is entered, the model will generate content automatically. Review the “proposal_content.docx” document and keep the chapter for which you want to start generating the proposal. You can modify the body text and heading 1, 2, and 3 styles; just don’t rename the styles, or there may be errors. Adjust the parameters in Generate.py : Generate.py API_KEY and SECRET_KEY: Baidu Cloud large model keys. MAX_WIDTH_CM: Maximum image width; images larger than this will be resized automatically. The prompts for generating content have been customized for large data scenarios, but feel free to modify them. MoreSection=1 will read Column C to generate detailed tertiary headings (default is on). ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off). DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on). key_flag=1 adds the importance level of each requirement to the proposal headings (default is on). last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” API_KEY and SECRET_KEY : Baidu Cloud large model keys. API_KEY SECRET_KEY MAX_WIDTH_CM : Maximum image width; images larger than this will be resized automatically. MAX_WIDTH_CM The prompts for generating content have been customized for large data scenarios, but feel free to modify them. MoreSection=1 will read Column C to generate detailed tertiary headings (default is on). MoreSection=1 ReGenerateText=0 will re-generate text content automatically for different proposal needs (default is off). ReGenerateText=0 DDDAnswer=1 generates the point-to-point response content at the top of each feature point (default is on). DDDAnswer=1 key_flag=1 adds the importance level of each requirement to the proposal headings (default is on). key_flag=1 last_heading_1=2 specifies the starting chapter for the technical solution in “proposal_content.docx.” last_heading_1=2 8. Run Generate.py . Generate.py Summary With this tool, you only need to check the product features against the proposal requirements, and most of the proposal content is generated automatically. The business proposal section can also be generated similarly, so creating a 1,000-page proposal now only takes a few minutes.

I Built An Automatic Proposal Generation Large Language Model and Open-Sourced It on GitHub

Using GTM Strategies for Digital Tech Startups: My Guide from Launching 7x Businesses

We are back with another Company of the Week feature! Every week, we share an awesome tech brand from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we are proud to present Algolia - end-to-end AI search and discovery platform. The company delivers a combination of market-leading natural language processing and keyword via vector search – all uniquely packaged on a single API and supported by hyperscale indexing. The company’s vision, mission and purpose is ‘Powering Discovery’. Algolia achieves its vision by enabling more than 17,000 customers to build blazing fast and relevant search and discovery experiences for their in-app users and/or online visitors (using any web, mobile or voice device) – by surfacing the desired content instantly and at scale. Algolia powers 1.75 Trillion search requests a year or more than 30 Billion a week – that’s four times more than the combined volume of Bing, Yahoo, DuckDuckGo, Baidu and Yandex). Algolia is used by one in six online users and more than 5 million developers a month. According to a Forrester Consulting study, customers gain a 382% Return on Investment with less than a 6 month payback period when deploying the Algolia Search and Discovery platform. Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Meet Algolia: #FunFact Algolia had fun participating in a recent Web Dev Challenge with Jason Lengstorf and three other developers. The challenge was - “What could you create if you had 30 minutes to plan and 4 hours to build an e-commerce site... with a twist?” Sidney Buckner, Alex Trost, Shaundai Person, and Jason Lengstorf took on the Web Dev Challenge! Joining them with expert advice from Algolia was VP of Product Growth, Nick Vlku! 💪 THE CHALLENGE: Design an e-commerce site that leverages Algolia. The twist? Think beyond traditional e-commerce strategies and innovate! 🛠 THE TOOL: The builds had to incorporate Algolia, the go-to solution for lightning-fast AI search, smart recommendations, and seamless integration with pre-built components. Watch the trailer here: https://www.youtube.com/watch?v=LnM7iPdjTq0&embedable=true 📺 Watch the full episode Algolia’s Featured Business Stories on HackerNoon Algolia has shared some amazing content via the HackerNoon Business Blogging Program, and has generated 1 month 26 days 4 hours and 53 minutes of reading time! Here are favorite stories by Algolia: Understanding AI Search
A JavaScript API Client Can Be a SaaS Product. Find Out How.
