Origami

origami

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

Design, Manufacturing and Open-Loop Control of a Soft Pneumatic Arm: Conclusions

Authors:
(1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es);
(2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain;
(3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.2 Material Selection 3.3 Manufacturing 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.5 Weight Carrying Experiments 6 Conclusions Funding Information A. Conducted Experiments and References 2 Related Works 2.1 Pneumatic Actuation There are numerous possibilities for designing and constructing pneumatic actuators. As is common in soft robotics, bio-inspired actuators exist. In [21], an actuator is presented that combines both pneumatics and tendons to mimic and attempts to mimic the behaviour of an octopus arm. Similar in final operation, although inspired by the human finger, can be considered the work of [22]. This consists of three one-dimensional valves that swell, thus mimicking the movement of the phalanges. Nevertheless, the most common bio-inspired pneumatic actuators are Pneumatic Artificial Muscles (PAMs). They are based on achieving extension or contraction of a pneumatic chamber, similar to that of a muscle. The walls of the chamber are usually made of a very thin and flexible membrane, which facilitates large deformations with little air flow introduced [23]. Although some actuators lengthen longitudinally upon inflation [24], the prevalent approach involves utilising McKibben muscles, consisting of a bladder inserted into a braided mesh that constrains the movement of the bladder as it inflates, producing a contraction of the whole [25]. An arm of two PAM segments is designed and modelled in [26]. Another alternative that has gained particular importance in recent years is the Pneunet-type actuators, firstly introduced in [27]. Manipulators that use this type of action consist of a finned beam type structure and, in some cases, some material of varying stiffness. It has been widely used for grippers [28, 29], soft gloves [30] and for modelling human body parts [31]. Different authors have proposed evolutions to this structure. An optimisation of the geometry is carried out at [32], whereas [33] presents PneuFlex actuator, which has evolved the Pneunet concept by making the beam have a variable cross-section. On the other hand, the works of [34] and [35] show how it is possible to design pneumatic segments based on this actuator with bidirectional bending. Jamming, initially used for soft grippers [36] can also be used to create manipulators in the form of beams [37]. Their function is the reverse of that of Pneunets: in their natural state they are bent and, when negative pressure is applied, they stiffen. In [38], a TPU-printed segment at which pressure or vacuum can be applied, is introduced. 2.2 Pneumatic Arms In addition to the design of actuators, pneumatic soft robotics has to face the challenge of their integration for the construction of arms with various degrees of freedom. While some of the previous work, such as [26], does form small arms, several alternatives have been developed for the construction of manipulators. A first option are hybrid approaches, in which both rigid and purely soft elements are combined, which makes it possible to obtain a relatively stable mechanism in a simple way. An example of this can be found in the work [39], in which antagonistically actuated PAM pairs are used to move a rigid beam arm with seven degrees of freedom. A very similar procedure is followed in [40]. To prevent the robot from causing harm to humans, inflatable sleeves are added to the arm. In [41], a pneumatic segment with rigid bases is developed. This consists of six tubes which, due to their geometry, when inflated in groups of three, allow the assembly to rotate on the axis perpendicular to the bases. Although a whole robotic arm is not developed as such, but only a segment of one degree of freedom, it is integrated into an entirely soft robotic arm. The same authors had previously developed a six-degree-of-freedom robotic arm from 85 cm-long segments, capable of lifting loads of up to 3 kg [42]. The work of [43], on the other hand, presents an origami robotic arm made of TPU. This is inflated and deflated by pneumatic actuation, but its position is controlled by tendons which, while slowing down the system, make it much more precise. Among 3D printed robots, [44] presents a three-degree-of-freedom segment, whose movement is achieved by inflating or deflating one or more of its three pneumatic tubes. It also has cables that play the role of antagonist and stiffen the movements of the manipulator. Its working principle is very similar to that of the work of [1]. Also based on the philosophy of printable and deployable robots, [45, 46] present a Honeycomb Pneumatic Network (HPN) arm. It has been constructed by concatenating TPU honeycomb structures, each with an airbag inside. Several prototypes are presented in the paper, one of which can reach a length of 600 mm after joining four segments together. While several of its advantages are discussed, it presents the problem that its weight, without being exaggerated, is high: the arm, considering all the tubes, weighs 4.4 kg. The robot of [47] consists of two segments, each with three pneumatic tubes. Although this theoretically gives it six degrees of freedom, the controllers are only responsible for the upper module, which does not allow all positions and orientations to be freely fixed. The STIFF-FLOP manipulator was introduced in [48]. This consists of an elastomeric cylinder with a series of pneumatic chambers inside, the inflation and deflation of which causes deformation of the cylinder and therefore movement of the robot. Various iterations of this design exist, such as the STIFF-FLOP segment with stiffening tendons demonstrated in [31]. In this line, SoPrA, presented in [49], is made combining three fibre-reinforced silicone segments, each shaped like a conical trunk, so that the end of the robot is much narrower than the base. Although the truncated cone shape is advantageous because, as the authors point out, the upper segments require more torque and hold more tubes inside them, the manufacturing process used to achieve the taper prevents new segments from being easily added to the robot. In [50], a 3-segment arm is built using silicone rubber. Each segment is 110 mm long, has a diameter of 45 mm and, contrary to traditional STIFF-FLOP structure, it is equipped with four inflatable cavities. This implies an increase of weight and difficulty of control, as redundancy is increased compared to having only three degrees of freedom per segment. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Authors: Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.2 Pneumatic Arms 2.3 Control of Soft Robots 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.1 Robot Design 3.2 Material Selection 3.2 Material Selection 3.3 Manufacturing 3.3 Manufacturing 3.4 Actuation Bank 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.1 Hardware Setup 4.2 Vision Capture System 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.1 Final PAUL version 5.2 Workspace Analysis 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.4 Bending Experiments 5.5 Weight Carrying Experiments 5.5 Weight Carrying Experiments 6 Conclusions 6 Conclusions Funding Information Funding Information A. Conducted Experiments and References A. Conducted Experiments and References 2 Related Works 2.1 Pneumatic Actuation There are numerous possibilities for designing and constructing pneumatic actuators. As is common in soft robotics, bio-inspired actuators exist. In [21], an actuator is presented that combines both pneumatics and tendons to mimic and attempts to mimic the behaviour of an octopus arm. Similar in final operation, although inspired by the human finger, can be considered the work of [22]. This consists of three one-dimensional valves that swell, thus mimicking the movement of the phalanges. Nevertheless, the most common bio-inspired pneumatic actuators are Pneumatic Artificial Muscles (PAMs). They are based on achieving extension or contraction of a pneumatic chamber, similar to that of a muscle. The walls of the chamber are usually made of a very thin and flexible membrane, which facilitates large deformations with little air flow introduced [23]. Although some actuators lengthen longitudinally upon inflation [24], the prevalent approach involves utilising McKibben muscles, consisting of a bladder inserted into a braided mesh that constrains the movement of the bladder as it inflates, producing a contraction of the whole [25]. An arm of two PAM segments is designed and modelled in [26]. Another alternative that has gained particular importance in recent years is the Pneunet-type actuators, firstly introduced in [27]. Manipulators that use this type of action consist of a finned beam type structure and, in some cases, some material of varying stiffness. It has been widely used for grippers [28, 29], soft gloves [30] and for modelling human body parts [31]. Different authors have proposed evolutions to this structure. An optimisation of the geometry is carried out at [32], whereas [33] presents PneuFlex actuator, which has evolved the Pneunet concept by making the beam have a variable cross-section. On the other hand, the works of [34] and [35] show how it is possible to design pneumatic segments based on this actuator with bidirectional bending. Jamming, initially used for soft grippers [36] can also be used to create manipulators in the form of beams [37]. Their function is the reverse of that of Pneunets: in their natural state they are bent and, when negative pressure is applied, they stiffen. In [38], a TPU-printed segment at which pressure or vacuum can be applied, is introduced. 2.2 Pneumatic Arms In addition to the design of actuators, pneumatic soft robotics has to face the challenge of their integration for the construction of arms with various degrees of freedom. While some of the previous work, such as [26], does form small arms, several alternatives have been developed for the construction of manipulators. A first option are hybrid approaches, in which both rigid and purely soft elements are combined, which makes it possible to obtain a relatively stable mechanism in a simple way. An example of this can be found in the work [39], in which antagonistically actuated PAM pairs are used to move a rigid beam arm with seven degrees of freedom. A very similar procedure is followed in [40]. To prevent the robot from causing harm to humans, inflatable sleeves are added to the arm. In [41], a pneumatic segment with rigid bases is developed. This consists of six tubes which, due to their geometry, when inflated in groups of three, allow the assembly to rotate on the axis perpendicular to the bases. Although a whole robotic arm is not developed as such, but only a segment of one degree of freedom, it is integrated into an entirely soft robotic arm. The same authors had previously developed a six-degree-of-freedom robotic arm from 85 cm-long segments, capable of lifting loads of up to 3 kg [42]. The work of [43], on the other hand, presents an origami robotic arm made of TPU. This is inflated and deflated by pneumatic actuation, but its position is controlled by tendons which, while slowing down the system, make it much more precise. Among 3D printed robots, [44] presents a three-degree-of-freedom segment, whose movement is achieved by inflating or deflating one or more of its three pneumatic tubes. It also has cables that play the role of antagonist and stiffen the movements of the manipulator. Its working principle is very similar to that of the work of [1]. Also based on the philosophy of printable and deployable robots, [45, 46] present a Honeycomb Pneumatic Network (HPN) arm. It has been constructed by concatenating TPU honeycomb structures, each with an airbag inside. Several prototypes are presented in the paper, one of which can reach a length of 600 mm after joining four segments together. While several of its advantages are discussed, it presents the problem that its weight, without being exaggerated, is high: the arm, considering all the tubes, weighs 4.4 kg. The robot of [47] consists of two segments, each with three pneumatic tubes. Although this theoretically gives it six degrees of freedom, the controllers are only responsible for the upper module, which does not allow all positions and orientations to be freely fixed. The STIFF-FLOP manipulator was introduced in [48]. This consists of an elastomeric cylinder with a series of pneumatic chambers inside, the inflation and deflation of which causes deformation of the cylinder and therefore movement of the robot. Various iterations of this design exist, such as the STIFF-FLOP segment with stiffening tendons demonstrated in [31]. In this line, SoPrA, presented in [49], is made combining three fibre-reinforced silicone segments, each shaped like a conical trunk, so that the end of the robot is much narrower than the base. Although the truncated cone shape is advantageous because, as the authors point out, the upper segments require more torque and hold more tubes inside them, the manufacturing process used to achieve the taper prevents new segments from being easily added to the robot. In [50], a 3-segment arm is built using silicone rubber. Each segment is 110 mm long, has a diameter of 45 mm and, contrary to traditional STIFF-FLOP structure, it is equipped with four inflatable cavities. This implies an increase of weight and difficulty of control, as redundancy is increased compared to having only three degrees of freedom per segment. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Soft, Squeezable, and Smarter Than Ever: The Rise of Pneumatic Robots

