she also seemed surprised to see paparazzi at the location as she crossed a busy road. She can ebe heard saying, 'Kidhar se aaye aap? But aise kaise matlab... Kahan se aaye ho aap log.' Take a look at the video.

'Aap kidhar se aa gaye': Kalki Koechlin is SURPRISED to see paparazzi

Amar Upadhyay revealed how his show Molkki ended the age-old custom of bride-buying in Rajasthan village.

Amar Upadhyay reveals how his show Molkki ended social evil of bride-buying in Rajasthan village | Exclusive

Patrick Dai, who has been accused of making violent threats against Jewish students, entered Cornell as one of the five National Merit Scholars from P

Who is Patrick Dai, Cornell University student accused of threatening Jewish peers?

Obtaining high-resolution images in the world of microscopy has long been a challenge. Deconvolution, a method to enhance image clarity, often amplifies noise between the sample and the image. Researchers at Boston University have recently developed a novel deblurring algorithm that avoids these issues,

Novel algorithm successfully improves microscopy resolution through pixel reassignment

Apple says that dynamic system simulation in MathWorks MATLAB is 5.5x faster than the most robust Intel-based Macs. For those who haven't upgraded to the Apple Silicon family, you have a good reason to now, as the M3 Max model is a whopping 11x faster than ...

New Apple Silicon has arrived with M3, M3 Pro, and M3 Max chips

While everything inside the new MacBook Pro models gets a lift from the M3 processors, it's really all about the graphics.

M3 MacBook Pro First Look: Apple Scares Up Serious Performance

Apple just announced a (nearly) complete overhaul of its first-party silicon line, in the form of the M3, M3 Pro and M3 Max. While the new 24-inch iMac is arriving with the M3, the MacBook Pro line is easily the biggest benefactor of today’s Scary Fast ...

Apple’s 14- and 16-inch M3 MacBook Pros arrive in ‘Space Black’ color, starting at $1,599

Samarth Jurel, Isha Malviya's alleged boyfriend, entered the reality show on Saturday. Upon his entry, he made some major claims about Abhishek Kumar. Read details.

Bigg Boss 17: Three SHOCKING claims Samarth Jurel made about Isha Malviya's ex Abhishek

Ashneer Grover expressed his admiration for Salman Khan's intelligence. He emphasised that the actor is a smart individual who comprehends the nuances of branding and maintains a clear and distinct public image as he recalls the incident where Ashneer was denied a photo with Salman.

Ashneer Grover on not getting photo with Salman Khan: 'Smart banda hai...Bhaad mein jaa' | Viral Video

Businessman Ashneer Grover said in a podcast that Salman Khans manager, during an ad shoot discussion, told him to ask for a photo from the star.

Bhaad Mein Jaa Tu: Ashneer Grover On Salman Khan Refusing To Click Photo With Him; WATCH