A New Era for Shopping: The Rise of AI-powered Conversational Commerce That's all this week, hackers! Stay Creative, Stay Iconic. We are back with another Company of the Week feature! Every week, we share an awesome tech brand from our tech company database , making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. Company of the Week tech company database This week, we are proud to present Algolia - end-to-end AI search and discovery platform. The company delivers a combination of market-leading natural language processing and keyword via vector search – all uniquely packaged on a single API and supported by hyperscale indexing. The company’s vision, mission and purpose is ‘Powering Discovery’. Algolia Algolia achieves its vision by enabling more than 17,000 customers to build blazing fast and relevant search and discovery experiences for their in-app users and/or online visitors (using any web, mobile or voice device) – by surfacing the desired content instantly and at scale. Algolia powers 1.75 Trillion search requests a year or more than 30 Billion a week – that’s four times more than the combined volume of Bing, Yahoo, DuckDuckGo, Baidu and Yandex). Algolia is used by one in six online users and more than 5 million developers a month. According to a Forrester Consulting study, customers gain a 382% Return on Investment with less than a 6 month payback period when deploying the Algolia Search and Discovery platform. Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Meet Algolia: #FunFact Meet Algolia: #FunFact Algolia had fun participating in a recent Web Dev Challenge with Jason Lengstorf and three other developers. The challenge was - “What could you create if you had 30 minutes to plan and 4 hours to build an e-commerce site... with a twist?” Sidney Buckner , Alex Trost , Shaundai Person , and Jason Lengstorf took on the Web Dev Challenge! Joining them with expert advice from Algolia was VP of Product Growth, Nick Vlku ! Jason Lengstorf Jason Lengstorf Sidney Buckner Sidney Buckner Alex Trost Alex Trost Shaundai Person Shaundai Person Jason Lengstorf Jason Lengstorf Nick Vlku Nick Vlku 💪 THE CHALLENGE: Design an e-commerce site that leverages Algolia. The twist? Think beyond traditional e-commerce strategies and innovate! 🛠 THE TOOL: The builds had to incorporate Algolia, the go-to solution for lightning-fast AI search, smart recommendations, and seamless integration with pre-built components. Watch the trailer here: https://www.youtube.com/watch?v=LnM7iPdjTq0&embedable=true https://www.youtube.com/watch?v=LnM7iPdjTq0&embedable=true 📺 Watch the full episode Watch the full episode Algolia’s Featured Business Stories on HackerNoon Algolia has shared some amazing content via the HackerNoon Business Blogging Program , and has generated 1 month 26 days 4 hours and 53 minutes of reading time! Here are favorite stories by Algolia: Algolia HackerNoon Business Blogging Program Understanding AI Search A JavaScript API Client Can Be a SaaS Product. Find Out How. A New Era for Shopping: The Rise of AI-powered Conversational Commerce Understanding AI Search Understanding AI Search Understanding AI Search A JavaScript API Client Can Be a SaaS Product. Find Out How. A JavaScript API Client Can Be a SaaS Product. Find Out How. A JavaScript API Client Can Be a SaaS Product. Find Out How. A New Era for Shopping: The Rise of AI-powered Conversational Commerce A New Era for Shopping: The Rise of AI-powered Conversational Commerce A New Era for Shopping: The Rise of AI-powered Conversational Commerce That's all this week, hackers! Stay Creative, Stay Iconic.

