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The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

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今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

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Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

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Security is important in all facets of the tech industry. Pertinent examples would be Web development, low-level programming, mobile development, blockchain development, etc. With security being so important, we must ask ourselves. How are the fastest-growing tech niches handling their security? We will specifically look at Developer operations, aka: “ ,” and view the industry’s security trends. In this written piece, Let us take a peek at how PaaS products handle their users’ security. DevOps SaaS? PaaS? The Cloud? In order to fully grasp the topic at hand, let us first define the most important terms to be used continuously throw this piece. What is SaaS? SaaS is an abbreviation of Software-as-a-Service. “ ”. This is an effective description given by TechTarget.com. In simpler terms, Saas is when you, the end user, pay to rent software solutions. Saas is a software distribution model in which a cloud provider hosts applications and makes them available to end users over the internet. E.g: An independent software vendor (ISV) may contract a third-party cloud provider to host the application What is PaaS? PaaS is an abbreviation of Platform-as-a-Service. “ This effective description was given by Microsoft. In Simpler terms, PaaS is a subset of SaaS but very specific to renting an online platform for development aid. .” Platform as a service (PaaS) is a complete development and deployment environment in the cloud, with resources that enable you to deliver everything from simple cloud-based apps to sophisticated, cloud-enabled enterprise applications The Cloud? By mentioning the cloud am sure it triggers a light-bulb moment within you. We have all heard of “the cloud” at this point. “The Cloud” can mean different things depending on the context it is used. In the workplace, “The Cloud” is the online database where everyone stores the document i.e: Google Drive. When you are talking about backing up your photos, “The Cloud” could mean iCloud, Apple’s storage service. There are other meanings for “The Cloud,” but let’s not dwell on them. What does “The Cloud” mean exactly? “ ” This was the definition given by Cloudflare.com. This is a funnily apt but brute definition. Let’s define it a bit more clearly together: . The cloud is made up of servers in data centers all over the world The cloud is an online network of connected databases and servers all over the world. So iCloud is a collection of servers and databases made for the purpose of safekeeping your information. Google Drive is also a cloud service similar to iCloud. The reason the cloud is important for us here is that most, if not all, PaaS products are cloud-based. What does this mean? They are products orientated to handling server and database issues for developers on one platform. This is what makes them one-stop-go platform services. This being the case, we need to know, How do PaaS products implement security? PaaS Security: To explore the security that PaaS offers, let’s look at an industry giant in PaaS. Let’s look at Google Cloud and its Security! You might be wondering, what is Google Cloud? What products does the platform offer? What does it even do? For the full, unfiltered description of Google Cloud and its services, . you can find Google’s description here What is Google Cloud? Stephen J. Bigelow from Techtarget.com . offers a more succinct explanation “ ” This is not only an excellent and short explanation of Google Cloud but also of most PaaS services, i.e., Amazon Web Services, Microsoft Azure, etc. Google Cloud is a suite of computing services offered by Google. The platform includes a range of hosted services for computing, storage, and application development that run on Google hardware. Google Cloud services can be accessed by software developers, cloud administrators, and other enterprise IT professionals. public cloud Google Cloud offers services such as: Google App Engine: This is a PaaS that gives software developers access to Google’s scalable hosting. Google Kubernetes Engine: A management and orchestration system for Docker container and the container clusters that run in tandem with Google’s other cloud services. Serverless Computing: Provides developers and IT professionals tools to handle event-based workload execution. i.e., Cloud Functions. There are a few more services, but you get the idea. Now that we have established what Google Cloud is and the services they offer, let’s take the next step. In what ways do they actively secure themselves and protect their PaaS operations? All Data back and forth between the Google Cloud and the end users They offer virtual networking (Virtual Private Cloud), which enables inner cloud segmentation in public cloud spaces, which is an extra layer of security for developers. They require binary authorization if you want your Kubernetes (docker) container deployed on the Kubernetes engine. They have their own internal undisclosed IDS (Intrusion Detection System) active at all times. They have standard Anti-DDoS, anti-bot, and API protection. That was a listing of the main security protection and protocols for the Google Cloud PaaS. This is surprising; it doesn’t seem like much, does it? On the surface, it seems the same as any other cloud platform or web-based software. This is because it is. In many ways, PaaS products and SaaS products are protected in much the same way as other web-based tech products. Keeping security in mind, there is another PaaS product that has similar but also special security measures. We should keep this platform in mind as it adds a special level of security in the PaaS space. It is ! Aptible What is Aptible? Let’s answer this by reading how they define themselves: “ ”  Essentially, you, as a developer, upload your code in a file, and Aptible takes care of all deployment and tech-related issues. Aptible is the No Infrastructure Platform as a Service that startups use to deploy in seconds, scale infinitely, and forget about infrastructure. This is all well and good; now let’s look into Aptibles security. We are using them as an example because, unlike other typical PaaS, where you are on an online IDE or cloud interface and interacting with your code and deploying from there. With Aptible since you just upload a folder with your app in it. As a result, you don’t need: Wide-ranging API protection Private Cloud protection Binary authorization etc. You get the idea. They don’t need a lot of the usual security protocols. So what protocols or functions do they use? Security compliance dashboard: A dashboard that provides continuous security monitoring of your app’s security status on all vulnerability levels of your app. All inner app APIs will be protected in their own private cloud network infrastructure. Automatic vulnerability management: You upload your app file, and they tell you all the places it is vulnerable and unsafe and fixes most of it for you. (that it’s allowed to). This is all very interesting. This is because, by the nature of uploading your app as a folder, Aptibles security is wholly focused on securing the folder internally, as the external threats are already secured. Interconnected Security in PaaS: So, How important are security protocols in PaaS products? Very important. Whether you’re using a more mainstream PaaS like Google Cloud, they have different security protocols at every facet of their network. The same applies to Aptible. You are just uploading a folder, but there are still numerous security protocols and safety mechanisms for the end user. As the overall SaaS industry grows, both sub-industries of PaaS and IaaS grow too. With this growth, it is always important to keep security in mind. The developer operations field is expanding, and as it expands, always ask yourself, what security mechanisms does this “xxx” product have? The answer will probably shock and interest you!