Meko, stisljivo i pametnije nego ikad: Uspon pneumatskih robota

Měkké, stlačitelné a chytřejší než kdy jindy: Vzestup pneumatických robotů

Suaves, flexibles y más inteligentes que nunca: el auge de los robots neumáticos

नरम, दबाने योग्य और पहले से कहीं अधिक स्मार्ट: न्यूमेटिक रोबोट का उदय

柔らかく、握りやすく、かつてないほどスマート：空気圧ロボットの台頭

Меки, стискачки и попаметни од кога било: Подемот на пневматските роботи

نرم، د فشار وړ، او تر بل هر وخت هوښیار: د نیوماتیک روبوټونو عروج

Yakapfava, Inosvinwa, uye Yakangwara Kupfuura Zvakamboitika: Kukwidzwa kwePneumatic Robots

Нарм, фишурдашаванда ва аз ҳарвақта донотар: Афзоиши роботҳои пневматикӣ

Everumşak, gysylýan we öňküsinden has akylly: Pnewmatik robotlaryň döremegi

Ku Olova, Ku Pfuxetiwa Ni Ku Tlhariha Ku Tlula Rini Na Rini: Ku Tlakuka Ka Tirhoboto Ta Pneumatic

Yumshoq, siqib chiquvchi va har qachongidan ham aqlli: pnevmatik robotlarning yuksalishi

Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Method 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 6 Discussion 7 Acknowledgement and References A Implementation Details 5.1 QUALITATIVE EVALUATION Fig. 6 provides a qualitative comparison of our method to prominent baselines; please refer to SM for the full videos. Our method (bottom row) outputs videos that better adhere to the edit prompt while maintaining temporal consistency of the resulting edited video, while other methods struggle to meet both these goals. Tune-A-Video (second row) inflates the 2D image model into a video model, and fine-tunes it to overfit the motion of the video; thus, it is suitable for short clips. For long videos it struggles to capture the motion resulting with meaningless edits, e.g., the shiny metal sculpture. Applying PnP for each frame independently (third row) results in exquisite edits adhering to the edit prompt but, as expected, lack any temporal consistency. The results of Gen-1 (fourth row) also suffer from some temporal inconsistencies (the beak of the origami stork changes color). Moreover, their frame quality is significantly worse than that of a text-to-image diffusion model. The edits of Text2Video-Zero and Fate-Zero (fifth and sixth row) suffer from severe jittering as these methods rely heavily on the extended attention mechanism to implicitly encourage consistency. The results of Rerender-a-Video exhibit notable long-range inconsistencies and artifacts arising primarily from their reliance on optical flow estimation for distant frames (e.g. keyframes), which is known to be sub-optimal (See our video results in the SM; when the wolf turns its head, the nose color changes). We provide qualitative comparison to Text2LIVE and to a RGB propagation baseline in the SM. 5.2 QUANTITATIVE EVALUATION We evaluate our method in terms of: (i) edit fidelity measured by computing the average similarity between the CLIP embedding (Radford et al., 2021) of each edited frame and the target text prompt; (ii) temporal consistency. Following Ceylan et al. (2023); Lai et al. (2018a), temporal consistency is measured by (a) computing the optical flow of the original video using Teed & Deng (2020), warping the edited frames according to it, and measuring the warping error, and (b) a user study; We adopt a Two-alternative Forced Choice (2AFC) protocol suggested in Kolkin et al. (2019); Park et al. (2020), where participants are shown the input video, ours and a baseline result, and are asked to determine which video is more temporally consistent and better preserves the motion of the original video. The survey consists of 2000-3000 judgments per baseline obtained using Amazon mechanical turk. We note that warpingerror could not be measured for Gen1 since their product platform does not output the same number of input frames. Table 1 compares our method to baselines. Our method achieves the highest CLI score, showing a good fit between the edited video and the input guidance prompt. Furthermore, our method has a low warping error, indicating temporally consistent results. We note that Re-rendera-Video optimizes for the warping error and uses optical flow to propagate the edit, and hence has the lowest warping error; However, this reliance on optical flow often creates artifacts and longrange inconsistencies which are not reflected in the warping error. Nonetheless, they are apparent in the user study, that shows users significantly favoured our method over all baselines in terms of temporal consistency. Additionally, we consider the reference baseline of passing the original video through the LDM auto-encoder without performing editing (LDM recon.). This baseline provides an upper bound on the temporal consistency achievable by LDM auto-encoder. As expected, the CLIP similarity of this baseline is poor as it does not involve any editing. However, this baseline does not achieve zero warp error either due to the imperfect reconstruction of the LDM auto-encoder, which hallucinates high-frequency information We further evaluate our correspondences and video representation by measuring the accuracy of video reconstruction using TokenFlow. Specifically, we reconstruct the video using the same pipeline of our editing method, only removing the keyframes editing part. Table 2 reports the PSNR and LPIPS distance of this reconstruction, compared to vanilla DDIM reconstruction. As seen, TokenFlow reconstruction slightly improves DDIM inversion, demonstrating robust frame representation. This improvement can be attributed to the keyframe randomization; It increases robustness to challenging frames since each frame is reconstructed from multiple other frames during the generation. Notably, our evaluation focuses on accurate correspondences within the feature space during generation, rather than RGB frame correspondences evaluation, which is not essential to our method. This paper is available on arxiv under CC BY 4.0 DEED DEED license. Authors:
(1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution;
(2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution;
(3) Shai Bagon, Weizmann Institute of Science;
(4) Tali Dekel, Weizmann Institute of Science. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Method 4 Method 4.1 Key Sample and Joint Editing 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 4.2 Edit Propagation Via TokenFlow 5 Results 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 5.3 Ablation Study 6 Discussion 6 Discussion 7 Acknowledgement and References 7 Acknowledgement and References A Implementation Details A Implementation Details 5.1 QUALITATIVE EVALUATION Fig. 6 provides a qualitative comparison of our method to prominent baselines; please refer to SM for the full videos. Our method (bottom row) outputs videos that better adhere to the edit prompt while maintaining temporal consistency of the resulting edited video, while other methods struggle to meet both these goals. Tune-A-Video (second row) inflates the 2D image model into a video model, and fine-tunes it to overfit the motion of the video; thus, it is suitable for short clips. For long videos it struggles to capture the motion resulting with meaningless edits, e.g., the shiny metal sculpture. Applying PnP for each frame independently (third row) results in exquisite edits adhering to the edit prompt but, as expected, lack any temporal consistency. The results of Gen-1 (fourth row) also suffer from some temporal inconsistencies (the beak of the origami stork changes color). Moreover, their frame quality is significantly worse than that of a text-to-image diffusion model. The edits of Text2Video-Zero and Fate-Zero (fifth and sixth row) suffer from severe jittering as these methods rely heavily on the extended attention mechanism to implicitly encourage consistency. The results of Rerender-a-Video exhibit notable long-range inconsistencies and artifacts arising primarily from their reliance on optical flow estimation for distant frames (e.g. keyframes), which is known to be sub-optimal (See our video results in the SM; when the wolf turns its head, the nose color changes). We provide qualitative comparison to Text2LIVE and to a RGB propagation baseline in the SM. 5.2 QUANTITATIVE EVALUATION We evaluate our method in terms of: (i) edit fidelity measured by computing the average similarity between the CLIP embedding (Radford et al., 2021) of each edited frame and the target text prompt; (ii) temporal consistency. Following Ceylan et al. (2023); Lai et al. (2018a), temporal consistency is measured by (a) computing the optical flow of the original video using Teed & Deng (2020), warping the edited frames according to it, and measuring the warping error, and (b) a user study; We adopt a Two-alternative Forced Choice (2AFC) protocol suggested in Kolkin et al. (2019); Park et al. (2020), where participants are shown the input video, ours and a baseline result, and are asked to determine which video is more temporally consistent and better preserves the motion of the original video. The survey consists of 2000-3000 judgments per baseline obtained using Amazon mechanical turk. We note that warpingerror could not be measured for Gen1 since their product platform does not output the same number of input frames. Table 1 compares our method to baselines. Our method achieves the highest CLI score, showing a good fit between the edited video and the input guidance prompt. Furthermore, our method has a low warping error, indicating temporally consistent results. We note that Re-rendera-Video optimizes for the warping error and uses optical flow to propagate the edit, and hence has the lowest warping error; However, this reliance on optical flow often creates artifacts and longrange inconsistencies which are not reflected in the warping error. Nonetheless, they are apparent in the user study, that shows users significantly favoured our method over all baselines in terms of temporal consistency. Additionally, we consider the reference baseline of passing the original video through the LDM auto-encoder without performing editing (LDM recon.). This baseline provides an upper bound on the temporal consistency achievable by LDM auto-encoder. As expected, the CLIP similarity of this baseline is poor as it does not involve any editing. However, this baseline does not achieve zero warp error either due to the imperfect reconstruction of the LDM auto-encoder, which hallucinates high-frequency information We further evaluate our correspondences and video representation by measuring the accuracy of video reconstruction using TokenFlow. Specifically, we reconstruct the video using the same pipeline of our editing method, only removing the keyframes editing part. Table 2 reports the PSNR and LPIPS distance of this reconstruction, compared to vanilla DDIM reconstruction. As seen, TokenFlow reconstruction slightly improves DDIM inversion, demonstrating robust frame representation. This improvement can be attributed to the keyframe randomization; It increases robustness to challenging frames since each frame is reconstructed from multiple other frames during the generation. Notably, our evaluation focuses on accurate correspondences within the feature space during generation, rather than RGB frame correspondences evaluation, which is not essential to our method. This paper is available on arxiv under CC BY 4.0 DEED DEED license. This paper is available on arxiv under CC BY 4.0 DEED DEED license. available on arxiv Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science. Authors: Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science.

Qualitative Evaluation and Quantitative Evaluation: Comparing Our Method to Others

Welcome back to this series where we are learning how to integrate products into web application. AI Intro & Setup Your First AI Prompt Streaming Responses How Does AI Work Prompt Engineering AI-Generated Images Security & Reliability Deploying In this post, we are going to use AI to generate images. Before we get to that, let’s add a dialog component to our app. This is a modal-like pop-up designed to show content and be dismissed with the mouse or keyboard. https://www.youtube.com/watch?v=V3DjC6-sGBA&embedable=true Create A Dialog Component Before showing an image, we need a place to put it. I think it would be nice to have a dialog that pops up to showcase the image. This is a good opportunity to spend more time with Qwik components. By convention, our new component should go in the folder. I’ll call mine (or if you prefer TypeScript). A Qwik component file should have a default export of a Qwick component. ./src/components Dialog.jsx .tsx To create one, we use the function from . This function takes a function component that returns JSX: component$ @builder.io/qwik import { component$ } from "@builder.io/qwik";

export default component$((props) => {

  return (
    <div>
      
    </div>
  )
}) At the moment, it’s not very useful. Let’s plan out the API design for this dialog. It should have the following characteristics: Opened by clicking on a button. Can be opened programmatically from the parent. Closed by pressing the Esc key. Closed by clicking the dialog background. Can be closed programmatically from the parent. has a element which is great, but it doesn’t quite offer the API that I’m looking for. With a little bit of work, we can fill in the gaps. HTML <dialog> Let’s start with a component that provides a element for controlling the dialog and the element that will contain any content you put inside. Clicking the button should trigger the method. Clicking outside the dialog should trigger the method. <button> <dialog> dialog.showModal() dialog.close() Pressing the Esc key closes the dialog already, so that’s sorted. To trigger the dialog methods, we need access to the DOM node, which we can get by . In our dialog component, we can add content using a , a flexible space inside the dialog where you can put content. using a ref and a signal <Slot> We should also provide a btnText prop to allow for customization of the text on the button that opens the dialogue. Here’s what I have so far: import { component$, useSignal, Slot } from "@builder.io/qwik";

export default component$(({ btnText }) => {
  const dialogRef = useSignal()

  return (
    <div>
      <button onClick$={() => dialogRef.value.showModal()}>
        {btnText}
      </button>

