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This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References ABSTRACT Keywords: Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References ABSTRACT Keywords : Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) Keywords 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Bridging Scales in Black Hole Accretion and Feedback: Studying Supermassive Black Holes

What are DePINs? Decentralized Physical Infrastructure Networks (DePINs) are real-world infrastructures on-chain. In more depth, DePINs offer a community-driven, cost-effective means of scaling projects without relying on traditional, centralized models. They are platforms where organizations and individuals can benefit from higher levels of control over their data and products through decentralized infrastructure. As for the term "DePIN,” this gained prominence in 2023 and continues to grow in mention into 2024. Initially, Web3 was used to reference projects focused on networks (replicating Web2 internet networks, but decentralized) but has since expanded to include all crypto or blockchain work, resulting in the need for this specific terminology. DePIN 101 On a fundamental level, DePINs function as bridges between physical facilities and the blockchain ecosystem. They operate through three main components. These are physical infrastructure controlled by a provider, a middleware connection to the blockchain, and a public ledger. These connections are managed by record-keeping and offering remittances to both the provider and the user.The physical infrastructure could be a smart agriculture system where farmers use IoT (Internet of Things) devices such as soil moisture sensors, weather stations, and crop health monitors to gather data about their fields. The middleware would then relay this information to the blockchain. Then, based on the data provided by the middleware, the blockchain would distribute rewards to the provider and users in the form of tokens. Additionally, for DePINs to operate effectively, four distinct parties must be involved in the network. This includes: Hardware (PRNs & DRNs): a physical component connecting networks to the real world
Hardware operators (Providers): Contributors buying or lending their hardware to the given network
Token: A financial incentive paid out to hardware operators based on data provided by the middleware
Users: DePINs need users who are willing to use and pay for the service The Current DePIN Landscape DePIN projects operate in six core business niches: compute marketplaces, wireless coverage, wholesale data, services marketplaces, energy services, and vertical ad networks. At present, there are several existing projects emerging in all six of these business models, including Filecoin, Helium, Hivemapper, Braintrust, Entheos and Sweatcoin. Out of these six sectors, compute marketplaces are where the most potential is seen, making up the majority of the market capitalization of DePIN crypto projects. Unpacking a DePIN example As we can see from the current DePIN landscape, there are many sectors in which DePINs currently operate. One real-world example that makes sense, at its core, is a decentralized energy grid. The basis for this is that traditional energy grids are often centralized, owned, and operated by utility companies, which can lead to inefficiencies and a lack of flexibility in distribution. Contrastingly, a decentralized energy grid built on blockchain technology could enable peer-to-peer energy trading between consumers and producers. Imagine a scenario where homeowners could sell excess energy to neighbors from their solar panels. This would facilitate more efficient use of renewable energy resources and incentivize individuals and communities to invest in sustainable energy production. By leveraging blockchain and smart contracts, this DePIN could ensure transparent and secure transactions while empowering users to have more control over their energy usage and production. The Flywheel Effect The concept of the flywheel effect is crucial to understanding DePINs potential for network growth and scalability. Unlike traditional models that require massive upfront capital investments, DePINs rely on grassroots efforts and community-driven initiatives to scale. Tokens play their part in these ecosystems by serving as an incentive for the community to contribute to the maintenance of the network. The Flywheel (above) visualizes the different steps involved in this maintenance and also helps to explain how DePIN projects can utilize the flywheel effect to catalyze their network growth.There are several different ways projects can increase the value of their token. For example, a stake-weighted random selection algorithm can be utilized to increase the value of a token, as providers must stake the project's token to get user deal flow. This adds an added incentive for contributors to continue building out the network. Therefore, the native token will increase in price as the network grows, resulting in more rewards for providers and a higher attraction for investors to invest. Fundamentally, this flywheel creates infrastructure networks that get stronger as they get bigger. As explained above, the flywheel effect of DePINs offers an alternative approach to traditional business models that fundamentally makes sense. However, we must consider the obstacles DePINs face in their journey to realizing their potential. The Future of DePINs By leveraging blockchain technology and tokenomics (The Flywheel Effect), DePINs are expected to disrupt existing IoT business models and enable on-chain communities to build innovative decentralized networks and applications. However, some limitations are currently holding DePINs back. There are several architectural considerations for scaling DePIN applications on-chain that need to be addressed if DePINs are going to be the bridge between decentralized technology and the real world. The most significant challenges to overcome are scalability, interoperability, security, and usability issues. The on-chain infrastructure needs to be able to handle a high volume of transactions and data throughput effectively to support the requirements of decentralized physical networks. To then communicate and exchange this data effectively, seamless interoperability with other blockchain networks and traditional systems is required. Robust security mechanisms, including encryption and authentication, are vital to safeguarding sensitive information and ensuring the integrity of DePIN networks. On top of this, DePINs must have systems in place, ensuring the security of external data feeds (oracles) used to verify real-world information and the prevention of Sybil attacks, where malicious actors create fake identities to gain undue influence within the network. A final consideration is that specialized technical knowledge is currently required to participate in a DePIN ecosystem. A more usable interface and intuitive design will be required for further adoption. The future of DePINs relies heavily on collaboration among stakeholders and ongoing technological innovation to address these challenges and unlock their full potential. What are DePINs? What are DePINs? Decentralized Physical Infrastructure Networks (DePINs) are real-world infrastructures on-chain. In more depth, DePINs offer a community-driven, cost-effective means of scaling projects without relying on traditional, centralized models. They are platforms where organizations and individuals can benefit from higher levels of control over their data and products through decentralized infrastructure. As for the term "DePIN,” this gained prominence in 2023 and continues to grow in mention into 2024. Initially, Web3 was used to reference projects focused on networks (replicating Web2 internet networks, but decentralized) but has since expanded to include all crypto or blockchain work, resulting in the need for this specific terminology. DePIN 101 DePIN 101 On a fundamental level, DePINs function as bridges between physical facilities and the blockchain ecosystem. They operate through three main components. These are physical infrastructure controlled by a provider, a middleware connection to the blockchain, and a public ledger. These connections are managed by record-keeping and offering remittances to both the provider and the user. The physical infrastructure could be a smart agriculture system where farmers use IoT (Internet of Things) devices such as soil moisture sensors, weather stations, and crop health monitors to gather data about their fields. The middleware would then relay this information to the blockchain. Then, based on the data provided by the middleware, the blockchain would distribute rewards to the provider and users in the form of tokens. Additionally, for DePINs to operate effectively, four distinct parties must be involved in the network. This includes: Hardware (PRNs & DRNs): a physical component connecting networks to the real world Hardware operators (Providers): Contributors buying or lending their hardware to the given network Token: A financial incentive paid out to hardware operators based on data provided by the middleware Users: DePINs need users who are willing to use and pay for the service Hardware (PRNs & DRNs): a physical component connecting networks to the real world Hardware (PRNs & DRNs): Hardware operators (Providers): Contributors buying or lending their hardware to the given network Hardware operators (Providers): Token: A financial incentive paid out to hardware operators based on data provided by the middleware Token: Users: DePINs need users who are willing to use and pay for the service Users: The Current DePIN Landscape The Current DePIN Landscape DePIN projects operate in six core business niches: compute marketplaces, wireless coverage, wholesale data, services marketplaces, energy services, and vertical ad networks. At present, there are several existing projects emerging in all six of these business models, including Filecoin , Helium , Hivemapper , Braintrust , Entheos and Sweatcoin . Filecoin Filecoin Helium Helium Hivemapper Hivemapper Braintrust Braintrust Entheos Entheos Sweatcoin Sweatcoin Out of these six sectors, compute marketplaces are where the most potential is seen, making up the majority of the market capitalization of DePIN crypto projects. Unpacking a DePIN example Unpacking a DePIN example As we can see from the current DePIN landscape, there are many sectors in which DePINs currently operate. One real-world example that makes sense, at its core, is a decentralized energy grid. The basis for this is that traditional energy grids are often centralized, owned, and operated by utility companies, which can lead to inefficiencies and a lack of flexibility in distribution. Contrastingly, a decentralized energy grid built on blockchain technology could enable peer-to-peer energy trading between consumers and producers. Imagine a scenario where homeowners could sell excess energy to neighbors from their solar panels. This would facilitate more efficient use of renewable energy resources and incentivize individuals and communities to invest in sustainable energy production. By leveraging blockchain and smart contracts, this DePIN could ensure transparent and secure transactions while empowering users to have more control over their energy usage and production. The Flywheel Effect The Flywheel Effect The concept of the flywheel effect is crucial to understanding DePINs potential for network growth and scalability. Unlike traditional models that require massive upfront capital investments, DePINs rely on grassroots efforts and community-driven initiatives to scale. Tokens play their part in these ecosystems by serving as an incentive for the community to contribute to the maintenance of the network. The Flywheel (above) visualizes the different steps involved in this maintenance and also helps to explain how DePIN projects can utilize the flywheel effect to catalyze their network growth. There are several different ways projects can increase the value of their token. For example, a stake-weighted random selection algorithm can be utilized to increase the value of a token, as providers must stake the project's token to get user deal flow. This adds an added incentive for contributors to continue building out the network. Therefore, the native token will increase in price as the network grows, resulting in more rewards for providers and a higher attraction for investors to invest. Fundamentally, this flywheel creates infrastructure networks that get stronger as they get bigger. As explained above, the flywheel effect of DePINs offers an alternative approach to traditional business models that fundamentally makes sense. However, we must consider the obstacles DePINs face in their journey to realizing their potential. The Future of DePINs The Future of DePINs By leveraging blockchain technology and tokenomics (The Flywheel Effect), DePINs are expected to disrupt existing IoT business models and enable on-chain communities to build innovative decentralized networks and applications. However, some limitations are currently holding DePINs back. There are several architectural considerations for scaling DePIN applications on-chain that need to be addressed if DePINs are going to be the bridge between decentralized technology and the real world. The most significant challenges to overcome are scalability, interoperability, security, and usability issues. The on-chain infrastructure needs to be able to handle a high volume of transactions and data throughput effectively to support the requirements of decentralized physical networks. To then communicate and exchange this data effectively, seamless interoperability with other blockchain networks and traditional systems is required. Robust security mechanisms, including encryption and authentication, are vital to safeguarding sensitive information and ensuring the integrity of DePIN networks. On top of this, DePINs must have systems in place, ensuring the security of external data feeds (oracles) used to verify real-world information and the prevention of Sybil attacks, where malicious actors create fake identities to gain undue influence within the network. A final consideration is that specialized technical knowledge is currently required to participate in a DePIN ecosystem. A more usable interface and intuitive design will be required for further adoption. The future of DePINs relies heavily on collaboration among stakeholders and ongoing technological innovation to address these challenges and unlock their full potential.