The Importance of Security in PaaS

Hi Kevin, welcome to our series “Behind the Startup.” Please tell us about yourself and the story behind ? Ishan Pandey: 360Wellness I used to be a French Product Engineer. I completed my MBA from and moved to Singapore in January 2013, where I lived for 7 years working for the world’s 2nd largest automotive parts manufacturer, a German company named ZF Friedrichshafen. At ZF, I rose to become Head of Product Management and Engineering for the Asia Pacific region and led a team of 60+ people across 6 locations. My first-hand experience in the world of sports and fitness was competing professionally in endurance sports and Triathlon. I notably qualified for the Ironman World Championships in 2019. Shortly before leaving my job at ZF group, I began coaching athletes online as a side hustle. This eventually led him to the initial project idea of 360Wellness. Kevin Serou: ESMA The full team internally and 3rd parties are over 40+ people, so we are growing a lot in the last few months due to the covid pandemic, which has moved people’s fitness regime to home. The team currently has 15 engineers full time on the mobile and web platforms, building unique new features. Please tell us about digital fitness and how can the blockchain disrupt the industry? Ishan Pandey: Digital Fitness is not for everyone and to let everyone exercise against their joint will, it is crucial to incentivize fitness activities with rewards based on cryptocurrency. It incentivizes cryptocurrency use and allows for the adoption of groundbreaking decentralized fitness and wellness app that connects people worldwide with upcoming loyalty rewards for staying fit and healthy. The cryptocurrency is fully integrated into the ecosystem as a credible alternative to fiat currencies for consuming health and fitness services and products. Kevin Serou: The main revenue generation for crypto-based fitness app is to allow more users to join the live classes, where the users earn more token, which is used for extra discounts. Imagine bringing on only a few hundred coaches onto our pay as you go platform and the growth they can have with only 10-20 users on each of their live classes. What are your views on the NFT art craze? Further, how will NFTs work on DEFIT? Ishan Pandey: The NFT art craze is here to stay. Before taking a picture of a rare piece of art was a visual you could use and easily fake in your own possession. This time the blockchain really validates with the world who is in ownership of the art. It’s a true game-changer and we don’t believe we will go back at all to the old ways of verifying art. The world is indeed moving into the digital space, and blockchain-based token will be the next frontier of digital fitness. Kevin Serou: NFT integration fitness app can make fitness easy and fun. To incorporate such things into the mobile application and the NFTs evolve with your persona as an athlete. According to you, which business model is most appropriate for a digital fitness business? Ishan Pandey: The ideal type of business model is based on the specific type of fitness activity and mode of business the company is choosing.  I can, for example, given insights from how we based our business model. We started off with the commission-based approach because our mission is to make physical activity affordable and accessible to anyone. Kevin Serou: To do this, we believe that the is definitely the most viable option to do. The main reason behind this idea is because many coaches out there don’t have the marketing or backing they need to create their own business in the fitness world. Instead, they have to start from scratch with an Instagram or Facebook page, set up a website or only a Google Form sheet for their clients. ‘Pay As You Go Model’ The solution to this is to give the coaches a platform to get the clients they need without spending money themselves to start their own business. Also, we aren’t entirely disregarding the subscription-based model, as it can also be applied in a decentralized manner. We do see the advantages of giving the user the option they need to purchase classes and services from the app. Additionally, we believe that a subscription-based model can be applied for coaches directly with their clients if they feel the need to do so as this has a motivation factor with clients who have paid monthly and don’t want that money to go to waste miss classes or training. Do you think decentralization of the digital fitness ecosystem terminates all chances of third-party exposure to user data? If so, do you think it can lead to mass blockchain adoption? Ishan Pandey: Decentralization is happening before our eyes in the Crypto space, but there will always be parts of centralization that are critical for a society to operate. Even if there are decentralized coins that do all they can to make sure it is as decentralized as possible, the people of the projects in some way or another have to come together to drive that decentralization which in itself is centralization. Kevin Serou: Therefore we don’t believe centralization is going to be terminated at all. The percentages of decentralized systems to centralized systems is and will continue to change, such as getting away from Google, Facebook and all those big players that collect our data for free. For the fitness space, we believe it will be the same movement and the people that do want to share their user data to get back money for it can be rewarded for this. In what other ways can blockchain technology be leveraged into boosting and assisting the fitness industry whilst fuelling a future of fit and healthy people? What are some major challenges in the fitness world which can be eradicated with the adoption of blockchain technology? Ishan Pandey: It is safe to say that people have become more aware of the value of living a healthy lifestyle as a result of the COVID-19 pandemic. People all over the world have begun to prioritize fitness, from bettering their eating habits to incorporating exercise into everyday routines. As a result, Internet-based health & fitness applications, especially mobile apps, have become an indispensable part of modern life. Kevin Serou: However, while these applications have a lot of appeals, they also have a lot of disadvantages - the lack of data protection being the most significant issue at play. Third-party apps can access a user’s personal data through these centralized applications and this personal information is then used for a variety of purposes. Research institutes use our data to evaluate performance based on our age, place, physical characteristics, eating habits, gender, and other factors. Furthermore, the legitimacy of services offered by such apps is reliant on a central authority. This is where blockchain comes in. The introduction of blockchain has opened up the possibility of building a decentralized ecosystem that can revolutionize any industry. The immutable and distributed ledger of blockchain enables the network to be open, impartial, and trustworthy. 360Wellness is an example of a decentralized application designed to make fitness easy. Our application offers the safest and reliable ecosystem while still meeting all of the user’s requirements in one convenient location. The dApp offers a comprehensive service in terms of fitness, whether it is a workout regimen, eating schedule, sleep monitoring, mental health, or even instructional content. COVID-19 has affected every aspect of our lives. In what ways, according to you, will the pandemic affect the adoption of the blockchain industry within the financial ecosystem? Ishan Pandey: Recently, the need for blockchain was intensified when unexpected demand for vital products (personal protective equipment and testing kits) emerged due to the COVID-19 pandemic. Because of COVID-19 issues, different levels of legislation levied by national and local governments across the world slowed and disrupted nearly all supply-chain networks. Kevin Serou: The world has relied on globalization to reap the advantages of these dynamic supply chains; however, severe bottlenecks, stalled production lines, and the unavailability of vital materials have forced countries and businesses around the world to reconsider their procurement strategies during this pandemic. COVID-19 delayed the flow of products and people and the development and advancement of blockchain technology itself, resulting in a visible downturn in blockchain investments, delays in project developments, and major blockchain and cryptocurrency conventions being either postponed or cancelled of pandemic restrictions. However, as the pandemic subsides, this should fade and blockchain investments should recover. Thus, although the current disruption has posed short-term challenges for the blockchain industry, it will also open up new opportunities in the medium and long term. Ultimately, blockchain technology will continue to move forward in the financial sector despite these recent challenges. “temporary slowdown” In conclusion, the potential of blockchain technology is enormous, and the delays caused by COVID-19 will have little effect on its ability to build stable ecosystems. Furthermore, there will be a strong desire to put the supply chain onshore or nearshore in the post-COVID world in the name of resiliency, reduced risk, quicker distribution, and data security. What does the roadmap ahead look like for the fitness ecosystem, especially after adopting the blockchain industry? What new developments can we expect to see within the industry? Ishan Pandey: Many health and wellness enthusiasts are gradually incorporating fitness apps into their routines. The use of gamification in the healthcare sector has resulted in increased participation, which has improved outcomes, and when combined with blockchain technology, it can help enhance the process by providing transparency that lacks centralized applications. Kevin Serou: Since all transactions are made on the encrypted blockchain network, blockchain technology ensures the protection and privacy of the user’s data while also eliminating the need for intermediaries. Users will have full access and control over their health records since the data will be stored on the blockchain. Smart contracts, enabled by blockchain technology, allow users to connect and receive tips and lessons. Users can also benefit from communicating with their trainer through public blockchain videos, and the industry can offer loyalty programs to their customers. Users will simply pay to download lessons and material posted by their instructors. The purpose of this article is to remove informational asymmetry existing today in our digital markets by performing due diligence by asking the right questions and equipping readers with better opinions to make informed decisions. The material does not constitute any investment, financial, or legal advice. Please do your research before investing in any digital assets or tokens, etc. The writer does not have any vested interest in the company. Ishan Pandey.

Gamification through Tokenization Can Disrupt the Fitness Industry

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When Disruption Is The Only Evolution Gem & Lauris R, Unsplash.com The history of the world is none other than the progress of the consciousness of freedom — Georg Wilhelm Friedrich Hegel Innovation is not a very well understood process. By its very nature, it includes some hits and misses. That is because there is an element of preempting the future. A future, that by today’s standards, is extremely unpredictable. As I witness many companies responding to disruption, I cannot help but notice some common ways to innovate. At the end, the essence of disruption lies in speed to market. In the FinTech world, nobody ever sleeps i.e. startups are extremely busy creating new business models using technology and favorable regulatory climate. This article examines three common ways in which companies can unleash the startup inside and ends with broader implications for people around the world including my readers. Identify Mega Trends Every decade or so, there are certain inescapable mega trends that affect the world. Some of them are irreversible i.e. they can be managed but not prevented. Every innovation must ride the tsunami of these trends to be truly disruptive. Let’s look at three examples. 1. If there is one mission that unites all of Elon Musk’s endeavors, it is saving humanity from itself. While the rest of the world is busy releasing green house gases, Elon wants to combat the existential threat of climate change through electronic cars (Tesla) and solar roofing (Solar City). As countries and companies think about AI, Elon is busy creating a backup plan to combat an evil AI scenario (Neuralink). If the human race runs out of real estate, Elon plans to help colonize Mars through SpaceX 2. The shape of money is changing everyday. What began as a proof of concept in the form of Bitcoin now spans roughly 2,000 cryptographic currencies and growing. Although nobody knows the timeline yet, It seems almost inevitable that these currencies will soon be fungible i.e. can be converted to fiat currencies and vice versa. They are here to stay and in the process, reinvent money and disrupt global money transfers, payment networks and financial services in general. 3. Larry Fink, CEO of Black Rock, wrote an open letter to the CEO’s of Fortune 500 companies saying: “Society is demanding that companies, both public and private, serve a social purpose,” Indra Nooyi, CEO of PepsiCo announced in the company’s 2006 annual report a company wide commitment to a greater social purpose called Performance With Purpose. It is aligned with the United Nations (UN) sustainable development goals and aims to create healthier products for consumers. Paul Polman, CEO of Unilever has long been advocating service to society as a company’s ultimate objective. The message here is clear: innovation, including technological disruption must serve humanity and not vice versa. Businesses, including established companies and startups, need to keep a higher purpose in mind to create long lasting value. Keep It Simple The user experience offered by any product or service has to be incredibly simple and without cluttered with possibly very few elements on the screen for the user to understand and play with. Design philosophy has never been more important in creating value. Personally, three examples spring to mind: The classic example that allowed Apple to tower over its competitors is the emphasis on simplicity. With clean iconography, beautiful industrial design and consistent growth in technology, Apple represents an excellent user experience. Coinbase, in recent times, is a surprisingly simple App to use. It provides a clean overview of prices, easy to trade menus and a large screen with elegant fonts. RobinHood, the stock trading application, is yet another example of a clean, easy to use and intuitive user interface. While it allows access to a large amount of data about the stock in question, it maintains its elegance throughout the experience. Design has come to occupy as much importance as the technology itself. To ensure wider social acceptability, users may not need to know what’s under the hood of a website, application or service but the way they interact with the interface in question should be as simple as child’s play. For more, checkout Amazon’s checkout free store named Amazon Go. Constant Paranoia The key to success is both failing fast but scaling fast. Scaling fast builds a moat around the business which buys time. Time is the single most precious resource any business can have. Most businesses that have scaled rapidly live in a state of paranoia. Andrew Grove, CEO of Intel once famously talked about paranoia in his book ‘Only The Paranoid Survive’. However, today, if you are asleep in one part of the world, someone else is busy disrupting your business or livelihood in a different part of the world. While that is true, paranoia should always be customer centric. Businesses that don’t evolve with their clients’ lives don’t survive too long. Today, most businesses are a pay as you go platform where monthly subscriptions are the way most users consume products and services. This is a direct outcome of technology driving down the cost of distribution to almost zero. The Inescapable Conclusion The conclusion, I have drawn, in the last 5 years of my life is that disruption is the only constant. The process, of picking up the pieces when your plans shatter by colliding with very rapid cycles of changes in the external environment also, has to be continuous. Every sphere of human life today is undergoing rapid technological disruption. From cryptographic currencies changing the shape of money to quantum computing, Internet of Things and blockchains creating a new internet of everything. In fact, the very process of creating designer human beings has been accelerated through CRISPR CAS 9 Gene Editing technologies, AI and efforts to embed chips in the human body. It is no longer a theory. offering it’s employees an option to embed an RFID chip in their body to make purchases and unlock doors. The company also known as 32 M offers self service micro market solutions for office break rooms. A company in Wisconsin — Three Square Market is Body hacking is now considered by many people who have already embedded RFID chips in their body as a way to improve it rather than degrade it as portrayed in dystopian versions of futuristic societies. What such profound and mind boggling disruption means for most people around the world, including you and I, is the following: 1. Education has become a lifelong pursuit. At the at Davos this year, re-skilling became an obvious solution to technological change. World Economic Forum 2. The ability to process vast amounts of information is about to become the difference between growth and atrophy. Thereby, making the habit of reading a most important and indispensable habit. 3. Newer technologies will develop faster than the speed with which we can rein in its unintended consequences. Simple technologies such as creating a smart home or allowing social media to track your every move reduces privacy and opens ports to access your personal experiences. 4. Businesses are becoming platform businesses which in turn are in the process of getting disrupted by the Blockchain which promotes disintermediation and elimination of third parties. The biggest human experiment in a Hegelian pursuit of the one truth. 5. Wisdom, in every discipline, including medicine is now crowdsourced. Crowdsourcing is the process of creating the single largest repository of data on any topic. As science fiction becomes science, I wonder: “what next?”. Irrespective of the answer to that question, this is an extremely tumultuous time where education and cooperation seem to be the only responses to the only constant.

