Sintecsys

sintecsys

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, with compromised reviews, are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series Apparently, South America startups did incredibly well in that regard. With so many, until now, uncrowned jewels, it’s time we present to you... * drumroll* 🥳 The South America Startups of the Year Winners! Santiago, Chile Chile is known for being the most stable country to do business in Latin America, ranking 13th in The Economist’s Business Environment Index. Moreover, according to the Global Innovation Index, Chile ranks first in Latin America for innovation and is recognised as Chilean Silicon Valley with Santiago being the central city. The winner is…. QCObjects It is the 1st world class open-source framework made in Latin-America. São Paulo, Brazil And the winner of Brazil’s vibrant financial center is... Innvo Digital Their mission is to create the best ecosystem for a technology professional to work and undertake while sharing it with our customers and society. São Carlos The absolute leader of Sao Carlos seems to be: Predify Predify is an online software for optimizing pricing proccess and improving profits with AI and Big Data Analytics. Belo Horizonte, Brazil With a more than noble goal, Belo Horizone’s winner is: CM Tecnologia CM Tecnologia is an online scheduling system for healthcare providers in Brazil. Porto Alegre, Brazil With the focus on data protection, Porto Alegre, Brazil’s winner is…. Privacy Tools Privacy compliance platform, based on AI/Blockchain, which helps global companies to keep compliant with the data protection requirements. Brasil, Brazil With the focus on EdTech, Brasil is bringing us: Awari Awari is a YC-W21 company focusing on education for the Tech Industry. The company is growing at a fast pace and has gathered experts from all leading tech companies into a full education experience to prepare the next cohort of tech talent. Quito The capital of Ecuador, and also the closest capital to the actual equator, as the winner brings us: Agrodemanda The most comprehensive agricultural directory in Ecuador. Rio De Janeiro, Brazil Rio de Janeiro has more than 300 startups and is ranked 3rd in Brazil in terms of startup ecosystem development. The city is famous for the startups in the spheres of education, energy & environment and foodtech. Their winner is…. Bybrand Bybrand is the simplest way to save time and avoid frustration when managing your employee's email signatures. Buenos Aires, Argentina Buenos Aires, Argentina is not only the cosmopolitan capital with a vibrant history. It’s also Latin America’s most important port. Which makes their winner actually a very logical choice. Please meet: Liftit Liftit is the leading technology platform to automate and execute last-mile truck deliveries across Latin America. Curitiba, Brazil With a more noble mission and sustainability in mind, Curitiba, Brazil, as the winner brings us... Discrepante is the marketplace for a more human and sustainable economy Discrepante.com Florianópolis, Brazil Would you really be surprised one of the Brazil’s winners is related to football training? We neither. Please meet... Fútbol Fit Club Football FitClub® is a fitness training firm that gives fitness training to improve like a professional football player. Medellín, Colombia And the first Colombian Startup Winner is is a data intelligence solution powered by a high quality team. Meet... LogComex Market intelligence for international trade in Latin America Montevideo, Uruguay The host of inaugural FIFA world cup of 1930 comes through when it comes to innovation even in 2021. I’d like to introduce you to: Drop Technologies First electronic hand antiperspirant device Lima, Peru Lima, Peru, has in 2021 delivered some amazing results when it comes to blockchain technology. Their winner is very well deserved: RSK Labs RSK is the first open-source smart contract platform São Carlos Do Pinhal Well this is a unique case. The absolute unanimous winner of São Carlos Do Pinhal is: brain4care brain4care solution enables monitoring intracranial compliance noninvasively anywhere Blumenau, Brazil We’re sensing a theme here. Another stellar EdTech winner is coming from Blumenau, Brazil. Please meet... Uniasselvi Uniasselvi is a Brazilian distance-learning company that offers more than 60 undergraduate and postgraduate courses, in person and distance. Bogotá, Colombia Startups of Bogotá, Colombia definitely showed they’re not to be underestimated. With a very noble mission, let me introduce you to: Risü Risü is an online platform that links online consumption to donation to social institutions. Campina, Brazil If there’s one typo of business we’d single out as taking the biggest hit out of the pandemic, it would have to be small businesses. Thankfully, the winner of Campina, Brazil, operates with small businesses in mind Superlógica Tecnologias Ltda. Superlogica is a management system company created for small service businesses. Cerqueira César, Brazil Swooping half the points from the entire City, this year’s winners of Cerqueira César are InfoPrice INFOPRICE is a business intelligence company focused on physical retail pricing. Governador Valadares, Brazil The same can be said for the winners here. Let’s hear it for: Grupo KPG Saas, Payment, Manufacture, Pharmaceutical, E-commerce, Marketplace, Logistics Caxias Do Sul Known for wine making and Italian cultural influence, Caxias Do Sul brings as an interesting winner. Studio 360 Studio 360 is a proptech focused on easing the purchase experience for real state companies, brokers, and costumers. Sorocaba From Sorocaba, we have an interesting concept coming. Looking forward to seeing it further develop. Let’s meet: Quinto Quinto is a social network that represents the collective opinion of society—to help democracy evolve through technology Viña Del Mar To be a venue-oriented business and survive through the pandemic definitely deserves some recognition. In the name of that, let’s meet: SonicPollen SonicPollen, based in Chile, is a web-based booking platform that connects bands with venues and organizers. Lages Learn more about high-growth startups in Lages, South America. and the winner is…. Qualiall Qualiall develops technologies for training in operational processes such as work security, equipment maintenance, and more. Córdoba, Argentina Another gem from Argentina brings us unique agricultural solutions. Please meet: Kilimo Kilimo is a Big Data solution for water management in agriculture. São José Dos Campos, Brazil Another great agricultural solution comes this time from Brasil. Meet São José Dos Campos Startup winner: TerraMagna TerraMagna is a fintech company that focuses on risk mitigation for agribusinesses. São Leopoldo, Brazil Event-oriented businesses that have survived in the previous years should definitely bottle their perseverance and start selling it. One of those is definitely this winner: Wedy Wedy offers a one-stop-social marketplace for weddings. Pelotas, Brazil Medical scheduling platforms are definitely something we need to see more of. So glad Pelotos winner comes from this particular niche. Fácil Consulta Fácil Consulta is a booking platform that allows people to schedule an appointment with a Specialist Physician in their area. Santa Maria Could you believe how versatile the startups of South America are when we are only now coming across a game development studio? Let’s meet them Imgnation Studios Imgnation Studios is a game development studio that focuses on virtual reality. Asunción, Paraguay A FinTech solution coming from Paraguay. Meet Asunción Startup of the Year winner Vision Banco Vision Banco is a solid and growing financial institution serving the financial needs of the micro, small and medium enterprises. Caracas Venezualan food delivery app that seemed to have your heart. Meet Caracas Startup of the Year winner Yummy Inc. First Food Delivery App in Venezuela Recife A suitable combo between gaming and education, Recife Startup of the Year winner brings revolutionary solutions. Let’s meet them. Joy Street Joy Street offers develops video games focused on educational needs. Rosario, Argentina The winner of Rosario Startup of the Year award for 2021 is... Gbot Gbot is an autonomous robot that controls weeds without using agrochemicals. We