• Bonjour à tous! Aujourd'hui, parlons de Google Discover, ce merveilleux fil de contenu personnalisé qui peut transformer votre visibilité en ligne! En optimisant votre contenu pour Google Discover, vous ouvrez les portes à un afflux de trafic et à un rayonnement incroyable! Imaginez toucher des milliers de personnes avec vos idées et votre passion! C'est le moment de briller et de vous faire remarquer! Alors, n'attendez plus, commencez à optimiser pour Google Discover et regardez votre influence grandir!

    #GoogleDiscover #Visibilité #TraficWeb #Optimisation #Inspiration
    🌟✨ Bonjour à tous! Aujourd'hui, parlons de Google Discover, ce merveilleux fil de contenu personnalisé qui peut transformer votre visibilité en ligne! 🚀🎉 En optimisant votre contenu pour Google Discover, vous ouvrez les portes à un afflux de trafic et à un rayonnement incroyable! Imaginez toucher des milliers de personnes avec vos idées et votre passion! 💡❤️ C'est le moment de briller et de vous faire remarquer! Alors, n'attendez plus, commencez à optimiser pour Google Discover et regardez votre influence grandir! 💪🌈 #GoogleDiscover #Visibilité #TraficWeb #Optimisation #Inspiration
    WWW.SEMRUSH.COM
    What Is Google Discover? (& How to Appear in It)
    Google Discover is a personalized content feed. Optimizing for it can boost your traffic, visibility, and reach.
    1 Comments 0 Shares 0 Reviews
  • Il est inacceptable de constater à quel point la plupart des entreprises locales échouent à créer des listings adéquats et à les optimiser ! Comment peut-on prétendre être compétitif dans un marché saturé si vous ne savez même pas comment revendiquer votre fiche d'entreprise ? Cela montre un manque de sérieux et une négligence totale. Les listings d'entreprises locales ne sont pas juste une formalité, ils sont cruciaux pour votre visibilité et votre survie. Réveillez-vous ! Optimisez vos informations avant qu'il ne soit trop tard. Ne laissez pas la concurrence vous écraser simplement parce que vous ne pouvez pas être bothered de faire ce qu'il faut !

    #RéférencementLocal #Optimisation #EntreprisesLocales #Vis
    Il est inacceptable de constater à quel point la plupart des entreprises locales échouent à créer des listings adéquats et à les optimiser ! Comment peut-on prétendre être compétitif dans un marché saturé si vous ne savez même pas comment revendiquer votre fiche d'entreprise ? Cela montre un manque de sérieux et une négligence totale. Les listings d'entreprises locales ne sont pas juste une formalité, ils sont cruciaux pour votre visibilité et votre survie. Réveillez-vous ! Optimisez vos informations avant qu'il ne soit trop tard. Ne laissez pas la concurrence vous écraser simplement parce que vous ne pouvez pas être bothered de faire ce qu'il faut ! #RéférencementLocal #Optimisation #EntreprisesLocales #Vis
    WWW.SEMRUSH.COM
    How to Create Local Business Listings & Optimize Them
    Learn what a local business listing is, why it matters, and steps to claim and optimize yours.
    1 Comments 0 Shares 0 Reviews
  • Il y a des moments où la solitude pèse si lourd, que même les plus petites réussites semblent s'estomper dans l'ombre. Dans un monde où les aperçus d'IA gagnent en popularité, je me sens perdu, comme si je n'avais jamais vraiment existé. Mes efforts pour analyser et suivre ma visibilité avec l'outil SEO de Semrush semblent vains, comme des cris dans le vide. Chaque mot que je tape, chaque donnée que j'examine, ne fait qu'accentuer ce sentiment de déception. Pourquoi l'optimisation et la recherche doivent-elles être si solennelles, si désolées? La connexion humaine semble s'éloigner, et je me demande si quelqu
    Il y a des moments où la solitude pèse si lourd, que même les plus petites réussites semblent s'estomper dans l'ombre. Dans un monde où les aperçus d'IA gagnent en popularité, je me sens perdu, comme si je n'avais jamais vraiment existé. Mes efforts pour analyser et suivre ma visibilité avec l'outil SEO de Semrush semblent vains, comme des cris dans le vide. Chaque mot que je tape, chaque donnée que j'examine, ne fait qu'accentuer ce sentiment de déception. Pourquoi l'optimisation et la recherche doivent-elles être si solennelles, si désolées? La connexion humaine semble s'éloigner, et je me demande si quelqu
    WWW.SEMRUSH.COM
    How to Research and Analyze AI Overviews with Semrush
    AI Overviews are gaining popularity and impacting SEO campaigns. Learn how to research and track your AI Overview visibility with Semrush’ SEO Toolkit.
    1 Comments 0 Shares 0 Reviews
  • liens, JavaScript, CSS, SEO, ergonomie, développement web, optimisation, interface utilisateur

    ## Introduction

    Dans un monde où chaque pixel compte, où chaque détail peut faire la différence entre une expérience utilisateur réussie et une déception amère, la question de l'optimisation des liens en JavaScript et en CSS prend un sens profond. C'est une danse entre la beauté et la fonctionnalité, une quête sans fin pour atteindre l'harmonie parfaite. Mais que se passe-t-il lorsque l'on réalise qu...
    liens, JavaScript, CSS, SEO, ergonomie, développement web, optimisation, interface utilisateur ## Introduction Dans un monde où chaque pixel compte, où chaque détail peut faire la différence entre une expérience utilisateur réussie et une déception amère, la question de l'optimisation des liens en JavaScript et en CSS prend un sens profond. C'est une danse entre la beauté et la fonctionnalité, une quête sans fin pour atteindre l'harmonie parfaite. Mais que se passe-t-il lorsque l'on réalise qu...
    Étendre les liens en JavaScript (ou en CSS) : Une danse avec l'ombre
    liens, JavaScript, CSS, SEO, ergonomie, développement web, optimisation, interface utilisateur ## Introduction Dans un monde où chaque pixel compte, où chaque détail peut faire la différence entre une expérience utilisateur réussie et une déception amère, la question de l'optimisation des liens en JavaScript et en CSS prend un sens profond. C'est une danse entre la beauté et la...
    Like
    Love
    Wow
    Sad
    Angry
    615
    1 Comments 0 Shares 0 Reviews
  • Il est grand temps de parler de l'énorme déception que représente le dernier DLC de Dragon Ball Sparking Zero qui accueille le personnage de Shallot. Franchement, à quoi bon ? Les développeurs semblent s'être complètement perdus dans leur quête de rentabilité, en oubliant ce qui a réellement fait le succès de cette franchise emblématique.

    Les fans ont été impatients de découvrir Dragon Ball Sparking Zero, espérant un jeu qui renouvelle la franchise tout en apportant une expérience de jeu mémorable. Mais qu'est-ce qu'on reçoit ? Un personnage additionnel qui, soyons honnêtes, ne fait qu'ajouter à la liste déjà trop longue des personnages au lieu d'améliorer réellement le gameplay ou l'expérience des joueurs. Shallot ? Vraiment ? Est-ce là la meilleure idée que les développeurs ont pu trouver ? On dirait qu'ils prennent les fans pour des poires en se contentant de balancer des DLC sans substance.