Demystifying DePINs and More: Bridging Real-World Infrastructure

AI/ML is a wildfire trend. It’s being integrated into just about every application you can think of. Compared to other technical innovations over the past years—like blockchain—infrastructure and tooling were being built (and still are) in parallel with discovering a meaningful application. AI/ML, on the other hand, has seemingly jumped straight to real-world application, leaving tooling and infrastructure largely lacking in comparison to adoption. We’ve built a lot of tools for developing and tuning models, but moving them into production is relatively new, and the few MLOps tools that do exist were built before AI/ML became all the rage. At some point, we have to stop and ask ourselves if we’re building long-term solutions or just bootstrapping our way to production. In my opinion, we’re standing at a crossroads—specifically, a crossroads of DevOps and MLOps. Like DevOps 10 years ago, MLOps is buzzy, and tech companies are racing to incorporate these practices into their workflows. However, the more I dig into it, the more I come to believe that MLOps is a momentary trend. I find it hard to believe that the future lies in further segmentation of XOps practices but in a return to a unified approach under the well-established (and adopted) practices of DevOps. The model handoff conundrum A major stumbling block in the current AI/ML workflow is the model handoff process. Moving a model from a development stage (typically in a Jupyter Notebook) to an ML tool, then onto a development server, and finally to a production environment like Kubernetes is…let’s say, challenging at best. Each of these transitions requires repackaging the model to suit the target environment. This is not just a pain for the team (usually DevOps) that owns everything to the right of training (see diagram below) but introduces significant risk and delay into the deployment cycle. If that’s not enough, the diversity of packaging mechanisms without a standardized procedure amplifies these issues, turning what we’ve come to enjoy as a straightforward process into a real “pain in the ops.” So we decided to do something about it. We had a crazy idea–Why not package models into a format that we’ve already standardized on? Back in 2013, a little company called Docker made it really easy to start using containers to package up applications. A big key to their success was the OCI ( ), an industry-wide initiative to have standards around how we package up our applications. Because of OCI standards, we have hundreds (maybe thousands?) of tools that can be combined to manage and deploy applications. So why aren’t we using this to package Notebooks and AI models as well? It would make deploying, sharing, and managing our models easier for everyone involved. you can learn about that here From Notebook to OCI-standard file type As I said earlier, we set out to do something about how we’re packaging up our models, and what we did was build Kit. Kit is that packages your model, datasets, code, and configuration so data scientists and developers can use their preferred tools while collaborating effortlessly. an open source MLOps project Our goal is to standardize the packaging, reproduction, deployment, and tracking of AI/ML models. We want models to be managed with the same agility as application code. Kit streamlines the deployment process and ensures models can be executed universally with minimal friction. E.g., when you hand a model to the DevOps team, it’s already good to go, meaning they can deploy it to prod! Your tools have been standardized, and best of all, you didn’t have to learn how to write a Docker or Kubernetes file. The Kit CLI packages up all of the assets/resources included in your Notebook and turns them into a ModelKit, to make handoffs between ML engineers using Jupyter Notebooks and application teams easy, letting both teams work with the tools they already use and love. . Checkout a video of the Kit CLI here KitOps' philosophy is centered around improving collaboration without requiring an overhaul of existing processes. By building on industry standards, we facilitate cross-team engagement beyond merely data scientists in the model development process. The compatibility of ModelKits and Kitfiles with current tools eliminates the need for significant modifications to established deployment pipelines or endpoints. Unmatched model traceability and reproducibility KitOps's approach to model traceability and reproducibility is different from the common practice of simply using a Docker file. To be clear, there’s nothing wrong with using Docker files, however, including your hyperparameters and data sets in a container are not a practical approach. Kit provides a unified package for everything you need to reproduce, integrate with, or test the model throughout its lifecycle in a modular package that gives users the flexibility to extract specific components of a ModelKit, such as the dataset or the model itself, or to retrieve the entire package. Furthermore, the storage of ModelKits within an organization’s registry facilitates a detailed record of state changes, crucial for auditing purposes. And, since ModelKits are OCI-compliant, you can use the registries that you’ve already adopted (Docker Hub, Amazon ECR, Artifactory, Quay, etc). In addition, ModelKits are ideally suited for secure bill-of-materials (SBOM) initiatives, addressing the growing concerns regarding security and compliance in AI/ML model deployment. Something that is becoming increasingly important. The inevitability of DevOps integration We believe that KitOps signifies a critical moment for AI/MLOps and DevOps. By addressing the issues plaguing model deployment and collaboration, KitOps paves the way for an era where the delineation between MLOps and DevOps becomes increasingly indistinct. To be clear, I’m not saying that the existing MLOps practices are not valuable; rather, I’m advocating that the foundational practices and tools being implemented in our DevOps workflows are flexible enough to incorporate the requisites of AI/ML deployment. We're still early in the development process and are really interested in collecting community feedback. You can learn more about KitOps at https://kitops.ml And check out the source code here: https://github.com/jozu-ai/kitops Also published . here