Meet Algolia: HackerNoon Company of the Week

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Google Has Monopoly Power In The U.S. Search Ads Market

United States of America v. Google LLC., Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 7 of 37. B. General Search Competition 1. Search Competitors 73. General search competitors—all of which are significantly smaller than Google in the United States—include Bing, Yahoo, DuckDuckGo, You.com, Ask.com, Brave, and Ecosia. Tr. 1232:22–1233:18 (Dischler (Google)); Tr. 8093:10–8094:5 (Gomes (Google)); Tr. 2167:21– 2168:4; (Giannandrea (Apple)); Tr. 2169:17–22 (Giannandrea (Apple)); Tr. 1942:11–21 (Weinberg) (DuckDuckGo)); UPX0599 at -698 (2008 email by Barton (Google) identifying Ask.com as offering web search); UPX0913 at -017 (Dr. Varian’s presentation listing Google, Bing, and DuckDuckGo as “general purpose” search engines). Outside of the United States, GSEs include Yandex (in Russia), Baidu (in China), and Naver (in South Korea). Tr. 2720:5– 2721:5 (Parakhin (Microsoft)); Tr. 7124:24–7125:14 (Baker (Mozilla)). a) Independent Versus Syndicated Engines 74. Search syndication occurs when one search engine is powered by, and receives results from, another in a white-label manner. Tr. 3520:13–22 (Nadella (Microsoft)); Tr. 320:10– 20 (Barton (Google)) (“[S]yndication . . . is when you’re powering someone else’s search.”). GSEs, which are typically free for users and funded by the sale of ads, can operate independently or through a syndication agreement with an independent GSE. Tr. 7345:8–19 (Raghavan (Google)) (Revenue from advertising supports Google’s search engine.); Tr. 184:11–12 (Varian (Google)) (GSEs are funded by the sale of ads); UPX0119 at -534 (Google analyzing its search syndication partnership with Yahoo in the U.S.). 75. Google and Bing are the only meaningful companies that independently own all the elements of a GSE. UPX0332 at -673 (Google listing only itself and Microsoft (Bing) as “general purpose search engines”); UPX0266 at -983 (Giannandrea (Apple)) (“there are so few serious contenders in [general web search] because of the depth of R&D needed”); UPX0333 at -116 (Varian declaring that “Google is [an] ad supported general purpose search engine. There aren’t very many of these in part because they are very expensive to build and maintain”); Des. Tr. 105:18–106:24 (Connell (Microsoft) Dep.) (“there are only a few search systems on the planet that allow you to understand queries and crawl the web and index the Internet”). 76. Nearly all other GSEs in the United States syndicate results from Google or Bing. Tr. 2061:18–2062:9 (Weinberg (DuckDuckGo)) (when considering syndication partners, DuckDuckGo looked at [redacted]); Des. Tr. 279:14–17; 279:19–25; 280:2–7 (Stein (IAC) Dep.) (Ask.com syndicates from Google and Bing); Tr. 5916:10–19 (Whinston (Pls. Expert)) (including DuckDuckGo and Yahoo in market share statistics is conservative as they syndicate results from Bing and are not clearly independent competitors). Yahoo and DuckDuckGo, the two major syndicated GSEs in the United States, syndicate from Microsoft’s Bing. Tr. 3520:13–22 (Nadella (Microsoft)); Des. Tr. 34:25–35:18, 35:24–36:8 (Ramalingam (Yahoo) Dep.) (although Yahoo used to crawl and index the web, it now gets results from Microsoft). b) Differentiation In General Search Engines 77. Bing and Yahoo offer general search engine experiences similar to Google. Bing is Google’s most significant competitor among GSEs. Tr. 8094:8–10 (Gomes (Google)); UPX0888 at -852 (Bing mimics Google in “look-and-feel” and if “Apple switches to Bing” from Google as its default, “most people won’t even notice a difference.”); Des. Tr. 216:3–217:2 (Baker (Mozilla) Dep.) (Bing, Yahoo, and Google would be products able to “fulfill the search engine function” in the Firefox browser.). DuckDuckGo, however, offers a GSE that emphasizes privacy. Tr. 1945:9–1946:4, 1947:3–1948:25 (Weinberg (DuckDuckGo)); UPX0408 at -030–36 (identifying DuckDuckGo as a “privacy-focus[]ed search engine[]”). 