    Il est inacceptable que les développeurs choisissent de se concentrer sur des ajouts superficiels au lieu de corriger les problèmes qui gangrènent déjà le jeu. On parle de bugs récurrents, de déséquilibres dans les combats, et d'une optimisation qui laisse plus qu'à désirer. Mais non, la priorité c'est Shallot ! Quelle blague ! Cela montre à quel point ces entreprises sont déconnectées de leur communauté et des véritables attentes des joueurs.

    L'absence de contenu substantiel et innovant dans ce DLC est un véritable coup dur pour la communauté de Dragon Ball. Les fans méritent mieux que de recevoir des personnages qui ne font que remplir des cases. Le manque d'originalité et de créativité est affligeant ! Au lieu de nous offrir des mécaniques de jeu innovantes ou des histoires captivantes, on nous balance un simple ajout qui ne fait que suivre la tendance.

    Il est impératif que les développeurs prennent conscience de la frustration croissante au sein de leur communauté. Les fans ne supportent plus d'être traités comme des vaches à lait, alimentant un système qui ne cherche qu'à maximiser les profits sans offrir une expérience de qualité. Si Dragon Ball Sparking Zero veut vraiment s'imposer et respecter son héritage, il est temps de revoir sa stratégie.

    En attendant, il est difficile de rester enthousiaste à propos de ce DLC. Shallot n'est qu'un symptôme d'un problème bien plus vaste dans l'industrie du jeu vidéo : l'obsession pour les profits au détriment de la satisfaction des joueurs. Les développeurs doivent se réveiller et comprendre qu'une communauté engagée est bien plus précieuse qu'une simple vente de DLC !

    #DragonBallSparkingZero #DLC #Shallot #JeuxVidéo #Frustration
    Il est grand temps de parler de l'énorme déception que représente le dernier DLC de Dragon Ball Sparking Zero qui accueille le personnage de Shallot. Franchement, à quoi bon ? Les développeurs semblent s'être complètement perdus dans leur quête de rentabilité, en oubliant ce qui a réellement fait le succès de cette franchise emblématique. Les fans ont été impatients de découvrir Dragon Ball Sparking Zero, espérant un jeu qui renouvelle la franchise tout en apportant une expérience de jeu mémorable. Mais qu'est-ce qu'on reçoit ? Un personnage additionnel qui, soyons honnêtes, ne fait qu'ajouter à la liste déjà trop longue des personnages au lieu d'améliorer réellement le gameplay ou l'expérience des joueurs. Shallot ? Vraiment ? Est-ce là la meilleure idée que les développeurs ont pu trouver ? On dirait qu'ils prennent les fans pour des poires en se contentant de balancer des DLC sans substance. Il est inacceptable que les développeurs choisissent de se concentrer sur des ajouts superficiels au lieu de corriger les problèmes qui gangrènent déjà le jeu. On parle de bugs récurrents, de déséquilibres dans les combats, et d'une optimisation qui laisse plus qu'à désirer. Mais non, la priorité c'est Shallot ! Quelle blague ! Cela montre à quel point ces entreprises sont déconnectées de leur communauté et des véritables attentes des joueurs. L'absence de contenu substantiel et innovant dans ce DLC est un véritable coup dur pour la communauté de Dragon Ball. Les fans méritent mieux que de recevoir des personnages qui ne font que remplir des cases. Le manque d'originalité et de créativité est affligeant ! Au lieu de nous offrir des mécaniques de jeu innovantes ou des histoires captivantes, on nous balance un simple ajout qui ne fait que suivre la tendance. Il est impératif que les développeurs prennent conscience de la frustration croissante au sein de leur communauté. Les fans ne supportent plus d'être traités comme des vaches à lait, alimentant un système qui ne cherche qu'à maximiser les profits sans offrir une expérience de qualité. Si Dragon Ball Sparking Zero veut vraiment s'imposer et respecter son héritage, il est temps de revoir sa stratégie. En attendant, il est difficile de rester enthousiaste à propos de ce DLC. Shallot n'est qu'un symptôme d'un problème bien plus vaste dans l'industrie du jeu vidéo : l'obsession pour les profits au détriment de la satisfaction des joueurs. Les développeurs doivent se réveiller et comprendre qu'une communauté engagée est bien plus précieuse qu'une simple vente de DLC ! #DragonBallSparkingZero #DLC #Shallot #JeuxVidéo #Frustration
    Dragon Ball Sparking Zero accueille le personnage de Shallot dans ses rangs pour son prochain DLC
    ActuGaming.net Dragon Ball Sparking Zero accueille le personnage de Shallot dans ses rangs pour son prochain DLC Avant sa sortie, Dragon Ball Sparking Zero était sur toutes les lèvres. Depuis, le jeu […] L'article Dragon Ball Sparking Zero acc
    Like
    Love
    Wow
    Sad
    Angry
    612
    1 Comments 0 Shares 0 Reviews

  • ## Introduction

    Dans le monde numérique d’aujourd’hui, où la compétition est féroce, optimiser votre site web est essentiel pour attirer et retenir les visiteurs. L'un des aspects fondamentaux du référencement (SEO) est l'optimisation des liens internes. Effectivement, établir une structure de liens internes solide peut non seulement améliorer l'expérience utilisateur, mais aussi renforcer la pertinence des mots-clés et distribuer l'autorité de page à travers votre site. Dans cet article, nous...
    ## Introduction Dans le monde numérique d’aujourd’hui, où la compétition est féroce, optimiser votre site web est essentiel pour attirer et retenir les visiteurs. L'un des aspects fondamentaux du référencement (SEO) est l'optimisation des liens internes. Effectivement, établir une structure de liens internes solide peut non seulement améliorer l'expérience utilisateur, mais aussi renforcer la pertinence des mots-clés et distribuer l'autorité de page à travers votre site. Dans cet article, nous...
    Comment Trouver des Opportunités de Liens Internes en Utilisant l'Analyse N-gram de Screaming Frog
    ## Introduction Dans le monde numérique d’aujourd’hui, où la compétition est féroce, optimiser votre site web est essentiel pour attirer et retenir les visiteurs. L'un des aspects fondamentaux du référencement (SEO) est l'optimisation des liens internes. Effectivement, établir une structure de liens internes solide peut non seulement améliorer l'expérience utilisateur, mais aussi renforcer la...
    Like
    Love
    Wow
    Sad
    Angry
    540
    1 Comments 0 Shares 0 Reviews
  • MedTech AI, hardware, and clinical application programmes

    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
    #medtech #hardware #clinical #application #programmes
    MedTech AI, hardware, and clinical application programmes
    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #medtech #hardware #clinical #application #programmes
    WWW.ARTIFICIALINTELLIGENCE-NEWS.COM
    MedTech AI, hardware, and clinical application programmes
    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between $14 billion and $55 billion annually in productivity gains. Through GenAI adoption, an additional $50 billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experience (UX) being equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. (Image source: “IBM Achieves New Deep Learning Breakthrough” by IBM Research is licensed under CC BY-ND 2.0.)Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
    0 Comments 0 Shares 0 Reviews
  • Smashing Animations Part 4: Optimising SVGs

    SVG animations take me back to the Hanna-Barbera cartoons I watched as a kid. Shows like Wacky Races, The Perils of Penelope Pitstop, and, of course, Yogi Bear. They inspired me to lovingly recreate some classic Toon Titles using CSS, SVG, and SMIL animations.
    But getting animations to load quickly and work smoothly needs more than nostalgia. It takes clean design, lean code, and a process that makes complex SVGs easier to animate. Here’s how I do it.