KitOps: Bridging the Gap Between AI/ML and DevOps with Standardized Packaging

Ishan Pandey: Hi Kamen, I am delighted to welcome you to our "Behind the Startup" series. Can you tell us what inspired the concept behind Nolus? Hi Ishan, I am delighted for the opportunity to be here! Let me introduce you to Nolus. The design of Nolus was inspired by traditional lease products where one can obtain an asset by providing only a fraction of the cost upfront, use the asset throughout the lease period and receive full ownership once the loan is repaid back in full. Kamen Trendafilov: Applying a similar concept in the crypto space, Nolus created the DeFi Lease: a lending solution that unlocks the full potential of crypto money markets by reducing the industry's steep over-collateralization requirements. This results in significantly improved capital efficiency and more favorable lending options for borrowers. Ishan Pandey: Could you explain the significance of Nolus' emphasis on interoperability and its ability to connect to various liquidity sources across different chains? Kamen Trendafilov: Under the hood, Nolus utilizes a semi-permissioned PoS blockchain built using the Cosmos SDK and a CosmWasm smart contract platform. Nolus emphasizes interoperability, connecting to various liquidity sources across chains without fragmenting assets. The system can swiftly swap assets on any integrated DEX, streamlining lending by negating the need for multiple pools and ensuring liquidity providers handle only stable assets. Ishan Pandey: How does Nolus navigate the complexities of the DeFi space, particularly in terms of regulatory compliance and building trust among users? Kamen Trendafilov: Nolus is a non-custodial DeFi cross-chain money market. It ultimately defines a money market between lenders looking to earn real yield on deposited stablecoins and borrowers looking to amplify holdings with more assets than their current equity. And Nolus blockchain and smart contracts connect all participants in our ecosystem. Nolus is an open-sourced protocol with multiple audits and a high appetite for simplifying the complexity of DeFi and making it more accessible to a broader user base. Ishan Pandey: How do you approach risk management, especially in ensuring a lower liquidation rate compared to other money markets? Kamen Trendafilov: Nolus boasts lower liquidation terms compared to competitors courtesy of its unique design. In Nolus, the downpayment provided by the borrower and the loan in stables from liquidity providers both are used to purchase the desired digital asset. This represents three times more exposure compared to regular crypto lenders. Because both the down payment together with the loan are locked into a DeFi Lease position and act as collateral, borrower has larger exposure to the asset bu also lower margin call risk by 40% compared to other blockchain lenders. During the amplified position with lower risk, the borrowers can utilize the assets within their active lease position in yield strategies whitelisted by the Protocol, basically creating self-repaying loans. For example, liquid staking derivatives will earn 20% APY which will create a margin of profits on the amplified position once interest is covered. The DeFi Lease comes at a never-changing interest rate and has no other related costs. Also, the price of the leveraged asset is fixed at the lease creation, thus any upside of the asset is for the borrower to keep. In short, the borrower actually gets more exposure at lower risk in a fully decentralized manner, together with yield strategies and upside appreciation. Ishan Pandey: Nolus aims to simplify and enhance cross-chain DeFi experiences. Could you share the near-term plans? Kamen Trendafilov: Nolus aims at being inclusive for beginner DeFi users, as well as proficient and experienced ones. Ultimately, Nolus aims to simplify and enhance cross-chain DeFi experiences for everyone by becoming the one-stop-shop for your crypto transaction. In the pipeline we have DEX integrations (including EVM chains), asset listings, product expansion, tired staking system. We also aim at eliminating the user’s need to perform many transactions on different chains but rather use only Nolus to interact with different liquidity hubs and assets. Ishan Pandey: Nolus is actively involved in the Cosmos ecosystem. How do you navigate the competitive landscape within Cosmos and position Nolus as a standout platform in comparison to competitors? Kamen Trendafilov: We firmly believe that Nolus’ offerings are unique and Nolus is positioned to technologically and financially become a safe harbor during crypto market’s volatility. Nolus’ rapid growth is a testament to its unique selling propositions and user demand for safe and long-term investment opportunities. In the past 8 months after launch, a total of $50m of transactional volume have been processed with more than 50 thousand wallets interacting with the blockchain. DeFi leases exceed $25m in 25 different assets, while TVL currently stands at $5m. We are certain this is just the beginning of a long road for Nolus. Ishan Pandey: With a focus on education, what strategies does Nolus plan to employ to simplify technical aspects for new users and position itself as a go-to solution within the Cosmos ecosystem? Kamen Trendafilov: First of all, Nolus has carefully crafted a simple, easy-to-grasp UI that comes from best practices of traditional financial platforms. Everything on Nolus (including swaps, buys, lending, borrowing and staking) comes at a simple one-click step. We are also actively engaged in providing simplified and elaborate explanations of every single functionality of the protocol. The main goal here is to increase crypto adoption and usage though well-known web2 practices in the world of finance. Ishan Pandey: How do you perceive the current challenges associated with the present bull run, such as increased market volatility and potential risks of liquidation, affecting the lending and borrowing market? Kamen Trendafilov: As every cycle on the crypto and regular markets, the biggest inefficiency has always been the increased leverage we observe at the very top of the bull. Market volatility in the crypto market is set to decrease as we embrace more and more the technology and core capabilities of the network. Risk of liquidation, however, is always highest when we increase the leverage in the system. This is really where we position Nolus - our solution actually cuts down the DeFi sector's high over-collateralization standards, thus decreasing the levels of liquidations and protects borrowers from getting trimmed. Furthermore, Nolus has embedded a partial liquidation model (rather than full liquidation model), thus further protecting its borrowers. If a liquidation trigger is reached, a portion of the collateral will be sold to cover part of the debt and return the lease to a healthier level. But the beauty of it is that the position is still active and potentially can recover in time thus providing yet another safeguard to the borrower. Ishan Pandey: How do you see the adoption of blockchain and DeFi transforming the traditional financial sector, and what challenges and opportunities do you anticipate in this process? Kamen Trendafilov: Absolutely. It will be a long way to go, of course, but principles and methodologies in DeFi are already transforming different aspects of traditional finance operations. The blockchain technology connects people, wallets and institutions like nothing before and it will be more than widely used in many game-changing transformations we will encounter in the following years. The opportunity is obvious - large scale transactions, cross-border, cheap and simple; a one-click to reach any point of the world, for every service and product. The challenge lies in scaling securely and safely. Ishan Pandey: In light of the increasing regulatory scrutiny in the crypto space, how does Nolus navigate compliance challenges and work towards ensuring a secure and regulatory-friendly environment for its users? Kamen Trendafilov: Regulations and scrutiny are absolutely essential for mass adoption in the crypto space. As a governance appchain, Nolus is entirely committed to embracing all regulatory developments to follow. We actually welcome such because with regulations comes further growth and prosperity in the space. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Nolus: Bridging the Gap in DeFi with Interoperability Across Blockchain Chains

Ishan Pandey: Hi John, welcome to our "Behind the Startup" series. Could you share some insights into your transition from a traditional investment banking background to becoming a FinTech entrepreneur and how your experiences have influenced the vision and direction of MANTRA? I had been in crypto for. For a long time before I actually joined a bank. I bought my first bitcoin in 2013, which was when I was back in university. So I think I've been pretty crypto native in technology, technologically driven, since a very young age. Even when I was at the bank, I was researching fintech and blockchain and other types of financial technology applications. So it was more about the culture and the type of business I wanted to work in. John Patrick Mullin: I definitely like the convergence of both the tech and the finance part, but it was more about this. I think the reason that I ended up shifting out and going into entrepreneurship is the ability to build something from scratch, lead people, work with people on incredibly tough challenges every day, and I'm just a bit of a workaholic, so entrepreneurship suits me. Ishan Pandey: Could you elaborate on the specific products and features that MANTRA is planning to launch to facilitate DeFi participation for both institutional and retail investors? At MANTRA we are building our Module layer which has MANTRA Token Service Module, MANTRA Assets Module, Guard Layer, MANTRA Compliance Module that enable creation, management and trading of RWAs for institutions, businesses, retailers, developers and users at ease. John Patrick Mullin: Ishan Pandey: How does MANTRA collaborate with regulatory authorities and industry partners to establish best practices and standards for tokenizing RWAs within the DeFi ecosystem? MANTRA is in the process of applying for requisite licensing for on chain exchange and brokerage, as well as asset investment management products in the UAE. We also work with regulated partners in other jurisdictions. One of the ways that we're able to cooperate and adhere to regulatory requirements is through our unique custom guard module on the MANTRA Chain, which allows us to create a compliant cocoon for many of the on chain applications that would require oversight, regulatory oversight and compliance. John Patrick Mullin: Ishan Pandey: How do tokenized real-world assets (RWAs) contribute to MANTRA’s strategy and offer unique opportunities for investors? Can you provide examples of tokenized RWAs that  MANTRA Finance plans to offer? MANTRA plans to offers a variety of RWAs to offer unique and extensive opportunities that includes stocks, bonds, commodities, real estate, art work and varied illiquid Traditional Assets. John Patrick Mullin: Ishan Pandey: What motivated the decision to debut yield-bearing DeFi products initially, followed by the launch of a Central Limit Order Book (CLOB) DEX and derivative products? Initially, we decided that it would make the most sense to launch with vault style products or yield bearing products, which obviously allow users to get exposure to not just RWAs, but other types of crypto yield bearing instruments. I think the two things in crypto that people are interested in is both the yield seeking part as well as the speculative part. So we want to cater to both parts of this user experience. We believe that naturally an exchange is really important to have within an ecosystem, whether that's a DEX, an order book exchange, etc. but we also believe in bringing finance onchain. So it made sense to launch an onchain sentiment order book that sits on top of an AMM, allowing for both. Simple swapping as well as more sophisticated trading structures. John Patrick Mullin: Ishan Pandey: What role does MANTRA Chain play within the MANTRA ecosystem, and how does it generate revenue? John Patrick Mullin: MANTRA Chain is the underpinning of everything within the MANTRA ecosystem. The OM token is the center of all this, which is the native token of the MANTRA Chain. We believe in this idea of the app chain thesis. So the protocol that we're developing, which is MANTRA Chain, has created specifically purpose built technology and software that allows applications that build on top of it to comply with local regulations across the globe. It's really the protocol for the entirety of our ecosystem. Ishan Pandey: As MANTRA moves towards its initial equity financing round, what are the key priorities for the company in terms of investor relations and fundraising activities? We raised $11 million, led by Shorooq Partners, which is a leading fintech VC in the UAE. Some of the priorities for the company, now that we've raised this capital, is hiring, expanding the team, growing our licensing capabilities, etc. John Patrick Mullin: Ishan Pandey: How does MANTRA ensure the integrity and authenticity of tokenized RWAs on its platform, especially considering the potential for fraud or misrepresentation in the tokenization process? MANTRA has its own Guard Module and Passportable DIDs that ensures a walled garden of KYC/KYB and Anti Money Laundering system. John Patrick Mullin: Ishan Pandey: Could you elaborate on the potential challenges associated with cross-border compliance and regulatory differences when dealing with tokenized RWAs from various jurisdictions, and how MANTRA plans to address them? One of the things that is obviously incredibly important when it comes to dealing with RWAs and other types of products is the ability to adhere to local regulations. So because of that, we built this idea of the guard module, which allows for permissioning across the wallet, the product, the pool, the asset, etc. and we have this customizable ability to allow and permission usage from individuals from different jurisdictions and also block them from others. So we can actually stop people who are not able to touch and interact with products at the smart contract level via this guard module. John Patrick Mullin: Ishan Pandey: How does MANTRA plan to educate investors about the benefits and risks of investing in tokenized RWAs compared to traditional financial instruments? Education is a really important thing when you're creating a new market, and we're definitely beating the drum for RWAs and tokenized on chain financial products. We're doing interesting things with respect to various hackathons. Obviously a lot of in person engagement, educational content from our social media team and marketing team, and we'll also have some unique incubator style arrangements where we'll be actually supporting teams to build on this RWA focused ecosystem. John Patrick Mullin: Additionally, we're cooperating with major academic institutions, particularly their blockchain clubs, that will allow us to educate the next generation about onchain financial products. Ishan Pandey: How does MANTRA perceive the role of RWAs in the future of crypto? Additionally, how does MANTRA aim to bring RWAs into the mainstream investor's radar? John Patrick Mullin: I believe that RWAs are just an extension of the financial system bringing it on chain. RWAs is frankly a bad acronym, it was called STO not too long ago. So I think there's a better way to actually describe these type of products and it's mainly just geared around tokenization. So I believe almost everything can be tokenized. That doesn't mean everything should be tokenized, but most financial products and platforms in my opinion in the future will be on some degree of a blockchain, although you may not know that it's on a blockchain. So I think this is incredibly important for our strategy. We believe the financial ecosystem is moving onchain and we'll be at the forefront of that. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Bridging Traditional Finance and Crypto: The Rise of MANTRA's Tokenization Ecosystem

