Contilio

contilio

Gnosis conditional token framework (CTF): tokenizing potential outcomes in prediction markets

Amazon has been salivating at the multitrillion-dollar United States healthcare market since 2018. Although Amazon Health played a vital role in public health during the pandemic, most still don’t view it as a disruptor. A new chapter in the American conglomerate’s move into the industry is upon us — Health Condition Programs. How does this initiative fit into the tech titan’s grand healthcare plan? It’s anybody’s guess. I can only speculate about the countless scenarios surrounding the interplay between Health Condition Programs and Amazon’s diverse solutions. However, the real question we should all be asking is — will it buck the trend of the organization’s cybersecurity, patient safety, and privacy scandals? A Treasure Trove of Patient Data On paper, Health Condition Programs aim to encourage millions of Americans to maximize their unused healthcare features. This mission has merit because over one-third of workers don’t fully understand the employee benefits they signed up for. Many American adults might be unaware that they can receive care to manage chronic conditions — such as diabetes and hypertension — between doctor visits at no extra cost to them. Enrolling in these programs allows insured individuals to maximize their coverage and squeeze more value out of the rising premiums or contributions they shoulder. In practice, Health Condition Programs enable the Andy Jassy-led all-encompassing empire to grow its patient data repository by tens of millions. This offering’s coverage check involves eligibility verification, necessitating personal data collection. Enrollees must share their legal name, date of birth, employer’s name, insurer's name, email address, and other sensitive information. This will determine whether their insurance plan or employee benefits cover health programs Amazon’s partners offer. After confirming an applicant is covered, Amazon Health directs the patient to third-party digital health companies — the actual virtual care providers — to complete registration and program enrollment. While Amazon may say it stops collecting personal data after a coverage check, it may still follow patients beyond that point. This Big Tech firm is a known participant in server-to-server tracking, so I would be more surprised if it doesn’t monitor the rest of an enrollee’s telehealth journey through a backdoor. Connected devices are notable program features that covered individuals can access. Continuous glucose monitor sensors, blood sugar meters, blood pressure monitors, and smart scales generate personalized quantitative data that forms a part of medical records and is personal. This is protected health information tech conglomerates could leverage to enrich their subsidiaries. A Slowly Expanding Network As disturbing as it is to think that Amazon could acquire bits and pieces of millions of medical records under the table through its Health Condition Programs, this multinational’s reach is proportional to its network’s size. As of October 2024, only two digital health companies have participated — Omada Health and Talkspace. Omada specializes in diabetes prevention, weight management, and hypertension care, while Talkspace delivers virtual therapy and psychiatric care. Between the two, insured Americans can choose from only three programs. It’s unclear why Amazon Health could only ink two partnerships in nine months — a departure from the online retail behemoth’s “Get Big Fast” mantra. I bet telehealth providers would move mountains to be in this exclusive club, so Amazon must have been overly selective about its partners for reasons unknown. Only time will tell if Amazon will bring more digital health companies into its fold sooner rather than later. A safe guess is that it would prioritize partner diversity to prevent cannibalization and harvest data with variety. A New Piece to the Amazon Health Care Strategy Puzzle Amazon plays the long game. For all we know, it might envision itself as the savior of health care — the ultimate medical data synthesizer the sector sorely needs. It’s impossible to connect all the dots when many of them haven’t come to light. Still, recognizing this offering’s potential value to this tech colossus isn’t brain surgery. If Health Condition Programs gain currency and the number of participating digital health companies on the network balloons, the company will better understand our health needs. These insights can be the rising tide that lifts the boats in the Amazon Health sub-ecosystem and other related subsidiaries. A quick thought experiment would reveal that more program enrollees could increase Amazon One Medical’s in-person visits. Health Condition Programs almost always require a prior medical diagnosis. Health care professionals on the network — health coaches, licensed therapists and psychiatrists, nutritionists, dieticians, and personal care specialists — could help train AWS HealthScribe with large volumes of recordings. The aim would be to churn out error-free transcripts and clinical notes consistently. The company could expand Alexa’s footprint by integrating its partners’ services into the voice assistant. It’s a tried-and-true move, as evidenced by the partnership between Amazon and Teladoc Health in 2022. Prescriptions from digital health companies can also boost Amazon Pharmacy’s sales. Analyzing mountains of medical data can shed light on the usual indicators of events that can endanger patients’ lives. Tech-forward hospitals have taken this route. They proved it’s feasible to predict signs of upcoming acute events — which increase the chances of patient mortality by three to four times — early enough to address them before they happen. Amazon can do the same thing for people living with chronic diseases. It can mobilize Whole Foods to make healthier food options more widely available to those who need them most. Accessible healthy grocery items empower chronically ill individuals to meet their unique nutritional needs and keep symptoms manageable. Are Health Condition Programs a Scandal Waiting to Happen? Amazon’s venture into health care has been riddled with controversies. In 2023, hackers successfully victimized more than 19,000 PillPack customers, stealing their names, residential addresses, phone numbers, and email addresses. About 3,600 of the impacted accounts included prescription information. That same year, senators grilled the company for forcing patients to waive their right to health information privacy to enroll in Amazon Clinic. In mid-2024, Amazon found itself in hot water for allegedly compromising the safety of One Medical patients. Incidents of awful misjudgments by staff assisting customers by phone have risen since the subsidiary entrusted the job to inadequately trained agents. Amazon has failed to give a good account of itself in the sector on various occasions. At this rate, I feel like someone would expose a dubious practice underpinning Health Condition Programs any moment now. Can We Trust Amazon’s Health Condition Programs? Criticisms toward this initiative are unsubstantiated and premature. However, it would be naive to expect Amazon not to supercharge its health data collection prowess through coverage checks and its partners when the alternative does nothing to improve other parts of its business. We’ll just have to wait and see whether this Amazon Health offering’s practical benefits outweigh its perceived privacy drawbacks — but let’s not hold our breaths. Amazon has been salivating at the multitrillion-dollar United States healthcare market since 2018. Although Amazon Health played a vital role in public health during the