also collect data in fields and generate precision maps. Vila Olímpia, Brazil The winner of Vila Olímpia Startup of the Year award for 2021 is... Seekr Seekr operates in the Information Technology sector, providing tools for managing and monitoring of brands. Barueri The winner of Barueri Startup of the Year award for 2021 is.... Glic Brazilian mhealth (web/app) platform for diabetes management Brasília The winner of Brasília Startup of the Year award for 2021 is... Ticies Ticies is a social network that helps people plan where to go with their friends, relatives and acquaintances, in all cities of the world. Juiz De Fora The winner of Juiz De Fora Startup of the Year award for 2021 is... Qranio Qranio is a gamified mobile learning platform for enterprises that rewards learning and stimulates users to engage with educational content. La Paz The winner of La Paz Startup of the Year award for 2021 is... Jukumari Advice 360 photo based market place for responsible tour operators Temuco The winner of Temuco Startup of the Year award for 2021 is... GoFeels GoFeels is the first independent hospitality network in the world. We promote collaborative work among hotels around the world. Vitacura The winner of Vitacura Startup of the Year award for 2021 is... MeatMe MeatMe delivers meats and accessories to have a culinary experience. Barranquilla The winner of Barranquilla Startup of the Year award for 2021 is... Mandaos Mandaos is a digital platform that connects companies and people with a network of independent messengers Ceará The winner of Ceará Startup of the Year award for 2021 is... Caroá Caroá is a cooperative that sells 34 varieties of vegetables, organic fruits, and vegetables. Chapecó The winner of Chapecó Startup of the Year award for 2021 is... HortiAgro HortiAgro is a seed and fertilizer market. Chile Chico The winner of Chile Chico Startup of the Year award for 2021 is... Gleengo Gleengo connects children with their favorite toys, through a tiny device and an application based on text-to-voice. Goiânia The winner of Goiânia Startup of the Year award for 2021 is... Auvo Auvo is an external teams management software. Itá The winner of Itá Startup of the Year award for 2021 is... TISSUELAB Tissuelab is a research and development center focused on biotechnology, and systems for reconstructive musculoskeletal surgery. Jardim Paulista The winner of Jardim Paulista Startup of the Year award for 2021 is... DiviCom DiviCom is a digital video networking company that designs, integrates, and markets complete systems. João Pessoa The winner of João Pessoa Startup of the Year award for 2021 is... YupiPlay YupiPlay develops and offers an educational gaming platform. La Reina The winner of La Reina Startup of the Year award for 2021 is... INTI-TECH INTI-TECH is the fastest growing renewable energy in Chile and the world's leading unconventional source. Leme The winner of Leme Startup of the Year award for 2021 is... EcoStart EcoStart focuses on manufacturing sustainable electric bicycles, fans, and accessories. Loja The winner of Loja Startup of the Year award for 2021 is... Enerwi Corporation Keeping people connected and secured through wireless charging novel solutions Manizales The winner of Manizales Startup of the Year award for 2021 is... Porter Metrics Porter Metrics has developed an automated solution for generating social media reports in minutes. Nova Lima The winner of Nova Lima Startup of the Year award for 2021 is... Biomimetic Solutions Biomimetic Solutions is an innovative tissue engineering company. Paraná The winner of Paraná Startup of the Year award for 2021 is... Laura Laura is a provider of artificial intelligence and predictive analytics platform for hospitala and clinics to prevent damage & reduce costs. Salvador The winner of Salvador Startup of the Year award for 2021 is... Sanar Sanar is a platform that focuses on being the doctor's guide and support, offering specialized content that helps in their development. Santa Catarina The winner of Santa Catarina Startup of the Year award for 2021 is... DrPOCKET Sunchales The winner of Sunchales Startup of the Year award for 2021 is... Eolo Pharma Eolo Pharma focuses on developing drugs targeting the molecular and cellular basis of inflammation-related diseases Valparaiso The winner of Valparaiso Startup of the Year award for 2021 is... Invitta Invitta transforms plastic waste into a high-performance construction material for manufacturing sustainable urban furniture. Vila Velha The winner of Vila Velha Startup of the Year award for 2021 is... OneGrid OneGrid is a developer of hardware and software solutions focused on energy management. Indaiatuba The winner of Indaiatuba Startup of the Year award for 2021 is... DWUL Energy Renewable Energy Campinas, Brazil The winner of Campinas Startup of the Year award for 2021 is... Ativy Ativy is an IT company specialized in cloud computing, data centers, IT outsourcing, service desk, and IT management. Las Condes, Chile The winner of Las Condes Startup of the Year award for 2021 is... # MuvPass Subscription services that allows it's users unlimited access to fitness classes in the best gyms and studios in Latin America. Pinheiros, Brazil The winner of Pinheiros Startup of the Year award for 2021 is... GuiaBolso GuiaBolso is a personal finance platform that empowers people to take control of financial well-being. Rio, Brazil The winner of Rio Startup of the Year award for 2021 is... EmCasa EmCasa is a vertically-integrated platform that helps customers buy and sell a home in Latin America, starting in Brazil. Guayaquil The winner of Guayaquil Startup of the Year award for 2021 is... Sabroso Online platform for LatAm's enterprises to order low-cost meals Jundiaí The winner of Jundiaí Startup of the Year award for 2021 is... Sintecsys Sintecsys offers a fast and effective detection, reducing losses and costs with fire brigades. Minas Gerais The winner of Minas Gerais Startup of the Year award for 2021 is... Rizoflora Biotechnology Rizoflora Biotechnology is a producer of biological product designed for nematodes control. São Caetano Do Sul The winner of São Caetano Do Sul Startup of the Year award for 2021 is... Wevo 1 iPaaS in Latam - Enabling companies to be truly digital companies with APIs, systems and data integrated into a single platform Canoas The winner of Canoas Startup of the Year award for 2021 is... ContaÁgil ContaÁgil facilitates and reduces bureaucracy in accounting routines of companies. Chile The winner of Chile Startup of the Year award for 2021 is... Unight Unight is the future of nightlife social media. Israelândia The winner of Israelândia Startup of the Year award for 2021 is... Matchpool Matchpool is a platform that creates human connections. La Plata The winner of La Plata Startup of the Year award for 2021 is... Pixivus Pixivus offers a community-based application that enables its users to collect, store, and share pictures of events. Novo Hamburgo The winner of Novo Hamburgo Startup of the Year award for 2021 is... Devorando Devorando offers an online food ordering system and menus available in restaurants based in Brazil. Pernambuco The winner of Pernambuco Startup of the Year award for 2021 is... Redu Redu is a distant learning platform which is built on a social networking structure. Providencia The winner of Providencia Startup of the Year award for 2021 is... # TrainFES TrainFES is a medical Devices for the rehabilitation of people with paralysis. Ribeirão Prêto The winner of Ribeirão Prêto Startup of the Year award for 2021 is... AllyHub Ally is a sales distribution channel technology that connects Schools and Service Providers with Education Agencies. Santa Cruz De La Sierra The winner of Santa Cruz De La Sierra Startup of the Year award for 2021 is... Mobi Latam Mobi Latam is a clean energy startup that focuses on transportation systems based on electric mobility. Santana De Parnaíba The winner of Santana De Parnaíba Startup of the Year award for 2021 is... MercadoLivre.com is an e-commerce technology company providing solutions to individuals and companies buying, selling, and more online. MercandoLivre.com More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written/compiled by Shreya Kundu and Katarina Andrejević! If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in South America