ঐতিহ্যগত অর্থ এবং ক্রিপ্টো ব্রিজিং: MANTRA এর টোকেনাইজেশন ইকোসিস্টেমের উত্থান

Brücke zwischen traditionellem Finanzwesen und Kryptowährungen: Der Aufstieg des Tokenisierungs-Ökosystems von MANTRA

Uniendo las finanzas tradicionales y las criptomonedas: el auge del ecosistema de tokenización de MANTRA

Faire le lien entre la finance traditionnelle et la cryptographie : l'essor de l'écosystème de tokenisation de MANTRA

पारंपरिक वित्त और क्रिप्टो को जोड़ना: MANTRA के टोकनाइजेशन इकोसिस्टम का उदय

従来の金融と仮想通貨の橋渡し: MANTRA のトークン化エコシステムの台頭

전통적인 금융과 암호화폐 연결: MANTRA 토큰화 생태계의 부상

Unindo finanças tradicionais e criptografia: a ascensão do ecossistema de tokenização do MANTRA

Соединение традиционных финансов и криптовалюты: развитие экосистемы токенизации MANTRA

Geleneksel Finans ve Kripto Arasında Köprü Kurmak: MANTRA'nın Tokenizasyon Ekosisteminin Yükselişi

Kết nối tài chính truyền thống và tiền điện tử: Sự trỗi dậy của hệ sinh thái mã thông báo của MANTRA

连接传统金融和加密货币：MANTRA 代币化生态系统的崛起

Navigating New RWA Horizons: Rick Schmitz on Bridging Blockchain and Traditional Finance

A Revolution is Underway in Finance Decentralized finance has experienced rapid growth at an amazing pace as compared to the adoption of teleview, laptops and other technology appliances, but challenges around data and transparency still loom large if it is to reach its full potential. Issues such as ensuring reliable access to information and navigating an evolving regulatory landscape don't have straightforward solutions. Building trust also remains difficult when centralized control leaves systems open to influence. A new partnership between financial powerhouse Nomura and upstart DeFi project Pyth signals that mindsets may finally be shifting. In a surprise alliance, Laser Digital - a Nomura subsidiary - has teamed up with the Pyth Network. This is awesome for the DeFi industry. Their goal is to address precisely the problems that have held the sector back: namely, concerns related to data reliability and transparency. By providing Pyth's platform institutional-grade market data via Laser Digital, while leveraging blockchain's inherent transparency, this collaboration aims to strengthen the very foundations of the burgeoning industry. It might even help accelerate DeFi's integration with traditional finance models. Is this indication that the tide is turning in decentralized finance's favor? Some believe that with major institutional players now paying attention, DeFi could be on the brink of fundamentally upending global finance in the years ahead. While uncertainty still abounds, one thing is clear - change is most definitely underway. This partnership suggests the revolution may at last be gaining real impetus. Only time will tell where the intertwining of Wall Street and decentralized technologies ultimately leads. But it seems the future of finance is growing increasingly difficult to predict. Nomura Steps into DeFi The alliance between the organizations signals that Nomura sees real potential in the decentralized finance space to disrupt and transform the traditional banking industry in APAC and America, the company is taking a long-term view on how TradiFi and DeFi will merge together, providing better financial opportunities to retail investors. By collaborating with innovative young companies in DeFi, Nomura is aiming to get on the ground floor of this tech-driven evolution in finance and be part of the future of finance, which democratizes financial opportunities for everyone. Ensuring Reliable Data for DeFi At the core of the partnership is Pyth Network's goal of providing decentralized apps (or dapps) with more dependable market data. Using blockchain tech, Pyth believes it can deliver information with greater efficiency and stability than traditional sources. I believe this is great for the industry! This is where Laser Digital comes in. By supplying Pyth with the types of institutional-grade financial data it's accustomed to, Laser Digital acknowledges how critical trustworthy information is for the budding DeFi ecosystem to flourish. Reliable stats underpin not just dapps themselves but also build confidence in users. Gaining Traction via Transparency One of Pyth's key selling points is its focus on transparency and decentralization. This fosters a secure, verifiable environment for DeFi platforms to gain wider acceptance. Transparency also helps address past concerns about data manipulation holding the industry back. Bridging Wall Street and DeFi Innovation The tie-up between Laser Digital and Pyth Network signifies efforts to blend traditional high finance with novel decentralized models. It underscores a growing openness within the industry to DeFi's potential to revolutionize banking as we know it. Over time, the lines between Wall Street and blockchain tech may continue to blur. Overcoming Hurdles on the Horizon While alliances like this open doors, challenges around regulation and compliance still loom large on DeFi's road ahead. As adoption expands, proactively navigating these issues will be paramount. If successful, partnerships could help DeFi deliver on its promise of accessible, transparent finance for all. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR ested Interest Disclosure:

Nomura and Pyth Pioneer a New Era in Finance: Bridging the Gap Between Traditional Banking and DeFi 

নোমুরা এবং পাইথ ফিনান্সে একটি নতুন যুগের পথপ্রদর্শক: প্রথাগত ব্যাঙ্কিং এবং ডিফাইয়ের মধ্যে ব্যবধান কমানো

Nomura und Pyth läuten eine neue Ära im Finanzwesen ein: Überbrückung der Lücke zwischen traditionellem Bankwesen und DeFi

Nomura y Pyth son pioneros en una nueva era en las finanzas: cerrar la brecha entre la banca tradicional y DeFi

Nomura et Pyth ouvrent une nouvelle ère en matière de finance : combler le fossé entre la banque traditionnelle et la DeFi

नोमुरा और पाइथ ने वित्त में एक नए युग की शुरुआत की: पारंपरिक बैंकिंग और डेफी के बीच अंतर को पाटना

野村とパイスが金融の新時代を開拓：従来の銀行業務とDeFiの間のギャップを埋める

Nomura와 Pyth는 금융의 새로운 시대를 개척합니다: 기존 은행과 DeFi 간의 격차 해소

Nomura e Pyth são os pioneiros de uma nova era nas finanças: preenchendo a lacuna entre o sistema bancário tradicional e o DeFi

Nomura и Pyth открывают новую эру в финансах: преодоление разрыва между традиционным банкингом и DeFi

Nomura ve Pyth Finansta Yeni Bir Döneme Öncülük Ediyor: Geleneksel Bankacılık ile DeFi Arasındaki Boşluğu Kapatmak

Nomura và Pyth Tiên phong kỷ nguyên mới trong lĩnh vực tài chính: Thu hẹp khoảng cách giữa ngân hàng truyền thống và DeFi