      <dialog
        ref={dialogRef}
        onClick$={(event) => {
          if (event.target.localName !== 'dialog') return
          dialogRef.value.close()
        }}
      >
        <div class="p-2">
          <Slot></Slot>
        </div>
      </dialog>
    </div>
  )
}) Note the additional inside the . This lets me add some padding, but more importantly, it helps me track whether a click on the dialog happened in the background (the dialog element) or on the content (the div and children). This lets us close the dialog only when the background is clicked. <div> <dialog> That gets us some basic functionality, but for my use case, I want to be able to programmatically open the dialog from the parent, not just when the toggle button is clicked. For that, we need a small refactor. To control the dialog from a parent, we need to add a new prop and trigger the dialog methods as it changes. Rather than repeat the open/close functionality for internal external changes, let’s create a local state with to track whether the dialog should be shown or not. and useStore() Then, we can simply toggle the state and respond to those changes using a (it’s like in React). If the prop from the parent changes, we need to respond to that change using another task, but this time, we’ll use . Qwik task useEffect open useVisibleTask$() We also need to provide a way for the parent component to be aware of changes to the dialog’s visibility. We can do that by providing a custom prop that will call the function whenever the dialog closes. And lastly, if we’re providing programmatic control from the parent, we may want to provide a way to hide the . onClose$ <button> import { component$, useSignal, Slot, useStore, useTask$, useVisibleTask$ } from "@builder.io/qwik";

export default component$(({ btnText, open, onClose$ }) => {
  const dialogRef = useSignal()
  const state = useStore({
    isOpen: false,
  })

  useTask$(({ track }) => {
    track(() => state.isOpen)

    const dialog = dialogRef.value
    if (!dialog) return

    if (state.isOpen) {
      dialog.showModal()
    } else {
      dialog.close()
      onClose$ && onClose$()
    }
  })
  useVisibleTask$(({ track }) => {
    track(() => open)
    state.isOpen = open || false
  })

  return (
    <div>
      {btnText && (
        <button onClick$={() => state.isOpen = true}>
          {btnText}
        </button>
      )}

      <dialog
        ref={dialogRef}
        onClick$={(event) => {
          if (event.target.localName !== 'dialog') return
          state.isOpen = false
        }}
      >
        <div class="p-2">
          <Slot></Slot>
        </div>
      </dialog>
    </div>
  )
}) This is a lot better, but there’s still a little work to do. I like to make sure my components are and typed. In this case, since we’re using a button to control the visibility of the dialog, it makes sense to add and attributes to the button. accessible aria-controls aria-expanded To connect them to the dialog, the dialog needs an ID, which we can either take from the props or dynamically generate. Lastly, pressing escape will close the dialog, but we also need to track the dialog’s native event by attaching an handler. "close" onClose$ Here is my finished component, including : JSDoc type definitions import {component$, useSignal, Slot, useStore, useTask$, useVisibleTask$ } from "@builder.io/qwik";
import { randomString } from "~/utils.js";

/**
 * @typedef {HTMLAttributes<HTMLDialogElement>} DialogAttributes
 *
 * @type {Component<DialogAttributes  & {
 * toggle: string|false,
 * open?: Boolean,
 * onClose$?: import('@builder.io/qwik').PropFunction<() => any>
 * }>}
 */
export default component$(({ toggle, open, onClose$, ...props }) => {
  const id = props.id || randomString(8)

  const dialogRef = useSignal()
  const state = useStore({
    isOpen: false,
  })

  useTask$(({ track }) => {
    track(() => state.isOpen)

    const dialog = dialogRef.value
    if (!dialog) return

    if (state.isOpen) {
      dialog.showModal()
    } else {
      dialog.close()
      onClose$ && onClose$()
    }
  })
  useVisibleTask$(({ track }) => {
    track(() => open)
    state.isOpen = open || false
  })

  return (
    <div>
      {toggle && (
        <button aria-controls={id} aria-expanded={state.isOpen} onClick$={() => state.isOpen = true}>
          {toggle}
        </button>
      )}

      <dialog
        ref={dialogRef}
        id={id}
        onClick$={(event) => {
          if (event.target.localName !== 'dialog') return
          state.isOpen = false
        }}
        onClose$={() => state.isOpen = false}
        {...props}
      >
        <div class="p-2">
          <Slot></Slot>
        </div>
      </dialog>
    </div>
  )
}) Cool! Now that we have a working dialog component, we need to put something in it. Generate AI Images With OpenAI Once the AI has determined a winner between opponent1 and opponent2, it would be cool to offer an image of them in combat. So why not add a button that says “Show me” after the results are available? After the text in the template, we could add a conditional like this: {state.winner && (
  <button>
    Show me
  </button>
)} Awesome! It’s just too bad it doesn’t do anything… To actually generate the AI image, we need to make another API request to OpenAI which means we need another API endpoint in our backend. We’ve already assigned a request handler to the current route. Let’s add a new route to handle GET requests to by adding a new file in . /ai-image /src/routes/ai-image/index.js In many cases, a new route may need to return HTML from the server to generate a page. That’s not the case for this route. This will only ever return JSON, so it doesn’t need to be a JSX file or return a component. Instead, we can export a custom like we did for post requests on the first page. To do that, we create a named export called that can look something like this: middleware onGet export const onGet = async (requestEvent) => {
  requestEvent.send(200, JSON.stringify({ some: 'data' }))
} Now, to get the route working, we need to do the following: Grab opponent1 and opponent2 from the request (we’ll use query parameters). Use the opponents to construct a prompt using LangChain templates. Create a body for the OpenAI API request containing the prompt and the image size we want (I’ll use ). "512x512" Create the authenticated HTTP request to OpenAI. Respond to the initial request with the URL of the generated image. For more details on working with images through OpenAI, refer to their . documentation Here’s how my implementation looks: import { PromptTemplate } from 'langchain/prompts'

const mods = [
  'cinematic',
  'high resolution',
  'epic',
];

const promptTemplate = new PromptTemplate({
  template: `{opponent1} and {opponent2} in a battle to the death, ${mods.join(', ')}`,
  inputVariables: ['opponent1', 'opponent2']
})

/** @type {import('@builder.io/qwik-city').RequestHandler} */
export const onGet = async (requestEvent) => {
  const OPENAI_API_KEY = requestEvent.env.get('OPENAI_API_KEY')

  const opponent1 = requestEvent.query.get('opponent1')
  const opponent2 = requestEvent.query.get('opponent2')

  const prompt = await promptTemplate.format({
    opponent1: opponent1,
    opponent2: opponent2
  })

  const body = {
    prompt: prompt,
    size: '512x512',
  }

  const response = await fetch('https://api.openai.com/v1/images/generations', {
    method: 'post',
    headers: {
      'Content-Type': 'application/json',
      Authorization: `Bearer ${OPENAI_API_KEY}`,
    },
    body: JSON.stringify(body)
  })
  const results = await response.json()

  requestEvent.send(200, JSON.stringify(results.data[0]))
} It’s worth noting that the response from OpenAI should look something like this: {
  "created": 1589478378,
  "data": [
    {
      "url": "https://..."
    },
    {
      "url": "https://..."
    },
  ]
} Also worth mentioning is the lack of validation for the opponents. It’s always a good idea to validate user input before processing it. Why don’t you try addressing that as an additional challenge? Provide AI Images With Art Direction In the code example above, you may have noticed the array that gets joined and appended to the end of the prompt. This is worth a callout. mods Generative images are tricky because the same prompt can return drastically different results. You might get a cartoon, an oil painting, or a sketch. So, it’s important to include a couple of hints to the AI to match the aesthetic of your application. I found that using an array with several different options allowed me to easily toggle off various features. In fact, in my actual project, I keep a much longer list of options, organized by style, format, quality, and effect. const mods = [
  /** Style */
  // 'Abstract',
  // 'Academic',
  // 'Action painting',
  // 'Aesthetic',
  // 'Angular',
  // 'Automatism',
  // 'Avant-garde',
  // 'Baroque',
  // 'Bauhaus',
  // 'Contemporary',
  // 'Cubism',
  // 'Cyberpunk',
  // 'Digital art',
  // 'photo',
  // 'vector art',
  // 'Expressionism',
  // 'Fantasy',
  // 'Impressionism',
  // 'kiyo-e',
  // 'Medieval',
  // 'Minimal',
  // 'Modern',
  // 'Pixel art',
  // 'Realism',
  // 'sci-fi',
  // 'Surrealism',
  // 'synthwave',
  // '3d-model',
  // 'analog-film',
  // 'anime',
  // 'comic-book',
  // 'enhance',
  // 'fantasy-art',
  // 'isometric',
  // 'line-art',
  // 'low-poly',
  // 'modeling-compound',
  // 'origami',
  // 'photographic',
  // 'tile-texture',