The Only Constant

I love the Go programming language, or as some refer to it, Golang. It’s simple and it’s great. I write this on a tangent. Didn’t expect Golang to be so good. I first picked up go around January 2016, it had a relative small but enthusiastic community here in Israel. I didn’t think much of it at the time, I was honing my programming skills and Golang was just a tool I’ve used to accomplish a task. Even one year ago, using go was brilliant. The process was straightforward once I’ve got the general hang of the language. I wrote a crucial piece of code for , the company I work for, and it didn’t let us down, still running in production a year later with zero maintenance since then. Visualead Recently I found myself again using Golang again, and I felt compelled to write about the reasons I fell in love with Golang. The GOPATH environment This is one of the first things you’ll have to handle once you begin writing in Go. Setup your GOPATH directory anywhere on your computer, complete with bin, src, and pkg directories and you are ready to begin writing. // How the directory structure looks like:
go
- bin
- pkg
- src Under my home directory I create a new directory for every programming language I write in. My user home directory is filled with folders like these: chef
cpp
elixir
elm
go
javascript
meteor
ocaml
php
python
scala None of these languages enforce this structure besides Go. Go forces you to define a root directory for all of your Golang goodies, and it does it for a good reasons which are coming right up. The Golang Application Want to create a new Golang applications? Simple. Go to your $GOPATH/src directory, create a new folder, create a new file.go , call the package main , add a func main() {} function and that’s it. all of the Go goodies are available to you since that moment forward. More on Go goodies coming up! but first a few words about the Golang modules. The Golang Modules My favorite programming paradigm done right. I see the module system as a sort of alternative to the Object Oriented classes system. Golang calls it’s modules packages. So from now on everytime we say a module we mean a package and vise-versa. Module == Package == Module In Golang every directory you create becomes a package. A package can become an application if it’s name is main.This is helps with a natural separation of code into manageable logical chunks. You always want your business logic to be created from reusable components, or packages in case of Golang. In my current app I have to manipulate some lines in some files, and upload them to AWS S3 once i’m done. My app structure looks like this: src
- my_app
- - main.go
- - main_test.go
- - uploader
- - - uploader.go
- - - uploader_test.go
- - replace_file_lines
- - - replace.go
- - - replace_test.go
- - - strings_util.go
- - - strings_util_test.go The _test.go files are of course the way to do unit test files for Golang, which has a testing framework built in it’s core. Also to those who are coming from an object oriented language, it helps to think that every directory here is a complete static class, and every function or variable inside the .go files becomes it either a property or method. Here’s a possible example of how replace.go can look like: <a href="https://medium.com/media/5fd49f65bc6cc1843e6727541a244ae3/href">https://medium.com/media/5fd49f65bc6cc1843e6727541a244ae3/href</a> If a directory shares more than one .go file, all the methods and variables are shared between the entire package, even the private ones. It helps for separating a package into even smaller chunks and avoiding large single files. Without going into Object Oriented vs Functional/Procedural, it’s just important to know that the Go creators decided to not include classic classes it the language. Instead opted to use structs/interfaces, and packages of course. The Golang gofmt Golang has a convention of how everything is supposed to look, the exact spacing which is needed in each case and each line. This allows developers to focus on writing code instead waging wars about curly braces locations. The old {} vs { \n } war: Or perhaps I am only happy because my side won in Go? Read more about it here: https://golang.org/cmd/gofmt/ The Golang Import imports are always relative to GOPATH/src . Always. I cannot overstate how much this decisions saves me in frustration. the next line is confusing by intention. Warning: When working with other languages you either can use relative/absolute imports, or somehow set a weird import that will allow you to import a file from whoknowswhere location ( I am looking at you Python ). Go solves it in a unique way. All imports, no matter in which file, will always be relative to GOPATH/src So in case of my_app, it’s main imports will look like this: <a href="https://medium.com/media/b25aa90e6c930bda3804c3c39789491b/href">https://medium.com/media/b25aa90e6c930bda3804c3c39789491b/href</a> my_app is under src, so we need to mention it first, then we import the packages that exist under my_app , like uploader and replace_file_lines . Notice we are not importing single files, but instead whole packages. Works like a charm and causes ZERO confusion. Furthermore, Golang won’t compile unless you actually use the packages you import, this tiny feature helps you to know that every import is actually used. Expecting your programmers to write a clean code without unused imports? why won’t you let your local Gopher take care of that? The Golang Get System The import sections leads us to the next great feature of Golang, the go get feature. While others are impressed with package manager, Go just uses any git repository as it’s package manager. JavaScripts NPM How does it work? I’ve wrote before that I needed to upload to S3 right? well then that means I need to AWS SDK to accomplish that. To make this work, I just open my terminal and write: go get github.com/aws/aws-sdk-go What’s going on here? Nothing special, Golang just downloads the repo from into your GOPATH/src directory. https://github.com/aws/aws-sdk-go Then all you have to do to use the aws-sdk is just import it: <a href="https://medium.com/media/7265ea218a520ebb4556006a37fe241f/href">https://medium.com/media/7265ea218a520ebb4556006a37fe241f/href</a> Remember that all imports are relative to the GOPATH/src ? From that you can understand that the s3 package for example resides now under GOPATH/src/github.com/aws/aws-sdk-go/services/s3 . Simple. Elegant. Another star for the Gopher. The Golang Build and Package Systems We’ve been focusing so far on GOPATH/src but there are still other directories we need to address, GOPATH/pkg and GOPATH/bin . Golang is a compiled language, it means that the code has to be compiled in order to run. Golang is compiled fast. How does it work? Everytime your compile your code to run it, Golang creates a .a file in the same path as your package, only under the GOPATH/pkg directory. It means that if you compiled the aws-sdk for example. It’ll be compiled once, and shared between all of your other code. Of course this is not the only reason Golang compiles fast, but it’s just an intro that helps you to understand the role of GOPATH/pkg Now, what about GOPATH/bin ? When you run the go install command, go creates a binary file, and places it under GOPATH/bin , the file gets the name of your main package’s directory my-app in our case. Why is it so great? Well you can add GOPATH/bin into your global PATH variable, and all binaries your build are available in your command line, without you doing any extra work! ( Isn’t it great :D ? ) The Golang Cross Platform Builds Deploying to other distributions instead of the one you are writing your code on? Well don’t worry, you don’t need to have a windows machine to build your code for windows. Golang got you covered. Just run: GOOS=windows GOARCH=amd64 go install And our code will output a binary ready to deploy on a windows machine. The .exe file will appear at GOPATH/bin/windows_amd64/my_app.exe . Like a walk in the park. The Golang Language Golang is aimed to be a simple language. I love the following post asking why Go wasn’t designed to be a functional language: https://www.quora.com/Why-wasnt-Go-written-as-a-functional-language “Programmers working at Google are early in their careers and are most familiar with procedural languages, particularly from the C family. The need to get programmers productive quickly in a new language means that the language cannot be too radical” And simple it is. Here’s a list of some features Golang doesn’t support: Classes Operators overloading Function overloading Optional Parameters exceptions While sometimes I find myself missing language features when writing in Go, I can always do everything that I want. Just sometimes it requires more writing and sometimes thinking. The bottom line is, it leads to code which is easier to reason with because it’s less abstract. It sometimes amazes me how fast I can achieve a goal when writing in Go, without having years of experience, just because the language is so accommodating and clear. The Golang Concurrency I have left the concurrency talk by intention to be last. Why? because I don’t think it’s so important. It’s an amazing feature of the language. But sometimes it’s presented as the essence of Go, which I believe it is not. Therefore let me try to sum it up in one paragraph. Yeah, Golang has great concurrency. You don’t deal with threads but rather create cheap goroutines which are simple to make and manage. Goroutines allow you to distribute the load across all of your CPUs without worrying on how to manage it all. Still interested in understanding goroutines? Read about them here: https://tour.golang.org/concurrency/1 The Golang Gopher Come on, he’s just so CUTE! Did I mention I am a Functional Programming enthusiast? Then why I am so attracted to Golang? it doesn’t even have a concept for mondas. Who knows… The universe works in mysterious ways.