野村证券和 Pyth 开创了金融新时代：弥合传统银行与 DeFi 之间的差距

For decades, traditional finance and decentralized protocols have operated in separate spheres. But a new development is poised to break down the barriers between them: the rise of real-world asset tokenization. By bringing tangible, "brick-and-mortar" assets onto blockchain networks, projects are literally bridging the gap between centralized and decentralized worlds. Real estate, commodities, loans, and more are now represented as cryptographic tokens thanks to innovative platforms disrupting the status quo. The implications could reshape the entire financial landscape. By unlocking trillions currently locked up in illiquid assets, a huge amount of new value is opening up for investors and economies worldwide. Tokenizing real estate, commodities, loans, and more allows these large assets to be divided into affordable fractional pieces. Now, everyday investors can directly participate in markets usually reserved for big players. Welcome to the new world of inclusive investing. Pioneers Blazing the Trail Outlets like MakerDAO, whose stablecoin DAI is collateralized by real estate deeds, are leading the charge. Creditcoin puts private loans on-chain, while Maple Finance connects institutional lenders and crypto borrowers using tokenized real assets. Centrifuge and Goldfinch similarly securitize and fund everything from invoices to equipment on decentralized networks. Their success points to massive untapped potential. As more asset classes become tokenized - think carbon credits, intellectual property, and beyond - an entirely new asset class is emerging where crypto and traditional holdings intersect. Investment opportunities and wealth creation possibilities are multiplying at an exponential rate. For now, let's first explore the benefits of RWA in DeF. In our work with DeFi projects, we mostly see: 1. Liquidity enhancement Tokenizing traditionally illiquid assets enables their conversion into divisible and tradable tokens, enhancing liquidity and facilitating more efficient transactions. This improved liquidity leads to more accurate price discovery and reduced transaction costs. 2. Fractional ownership By breaking down high-value assets into smaller, affordable units, tokenization opens up investment opportunities to a broader audience. This democratization of access allows retail investors to participate in markets previously reserved for institutional players. 3. Diversification Integrating RWAs into DeFi platforms enables users to diversify their investment portfolios by including real-world assets alongside cryptocurrencies. This diversification can help mitigate risk and optimize returns. But Bridging Worlds Is No Easy Feat For all its benefits, merging physical and digital finance comes with dilemmas to unravel. Chief among them is maintaining a clear, verifiable link between on- and off-chain assets - no easy task across disparate jurisdictions and compliance frameworks. Regulation, too, poses knotty issues, as decentralized networks interact with regulated, "real world" assets in novel ways. Navigating rules sensitively will be paramount, requiring cooperation between authorities, firms, and communities. Technology alone won't solve governance challenges. P.S. Check out some of my previous articles at HackerNoon: Google Introduces Gemma: Revolutionary Access to Pre-Trained AI Models Sora's AI Innovation Set to Disrupt Video Content Creation Breaking Down the Next Big Thing in NFTs: ERC-404 Spotlight Gemini 1.5 Unleashes Unprecedented Context for AI Applications The AI Revolution in Journalism: A New Era of Enhanced Reporting DeFi Community Building: A Step-by-Step Guide for Crypto Startups

Bridging the Great Divide: How Real-World Assets Are Combining TradFi and DeFi

Welcome to our enlightening session of the "Behind the Startup" series. Today, we're thrilled to host Robby Greenfield IV, the visionary founder of Umoja, a trailblazing platform at the intersection of blockchain technology and financial innovation. With a rich background in financial engineering, product management, and a remarkable journey through giants like Goldman Sachs, Amazon, and ConsenSys, Robby brings a wealth of experience and insights. Umoja, born from a passion to democratize financial services, stands out as a beacon of innovation in the crypto world. Join us as Robby shares his inspiring journey, the genesis of Umoja, and how it's shaping the future of hedging in the cryptocurrency market. Revolutionizing Crypto Hedging: How Umoja Guarantees Market-Loss Insurance Without Liquidation Fears Ishan Pandey: Hi Robby, delighted to welcome you to our "Behind the Startup" series. Can you share insights into your background, and your involvement in prior ventures also what inspired you to found Umoja? Absolutely! Glad to be a part of the series. I’ve been a financial engineer & product manager for over a decade, and started my journey in crypto in 2011. I started my career at Goldman Sachs, and have since worked at Cisco Systems, Amazon, and ConsenSys. At ConsenSys, I was their Head of Social Impact, and deployed over a dozen dApps & blockchain initiatives across 17 emerging markets globally, having worked with the likes of the U.S. State Dept., the UNOPs, Oxfam International, Harvard, and the World Bank. Robby Greenfield IV: Umoja actually spun out of ConsenSys originally as ConsenSys Social Impact, focused on enabling digital humanitarian aid on-chain for the underbanked by offering digital wallets compatible with WhatsApp, NFC Cards, and feature phones. After disbursing over $3M to tens of thousands of beneficiaries across 6 countries, we pivoted into hedging after realizing many of our customers and competitors greatly struggled with FX hedging due to the international nature of their organizations. Given that we know fiat, and all RWAs will come on chain, and the fact that our team has decades of Web3 and TradFi experience,  it made sense to capture this opportunity and protocolize hedging. Ishan Pandey: How does Umoja address the unique challenges of the crypto market through its hedging solutions? Umoja enables anyone to hedge their crypto losses like a hedge fund without the fear of liquidation. To put it simply - it’s like a market-loss insurance plan to lock in your gains with zero technical knowledge. We do this by creating synthetic options replicated through the algorithmic trade of leveraged, perpetual futures. In this way, we don’t need LPs and can offer option-like exposure more flexibly and affordably than nearly every other options protocol and exchange in crypto - all while making it easy to use and understand by retail investors. Robby Greenfield IV: Ishan Pandey: How does Umoja's automated hedging solution stand out in the market, especially in comparison to other options exchanges? Robby Greenfield IV: We don’t require any LP liquidity, we’re up to 30x more affordable on up-front costs. We’re much more flexible to use (can opt out anytime from a hedge). We’re the simplest to use. We’re more customizable - able to create DeFi-niche hedges like that for Uniswap LP positions, or de-peg derivatives. This is particularly true for non ETH/BTC markets, where the options markets are very illiquid. What we’re creating is DeFi’s first risk primitive - enabling tokenized investment strategies with strong downside protection - rather than just something that generates yield on a declining asset. Ishan Pandey: Can you elaborate on how Umoja provides more flexibility in terms of the duration of hedges and strike options than any other options exchange? How does it enhance user experience? When you purchase a traditional option - whether in TradFi or in Crypto, you have to purchase it at a standardized strike rate and term (e.g., 5% at 1 Month). This is because crypto options markets are much more illiquid than TradFi options markets, and because options have attributes like strike rates and terms, liquidity has to be concentrated into separate, standardized bands. Too much flexibility will cause even more illiquidity - and thus it is not currently possible to hyper-customize your strike and terms with crypto options in the traditional market. Additionally, traditional crypto options face much higher counterparty risk, as there has to be someone who wants your exact option in order for you to get out of that position. Robby Greenfield IV: In contrast, since Umoja replicates the value of options through algo trading perpetual futures, our counterparty risk is much lower. No one needs to buy an option from Umoja hedgers, because they didn’t purchase one in the first place. The counterparty is the underlying asset’s future’s market. For example, BTC’s future’s market monthly trading volume for , whereas its monthly options trading volume for that same month was . That means Umoja’s synthetic options are nearly 30x more liquid. Additionally, we can hyper-customize strikes and terms because - again - it’s a synthetic option not bound by the standardized liquidity constraints of the traditional options market. January 2024 was $1.29T $43.34B Our UX is the best in crypto derivatives. Inspired by Uniswap, you can literally hedge your market losses in less than 20 seconds. By simplifying the options experience by making them more customizable and attaching them to a useful context - protecting one’s money & locking in your gains - it doesn’t make sense for any trader or retail investor to go anywhere else. Ishan Pandey: Can you walk us through the process of how users can select the right fund, pay a fee, provide collateral, and receive Hedge Tokens? How does this process make it easy for users to interact with the platform? Hedging on Umoja takes 5 simple steps: Robby Greenfield IV: Choose a token you want to protect. Choose how much of that token you want to protect. Choose the date up until you want to protect it. Choose a price at which your protection should start. Put down 10% collateral and pay a small fee to Umoja. That’s it. Hedgers are never liquidated and can opt-out the hedge at any time without suffering from high counterparty risk. Hedgers may, however, need to top-up their collateral from time to time depending on the length of their hedge and market volatility if they want to maintain their full coverage. If they decide not to, their coverage will gradually decay over time. Ishan Pandey: Can you discuss the significance of your partnership with the first Crypto Hedge Fund and Chainlink, and the pending partnerships with Avalanche, Gogopool, and Benqi? Though I don’t want to spill the beans too much - let’s say that there are some exciting developments coming to DeFi. ‘Zero-Loss’ staking via hedging-embedded, liquid-staking tokens, crypto hedge funds leveraging the protocol, and LP position hedging via price feeds are some of those developments. Robby Greenfield IV: Ishan Pandey: With your background in blockchain and social impact, how do you see Umoja contributing to financial inclusion? Retail traders lost $1.8T over the last bear market - mainly due to not being able to protect themselves and buying the downside of whales and hedge funds. According to Pew Research Center polls in 2021 and 2022, some 20% of Black, Hispanic and Asian U.S. adults have bought, traded or used cryptocurrency, compared with 13% of white adults. This means that bear markets disproportionately affect communities of color, and having a way for those communities, and quite frankly, everyone to protect themselves from losses in a way that’s easy to use is an essential public good. Robby Greenfield IV: Ishan Pandey: In your opinion, how can widespread hedging positively impact the crypto ecosystem? It will create a world where people can opt to take on downside risk of a native asset. You can choose to either holde hedged ETH (uETH) at a premium, or normal ETH (and take on losses). Simply put - this makes DeFi and crypto at large more financially responsible. No more betting the mortgage or the dichotomy between WAGMI and going broke. Robby Greenfield IV: Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Robby Greenfield IV on Founding Umoja: Bridging Traditional Finance and Web3 with a Decade of Expert