Wildfires are more and more present in modern society, mainly caused by heat waves, lightning, droughts, climate change, or even human actions like car fires, or cigarette butts. We've seen it everywhere recently Brazil, Australia, United States, Canada, etc., destroying plant, human, and animal life, property damage, and contributing to global warming through the high amount of CO2 produced. But thanks to AI, we may be able to spot these fires much sooner and take action sooner. Here's how artificial intelligence can be used to reduce fire detection time from an average of 40 minutes to ! less than five minutes Watch the video References ►Read the full article: ►My Newsletter (A new AI application explained weekly to your emails!): ►Syntecsys, 2021: ►Odemna article " ", December 2020: ►Odemna's article " ", 2021: https://www.louisbouchard.ai https://www.louisbouchard.ai/newsletter/ https://umgrauemeio.com/ Artificial Intelligence For Wildfires: Scaling a Solution To Save Lives, Infrastructure, and The Environment https://omdena.com/blog/artificial-intelligence-wildfires/ Leveraging AI to fight wildfires https://omdena.co1m/projects/ai-wildfires Video Transcript 00:00 Wildfires are more and more present in modern society, mainly caused by heat waves, lightning, 00:06 droughts, climate change, or even human actions like car fires and cigarette butts. 00:11 We've seen it everywhere recently Brazil, Australia, United States, Canada, etc., destroying 00:16 plant, human and animal life, property damage, and contributing to global warming through 00:22 the high amount of CO2 produced. 00:24 These countries all have walls of videos like this in the county’s fire emergency where 00:29 they can see if something is going on. 00:31 The most common problem is that they are spotted too late and already widely spread out. 00:37 This is because you cannot have somebody staring at that wall all day, waiting to spot smoke 00:42 or fire. 00:43 And now you see where this is going; that's where artificial intelligence comes into play. 00:47 Using a good enough AI, you can have something even better: 00:51 it will be staring at all of these cameras simultaneously all day and will automatically 00:56 ping the authorities as soon as it detects something weird within a split second. 01:01 The best thing is that it can save the video frames with the suspicious smoke and send 01:06 it with this ping along with recommended fighting action, making the process much more efficient, 01:11 and the worst case being a false alert that authorities decide to ignore. 01:16 It's such a cool and practical application of AI, and it has already been deployed in 01:20 the real world! 01:21 Indeed, such an AI-based system has been running in Brazil for the past three years, and it 01:27 reduced fire detection time from an average of 40 minutes to less than five. 01:32 This system, built by a Brazilian company called Sintecsys, started using cameras installed 01:37 on top of 50 towers distributed in Brazil. 01:41 With the help of Omdena’s AI community, where many teams were assembled to attack 01:45 this task, they managed to build the best AI model for this use case. 01:50 The main problems they had to face are that 1. 01:52 the images sent to the model came from different times of the day. 01:56 Meaning that not only the luminosity will be different between day and night, which 01:59 is a massive factor for a model since it affects and changes the whole image giving it a hard 02:04 time to understand what's going on, but also that day fires are easily detected 02:09 through smoke while night fires are much more easily detected through live fire, due to 02:14 obvious reasons. 02:15 To attack this issue, teams either could build two separate models, one for the night and 02:20 one for the day, or build one larger model and assume that 02:24 the smoke is also detectable during night time. 02:27 The latter could work with sufficient training data and parameters to learn from this data. 02:32 Of course, the first way is problematic since there is still the sunset and dawn problem 02:37 where both live fire and smoke could be detected. 02:40 They do not mention how they decided to build the final model, but both were tested by different 02:45 teams from the Omdena's AI community. 02:48 In your opinion, what would you say could be the best solution for this situation? 02:51 I would assume a large enough model would be their best shot to fix the dawn and sunset 02:56 boundary problem without training a model for each sub-case. 03:00 They had to face a second problem: differentiate real smoke and 'smoke-like' anomalies such 03:05 as camera glare, fog, clouds, and smoke released from boilers that appear in the images. 03:11 The final problem was the low definition of the images they received from the cameras. 03:16 This model initially received heavily compressed images sent from the cameras, so they had 03:20 to upscale it before sending it to their model. 03:23 As you know by now, AI is hugely data dependant, so they had to have the best training data 03:28 possible in terms of quality and quantity to solve these problems successfully. 03:33 Such a model can only be as good as the data it is given during its training, so it had 03:38 to be very broad and contain all possible artifacts that may appear in the real world, 03:43 like clouds, fog, and camera glare we just discussed. 03:46 To start, they had 20 people manually labeling 9'000 images as precisely as possible. 03:53 This means that they manually went through all the images painting over the smoke to 03:57 help the model understand what smoke looks like. 04:00 This is undoubtedly the most expensive and tedious task, but it's crucial to build most 04:05 deep learning-based models used in real-world applications. 04:08 If you are not familiar with data annotation, I invite you to watch this short video I made 04:15 last year explaining it. 04:16 After doing so, they could start diving into how to attack the smoke detection, which means 04:21 finding the best way to detect whether there is smoke or not in the picture. 04:26 We do not have the details on the exact chosen model. 04:29 Still, they shared that they ended up using a convolutional neural network (CNN) approach 04:34 with some modifications to the images before they send them to the network. 04:38 As you may be aware, CNNs are a powerful deep learning architecture for vision-based applications 04:44 in which, simply said, the image is iteratively compressed, 04:48 focusing on the information we need about the image while removing redundant and uninformative 04:53 spatial features, ending up with a confidence rate informing us whether the image contains 04:58 what we were looking for or not. 05:01 This focus can be on anything, from detecting cats, humans, objects, to detecting smoke 05:07 in this case. 05:08 It all depends on the data it is trained on, but the overall architecture and working will 05:12 stay the same. 05:13 You can see CNNs as compressing the image, focusing on a specific feature of the image 05:18 at every step, getting more compressed and relevant to what we want the deeper we get 05:23 in the network. 05:24 This is done using filters that will go through the whole image, putting its focus on specific 05:29 features like edges with specific orientations. 05:32 This process is repeated with multiple filters making one convolution, and those filters 05:37 are what is learned during training. 05:40 After the first convolution, we get a new smaller image for each filter, which we call 05:45 a feature map, each of them focusing on specific edges or features. 05:51 So they will all look like a weird and blurry zoomed version of the image giving an accent 05:56 on specific features. 05:57 And we can use as many filters as needed to optimize our task. 06:01 Then, each of these new images is sent to the same process repeated over and over until 06:07 the image is so compressed that we have a lot of these tiny feature maps optimized on 06:12 the information we need adapted for the many different images our dataset contains. 06:18 Lastly, these tiny feature maps are sent into what we call "fully connected layers" to extract 06:23 the relevant information using weights. 06:25 These last few layers contain all connected weights that will learn which feature the 06:30 model should focus on based on the images fed and pass the information forward for our 06:35 final classification. 06:37 This process will further compress the information and finally tell us if there is smoke or not 06:43 with a confidence level. 06:44 So assuming the model is well trained, the final results would be a model focusing its 06:49 compression on smoke features in the image, which is why it is so appropriate to this 06:53 task or any task involving images. 06:56 If there is smoke, the filters will produce high responses, and we will end up with a 07:01 network telling us that there is smoke in the image with high confidence. 07:05 If there is no smoke, these compression results will produce low responses, letting us know 07:09 that nothing is going on in the picture regarding what we are trying to detect, which is a fire 07:15 in this case. 07:16 It will also produce results with a confidence rate anywhere in between no smoke and evident 07:20 smoke. 07:21 As they shared, the final model detected smoke in images with an impressive 95% to 97% accuracy! 07:28 Of course, the model isn't perfect yet, and as I said, it is very dependant on its training 07:33 data like most deep learning-based approaches. 07:36 This means that it may not be as good when trying to use this same model on different 07:40 types of environments, and we may need to use more data to adapt the model to the new 07:45 environment. 07:46 Fortunately, there are many ways to adapt a model with very little available data, which 07:51 we call fine-tuning a model, and it's done with new data different from the ones used 07:55 in training. 07:56 So, starting with this strong baseline they have, you won't need to label 9'000 images 08:01 for any new country you want to run your model on. 08:04 For example, this model trained on Brazilian forest images may need to be trained again 08:09 on a few hundred to a few thousand more images from Canadian forests if we would like to 08:14 use it in Canada. 08:16 This is an amazing real-world application of machine learning with a great use case 08:20 that will benefit everyone, especially these days with many wildfires around the world. 08:25 Before ending this video, are there any environment-related applications where you would see AI could 08:30 help? 08:31 Let me know in the comments, and I will look for it to cover any of them! 08:34 As always, you can find my blog article and newsletter in the description below, where 08:38 I send out a new AI application every week and more. 08:42 Thank you for watching.