ATLAB

atlab

How big of a problem is digital piracy? It can be argued that regulation and litigation have ensured mitigation from the mainstream. However, when some AI companies needed training data, why were pirated copies of contents allegedly [accessible to be] crawled and scraped? Why are several other unlawful events possible online, in ways that are not in the physical world—so to speak? What is peculiar about digital that makes it difficult for regulation and litigation to be decisive in eradicating problems? One easy-to-identify issue, especially with the internet—in recent decades—is that development has been ahead of safety. Safety is often added by some of the initializing companies, then tests in the real world with flaks that have resulted in adjustments. Though policy, framework, governance, terms, best practices, and so forth have been necessary to internet platforms, what would have been efficient is broad possibilities for technical angles to safety. Technical safety with evolving approaches from several corners would have done better than anything else, for the internet, than reliance on a traditional law-led approach. The same problem may follow AI safety, which regulations are trying early to correct—after the errors from social media and the internet. Regulating social media earlier may have worked in many forms, but there would have been numerous lapses that would have resulted in some of the same problems that eventually happened. Social media is as harmful as the technical shortages for safety. Assuming for some social media users, there were plugins that went through pages for them, even before seeing what was there, or there were plugins that sort to reinterpret what they were seeing or hearing, not just to fact-check, or there were plugins that developed usage parallels so that going to social media would have been unnecessary in some instances, they may have helped much. These are not just descriptions of what to do in retrospect, but things that are still necessary, which do not seem like any team is doing. Reliance is generally on the companies for safety, but they may do as it is possible or as it benefits the business. Assuming there are efforts from too many sources on technical solutions, maybe more would have been possible against past and current disadvantages of social media. In a way, seeking to correct that led to the establishment of the US AI Safety Institute, which followed the UK AISI and was followed by the EU AI Office. The purpose, in part, is to do technical research for AI safety. The US AISI also has a safety consortium, consisting of several organizations whose CRADA—Consortium Cooperative Research and Development Agreement, states that, "The purpose of the NIST Artificial Intelligence Safety Institute Consortium (“Consortium”) is to establish a new measurement science that will enable the identification of proven, scalable, and interoperable measurements and methodologies to promote safe and trustworthy development and use of Artificial Intelligence (AI), particularly for the most advanced AI (“Purpose”). Technical expertise, Models, data, and/or products to support and demonstrate pathways to enable safe and trustworthy AI systems,  Infrastructure support for Consortium projects in the performance of the Research Plan, Facility space and hosting of Consortium Members’ participants, workshops, and conferences." The consortium was announced in February, which is enough time for all the organizations included to have an AI safety department, lab, or desk, with a link available on the consortium's list. It is probably not enough to have a mandate, but out of necessity to actually follow through actively with work and varying approaches for the objectives. AI companies on the list are developing products and safety. Others may have their paths, but it appears that it is the same pattern where engineering and safety are bundled by major firms as they did for social media. Having more than 200 organizations is the opportunity for more than 200 different technical approaches to AI safety. The abundance would be useful to combat AI threats, misuses, and risks, both present and future, from all sources and destinations, not just major sources, commercial ones, or friendly locations. There is no evidence that all of the companies are currently developing technical research in AI safety. There is no aggregation of their work, collaborative collection, or lookout for technical approaches to safety on display for all of them, to show that there is a major difference to the consortium. Those who are working in AI safety would have been doing it anyway without the consortium, limiting the approach to solutions that are necessary for the unknowns ahead for AI safety, alignment, and governance. California lawmakers just passed SB 1047, which is symbolic but unlikely to be efficient without thorough technical solutions. The targets may comply because it is what they can also do and because they want to survive. But risks from others that the regulations would not encase would remain and may overwhelm compliant models. There are several angles that AI safety can proceed from, from theoretical neuroscience of how the human mind is cautious to novel technical areas, away from the regimentation of the paths of some AI corporations. There should be as many AI safety institutes as possible and as many AI safety labs and departments as possible, for broad approaches against an intelligence that is capable and not natural which, for