উমোজা প্রতিষ্ঠায় রবি গ্রিনফিল্ড IV: এক দশকের বিশেষজ্ঞের সাথে ঐতিহ্যগত অর্থায়ন এবং ওয়েব3 ব্রিজিং

Robby Greenfield IV über die Gründung von Umoja: Mit einem Jahrzehnt Erfahrung verbindet er traditionelle Finanzen und Web3

Robby Greenfield IV sobre la fundación de Umoja: uniendo las finanzas tradicionales y la Web3 con una década de expertos

Robby Greenfield IV sur la création d'Umoja : relier la finance traditionnelle et le Web3 avec une décennie d'experts

उमोजा की स्थापना पर रॉबी ग्रीनफील्ड IV: एक दशक के विशेषज्ञ के साथ पारंपरिक वित्त और वेब3 को जोड़ना

Robby Greenfield IV が Umoja の設立について語る: 10 年にわたる専門家による従来の金融と Web3 の橋渡し

Umoja 설립에 관한 Robby Greenfield IV: 10년의 전문가와 전통적인 금융 및 Web3 연결

Robby Greenfield IV sobre a fundação do Umoja: unindo finanças tradicionais e Web3 com uma década de especialistas

Робби Гринфилд IV о создании «Умоджи»: соединение традиционных финансов и Web3 с десятилетним опытом экспертов

Robby Greenfield IV, Umoja'nın Kuruluşu: Geleneksel Finans ve Web3'ü On Yıllık Uzmanla Birleştirmek

Robby Greenfield IV về việc thành lập Umoja: Kết nối tài chính truyền thống và Web3 với một thập kỷ chuyên gia

罗比·格林菲尔德四世 (Robby Greenfield IV) 谈创立“团结”系统：通过十年的专家在传统金融和 Web3 之间架起桥梁

In an enlightening conversation for the "Behind the Startup" series, Louis Bellet, the dynamic force behind Yellow Network, shares the intriguing story of its inception and its ambitious mission to redefine the cryptocurrency trading landscape. Born from the strategic merger of Openware.com and Yellow Trading, Yellow Network emerges as a groundbreaking solution, tackling the industry's pressing challenges head-on. With a focus on security, efficiency, and decentralization, Bellet details the innovative mechanisms and future-forward technologies that make Yellow Network a beacon of progress in the digital asset ecosystem. Revolutionizing Crypto Trade: Louis Bellet Unveils Yellow Network's Vision Ishan Pandey: Hi Louis, great to have you here for our "Behind the Startup" series. Can you share the story behind the founding of Yellow Network and what inspired you to start this venture? Louis Bellet: Yellow Network was born as a result of a collaboration of two established players in the industry. My journey began with Openware.com, where we built over 150 trading platforms, gaining unique insights into the pains and gains of both institutional and retail crypto businesses. Meanwhile, my partner Alexis, one of the founders of GSR and Yellow Trading, brought an in-depth perspective from the trading side. Our combined understanding of the industry's challenges led us to create Yellow Network. It represents our collaborative effort to innovate and solve persistent issues in the digital asset ecosystem, aiming to revolutionize the way digital assets are traded. Ishan Pandey: Can you elaborate on how Yellow Network aims to make cryptocurrency trading leaner, more efficient, and more secure in the current blockchain landscape? Louis Bellet: One of the main issues of the crypto industry today is the absence of the proper trading infrastructure. Yellow Network solves this by introducing a decentralized clearing system. It is designed to minimize reliance on a single market player with no separation of responsibilities, thus greatly reducing counterparty risks and removing potential points of failure. This approach connects brokers and exchanges across blockchains, enabling efficient and secure cross-chain trading and clearing. Yellow Network optimizes capital management through position netting, and streamlined settlement processes that consolidate a batch of trades into a single transaction. Ishan Pandey: How does Yellow Network utilize a P2P network and state channels to connect brokers across various blockchains? Louis Bellet: The P2P approach enables brokers to instantly connect to other network participants to match orders and execute trades directly with each other in a decentralized manner, bypassing the traditional centralized framework. State channels facilitate private, secure off-chain transactions between parties, ensuring privacy, scalability and efficiency without compromising the blockchain's integrity. In this way, we can bring ultra-high-frequency trading and minimize transaction costs, while also being able to trade assets cross-chain. Ishan Pandey: Could you explain in more detail how Yellow Network's automated smart clearing protocol and its significance in the context of cryptocurrency trading? Louis Bellet: Our core component is called ClearSyncTM; it is a protocol that automates the post-trade process, including the clearing and settlement of trades, through smart contracts. It handles all activities from the moment a transaction commitment is made until it is settled, similar to traditional finance clearing processes​​. Key features of ClearSyncTM include the ability to open and close trading channels, deposit collateral in various cryptocurrencies, and send high-frequency margin-calls requests off-chain. In addition, it offers the flexibility to request settlements using different methods, including atomic cross chain swaps, or even third-party custodians. It also handles disputes on a portofolio state to unlock the collateral, enhancing the security and reliability of the trading process​​. Essentially, ClearSyncTM reduces counterparty risk and eliminates the need for intermediaries, leading to a more streamlined and less costly trading process. Ishan Pandey: What features make Yellow Network an all-in-one Web3 solution, and how is it tailored for the global retail market? Louis Bellet: Yellow Network offers a comprehensive suite of tools and services tailored to the needs and expectations of a global crypto market. Features such as cross-chain liquidity access, decentralized identity management, and integrated wallet services serve well for both beginners and experienced traders. Among other things, the platform also features: Decentralized trading, allowing participants to trade using their nearest node; Scalable infrastructure: with the use of state channels to enable swift, efficient off-chain trading and clearing suitable for high-frequency trading​​; Enhanced liquidity access as opposed to traditional silos, offering seamless liquidity exchange across platforms and chains​​; Minimized counterparty risks with the automated netting and operates collateral and safety deposits for secure settlements​​; Capital efficiency for consolidating trades and lowering margin requirements, optimizing capital usage​​; A broker-dealer model that aligns with regulatory preferences and streamlines broker operations​​. Ishan Pandey: Can you provide insights into the decision to merge Openware and Yellow Trading to form Yellow Network? How has this merger contributed to the company's overall strategy? Louis Bellet: The decision to merge was strategic in nature and aimed at uniting our expertise in blockchain development and financial markets. When we first started working together with Alexis, we quickly realized that we have an aligned vision of the future of crypto and a similar outlook on how the main problems of the industry can be tackled. We have leveraged Openware's technical expertise in building infrastructure for exchanges with Yellow Trading's market insights, which created Yellow Network as we know it now. Ishan Pandey: Can you elaborate on Yellow Trading's role as a market maker and startup incubator within the Yellow Network ecosystem? Louis Bellet: Yellow Trading plays a dual role within Yellow Network. As a market maker, it provides liquidity, ensuring efficient and stable market operations. This is crucial in creating a fruitful trading environment, especially in emerging or less liquid markets. As a startup incubator, Yellow Trading nurtures innovative blockchain projects, providing them with the necessary resources, mentorship, and network support to succeed, further enriching and integrating them into the Yellow Network ecosystem. Ishan Pandey: How do you perceive the current state of blockchain adoption globally, and what role does Yellow Network play in driving adoption? Louis Bellet: The global adoption of blockchain is primarily focused on trading and speculation. However, most traders don't fully utilize the benefits of the blockchain; they simply can’t; due to the isolated nature of exchanges and scalability issues. Blockchains are not the complete solution for decentralizing trading, but rather a component of the infrastructure. Yellow Network bridges the gap between isolated trading platforms and chains, facilitating efficient, real-time cross-chain transactions. This innovative approach progresses blockchain usage beyond current limitations, positioning Yellow Network at the forefront of promoting broader blockchain adoption and transforming the cryptocurrency trading landscape. Ishan Pandey: Looking ahead, how do you anticipate the regulatory landscape for blockchain and cryptocurrency evolving, and what preparations is Yellow Network making for potential regulatory changes? Louis Bellet: The regulatory framework for blockchain and cryptocurrency is expected to evolve towards greater clarity and structure, much like in traditional finance. In traditional finance, separating responsibilities like custody, exchange, and clearing is standard practice for risk mitigation and regulatory compliance. We are anticipating and aligning with this trend by distributing these functions. Our broker-dealer model, part of the network's design, separates these roles, reducing the risk of having all operations centralized in one entity. Our goal is to remain adaptable and responsive to regulatory changes, ensuring the longevity and legitimacy of our platform. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Bridging Gaps in Crypto Trading: The Strategic Vision of Yellow Network