How AI Can Spot Wildfires Faster Than Humans 

Apresentarei aqui algumas abordagens que ajudam( ou podem ajudar) no desempenho e qualidade de seu Front End. Estas ideias foram fundamentadas em minha experiência profissional e no curso de Arquitetura de Software. Serei mais sucinto possível. Qualquer dúvida entrem contato comigo. Antes de entrar nas dicas É importante antes de iniciar ter em mente o funcionamento da Web e da comunicação do HTTP. Pesquisem rapidamente sobre Three Way Handshake, renderização de conteúdo nos browser web. Responsabilidade do HTML, CSS e JS. Vamos as Dicas 1 — Mantenha seu HTML, CSS e JS em arquivos separados; 1.1 — Facilitamos o processo de manutenção de nosso Front End; 1.2 — Conseguimos manter o princípio da responsabilidade única; 1.3 — Facilidade em adotar ferramentar de transpilação de CSS (SASS e LESS) e Bundles (Webpack). Outros formas de utilizar os recurso no HTML: Inline e Embedded. 2 — CSS no início e JS no fim do HTML. 2.1 — Coloque o CSS no início para que a página não carregar inicialmente sem estilo e coloque o JS no final para que a página não se torne lenta em sua primeira resposta; O fonte fica assim: Browser Diet Estilo no início e JS no fim < > html < > head < = > meta charset "UTF-8" < > title </ > title  < = = = > link rel "stylesheet" href "/css/siteCSS.css" media "all" </ > head < > body < > p </ > p   < = > script src "/js/siteJS.js" </ > script </ > body </ > html Indo mais além: uma outra abordagem é utilizar a requisição assíncrona para o JS. Isso faz com que a página não espera o retorno do arquivo para continuar a renderização. (<script async src=”siteJS.js”></script>) 3 — Sempre minifique para produção Ele faz com que um texto legível para nós se torne uma versão menos verbosa e com isso realizar a diminuição dos arquivos( remoção de comentários, redução de nomes de variáveis, remoção de quebra de linhas, etc). 3.1 — Transporte de arquivos menores; 3.2 — Maior velocidade no carregamento pelo Browser; 3.3 — Menor carga de dados na rede e no servidor de aplicação; Dica: pesquise por ferramentas de minificação. 4 —Sempre bundling para produção O processo se resume em criar um (ou alguns) arquivos a partir de vários outros. 4.1 — Redução da quantidade de requisições HTTP ao servidor de aplicação; 4.2 — Melhoria desempenho da página web; Ferramenta: (querido) Webpack. 5 —Imagem Sprites “Cria uma imagem com todas dentro”.Observe o quadro abaixo, ele será a melhor explicação dessa técnica: 5.1 — Redução de requisições HTTP; 5.2 — Redução no tempo de carregamento do site; Ferramenta: vale testar o processo com estas ferramentas https://css.spritegen.com/ , https://www.toptal.com/developers/css/sprite-generator/ . 6 — Remoção de metadados de imagens As imagens possuem metadados que não influenciam na funcionalidade delas para os websites( modelo da câmera, data, local). 6.1 — Diminuição do tráfego na rede; 6.2 — Carregamento mais rápido das imagens; Exemplo de ferramentas: https://www.verexif.com/en/ , https://www.exifremove.com/ . E por fim Existem muitos outras técnicas para melhorar suas páginas web, porém comece com estas pois elas resultarão em grande mudanças na organização e no desempenho do seu site. Não existe bala de prata e além disso sempre vale analisar se para o projeto em questão o aumento da complexidade justifica os ganhos. Referências: http://lesscss.org/ https://sass-lang.com/ https://browserdiet.com/ https://webpack.js.org/