now, can be used for some negative purposes, with uncertainties as it improves. There is a recent story on Reuters, Contentious California AI bill passes legislature, awaits governor's signature, stating that, "California lawmakers passed a hotly contested artificial-intelligence safety bill on Wednesday, after which it will need one more process vote before its fate is in the hands of Governor Gavin Newsom, who has until Sept. 30 to decide whether to sign it into law or veto it. Tech companies developing generative AI - which can respond to prompts with fully formed text, images or audio as well as run repetitive tasks with minimal intervention – have largely balked at the legislation, called SB 1047, saying it could drive AI companies from the state and hinder innovation. The measure mandates safety testing for many of the most advanced AI models that cost more than $100 million to develop or those that require a defined amount of computing power. Developers of AI software operating in the state also need to outline methods for turning off the AI models if they go awry, effectively a kill switch. The bill also gives the state attorney general the power to sue if developers are not compliant, particularly in the event of an ongoing threat, such as the AI taking over government systems like the power grid. As well, the bill requires developers to hire third-party auditors to assess their safety practices and provide additional protections to whistleblowers speaking out against AI abuses." There is a new press release, U.S. AI Safety Institute Signs Agreements Regarding AI Safety Research, Testing and Evaluation With Anthropic and OpenAI, stating that, "Today, the U.S. Artificial Intelligence Safety Institute at the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) announced agreements that enable formal collaboration on AI safety research, testing and evaluation with both Anthropic and OpenAI. Each company’s Memorandum of Understanding establishes the framework for the U.S. AI Safety Institute to receive access to major new models from each company prior to and following their public release. The agreements will enable collaborative research on how to evaluate capabilities and safety risks, as well as methods to mitigate those risks. Additionally, the U.S. AI Safety Institute plans to provide feedback to Anthropic and OpenAI on potential safety improvements to their models, in close collaboration with its partners at the U.K. AI Safety Institute." Feature image source How big of a problem is digital piracy? It can be argued that regulation and litigation have ensured mitigation from the mainstream. However, when some AI companies needed training data, why were pirated copies of contents allegedly [accessible to be] crawled and scraped? Why are several other unlawful events possible online, in ways that are not in the physical world—so to speak? What is peculiar about digital that makes it difficult for regulation and litigation to be decisive in eradicating problems? it difficult for regulation and litigation One easy-to-identify issue, especially with the internet—in recent decades—is that development has been ahead of safety. Safety is often added by some of the initializing companies, then tests in the real world with flaks that have resulted in adjustments. Though policy, framework, governance, terms, best practices, and so forth have been necessary to internet platforms, what would have been efficient is broad possibilities for technical angles to safety. Technical safety with evolving approaches from several corners would have done better than anything else, for the internet, than reliance on a traditional law-led approach. The same problem may follow AI safety, which regulations are trying early to correct—after the errors from social media and the internet. Regulating social media earlier may have worked in many forms, but there would have been numerous lapses that would have resulted in some of the same problems that eventually happened. Social media is as harmful as the technical shortages for safety. Assuming for some social media users, there were plugins that went through pages for them, even before seeing what was there, or there were plugins that sort to reinterpret what they were seeing or hearing, not just to fact-check, or there were plugins that developed usage parallels so that going to social media would have been unnecessary in some instances, they may have helped much. These are not just descriptions of what to do in retrospect, but things that are still necessary, which do not seem like any team is doing. Reliance is generally on the companies for safety, but they may do as it is possible or as it benefits the business. Assuming there are efforts from too many sources on technical solutions, maybe more would have been possible against past and current disadvantages of social media. In a way, seeking to correct that led to the establishment of the US AI Safety Institute , which followed the UK AISI and was followed by the EU AI Office . The purpose, in part, is to do technical research for AI safety. US AI Safety Institute UK AISI EU AI Office The US AISI also has a safety consortium, consisting of several organizations whose CRADA— Consortium Cooperative Research and Development Agreement , states that, "The purpose of the NIST Artificial Intelligence Safety Institute Consortium (“Consortium”) is to establish a new measurement science that will enable the identification of proven, scalable, and interoperable measurements