Why I Love Golang

Optimizing the way open-source SDK Flutter deals with external texture for Android and iOS apps This article is part of Alibaba’s Utilizing Flutter series. In computing, as in much of life, any given method can see a lot of use before its latent flaws reach a decisive impasse. For Alibaba, discovering one such flaw in software development kit Flutter meant the difference between success and failure in the group’s recent work on a mobile app for its Xianyu(闲鱼) second-hand trading platform. Now the Alibaba team has successfully optimized Flutter for a new range of uses unique to Xianyu’s marketplace, implementing an OpenGL process for the entire UI rendering process to reduce CPU and GPU resource overhead. In today’s article, we look at the group’s optimization efforts alongside a detailed view of the inner workings and “external texture” of Flutter that technical audiences can explore in their own work. Flutter Rendering Framework The Flutter rendering framework is organized into a series of layers, with each layer building upon the previous layer. The architecture design of the Flutter Rendering Framework is shown below: The Flutter rendering framework, by layer 1. : The rendering pipeline is a tree-like structure output by the Dart API at runtime. Each leaf node on the tree represents an interface element, such as buttons, images, and so on. Layer Tree 2. : A cross-platform rendering framework sponsored and managed by Google. It serves as graphics engine for iOS/Android applications. The bottom layer of Skia is known as OpenGL drawing. Vulkan support is very limited and Metal does not provide the support. Skia 3. : A platform feature that includes iOS/Android platform implementations, EAGLContext management, returning data on a screen, and the external texture implementations. Shell The Layout process is executed at the Dart runtime and outputs a Layer tree. Each leaf node of the Layer tree is traversed in the pipeline to call the Skia engine for completing the drawing of the interface elements. After completing the above-mentioned process, do the following: 1. iOS: Run the command to display the render buffers contents on the screen. Android: Run the command to perform a buffer swap on the layer in use for the current window. glPresentRenderBuffer glSwapBuffer 2. Click the Complete display-on-screen link. Based on this principle, Flutter can implement UI isolation on Native and Flutter Engine. It also captures the UI code without analyzing the platform implementation on the cross-platform solutions. Implementation Problems There are pros and cons to this implementation. Flutter is isolated from Native, which can sometimes make it feel like there is a mountain separating Flutter Engine and Native. This poses issues when Flutter wants to capture high-memory images of the Native side, such as camera frames, video frames, album images, and so on. Traditional applications (RN, Weex, and so on) can directly obtain this data by bridging the NativeAPI. Flutter, meanwhile, determines whether the data can be directly captured and sends a notification message based on the defined channel mechanism, inevitably leading to huge memory usage and CPU utilization while transmitting the data. Bridging the Gap with External Texture Flutter provides a special mechanism known as an external texture. Note that textures are images that can be applied to an area of the Flutter view. They are created, managed, and updated using a platform-specific texture registry. This is typically done by a plugin that integrates with host platform video player, camera, or OpenGL APIs, or similar image sources. The architecture diagram of LayerTree is shown below: LayerTree architecture Each leaf node represents a control of dart code layout. TextureLayer node at the end corresponds to the texture control in Flutter. When a texture control is created in Flutter, it represents the data displayed on this control that needs to be provided by Native. Note that this texture is different from GPU’s texture. It is for Flutter’s control. The following is the final drawing code of the TextureLayer node on the iOS platform. The code for the Android platform is similar, but its method of obtaining texture is slightly different. It is recommended to run the code following these three steps: 1. Make the call of external texture’s copyPixelBuffer function to get CVPixelBuffer 2. Create the OpenGL ES Texture — CVOpenGLESTextureCacheCreateTextureFromImage 3. Capture the OpenGL ES texture into a SKImage and make the call of Skia’s DrawImage function to complete drawing. void IOSExternalTextureGL::Paint(SkCanvas& canvas, const SkRect& bounds) { if (!cache_ref_) { CVOpenGLESTextureCacheRef cache; CVReturn err = CVOpenGLESTextureCacheCreate(kCFAllocatorDefault, NULL, [EAGLContext currentContext], NULL, &cache); if (err == noErr) { cache_ref_.Reset(cache); } else { FXL_LOG(WARNING) << "Failed to create GLES texture cache: " << err; return; } } fml::CFRef<CVPixelBufferRef> bufferRef; bufferRef.Reset([external_texture_ copyPixelBuffer]); if (bufferRef != nullptr) { CVOpenGLESTextureRef texture; CVReturn err = CVOpenGLESTextureCacheCreateTextureFromImage( kCFAllocatorDefault, cache_ref_, bufferRef, nullptr, GL_TEXTURE_2D, GL_RGBA, static_cast<int>(CVPixelBufferGetWidth(bufferRef)), static_cast<int>(CVPixelBufferGetHeight(bufferRef)), GL_BGRA, GL_UNSIGNED_BYTE, 0, &texture); texture_ref_.Reset(texture); if (err != noErr) { FXL_LOG(WARNING) << "Could not create texture from pixel buffer: " << err; return; } } if (!texture_ref_) { return; } GrGLTextureInfo textureInfo = {CVOpenGLESTextureGetTarget(texture_ref_), CVOpenGLESTextureGetName(texture_ref_), GL_RGBA8_OES}; GrBackendTexture backendTexture(bounds.width(), bounds.height(), GrMipMapped::kNo, textureInfo); sk_sp<SkImage> image = SkImage::MakeFromTexture(canvas.getGrContext(), backendTexture, kTopLeft_GrSurfaceOrigin, kRGBA_8888_SkColorType, kPremul_SkAlphaType, nullptr); if (image) { canvas.drawImage(image, bounds.x(), bounds.y()); }} Where does the external_texture_object come from? Before making the call of RegisterExternalTexture on the Native side, create an object for implementing the FlutterTexture protocol, which is assigned to the external_texture object. The external texture is a bridge between Flutter and Native, used to continuously obtain the image data to be displayed. As shown in the figure, the PixelBuffer is the carrier of data transmitted by Flutter and Native while using the external texture. The data source (camera, player, and so on) of the Native side transmits the data into PixelBuffer. Flutter takes PixelBuffer and converts it into OpenGL ES texture for Skia to complete the drawing. At this point, Flutter can easily draw all the data that the Native side wants to draw. In addition to the dynamic image data (the camera player), the display of the image provides another possibility beyond the Image control, especially for the Native side when there are large-scale image loading libraries like SDWebImage. This process is very time consuming and laborious when there is a need to write a copy with dart on the Flutter side. Optimizing Processing Speed The entire process described above seems to solve Flutter’s problem of displaying native-side big data, but it has the following limitations: As shown in the above diagram, the video image data processing generally uses GPU processing on the Native side to improve the performance. The copyPixelBuffer interface is defined by the Flutter side. The entire data flow goes through GPU > CPU > GPU process. Note that the memory swapping between CPU and GPU is the most time-consuming in all operations. One go-and-back usually takes longer to complete than the process time of the entire pipeline. The Skia rendering engine requires GPU Texture and the Native data processing output is GPU Texture. This GPU Texture can be used directly using a shared resource of EAGLContext. An EAGLContext object manages an OpenGL ES rendering context — the state information, commands, and resources needed to draw using OpenGL ES. The thread structure of Flutter is introduced as below: Flutter usually creates 4 Runners. The TaskRunner is like the iOS Grand Central Dispatch (GCD). It is a mechanism for executing tasks in a queue. Generally, the TaskRunner communicates to a thread and Platform Runner is running on the major thread. The following 3 TaskRunners are relevant to this article: 1. : Responsible for GPU rendering related operations. GPU TaskRunner 2. : Responsible for the loading of resources. IO TaskRunner 3. : Responsible for all swapping between Native and Flutter Engine and running on the major thread. Platform TaskRunner Usually, an app thread design that uses OpenGL will have two threads — one thread for loading resources (from image to texture) and another thread for rendering. However, it is often the case that in order to enable the texture created by the load thread to be used in the render thread, the two threads share one EAGLContext. This is not standard practice, as it is unsafe. Multi-threaded access to the same object with lock will inevitably affect performance. It will even cause deadlock when the code is not well processed. To avoid this problem, Flutter provides a new mechanism for the use of EAGLContext — each thread uses its own EAGLContext and shares texture data via ShareGroup for iOS apps and shareContext for Android apps. (Although the users of the two Contexts are the GPU and the IO Runner respectively, the two Contexts of the existing Flutter logic are created under the Platform Runner. This aspect of Flutter’s design is rather puzzling and creates all sorts of issues, but these fall outside the scope of this article.) For the module that uses OpenGL on the Native side, that also creates a Context corresponding to and under its own thread. To deliver the Texture created under this Context to the Flutter and send this data to Skia for complete drawing, disclose the ShareGroup for iOS apps while creating two internal Contexts in Flutter and then save the ShareGroup on the Native side. When Native creates Context, it will also use this ShareGroup to create it. In this way, Native and Flutter can share texture. The following are two benefits that come from using external_texture: 1. Alibaba tests concluded that a frame of 720P RGBA format video on an Android model takes about 5ms from reading GPU to CPU, then takes another 5ms from CPU to GPU. Even when PBO is introduced, there is also about 5ms time consumption, which is obviously unacceptable for high frame rate scenarios. Reduced compute time 2. As one might intuit, data is passed on the GPU, especially in picture scenes (because there are many pictures to be displayed at the same time). Reduced CPU memory consumption Some Side Notes Now that the basic principles of Flutter external textures and optimization strategies have been introduced, this section deals with some drawbacks and exceptions to these principles. Integrity of Flutter’s Texture vs. Pixelbuffer A question many people ask at this point is: If using Texture directly as an external texture is as good as claimed, then why is Google using Pixelbuffer? If you use Texture, you will have to disclose the ShareGroup, which means the GL environment of Flutter is open. If the external OpenGL is not working properly, the OpenGL object is just a number, a Texture or a FrameBuffer to the CPU and to the user is a GLuint (Unsigned binary integer) when breaking points. If the environment is isolated, the user can operate deleteTexture and deleteFrameBuffer as they wish without affecting the objects in other environments. Otherwise, these operations would probably affect the objects under the Context of Flutter. As a framework designer, the biggest priority is making sure the framework is a closed environment so as to maintain its integrity. Troubleshooting: Crashes During the development process, the team encountered a strange problem: Flutter would routinely crash during rendering, but nobody could figure out why. After much searching, the cause was finally identified as being making the call of glDeleteFrameBuffer when the main thread did not have setCurrentContext, which meant Flutter’s FrameBuffer was accidentally deleted. If you do choose to use this program, the relevant GL operations on the Native side should follow at least one of the points below: 1. Try not to perform GL operations on the main thread; 2. Always add setCurrentContext before the call of function with GL operation. iOS vs. Android Most of the logic in this article is based on iOS examples. The overall principles are the same for Android, but the implementation is slightly different. The external texture of Flutter on the Android side is implemented by SurfaceTexture. The mechanism is actually the copy from CPU to GPU memory. There is no concept of ShareGroup in Android OpenGL. Instead, it uses shareContext, which means Context is transmitted out directly. Additionally, the GL implementation of Android in the Shell layer is based on C++, so Context is a C++ object. To share this C++ object with the Java Context object of the Android Native side, you need to make the call in the jni layer as follows: static jobject GetContext(JNIEnv* env, jobject jcaller, jlong shell_holder) { jclass eglcontextClassLocal = env->FindClass("android/opengl/EGLContext"); jmethodID eglcontextConstructor = env->GetMethodID(eglcontextClassLocal, "<init>", "(J)V"); void * cxt = ANDROID_SHELL_HOLDER->GetPlatformView()->GetContext(); if((EGLContext)cxt == EGL_NO_CONTEXT) { return env->NewObject(eglcontextClassLocal, eglcontextConstructor, reinterpret_cast<jlong>(EGL_NO_CONTEXT)); } return env->NewObject(eglcontextClassLocal, eglcontextConstructor, reinterpret_cast<jlong>(cxt));} (Original article by Chen Lujun陈炉军) Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