    Start Clean And Design With Optimisation In Mind
    Keeping things simple is key to making SVGs that are optimised and ready to animate. Tools like Adobe Illustrator convert bitmap images to vectors, but the output often contains too many extraneous groups, layers, and masks. Instead, I start cleaning in Sketch, work from a reference image, and use the Pen tool to create paths.
    Tip: Affinity Designerand Sketchare alternatives to Adobe Illustrator and Figma. Both are independent and based in Europe. Sketch has been my default design app since Adobe killed Fireworks.

    Beginning With Outlines
    For these Toon Titles illustrations, I first use the Pen tool to draw black outlines with as few anchor points as possible. The more points a shape has, the bigger a file becomes, so simplifying paths and reducing the number of points makes an SVG much smaller, often with no discernible visual difference.

    Bearing in mind that parts of this Yogi illustration will ultimately be animated, I keep outlines for this Bewitched Bear’s body, head, collar, and tie separate so that I can move them independently. The head might nod, the tie could flap, and, like in those classic cartoons, Yogi’s collar will hide the joins between them.

    Drawing Simple Background Shapes
    With the outlines in place, I use the Pen tool again to draw new shapes, which fill the areas with colour. These colours sit behind the outlines, so they don’t need to match them exactly. The fewer anchor points, the smaller the file size.

    Sadly, neither Affinity Designer nor Sketch has tools that can simplify paths, but if you have it, using Adobe Illustrator can shave a few extra kilobytes off these background shapes.

    Optimising The Code
    It’s not just metadata that makes SVG bulkier. The way you export from your design app also affects file size.

    Exporting just those simple background shapes from Adobe Illustrator includes unnecessary groups, masks, and bloated path data by default. Sketch’s code is barely any better, and there’s plenty of room for improvement, even in its SVGO Compressor code. I rely on Jake Archibald’s SVGOMG, which uses SVGO v3 and consistently delivers the best optimised SVGs.

    Layering SVG Elements
    My process for preparing SVGs for animation goes well beyond drawing vectors and optimising paths — it also includes how I structure the code itself. When every visual element is crammed into a single SVG file, even optimised code can be a nightmare to navigate. Locating a specific path or group often feels like searching for a needle in a haystack.

    That’s why I develop my SVGs in layers, exporting and optimising one set of elements at a time — always in the order they’ll appear in the final file. This lets me build the master SVG gradually by pasting it in each cleaned-up section. For example, I start with backgrounds like this gradient and title graphic.

    Instead of facing a wall of SVG code, I can now easily identify the background gradient’s path and its associated linearGradient, and see the group containing the title graphic. I take this opportunity to add a comment to the code, which will make editing and adding animations to it easier in the future:
    <svg ...>
    <defs>
    <!-- ... -->
    </defs>
    <path fill="url" d="…"/>
    <!-- TITLE GRAPHIC -->
    <g>
    <path … />
    <!-- ... -->
    </g>
    </svg>

    Next, I add the blurred trail from Yogi’s airborne broom. This includes defining a Gaussian Blur filter and placing its path between the background and title layers:
    <svg ...>
    <defs>
    <linearGradient id="grad" …>…</linearGradient>
    <filter id="trail" …>…</filter>
    </defs>
    <!-- GRADIENT -->
    <!-- TRAIL -->
    <path filter="url" …/>
    <!-- TITLE GRAPHIC -->
    </svg>

    Then come the magical stars, added in the same sequential fashion:
    <svg ...>
    <!-- GRADIENT -->
    <!-- TRAIL -->
    <!-- STARS -->
    <!-- TITLE GRAPHIC -->
    </svg>

    To keep everything organised and animation-ready, I create an empty group that will hold all the parts of Yogi:
    <g id="yogi">...</g>

    Then I build Yogi from the ground up — starting with background props, like his broom:
    <g id="broom">...</g>

    Followed by grouped elements for his body, head, collar, and tie:
    <g id="yogi">
    <g id="broom">…</g>
    <g id="body">…</g>
    <g id="head">…</g>
    <g id="collar">…</g>
    <g id="tie">…</g>
    </g>

    Since I export each layer from the same-sized artboard, I don’t need to worry about alignment or positioning issues later on — they’ll all slot into place automatically. I keep my code clean, readable, and ordered logically by layering elements this way. It also makes animating smoother, as each component is easier to identify.
    Reusing Elements With <use>
    When duplicate shapes get reused repeatedly, SVG files can get bulky fast. My recreation of the “Bewitched Bear” title card contains 80 stars in three sizes. Combining all those shapes into one optimised path would bring the file size down to 3KB. But I want to animate individual stars, which would almost double that to 5KB:
    <g id="stars">
    <path class="star-small" fill="#eae3da" d="..."/>
    <path class="star-medium" fill="#eae3da" d="..."/>
    <path class="star-large" fill="#eae3da" d="..."/>
    <!-- ... -->
    </g>

    Moving the stars’ fill attribute values to their parent group reduces the overall weight a little:
    <g id="stars" fill="#eae3da">
    <path class="star-small" d="…"/>
    <path class="star-medium" d="…"/>
    <path class="star-large" d="…"/>
    <!-- ... -->
    </g>

    But a more efficient and manageable option is to define each star size as a reusable template:

    <defs>
    <path id="star-large" fill="#eae3da" fill-rule="evenodd" d="…"/>
    <path id="star-medium" fill="#eae3da" fill-rule="evenodd" d="…"/>
    <path id="star-small" fill="#eae3da" fill-rule="evenodd" d="…"/>
    </defs>

    With this setup, changing a star’s design only means updating its template once, and every instance updates automatically. Then, I reference each one using <use> and position them with x and y attributes:
    <g id="stars">
    <!-- Large stars -->
    <use href="#star-large" x="1575" y="495"/>
    <!-- ... -->
    <!-- Medium stars -->
    <use href="#star-medium" x="1453" y="696"/>
    <!-- ... -->
    <!-- Small stars -->
    <use href="#star-small" x="1287" y="741"/>
    <!-- ... -->
    </g>