pandemic, most still don’t view it as a disruptor. A new chapter in the American conglomerate’s move into the industry is upon us — Health Condition Programs. How does this initiative fit into the tech titan’s grand healthcare plan? It’s anybody’s guess. I can only speculate about the countless scenarios surrounding the interplay between Health Condition Programs and Amazon’s diverse solutions. However, the real question we should all be asking is — will it buck the trend of the organization’s cybersecurity, patient safety, and privacy scandals? A Treasure Trove of Patient Data On paper, Health Condition Programs aim to encourage millions of Americans to maximize their unused healthcare features. This mission has merit because over one-third of workers don’t fully understand the employee benefits they signed up for. one-third of workers don’t fully understand one-third of workers don’t fully understand Many American adults might be unaware that they can receive care to manage chronic conditions — such as diabetes and hypertension — between doctor visits at no extra cost to them. Enrolling in these programs allows insured individuals to maximize their coverage and squeeze more value out of the rising premiums or contributions they shoulder. In practice, Health Condition Programs enable the Andy Jassy-led all-encompassing empire to grow its patient data repository by tens of millions. This offering’s coverage check involves eligibility verification, necessitating personal data collection. Enrollees must share their legal name, date of birth, employer’s name, insurer's name, email address, and other sensitive information. This will determine whether their insurance plan or employee benefits cover health programs Amazon’s partners offer. After confirming an applicant is covered, Amazon Health directs the patient to third-party digital health companies — the actual virtual care providers — to complete registration and program enrollment. While Amazon may say it stops collecting personal data after a coverage check, it may still follow patients beyond that point. This Big Tech firm is a known participant in server-to-server tracking , so I would be more surprised if it doesn’t monitor the rest of an enrollee’s telehealth journey through a backdoor. a known participant in server-to-server tracking a known participant in server-to-server tracking Connected devices are notable program features that covered individuals can access. Continuous glucose monitor sensors, blood sugar meters, blood pressure monitors, and smart scales generate personalized quantitative data that forms a part of medical records and is personal. This is protected health information tech conglomerates could leverage to enrich their subsidiaries. forms a part of medical records forms a part of medical records A Slowly Expanding Network As disturbing as it is to think that Amazon could acquire bits and pieces of millions of medical records under the table through its Health Condition Programs, this multinational’s reach is proportional to its network’s size. As of October 2024, only two digital health companies have participated — Omada Health and Talkspace. Omada specializes in diabetes prevention, weight management, and hypertension care, while Talkspace delivers virtual therapy and psychiatric care. Between the two, insured Americans can choose from only three programs. It’s unclear why Amazon Health could only ink two partnerships in nine months — a departure from the online retail behemoth’s “Get Big Fast” mantra. I bet telehealth providers would move mountains to be in this exclusive club, so Amazon must have been overly selective about its partners for reasons unknown. Only time will tell if Amazon will bring more digital health companies into its fold sooner rather than later. A safe guess is that it would prioritize partner diversity to prevent cannibalization and harvest data with variety. A New Piece to the Amazon Health Care Strategy Puzzle Amazon plays the long game. For all we know, it might envision itself as the savior of health care — the ultimate medical data synthesizer the sector sorely needs. It’s impossible to connect all the dots when many of them haven’t come to light. Still, recognizing this offering’s potential value to this tech colossus isn’t brain surgery. If Health Condition Programs gain currency and the number of participating digital health companies on the network balloons, the company will better understand our health needs. These insights can be the rising tide that lifts the boats in the Amazon Health sub-ecosystem and other related subsidiaries. A quick thought experiment would reveal that more program enrollees could increase Amazon One Medical’s in-person visits. Health Condition Programs almost always require a prior medical diagnosis. Health care professionals on the network — health coaches, licensed therapists and psychiatrists, nutritionists, dieticians, and personal care specialists — could help train AWS HealthScribe with large volumes of recordings. The aim would be to churn out error-free transcripts and clinical notes consistently. The company could expand Alexa’s footprint by integrating its partners’ services into the voice assistant. It’s a tried-and-true move, as evidenced by the partnership between Amazon and Teladoc Health in 2022. Prescriptions from digital health companies can also boost Amazon Pharmacy’s sales. partnership between Amazon and Teladoc Health partnership between Amazon and Teladoc Health Analyzing mountains of medical data can shed light on the usual indicators of events that can endanger patients’ lives. Tech-forward hospitals have taken this route. They proved it’s feasible to predict signs of upcoming acute events — which increase the chances of patient mortality by three to four times — early enough to address them before they happen. by three to four times by three to four times Amazon can do the same thing for people living with chronic diseases. It can mobilize Whole Foods to make healthier food options more widely available to those who need them most. Accessible healthy grocery items empower chronically ill individuals to meet their unique nutritional needs and keep symptoms manageable. Are Health Condition Programs a Scandal Waiting to Happen? Amazon’s venture into health care has been riddled with controversies. In 2023, hackers successfully victimized more than 19,000 PillPack customers, stealing their names, residential addresses, phone numbers, and email addresses. About 3,600 of the impacted accounts included prescription information. 3,600 of the impacted accounts 3,600 of the impacted accounts That same year, senators grilled the company for forcing patients to waive their right to health information privacy to enroll in Amazon Clinic. In mid-2024, Amazon found itself in hot water for allegedly compromising the safety of One Medical patients. Incidents of awful misjudgments by staff assisting customers by phone have risen since the subsidiary entrusted the job to inadequately trained agents. Amazon has failed to give a good account of itself in the sector on various occasions. At this rate, I feel like someone would expose a dubious practice underpinning Health Condition Programs any moment now. Can We Trust Amazon’s Health Condition Programs? Criticisms toward this initiative are unsubstantiated and premature. However, it would be naive to expect Amazon not to supercharge its health data collection prowess through coverage checks and its partners when the alternative does nothing to improve other parts of its business. We’ll just have to wait and see whether this Amazon Health offering’s practical benefits outweigh its perceived privacy drawbacks — but let’s not hold our breaths.