  /** Format */
  // '3D render',
  // 'Blender Model',
  // 'CGI rendering',
  'cinematic',
  // 'Detailed render',
  // 'oil painting',
  // 'unreal engine 5',
  // 'watercolor',
  // 'cartoon',
  // 'anime',
  // 'colored pencil',

  /** Quality */
  'high resolution',
  // 'high-detail',
  // 'low-poly',
  // 'photographic',
  // 'photorealistic',
  // 'realistic',

  /** Effects */
  // 'Beautiful lighting',
  // 'Cinematic lighting',
  // 'Dramatic',
  // 'dramatic lighting',
  // 'Dynamic lighting',
  'epic',
  // 'Portrait lighting',
  // 'Volumetric lighting',
]; I’ve also found that it’s better to include these modifiers at the end of the prompt, otherwise, they can be forgotten. Request an AI-Generated Image Now that our endpoint is ready, we can start using it. We need to send an HTTP request with query parameters including opponent1 and opponent2. We can pull those values from the s on demand, but I prefer to maintain some reactive state that gets updated any time a user types into the s. <textarea> <textarea> Let’s modify our to include properties for opponent1 and opponent2: state const state = useStore({
  isLoading: false,
  text: '',
  winner: '',
  opponent1: '',
  opponent2: '',
}) Next, let’s add an event handler that will update the state. The event handler should probably also clear any previous text results and winners. Note that we need to do this for both inputs. onInput$ <Input
  label="Opponent 1"
  name="opponent1"
  value={state.opponent1}
  class={{
    rainbow: state.winner === 'opponent1'
  }}
  onInput$={(event) => {
    state.winner = ''
    state.text = ''
    state.opponent1 = event.target.value
  }}
/> Now that we have the values conveniently available, we can construct the HTTP request. We could do this when the “Show me” button is clicked, but we already made the function in . Might as well reuse it. All it needs is a with the data to send. jsFormSubmit the third post of the series <form> Let’s create a form that submits to our route, prevents the default behavior, and submits the data with instead. We can use hidden inputs to put the data in the form without impacting the UI. /ai-image jsFormSubmit {state.winner && (
  <form
    action="/ai-image"
    preventdefault:submit
    onSubmit$={async (event) => {
      const form = event.target
      console.log(await jsFormSubmit(form))
    }}
    class="mt-4"
  >
    <input
      type="hidden"
      name="opponent1"
      value={state.opponent1}
      required
    />
    <input
      type="hidden"
      name="opponent2"
      value={state.opponent2}
      required
    />
    <button type="submit">
      Show me
    </button>
  </form>
)} It looks the same as it did before, but now, it actually does something. I would show you a screenshot, but it’s pretty much just a button. Unremarkable, but effective. Show the Image in the Dialog The last step is to put it all together. The user submits the two opponents, and the AI returns a winner. The image generation will be available, showing the user the “Show me” button. <form> When the user clicks the button, the API request gets submitted. At the same time, we’ll programmatically open the dialog with some initial loading state. When the request returns, we’ll display the image. For that, I’ll create a new store for the image state using . It’ll hold a state, an state, and the . I’m also going to move the form’s submit handler into a dedicated function called so it’s not nested in the template and all the logic can live together. useStore showDialog isLoading url onSubmitImg The body of the function will activate the dialog, set the loading state, submit the form with , set the image URL from the results, and disable the loading state. obSubmitImg jsFormSubmit const imgState = useStore({
  showDialog: false,
  isLoading: false,
  url: ''
})
const onSubmitImg = $(async (event) => {
  imgState.showDialog = true
  imgState.isLoading = true

  const form = event.target

  const response = await jsFormSubmit(form)
  const results = await response.json()

  imgState.url = results.url
  imgState.isLoading = false
}) Man, I love that function! It lets the HTML provide the declarative HTTP logic and simplifies the business logic. jsFormSubmit Ok – with the state setup, the last thing to do is connect the component. Since we’ll be opening it programmatically, we don’t need the built-in button. We can disable that by making the prop falsy. We can connect the prop to . We’ll also want to update that state when the dialog closes via the event. <Dialog> btnText open imgState.showDialog onClose$ And the contents of the dialog should either show something for the loading state or show the generated image. <Dialog
  btnText={false}
  open={imgState.showDialog}
  onClose$={() => imgState.showDialog = false}
>
  {imgState.isLoading && (
    "Working on it..."
  )}
  {!imgState.isLoading && imgState.url && (
    <img src={imgState.url} alt={`An epic battle between ${state.opponent1} and ${state.opponent2}`} />
  )}
</Dialog> Unfortunately, we now have a button that programmatically opens a dialog element without accessibility considerations. I thought about including the same ARIA attributes as we have on the toggle, but if I’m honest, I’m not sure if a submit button control a dialog. should In this case, I’m leaving it out because I don’t know the right approach, and sometimes doing accessibility wrong leads to a worse experience than doing nothing at all. Open to suggestions. :) Closing Okay, I think that’s as far as we’ll get today. It’s time to stop and enjoy the fruits of our labor. Okay, OpenAI isn’t the best AI image generator, or maybe my prompt skills need some work. I’d love to see how yours turned out. A lot of the things we covered today were a review: building Qwik components, making HTTP requests to OpenAI, state, and conditional rendering in the template. The biggest difference, I think, is how we treat prompts for AI images. I find they require a little more creative thinking and finagling to get right. Hopefully, you found this helpful. In the video version, we covered a really cool SVG component that I think is worth checking out, but this post was already long enough. Functionally, the app is as far as I want to take it, so In the next post, we’ll focus on reliability and security before we get into launching to production. Intro & Setup Your First AI Prompt Streaming Responses How Does AI Work Prompt Engineering AI-Generated Images Security & Reliability Deploying Thank you so much for reading. If you liked this article, and want to support me, the best ways to do so are to , , and . share it sign up for my newsletter follow me on Twitter Originally published on . austingil.com