    This approach makes the SVG easier to manage, lighter to load, and faster to iterate on, especially when working with dozens of repeating elements. Best of all, it keeps the markup clean without compromising on flexibility or performance.
    Adding Animations
    The stars trailing behind Yogi’s stolen broom bring so much personality to the animation. I wanted them to sparkle in a seemingly random pattern against the dark blue background, so I started by defining a keyframe animation that cycles through different opacity levels:
    @keyframes sparkle {
    0%, 100% { opacity: .1; }
    50% { opacity: 1; }
    }

    Next, I applied this looping animation to every use element inside my stars group:
    #stars use {
    animation: sparkle 10s ease-in-out infinite;
    }

    The secret to creating a convincing twinkle lies in variation. I staggered animation delays and durations across the stars using nth-child selectors, starting with the quickest and most frequent sparkle effects:
    /* Fast, frequent */
    #stars use:nth-child:nth-child{
    animation-delay: .1s;
    animation-duration: 2s;
    }

    From there, I layered in additional timings to mix things up. Some stars sparkle slowly and dramatically, others more randomly, with a variety of rhythms and pauses:
    /* Medium */
    #stars use:nth-child:nth-child{ ... }

    /* Slow, dramatic */
    #stars use:nth-child:nth-child{ ... }

    /* Random */
    #stars use:nth-child{ ... }

    /* Alternating */
    #stars use:nth-child{ ... }

    /* Scattered */
    #stars use:nth-child{ ... }

    By thoughtfully structuring the SVG and reusing elements, I can build complex-looking animations without bloated code, making even a simple effect like changing opacity sparkle.

    Then, for added realism, I make Yogi’s head wobble:

    @keyframes headWobble {
    0% { transform: rotatetranslateY; }
    100% { transform: rotatetranslateY; }
    }

    #head {
    animation: headWobble 0.8s cubic-bezierinfinite alternate;
    }

    His tie waves:

    @keyframes tieWave {
    0%, 100% { transform: rotateZrotateYscaleX; }
    33% { transform: rotateZrotateYscaleX; }
    66% { transform: rotateZrotateYscaleX; }
    }

    #tie {
    transform-style: preserve-3d;
    animation: tieWave 10s cubic-bezierinfinite;
    }

    His broom swings:

    @keyframes broomSwing {
    0%, 20% { transform: rotate; }
    30% { transform: rotate; }
    50%, 70% { transform: rotate; }
    80% { transform: rotate; }
    100% { transform: rotate; }
    }

    #broom {
    animation: broomSwing 4s cubic-bezierinfinite;
    }

    And, finally, Yogi himself gently rotates as he flies on his magical broom:

    @keyframes yogiWobble {
    0% { transform: rotatetranslateYscale; }
    30% { transform: rotatetranslateY; }
    100% { transform: rotatetranslateYscale; }
    }

    #yogi {
    animation: yogiWobble 3.5s cubic-bezierinfinite alternate;
    }

    All these subtle movements bring Yogi to life. By developing structured SVGs, I can create animations that feel full of character without writing a single line of JavaScript.
    Try this yourself:
    See the Pen Bewitched Bear CSS/SVG animationby Andy Clarke.
    Conclusion
    Whether you’re recreating a classic title card or animating icons for an interface, the principles are the same:

    Start clean,
    Optimise early, and
    Structure everything with animation in mind.