Did You Know About Amazon’s Health Condition Programs? What This Means for Users

(1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu);
(2) Bichen Wu, Meta GenAI and Corresponding author;
(3) Jialiang Wang, Meta GenAI;
(4) Licheng Yu, Meta GenAI;
(5) Kunpeng Li, Meta GenAI;
(6) Yinan Zhao, Meta GenAI;
(7) Ishan Misra, Meta GenAI;
(8) Jia-Bin Huang, Meta GenAI;
(9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com);
(10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com);
(11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction
2. Related Work
3. Preliminary
4. FlowVid
4.1. Inflating image U-Net to accommodate video
4.2. Training with joint spatial-temporal conditions
4.3. Generation: edit the first frame then propagate


Experiments


5.1. Settings
5.2. Qualitative results
5.3. Quantitative results
5.4. Ablation study and 5.5. Limitations
Conclusion, Acknowledgments and References
A. Webpage Demo and B. Quantitative comparisons 4.2. Training with joint spatial-temporal conditions Upon expanding the image model, a straightforward method might be to train the video model using paired depth-video data. Yet, our empirical analysis indicates that this leads to sub-optimal results, as detailed in the ablation study in Section 5.4. We hypothesize that this method neglects the temporal clue within the video, making the frame consistency hard to maintain. While some studies, such as Rerender [49] and CoDeF [32], incorporate optical flow in video synthesis, they typically apply it as a rigid constraint. In contrast, our approach uses flow as a soft condition, allowing us to manage the imperfections commonly found in flow estimation. This paper is available on arxiv under CC 4.0 license. (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction 2. Related Work 3. Preliminary 4. FlowVid 4.1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate Experiments 5.1. Settings 5.2. Qualitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons Abstract and Introduction Abstract and Introduction 2. Related Work 2. Related Work 3. Preliminary 3. Preliminary 4. FlowVid 4. FlowVid 4 .1. Inflating image U-Net to accommodate video .1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate 4.3. Generation: edit the first frame then propagate Experiments Experiments Experiments 5.1. Settings 5.1. Settings 5.2. Qualitative results 5.2. Qualitative results 5.3. Quantitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons A. Webpage Demo and B. Quantitative comparisons 4.2. Training with joint spatial-temporal conditions Upon expanding the image model, a straightforward method might be to train the video model using paired depth-video data. Yet, our empirical analysis indicates that this leads to sub-optimal results, as detailed in the ablation study in Section 5.4. We hypothesize that this method neglects the temporal clue within the video, making the frame consistency hard to maintain. While some studies, such as Rerender [49] and CoDeF [32], incorporate optical flow in video synthesis, they typically apply it as a rigid constraint. In contrast, our approach uses flow as a soft condition, allowing us to manage the imperfections commonly found in flow estimation. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

FlowVid: Taming Imperfect Optical Flows: Training With Joint Spatial-temporal Conditions

Authors:
(1) Dustin Podell, Stability AI, Applied Research;
(2) Zion English, Stability AI, Applied Research;
(3) Kyle Lacey, Stability AI, Applied Research;
(4) Andreas Blattmann, Stability AI, Applied Research;
(5) Tim Dockhorn, Stability AI, Applied Research;
(6) Jonas Müller, Stability AI, Applied Research;
(7) Joe Penna, Stability AI, Applied Research;
(8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work Appendix A Acknowledgements B Limitations C Diffusion Models D Comparison to the State of the Art E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline References 2.2 Micro-Conditioning Conditioning the Model on Image Size A notorious shortcoming of the LDM paradigm [38] is the fact that training a model requires a minimal image size, due to its two-stage architecture. The two main approaches to tackle this problem are either to discard all training images below a certain minimal resolution (for example, Stable Diffusion 1.4/1.5 discarded all images with any size below 512 pixels), or, alternatively, upscale images that are too small. However, depending on the desired image resolution, the former method can lead to significant portions of the training data being discarded, what will likely lead to a loss in performance and hurt generalization. We visualize such effects in Fig. 2 for the dataset on which SDXL was pretrained. For this particular choice of data, discarding all samples below our pretraining resolution of 256 2 pixels would lead to a significant 39% of discarded data. The second method, on the other hand, usually introduces upscaling artifacts which may leak into the final model outputs, causing, for example, blurry samples. Conditioning the Model on Cropping Parameters The first two rows of Fig. 4 illustrate a typical failure mode of previous SD models: Synthesized objects can be cropped, such as the cut-off head of the cat in the left examples for SD 1-5 and SD 2-1. An intuitive explanation for this behavior is the use of random cropping during training of the model: As collating a batch in DL frameworks such as PyTorch [32] requires tensors of the same size, a typical processing pipeline is to (i) resize an image such that the shortest size matches the desired target size, followed by (ii) randomly cropping the image along the longer axis. While random cropping is a natural form of data augmentation, it can leak into the generated samples, causing the malicious effects shown above. While other methods like data bucketing [31] successfully tackle the same task, we still benefit from cropping-induced data augmentation, while making sure that it does not leak into the generation process - we actually use it to our advantage to gain more control over the image synthesis process. Furthermore, it is easy to implement and can be applied in an online fashion during training, without additional data preprocessing. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Authors: Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Improving Stable Diffusion 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work 3 Future Work Appendix Appendix A Acknowledgements A Acknowledgements B Limitations B Limitations C Diffusion Models C Diffusion Models D Comparison to the State of the Art D Comparison to the State of the Art E Comparison to Midjourney v5.1 E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline G Additional Comparison between Single- and Two-Stage SDXL pipeline References References 2.2 Micro-Conditioning Conditioning the Model on Image Size A notorious shortcoming of the LDM paradigm [38] is the fact that training a model requires a minimal image size, due to its two-stage architecture. The two main approaches to tackle this problem are either to discard all training images below a certain minimal resolution (for example, Stable Diffusion 1.4/1.5 discarded all images with any size below 512 pixels), or, alternatively, upscale images that are too small. However, depending on the desired image resolution, the former method can lead to significant portions of the training data being discarded, what will likely lead to a loss in performance and hurt generalization. We visualize such effects in Fig. 2 for the dataset on which SDXL was pretrained. For this particular choice of data, discarding all samples below our pretraining resolution of 256 2 pixels would lead to a significant 39% of discarded data. The second method, on the other hand, usually introduces upscaling artifacts which may leak into the final model outputs, causing, for example, blurry samples. Conditioning the Model on Cropping Parameters The first two rows of Fig. 4 illustrate a typical failure mode of previous SD models: Synthesized objects can be cropped, such as the cut-off head of the cat in the left examples for SD 1-5 and SD 2-1. An intuitive explanation for this behavior is the use of random cropping during training of the model: As collating a batch in DL frameworks such as Conditioning the Model on Cropping Parameters PyTorch [32] requires tensors of the same size, a typical processing pipeline is to (i) resize an image such that the shortest size matches the desired target size, followed by (ii) randomly cropping the image along the longer axis. While random cropping is a natural form of data augmentation, it can leak into the generated samples, causing the malicious effects shown above. While other methods like data bucketing [31] successfully tackle the same task, we still benefit from cropping-induced data augmentation, while making sure that it does not leak into the generation process - we actually use it to our advantage to gain more control over the image synthesis process. Furthermore, it is easy to implement and can be applied in an online fashion during training, without additional data preprocessing. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Micro-Conditioning Strategies for Superior Image Generation in SDXL