Rendering “External Texture”: A Flutter Optimization Story

This post require to know how write and read the .yaml syntaxe only : easy ! ;) What we need ? A terminal with php on your local machine A git repository for your project What we use ? automate ! This is the official website of automate : https://automate-deployer.com/ Automate It’s time to start ! First, the file .automate.yml First, add the file at the root of your project (not the public directory), write this lines : .automate.yml repository: :romaricp/kit-starter-symfony-4-docker.gitplatforms:development:default_branch: mastermax_releases: 2servers:dddv-server:host: 10.128.12.12user: devpassword: %dev_password%path: /home/wwwroot/sf4preprod:default_branch: preprodmax_releases: 2servers:pddv-server:host: 10.128.12.13user: devpassword: %preprod_password%path: /home/wwwroot/sf4shared_files:- .envshared_folders:- public/uploadon_deploy:- "composer install"- "setfacl -R -m u:www-data:rwX -m u:`whoami`:rwX var"- "setfacl -dR -m u:www-data:rwX -m u:`whoami`:rwX var"- "php bin/console c:c --env=prod --no-debug"post_deploy:- "php bin/console doctrine:schema:update --force"- "php bin/console cache:war --no-debug" git@github.com Okay so what we have write here ? It’s just setting, not coding ;) repository: :romaricp/kit-starter-symfony-4-docker.git git@github.com Here, we just wrote the address of your project, for example here a sample project with Symfony4. platforms:development:default_branch: mastermax_releases: 2servers:dddv-server:host: 10.128.12.12user: devpassword: %dev_password%path: /home/wwwroot/sf4preprod:default_branch: preprodmax_releases: 2servers:pddv-server:host: 10.128.12.13user: devpassword: %preprod_password%path: /home/wwwroot/sf4 Here, we defined all servers we want to deploy our project. So as you can see, 2 servers are defined : and one. development preproduction For each server we defined which git branch is used via ; so for the server I want to use the branch and for the server I want to use the branch. default_branch development master preprod preprod We have to defined how many maximum releases we want to stock on each server. 2 seems good because if you get some bug after a new deployment you can rollback with one command, we will see later. servers:pddv-server:host: 10.128.12.13user: devpassword: %preprod_password%path: /home/wwwroot/sf4 Here, we defined all servers by platform, In this case we have just one server / platform but you can easely add 2 or 3 servers to deploy your project. For each server you have to define : Host User (with ssh access) The path of you project on the remote server For the password, it’s not a good practice to write this one here in this file, so that’s why we are using each side, because when you will start the deployment the terminal will ask you to enter the right password. % shared_files:- .env Here, we defined all files shared for all releases, in this case this file is for Symfony which contains all the necessary environment variables. So no matter which release is current there is just one config file for all my releases ! environment shared_folders:- public/upload Here, same story with folders. We defined all folders that we need for all releases. The best example is the upload directory. on_deploy:- "composer install"- "php bin/console c:c --env=prod --no-debug"post_deploy:- "php bin/console doctrine:schema:update --force"- "php bin/console cache:war --no-debug" Here, it’s the list of commands lines that your project need to be deploy perfectly. There are 3 events availables with automate : , and all are well explain here : pre_deploy on_deploy post_deploy https://automate-deployer.com/ doc So now your .automate.yml file is ready ! Second, install the automate.phar Go to your root path of project cd /your/path/project You can download the latest version of Automate with the following command: curl -LSs https:// Now you should get something like that : Sample with Symfony4 project It’s time to deploy ! Go to your root path of project and launch automate ! cd /your/path/project start the deployment Enter the and enjoy the show ! ;) dev_password You should get something like that : End point of the deployment What happen to our server ? Well if you go to our server, we should get something like that : our development server remote current directory is a symbolic link to the lastest release releases directory contain all last releases (depend the max_releases number you set in the .automate.yml file) shared directory contain all folders shared between every releases If we go to the you can see : shared directory the shared directory Which contain the “real” folders and files. On the other hand if we go to the current directory you can see : The file is a symbolic link to the real file which is in the shared directory. .env .env So as you can see, we deployed without a single line of code ! Automate is very easy to use and to set. Automate has a lot of plugins by default, you can see all of this to the official documentation : https://automate-deployer.com/ doc To conclude, we can now imagine to use automate and gitlab with an automatic deployment on each merge request ! ;) Thanks to & for rereading. :) Solène Louvrier Nicolas Legendre

How to deploy on multiple servers a project without a single line of code!

Forward I’m an 1.x developer. I work in Angular 1.x every day and would say I know my way around a digest cycle. I took a very learn as you go approach to Angular 1.x, and with Angular 2 I’m going to be a little more pragmatic. Angular I’m going to attempt to pick apart and document each step and section that an Angular 2 app goes through in order to go from to DOM elements on the screen. javascript Setup A little background for where my app, lovingly referred to as “client,” is starting from. I installed the angular-cli package and simply called > ng init Pretty complicated setup there, actually I really like this for starter apps. Compared to a long and arduous manual setup, it’s a very nice way to get started fast. Now, most of the compilation/packing steps are wrapped up in a blackbox webpack system included with angular-cli, so we’ll start with src/main.ts. main.ts imports some polyfills and basic stuff that the typescript compiler and angular will need to run properly in the browser. Now let’s get into some actual meat. And so it begins… it’s turtles all the way down. platformBrowserDynamic().bootstrapModule(AppModule); What in the world does this mean? Let’s dig, first we go , this is the declaration for platformBrowserDynamic. In it, we find that we see here export const platformBrowserDynamic = createPlatformFactory(platformCoreDynamic,'browserDynamic',INTERNAL_BROWSER_DYNAMIC_PLATFORM_PROVIDERS); Now we’ve established that there’s something called a Platform (as well as a platform factory.) Following this call, we’re redirected to a file called . We see that the argument names, in order, are application_ref.ts parentPlaformFactory: (extraProviders?: Provider[]) => PlatformRef name: string providers: Provider[] = [] The names seem pretty self explanatory. The first thing to note is the INTERNAL_BROWSER_DYNAMIC_PLATFORM_PROVIDERS. You can find all the things that provides , or if you just want me to tell you, it’s all of the modules necessary for doing . This is basically the platform specific part of these calls. Everything else is, well, Core. here browser specific rendering Let’s dig into the first argument, “parentPlatformFactory”. In the case of the starter app, we can see that this is platformCoreDynamic which we get from the . What lies here you may ask? ! It’s turtles all the way down. compiler module More platform factories export const platformCoreDynamic = createPlatformFactory(platformCore,'coreDynamic',[{provide: COMPILER_OPTIONS,useValue: {},multi: true}, {provide: CompilerFactory,useClass: RuntimeCompilerFactory}, {provide: PLATFORM_INITIALIZER,useValue: _initReflector,multi: true}]); So now we have another platform based off of something called “platformCore,” which we’ll dig further into in a second. Note that it looks as though we’re providing a lot of compiler related modules. These are related to taking the angular code and templates and compiling them into render-able components. Most notable here is that the RuntimeCompilerFactory is supplied as the CompilerFactory. This means that we’re doing JIT compilation, as opposed to compiling the angular app ahead of time. platformCore. What are you? Let’s find out. It comes from angular/core, and we find… export const platformCore = createPlatformFactory(null,'core',_CORE_PLATFORM_PROVIDERS); We have reached the end though, the granddaddy platformFactory. ‘core.’ It even has a great name, it is the core. the last. the center. If you look into what the _CORE_PLATFORM_PROVIDERS are, they look to be a bunch of pretty general stuff, reflector, console, generally providers that provide basic functionality for any app. Great, let’s unwind, all the way back up to our original call to createPlatformFactory to figure out what all this means. The first line of createPlatformFactory looks like this; const marker = new OpaqueToken(`Platform: ${name}`); OpaqueToken is just a container for the name, basically Angular2’s version of a Symbol. So far so good. return (extraProviders: Provider[] = []) => {if (!getPlatform()) {if (parentPlaformFactory) {parentPlaformFactory(providers.concat(extraProviders).concat({provide: marker, useValue: true}));} else {createPlatform(ReflectiveInjector.resolveAndCreate(providers.concat(extraProviders).concat({provide: marker, useValue: true})));}} return assertPlatform(marker);}; The first statement, getPlatform(), checks to see if createPlatform() has ever been called, i.e. if there is an assigned platform. The first time we call createPlatform (way back in core), we create a platform according to the created injector. . As we can see, PlatformRef has been assigned to PlatformRef_ from the application_ref file. Back over there, that it’s an implementation of the abstract class . Let’s take a look at that file again we see PlatformRef The Angular platform is the entry point for Angular on a web page. Each page has exactly one platform, and services (such as reflection) which are common to every Angular application running on the page are bound in its scope. * * A page’s platform is initialized implicitly when {@link bootstrap}() is called, or * explicitly by calling {@link createPlatform}(). So, we’ve finally made it to our Angular application. In the next part we’ll investigate what actually happens when you call bootstrapModule. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