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 4 Conclusion In the multifaceted quest to understand mutation-induced solubility changes, various scientific domains including quantum mechanics, molecular mechanics, biochemistry, biophysics, and molecular biology have made significant contributions. Despite these concerted efforts, state of-art models have limitations, as evidenced by their normalized CPR value of 0.656 even after employing feature selection methods. Persistent homology (PH) has emerged as a powerful tool for capturing the complexity of biomolecular structures and has achieved noteworthy success in drug discovery applications. However, its inability to capture the nuances of homotopic shape evolution, crucial for delineating molecular interactions in proteins, presents a critical shortcoming. Our study introduces TopLapGBT, a novel model that integrates persistent Laplacian (PL) features with pretrained transformer features, thereby bridging the gap in capturing both topology and homotopic shape evolution. This innovative fusion leads to significant advancements in classification performance. Specifically, TopLapGBT achieves normalized CPR and GC2 scores of 0.688 and 0.361, respectively, marking improvements of 4.88% and 15.71% over the state-of-the-art PON-Sol2. These findings are further corroborated by an independent blind test, where TopLapGBT continues to outperform existing models. In summary, our proposed TopLapGBT model not only achieves superior performance over existing state-of-the-art methods but also introduces a more nuanced approach for the classification of protein solubility changes upon mutation. These results underscore the transformative potential of integrating geometric and topological features with machine learning in advancing the field of molecular biology. This paper is under CC 4.0 license. available on arxiv

TopLapGBT: Bridging Gaps in Protein Solubility Prediction with Cutting-Edge Fusion

Bridging the Test Coverage Gap With Proactive Monitoring in Production and Testing Environments

উত্পাদন এবং পরীক্ষার পরিবেশে সক্রিয় পর্যবেক্ষণের মাধ্যমে পরীক্ষার কভারেজ গ্যাপ পূরণ করা

Überbrückung der Testabdeckungslücke durch proaktive Überwachung in Produktions- und Testumgebungen

Cerrar la brecha en la cobertura de pruebas con monitoreo proactivo en entornos de producción y pruebas

Combler le déficit de couverture des tests grâce à une surveillance proactive dans les environnements de production et de test

उत्पादन और परीक्षण परिवेश में सक्रिय निगरानी के साथ परीक्षण कवरेज अंतर को पाटना

実稼働環境とテスト環境でのプロアクティブなモニタリングによりテスト カバレッジのギャップを埋める

프로덕션 및 테스트 환경에서 사전 모니터링을 통해 테스트 범위 격차 해소

Preenchendo a lacuna de cobertura de teste com monitoramento proativo em ambientes de produção e teste

Устранение разрыва в тестовом покрытии с помощью упреждающего мониторинга в производственных и тестовых средах

Üretim ve Test Ortamlarında Proaktif İzleme ile Test Kapsamı Açığının Kapatılması

Thu hẹp khoảng cách về phạm vi kiểm thử bằng tính năng giám sát chủ động trong môi trường sản xuất và thử nghiệm

通过生产和测试环境中的主动监控来缩小测试覆盖率差距

The history of AI goes back to myths and literature. However, from the first step taken by Alan Turing to the very recent ChatGPT, understanding AI has always been complex. Understanding AI didn’t feel like a requirement in the past. This is especially true when the concepts were only discussed in books and movies. However, today, AI technologies are already being used in multiple industries for different use cases. This article aims to cover a very important and relevant topic in contemporary times, i.e., AI explainability. We will cover what it means, several techniques, and much more. So, let’s begin. What is AI Explainability? The concept of AI explainability or explainable AI refers to the ability to understand and interpret the decisions or outputs made by artificial intelligence (AI) systems. As AI technologies are becoming way more pervasive and complex, there is an imminent need for transparency and interpretability. This is to build trust, ensure accountability, and address ethical concerns. Why do we need AI Explainability? AI explainability is important for several reasons. Reasons that address transparency, accountability, trust, and ethical considerations. Let’s check some of the most common ones: Ensure greater acceptance and adoption of AI technologies in various domains Helps in understanding how AI models make decisions Uncover and address biases in AI models Identification of potential ethical issues, promoting fairness, and mitigating discrimination Accountability of AI systems Attribution of responsibility Transparency in the AI decision-making process Transparency in models helps in identifying errors or inaccuracies Quick detection of mistakes Continuous feedback and refinement in terms of accuracy and reliability Facilitating communication between AI systems and humans Explainability supports efforts to create fair and unbiased AI models Public perception of AI In times when AI is taking over most critical applications in different sectors such as healthcare, finance, automobile, etc. Understanding AI is a lucrative need. It will not only impact how we operate but will directly impact effectiveness and efficiency. Additionally, with the hyper-adoption of AI, the implementers need to stay aligned with regulatory bodies and ethical guidelines. Techniques Used for AI Explainability Several techniques are used to enhance explainability in AI models. Here are some common techniques used: Interpretable Models These are simpler models that are inherently easier to understand. However, they sometimes sacrifice predictive performance. Some examples of these models are decision trees, linear models, rule-based systems, etc. Ethical AI Auditing This technique conducts audits to identify biases and ethical concerns related to AI models. With regular assessments, it is ensured that ethical standards and regulatory requirements are met. Feature Importance Methods This technique identifies and communicates relevant input features that influence model predictions. There are methods like permutation and information gain that help assess each feature. Local Explanations This one is used to provide insights. Insights like why a specific decision was made for a particular instance or prediction. Approaches like LIME (Local Interpretable Model-agnostic Explanations) help generate locally faithful explanations for the models. Global Expansions Global expansions provide a holistic view of the model's operation across various inputs and scenarios. Several methods, like Shapley values, integrated gradients, etc., provide global insights into feature contributions. Partial Dependence Plots (PDP) This technique illustrates the relationship between a feature and the model’s prediction. While doing so, it is important to ensure that all the other features remain constant. PDPs are great for visualizing the impact of individual features on the model’s output. Counterfactual Explanations In this, we generate instances that are similar to a given input. However, they have different model predictions. Counterfactuals provide insights into how minuscule changes in input variables affect the model’s decisions. Layer-wise Relevance Propagation (LRP) In layer-wise relevance propagation, we use attribute relevance scores to input features. This is based on the contribution of each feature at different layers of the neural network. LRP is a great technique for understanding the importance of different features throughout the model. Attention Mechanisms This one is commonly used for natural language processing.  We highlight specific input parts crucial for the model’s decision in attention mechanisms. For instance, Transformer-based models often use attention mechanisms for language. For example, GPT-3, GPT-4, Google BERT, Dall-E, Hugging-Face’s Transformers, etc. Model Distillation This technique is used to train a simpler model. It is done by mimicking the behavior of a much more complex model. It is often easier to interpret a distilled model while retaining essential characteristics of the original model. Explainable AI Libraries This technique uses dedicated libraries and frameworks that provide tools for explaining AI models. Some examples are SHAP (SHapley Additive exPlanations), Lime, and Alibi Explain. Visualizations It is used to create visual representations of internal models or decision processes. This technique uses saliency maps, decision boundaries, etc., that help users understand the model’s behavior. User-Friendly Interfaces These are used to develop interfaces that provide clear explanations and understandability for non-experts. It includes dashboards and interactive tools that enhance user engagement with AI systems. These techniques can be applied individually or in combination to enhance the explainability factor of AI models. These give users the required insights to trust, validate, and even understand decision-making. Challenges associated with AI Explainability While AI explainability is crucial to understanding complex models often useful for startups, entrepreneurs, etc., approaching the best AI development companies for outsourcing. It does pose significant challenges. Therefore, let’s check them out: Modern AI models can have millions and billions of parameters, making it incredibly difficult to arrive at specific decisions Modern systems are often called “Black Boxes” as their decision process is not visible to the users, making them hard to interpret Model complexity and explainability are often compromised because simpler models like decision trees are interpretable but don’t cover complex tasks AI is capable of learning biases based on the training data fed Explanations for AI models are not always accurate and don’t represent the decision-making process There is no universally accepted framework or standard for AI explainability End users are not equipped with technical acumen even for explained models As AI becomes more pervasive, meeting regulatory and ethical requirements without compromising the performance or practicality of AI systems is a complex challenge AI models are trained on specific data; however, they are expected to operate and provide explanations in environments unknown AI Explainability - Ethical Considerations, Industry Standards, and Regulations With so much skepticism around AI, AI explainability must address ethical concerns, adhere to industry standards, and comply with regulations. Here’s a closer at each of these aspects: Ethical Considerations AI systems should be free from biases and should treat individuals or groups fairly There should be clarity as to who is responsible for the decisions that are made by the AI systems Users should be able to understand and trust AI systems AI systems should provide users with sufficient information to support user autonomy Industry Standards The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) already have standards to mitigate risks and maximize the rewards of using AI. However, these are primarily laid from the perspective of improving transparency and explainability. ISO Standards: The Institute of Electrical and Electronics Engineers (IEEE) is another standard with multiple subdivisions. These subdivisions range from ethics and governance to technical standards. IEEE Standards: Different industries have developed different standards for explainability based on their needs and associated risks. For example, IMAI is in healthcare, ACORD is in finance, SAE International is in automotive, etc. Sector-Specific Standards: Regulation GDPR is prevalent in the European Union. It includes provisions that are related to the explainability of AI decisions, especially in the context of automation decision-making and profiling GDPR (General Data Protection Regulation): This legislation in the United States aims to make it mandatory for companies to conduct impact assessments of automated decision systems. This should be done to check biases, effectiveness, and other factors. Algorithmic Accountability Act: Several countries and regions are actively developing regulatory frameworks for AI. For example, the European Union proposed the “Artificial Intelligence Act”. Regulatory Bodies and Frameworks: Conclusion The need for explainable AI rose from the increasing complexity of the models. Most stakeholders are required to understand the ethical and practical implications. It is to enable more transparency in decision-making and help understand the root causes of AI-associated problems. Symbiotically, this would benefit in terms of improving the existing models and understanding the system internally. AI explainability is the need of the hour as the future holds a lot of AI systems to be integrated into our current workflows.