Robots work in all kinds of places with varying network conditions, from stable Ethernet to inconsistent cellular or satellite connections. Learn how you can use gRPC and WebRTC for robots and other smart machines to communicate more reliably. Why use gRPC AND WebRTC for controlling robots? I recently started a new job at Viam, and was curious to learn more about their system architecture. Many robotics systems are composed of low-power and low-bandwidth devices, or operate in poor network conditions. Viam is an open-source robotics software platform that was designed specifically to help robots and other smart machines communicate more reliably. It uses gRPC for fast, structured client-server communication and WebRTC for direct, peer-to-peer communication between machines. Let’s explore the benefits of using gRPC and WebRTC in more detail. Benefits of using gRPC and Protobuf in robotics There’s a number of reasons to use gRPC for robotics, such as for improved developer productivity and developer experience. However, the most compelling reason is that it optimized for speed, perhaps up to 7–10x faster than REST APIs over HTTP, with lightweight Protobuf messaging. Performance and Efficiency Encoding: Protobuf offers efficient binary encoding, ideal for machines with low power, memory, compute, or bandwidth constraints compared to JSON-RPC.
Transport: Along with the default transport of HTTP/2, gRPC supports alternative transports like HTTP/1.1 with gRPC-Web and WebRTC. This provides a consistent interface with the flexibility of underlying transport technology, especially with streaming. Components and services in the Viam API are represented as language-agnostic Protocol Buffer (protobuf) services, and gRPC is responsible for the transport and communication of protobuf messages when calling protobuf methods. Specifically, Viam uses gRPC for high-level interactions between machine parts and SDK-based applications, enabling structured, low-latency data exchange and control commands. This is an efficient way to coordinate work to be done, such as retrieving point clouds from cameras, analyzing data for object segmentation, and instructing robotic arms to perform tasks based on computed poses. Protobuf minimizes the size of the transmitted data, saving network bandwidth during transmission. Benefits of using WebRTC in robotics There’s a number of reasons to use WebRTC in robotics, such as for security and cross-platform compatibility. However, the most compelling reason is that it uses a direct connection. Peer-to-peer communication Direct: WebRTC facilitates direct connections between devices, reducing reliance on central servers and minimizing the traversal route of data.
Network adaptability: Adjusts media quality based on network conditions for smooth performance, such as for video streaming. Viam relies on WebRTC for peer-to-peer communication, allowing direct video and data streams between machine parts and the Viam SDK. This means once the initial connection is established, your data doesn’t need to take the long way going through a central server on its way to talking to another machine. This setup optimizes real-time responsiveness and operational efficiency especially with complex machine configurations. WebRTC enables direct peer-to-peer communication, avoiding intermediate servers that could introduce delays or points of failure. How Viam uses gRPC and WebRTC to enable peer-to-peer robot communication Let’s see how this works in the example of an arcade claw game composed of two machine parts: a camera and an arm. The Viam app (app.viam.com) listens for connection requests, sends connection details, and then lets the two peers communicate directly. The initial connections are facilitated over gRPC. Once connected, the machine parts communicate over WebRTC, allowing all interactions between SDKs and machines to happen over WebRTC using gRPC method calls. In other words, the gRPC schema decides what the data looks like and how it’s packaged, while WebRTC handles the actual delivery of that data from one place to another. Get started with robotics using Viam While you could build out a similar infrastructure on your own, Viam provides this capability with off-the-shelf robotics software. The on-machine functionality is open-source and free to operate. If you eventually start managing a fleet, it’s usage-based billing for cloud services and data storage. The Viam web app and SDKs were designed to help you work with your machines. However, if you want to explore what’s happening behind-the-scenes, check out the Viam APIs public workspace in Postman for step-by-step instructions on working with the underlying Viam gRPC APIs, including: Authorizing gRPC API calls


Invoking methods to control hardware components


Invoking methods to manage software services You may not have a robotic arm at your disposal, but you can try this out with a more affordable machine, like a rover, such as Yahboom or SCUTTLE. Viam also designed an open-source rover to help folks learn about robotics. Use gRPC and WebRTC to operate a rover To get started, fork the Spin a Viam rover sample collection to your own workspace by clicking the Run in Postman button below. And follow along with these resources: Step-by-step codelab: Postman tutorial for Viam’s gRPC APIs
Browse the collection documentation for more details about each request
Watch the video: Spin a rover with the Viam gRPC API https://www.youtube.com/watch?v=KaJIXznpssw&t=1s&embedable=true Technical review by: Nick Hehr Robots work in all kinds of places with varying network conditions, from stable Ethernet to inconsistent cellular or satellite connections. Learn how you can use gRPC and WebRTC for robots and other smart machines to communicate more reliably. Why use gRPC AND WebRTC for controlling robots? I recently started a new job at Viam, and was curious to learn more about their system architecture. Many robotics systems are composed of low-power and low-bandwidth devices, or operate in poor network conditions. Viam is an open-source robotics software platform that was designed specifically to help robots and other smart machines communicate more reliably. Viam Viam It uses gRPC for fast, structured client-server communication and WebRTC for direct, peer-to-peer communication between machines. Let’s explore the benefits of using gRPC and WebRTC in more detail. Benefits of using gRPC and Protobuf in robotics There’s a number of reasons to use gRPC for robotics, such as for improved developer productivity and developer experience. However, the most compelling reason is that it optimized for speed, perhaps up to 7–10x faster than REST APIs over HTTP, with lightweight Protobuf messaging. gRPC 7–10x faster than REST APIs Performance and Efficiency Encoding: Protobuf offers efficient binary encoding, ideal for machines with low power, memory, compute, or bandwidth constraints compared to JSON-RPC. Transport: Along with the default transport of HTTP/2, gRPC supports alternative transports like HTTP/1.1 with gRPC-Web and WebRTC. This provides a consistent interface with the flexibility of underlying transport technology, especially with streaming. Encoding: Protobuf offers efficient binary encoding, ideal for machines with low power, memory, compute, or bandwidth constraints compared to JSON-RPC . Encoding: JSON-RPC Transport: Along with the default transport of HTTP/2, gRPC supports alternative transports like HTTP/1.1 with gRPC-Web and WebRTC. This provides a consistent interface with the flexibility of underlying transport technology, especially with streaming. Transport: Components and services in the Viam API are represented as language-agnostic Protocol Buffer (protobuf) services, and gRPC is responsible for the transport and communication of protobuf messages when calling protobuf methods. Viam API Specifically, Viam uses gRPC for high-level interactions between machine parts and SDK-based applications, enabling structured, low-latency data exchange and control commands. This is an efficient way to coordinate work to be done, such as retrieving point clouds from cameras, analyzing data for object segmentation, and instructing robotic arms to perform tasks based on computed poses. Protobuf minimizes the size of the transmitted data, saving network bandwidth during transmission. Protobuf minimizes the size of the transmitted data, saving network bandwidth during transmission. Benefits of using WebRTC in robotics There’s a number of reasons to use WebRTC in robotics, such as for security and cross-platform compatibility. However, the most compelling reason is that it uses a direct connection. WebRTC Peer-to-peer communication Direct: WebRTC facilitates direct connections between devices, reducing reliance on central servers and minimizing the traversal route of data. Network adaptability: Adjusts media quality based on network conditions for smooth performance, such as for video streaming. Direct : WebRTC facilitates direct connections between devices, reducing reliance on central servers and minimizing the traversal route of data. Direct Network adaptability : Adjusts media quality based on network conditions for smooth performance, such as for video streaming. Network adaptability Viam relies on WebRTC for peer-to-peer communication, allowing direct video and data streams between machine parts and the Viam SDK. This means once the initial connection is established, your data doesn’t need to take the long way going through a central server on its way to talking to another machine. This setup optimizes real-time responsiveness and operational efficiency especially with complex machine configurations. WebRTC enables direct peer-to-peer communication, avoiding intermediate servers that could introduce delays or points of failure. WebRTC enables direct peer-to-peer communication, avoiding intermediate servers that could introduce delays or points of failure. How Viam uses gRPC and WebRTC to enable peer-to-peer robot communication Let’s see how this works in the example of an arcade claw game composed of two machine parts: a camera and an arm. The Viam app (app.viam.com) listens for connection requests, sends connection details, and then lets the two peers communicate directly. The initial connections are facilitated over gRPC. Once connected, the machine parts communicate over WebRTC, allowing all interactions between SDKs and machines to happen over WebRTC using gRPC method calls. In other words, the gRPC schema decides what the data looks like and how it’s packaged, while WebRTC handles the actual delivery of that data from one place to another. Get started with robotics using Viam While you could build out a similar infrastructure on your own, Viam provides this capability with off-the-shelf robotics software. The on-machine functionality is open-source and free to operate. If you eventually start managing a fleet, it’s usage-based billing for cloud services and data storage. on-machine functionality The Viam web app and SDKs were designed to help you work with your machines. However, if you want to explore what’s happening behind-the-scenes, check out the Viam APIs public workspace in Postman for step-by-step instructions on working with the underlying Viam gRPC APIs, including: Viam web app SDKs Viam APIs public workspace Authorizing gRPC API calls Invoking methods to control hardware components Invoking methods to manage software services Authorizing gRPC API calls Authorizing gRPC API calls Invoking methods to control hardware components Invoking methods to control hardware components Invoking methods to manage software services Invoking methods to manage software services You may not have a robotic arm at your disposal, but you can try this out with a more affordable machine, like a rover, such as Yahboom or SCUTTLE . Viam also designed an open-source rover to help folks learn about robotics. Yahboom or SCUTTLE an open-source rover Use gRPC and WebRTC to operate a rover Use gRPC and WebRTC to operate a rover To get started, fork the Spin a Viam rover sample collection to your own workspace by clicking the Run in Postman button below. Spin a Viam rover Run in Postman And follow along with these resources: Step-by-step codelab: Postman tutorial for Viam’s gRPC APIs Browse the collection documentation for more details about each request Watch the video: Spin a rover with the Viam gRPC API Step-by-step codelab: Postman tutorial for Viam’s gRPC APIs Postman tutorial for Viam’s gRPC APIs Browse the collection documentation for more details about each request the collection documentation Watch the video: Spin a rover with the Viam gRPC API Spin a rover with the Viam gRPC API https://www.youtube.com/watch?v=KaJIXznpssw&t=1s&embedable=true https://www.youtube.com/watch?v=KaJIXznpssw&t=1s&embedable=true Technical review by: Nick Hehr Technical review by: Nick Hehr Nick Hehr