How does Angular2 work? Part 1 — Application Refs

**SINGAPORE, Singapore, May 17th, 2024/Chainwire/--**Mining Now, a trusted and reputable real-time crypto-mining insights provider company, is thrilled to announce the launch of its versatile platform that attempts to give unbiased insights associated with crypto mining as well as real-time Asic profitability tracking where people can analyze and compare the crypto mining hardware to draw well-informed inferences. It is designed to bridge the gap between miners around the world and the crypto landscape with Mining Now’s user-friendly interface, with the intent to boost crypto mining endeavors. With a solid commitment to delivering crypto mining insights, the leading-edge platform not only pertains to benefit the miners worldwide to accomplish swift and unmatched mining outputs but also curated a whole range of ASIC miners showcasing intricate data and specifications in entirely uncomplicated form in one place to create heightened awareness as well as maximizing the profits. With its recently launched platform, the entire team of Mining Now has big plans to expand their horizons to cover other forms of crypto mining hardware as well, such as GPU and CPU miners, for maximized research potential, and they are persistently working to accomplish it. Not only this, but they are also on the go to forge a full-fledged mining community that thrives to unify diversified entities. Mining Now’s exclusive community will have crypto Asic enthusiasts, technicians, supporters, and mining farms that will aid in promoting exquisite experiences and exponential growth. Another distinctive approach to providing its users with P2P deals is the cherry on top, as they will have accessibility to sellers from around the world, which would save them thousands of dollars. No such groundbreaking platform has been in the game yet, and it can be well-anticipated that Mining Now is now all set to pioneer the entire world with its distinctive features and qualities. This move by Mining Now will not only forge a more profound sense of reliability amongst its users but also bring a revolution that would shake the entire industry with its vision and innovations, leaving a benchmark of unbiasedness, futuristic approach and unseen innovations. Embark on this exquisite journey with Mining Now and raise the bars of your crypto mining ventures. Website: https://miningnow.com/ Social Media Handles Discord: https://discord.com/invite/9JVK9trkG7
Twitter: https://twitter.com/Realminingnow
Telegram: https://t.me/RealMiningNow Contact Selina Mining Now selina@miningnow.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SINGAPORE, Singapore, May 17th, 2024/Chainwire/--**Mining Now, a trusted and reputable real-time crypto-mining insights provider company, is thrilled to announce the launch of its versatile platform that attempts to give unbiased insights associated with crypto mining as well as real-time Asic profitability tracking where people can analyze and compare the crypto mining hardware to draw well-informed inferences. It is designed to bridge the gap between miners around the world and the crypto landscape with Mining Now’s user-friendly interface, with the intent to boost crypto mining endeavors. With a solid commitment to delivering crypto mining insights, the leading-edge platform not only pertains to benefit the miners worldwide to accomplish swift and unmatched mining outputs but also curated a whole range of ASIC miners showcasing intricate data and specifications in entirely uncomplicated form in one place to create heightened awareness as well as maximizing the profits. ASIC miners ASIC miners With its recently launched platform, the entire team of Mining Now has big plans to expand their horizons to cover other forms of crypto mining hardware as well, such as GPU and CPU miners, for maximized research potential, and they are persistently working to accomplish it. Not only this, but they are also on the go to forge a full-fledged mining community that thrives to unify diversified entities. Mining Now’s exclusive community will have crypto Asic enthusiasts, technicians, supporters, and mining farms that will aid in promoting exquisite experiences and exponential growth. Another distinctive approach to providing its users with P2P deals is the cherry on top, as they will have accessibility to sellers from around the world, which would save them thousands of dollars. No such groundbreaking platform has been in the game yet, and it can be well-anticipated that Mining Now is now all set to pioneer the entire world with its distinctive features and qualities. This move by Mining Now will not only forge a more profound sense of reliability amongst its users but also bring a revolution that would shake the entire industry with its vision and innovations, leaving a benchmark of unbiasedness, futuristic approach and unseen innovations. Embark on this exquisite journey with Mining Now and raise the bars of your crypto mining ventures. Website: https://miningnow.com/ https://miningnow.com/ https://miningnow.com/ Social Media Handles Discord: https://discord.com/invite/9JVK9trkG7 Twitter: https://twitter.com/Realminingnow Telegram: https://t.me/RealMiningNow Discord: https://discord.com/invite/9JVK9trkG7 https://discord.com/invite/9JVK9trkG7 https://discord.com/invite/9JVK9trkG7 Twitter: https://twitter.com/Realminingnow https://twitter.com/Realminingnow https://twitter.com/Realminingnow Telegram: https://t.me/RealMiningNow https://t.me/RealMiningNow https://t.me/RealMiningNow Contact Selina Mining Now selina@miningnow.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Mining Now Launches Real-Time Mining Insights And Profit Analysis Platform