AI for Web Devs: How to Generate Images Using AI

121 Stories To Learn About Nintendo

Though most fans are familiar with Mario’s usual platforming adventures, some of his biggest journeys weren’t part of his mainline titles. His most high-stakes, world-ending games were done in a role-playing game format, bringing a new flavor of gameplay to the mustachioed plumber. Whether partnered with Luigi, traveling in a world of paper, or fighting monstrous machines and weapons, Mario’s RPGs offer some of the most intense experiences players can have in a Nintendo title. That’s why this article will take a closer look at the 10 best Mario RPGs in terms of copies sold, from Super Mario RPG to Paper Mario: The Origami King! All information was . Remakes will be considered separate from their predecessors, so don’t expect to see the 3DS releases of Superstar Saga or Bowser’s Inside Story on this list! taken from VGSales The links in this article include affiliate links to Amazon and eBay. You won’t be charged anything extra for clicking on these links, but you will be supporting HackerNoon if you do choose to purchase something through one of these links. Note: List of the Best Mario RPGs of All Time Ranked by Copies Sold 10. Paper Mario 9. Mario & Luigi: Partners in Time 8. Paper Mario: The Thousand-Year Door 7. Super Mario RPG: Legend of the Seven Stars 6. Mario & Luigi: Superstar Saga 5. Paper Mario: Sticker Star 4. Mario & Luigi: Dream Team 3. Paper Mario: The Origami King 2. Super Paper Mario 1. Mario & Luigi: Bowser’s Inside Story 10. Paper Mario — ~1.37 Million Copies Sold The original Paper Mario took a storybook approach to what players were familiar with. Bowser kidnaps Princess Peach and Mario must rescue her, but an encounter with the Star Rod forces the plumber to find seven Star Spirits across the Mushroom Kingdom. New characters and locations based off of the main Mario titles were introduced, making everything feel much more lively and interesting. Plus, the battle system refined the Timed Hits of Super Mario RPG, allowing for more engagement and ease-of-access than ever before. Top it all off with stylized art, and it’s clear that Paper Mario is a perfect start for anyone looking to get into these spinoffs! Buy now on or Amazon eBay 9. Mario & Luigi: Partners in Time — ~1.73 Million Copies Sold As a game focused on time travel, Partners in Time let fans of Mario and Luigi pair up with their baby selves. This allowed for some unique gameplay featuring the four heroes as they fought against unknown invaders from outer space. There was a lot to keep track of, especially with factors such as four separate health bars and limited special attacks, but that also meant there were many ways players could improve themselves during battles. Partners in Time is a game unlike any that came before or after it, making it an especially worthwhile experience for those unfamiliar with its story. Buy now on or Amazon GameStop 8. Paper Mario: The Thousand-Year Door — ~1.91 Million Copies Sold To many, The Thousand-Year Door was the last “true” Paper Mario experience. It’s not hard to see why, as it built even further upon the improvements to the battle system used in its predecessor. It also featured a brand-new region with iconic characters like Rawk Hawk, the Shadow Sisters, Cortez, and — of course — the villainous X-Nauts. There were many ways players could battle these unique foes thanks to the help of powerful badges, even allowing for entire builds focused on keeping Mario near death’s door. This title was stuck on the GameCube, preventing it from reaching higher sales, but it’s hard to argue against it being one of the most definitive ways to play a Mario RPG. Buy now on or Amazon eBay 7. Super Mario RPG: Legend of the Seven Stars — ~2.14 Million Copies Sold This was the game that introduced the very concept of a Mario RPG to fans. Its unique system of Timed Hits would influence the Action Commands many players know today, making each battle more than a series of menus and numbers. Super Mario RPG also included many characters that are still fan-favorites today, such as Mallow, Geno, Booster, and Smithy. Sadly, as this game was co-developed by Square, many of the unique elements from this game are unlikely to be used by Nintendo again. Despite this, , especially one that involves the return of characters like Geno. Hopefully both companies will consider these requests, as it wouldn’t be right to simply let the inspiration behind both Paper Mario and Mario & Luigi die out in silence. some fans still wish for a sequel Buy now on or Amazon eBay 6. Mario & Luigi: Superstar Saga — ~2.15 Million Copies Sold Superstar Saga was the first Mario & Luigi game, putting a spin on the RPG formula in ways few titles managed at the time. Here, instead of simply waiting for attacks to come, players are able to dodge and deflect them with good timing. This means skilled players can have just as much fun as strategic ones, focusing on improving their dodging skills or using the right gear to take bosses down in one blow. The story was also quite fun, introducing beloved villains like Cackletta and Fawful while giving both Mario Bros. a chance to shine in different ways. There are some ways it could have improved gameplay-wise, but it’s still easy to see why it has a place in the hearts of many different fans. This game also received a remake on the 3DS. It changed quite a few things about the original, improving the battles and graphics in some ways but also messing with some designs and making other arguably unnecessary changes. It’s still a fine way to experience the game, but the decision of “remake or original” might be easier for . those with the Nintendo Switch Online Expansion Pack Buy now on (Remake) or (Original) Amazon eBay 5. Paper Mario: Sticker Star — ~2.48 Million Copies Sold For fans alienated by Super Paper Mario’s removal of turn-based battles, Sticker Star seemed like a promising release. However, it changed a lot more than Super ever did, making every attack limited and removing almost every unique design from the franchise. This led to a Toad-covered gimmicky mess of a game that completely redefined Paper Mario in a way many fans utterly despised. This game was still relatively successful, no doubt thanks to the massive number of those owning a 3DS. But Sticker Star and its sequels are notably lacking when compared to the content, customization, and general uniqueness of its predecessors. Still, its simplicity might be appealing for especially young players, making it even more accessible than the first Paper Mario. It also introduced some more mainline Mario enemies to the series, so there was at least some variety in terms of foes. If you want to check this game out for yourself, be sure to do so quickly as ! the 3DS eShop will close in March 2023 Buy now on or Amazon the Nintendo eShop 4. Mario & Luigi: Dream Team — ~2.67 Million Copies Sold Some might argue that Dream Team was the final truly great game from the Mario & Luigi series. Released as part of the Year of Luigi — which introduced — this 3DS title had the duo explore a world of dreams to stop the schemes of the evil bat king Antasma. It improved upon systems introduced in Bowser’s Inside Story and brought back a few familiar faces, even including some character development for some of the main cast. The game is admittedly filled to the brim with tutorials, which can be particularly annoying on repeat playthroughs, but it still makes for a great experience once you get past all of that. other games focused on Mario’s brother Buy now on or Amazon the Nintendo eShop 3. Paper Mario: The Origami King — ~3.12 Million Copies Sold The Origami King follows the lead set by Sticker Star, lacking many unique designs and having a battle system filled with gimmicks. However, it also struck a chord with long-time fans of the series, allowing the reason for its success to be more than simply having been released on the Switch. Characters like Bowser Jr. and Bowser were able to join Mario’s side in battle, allies like Olivia drew favorable attention from players, and boss battles required some reaction time and skill. It’s still not quite at the level of the first three Paper Mario games, but it’s as close to a step in the right direction as what fans have been offered since Sticker Star. Buy now on or Amazon the Nintendo eShop 2. Super Paper Mario — ~4.23 Million Copies Sold Super Paper Mario was the first experimental title of its series, becoming a platforming-RPG hybrid that abandoned turn-based combat. While it garnered some criticism at the time for this change, it also kept the overall feel of previous games through its inclusion of unique characters and partners. Its story was also notably dark and full of depth, with some considering Super Paper Mario to have the best story of any Mario game out there. For better or worse, fans are unlikely to get another game like this one again, making it an essential title for anyone wishing for the most unique Mario experience they can get. Buy now on or Amazon GameStop 1. Mario & Luigi: Bowser’s Inside Story — ~4.56 Million Copies Sold Bowser’s Inside Story is out there — and, by extension, the best-selling Mario RPG of all time. Players were able to meet Fawful once again as they took control of Mario, Luigi, and even Bowser in a grand adventure throughout the Mushroom Kingdom. The game’s giant battles offered more variations in bosses, the music is still arguably unmatched by most other games in general, and the story — while simplistic — gave fans more reasons to love Bowser and the Mario brothers than ever before. the best-selling Mario & Luigi game This game also had a remake, changing less overall than Superstar Saga but still making alterations that some viewed as unnecessary. Combined with poor timing and a lack of marketing, this made the 3DS remake the worst-selling Mario RPG, ironically enough. It also led to the developers going outright bankrupt, leaving the question as to whether will ever be made. Despite this, there’s nothing particularly wrong with the remake itself, as it’s still Bowser’s Inside Story with some extra content. If you’ve never played the game before, you could go for either version and still have the time of your life. a new Mario & Luigi game Buy now on (Remake) or (Original) Amazon eBay Final Thoughts Mario RPGs offered unique adventures that Nintendo hasn’t tried to replicate for years. While some newer games take steps in the right direction, we’re still a long way off from realizing the full potential a traditional Mario RPG could have on the Switch. Hopefully, Nintendo will seek to realize that potential, giving fans one more taste of the never-before-seen characters, genre-changing gameplay, and shockingly good stories that these titles used to have.