Poor Network Conditions? No Problem! gRPC and WebRTC for Robots Can Help

দুর্বল নেটওয়ার্ক শর্ত? কোন সমস্যা নেই! রোবটের জন্য gRPC এবং WebRTC সাহায্য করতে পারে

Schlechte Netzwerkbedingungen? Kein Problem! gRPC und WebRTC für Roboter können helfen

¿Condiciones de red deficientes? ¡No hay problema! gRPC y WebRTC para robots pueden ayudar

Mauvaise qualité du réseau ? Pas de problème ! gRPC et WebRTC pour robots peuvent vous aider

खराब नेटवर्क की स्थिति? कोई समस्या नहीं! रोबोट के लिए gRPC और WebRTC मदद कर सकते हैं

ネットワーク状態が悪い？問題なし！ロボット向けのgRPCとWebRTCが役に立つ

네트워크 상태가 좋지 않으신가요? 문제 없습니다! 로봇을 위한 gRPC와 WebRTC가 도와드릴 수 있습니다.

Condições de rede ruins? Sem problemas! gRPC e WebRTC para robôs podem ajudar

Плохое состояние сети? Не проблема! gRPC и WebRTC для роботов могут помочь

Kötü Ağ Koşulları mı? Sorun Değil! Robotlar için gRPC ve WebRTC Yardımcı Olabilir

Điều kiện mạng kém? Không vấn đề gì! gRPC và WebRTC cho Robot có thể giúp ích

网络条件差？没问题！机器人的 gRPC 和 WebRTC 可以提供帮助

Authors:
(1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu);
(2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com);
(3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Related Work Project Prerequisites Project Implementation Insight and Reflections Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Authors: Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Project Prerequisites Project Prerequisites Project Implementation Project Implementation Insight and Reflections Insight and Reflections Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Introducing High School Students to Version Control, Continuous Integration, and Quality Assurance

Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals, handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Transparency International, which publishes the Corruption Perceptions Index annually, confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy.


Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management.


Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals (SDGs), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole. Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals , handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. as equal goals But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Corruption and Government: Causes, Consequences, and Reform , Corruption and Government: Causes, Consequences, and Reform Corruption and Government: Causes, Consequences, and Reform Transparency International, which publishes the Corruption Perceptions Index annually , confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. Corruption Perceptions Index Corruption Perceptions Index Corruption Perceptions Index annually annually It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. Syndromes of Corruption: Wealth, Power, and Democracy , Syndromes of Corruption: Wealth, Power, and Democracy Syndromes of Corruption: Wealth, Power, and Democracy In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality . Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals ( SDGs ), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. SDGs SDGs SDGs Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. The Age of Sustainable Development , The Age of Sustainable Development The Age of Sustainable Development But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. Life 3.0: Being Human in the Age of Artificial Intelligence , Life 3.0: Being Human in the Age of Artificial Intelligence Life 3.0: Being Human in the Age of Artificial Intelligence The Second Machine Age The Second Machine Age The Second Machine Age To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole.

Could Artificial General Intelligence Make Government Corruption Obsolete? 

Može li vještačka opća inteligencija učiniti korupciju vlade zastarjelom? 

¿Podría la Inteligencia Artificial General hacer obsoleta la corrupción gubernamental?

Inteligentzia Orokor Artifizialak Gobernuaren ustelkeria zaharkitu al dezake? 

Podería a intelixencia xeral artificial facer obsoleta a corrupción do goberno? 

क्या कृत्रिम सामान्य बुद्धिमत्ता सरकारी भ्रष्टाचार को ख़त्म कर सकती है?

A mesterséges általános intelligencia elavulttá teheti a kormányzati korrupciót? 