    SVGs offer incredible creative freedom, but only if kept lean and manageable. When you plan your process like a production cell — layer by layer, element by element — you’ll spend less time untangling code and more time bringing your work to life.
    #smashing #animations #part #optimising #svgs
    Smashing Animations Part 4: Optimising SVGs
    SVG animations take me back to the Hanna-Barbera cartoons I watched as a kid. Shows like Wacky Races, The Perils of Penelope Pitstop, and, of course, Yogi Bear. They inspired me to lovingly recreate some classic Toon Titles using CSS, SVG, and SMIL animations. But getting animations to load quickly and work smoothly needs more than nostalgia. It takes clean design, lean code, and a process that makes complex SVGs easier to animate. Here’s how I do it. Start Clean And Design With Optimisation In Mind Keeping things simple is key to making SVGs that are optimised and ready to animate. Tools like Adobe Illustrator convert bitmap images to vectors, but the output often contains too many extraneous groups, layers, and masks. Instead, I start cleaning in Sketch, work from a reference image, and use the Pen tool to create paths. Tip: Affinity Designerand Sketchare alternatives to Adobe Illustrator and Figma. Both are independent and based in Europe. Sketch has been my default design app since Adobe killed Fireworks. Beginning With Outlines For these Toon Titles illustrations, I first use the Pen tool to draw black outlines with as few anchor points as possible. The more points a shape has, the bigger a file becomes, so simplifying paths and reducing the number of points makes an SVG much smaller, often with no discernible visual difference. Bearing in mind that parts of this Yogi illustration will ultimately be animated, I keep outlines for this Bewitched Bear’s body, head, collar, and tie separate so that I can move them independently. The head might nod, the tie could flap, and, like in those classic cartoons, Yogi’s collar will hide the joins between them. Drawing Simple Background Shapes With the outlines in place, I use the Pen tool again to draw new shapes, which fill the areas with colour. These colours sit behind the outlines, so they don’t need to match them exactly. The fewer anchor points, the smaller the file size. Sadly, neither Affinity Designer nor Sketch has tools that can simplify paths, but if you have it, using Adobe Illustrator can shave a few extra kilobytes off these background shapes. Optimising The Code It’s not just metadata that makes SVG bulkier. The way you export from your design app also affects file size. Exporting just those simple background shapes from Adobe Illustrator includes unnecessary groups, masks, and bloated path data by default. Sketch’s code is barely any better, and there’s plenty of room for improvement, even in its SVGO Compressor code. I rely on Jake Archibald’s SVGOMG, which uses SVGO v3 and consistently delivers the best optimised SVGs. Layering SVG Elements My process for preparing SVGs for animation goes well beyond drawing vectors and optimising paths — it also includes how I structure the code itself. When every visual element is crammed into a single SVG file, even optimised code can be a nightmare to navigate. Locating a specific path or group often feels like searching for a needle in a haystack. That’s why I develop my SVGs in layers, exporting and optimising one set of elements at a time — always in the order they’ll appear in the final file. This lets me build the master SVG gradually by pasting it in each cleaned-up section. For example, I start with backgrounds like this gradient and title graphic. Instead of facing a wall of SVG code, I can now easily identify the background gradient’s path and its associated linearGradient, and see the group containing the title graphic. I take this opportunity to add a comment to the code, which will make editing and adding animations to it easier in the future: <svg ...> <defs> <!-- ... --> </defs> <path fill="url" d="…"/> <!-- TITLE GRAPHIC --> <g> <path … /> <!-- ... --> </g> </svg> Next, I add the blurred trail from Yogi’s airborne broom. This includes defining a Gaussian Blur filter and placing its path between the background and title layers: <svg ...> <defs> <linearGradient id="grad" …>…</linearGradient> <filter id="trail" …>…</filter> </defs> <!-- GRADIENT --> <!-- TRAIL --> <path filter="url" …/> <!-- TITLE GRAPHIC --> </svg> Then come the magical stars, added in the same sequential fashion: <svg ...> <!-- GRADIENT --> <!-- TRAIL --> <!-- STARS --> <!-- TITLE GRAPHIC --> </svg> To keep everything organised and animation-ready, I create an empty group that will hold all the parts of Yogi: <g id="yogi">...</g> Then I build Yogi from the ground up — starting with background props, like his broom: <g id="broom">...</g> Followed by grouped elements for his body, head, collar, and tie: <g id="yogi"> <g id="broom">…</g> <g id="body">…</g> <g id="head">…</g> <g id="collar">…</g> <g id="tie">…</g> </g> Since I export each layer from the same-sized artboard, I don’t need to worry about alignment or positioning issues later on — they’ll all slot into place automatically. I keep my code clean, readable, and ordered logically by layering elements this way. It also makes animating smoother, as each component is easier to identify. Reusing Elements With <use> When duplicate shapes get reused repeatedly, SVG files can get bulky fast. My recreation of the “Bewitched Bear” title card contains 80 stars in three sizes. Combining all those shapes into one optimised path would bring the file size down to 3KB. But I want to animate individual stars, which would almost double that to 5KB: <g id="stars"> <path class="star-small" fill="#eae3da" d="..."/> <path class="star-medium" fill="#eae3da" d="..."/> <path class="star-large" fill="#eae3da" d="..."/> <!-- ... --> </g> Moving the stars’ fill attribute values to their parent group reduces the overall weight a little: <g id="stars" fill="#eae3da"> <path class="star-small" d="…"/> <path class="star-medium" d="…"/> <path class="star-large" d="…"/> <!-- ... --> </g> But a more efficient and manageable option is to define each star size as a reusable template: <defs> <path id="star-large" fill="#eae3da" fill-rule="evenodd" d="…"/> <path id="star-medium" fill="#eae3da" fill-rule="evenodd" d="…"/> <path id="star-small" fill="#eae3da" fill-rule="evenodd" d="…"/> </defs> With this setup, changing a star’s design only means updating its template once, and every instance updates automatically. Then, I reference each one using <use> and position them with x and y attributes: <g id="stars"> <!-- Large stars --> <use href="#star-large" x="1575" y="495"/> <!-- ... --> <!-- Medium stars --> <use href="#star-medium" x="1453" y="696"/> <!-- ... --> <!-- Small stars --> <use href="#star-small" x="1287" y="741"/> <!