Dicas para ter um “bom” Front End

is the market leading issue-tracking and agile project management tool, and is in heavy use at companies around the globe. Atlassian JIRA For I wanted to build a slick integration to to help my customers to search their JIRA issues alongside their Slack messages, Trello cards, files, GitHub issues and emails. CTX, my team search app JIRA When it came to actually building the integration, I was pleasantly surprised by the , and especially the and the simple integration patterns it promotes. APIs available Atlassian Connect API While there’s — and even a short , I couldn’t find a single, simple tutorial that covered Connect, the webhook interface and the main API in one place. lots of documentation blog series about integrating Bitbucket with a Clojure application JIRA So I wrote one. . The full source of the application we’re building is available on GitHub here What are we building? We’re going to build a JIRA integration in Clojure, that listens for issues being created and mutated and creates a basic activity feed of changes. To do this, we need a Clojure web application, listening on an HTTPS endpoint.  We’ll register the application with JIRA, and every time a user creates or updates a ticket, JIRA will send us an event on a Webhook. We’ll record these in an in-memory database, and write a little front-end application that polls our app to display a list of changes to issues. What is Atlassian Connect? Connect provides a simple framework for integrating with Atlassian applications like JIRA and Confluence. It leverages OAuth2 and JWT, along with some simple integration patterns, to give developers a nice and consistent way to talk to the Atlassian API suite. In its’ most basic form, all you need to do is publish a Descriptor listing the endpoints you provide and the services you require, point your Atlassian application at it, and then deploy some code on the listed endpoints that talks HTTP / JSON. Pre-conditions First, install , and make sure it’s in your PATH.  And get yourself a Java JDK if you haven’t got one. Leiningen Check it all works: $ lein --versionLeiningen 2.8.1 on Java 9.0.4 Java HotSpot(TM) 64-Bit Server VM Basic project setup Let’s create a basic Clojure app skeleton using the . Compojure template $ lein new compojure connect-example$ cd connect-example$ ls-rw-r--r--  1 rory  Users  273 20 Mar 16:21 README.md-rw-r--r--  1 rory  Users  483 20 Mar 16:21 project.cljdrwxr-xr-x  3 rory  Users  102 20 Mar 16:21 resources  drwxr-xr-x  3 rory  Users  102 20 Mar 16:21 src  drwxr-xr-x  3 rory  Users  102 20 Mar 16:21 test$ lein ring server-headlessRetrieving lein-ring/lein-ring/0.9.7/lein-ring-0.9.7.pom from clojars  ...2018-03-20 16:24:40.501:INFO:oejs.Server:jetty-7.6.13.v20130916  2018-03-20 16:24:40.550:INFO:oejs.AbstractConnector:Started SelectChannelConnector@0.0.0.0:3000 In a second tab, use to test it's running: curl $ curl http://localhost:3000Hello World Great! We’ve got a running app. Import basic dependencies Now we need to add some libraries to our Clojure project to let us do the basics of HTTP comms, the Connect flow and so on. Open up the file in an editor of your choice, and make it look like this: project.clj (defproject connect-example "0.1.0-SNAPSHOT"  :min-lein-version "2.0.0"  :dependencies [[org.clojure/clojure "1.9.0"]                 [compojure "1.6.0"]                 [ring/ring-defaults "0.3.1"]                 [ring/ring-json "0.4.0"]                 [clj-connect "0.2.4"]]  :plugins [[lein-ring "0.9.7"]]  :ring {:handler connect-example.handler/app}  :resource-paths ["resources"]  :profiles  {:dev {:dependencies [[javax.servlet/servlet-api "2.5"]                        [ring/ring-mock "0.3.0"]]}}) The libraries in the clause give us HTTP serving ( ), routing ( ) and a simple wrapper around the Atlassian Connect API & underlying JWT technology ( ) :dependencies ring compojure clj-connect Connecting to JIRA JIRA will only talk to our application over HTTPS, so the first hurdle is how to get it on the internet. Without setting up a server, or using something like Heroku, this can be annoyingly time consuming, so here’s a simple way round it: . ngrok ngrok lets you create an externally accessible SSL endpoint to test your integration software. You just run it like this; $ ngrok tls 3000ngrok by @inconshreveable    Session Status                online  Account                       Rory Gibson  Version                       2.2.8  Region                        United States (us)  Web Interface                 http://127.0.0.1:4040  Forwarding                    http://9a69173f.ngrok.io -> localhost:3000  Forwarding                    https://9a69173f.ngrok.io -> localhost:3000Connections                   ttl     opn     rt1     rt5     p50     p90                                0       0       0.00    0.00    0.00    0.00 and it will create a tunnel, and give you a URL — e.g. - you can use to access your software, running on your development machine but accessible over SSL on the internet. https://9a69173f.ngrok.io Now that we have a service that’s discoverable externally, we can generate our OAuth credentials in JIRA. Serving the Descriptor Now we’ve got all the plumbing sorted, we can serve up the Connect Descriptor. This is a simple JSON file that declares the application’s capabilities and dependencies, so that JIRA (and other Atlassian applications) know how to talk to it. Create a file at and paste the following content into it (ensure that the baseUrl property matches your ngrok URL): <project>/resources/public/connect.json {    "key": "connect-example",    "name": "connect-example",    "description": "Demo JIRA / Clojure integration",    "baseUrl": "https://9a69173f.ngrok.io",    "authentication": {        "type": "jwt"    },    "lifecycle": {        "installed": "/installed"    },    "modules": {        "oauthConsumer": {            "clientId": "PLACEHOLDER"        },        "webhooks": [            {                "event": "*",                "url": "/recv"            }        ]            },    "scopes": ["READ"]} Handling lifecycle callbacks The Connect API works by loading your descriptor, then making a call to on your app as a "lifecycle callback" (there are others, like Uninstalled, that we're ignoring here). /installed All it wants is a 200 response. We’ll use Compojure and Ring to create a simple handler that serves this function (and provides basic API plumbing) (ns connect-example.handler  (:require [compojure.core :refer :all]            [compojure.route :as route]            [ring.middleware.defaults :refer [wrap-defaults api-defaults]]            [ring.middleware.json :refer [wrap-json-body wrap-json-response]]            [ring.util.response :refer [response]]))(defroutes app-routes  (POST "/installed" [] {:status 200 :body "OK"}))(def app  (-> app-routes    wrap-json-body    wrap-json-response    (wrap-defaults api-defaults))) Now that we have this much, we can wire it up to JIRA in the Cloud. Save everything and restart the app ( ) lein ring server-headless Go and login to your JIRA instance and click (the cog icon) . Settings > Add-Ons Go to the tiny link at the bottom of the page. Settings and ensure that and are both ticked. Enable development mode Enable private listings Now you should see a link to — click this and paste in the fully-qualified URL to your descriptor (e.g. ) Upload add-on HTTPS https://abcd1234.ngrok.com/connect.json Handling events Once Connect has hooked up to your app, you’ll start receiving the events we specified in the Descriptor whenever they occur.  All we need is some handlers and some logic on our side. Add 2 routes to the routing block — one for JIRA to call when an event happens, and one for us to call to list events. (POST "/recv" req (recv req))(GET "/events" req (response (events req))) and the functions for them to call. gets the payload, extracts the data we want and stores it in an in the server (instead of a DB, this is only a demo :) recv atom (def event-store (atom '()))(defn persist!  "Save event to local in-memory structure"  [e]  (swap! event-store conj e)  (println e) ;; debug info for development  e)(defn transform  "Get the fields we care about from the JIRA payload"  [e1]  (let [id        (-> e1 :issue :key)        user      (-> e1 :user :displayName)        timestamp (-> e1 :timestamp)        type      (-> e1 :webhookEvent)        summary   (-> e1 :issue :fields :summary)]    {:user user :id id :timestamp timestamp :summary summary :type type}))(defn handle-event  [raw]  (-> raw    clojure.walk/keywordize-keys    transform    persist!))(defn events  [_]  @event-store)(defn recv  "Handler function for all events"  [req]  (handle-event (:body req))  {:status 200 :body "OK"}) Restart your app Hit then again. Ctrl-C lein ring server-headless If you change an Issue in your JIRA (say by moving it from To Do to Done in the agile view) you should see some output in your terminal as JIRA calls our app and we save the data, and dump it to screen with the above. println Hit the endpoint to check we're saving the data and you should see something like this: /events $ curl http://localhost:3000/events[{"summary":"My issue 1,"timestamp":1521891438142,"type":"jira:issue_updated","id":"TEST-1"}] Adding a UI That’s great! We have an inbound and outbound API.  