and methodologies to promote safe and trustworthy development and use of Artificial Intelligence (AI), particularly for the most advanced AI (“Purpose”). Technical expertise, Models, data, and/or products to support and demonstrate pathways to enable safe and trustworthy AI systems,  Infrastructure support for Consortium projects in the performance of the Research Plan, Facility space and hosting of Consortium Members’ participants, workshops, and conferences." Consortium Cooperative Research and Development Agreement The consortium was announced in February, which is enough time for all the organizations included to have an AI safety department, lab, or desk, with a link available on the consortium's list. It is probably not enough to have a mandate, but out of necessity to actually follow through actively with work and varying approaches for the objectives. AI companies on the list are developing products and safety. Others may have their paths, but it appears that it is the same pattern where engineering and safety are bundled by major firms as they did for social media. Having more than 200 organizations is the opportunity for more than 200 different technical approaches to AI safety. The abundance would be useful to combat AI threats, misuses, and risks, both present and future, from all sources and destinations, not just major sources, commercial ones, or friendly locations. There is no evidence that all of the companies are currently developing technical research in AI safety. Having more than 200 organizations There is no aggregation of their work, collaborative collection, or lookout for technical approaches to safety on display for all of them, to show that there is a major difference to the consortium. Those who are working in AI safety would have been doing it anyway without the consortium, limiting the approach to solutions that are necessary for the unknowns ahead for AI safety, alignment, and governance. California lawmakers just passed SB 1047, which is symbolic but unlikely to be efficient without thorough technical solutions . The targets may comply because it is what they can also do and because they want to survive. But risks from others that the regulations would not encase would remain and may overwhelm compliant models. efficient without thorough technical solutions There are several angles that AI safety can proceed from, from theoretical neuroscience of how the human mind is cautious to novel technical areas, away from the regimentation of the paths of some AI corporations. There should be as many AI safety institutes as possible and as many AI safety labs and departments as possible, for broad approaches against an intelligence that is capable and not natural which, for now, can be used for some negative purposes, with uncertainties as it improves. theoretical neuroscience of how the human mind is cautious There is a recent story on Reuters , Contentious California AI bill passes legislature, awaits governor's signature , stating that, "California lawmakers passed a hotly contested artificial-intelligence safety bill on Wednesday, after which it will need one more process vote before its fate is in the hands of Governor Gavin Newsom, who has until Sept. 30 to decide whether to sign it into law or veto it. Tech companies developing generative AI - which can respond to prompts with fully formed text, images or audio as well as run repetitive tasks with minimal intervention – have largely balked at the legislation, called SB 1047, saying it could drive AI companies from the state and hinder innovation. The measure mandates safety testing for many of the most advanced AI models that cost more than $100 million to develop or those that require a defined amount of computing power. Developers of AI software operating in the state also need to outline methods for turning off the AI models if they go awry, effectively a kill switch. The bill also gives the state attorney general the power to sue if developers are not compliant, particularly in the event of an ongoing threat, such as the AI taking over government systems like the power grid. As well, the bill requires developers to hire third-party auditors to assess their safety practices and provide additional protections to whistleblowers speaking out against AI abuses." Reuters Contentious California AI bill passes legislature, awaits governor's signature There is a new press release, U.S. AI Safety Institute Signs Agreements Regarding AI Safety Research, Testing and Evaluation With Anthropic and OpenAI , stating that, "Today, the U.S. Artificial Intelligence Safety Institute at the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) announced agreements that enable formal collaboration on AI safety research, testing and evaluation with both Anthropic and OpenAI. Each company’s Memorandum of Understanding establishes the framework for the U.S. AI Safety Institute to receive access to major new models from each company prior to and following their public release. The agreements will enable collaborative research on how to evaluate capabilities and safety risks, as well as methods to mitigate those risks. Additionally, the U.S. AI Safety Institute plans to provide feedback to Anthropic and OpenAI on potential safety improvements to their models, in close collaboration with its partners at the U.K. AI Safety Institute." U.S. AI Safety Institute Signs Agreements Regarding AI Safety Research, Testing and Evaluation With Anthropic and OpenAI Feature image source Feature image source source