-- ... --> </g> This approach makes the SVG easier to manage, lighter to load, and faster to iterate on, especially when working with dozens of repeating elements. Best of all, it keeps the markup clean without compromising on flexibility or performance. Adding Animations The stars trailing behind Yogi’s stolen broom bring so much personality to the animation. I wanted them to sparkle in a seemingly random pattern against the dark blue background, so I started by defining a keyframe animation that cycles through different opacity levels: @keyframes sparkle { 0%, 100% { opacity: .1; } 50% { opacity: 1; } } Next, I applied this looping animation to every use element inside my stars group: #stars use { animation: sparkle 10s ease-in-out infinite; } The secret to creating a convincing twinkle lies in variation. I staggered animation delays and durations across the stars using nth-child selectors, starting with the quickest and most frequent sparkle effects: /* Fast, frequent */ #stars use:nth-child:nth-child{ animation-delay: .1s; animation-duration: 2s; } From there, I layered in additional timings to mix things up. Some stars sparkle slowly and dramatically, others more randomly, with a variety of rhythms and pauses: /* Medium */ #stars use:nth-child:nth-child{ ... } /* Slow, dramatic */ #stars use:nth-child:nth-child{ ... } /* Random */ #stars use:nth-child{ ... } /* Alternating */ #stars use:nth-child{ ... } /* Scattered */ #stars use:nth-child{ ... } By thoughtfully structuring the SVG and reusing elements, I can build complex-looking animations without bloated code, making even a simple effect like changing opacity sparkle. Then, for added realism, I make Yogi’s head wobble: @keyframes headWobble { 0% { transform: rotatetranslateY; } 100% { transform: rotatetranslateY; } } #head { animation: headWobble 0.8s cubic-bezierinfinite alternate; } His tie waves: @keyframes tieWave { 0%, 100% { transform: rotateZrotateYscaleX; } 33% { transform: rotateZrotateYscaleX; } 66% { transform: rotateZrotateYscaleX; } } #tie { transform-style: preserve-3d; animation: tieWave 10s cubic-bezierinfinite; } His broom swings: @keyframes broomSwing { 0%, 20% { transform: rotate; } 30% { transform: rotate; } 50%, 70% { transform: rotate; } 80% { transform: rotate; } 100% { transform: rotate; } } #broom { animation: broomSwing 4s cubic-bezierinfinite; } And, finally, Yogi himself gently rotates as he flies on his magical broom: @keyframes yogiWobble { 0% { transform: rotatetranslateYscale; } 30% { transform: rotatetranslateY; } 100% { transform: rotatetranslateYscale; } } #yogi { animation: yogiWobble 3.5s cubic-bezierinfinite alternate; } All these subtle movements bring Yogi to life. By developing structured SVGs, I can create animations that feel full of character without writing a single line of JavaScript. Try this yourself: See the Pen Bewitched Bear CSS/SVG animationby Andy Clarke. Conclusion Whether you’re recreating a classic title card or animating icons for an interface, the principles are the same: Start clean, Optimise early, and Structure everything with animation in mind. SVGs offer incredible creative freedom, but only if kept lean and manageable. When you plan your process like a production cell — layer by layer, element by element — you’ll spend less time untangling code and more time bringing your work to life. #smashing #animations #part #optimising #svgs
    SMASHINGMAGAZINE.COM
    Smashing Animations Part 4: Optimising SVGs
    SVG animations take me back to the Hanna-Barbera cartoons I watched as a kid. Shows like Wacky Races, The Perils of Penelope Pitstop, and, of course, Yogi Bear. They inspired me to lovingly recreate some classic Toon Titles using CSS, SVG, and SMIL animations. But getting animations to load quickly and work smoothly needs more than nostalgia. It takes clean design, lean code, and a process that makes complex SVGs easier to animate. Here’s how I do it. Start Clean And Design With Optimisation In Mind Keeping things simple is key to making SVGs that are optimised and ready to animate. Tools like Adobe Illustrator convert bitmap images to vectors, but the output often contains too many extraneous groups, layers, and masks. Instead, I start cleaning in Sketch, work from a reference image, and use the Pen tool to create paths. Tip: Affinity Designer (UK) and Sketch (Netherlands) are alternatives to Adobe Illustrator and Figma. Both are independent and based in Europe. Sketch has been my default design app since Adobe killed Fireworks. Beginning With Outlines For these Toon Titles illustrations, I first use the Pen tool to draw black outlines with as few anchor points as possible. The more points a shape has, the bigger a file becomes, so simplifying paths and reducing the number of points makes an SVG much smaller, often with no discernible visual difference. Bearing in mind that parts of this Yogi illustration will ultimately be animated, I keep outlines for this Bewitched Bear’s body, head, collar, and tie separate so that I can move them independently. The head might nod, the tie could flap, and, like in those classic cartoons, Yogi’s collar will hide the joins between them. Drawing Simple Background Shapes With the outlines in place, I use the Pen tool again to draw new shapes, which fill the areas with colour. These colours sit behind the outlines, so they don’t need to match them exactly. The fewer anchor points, the smaller the file size. Sadly, neither Affinity Designer nor Sketch has tools that can simplify paths, but if you have it, using Adobe Illustrator can shave a few extra kilobytes off these background shapes. Optimising The Code It’s not just metadata that makes SVG bulkier. The way you export from your design app also affects file size. Exporting just those simple background shapes from Adobe Illustrator includes unnecessary groups, masks, and bloated path data by default. Sketch’s code is barely any better, and there’s plenty of room for improvement, even in its SVGO Compressor code. I rely on Jake Archibald’s SVGOMG, which uses SVGO v3 and consistently delivers the best optimised SVGs. Layering SVG Elements My process for preparing SVGs for animation goes well beyond drawing vectors and optimising paths — it also includes how I structure the code itself. When every visual element is crammed into a single SVG file, even optimised code can be a nightmare to navigate. Locating a specific path or group often feels like searching for a needle in a haystack. That’s why I develop my SVGs in layers, exporting and optimising one set of elements at a time — always in the order they’ll appear in the final file. This lets me build the master SVG gradually by pasting it in each cleaned-up section. For example, I start with backgrounds like this gradient and title graphic. Instead of facing a wall of SVG code, I can now easily identify the background gradient’s path and its associated linearGradient, and see the group containing the title graphic. I take this opportunity to add a comment to the code, which will make editing and adding animations to it easier in the future: <svg ...