But most non-developers don’t like to use for everything, so we'd better add a UI. curl We’ll build something in ClojureScript, so that we can use the same paradigms and dialect across front and back-end codebases. $ mkdir -p src/clj$ mv src/connect-example src/clj$ mkdir -p src/cljs/connect-example$ touch resources/public/index.html Now edit your to the following (pulling in the new directories and adding the ClojureScript libraries and build configuration) project.clj (defproject connect-example "0.1.0-SNAPSHOT"  :min-lein-version "2.0.0"  :dependencies [[org.clojure/clojure "1.9.0"]                 [org.clojure/clojurescript "1.10.217"]                 [compojure "1.6.0"]                 [ring/ring-defaults "0.3.1"]                 [ring/ring-json "0.4.0"]                 [rum "0.11.2"]                 [clj-connect "0.2.4"]                 [cljs-ajax "0.7.3"]]  :plugins [[lein-ring "0.9.7"]            [lein-figwheel "0.5.13"]]  :source-paths ["src/clj"]  :resource-pahts ["resources" "target/cljsbuild"]  :ring {:handler connect-example.handler/app}  :profiles  {:dev {:dependencies [[javax.servlet/servlet-api "2.5"]                        [ring/ring-mock "0.3.2"]]}}  :clean-targets ^{:protect false} [:target-path "out" "resources/public/cljs"]  :figwheel {:css-dirs ["resources/public/css"]             :server-port 3000             :ring-handler connect-example.handler/app}  :cljsbuild {              :builds [{:id "dev"                        :source-paths ["src/cljs"]                        :figwheel true                        :compiler {:main "connect-example.core"                                   :asset-path "cljs/out"                                   :output-to  "resources/public/cljs/main.js"                                   :output-dir "resources/public/cljs/out"}                        }]              }) Create and fill in your resources/public/index.html <!DOCTYPE html>  <html>    <head></head>  <body>    <div id="content"></div>    <script src="app.js" type="text/javascript"></script>  </body></html> and ... src/cljs/connect-example/core.cljs (ns connect-example.core)(let [el (.getElementById js/document "content")]  (set! (.-innerText el) "Hello World")) Which will, when it’s loaded, find the with ID and put an informative message into it. div content Now let’s try to run it, using the ; awesome Figwheel plugin $ lein figwheelRetrieving figwheel/figwheel/0.5.13/figwheel-0.5.13.pom from clojars  Retrieving org/clojure/core.async/0.3.443/core.async-0.3.443.pom from central  ...Figwheel: Validating the configuration found in project.clj  Spec Warning:  missing an :output-to option - you probably will want this ...  Figwheel: Configuration Valid ;)  Figwheel: Starting server at http://0.0.0.0:3449  Figwheel: Watching build - dev  Figwheel: Cleaning build - dev  Figwheel: Starting server at http://0.0.0.0:3449  Figwheel: Watching build - dev  Compiling "main.js" from ["src/cljs"]...  Successfully compiled "main.js" in 1.95 seconds.  Launching ClojureScript REPL for build: dev  Figwheel Controls:            (stop-autobuild)                ;; stops Figwheel autobuilder          (start-autobuild [id ...])      ;; starts autobuilder focused on optional ids          (switch-to-build id ...)        ;; switches autobuilder to different build          (reset-autobuild)               ;; stops, cleans, and starts autobuilder          (reload-config)                 ;; reloads build config and resets autobuild          (build-once [id ...])           ;; builds source one time          (clean-builds [id ..])          ;; deletes compiled cljs target files          (print-config [id ...])         ;; prints out build configurations          (fig-status)                    ;; displays current state of system          (figwheel.client/set-autoload false)    ;; will turn autoloading off          (figwheel.client/set-repl-pprint false) ;; will turn pretty printing off  Switch REPL build focus:          :cljs/quit                      ;; allows you to switch REPL to another build    Docs: (doc function-name-here)    Exit: Control+C or :cljs/quit Results: Stored in vars *1, *2, *3, *e holds last exception objectPrompt will show when Figwheel connects to your application Load up a browser tab pointed to and you should see, in your browser, the message "Hello World". http://localhost:3000 Hello World Some React magic ClojureScript web development is heavily focussed on the React ecosystem. Several React bindings exist; one of the easiest to get started with is . Rum Add the following to the key in your : :dependencies project.clj [cljs-ajax "0.7.3"][rum "0.11.2"] Modify so it includes the for Rum and a component definition (which creates a React component under the hood), then mounts it on the document body. core.cljs require (ns connect-example.core  (:require [rum.core :as rum :refer [defc mount]))(defc label [text]  [:div {:class "label"} text])(mount (label "Hello again") js/document.body) Restart Figwheel (Ctrl-C in your terminal then again) and refresh your browser, and that's it - you're seeing a React rendered application! lein figwheel A simple list-based UI Now we have the basic structure to put a React UI on screen, let’s fill it in with something that’s actually useful. To match the wireframe above, we’ll have an H1 for the title: (defc heading []  [:h1 "JIRA Activity Feed"]) And a component to iterate over a list of JIRA issues — so we’ll call the display of each result an “issue”. We’ll create some dummy data to render initially, too. (defc issue  [{:keys [id summary type timestamp] :as issue}]  [:div.issue   [:div.id id]   [:div.info    [:div.title summary]    [:div.detail type]    [:div.date timestamp]]])(defc issues  [is]  [:div.issues   (map issue is)])(def fake-issues '({:id "FOO-1" :summary "The Title" :type "jira:issue_updated" :timestamp 123456789}))(defc content  [data]  [:div.container   (heading)   (if data     (issues data)     [:div "No activity found in feed."])])(mount (content fake-issues) (.getElementById js/document "content")) Save everything and reload your browser and you should see a very basic list and title. It could probably do with some styling, so let’s tweak our as follows: index.html <!DOCTYPE html>  <html>    <head>    <link rel="stylesheet" href="/site.css" />  </head>  <body>    <div id="content"></div>    <script src="cljs/main.js" type="text/javascript"></script>  </body></html> Create a file with some simple styles: resources/public/css/site.css body {    font-family: sans-serif;}.container {  margin-left: auto;  margin-right: auto;  padding-top: 30px;  width: 800px;}div.issue {    font-size: 16px;  width: 600px;  border: solid black 1px;  padding: 1em;  margin-bottom: 1em;}.issue .id {  font-weight: bold;  float: left;  width: 75px;  height: 50px;  text-align: center;  padding: 1em .5em 0 0;}.issue .info {  height: 100%;}.issue .summary {  font-weight: bold;  padding-bottom: 0.5em} Restart again and reload and everything should be perfect. lein figwheel NB: once you get to this stage, Figwheel’s most awesome benefit kicks in — hot auto-reload of ClojureScript and CSS files. Just save in your editor and the browser updates on its’ own! AJAX comms Now let’s get the UI to query the server side for our JIRA information… Add a new so the top of looks like this: require core.cljs (ns connect-example.core  (:require [rum.core :as rum]            [ajax.core :refer [GET]])) and then let’s add a function to fetch and display the data (and actually call that function to do the display) Remove the existing form and add this to the end of : mount core.cljs (defn handler [data]  (mount (content data) (.getElementById js/document "content")))(fetch-issues handler) Finally, let’s get the browser app to poll the server every 5 seconds to update the feed. (js/setInterval  #(fetch-issues handler)  5000) What have we got? We’ve built a dynamically-updating ClojureScript React application that’s showing live changes from JIRA. It turns out it’s surprisingly easy to integrate a Clojure app with JIRA through Atlassian Connect. Evidently, what we’ve built here is only the barest beginnings of a production app; in real life you’d want to check the validity of inbound events using the JWT functionality built into the Connect API, and you’d at least want a proper database of some sort to persist your data into. And remember — the full working source to this application is on GitHub here for your reference: https://github.com/getctx/connect-example One more thing… If you like what you see here, please check out our application, like JIRA, Trello, Slack, GitHub, Google Drive, email and more in one shot. We offer a free trial with no credit card required.  Thanks! CTX, which lets you and your team search your apps Originally published at getctx.io on March 24, 2018.