האם בינה כללית מלאכותית יכולה להפוך את השחיתות השלטונית למיושנת? 

汎用人工知能は政府の腐敗をなくすことができるか？

ປັນຍາທຽມທົ່ວໄປສາມາດເຮັດໃຫ້ການສໍ້ລາດບັງຫຼວງຂອງລັດຖະບານລ້າສະໄຫມໄດ້ບໍ? 

Может ли искусственный интеллект сделать коррупцию в правительстве ненужной?

Sirdoonka Guud ee macmalka ahi ma ka dhigi karaa musuqmaasuqa dawladda mid duugoobay? 

Kan artificiell allmän intelligens göra statlig korruption föråldrad? 

Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 What Is Gradient Descent? Gradient descent is like hiking downhill with your eyes closed, following the slope until you hit the bottom (or at least a nice flat spot to rest). Technically, it is a method to minimize an objective function F(θ), parameterized by a model’s parameters θ∈Rn, by updating them in the opposite direction of the gradient ∇F(θ). The size of each step is controlled by the learning rate α. Think of α as the cautious guide who ensures you don’t tumble down too fast. If this sounds like Greek, feel free to check out my previous article, Quick Glance At Gradient Descent In Machine Learning — I promise it’s friendlier than it sounds! 🙂 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values.
For example:In machine learning, it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks.The objective function maps the model’s predictions to a numerical value, where smaller values indicate better performance.In simpler terms:It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Gradient Descent Variants Gradient descent isn’t a one-size-fits-all deal. It comes in three variants, each like a different hiker — some take the scenic route, others sprint downhill, and a few prefer shortcuts (like me 😅). These variants balance accuracy and speed, depending on how much data they use to calculate the gradient. 1. Batch Gradient Descent Batch gradient descent, the “all-or-nothing” hiker, uses the entire dataset to compute the gradient of the cost function: Imagine stopping to look 👀 at every rock 🪨, tree 🌴, and bird 🕊 before deciding where to place your foot 🦶🏽next. It’s thorough, but not ideal if your dataset is as big as, say, the Amazon rainforest 😩. It’s also not great if you need to learn on the fly — like updating your hiking route after spotting a bear 🐻‍❄️. Code Example: for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad Batch gradient descent shines when you have all the time in the world and a dataset that fits neatly into memory. It’s guaranteed to find the global minimum for convex surfaces (smooth hills) or a local minimum for non-convex surfaces (rugged mountains). 2. Stochastic Gradient Descent (SGD) SGD is the “impulsive” hiker who takes one step at a time based on the current terrain: It’s faster because it doesn’t bother calculating gradients for the entire landscape. Instead, it uses one training example at a time. While this saves time, the frequent updates can make SGD look like it’s zigzagging downhill, which can be both exciting and a little chaotic. 😅 Imagine updating your grocery list after each aisle — you get to the essentials faster, but your cart might look wild in the process. However, with a learning rate that slows down over time, SGD can eventually reach the bottom (or the best local minimum). Code Example: for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad 3. The Hero of the Day: Adam 🙌🏽 Now let’s talk about Adam — the “hiking guru” who combines the wisdom of Momentum and RMSprop. Adaptive Moment Estimation (Adam) is like having a smart guide who tracks the terrain and adjusts your steps based on past experiences and current conditions. It’s the go-to optimizer when you want to train neural networks and still have time for coffee. Why You’ll Love Adam Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials.


Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate).


Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. How Adam Works Adam maintains moving averages of gradients (mt​) and squared gradients (vt​) to adapt learning rates for each parameter: Here’s the cool part: Adam corrects biases to ensure accuracy, and updates parameters using this formula: where m^t and v^t are bias-corrected estimates, and epsilon ϵ is a small number to prevent division by zero. Conclusion Adam is the Swiss Army knife of optimizers — versatile, efficient, and reliable. Whether you’re training neural networks to detect fraud or create next-gen chatbots, Adam helps you get there faster and with fewer headaches. So, embrace Adam, take confident steps, and enjoy the view from the summit of machine learning success! References: https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/ Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 What Is Gradient Descent? Gradient descent is like hiking downhill with your eyes closed, following the slope until you hit the bottom (or at least a nice flat spot to rest). Technically, it is a method to minimize an objective function F(θ ), parameterized by a model’s parameters θ∈Rn, by updating them in the opposite direction of the gradient ∇F(θ). objective function F(θ The size of each step is controlled by the learning rate α. Think of α as the cautious guide who ensures you don’t tumble down too fast. If this sounds like Greek, feel free to check out my previous article, Quick Glance At Gradient Descent In Machine Learning — I promise it’s friendlier than it sounds! 🙂 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values. For example: In machine learning, it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks. The objective function maps the model’s predictions to a numerical value, where smaller values indicate better performance. In simpler terms: It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values. Circle back: objective function For example: In machine learning , it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks. machine learning loss function The objective function maps the model’s predictions to a numerical value , where smaller values indicate better performance. numerical value In simpler terms: It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Gradient Descent Variants Gradient descent isn’t a one-size-fits-all deal. It comes in three variants, each like a different hiker — some take the scenic route, others sprint downhill, and a few prefer shortcuts (like me 😅). These variants balance accuracy and speed, depending on how much data they use to calculate the gradient. 1. Batch Gradient Descent Batch gradient descent, the “all-or-nothing” hiker, uses the entire dataset to compute the gradient of the cost function: Imagine stopping to look 👀 at every rock 🪨, tree 🌴, and bird 🕊 before deciding where to place your foot 🦶🏽next. It’s thorough, but not ideal if your dataset is as big as, say, the Amazon rainforest 😩. It’s also not great if you need to learn on the fly — like updating your hiking route after spotting a bear 🐻‍❄️. Code Example: Code Example: for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad Batch gradient descent shines when you have all the time in the world and a dataset that fits neatly into memory. It’s guaranteed to find the global minimum for convex surfaces (smooth hills) or a local minimum for non-convex surfaces (rugged mountains). 2. Stochastic Gradient Descent (SGD) SGD is the “impulsive” hiker who takes one step at a time based on the current terrain: It’s faster because it doesn’t bother calculating gradients for the entire landscape. Instead, it uses one training example at a time. While this saves time, the frequent updates can make SGD look like it’s zigzagging downhill, which can be both exciting and a little chaotic. 😅 Imagine updating your grocery list after each aisle — you get to the essentials faster, but your cart might look wild in the process. However, with a learning rate that slows down over time, SGD can eventually reach the bottom (or the best local minimum). Code Example: Code Example: for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad 3. The Hero of the Day: Adam 🙌🏽 Now let’s talk about Adam — the “hiking guru” who combines the wisdom of Momentum and RMSprop . Adaptive Moment Estimation (Adam) is like having a smart guide who tracks the terrain and adjusts your steps based on past experiences and current conditions. It’s the go-to optimizer when you want to train neural networks and still have time for coffee. Momentum RMSprop Why You’ll Love Adam Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Low Memory Requirements: Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Minimal Hyperparameter Tuning: Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Practical Use: How Adam Works Adam maintains moving averages of gradients (mt​) and squared gradients (vt​) to adapt learning rates for each parameter: Here’s the cool part: Adam corrects biases to ensure accuracy, and updates parameters using this formula: where m^t and v^t are bias-corrected estimates, and epsilon ϵ is a small number to prevent division by zero. Conclusion Adam is the Swiss Army knife of optimizers — versatile, efficient, and reliable. Whether you’re training neural networks to detect fraud or create next-gen chatbots, Adam helps you get there faster and with fewer headaches. So, embrace Adam, take confident steps, and enjoy the view from the summit of machine learning success! References: https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/ https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/