This paper is available on arxiv under CC 4.0 license. Authors: (1) Berrenur Saylam; (2) Ozlem Durmaz ¨ ˙Incel. Table of Links Highlights Abstract & Introduction Federated Learning: Collaborative and Privacy-Preserving Model Training State of the Art Federated Learning Software Platforms and Testbeds Sensors and Edge-Sensing Devices Federated Learning Applications on Sensing Devices Discussion Conclusions, Acknowledgment, and References 3. State of the Art Federated Learning Software Platforms and Testbeds In this section, we introduce some prominent software frameworks for building FL applications. In addition, we provide a list of popular testbeds crucial for the model deployment and evaluation on real devices. 3.1. Software Platforms Frameworks provide developers and researchers with powerful tools and resources to implement FL algorithms and conduct experiments in a distributed and collaborative manner Kholod et al. (2020). We review the commonly used FL software platforms in the literature: • TensorFlow Federated (TFF) is developed by Google specifically for applying deep learning over distributed data, with a particular focus on mobile keyboard prediction and ondevice learning. TFF consists of two API layers: Core and FL API. The Core API offers default implementations of FL algorithms such as FedAvg and FedSgd, while the FL API allows for the definition of custom federated algorithms. TensorFlow (TFF (2023)): TFF currently supports simulation mode which is a controlled environment rather than being executed on real distributed devices or edge nodes. It allows researchers and developers to fine-tune the algorithms and observe their behavior under various conditions without the complexity and overhead of dealing with real distributed systems. • LEAF is an open-source modular benchmarking framework. It comprises three components: datasets, evaluation frameworks, and reference implementations. LEAF supports various learning paradigms, including federated, meta-learning, multitask, and on-device learning. It provides a benchmarking environment for evaluating FL algorithms. LEAF (Caldas et al. (2018)): • PySyft is a framework focusing on secure and private deep learning applications compatible with popular deep learning libraries such as PyTorch and TensorFlow. It enables static and dynamic computations and incorporates privacy-preserving techniques into the FL workflow. PySyft leverages PyGrid, an API for managing and deploying PySyft-powered models to interact with edge devices. PySyft (Community (2023)): Flower is a user-friendly FL framework that provides a facility for large-scale data scenarios and is designed to support heterogeneous FL device environments. One of its significant contributions is the ability to run FL algorithms directly on edge devices. • Flower (Beutel et al. (2020)): Flower also facilitates conducting experiments at the algorithmic level and considers system-related factors such as computing capability and bandwidth. It has been successfully tested in scenarios involving millions of clients. Federated AI Technology Enabler (Fate) is developed by WeBank and offers extensive customization options for FL algorithms. It comprises various components, including FATEFlow, FederatedML, FATEBoard, FATE Serving, Federated Network, KubeFATE, and FATE-Client. • Fate (Fedai (2023)): Fate supports horizontal and vertical data partitions, allowing for flexibility in data distribution. However, it currently employs a centralized implementation, where a server coordinates all the FL processes. It can be deployed in both simulated and federated modes. OpenFL is an open-source project initiated by Intel. It provides a bash-script-based framework for FL and emphasizes secure communication between clients. OpenFL consists of two main components: the abrogator and the collaborator. • OpenFL (Intel (2023)): The framework allows for the customization of various aspects, including logging mechanisms, data split methods, and aggregation logic. However, it requires manual client-side handling. Among the listed FL software platforms, Flower and PySyft are particularly relevant for working with sensor data and edge devices, including IoT devices. Flower is a user-friendly framework supporting large-scale data scenarios and heterogeneous FL device environments. It enables running FL algorithms directly on edge devices, making it suitable for IoT devices. Flower considers system-related factors such as computing capability and bandwidth, making it a valuable choice for edge devices in sensor networks. PySyft, on the other hand, focuses on secure and private deep-learning applications. It is compatible with popular deep-learning libraries and allows for interactions with edge devices. With its privacy-preserving techniques, PySyft can be applied to IoT devices, ensuring data security during the FL workflow. These frameworks provide the necessary support and customization options to address the challenges associated with edge computing and facilitate efficient FL on resource-constrained devices. 3.2. Testbeds Testbeds play a crucial role in deploying and evaluating FL on the target devices. They provide open, accessible, and reliable facilities to validate technological solutions and foster research collaboration. One example testbed is the Fed4FIRE project[1] , which aims to develop a common federation framework integrating EU and US testbeds. By enabling experiments across different domains, the project facilitates the assessment of Software Defined Networks (SDN), explores edge computing use cases, and highlights the benefits of interconnecting these testbeds. In the realm of FL algorithms, the PTB-FLA Python Testbed Popovic et al. (2023) offers a Python-based framework specifically designed for smart IoT devices in edge systems. It supports centralized and decentralized algorithms, providing advantages such as a small application footprint and simple installation with no external dependencies. While the current version has limitations, such as a maximum number of nodes and edges and a lack of networking support, PTB-FLA shows promise for developing FL algorithms targeting IoT devices, and future implementations aim to expand its capabilities. This testbed includes swarms of simply IoT devices (no computers) on its roadmap that may utilize MicroPython as an OS, which is becoming more prevalent in embedded systems. The FeatureCloud testbed[2] provides a controlled environment for developers to test their FL applications. It offers workflow and testbed execution modes, allowing collaborative federated training and standalone app testing. With features like simultaneous test runs, monitoring panels, configuration settings, and result storage, developers can quickly validate and manage their FL apps before deployment in a federated environment. For realistic evaluation of FL systems, the FLAME testbed Cho et al. (2022), introduces a novel data partitioning scheme that distributes datasets among numerous client devices while preserving the multi-device nature of FL. This testbed generates many users, each owning a small number of labelled data samples, enabling developers to assess FL algorithms and models in a scalable setting. The FLAME testbed contributes to reproducibility and further research by open-sourcing its source code and federated HAR datasets. These testbeds provide valuable platforms for researchers and developers to experiment, validate, and refine their technologies in edge devices and FL domains. Among these testbeds, Fed4FIRE, PTB-FLA, FeatureCloud, and FLAME, demonstrate support for edge devices in federated learning. Fed4FIRE aims to integrate various testbeds, including those supporting IoT, to enable experiments across different domains and emphasize the importance of providing open and interoperable testing facilities for IoT researchers and innovators. PTB-FLA specifically targets smart IoT devices in edge systems, offering a Python-based framework with a small application footprint suitable for smart IoTs. FeatureCloud provides a controlled environment for testing FL applications and supports IoT devices, allowing developers to validate and manage their apps before deployment in a federated environment. Similarly, FLAME supports IoT devices and creates a realistic evaluation environment for FL systems by generating users with labelledW data samples, enabling developers to assess FL algorithms in a more realistic IoT setting. These testbeds collectively contribute to advancing research and innovation in federated learning on IoT devices by offering experimentation, validation, and refinement platforms. [1] https://www.fed4fire.eu/news/innovation-through-the-fed4fire-federated-testebds/ [2]  https://featurecloud.ai/ This paper is under a CC 4.0 license. available on Arxiv

Federated Learning on Edge Sensing Devices: State of the Art Federated Learning Software Platforms

If you think about the science fiction utopias of old, one thing was usually present: a widespread sharing of technology and ideas. Open source is the modern-day realization of that utopian dream. It's a way for developers to create and share software so that anyone can use, study, change, and improve it. The advantages of are numerous. Developers can save time and effort because they don't have to start from scratch. They can also be confident that their work will be compatible with other open-source projects. open source And, perhaps most importantly, they can trust that the platform will be well-maintained and supported by a community of developers. When I recently sat down with with Ian Tien to discuss his route from making HTML5 online video games to becoming a VC-backed CEO, I didn't think I was going to come out with a full-length explainer of the benefits of open source platforms. But that is kind of the point, isn't it? Ian was generous with his time and knowledge, happy to share anything that would drive the conversation forward, encouraging more people to follow his path down the open source path. From SpinPunch to MatterMost Like any good story, Ian’s CEO journey started with an inciting incident. While developing online games for a company called SpinPunch, an early messaging service suddenly put all of their communication data behind a paywall. What spun out of that would eventually become MatterMost, an open-source alternative to SaaS platforms like Slack and Notion. His decision to make it open was influenced by a friend who explained the choice simply: “If you open source it and it is popular, you can always close it without giving away that much. But if you open source it and no one cares, you should just stop working on it - go do something else.” Ian believes now that by making the platform available for developers all over the world to work on, you can save productivity and avoid those wasted hours of “1000 companies rebuilding the same thing. But he wasn’t going to stop with high-concept advice like that. Just like his company - he’s an open book. How to Find a Market Fit It’s great to think of things as a utopia but in today’s world, these projects still have to include a capitalist slant. There has to be a way to make money, and for that, you need customers and a market. “Follow the emotion,” says Ian, who explains that the best way to know what people will pay for is by seeing what they react to emotionally. What is so important for them to fix that they will spend money to do it? Anybody Could Just Copy it, Right? I’ll admit it. Before this conversation with Ian, I had the same thought that so many other people do when considering open-source software. Couldn’t someone just take all of your work, and copy it? While the answer is yes, technically, he offered some pretty compelling evidence to suggest that even though someone could doesn’t mean they actually would. The team at MatterMost uses the acronym TACTICAL to explain. Test Infrastructure A good open source project will have a comprehensive test infrastructure in place that can be used to verify the correctness of changes and additions. This includes unit tests, integration tests, end-to-end tests, and more. A well-tested project is less likely to break when someone makes a change. Since this testing work isn’t open source - much of it is done with actual human testing so it can’t be - you can “fork” the project, but you would need to rebuild that entire infrastructure from scratch. Alignment One of the biggest things that Ian explained was “don’t try to monetize developers” because it will “militarize every developer against you.” What he means by this is don’t make the paid part of the service the things that developers need; instead, offer them the free stuff so that you can benefit from their creativity while offering the paid features to IT professionals and executives. As Ian puts it, developers “don’t like building the compliance features” meaning they won’t try to steal the whole platform. Core Committers This is a key part of not only keeping your project safe but making sure that you have the best people working on it. Bring in the community members that are most dedicated and allow them to work directly for the company, instead of just building additional capabilities on the outside. Bring your potential competitors inside your tent, and they will never become your enemy. Threat Intelligence Security is a huge part of any open source project, and Ian is so obviously committed to this side of the business that he brought it up several times in our conversation. In this context, he explained how it can be used as a way to keep any potential competitors at bay. If a report is submitted that points out a security flaw, the company will fix it, and let their customers know it is fixed, but not release the details of the patch for 30 days. If someone “forked” the platform, not only would they not have access to that fix immediately but they wouldn’t have even received the report in the first place. Innovation If a platform continues to innovate and you try to copy the entire system, you won’t be able to keep up (I could barely keep up with Ian in a one-on-one talk, what chance do you have of matching his entire team!) Compatibility Once again mentioning security, Ian mentions how if you took one build of the software and tried to copy it, any future patches won’t work properly. You would have to be doing most of the work yourself to integrate the fix, making the whole project untenable. Architecture If you build it correctly in the first place, there will be little reason to recreate it anyway. The whole point is that you can change an open source project, meaning you can leverage all the work that has been done and make it specific to your needs without rebuilding the whole thing. Licensing The last thing, and perhaps the one that makes the most sense to the average investor, is that while the code is open source, the trademark is not. So if you were trying to take it all and build a new brand it would be just that - a new brand. You wouldn’t be benefiting from the work that MatterMost (or whatever other company you were taking from) has already done to build trust in the community. Taking Open Source SaaS to Market So while the philosophical debate over open and closed source software may be interesting, the more pressing question for many of you will be how you take an open source project and turn it into a valuable company. “It’s really about building something that people want.” Sounds simple, doesn’t it? Despite that, so many startups seem to forget the product development loop that is necessary - a loop that includes direct communication and feedback. Talk to customers: What do you think of this? What features would it need? Would you pay for it? Build product: Here is something that fixes your problem. Exercise: Keep your business healthy, lean, and growing with constant evaluation. This isn’t a one-time list of steps. You need to loop it over and over and over throughout the entire life cycle of a company. It’s never enough to know what a customer wants today; you need to know what they want tomorrow, six months from now, and three years down the line. Retaining Great Talent Without Investment One of the most interesting parts of the conversation with Ian revolved around some of the strategies he has employed in hiring and retaining the best talent available, even without the benefit of huge capital investment. Open source projects help in this way, though it isn’t with direct compensation. “The people that are money motivated - go work in hedge funds,” he said, laughing off the idea as if it is something that he easily could have done if he wanted to. Instead, he focuses on three things when he’s trying to attract talent that belongs in the open source world. : “If you do it right, it never has to be written again.” The idea is that if you want to make a mark in software history, this is how you’re going to do it - not counting the Netflix options in your portfolio. Impact : “Half our managers are promoted from within.” Enabling your employees to grow and develop within your company is integral to retention in any industry, and perhaps even more so in tech. No one wants to sit still, even if they are being compensated well. Growth : “The ability to walk off a plane in 20 different cities around the world and have people greet you at the airport.” Working with people all over the globe, no matter what culture, background, or location, is a key part of hiring for an open-source project. That is kind of the whole point - everyone can be a part of it. Connection But while these three things are the attributes to look for, it may be hard to foster that kind of talent pool without a little bit of work. Building the Community There is a skill shortage in so many different developer fields, so building an engaged, excited community is key to any sort of company growth. These are the people you will be drawing from for your next position, whether it is through a referral or, in the case of open source, the delivery of a ticket. Ian suggests that at least in this field, it comes down to three key principles. : You have to be crystal clear with which projects are available, and which will be accepted. Clarity : Focus on the people who can deliver something quickly, that they had a passion to create. Instead of someone who works half-heartedly on it for weeks or months. Speed : The last thing a developer wants is to have a pull request rejected, despite correctly following the instructions. You must create a space where developers know that their time isn’t being wasted. Safety If something is rejected, Ian explains that is “our failure, not the contributor’s.” That is the crux of the whole thing - take the responsibility of providing clear, efficient, processes that will give your developers a sense of security about the project they choose to work on. Wrap-Up There is so much more to learn from someone like Ian, especially given how willing he is to share his knowledge. “Success for me is living up to all the gifts that I’ve been given.” He knows he had some advantages over others, and he wants to make sure he doesn’t squander those. So beyond the open source discussion that we’ve distilled above, he also gave his thoughts on the remote work revolution, software security and how it could be improved, and some of the overlooked benefits of bringing in VC money early. If you want to check out the whole interview, head over to the Success Story youtube page , where we have hundreds of podcasts with some of the brightest minds in business today. We’ll be back with another one next week! Also published here