10 Best Mario RPGs Ranked by Sales

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Dynamic NFTs are a new trend in web3. In five questions and answers, we explain what kind of technology it is and why it is the next big step in web3. What is dynamic NFT technology, and how is it different from usual NFTs? With dynamic NFTs, there are two aspects of metadata — the visuals, and the written data, the same as regular NFTs. But, inputs can constantly change the metadata. Dynamic NFTs have more utility since they can respond to user interaction with the tokens, which unlocks potentially limitless utility. In other words, dynamic NFTs, unlike static ones, are responsive and interactive. The factors determining the change to the images can be anything in the online or real world, and they can be relatively predictable or completely algorithmically generated, depending on the creator's intentions. To summarize the difference, typical static NFTs have unchanging metadata, but dynamic NFTs can constantly grow, change, stay up to date, and provide a reason for users to want to keep them, collaborate on them, and acquire them. This technology can be applied in NFT tickets, move-to-earn apps, wallets, games, art, and documentation. Are there many web3 companies using this technology? It’s a new technology so there aren't many of them yet, but more and more such projects appear, and the technology is quickly gaining in popularity. Here are just a few examples. has become one of the most anticipated NFT projects launched in 2022 and surely the most attended to date. It almost broke Ethereum blockchain on its minting day for its high request demands. Dynamic NFT is behind its most remarkable features. The OthersideMeta by Yuga Labs The OthersideMeta is a highly engaging metaverse built of Otherdeeds lands, a unique mix of environment and sediment, resources or artifacts, some with aliens called Koda. These lands are dynamic NFTs and will evolve based on players’ chives. Players are the creators of this meraverse. They can interchange the traits of their lands by adding or removing resources and trade on the Otherside marketplace. Same year NBA, which is not new to the world of NFT, has launched NBAxNFT, official web3 home for the NBA, a collection of 30 thousand NFTs minted during the 2022 NBA playoffs. Each card is a dynamic NFT changing its appearance influenced by team and player on-court performance. How does that work? Each NBAxNFT was linked to real time data feeds. The data computations were carried out by Chainlink which powered the automated visual traits change of each dynamic NFT based on the preset achievements per player and team written into the smart contract. The visual changes of each NFT included background, special emojis designed by various artists, and also accessories. Some of the traits were progressive, meaning that an attribute could only be gained if a player reached the same achievement multiple times. What does it have to do with art? Dynamic NFT technology may be used to create impressive generative art collections. These art NFTs can grow, breed, fuse, and react to blockchain or IRL events, and they can continue to evolve even after they were bought. So, collectors interacting with this artwork can keep developing it – potentially forever. In art projects using this technology, everyone becomes a co-author, while digital art becomes an everlasting act of co-creation. In fact OG.Art platform is now creating a whole art platform for selected artists to experiment with this technology. They promise that with their technology rendering any transformations of NFTs will actually take just several minutes. Can you give an example? Probably the first NFT art collection that developed and tested this technology was , a collaboration between artists Michael Joo (ASomatica) and Danil Krivoruchko (Myshli) with the technical support of Snark.art. A collection of 10,301 unique NFT collectibles launched on October 16, 2021. Each OG:Crystal transformed and grew every time it was purchased from another collector. The facets and contours of each piece were generated algorithmically, according to each owner's crypto wallet history. OG:Crystals The collection became one of the top-5 art projects on OpenSea, earning $4.2 million in primary sales and $9 million in secondary sales. Such a success motivated the OG:CR team to even turn the technology developed for the art collection into a separate product called OG Protocol — a b2b solution for web3 companies to apply dynamic NFT technology. So dynamic NFT art is a collection of static generative pictures? Not at all. OG:Crystals were high-quality static 3D pictures, but the new collection by OG.Art called is a collection of dynamic generative 3D videos. Life in our Minds It’s a multi-dimensional artwork consisting of two major parts: an NFT collection to be launched in October and an interactive generative video sculpture entitled The Mother Flock. The Mother Flock and NFT collection are interrelated, as they are made from the same basic matter — 3D origami objects called Boids. Following the launch of the project, all the elements — Boids, NFTs, and The Mother Flock — will go through a complex evolutionary process that depends on the NFT collectors’ behaviors. LIOM NFTs gradually evolve as long as they stay in the collectors' wallet and are not put up for sale — they add new Boids to the formation, while the old Boids of the NFT gradually change their looks, sometimes adding special rare materials and decorative patterns, that represent other NFT collections in the owner’s wallet. The Evolution Mechanics here reward the holding of LIOM NFTs, rather than the typical trend common in the NFT space of simply flipping the items. So, the longer you hold onto a LIOM NFT, the more complex, beautiful, rare, and precious it becomes. LIOM project created by prominent artists Random International and Danil Krivoruchko is one of the most collaborative, technologically innovative NFT projects to date. “The NFTs are constantly updating dynamic 3D videos and we have to render each update. I had to make many optimizations to keep the NFTs looking sophisticated while still being able to render them fast enough and keep the cost of these updates reasonable. From a technological point of view, it was an exciting puzzle to solve” — Danil Krivoruchko. Despite the crypto slump the dynamic NFTs collections have completely boomed the market. The OthersideMeta by Yuga Labs has become one of the most anticipated NFT projects launched in 2022 and surely the most attended to date. It almost broke Ethereum blockchain on its minting day for its high request demands. Dynamic NFT is behind its most remarkable features.

Next Steps in NFTs: Which Innovations Stand Behind Dynamic NFTs?