78. Neeva offered a different approach to user privacy: a subscription-based GSE with no ads. Tr. 3671:2–3672:11 (Ramaswamy (Neeva)) (Neeva sought to create a search engine not beholden to advertisers). Specifically, Neeva differentiated itself through privacy, personalization, generative AI, and being ads-free. Tr. 3718:25–3719:16 (Ramaswamy (Neeva)). 2. Potential Search Competitors 79. Tools and services that are designed to search the contents of mobile apps are not substitutes for general search services today. Tr. 5851:5–5852:5 (Whinston (Pls. Expert)) (discussing Branch and UPX0694). Such app-search tools do not require their developers to index the web, and their core service does not involve returning web results in response to user queries. Tr. 2957:3–18 (Austin (Branch)) (Branch does not indexed the web); Tr. 847:23–848:9 (Kolotouros (Google)) (on-device search explores what is on the phone, even when it is not connected to the web). 80. For example, Branch offers an app-search tool that includes, among other things, an on-device search service. Tr. 2894:3–18 (Austin (Branch)) (Branch allows a user to search across app pages and then be directed to the app); Tr. 2789:12–18 (Kartasheva (Google)) (Branch indexes app content to allow users to search within those apps and can show customized ads to those users based on user activity.); id. 2796:20–2797:17 (Branch uses deeplinking); Tr. 4497:2–13 (Chang (Samsung Next)) (explaining that Branch was integrated with S Finder, Samsung’s on-device search product). 81. Some large distributors also provide search features. For example, although Apple does not operate a GSE, Tr. 2206:2–3 (Giannandrea (Apple)), it provides users certain search capabilities through Spotlight, Siri, and Suggestions, supra ¶¶ 8–10. Apple’s goal is to “give answers when we ha[ve] them” and “get people where they’re going faster[.]” Id. 2282:14–20. Apple has invested significantly in search technology to enable these features and could leverage this technology to answer more queries in the future. Infra ¶¶ 1093–1119 (§ VIII.B.3.a). 3. Evolution From Desktop To Mobile 82. Before 2010, very little if any general searches occurred on mobile phones. Tr. 3097:13–3099:03 (Tinter (Microsoft)) (When Bing first launched in 2009, most searches were on desktop); Tr. 316:1–9 (Barton (Google)) (When he joined Google in 2004, people used feature phones, which were “very basic phones” from companies like Nokia and Motorola). Since 2010, the number of mobile phone searches has grown rapidly. Id. 317:6–20 (between 2011 and 2013, mobile search became “a very significant portion of all search.”); Tr. 3663:7–18 (Nadella (Microsoft)) (in the early 2010s, mobile grew to be “the dominant platform” for search); UPX0006 at -330 (2019 Google deck showing actual and projected percentages of mobile, desktop, and tablet revenues and queries for 2018–2023). 83. Today, more than half of all general searches occur on mobile phones. Tr. 3097:13–3099:03 (Tinter (Microsoft)); UPX0006 at -330 (Google 2019 “Search State of the Union” with chart showing actual and projected percentages of mobile, desktop, and tablet revenues and queries for 2018–2023). “[T]he user behavior dynamics on mobile tend to be different than they are on PC, right.” Tr. 3102:12–3104:25 (Tinter (Microsoft)); Tr. 329:21– 330:1 (Barton (Google)). Some of this differentiation is a function of smaller screen real estate, and lack of keyboard. Tr. 9764:2–18 (Murphy (Def. Expert)) (conceding that a difficulty exists in changing the default on mobile devices because it lacks a keyboard and the screens are smaller); Tr. 1628:25–1629:4 (Roszak (Google)) (discussing UPX1050, agreeing that keyboards and screen size are different on mobile); Tr. 3102:12–3104:25 (Tinter (Microsoft)). Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on April 30, 2024, storage.courtlistener is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. United States of America v. Google LLC., Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 7 of 37. United States of America v. Google LLC., Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 7 of 37. HackerNoon’s Legal PDF Series here B. General Search Competition 1. Search Competitors 1. Search Competitors 73. General search competitors—all of which are significantly smaller than Google in the United States—include Bing, Yahoo, DuckDuckGo, You.com, Ask.com, Brave, and Ecosia. Tr. 1232:22–1233:18 (Dischler (Google)); Tr. 8093:10–8094:5 (Gomes (Google)); Tr. 2167:21– 2168:4; (Giannandrea (Apple)); Tr. 2169:17–22 (Giannandrea (Apple)); Tr. 1942:11–21 (Weinberg) (DuckDuckGo)); UPX0599 at -698 (2008 email by Barton (Google) identifying Ask.com as offering web search); UPX0913 at -017 (Dr. Varian’s presentation listing Google, Bing, and DuckDuckGo as “general purpose” search engines). Outside of the United States, GSEs include Yandex (in Russia), Baidu (in China), and Naver (in South Korea). Tr. 2720:5– 2721:5 (Parakhin (Microsoft)); Tr. 7124:24–7125:14 (Baker (Mozilla)). a) Independent Versus Syndicated Engines a) Independent Versus Syndicated Engines 74. Search syndication occurs when one search engine is powered by, and receives results from, another in a white-label manner. Tr. 3520:13–22 (Nadella (Microsoft)); Tr. 320:10– 20 (Barton (Google)) (“[S]yndication . . . is when you’re powering someone else’s search.”). GSEs, which are typically free for users and funded by the sale of ads, can operate independently or through a syndication agreement with an independent GSE. Tr. 7345:8–19 (Raghavan (Google)) (Revenue from advertising supports Google’s search engine.); Tr. 184:11–12 (Varian (Google)) (GSEs are funded by the sale of ads); UPX0119 at -534 (Google analyzing its search syndication partnership with Yahoo in the U.S.). 75. Google and Bing are the only meaningful companies that independently own all the elements of a GSE. UPX0332 at -673 (Google listing only itself and Microsoft (Bing) as “general purpose search engines”); UPX0266 at -983 (Giannandrea (Apple)) (“there are so few serious contenders in [general web search] because of the depth of R&D needed”); UPX0333 at -116 (Varian declaring that “Google is [an] ad supported general purpose search engine. There aren’t very many of these in part because they are very expensive to build and maintain”); Des. Tr. 105:18–106:24 (Connell (Microsoft) Dep.) (“there are only a few search systems on the planet that allow you to understand queries and crawl the web and index the Internet”). 76. Nearly all other GSEs in the United States syndicate results from Google or Bing. Tr. 2061:18–2062:9 (Weinberg (DuckDuckGo)) (when considering syndication partners, DuckDuckGo looked at [redacted]); Des. Tr. 279:14–17; 279:19–25; 280:2–7 (Stein (IAC) Dep.) (Ask.com syndicates from Google and Bing); Tr. 5916:10–19 (Whinston (Pls. Expert)) (including DuckDuckGo and Yahoo in market share statistics is conservative as they syndicate results from Bing and are not clearly independent competitors). Yahoo and DuckDuckGo, the two major syndicated GSEs in the United States, syndicate from Microsoft’s Bing. Tr. 3520:13–22 (Nadella (Microsoft)); Des. Tr. 34:25–35:18, 35:24–36:8 (Ramalingam (Yahoo) Dep.) (although Yahoo used to crawl and index the web, it now gets results from Microsoft). b) Differentiation In General Search Engines b) Differentiation In General Search Engines 77. Bing and Yahoo offer general search engine experiences similar to Google. Bing is Google’s most significant competitor among GSEs. Tr. 8094:8–10 (Gomes (Google)); UPX0888 at -852 (Bing mimics Google in “look-and-feel” and if “Apple switches to Bing” from Google as its default, “most people won’t even notice a difference.”); Des. Tr. 216:3–217:2 (Baker (Mozilla) Dep.) (Bing, Yahoo, and Google would be products able to “fulfill the search engine function” in the Firefox browser.). DuckDuckGo, however, offers a GSE that emphasizes privacy. Tr. 1945:9–1946:4, 1947:3–1948:25 (Weinberg (DuckDuckGo)); UPX0408 at -030–36 (identifying DuckDuckGo as a “privacy-focus[]ed search engine[]”). 