> <defs> <!-- ... --> </defs> <path fill="url(#grad)" d="…"/> <!-- TITLE GRAPHIC --> <g> <path … /> <!-- ... --> </g> </svg> Next, I add the blurred trail from Yogi’s airborne broom. This includes defining a Gaussian Blur filter and placing its path between the background and title layers: <svg ...> <defs> <linearGradient id="grad" …>…</linearGradient> <filter id="trail" …>…</filter> </defs> <!-- GRADIENT --> <!-- TRAIL --> <path filter="url(#trail)" …/> <!-- TITLE GRAPHIC --> </svg> Then come the magical stars, added in the same sequential fashion: <svg ...> <!-- GRADIENT --> <!-- TRAIL --> <!-- STARS --> <!-- TITLE GRAPHIC --> </svg> To keep everything organised and animation-ready, I create an empty group that will hold all the parts of Yogi: <g id="yogi">...</g> Then I build Yogi from the ground up — starting with background props, like his broom: <g id="broom">...</g> Followed by grouped elements for his body, head, collar, and tie: <g id="yogi"> <g id="broom">…</g> <g id="body">…</g> <g id="head">…</g> <g id="collar">…</g> <g id="tie">…</g> </g> Since I export each layer from the same-sized artboard, I don’t need to worry about alignment or positioning issues later on — they’ll all slot into place automatically. I keep my code clean, readable, and ordered logically by layering elements this way. It also makes animating smoother, as each component is easier to identify. Reusing Elements With <use> When duplicate shapes get reused repeatedly, SVG files can get bulky fast. My recreation of the “Bewitched Bear” title card contains 80 stars in three sizes. Combining all those shapes into one optimised path would bring the file size down to 3KB. But I want to animate individual stars, which would almost double that to 5KB: <g id="stars"> <path class="star-small" fill="#eae3da" d="..."/> <path class="star-medium" fill="#eae3da" d="..."/> <path class="star-large" fill="#eae3da" d="..."/> <!-- ... --> </g> Moving the stars’ fill attribute values to their parent group reduces the overall weight a little: <g id="stars" fill="#eae3da"> <path class="star-small" d="…"/> <path class="star-medium" d="…"/> <path class="star-large" d="…"/> <!-- ... --> </g> But a more efficient and manageable option is to define each star size as a reusable template: <defs> <path id="star-large" fill="#eae3da" fill-rule="evenodd" d="…"/> <path id="star-medium" fill="#eae3da" fill-rule="evenodd" d="…"/> <path id="star-small" fill="#eae3da" fill-rule="evenodd" d="…"/> </defs> With this setup, changing a star’s design only means updating its template once, and every instance updates automatically. Then, I reference each one using <use> and position them with x and y attributes: <g id="stars"> <!-- Large stars --> <use href="#star-large" x="1575" y="495"/> <!-- ... --> <!-- Medium stars --> <use href="#star-medium" x="1453" y="696"/> <!-- ... --> <!-- Small stars --> <use href="#star-small" x="1287" y="741"/> <!-- ... --> </g> This approach makes the SVG easier to manage, lighter to load, and faster to iterate on, especially when working with dozens of repeating elements. Best of all, it keeps the markup clean without compromising on flexibility or performance. Adding Animations The stars trailing behind Yogi’s stolen broom bring so much personality to the animation. I wanted them to sparkle in a seemingly random pattern against the dark blue background, so I started by defining a keyframe animation that cycles through different opacity levels: @keyframes sparkle { 0%, 100% { opacity: .1; } 50% { opacity: 1; } } Next, I applied this looping animation to every use element inside my stars group: #stars use { animation: sparkle 10s ease-in-out infinite; } The secret to creating a convincing twinkle lies in variation. I staggered animation delays and durations across the stars using nth-child selectors, starting with the quickest and most frequent sparkle effects: /* Fast, frequent */ #stars use:nth-child(n + 1):nth-child(-n + 10) { animation-delay: .1s; animation-duration: 2s; } From there, I layered in additional timings to mix things up. Some stars sparkle slowly and dramatically, others more randomly, with a variety of rhythms and pauses: /* Medium */ #stars use:nth-child(n + 11):nth-child(-n + 20) { ... } /* Slow, dramatic */ #stars use:nth-child(n + 21):nth-child(-n + 30) { ... } /* Random */ #stars use:nth-child(3n + 2) { ... } /* Alternating */ #stars use:nth-child(4n + 1) { ... } /* Scattered */ #stars use:nth-child(n + 31) { ... } By thoughtfully structuring the SVG and reusing elements, I can build complex-looking animations without bloated code, making even a simple effect like changing opacity sparkle. Then, for added realism, I make Yogi’s head wobble: @keyframes headWobble { 0% { transform: rotate(-0.8deg) translateY(-0.5px); } 100% { transform: rotate(0.9deg) translateY(0.3px); } } #head { animation: headWobble 0.8s cubic-bezier(0.5, 0.15, 0.5, 0.85) infinite alternate; } His tie waves: @keyframes tieWave { 0%, 100% { transform: rotateZ(-4deg) rotateY(15deg) scaleX(0.96); } 33% { transform: rotateZ(5deg) rotateY(-10deg) scaleX(1.05); } 66% { transform: rotateZ(-2deg) rotateY(5deg) scaleX(0.98); } } #tie { transform-style: preserve-3d; animation: tieWave 10s cubic-bezier(0.68, -0.55, 0.27, 1.55) infinite; } His broom swings: @keyframes broomSwing { 0%, 20% { transform: rotate(-5deg); } 30% { transform: rotate(-4deg); } 50%, 70% { transform: rotate(5deg); } 80% { transform: rotate(4deg); } 100% { transform: rotate(-5deg); } } #broom { animation: broomSwing 4s cubic-bezier(0.5, 0.05, 0.5, 0.95) infinite; } And, finally, Yogi himself gently rotates as he flies on his magical broom: @keyframes yogiWobble { 0% { transform: rotate(-2.8deg) translateY(-0.8px) scale(0.998); } 30% { transform: rotate(1.5deg) translateY(0.3px); } 100% { transform: rotate(3.2deg) translateY(1.2px) scale(1.002); } } #yogi { animation: yogiWobble 3.5s cubic-bezier(.37, .14, .3, .86) infinite alternate; } All these subtle movements bring Yogi to life. By developing structured SVGs, I can create animations that feel full of character without writing a single line of JavaScript. Try this yourself: See the Pen Bewitched Bear CSS/SVG animation [forked] by Andy Clarke. Conclusion Whether you’re recreating a classic title card or animating icons for an interface, the principles are the same: Start clean, Optimise early, and Structure everything with animation in mind. SVGs offer incredible creative freedom, but only if kept lean and manageable. When you plan your process like a production cell — layer by layer, element by element — you’ll spend less time untangling code and more time bringing your work to life.
    Like
    Love
    Wow
    Angry
    Sad
    273
    0 Comments 0 Shares 0 Reviews
  • Huawei Supernode 384 disrupts Nvidia’s AI market hold