Here we are at another stomach-churning moment for Cryptopians. The swan dive in the price of Bitcoin and Ethereum, , the consolidation of the NFT market… Even my beloved Axie Infinity Shards ( ) are down by more than a factor of seven from their peak.  All this carnage is generating a lot of attention. the $40B sudden evaporation of Luna hard hit by a crypto heist What are the Fundamentals? I’m an Axie Infinity guild owner, so I’m taking it upon myself to explain the fundamentals that have been missing in every single analysis that I’ve read: SLP is created through playing the game, it can be sold on the open market, or burned to create more Axie pets. Currently, almost no one is burning SLP, and no one is buying Axies, leading to a seemingly catastrophic drop in the token’s value. But is this the end, or is it something else that everyone is missing? Let me explain, starting with… What is Axie Infinity? Axie Infinity is a game that allows players to through competitive play and then use it to build their empire. The game is similar to CryptoKitties, but with a few key differences. The biggest difference is that in Axie Infinity the players are not just buying and selling virtual cats. earn their own cryptocurrency Instead, they are battling to earn their own cryptocurrency (“Smooth Love Potion,” SLP, won and minted after winning battles). Then, use it to build an empire of players who use the digital pets you create by breeding (burning SLP and another token called AXS) to play, battle, and win. The Mistake Many Analysts are Making Suddenly, Google reveals the world to be flooded with analysts and commentators, including those writing for influential gaming and crypto publications. They are, like the fable in Lunacia Chicken Axie, shouting that the sky is falling on Lunacia (the land that contains Axie). The critics point to a declining player base and the plummeting price of the token as proof that Play to Earn (we've actually started to call it "Play and Earn," but that's an article for another day) "doesn't work." As a guild owner, I can't help but cringe when I read these articles. Not because I'm worried that Axie Infinity is over (it has the word INFINITY in the title, come on!). I find the lack of understanding that these analysts have for the underlying reasons for the current decline cringeworthy. Lunacia’s Upgrade Since the beginning of Axie Infinity, Lunacians have been playing the same game, with the same fundamental rules, which has been incrementally upgraded. Like Pokemon, your combination of Axies must be strategically balanced, with the four-card combination per Axie being vital to a winning team. And the cards of the three Axies that play together must complement each other. Because there's a huge upgrade coming out, called Version 3, or Origin, (expected imminently, for the last 5 months or so), the players haven't known which team and card combinations will work after the upgrade. Markets hate bad news. But they hate uncertainty worse. This has Caused a Decline in the Axie Economy The issue is that people aren't using SLP (the token you earn) to breed Axies, because the new upgrade will actually, basically, be a completely new game with new cards and brand new dynamics. From the gameplay I’ve seen so far, the new game is likely a significant upgrade from the legacy game. Yet… some of the legacy Axie teams are going to be useless going forward.  The economist Schumpeter called this sort of progress “creative destruction.” We don’t know which ones won’t be effective moving forward. We won’t know until the moment of the Big Reveal. For this reason, the accumulation of SLP has been ever-increasing, without the usual mechanism (burning it to breed) to decrease the supply. Stakeholders are waiting for the new game to come out to breed. Additionally, very few are buying Axies. Who wants to buy a digital battling pet that might not be a competitive player going forward? This has caused SLP’s value to plummet. Meanwhile, the scholars (players who borrow your teams to play and earn and remit some of their earnings back to the owner) have been defecting as the price of SLP declines. Origin’s Release and the Start of a New Axie Infinity Adventure However painful this transition has been, I still believe a new game, and a large upgrade will be beneficial to the long-term health of the Axie economy. This is so despite how bitter the last five months have been. Once the new game comes out, guilds who have been waiting for final card balancing and new technical dynamics such as rune charms can rebalance their teams, and finally, start buying and breeding Axies again as they’ll have a better idea of their use moving forward. Currently, purchases have fallen to near zero, as you would almost be gambling by purchasing Axie teams now. But What's the Point of SLP? Finally, almost but not entirely unrelated, let’s look at the journalists who are dancing around spreading salt on the ground while saying "AHA! Play to earn doesn't work! SLP's value is under a cent. THIS PROVES IT." Journalists, and other humans, I have some bad news for you. All currency (crypto or not) -is basically useless in its pure form (although you can use it to make fire, origami, and to write on). SLP and other currencies minted through blockchain are no different, including Bitcoin, Ethereum, and all 18,465 cryptos out here. No, SLP has no intrinsic value. It's not even a physical coin, but like other cryptocurrencies, a marker of an input and output. So, how do we assess the fundamental value? By the underlying assets. What are the Underlying Assets of SLP? It is the players, who are (for the first time, and it matters) owning the game they play, who furnish the underlying value. It’s the developers, who were the first to show the world the true potential of crypto gaming. Finally, the game and network itself, including the coming upgrades. That’s where the value lies. But the Players Will Just Keep Minting More SLP True. Hopefully, the recent adjustments will bring more balance when that happens. We’re still early in crypto, and while the community starts to build out new games that can be used in part to spend or burn SLP, it may bring balance to the economy and stability. This is the first play to earn experiment, and while others are taking apparent glee in decrying it doesn’t work, I’m excited for the community making it work. I don’t want to get too much into boring technicals, but one important thing to point out is for most of Axie, almost any player with three Axies, even not great ones, could basically collect 75 free SLP per day, which the developers took away recently. With this new adjustment, it’s about 75% harder to mint SLP, and from a community participant, I suspect this will actually balance the mint to burn ratio a lot on its own, although it was very painful for the community at the same time. One day, we may be able to take our earnings out and actually spend them. . Or the grocery store, which will add balance to an entirely new degree. And I’m excited for that. At places like GameStop Note: This isn’t investment advice. I’m a guild owner and thus I have a stake in the Axie Economy and the tokens discussed in this article. At the same time, that didn’t affect this analysis—I wrote this piece to point out something that was missing from the viewpoint of analysts writing about the game from the outside without a player’s understanding of the economy. Do your own research, and don’t invest money you can’t afford to lose. The last few weeks have been gutting for the crypto market as a whole, and even with strong fundamentals, there are no guarantees in crypto. Origin is out in an Alpha test phase with no crypto rewards, without final balancing, and there’s currently no consensus on which teams will work when it’s given an official release. When I say Origin isn’t out yet, I am specifically saying the official game release with full dynamics, crypto rewards, and card balancing patches that players and guild owners could use to make informed financial decisions when buying new teams.

What Everyone Is Missing About Axie Infinity’s Economic Storm

The HackerNoon editorial team has launched this interview series with to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. ! women in tech Share your story today Tell us about yourself! My aim is to use technology for combating climate change and enhancing human well-being. By profession, I am an Architect with experience in sustainable design with a core focus on air quality and occupant health. This led me to BreatheIO, where I am the CEO and Co-founder. BreatheIO aims to create IoT products that promote human health and well-being. I am also the founder of a non-profit REsource Zero Waste that works with organizations/ institutions/ industries for waste reduction at source. Why did you choose this field in the first place? Most of us believe that someone will solve our major climate problems, but we don’t realize that this is our problem and we have to solve it. I have had a bond with Lahore since my childhood. When 2015 in its wake brought smog to the city, engulfing it in a haze with traces of smoke, it didn’t cause much alarm. However, it turned into an annual fest, which was worrisome. I am a mother of 2 kids, and watching my kids suffer from this polluted air made me worry about their health. Smog has adverse effects on the elderly as well. Scanning the market for a good air purifier led us to develop our own air purifier that was affordable, had ease of filter replacement, and gave users the power to monitor their indoor air quality using an array of gases. What tech are you most excited/passionate about right now and why? Internet of Things and smart connectivity! It fascinates me because research has shown IoT has the potential to reduce carbon emissions by 15% by 2030. We may not realize it consciously, but IoT has already been embedded in our daily lives. Smart connectivity & IoT has the capability to help transition towards smart cities and tackle climate changes issues using technology. This is vital for developing countries like Pakistan, which rank amongst the top 10 countries most affected by climate change. What tech are you most worried about right now and why? Artificial Intelligence. Why? Because such systems are vulnerable and can be destructive if they land in the wrong hands. For instance, autonomous weapons used to kill could cause havoc if hijacked by the wrong people. Similarly, an AI system programmed to reach the goal may develop a destructive method to reach that goal. Although there is research regarding AI safety, we need more research to make such systems safe and secure. What are your hobbies and interests outside of tech? I love to create, may it be origami or an air monitor. I like to make cement pottery, and stone homeware, for the desire to create is reflected in whatever I do. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? I believe in our local context, the glass ceiling initiates at home and thickens as you move through the corporate world. We have been conditioned in a manner where professions revolving around tech have been categorized male-centric, and girls are discouraged from pursuing such professions. For me, the challenges started when I shifted my vertical and transitioned towards tech. Explaining to people around me this transition and then convincing the people in tech of my ability has been a challenge itself. Any interesting misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam? It is not a story but a commonly recurring situation I face when interacting with people from a tech background. It is their non-serious attitude towards women founders working in tech. I have observed their questions and statement being directed towards my male counterparts/co-founders instead of me. What was the biggest setback/failure that you faced, and how did you manage it? COVID-19. When the pandemic hit and schools shut down with kids home for online classes, it was the biggest setback I have faced. To manage my work, my kids’ classes, and other responsibilities has been very difficult for me, and it continues to be this way as we keep shifting back to this system again and again with no end in sight. What's your biggest achievement that you're really proud of? Last year, the Government of Punjab selected us to represent our startup at EXPO Dubai 2020 Pakistan Pavilion. We had the opportunity to showcase our product at the Pakistan Pavilion. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? Like I stated before, it all starts with how we condition our girls. They need to be exposed to tech and the multiple uses of tech at an early age. Such subjects need to be incorporated into our curriculums with accessibility to all. This will eventually bring about a change in the mindset of our people regarding women in tech. Who is your tech idol? Why? Dr. Ayesha Khanna. She is breaking barriers in the tech world and has set up a tech institute that gives classes and workshops to young girls, encouraging them to join this domain and excel in it. Do you have any advice for aspiring girls who want to join the field? Find what you are passionate about and your purpose for joining this field, and let that passion be the force that drives you. Don’t be intimidated by the men around you thinking they know more, they also started at some point, and if you have a thirst for learning, you can stand parallel to them. Most importantly, be a resource for others, what you have learned and applied, share it with others so that they may also benefit from it because, as they say: “Empowered women empower women!”

Using Technology to Combat Climate Change: #Women-In-Tech Interview with Sana, CEO BreatheIO

Stories, News & Mentions about ORIGAMI

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Stories, News & Mentions about ORIGAMI

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