78. Neeva offered a different approach to user privacy: a subscription-based GSE with no ads. Tr. 3671:2–3672:11 (Ramaswamy (Neeva)) (Neeva sought to create a search engine not beholden to advertisers). Specifically, Neeva differentiated itself through privacy, personalization, generative AI, and being ads-free. Tr. 3718:25–3719:16 (Ramaswamy (Neeva)). 2. Potential Search Competitors 2. Potential Search Competitors 79. Tools and services that are designed to search the contents of mobile apps are not substitutes for general search services today. Tr. 5851:5–5852:5 (Whinston (Pls. Expert)) (discussing Branch and UPX0694). Such app-search tools do not require their developers to index the web, and their core service does not involve returning web results in response to user queries. Tr. 2957:3–18 (Austin (Branch)) (Branch does not indexed the web); Tr. 847:23–848:9 (Kolotouros (Google)) (on-device search explores what is on the phone, even when it is not connected to the web). 80. For example, Branch offers an app-search tool that includes, among other things, an on-device search service. Tr. 2894:3–18 (Austin (Branch)) (Branch allows a user to search across app pages and then be directed to the app); Tr. 2789:12–18 (Kartasheva (Google)) (Branch indexes app content to allow users to search within those apps and can show customized ads to those users based on user activity.); id. 2796:20–2797:17 (Branch uses deeplinking); Tr. 4497:2–13 (Chang (Samsung Next)) (explaining that Branch was integrated with S Finder, Samsung’s on-device search product). 81. Some large distributors also provide search features. For example, although Apple does not operate a GSE, Tr. 2206:2–3 (Giannandrea (Apple)), it provides users certain search capabilities through Spotlight, Siri, and Suggestions, supra ¶¶ 8–10. Apple’s goal is to “give answers when we ha[ve] them” and “get people where they’re going faster[.]” Id. 2282:14–20. Apple has invested significantly in search technology to enable these features and could leverage this technology to answer more queries in the future. Infra ¶¶ 1093–1119 (§ VIII.B.3.a). 3. Evolution From Desktop To Mobile 3. Evolution From Desktop To Mobile 82. Before 2010, very little if any general searches occurred on mobile phones. Tr. 3097:13–3099:03 (Tinter (Microsoft)) (When Bing first launched in 2009, most searches were on desktop); Tr. 316:1–9 (Barton (Google)) (When he joined Google in 2004, people used feature phones, which were “very basic phones” from companies like Nokia and Motorola). Since 2010, the number of mobile phone searches has grown rapidly. Id. 317:6–20 (between 2011 and 2013, mobile search became “a very significant portion of all search.”); Tr. 3663:7–18 (Nadella (Microsoft)) (in the early 2010s, mobile grew to be “the dominant platform” for search); UPX0006 at -330 (2019 Google deck showing actual and projected percentages of mobile, desktop, and tablet revenues and queries for 2018–2023). 83. Today, more than half of all general searches occur on mobile phones. Tr. 3097:13–3099:03 (Tinter (Microsoft)); UPX0006 at -330 (Google 2019 “Search State of the Union” with chart showing actual and projected percentages of mobile, desktop, and tablet revenues and queries for 2018–2023). “[T]he user behavior dynamics on mobile tend to be different than they are on PC, right.” Tr. 3102:12–3104:25 (Tinter (Microsoft)); Tr. 329:21– 330:1 (Barton (Google)). Some of this differentiation is a function of smaller screen real estate, and lack of keyboard. Tr. 9764:2–18 (Murphy (Def. Expert)) (conceding that a difficulty exists in changing the default on mobile devices because it lacks a keyboard and the screens are smaller); Tr. 1628:25–1629:4 (Roszak (Google)) (discussing UPX1050, agreeing that keyboards and screen size are different on mobile); Tr. 3102:12–3104:25 (Tinter (Microsoft)). Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on April 30, 2024, storage.courtlistener is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on April 30, 2024, storage.courtlistener is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Google vs Bing vs Yahoo: The Search Engine Competition

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