LLMs: Is NIST's AI Safety Consortium Relevant Amid California's SB 1047?

Multilingual Coarse Political Stance Classification of Media: Acknowledgments and References

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: স্বীকৃতি এবং উল্লেখ

Mehrsprachige grobe politische Haltung Klassifizierung von Medien: Danksagungen und Referenzen

Clasificación multilingüe de posturas políticas generales de los medios: reconocimientos y referencias

Classification multilingue grossière des positions politiques des médias : remerciements et références

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: आभार और संदर्भ

多言語メディアの粗い政治的立場の分類: 謝辞と参考文献

미디어의 다국어 거친 정치적 입장 분류: 감사의 글 및 참고 자료

Classificação Multilíngue da Posição Política Grosseira da Mídia: Agradecimentos e Referências

Многоязычная грубая политическая классификация СМИ: благодарности и ссылки

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Teşekkür ve Referanslar

Phân loại quan điểm chính trị thô sơ đa ngôn ngữ của phương tiện truyền thông: Lời cảm ơn và tài liệu tham khảo

多语言媒体粗略政治立场分类：致谢与参考文献

It was a big week for India as Prime Minister Narendra Modi made a state visit to the U.S., meeting his counterpart Joe Biden to discuss a wide range of issues and sign bilateral agreements on defense and technology; to contain China's growing influence on the world stage. During his time in the U.S., the head of the world's largest democracy also met or interacted with nearly every tech CEO worth their salt. We're talking Microsoft's Satya Nadella, Google's Sundar Pichai, Apple's Tim Cook, Amazon's Andy Jassy. Heck, even OpenAI's Sam Altman was there, and Modi even got to spend some time with Elon Musk. The meeting was part of the Indian PM's plan to get more and more American companies investing in India. Most recently, Apple opened its first-ever store in the nation of 1.4 billion people, while Musk is planning to introduce Tesla and Starlink in the country. Modi is making a strong push for investments, particularly with a focus on making products within India. Following his interaction, Amazon said it would invest an additional $15 billion in India while Google announced plans to open its global fintech operation centre in Gujarat, India. Meanwhile, Microsoft said it would continue to seek new areas to further introduce AI to the masses in the nation. Google ranked #5 on HackerNoon's Tech Company Rankings this week. Tesla was #6; Microsoft was #15; Apple was #66; and Amazon was #88. Google's Love Affair With AI: Down, But Not Out 🤖 Google may have failed to make a splash when it launched an AI chatbot to rival OpenAI's ChatGPT, but the execs at the company are not giving up hope yet. A new report from TechCrunch details attempts by Google's senior leadership to keep its skin in the game. The key? AlphaGo, the AI system that made waves after defeating world champions at the board game Go. The AI system was developed by an acquired research lab of Google called DeepMind which now plans on using its expertise in creating a chatbot that can truly rival or eclipse OpenAI's ChatGPT. There's a lot riding on here. Google's botched attempt to counter ChatGPT with its own chatbot cost the company $100 billion in market cap at the time of its release, and while the company may have recovered, the failure has been haunting CEO Sundar Pichai ever since. Whether Google will win this contest, only time will tell.. 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. In Other News.. 📰 Spywares are bad. Here's why: the Polish developer of a phone tracking app called LetMeSpy said a hacker gained unauthorized access to its data, making it the latest in a long list of companies that have been hacked, breached, or exposed victim’s data.
Unicorn social app IRL is shutting down after admitting 95% of its users were fake, TechCrunch reports.
Google's YouTube wants to try its hand in cloud gaming.
Parents will now have more control over what teens see and do over Instagram and Messenger.
Apple says proposed UK law ‘poses a serious threat’ to end-to-end encryption, The Verge reported.
IBM will acquire technology spend-management platform Apptio from Vista Equity Partners for $4.6 billion in cash, in the latest deal to bolster its capabilities in cloud and automation, Reuters reported. And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon It was a big week for India as Prime Minister Narendra Modi made a state visit to the U.S., meeting his counterpart Joe Biden to discuss a wide range of issues and sign bilateral agreements on defense and technology; to contain China's growing influence on the world stage. state visit state visit During his time in the U.S., the head of the world's largest democracy also met or interacted with nearly every tech CEO worth their salt. We're talking Microsoft 's Satya Nadella, Google 's Sundar Pichai, Apple 's Tim Cook, Amazon 's Andy Jassy. Heck, even OpenAI's Sam Altman was there, and Modi even got to spend some time with Elon Musk. met met Microsoft Microsoft Google Google Apple Apple Amazon Amazon The meeting was part of the Indian PM's plan to get more and more American companies investing in India. Most recently, Apple opened its first-ever store in the nation of 1.4 billion people, while Musk is planning to introduce Tesla and Starlink in the country. opened opened planning planning Tesla Tesla and Starlink Starlink Modi is making a strong push for investments, particularly with a focus on making products within India. Following his interaction, Amazon said it would invest an additional $15 billion in India while Google announced plans to open its global fintech operation centre in Gujarat, India. Meanwhile, Microsoft said it would continue to seek new areas to further introduce AI to the masses in the nation. said said Google ranked #5 on HackerNoon's Tech Company Rankings this week. Tesla was #6; Microsoft was #15; Apple was #66; and Amazon was #88. Tech Company Rankings Tech Company Rankings Google's Love Affair With AI: Down, But Not Out 🤖 Google's Love Affair With AI: Down, But Not Out 🤖 Google may have failed to make a splash when it launched an AI chatbot to rival OpenAI's ChatGPT, but the execs at the company are not giving up hope yet. A new report from TechCrunch details attempts by Google's senior leadership to keep its skin in the game. The key? AlphaGo, the AI system that made waves after defeating world champions at the board game Go. new report new report The AI system was developed by an acquired research lab of Google called DeepMind which now plans on using its expertise in creating a chatbot that can truly rival or eclipse OpenAI's ChatGPT. There's a lot riding on here. Google's botched attempt to counter ChatGPT with its own chatbot cost the company $100 billion in market cap at the time of its release, and while the company may have recovered, the failure has been haunting CEO Sundar Pichai ever since. botched attempt botched attempt haunting haunting Whether Google will win this contest, only time will tell.. 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. part 2 Part 1 Part 1 here complete In Other News.. 📰 In Other News.. 📰 Spywares are bad. Here's why: the Polish developer of a phone tracking app called LetMeSpy said a hacker gained unauthorized access to its data, making it the latest in a long list of companies that have been hacked, breached, or exposed victim’s data. Unicorn social app IRL is shutting down after admitting 95% of its users were fake, TechCrunch reports. Google's YouTube wants to try its hand in cloud gaming. Parents will now have more control over what teens see and do over Instagram and Messenger. Apple says proposed UK law ‘poses a serious threat’ to end-to-end encryption, The Verge reported. IBM will acquire technology spend-management platform Apptio from Vista Equity Partners for $4.6 billion in cash, in the latest deal to bolster its capabilities in cloud and automation, Reuters reported. Spywares are bad. Here's why: the Polish developer of a phone tracking app called LetMeSpy said a hacker gained unauthorized access to its data, making it the latest in a long list of companies that have been hacked, breached, or exposed victim’s data. latest latest Unicorn social app IRL is shutting down after admitting 95% of its users were fake, TechCrunch reports . reports reports Google's YouTube wants to try its hand in cloud gaming . cloud gaming cloud gaming Parents will now have more control over what teens see and do over Instagram and Messenger. more control more control Apple says proposed UK law ‘poses a serious threat’ to end-to-end encryption, The Verge reported . reported reported IBM will acquire technology spend-management platform Apptio from Vista Equity Partners for $4.6 billion in cash, in the latest deal to bolster its capabilities in cloud and automation, Reuters reported . reported reported And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon

Indian PM Woos Tech CEOs During U.S. State Visit 

মার্কিন রাষ্ট্রীয় সফরের সময় ভারতীয় প্রধানমন্ত্রী উওস টেক সিইও

Der indische Premierminister wirbt bei einem US-Staatsbesuch um Tech-CEOs

Primer ministro indio corteja a directores ejecutivos de tecnología durante visita de estado a EE. UU.

Le Premier ministre indien courtise les PDG de Tech lors d'une visite d'État aux États-Unis

अमेरिकी दौरे के दौरान भारतीय प्रधानमंत्री ने टेक सीईओ को लुभाया

インド首相、米国の国賓訪問中にテクノロジー企業のCEOらを説得

인도 PM Woos Tech CEO, 미국 국빈 방문 중

Primeiro-ministro indiano corteja CEOs de tecnologia durante visita de estado aos EUA

Hindistan Başbakanı Woos Tech CEO'ları ABD Devlet Ziyaretinde

Thủ tướng Ấn Độ Woos Tech CEO trong chuyến thăm cấp Nhà nước Hoa Kỳ

印度总理在美国国事访问期间向科技公司首席执行官求爱

A Portrait of the Hacker as a Young Man

Un retrato del hacker cuando era joven

Un portrait du hacker en jeune homme

यंग मैन के रूप में हैकर का एक पोर्ट्रेट

若者としてのハッカーの肖像

Um retrato do hacker quando jovem

Chân dung Hacker khi còn trẻ

青年黑客的写照

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less.
Reliability: No reliance on third-party services reduces failure chances.
Scalability: No extra costs as usage grows. Cons Resource Intensive: Each app instance consumes significant memory.
More Dependencies: Requires packages for decoding and looking up GeoIP data.
Challenging Updates: Updates require manual intervention or another self-hosted service. Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources.
Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint.
Fewer Dependencies: Often only requires an HTTP client.
Updates without hassle: Third-party will handle updates Cons Slower Responses: Calls to third-party services can introduce significant latency.
Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes.
Scaling Costs: Costs can escalate as usage increases. Introducing Atlas: Combining the Best of Both Worlds Faced with these challenges, I developed Atlas—a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Cost Efficiency: No additional costs with scale in terms of third-party fees. Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md. Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/. Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8: > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage.


Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement.


Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request.


Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable.


Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration! Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less. Reliability: No reliance on third-party services reduces failure chances. Scalability: No extra costs as usage grows. Speed : IP lookups in milliseconds or less. Speed Reliability : No reliance on third-party services reduces failure chances. Reliability Scalability : No extra costs as usage grows. Scalability Cons Resource Intensive: Each app instance consumes significant memory. More Dependencies: Requires packages for decoding and looking up GeoIP data. Challenging Updates: Updates require manual intervention or another self-hosted service. Resource Intensive : Each app instance consumes significant memory. Resource Intensive More Dependencies : Requires packages for decoding and looking up GeoIP data. More Dependencies Challenging Updates : Updates require manual intervention or another self-hosted service. Challenging Updates Using External GeoIP APIs (Often Paid) Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources. Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Fewer Dependencies: Often only requires an HTTP client. Updates without hassle: Third-party will handle updates Minimal Resource Use : Outsourcing GeoIP data and logic doesn't burden your app's resources. Minimal Resource Use Ease of Implementation : Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Ease of Implementation Fewer Dependencies : Often only requires an HTTP client. Fewer Dependencies Updates without hassle : Third-party will handle updates Updates without hassle Cons Slower Responses: Calls to third-party services can introduce significant latency. Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes. Scaling Costs: Costs can escalate as usage increases. Slower Responses : Calls to third-party services can introduce significant latency. Slower Responses Increased Unreliability : Greater likelihood of failures due to network issues or third-party downtimes. Increased Unreliability Scaling Costs : Costs can escalate as usage increases. Scaling Costs Introducing Atlas : Combining the Best of Both Worlds Introducing Atlas Faced with these challenges, I developed Atlas —a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Atlas Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency : Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates : It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates Cost Efficiency: No additional costs with scale in terms of third-party fees. Cost Efficiency : No additional costs with scale in terms of third-party fees. Cost Efficiency Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API : A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free : Atlas is Open Source and freely available under Apache-2.0 License. Open and Free By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Why Rust? Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md . Atlas README.md Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/ . https://atlas-rs.fly.dev/swagger-ui/ Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8 : Fly.io 8.8.8.8 > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas : The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues : If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features : Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation : As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word : If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. special thanks to oschwald oschwald maxminddb Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration!

IP Geo-Location: Local Database vs. Remote API—Finding the Middle Ground With Atlas