Building a JIRA integration with Clojure & Atlassian Connect

We’ve heard of , we’ve heard of NeRFs, and we’ve seen these kinds of applications allowing you to recreate someone’s face and pretty much make him or her say whatever you want. deepfakes What you might not know is how inefficient those methods are and how much computing and time they require. Plus, we only see the best results. Keep in mind that what we see online is the results associated with the faces we could find most examples of, so basically, internet personalities and the models producing those results are trained using lots of computing, meaning expensive resources like many graphics cards. Still, the results are really impressive and only getting better. Fortunately, some people like Jiaxian Tang and colleagues are working on making those methods more available and effective with a new model called RAD-NeRF. From a single video, they can synthesize the person talking for pretty much any word or sentence in real time with better quality. You can animate a talking head following any audio track in real-time. This is both so cool and so scary at the same time... Learn more in the video References ►Tang, J., Wang, K., Zhou, H., Chen, X., He, D., Hu, T., Liu, J., Zeng, G. and Wang, J., 2022. Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition. . ►Results/project page: ​​ arXiv preprint arXiv:2211.12368 https://me.kiui.moe/radnerf/ Video Transcript 0:02 [Music] 0:07 we've heard of deep fakes we've heard of 0:09 Nerfs and we've seen these kinds of 0:11 applications allowing you to recreate 0:13 someone's face and pretty much make him 0:15 say whatever you want what you might not 0:17 know is how inefficient those methods 0:20 are and how much Computing and time they 0:22 require plus we only see the best 0:24 results keep in mind that what we see 0:26 online is the results associated with 0:29 the faces we could find most examples of 0:31 so basically internet personalities and 0:34 the models producing those results are 0:36 trained using lots of computing meaning 0:38 expensive resources like many graphics 0:41 cards still the results are really 0:43 impressive and only getting better 0:45 fortunately some people like Jackson 0:47 tang and colleagues are working on 0:49 making those methods more available and 0:52 effective with a new model called red 0:54 Nerf but let's hear that from their own 0:57 model hello thanks for watching the 0:59 supplementary video for our paper 1:00 real-time neural Radiance talking head 1:03 synthesis via decomposed audio spatial 1:05 encoding 1:06 our method is person-specific and only 1:08 needs a three to five minutes monocular 1:10 video for training 1:11 after training the model can synthesize 1:14 realistic Talking Heads driven by 1:15 arbitrary audio in real time while 1:17 keeping comparable or better rendering 1:19 quality compared to previous methods so 1:21 you heard that right from a single video 1:23 they can synthesize the person talking 1:26 for pretty much any word or sentence in 1:28 real time with better quality you can 1:30 animate a talking head following any 1:33 audio track in real time this is both so 1:36 cool and so scary at the same time just 1:39 imagine what could be done if we could 1:40 make you say anything at least they 1:43 still need access to a video of you 1:45 speaking in front of the camera for 5 1:47 minutes so it's hard to achieve that 1:48 without you knowing still as soon as you 1:51 appear online anyone will be able to use 1:53 such a model and create infinite videos 1:56 of you talking about anything they want 1:58 they can even host live streams with 2:00 this method which is even more dangerous 2:03 and makes it even harder to say wetsuit 2:05 or not anyways even though this is 2:08 interesting and I'd love to hear your 2:10 thoughts in the comments and keep the 2:11 discussion question going here I wanted 2:13 to cover something that is only positive 2:15 and exciting science more precisely how 2:19 did they achieve to animate Talking 2:20 Heads in real time from any audio using 2:23 only a video of the face as they State 2:26 their red Nerf model can run 500 times 2:29 faster than the previous works with 2:31 better rendering quality and more 2:33 control you may ask how is that possible 2:36 we usually trade quality for efficiency 2:39 yet they achieve to improve both 2:41 incredibly these immense improvements 2:43 are possible thanks to three main points 2:46 the first two are related to the 2:48 architecture of the model more 2:50 specifically how they adapted the Nerf 2:52 approach to make it more efficient and 2:54 with improved motions of the Torso and 2:57 head the first step is to make nerves 2:59 more efficient I won't dive into how 3:02 Nerfs work since we covered it numerous 3:04 time basically it's an approach based on 3:06 neural networks to reconstruct 3D 3:09 volumetric scenes from a bunch of 2D in 3:11 images which means regular images this 3:14 is why they will take a video as input 3:17 as it basically gives you a lot of 3:19 images of a person from many different 3:21 angles so it usually uses a network to 3:24 predict all pixels colors and densities 3:26 from the camera Viewpoint you are 3:28 visualizing and does that for all 3:31 viewpoints you want to show when 3:32 rotating around the subject which is 3:34 extremely computation hungry as you are 3:37 predicting multiple parameters for each 3:39 coordinate in the image every time and 3:41 you are learning to predict all of them 3:43 Plus in their case it isn't only a Nerf 3:46 producing or 3D scene it also has to 3:49 match an audio input and fit the lips 3:51 mouth eyes and movements with what the 3:53 person says instead of predicting all 3:56 pixels densities and colors matching the 3:58 audio for a specific frame they will 4:00 work with two separate new and condensed 4:03 spaces called grid spaces or grid-based 4:06 Nerf they will translate their 4:08 coordinates into a smaller 3D grid space 4:11 trans laid their audio into a smaller 2D 4:13 grid space and then send them to render 4:16 the head this means they never merge the 4:19 audio data with the spatial data which 4:22 will increase the size exponentially 4:23 adding two dimensional inputs to each 4:26 coordinate so reducing the size of the 4:29 audio features along with keeping the 4:31 audio and spatial features separate is 4:34 what makes the approach so much more 4:36 efficient but how can the results be 4:38 better if they use condensed spaces that 4:40 have less information adding a few 4:42 controllable features like an eye 4:44 blinking control to our grid Nerf the 4:47 model will learn more realistic 4:48 behaviors for the eyes compared to 4:51 previous approaches something really 4:53 important for realism the second 4:55 Improvement they've done is to model the 4:57 Torso with another Nerf using the same 5:00 approach instead of trying to model it 5:02 with the same Nerf used further head 5:04 which will require much fewer parameters 5:07 and different needs as the goal here is 5:09 to animate moving heads and not whole 5:12 bodies since the Torso is pretty much 5:14 static in these cases they use a much 5:16 simpler and more efficient Nerf based 5:18 module that only works in 2D working in 5:21 the image space directly instead of 5:24 using camera arrays as we usually do 5:26 with Nerf to generate many different 5:28 angles which aren't needed for a torso 5:30 so it is basically much more efficient 5:32 because they modified the approach for 5:35 this very specific use case of the rigid 5:37 torso and moving head videos they then 5:40 recompose the head with the Torso to 5:42 produce the final video and voila this 5:45 is how you produce talking head videos 5:47 over any audio input super efficiently 5:50 of course this was just an overview of 5:53 this new exciting research publication 5:55 and they do other modifications during 5:57 the training of their algorithm to make 5:59 it more efficient which is the third 6:01 point I mentioned at the beginning of 6:03 the video if you were wondering I invite 6:05 you to read their paper for more 6:07 information the link is in the 6:09 description below before you leave I 6:10 just wanted to thank the people who 6:12 recently supported this channel through 6:14 patreon this is not necessary and 6:16 strictly to support the work I do here 6:18 huge thanks to artem vladiken Leopoldo 6:22 Alta Murano J Cole Michael carichao 6:25 Daniel gimness and a few Anonymous 6:28 generous donors it will be greatly 6:30 appreciated if you also want and can 6:33 afford to support my work financially 6:35 the link to my patreon page is in the 6:37 description below as well but no worries 6:39 if not a sincere comment below this 6:42 video is all I need to be happier I hope 6:45 you've enjoyed this video and I will see 6:47 you next week with another amazing paper 6:51 [Music]