Extending Stochastic Gradient Optimization with ADAM

No, You Don't Need a Highly Specialized Team to Build Your SaaS Application 

Bitcoin being the first blockchain network and cryptocurrency, has always been celebrated as the ultimate store of value. While its decentralized and immutable nature makes it ideal for this purpose, Bitcoin's original design limits its functionality beyond basic transactions. And here is Rootstock (RSK); a Bitcoin Layer 2 solution sidechain poised that unlocks limitless potential for the Bitcoin ecosystem. In this article, I will explain why Bitcoin needs a Layer 2, the pivotal role Rootstock plays in expanding Bitcoin utility, and how it is empowering decentralized finance (DeFi), smart contracts, and more, without compromising Bitcoin core principles. The Case for Bitcoin Layer 2 Bitcoin scalability challenges are no secret. With a block time of 10 minutes and limited throughput (approximately seven transactions per second), it was never designed for the high-speed, high-volume use cases we see in modern decentralized applications (dApps). While Bitcoin is unmatched as a digital gold standard, it lacks programmability and the ability to support complex logic like smart contracts, which are essential for use cases like DeFi, gaming, and tokenized assets. A Bitcoin Layer 2 is not just a technical upgrade, it is a philosophical evolution. Layer 2 solutions operate on top of the Bitcoin mainchain, inheriting its unmatched security while providing additional scalability, functionality, and flexibility. They allow developers and users to build a robust ecosystem around Bitcoin, expanding its utility far beyond simple payments. Rootstock: The Smart Contract Layer for Bitcoin Rootstock is a leading Layer 2 solution; a sidechain designed to merge Bitcoin security with Ethereum-like smart contract capabilities. Built as a sidechain to Bitcoin, Rootstock connects directly to the Bitcoin blockchain while adding a Turing-complete virtual machine (the Rootstock Virtual Machine, or RVM) that enables developers to write and deploy smart contracts. Here is why Rootstock stands out as a transformative force in the Bitcoin ecosystem: 1. Leverages Bitcoin Security Rootstock is anchored to the Bitcoin network through merged mining. Miners who secure the Bitcoin blockchain can simultaneously validate transactions on Rootstock, ensuring Rootstock inherits Bitcoin's unparalleled security while incentivizing miners with additional rewards. 2. Brings Smart Contracts to Bitcoin By integrating a version of the Ethereum Virtual Machine (EVM), Rootstock enables Bitcoin to support decentralized applications. This opens the door for use cases like DeFi, NFTs, gaming, and tokenized assets, all built on Bitcoin. 3. Seamless Two-Way Peg Rootstock's two-way peg system allows users to seamlessly transfer BTC to the Rootstock sidechain as RBTC, a 1:1 Bitcoin-backed token. This bridges Bitcoin liquidity to the Rootstock network, enabling the use of BTC in smart contract applications without leaving the ecosystem. 4. Scalability and Efficiency Rootstock significantly increases transaction throughput compared to Bitcoin’s base layer. Transactions on Rootstock are faster and cheaper, making it ideal for high-frequency applications like DeFi trading or microtransactions. Putting Bitcoin to Work: Real-World Applications of Rootstock Rootstock is not just theoretical; it is actively unlocking new use cases for Bitcoin, enabling the network to become a hub for innovation. Here is how: 1. Decentralized Finance (DeFi) DeFi has exploded in recent years, but most of its growth has occurred on Ethereum and other programmable chains. Rootstock brings this opportunity to Bitcoin. Platforms like Money on Chain (a stablecoin protocol) and Sovryn (a decentralized trading and lending platform) leverage Rootstock to create DeFi products secured by Bitcoin. This means Bitcoin holders can now earn yields, borrow, and lend without trusting centralized entities. 2. Tokenization Rootstock enables the creation of tokens backed by real-world assets, such as real estate or commodities, directly on the Bitcoin network. This expands Bitcoin’s utility as a base layer for a global financial ecosystem. 3. DAOs and Governance With Rootstock, Bitcoin can support Decentralized Autonomous Organizations (DAOs) that use smart contracts for governance and decision-making. Imagine a future where Bitcoin communities govern funds and projects democratically, all on-chain. 4. Identity and Reputation Systems Rootstock smart contracts can facilitate decentralized identity (DID) and reputation systems, laying the groundwork for trustless, self-sovereign identities anchored to Bitcoin. These systems could power use cases ranging from credit scoring to community-based voting. 5. Payment Channels While Bitcoin’s Lightning Network focuses on microtransactions, Rootstock complements this with programmable logic for payment channels. Businesses can automate recurring payments, escrow services, or conditional transfers directly on Rootstock. Why Rootstock Matters for The Future of Bitcoin Bitcoin's decentralized and censorship-resistant nature has made it the reserve asset of the crypto world, but this strength also limits its adaptability. Rootstock bridges this gap, allowing Bitcoin to participate in the programmable, DeFi-driven economy while remaining true to its ethos. By building on Bitcoin, Rootstock ensures that innovation in the ecosystem remains tied to the most secure and decentralized network in the world. This synergy creates a feedback loop: as more applications leverage Bitcoin liquidity and security through Rootstock, they attract new users and strengthen the network. The Road Ahead: Bitcoin Beyond a Store of Value Rootstock represents a paradigm shift for Bitcoin, transforming it from a passive store of value to an active participant in the broader blockchain ecosystem. As more developers, entrepreneurs, and users explore Rootstock capabilities, we are likely to see a wave of innovation that challenges the dominance of other smart contract platforms. For Bitcoin enthusiasts, Rootstock is an opportunity to see their favorite network evolve without compromising its core principles. For developers, it’s an invitation to build on the world's most secure blockchain. And for the crypto industry as a whole, it is proof that the Bitcoin story is far from over—it is just getting started. Bitcoin doesn’t need to be “fixed”; it needs to be unlocked. And Rootstock is the key. See You At $100,000 Bitcoin being the first blockchain network and cryptocurrency, has always been celebrated as the ultimate store of value. While its decentralized and immutable nature makes it ideal for this purpose, Bitcoin's original design limits its functionality beyond basic transactions. And here is Rootstock (RSK); a Bitcoin Layer 2 solution