    Huawei’s AI capabilities have made a breakthrough in the form of the company’s Supernode 384 architecture, marking an important moment in the global processor wars amid US-China tech tensions.The Chinese tech giant’s latest innovation emerged from last Friday’s Kunpeng Ascend Developer Conference in Shenzhen, where company executives demonstrated how the computing framework challenges Nvidia’s long-standing market dominance directly, as the company continues to operate under severe US-led trade restrictions.Architectural innovation born from necessityZhang Dixuan, president of Huawei’s Ascend computing business, articulated the fundamental problem driving the innovation during his conference keynote: “As the scale of parallel processing grows, cross-machine bandwidth in traditional server architectures has become a critical bottleneck for training.”The Supernode 384 abandons Von Neumann computing principles in favour of a peer-to-peer architecture engineered specifically for modern AI workloads. The change proves especially powerful for Mixture-of-Experts modelsHuawei’s CloudMatrix 384 implementation showcases impressive technical specifications: 384 Ascend AI processors spanning 12 computing cabinets and four bus cabinets, generating 300 petaflops of raw computational power paired with 48 terabytes of high-bandwidth memory, representing a leap in integrated AI computing infrastructure.Performance metrics challenge industry leadersReal-world benchmark testing reveals the system’s competitive positioning in comparison to established solutions. Dense AI models like Meta’s LLaMA 3 achieved 132 tokens per second per card on the Supernode 384 – delivering 2.5 times superior performance compared to traditional cluster architectures.Communications-intensive applications demonstrate even more dramatic improvements. Models from Alibaba’s Qwen and DeepSeek families reached 600 to 750 tokens per second per card, revealing the architecture’s optimisation for next-generation AI workloads.The performance gains stem from fundamental infrastructure redesigns. Huawei replaced conventional Ethernet interconnects with high-speed bus connections, improving communications bandwidth by 15 times while reducing single-hop latency from 2 microseconds to 200 nanoseconds – a tenfold improvement.Geopolitical strategy drives technical innovationThe Supernode 384’s development cannot be divorced from broader US-China technological competition. American sanctions have systematically restricted Huawei’s access to cutting-edge semiconductor technologies, forcing the company to maximise performance within existing constraints.Industry analysis from SemiAnalysis suggests the CloudMatrix 384 uses Huawei’s latest Ascend 910C AI processor, which acknowledges inherent performance limitations but highlights architectural advantages: “Huawei is a generation behind in chips, but its scale-up solution is arguably a generation ahead of Nvidia and AMD’s current products in the market.”The assessment reveals how Huawei AI computing strategies have evolved beyond traditional hardware specifications toward system-level optimisation and architectural innovation.Market implications and deployment realityBeyond laboratory demonstrations, Huawei has operationalised CloudMatrix 384 systems in multiple Chinese data centres in Anhui Province, Inner Mongolia, and Guizhou Province. Such practical deployments validate the architecture’s viability and establishes an infrastructure framework for broader market adoption.The system’s scalability potential – supporting tens of thousands of linked processors – positions it as a compelling platform for training increasingly sophisticated AI models. The capability addresses growing industry demands for massive-scale AI implementation in diverse sectors.Industry disruption and future considerationsHuawei’s architectural breakthrough introduces both opportunities and complications for the global AI ecosystem. While providing viable alternatives to Nvidia’s market-leading solutions, it simultaneously accelerates the fragmentation of international technology infrastructure along geopolitical lines.The success of Huawei AI computing initiatives will depend on developer ecosystem adoption and sustained performance validation. The company’s aggressive developer conference outreach indicated a recognition that technical innovation alone cannot guarantee market acceptance.For organisations evaluating AI infrastructure investments, the Supernode 384 represents a new option that combines competitive performance with independence from US-controlled supply chains. However, long-term viability remains contingent on continued innovation cycles and improved geopolitical stability.See also: Oracle plans B Nvidia chip deal for AI facility in TexasWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
    #huawei #supernode #disrupts #nvidias #market
    Huawei Supernode 384 disrupts Nvidia’s AI market hold
    Huawei’s AI capabilities have made a breakthrough in the form of the company’s Supernode 384 architecture, marking an important moment in the global processor wars amid US-China tech tensions.The Chinese tech giant’s latest innovation emerged from last Friday’s Kunpeng Ascend Developer Conference in Shenzhen, where company executives demonstrated how the computing framework challenges Nvidia’s long-standing market dominance directly, as the company continues to operate under severe US-led trade restrictions.Architectural innovation born from necessityZhang Dixuan, president of Huawei’s Ascend computing business, articulated the fundamental problem driving the innovation during his conference keynote: “As the scale of parallel processing grows, cross-machine bandwidth in traditional server architectures has become a critical bottleneck for training.”The Supernode 384 abandons Von Neumann computing principles in favour of a peer-to-peer architecture engineered specifically for modern AI workloads. The change proves especially powerful for Mixture-of-Experts modelsHuawei’s CloudMatrix 384 implementation showcases impressive technical specifications: 384 Ascend AI processors spanning 12 computing cabinets and four bus cabinets, generating 300 petaflops of raw computational power paired with 48 terabytes of high-bandwidth memory, representing a leap in integrated AI computing infrastructure.Performance metrics challenge industry leadersReal-world benchmark testing reveals the system’s competitive positioning in comparison to established solutions. Dense AI models like Meta’s LLaMA 3 achieved 132 tokens per second per card on the Supernode 384 – delivering 2.5 times superior performance compared to traditional cluster architectures.Communications-intensive applications demonstrate even more dramatic improvements. Models from Alibaba’s Qwen and DeepSeek families reached 600 to 750 tokens per second per card, revealing the architecture’s optimisation for next-generation AI workloads.The performance gains stem from fundamental infrastructure redesigns. Huawei replaced conventional Ethernet interconnects with high-speed bus connections, improving communications bandwidth by 15 times while reducing single-hop latency from 2 microseconds to 200 nanoseconds – a tenfold improvement.Geopolitical strategy drives technical innovationThe Supernode 384’s development cannot be divorced from broader US-China technological competition. American sanctions have systematically restricted Huawei’s access to cutting-edge semiconductor technologies, forcing the company to maximise performance within existing constraints.Industry analysis from SemiAnalysis suggests the CloudMatrix 384 uses Huawei’s latest Ascend 910C AI processor, which acknowledges inherent performance limitations but highlights architectural advantages: “Huawei is a generation behind in chips, but its scale-up solution is arguably a generation ahead of Nvidia and AMD’s current products in the market.”The assessment reveals how Huawei AI computing strategies have evolved beyond traditional hardware specifications toward system-level optimisation and architectural innovation.Market implications and deployment realityBeyond laboratory demonstrations, Huawei has operationalised CloudMatrix 384 systems in multiple Chinese data centres in Anhui Province, Inner Mongolia, and Guizhou Province. Such practical deployments validate the architecture’s viability and establishes an infrastructure framework for broader market adoption.The system’s scalability potential – supporting tens of thousands of linked processors – positions it as a compelling platform for training increasingly sophisticated AI models. The capability addresses growing industry demands for massive-scale AI implementation in diverse sectors.Industry disruption and future considerationsHuawei’s architectural breakthrough introduces both opportunities and complications for the global AI ecosystem. While providing viable alternatives to Nvidia’s market-leading solutions, it simultaneously accelerates the fragmentation of international technology infrastructure along geopolitical lines.The success of Huawei AI computing initiatives will depend on developer ecosystem adoption and sustained performance validation. The company’s aggressive developer conference outreach indicated a recognition that technical innovation alone cannot guarantee market acceptance.For organisations evaluating AI infrastructure investments, the Supernode 384 represents a new option that combines competitive performance with independence from US-controlled supply chains. However, long-term viability remains contingent on continued innovation cycles and improved geopolitical stability.See also: Oracle plans B Nvidia chip deal for AI facility in TexasWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #huawei #supernode #disrupts #nvidias #market
    WWW.ARTIFICIALINTELLIGENCE-NEWS.COM
    Huawei Supernode 384 disrupts Nvidia’s AI market hold
    Huawei’s AI capabilities have made a breakthrough in the form of the company’s Supernode 384 architecture, marking an important moment in the global processor wars amid US-China tech tensions.The Chinese tech giant’s latest innovation emerged from last Friday’s Kunpeng Ascend Developer Conference in Shenzhen, where company executives demonstrated how the computing framework challenges Nvidia’s long-standing market dominance directly, as the company continues to operate under severe US-led trade restrictions.Architectural innovation born from necessityZhang Dixuan, president of Huawei’s Ascend computing business, articulated the fundamental problem driving the innovation during his conference keynote: “As the scale of parallel processing grows, cross-machine bandwidth in traditional server architectures has become a critical bottleneck for training.”The Supernode 384 abandons Von Neumann computing principles in favour of a peer-to-peer architecture engineered specifically for modern AI workloads. The change proves especially powerful for Mixture-of-Experts models (machine-learning systems using multiple specialised sub-networks to solve complex computational challenges.)Huawei’s CloudMatrix 384 implementation showcases impressive technical specifications: 384 Ascend AI processors spanning 12 computing cabinets and four bus cabinets, generating 300 petaflops of raw computational power paired with 48 terabytes of high-bandwidth memory, representing a leap in integrated AI computing infrastructure.Performance metrics challenge industry leadersReal-world benchmark testing reveals the system’s competitive positioning in comparison to established solutions. Dense AI models like Meta’s LLaMA 3 achieved 132 tokens per second per card on the Supernode 384 – delivering 2.5 times superior performance compared to traditional cluster architectures.Communications-intensive applications demonstrate even more dramatic improvements. Models from Alibaba’s Qwen and DeepSeek families reached 600 to 750 tokens per second per card, revealing the architecture’s optimisation for next-generation AI workloads.The performance gains stem from fundamental infrastructure redesigns. Huawei replaced conventional Ethernet interconnects with high-speed bus connections, improving communications bandwidth by 15 times while reducing single-hop latency from 2 microseconds to 200 nanoseconds – a tenfold improvement.Geopolitical strategy drives technical innovationThe Supernode 384’s development cannot be divorced from broader US-China technological competition. American sanctions have systematically restricted Huawei’s access to cutting-edge semiconductor technologies, forcing the company to maximise performance within existing constraints.Industry analysis from SemiAnalysis suggests the CloudMatrix 384 uses Huawei’s latest Ascend 910C AI processor, which acknowledges inherent performance limitations but highlights architectural advantages: “Huawei is a generation behind in chips, but its scale-up solution is arguably a generation ahead of Nvidia and AMD’s current products in the market.”The assessment reveals how Huawei AI computing strategies have evolved beyond traditional hardware specifications toward system-level optimisation and architectural innovation.Market implications and deployment realityBeyond laboratory demonstrations, Huawei has operationalised CloudMatrix 384 systems in multiple Chinese data centres in Anhui Province, Inner Mongolia, and Guizhou Province. Such practical deployments validate the architecture’s viability and establishes an infrastructure framework for broader market adoption.The system’s scalability potential – supporting tens of thousands of linked processors – positions it as a compelling platform for training increasingly sophisticated AI models. The capability addresses growing industry demands for massive-scale AI implementation in diverse sectors.Industry disruption and future considerationsHuawei’s architectural breakthrough introduces both opportunities and complications for the global AI ecosystem. While providing viable alternatives to Nvidia’s market-leading solutions, it simultaneously accelerates the fragmentation of international technology infrastructure along geopolitical lines.The success of Huawei AI computing initiatives will depend on developer ecosystem adoption and sustained performance validation. The company’s aggressive developer conference outreach indicated a recognition that technical innovation alone cannot guarantee market acceptance.For organisations evaluating AI infrastructure investments, the Supernode 384 represents a new option that combines competitive performance with independence from US-controlled supply chains. However, long-term viability remains contingent on continued innovation cycles and improved geopolitical stability.(Image from Pixabay)See also: Oracle plans $40B Nvidia chip deal for AI facility in TexasWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
    0 Comments 0 Shares 0 Reviews
CGShares https://cgshares.com