Efficient NeRFs for Real-Time Portrait Synthesis (RAD-NeRF)

NeRF eficientes para la síntesis de retratos en tiempo real (RAD-NeRF)

रीयल-टाइम पोर्ट्रेट सिंथेसिस (आरएडी-एनईआरएफ) के लिए कुशल एनईआरएफ

リアルタイム ポートレート合成 (RAD-NeRF) のための効率的な NeRF

NeRFs eficientes para síntese de retrato em tempo real (RAD-NeRF)

NeRF hiệu quả để tổng hợp chân dung thời gian thực (RAD-NeRF)

用于实时人像合成的高效 NeRF (RAD-NeRF)

eDiffi, NVIDIA's most recent model, generates better-looking and more accurate images than all previous approaches like DALLE 2 or Stable Diffusion. eDiffi better understands the text you send and is more customizable, adding a feature we saw in a previous paper from NVIDIA: the painter tool. Learn more in the video... References ►Read the full article: ► Balaji, Y. et al., 2022, eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers, ►Project page: ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/ediffi/ https://arxiv.org/abs/2211.01324 https://deepimagination.cc/eDiffi/ https://www.louisbouchard.ai/newsletter/ Video Transcript 0:06 the new state-of-the-art approach for 0:08 image synthesis it generates better 0:10 looking and more accurate images than 0:13 all previous approaches like Delhi 2 or 0:15 stable diffusion either if he better 0:17 understands the text you send and is 0:19 more customizable adding a new feature 0:21 we saw in a previous paper from Nvidia 0:23 the painter tool as they see you can 0:26 paint with words in short this means you 0:29 can enter a few subjects and paint in 0:32 the image what should appear here and 0:34 there allowing you to create much more 0:36 customized images compared to a random 0:39 generation following a prompt this is 0:41 the next level enabling you to pretty 0:43 much get the exact image you have in 0:45 mind by simply drawing a horrible quick 0:47 sketch something even I can do as I 0:50 mentioned the results are not only Sota 0:52 and better looking than stable diffusion 0:55 but they are also way more controllable 0:57 of course it's a different use case as 0:59 it needs a bit more work and a clearer 1:02 ID in mind for creating such a draft but 1:04 it's definitely super very exciting and 1:06 interesting it's also why I wanted to 1:08 cover it on my channel since it's not 1:11 merely a better model but also a 1:13 different approach with much more 1:15 control over the output the tool isn't 1:17 available yet unfortunately but I sure 1:19 hope it will be soon by the way you 1:22 should definitely subscribe to the 1:23 channel and follow me on Twitter at what 1:25 say hi if you like this kind of video 1:27 and would like to have access to easily 1:30 digestible news on this heavily 1:32 complicated field another win which they 1:34 allow you to have more control in this 1:37 new model is by using the same feature 1:39 we saw but differently indeed the model 1:42 generates images Guided by a sentence 1:44 but it can also be influenced using a 1:47 quick sketch so it basically takes an 1:49 image and a text as inputs this means 1:52 you can do other stuff as it understands 1:54 images here they leverage this 1:56 capability by developing a style 1:58 transfer approach where you can 2:00 influence the style of the image 2:02 generation process giving an image with 2:04 a particular style well along with your 2:06 text input this is super cool and just 2:09 look at the results they speak for 2:11 themselves it's incredible beating both 2:14 Sota style transfer models and image 2:16 synthesis models with a single approach 2:18 now the question is how could Nvidia 2:22 develop a model that creates better 2:23 looking images enable more control over 2:26 both the style and the image structure 2:29 as well as better understanding and 2:31 representing what you actually want in 2:34 your text well they change the typical 2:36 diffusion architecture in two ways first 2:39 they encode the text using two different 2:41 approaches that I already covered on the 2:43 channel which we refer to as clip and T5 2:46 encoders this means they will use 2:48 pre-trained models to take text and 2:50 create various embeddings focusing on 2:52 different features as they are trained 2:55 and behaved differently and meanings are 2:57 just representations maximizing what the 3:00 sentence actually means for the 3:01 algorithm or the machine to understand 3:04 it regarding the input image they just 3:06 use the clip embeddings as well 3:08 basically encoding the image so that the 3:11 model can understand it which you can 3:13 learn more about in my other videos 3:14 covering generative models as they are 3:16 pretty much all built on clip this is 3:19 what allows them to have more control 3:21 over the output as well as processed 3:23 text and images rather than only text 3:25 the second modification is using a 3:28 Cascade of diffusion models instead of 3:31 reusing the same iteratively as we 3:33 usually do with diffusion based models 3:35 here the use models trained for the 3:38 specific part of the generative process 3:39 meaning that each model does not have to 3:42 be as general as the regular diffusion 3:44 denoiser since each model has to focus 3:46 on a specific part of the process it can 3:49 be much better at it they use this 3:51 approach because they observed that the 3:52 denoising models seemed to use the text 3:55 embeddings a lot more to orient its 3:57 generation towards the beginning of the 3:59 process and then use it less and less to 4:02 focus on output quality and Fidelity the 4:05 this naturally brings the hypothesis 4:07 that reusing the same denoising model 4:09 throughout the whole process might not 4:11 be the best ID since it automatically 4:13 focuses on different tasks and we know 4:15 that a generalist is far from the expert 4:18 level at all tasks why not use a few 4:20 experts instead of one generalist to get 4:23 much better results so this is what they 4:25 did and why they call them denoising 4:28 experts and the main reason for this 4:30 improves performance in quality and 4:32 faithfulness the rest of the 4:34 architecture is pretty similar to other 4:36 approaches of scaling the final results 4:38 with other models to get a high 4:40 definition final image the image and 4:43 video synthesis fields are just getting 4:45 crazy nowadays and we are seeing 4:47 impressive results coming out every week 4:49 I am super excited for the next releases 4:51 and I love to see different approaches 4:53 with both innovative ways of tackling 4:55 the problem and also going for different 4:57 use cases as a great person once said 5:01 what a time to be alive I hope you like 5:04 this quick overview of the approach a 5:06 bit more high level than what I usually 5:08 do as it takes most Parts I already 5:10 covered in numerous videos and changed 5:12 them to act differently I invite you to 5:15 watch my stable diffusion video to learn 5:17 a bit more about the diffusion approach 5:19 itself and read the nvidia's paper to 5:21 learn more about this specific approach 5:23 and its implementation I will see you 5:26 next week with another amazing paper 5:32 foreign 5:36 [Music]

An Intro to eDiffi: NVIDIA's New SOTA Image Synthesis Model

Una introducción a eDiffi: el nuevo modelo de síntesis de imágenes SOTA de NVIDIA

Une introduction à eDiffi : le nouveau modèle de synthèse d'images SOTA de NVIDIA

eDiffi का एक परिचय: NVIDIA का नया SOTA छवि संश्लेषण मॉडल

eDiffi の紹介: NVIDIA の新しい SOTA 画像合成モデル

Uma introdução ao eDiffi: o novo modelo de síntese de imagem SOTA da NVIDIA

Giới thiệu về eDiffi: Mô hình tổng hợp hình ảnh SOTA mới của NVIDIA

eDiffi 简介：NVIDIA 的新 SOTA 图像合成模型

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