Build on Top: Using Open Source Platforms to Increase Productivity

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro
Corpora Compilation
Political Stance Classification
Summary and Conclusions
Limitations and Ethics Statement
Acknowledgments and References
A. Newspapers in OSCAR 22.01
B. Topics
C. Distribution of Topics per Newspaper
D. Subjects for the ChatGPT and Bard Article Generation
E. Stance Classification at Article Level
F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com 2. https://mediabiasfactcheck.com 3. https://adfontesmedia.com This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro Corpora Compilation Political Stance Classification Summary and Conclusions Limitations and Ethics Statement Acknowledgments and References A. Newspapers in OSCAR 22.01 B. Topics C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level F. Training Details Abstract and Intro Abstract and Intro Corpora Compilation Corpora Compilation Political Stance Classification Political Stance Classification Summary and Conclusions Summary and Conclusions Limitations and Ethics Statement Limitations and Ethics Statement Acknowledgments and References Acknowledgments and References A. Newspapers in OSCAR 22.01 A. Newspapers in OSCAR 22.01 B. Topics B. Topics C. Distribution of Topics per Newspaper C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level E. Stance Classification at Article Level F. Training Details F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com https://www.allsides.com 2. https://mediabiasfactcheck.com https://mediabiasfactcheck.com 3. https://adfontesmedia.com https://adfontesmedia.com

Multilingual Coarse Political Stance Classification of Media: Abstract and Intro

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: বিমূর্ত এবং ভূমিকা

Mehrsprachige grobe politische Standpunktklassifizierung von Medien: Zusammenfassung und Einführung

Clasificación multilingüe de posturas políticas generales de los medios: resumen e introducción

Classification multilingue grossière des positions politiques des médias : résumé et introduction

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: सार और परिचय

多言語メディアの粗い政治的立場の分類：概要と序論

미디어의 다국어 거친 정치적 입장 분류: 개요 및 소개

Classificação Multilíngue da Posição Política Grosseira da Mídia: Resumo e Introdução

Многоязычная грубая политическая классификация СМИ: Аннотация и введение

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Özet ve Giriş

Phân loại lập trường chính trị thô sơ đa ngôn ngữ của truyền thông: Tóm tắt và giới thiệu

多语言媒体粗略政治立场分类：摘要和简介

Revolutionizing Healthcare: Andy Schoonover's Playbook for Disrupting Entrenched Industries

বিপ্লবী স্বাস্থ্যসেবা: প্রবেশ করা শিল্পগুলিকে ব্যাহত করার জন্য অ্যান্ডি শুনোভারের প্লেবুক

Revolutionierung des Gesundheitswesens: Andy Schoonovers Strategie zur Umwälzung etablierter Branchen

Revolucionando la atención médica: el manual de Andy Schoonover para revolucionar industrias arraigadas

Révolutionner les soins de santé : le manuel d'Andy Schoonover pour perturber les industries bien établies

स्वास्थ्य सेवा में क्रांति: स्थापित उद्योगों में बदलाव के लिए एंडी स्कूनओवर की कार्यपुस्तिका

ヘルスケアの革命: 定着した業界を変革するためのアンディ・シューノーバーの戦略

의료 혁신: 고착화된 산업을 혁신하기 위한 Andy Schoonover의 플레이북

Revolucionando a saúde: o manual de Andy Schoonover para desestabilizar indústrias entrincheiradas

Революция в здравоохранении: книга Энди Шуновера по разрушению устоявшихся отраслей

Sağlık Hizmetinde Devrim Yaratmak: Andy Schoonover'ın Yerleşik Endüstrileri Yıkmaya Yönelik Başucu Kitabı

Cách mạng hóa hoạt động chăm sóc sức khỏe: Sách hướng dẫn cách phá vỡ các ngành công nghiệp cố thủ của Andy Schoonover

医疗保健革命：安迪·斯库诺弗颠覆根深蒂固行业的策略

The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth)


As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform
Customers want to search and find information without having to do mental gymnastics
Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message.
Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads)
Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management
Assisting employees in their training, learning, and development programs
Synthesizing customer feedback efficiently
Assisting in the creation of impactful marketing assets easily If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers. The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. the this HBR article this HBR article All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG BCG | So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform Customers want to search and find information without having to do mental gymnastics Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Customers want to synthesize the information they create on the business’s platform synthesize synthesize Customers want to search and find information without having to do mental gymnastics search search Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. creative creative Customers want to be less involved in doing mundane and routine tasks on the business’s platform. routine routine tasks Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office .\ savings opportunities of $1.4 trillion to $2.6 trillion across operations impact on the back office Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management Assisting employees in their training, learning, and development programs Synthesizing customer feedback efficiently Assisting in the creation of impactful marketing assets easily Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) summarizing, Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management creating Assisting employees in their training, learning, and development programs Assisting Synthesizing customer feedback efficiently Synthesizing Assisting in the creation of impactful marketing assets easily creation If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features How big of a problem is it today? How well does GenAI natively do it? Top-line examples Bottom-line examples GenAI features GenAI features GenAI features How big of a problem is it today? How big of a problem is it today? How big of a problem is it today? How well does GenAI natively do it? How well does GenAI natively do it? How well does GenAI natively do it? Top-line examples Top-line examples Top-line examples Bottom-line examples Bottom-line examples Bottom-line examples Automated summarization Low Very well Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Automated summarization Automated summarization Automated summarization Low Low Very well Very well Publishers, Content platforms Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Business reporting, Meetings, and internal communication, Knowledge Management Creation High Good enough Marketing businesses, content creation platforms Documentation, internal communication Creation Creation Creation High High Good enough Good enough Marketing businesses, content creation platforms Marketing businesses, content creation platforms Documentation, internal communication Documentation, internal communication Synthesis of information High Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Synthesis of information Synthesis of information Synthesis of information High High Very well Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Learning and Development, Business Analytics, Research, Feedback processing Search in natural language Medium Very well Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Search in natural language Search in natural language Search in natural language Medium Medium Very well Very well Broad applicability Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions High Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Automated Actions Automated Actions Automated Actions High High Additional skills and fine-tuning to be added for each new use-case Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers.

Make Your GenAI Idea Obvious to Businesses

আপনার GenAI আইডিয়াকে ব্যবসার কাছে সুস্পষ্ট করুন

Machen Sie Ihre GenAI-Idee für Unternehmen offensichtlich

Haga que su idea GenAI sea obvia para las empresas

Rendez votre idée GenAI évidente pour les entreprises

अपने GenAI आइडिया को व्यवसायों के लिए स्पष्ट बनाएं

GenAIのアイデアを企業にわかりやすく伝える

귀하의 GenAI 아이디어를 기업에 분명하게 알리십시오

Torne sua ideia GenAI óbvia para as empresas

Сделайте свою идею GenAI очевидной для бизнеса

GenAI Fikrinizi İşletmelere Açık Hale Getirin

Làm cho ý tưởng GenAI của bạn trở nên rõ ràng với doanh nghiệp

让你的 GenAI 理念对企业显而易见

Are DEXs Finally Become the Bedrock of Crypto Trading?

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases.

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