sidechain poised that unlocks limitless potential for the Bitcoin ecosystem. In this article, I will explain why Bitcoin needs a Layer 2, the pivotal role Rootstock plays in expanding Bitcoin utility, and how it is empowering decentralized finance (DeFi), smart contracts, and more, without compromising Bitcoin core principles. The Case for Bitcoin Layer 2 The Case for Bitcoin Layer 2 Bitcoin scalability challenges are no secret. With a block time of 10 minutes and limited throughput (approximately seven transactions per second), it was never designed for the high-speed, high-volume use cases we see in modern decentralized applications (dApps). While Bitcoin is unmatched as a digital gold standard, it lacks programmability and the ability to support complex logic like smart contracts, which are essential for use cases like DeFi, gaming, and tokenized assets. A Bitcoin Layer 2 is not just a technical upgrade, it is a philosophical evolution. Layer 2 solutions operate on top of the Bitcoin mainchain, inheriting its unmatched security while providing additional scalability, functionality, and flexibility. They allow developers and users to build a robust ecosystem around Bitcoin, expanding its utility far beyond simple payments. Rootstock: The Smart Contract Layer for Bitcoin Rootstock: The Smart Contract Layer for Bitcoin Rootstock is a leading Layer 2 solution; a sidechain designed to merge Bitcoin security with Ethereum-like smart contract capabilities. Built as a sidechain to Bitcoin, Rootstock connects directly to the Bitcoin blockchain while adding a Turing-complete virtual machine (the Rootstock Virtual Machine, or RVM) that enables developers to write and deploy smart contracts. Here is why Rootstock stands out as a transformative force in the Bitcoin ecosystem: Here is why Rootstock stands out as a transformative force in the Bitcoin ecosystem: 1. Leverages Bitcoin Security Leverages Bitcoin Security Rootstock is anchored to the Bitcoin network through merged mining. Miners who secure the Bitcoin blockchain can simultaneously validate transactions on Rootstock, ensuring Rootstock inherits Bitcoin's unparalleled security while incentivizing miners with additional rewards. 2. Brings Smart Contracts to Bitcoin Brings Smart Contracts to Bitcoin By integrating a version of the Ethereum Virtual Machine (EVM), Rootstock enables Bitcoin to support decentralized applications. This opens the door for use cases like DeFi, NFTs, gaming, and tokenized assets, all built on Bitcoin. 3. Seamless Two-Way Peg Seamless Two-Way Peg Rootstock's two-way peg system allows users to seamlessly transfer BTC to the Rootstock sidechain as RBTC, a 1:1 Bitcoin-backed token. This bridges Bitcoin liquidity to the Rootstock network, enabling the use of BTC in smart contract applications without leaving the ecosystem. 4. Scalability and Efficiency Scalability and Efficiency Rootstock significantly increases transaction throughput compared to Bitcoin’s base layer. Transactions on Rootstock are faster and cheaper, making it ideal for high-frequency applications like DeFi trading or microtransactions. Putting Bitcoin to Work: Real-World Applications of Rootstock Putting Bitcoin to Work: Real-World Applications of Rootstock Rootstock is not just theoretical; it is actively unlocking new use cases for Bitcoin, enabling the network to become a hub for innovation. Here is how: 1. Decentralized Finance (DeFi) 1. Decentralized Finance (DeFi) DeFi has exploded in recent years, but most of its growth has occurred on Ethereum and other programmable chains. Rootstock brings this opportunity to Bitcoin. Platforms like Money on Chain (a stablecoin protocol) and Sovryn (a decentralized trading and lending platform) leverage Rootstock to create DeFi products secured by Bitcoin. This means Bitcoin holders can now earn yields, borrow, and lend without trusting centralized entities. 2. Tokenization 2. Tokenization Rootstock enables the creation of tokens backed by real-world assets, such as real estate or commodities, directly on the Bitcoin network. This expands Bitcoin’s utility as a base layer for a global financial ecosystem. 3. DAOs and Governance 3. DAOs and Governance With Rootstock, Bitcoin can support Decentralized Autonomous Organizations (DAOs) that use smart contracts for governance and decision-making. Imagine a future where Bitcoin communities govern funds and projects democratically, all on-chain. 4. Identity and Reputation Systems 4. Identity and Reputation Systems Rootstock smart contracts can facilitate decentralized identity (DID) and reputation systems, laying the groundwork for trustless, self-sovereign identities anchored to Bitcoin. These systems could power use cases ranging from credit scoring to community-based voting. 5. Payment Channels 5. Payment Channels While Bitcoin’s Lightning Network focuses on microtransactions, Rootstock complements this with programmable logic for payment channels. Businesses can automate recurring payments, escrow services, or conditional transfers directly on Rootstock. Why Rootstock Matters for The Future of Bitcoin Why Rootstock Matters for The Future of Bitcoin Bitcoin's decentralized and censorship-resistant nature has made it the reserve asset of the crypto world, but this strength also limits its adaptability. Rootstock bridges this gap, allowing Bitcoin to participate in the programmable, DeFi-driven economy while remaining true to its ethos. By building on Bitcoin, Rootstock ensures that innovation in the ecosystem remains tied to the most secure and decentralized network in the world. This synergy creates a feedback loop: as more applications leverage Bitcoin liquidity and security through Rootstock, they attract new users and strengthen the network. The Road Ahead: Bitcoin Beyond a Store of Value The Road Ahead: Bitcoin Beyond a Store of Value Rootstock represents a paradigm shift for Bitcoin, transforming it from a passive store of value to an active participant in the broader blockchain ecosystem. As more developers, entrepreneurs, and users explore Rootstock capabilities, we are likely to see a wave of innovation that challenges the dominance of other smart contract platforms. For Bitcoin enthusiasts, Rootstock is an opportunity to see their favorite network evolve without compromising its core principles. For developers, it’s an invitation to build on the world's most secure blockchain. And for the crypto industry as a whole, it is proof that the Bitcoin story is far from over—it is just getting started. Bitcoin doesn’t need to be “fixed”; it needs to be unlocked. And Rootstock is the key. See You At $100,000 Bitcoin doesn’t need to be “fixed”; it needs to be unlocked. And Rootstock is the key. See You At $100,000

From Hodling to Putting Bitcoin to Work With Sidechain: Bitcoin 2.0

Stories, News & Mentions about CONTILIO

Contilio’s Stories on HackerNoon