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

In today's world, where everything is interconnected, it's important to understand how our digital infrastructure, including the internet and our own smaller networks, performs. Whether you're a seasoned network expert or just starting out, having the right tools at your disposal can make a big difference. In this article, we’ll take a closer look at two helpful tools: and . Both are network measurement platforms that allow you to evaluate the performance and reliability of networks using a globally distributed probe network. RIPE Atlas Globalping We’ll also compare the two platforms and see in which use cases the newer Globalping platform can be a great RIPE Atlas alternative. By the end of the article, you may even want to use both platforms together to get the best insights! Introducing the Platforms To start, let's give you a brief introduction to the two tools: Introducing Globalping Founded by the jsDelivr team in 2021, Globalping is a platform that relies on an extensive probe network hosted in users' homes and offices worldwide. The platform aims to provide an excellent user experience, making it straightforward for users from all backgrounds to create their measurements effortlessly. The platform is entirely open source, and all tools, including the API, are free to use. You can also support the project and its users by hosting one or more probes. Globalping offers various official integrations and tools to ensure a seamless experience. For example, you can use the CLI tool to bring the platform's capabilities to your terminal. Or you can add the Slack app to your workspace, which enables your team to collaborate and troubleshoot effectively, making Globalping an interesting RIPE Atlas alternative. Introducing RIPE Atlas RIPE Atlas was founded by the RIPE Network Coordination Centre (NCC) in 2010, and it has since grown its network to consist of over 12000 probes to measure internet connectivity worldwide. All data collected by the platform is publicly available, which makes it popular with researchers and organizations interested in monitoring and learning about the state of the internet in real-time. By spending credits, you can create your own measurements and view the results in their dashboard, rendered over maps, or as raw data. You can earn credits in various ways, such as hosting a probe or purchasing credits directly, although that’s limited to bigger enterprises. In addition, there are several tools available, including data parsers, CLI tools, and apps, which have been created by both the RIPE NCC and the community. Comparing the Two Platforms Below, you'll find a list of criteria along with some comments on how each platform handles and performs in those areas. Our goal is to emphasize the strengths of both platforms, as each one has its own features and benefits, catering to a wide range of users and use cases. Probe Network Globalping: Has a quickly and continuously growing probe network. Anyone can host a probe – all you need to do is run the probe's Docker container on an internet-connected machine. RIPE Atlas: Offers an extensive probe network across the globe. If you want to host a probe, you need to apply for one first. If the network would benefit from your probe's location, you'll get a hardware probe sent to your address where you need to set it up. Measurement Types Globalping: All tests report their results back in real-time, creating a feeling of local testing and allowing different kinds of new use cases. Supported test types are: Ping, Traceroute, DNS, HTTP (including SSL), and MTR. RIPE Atlas: The platform is focused more on scheduled measurements resulting in delays that may happen due to RIPE's complexity. Supported test types are:
Ping, Traceroute, DNS, HTTP, SSL, and NTP. Measurements and Costs Globalping: Enables you to create measurements for free, regardless of the tool you use, whether it is the API, CLI tool, or Slack app. However, some rate limits are in place to safeguard the API, which are usually sufficient for most users. But, if you need more, you can increase the limit by becoming a GitHub sponsor or earn free credits by hosting multiple probes at home, in the office, or on hosting providers. RIPE Atlas: Allows you to create measurements, but it costs credits. You can obtain credits by hosting a probe, requesting credits as a researcher, receiving transfers from other users, or buying credits by becoming a corporate sponsor. However, becoming a sponsor may be too pricey for individuals. Internet Health Monitoring Globalping: Doesn't offer public internet health monitoring capabilities at the moment. Instead, it focuses on providing measurement creation capabilities to users. RIPE Atlas: Offers public access to all measurements. You can analyze internet health data by viewing maps and graphs, which provide detailed insights. Custom Monitoring Capabilities Globalping: You can leverage the API to develop your own monitoring solution, which many companies already do. The Globalping team is also working on building a custom and scheduled monitoring platform. This platform will use the probe network to provide real-time and historical data on the performance and quality of any internet endpoint. RIPE Atlas: You can specify intervals and timeframes for running measurements, and then analyze the results using maps and graphs that the platform generates and displays on its website. Use Cases Globalping: Designed to be used by users with various backgrounds and networking skills. It allows you to create network measurements effortlessly and for free, making it a great RIPE Atlas alternative when you want to learn more about networking or investigate your own networks in-depth. Overall, Globalping is suitable for both newcomers and network experts. RIPE Atlas: Tailored for researchers, academics, and anyone who needs access to aggregated internet connectivity data. The platform also allows you to investigate the impact of global or local incidents on internet health, such as natural catastrophes. Commercial Use Globalping: Doesn't differentiate between personal and commercial use, and you don't need an account or authentication for basic tests. RIPE Atlas: If you plan to use the RIPE Atlas platform to provide paid services or products, there are some rules you need to follow. These rules include, for example, displaying the platform's logo and description. The platform's credit-based usage system also applies here. Data Usage Globalping: Measurement results are stored for a limited time; only users with the measurement link can view them. Also, probe owners can see the tests their probe ran. RIPE Atlas: All measurements conducted on the platform are publicly accessible. Tools and Integrations Globalping: Provides user-friendly tools and integrations for custom development, Slack, and GitHub. The Globalping team officially maintains all tools, ensuring ongoing development, support, and a consistent and modern UX. RIPE Atlas: You can find many open-source tools, including CLI tools and visualization libraries, on the website and on GitHub. Some of these tools were created during hackathons and may not be regularly maintained, so keep this in mind when searching for tools. Getting Involved Globalping: Invites you to host one or more probes, become a GitHub sponsor, or contribute to the platform on GitHub. The team welcomes any feedback and feature requests, which are essential for the platform's continuous improvement and growth. RIPE Atlas: Encourages you to host a probe or an anchor, sponsor the project by buying credits, become an ambassador, or contribute to the open-source tools. Is Globalping a RIPE Atlas Alternative? Our comparison between Globalping and RIPE Atlas showed that both platforms have unique benefits: With its extensive public internet health data, RIPE Atlas is great for researchers and network experts who require detailed analysis. On the other hand, Globalping's modern approach, user-friendly UX, and free measurements make it compelling for a wide range of users. This makes it a great alternative to RIPE Atlas for individuals as well as companies ranging from startups to enterprises. However, each platform has its features and strengths, so we think it's worth using them together. By leveraging Globalping to run real-time measurements in a comfortable way and RIPE Atlas to obtain aggregated data, you can get a more in-depth view of the state of the internet and your own networks. If you're new to Globalping or looking for a straightforward way to get started with networking, we invite you to ! visit our website and create your first measurements

RIPE Atlas and Globalping: Choosing the Right Network Measurement Platform

RIPE Atlas এবং Globalping: সঠিক নেটওয়ার্ক পরিমাপ প্ল্যাটফর্ম নির্বাচন করা

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