AI Explainability: Bridging the Gap Between Complexity and Understanding

Cucumber-js and BDD are not new. Still, many developers are fairly unfamiliar with them; the two together can be very powerful tools for both non-tech people and developers. What Is BDD? BDD is short for Business-Driven-Development; it's a way to close the gap between business people and technical people. Basically, BDD has evolved from TDD; there's a high chance that you might even be doing BDD without knowing it, as sometimes the lines between them aren't clear. So what's the difference between them, both are ? Well, the difference is that we use the language of our end-users, i.e., the business or domain language such as , to capture a story in an executable format. For example, automated tests right Given-When-Then GIVEN User is on Wordpress Registration Page.
WHEN he enters all the required information.
THEN his account is created. The “given” part is where we declare preconditions. In our example above, we had a user. Next, the “when” part contains the action you want to test. And finally, you verify the outcome in the “then” part. Now, If you have more than one or you require more information than this, you can add them with . For example, AND GIVEN User is on Wordpress Registration Page.
WHEN he enters all the required information.
AND he hits ‘create account’
THEN his account is created.
AND his confirmation email is sent. Introducing Cucumber Cucumber-js is . The tests are written in the Gherkin language, which is human-readable and is stored in feature files that have the feature extension. This allows your tests to be a point of communication and collaboration with business people and can even serve as documentation that is automatically up-to-date. a test framework that supports BDD A test in the Gherkin language is called a scenario. And scenarios are organized into features. Feature: Automatic discounts for premium customers
    Premium customers should automatically get a
    discount of 25% on purchases over $500.

    Scenario: Purchase over $700
        Given a premium customer
        And an order containing
            | item   | amount | price |
            | pencil | 100    | 2     |
            | paper  | 10     | 35    |
        When the customer checks out
        Then the total price should be 412.5 In the above example, there are many , and every keyword has its own meaning and purpose. keywords Let’s explain each keyword with respect to the above example. : The feature file starts with the keyword Feature**.** Under feature, you can mention the feature name that is to be tested, as seen above. Feature Keyword : Each scenario must start with the keyword Scenario**,** followed by the scenario name. And under each feature file, there can be more than one scenario. Scenario Keyword : Given keyword is used when we have to give some pre-condition in our test case. Given Keyword : When the keyword is used to perform some action. When Keyword : It is used to two statements with the logical condition between any two statements. It can be used in combination with , and statements. And Keyword connect AND GIVEN WHEN, THEN : Then Keyword is used for the final outcome or for validation. Then Keyword Other than these, there are two more keywords, : This keyword is used to represent negative assertions in addition to the previous statement. But Keyword : Steps kept under Background would be run before every test case. Background Keyword Although, Initially, Cucumber was created for ' ' as a support for BDD framework for testing, Cucumber-js has evolved to support a variety of different programming languages such as Java, JavaScript, PHP, Net, Python, Perl, etc... Ruby Language RSpec Writing simple BDD test with Cucumber-js We will create a few tests to see how cucumber works, for this we will first define our scenario and feature before starting to write our code. Let's get started!! First, let's create our work directory and create our file: - package*.json mkdir bdd & cd bdd
npm init -y Now, it's time to install our dependencies. npm i chai @cucumber/cucumber @cucumber/cucumber-expressions cucumber-html-reporter Let's move towards writing the tests and setting up folder structures for cucumber-js; this is how the folder structure should look like: - ├── Features
│   ├── Support
│   │   ├── steps.js
│   │   ├── world.js
│   ├── simple_math.feature
├── utils
│   ├── report.js
├── cucumber.js
├── node_modules
├── package.json
├── package-lock.json
└── .gitignore Create a folder structure similar to the above before moving forward. Once we have our folder structure ready, let's start with file: - steps.js const { Given, When, Then } = require("@cucumber/cucumber");
const { expect } = require("chai");

Given("a variable set to {int}", function(number) {
  this.setTo(number);
});

When("I increment the variable by {int}", function(number) {
  this.incrementBy(number);
});

Then("the variable should contain {int}", function(number) {
  expect(this.variable).to.eql(number);
}); In Cucumber-js, “ ” is an isolated scope for each scenario. That means all the steps, hooks, etc., for a scenario are a part of a world, and this data can be accessed within the world anytime. Let's define our file world world.js const { setWorldConstructor } = require("@cucumber/cucumber");

class CustomWorld {
  constructor() {
    this.variable = 0;
  }
  setTo(number) {
    this.variable = number;
  }
  incrementBy(number) {
    this.variable += number;
  }
}
setWorldConstructor(CustomWorld); Finally, we have both steps and world ready. Now it's time to create our file. .feature Feature: Simple maths
  In order to do maths
  As a developer
  I want to increment variables

  Scenario: easy maths
    Given a variable set to 1
    When I increment the variable by 1
    Then the variable should contain 2

  Scenario Outline: much more complex stuff
    Given a variable set to <var>
    When I increment the variable by <increment>
    Then the variable should contain <result>
    Examples:
      | var | increment | result |
      | 100 |         5 |    105 |
      |  99 |      1234 |   1333 |
      |  12 |         5 |     17 | Above, we have defined our feature, and, in correspondence, created two different scenarios. Let's run to see if the test pass/fail : npx cucumber-js The output we get would be similar to below: Test Report File Now, to make it more understandable, let's add functionality to create the test report for each run. In file, we will add: - cucumber.js var _default = [
    '--format usage:reports/usage.txt',
    '--format json:reports/cucumber_report.json',
    '--format html:reports/cucumber_report.html'
  ].join(' ')
  
  module.exports = {
    default: _default,
  } This will create a JSON and HTML report to visualize and append in the same files each time we run our tests. Now, let's define what will be there in the report file. In , we will add a few lines to provide some more ideas about test environments in the form of metadata. report.js var reporter = require('cucumber-html-reporter');

var options = {
        theme: 'bootstrap',
        jsonFile: 'reports/cucumber_report.json',
        output: 'reports/cucumber_report.html',
        reportSuiteAsScenarios: true,
        scenarioTimestamp: true,
        launchReport: true,
        metadata: {
            "App Version":"0.3.2",
            "Test Environment": "STAGING",
            "Browser": "Edge  54.0.2840.98",
            "Platform": "MacOS",
            "Parallel": "Scenarios",
            "Executed": "Remote"
        }
    };

reporter.generate(options); Now run your tests once again, and you can see that under folder there are two files with name. Open the to see your test report HTML file would look something like below: - reports/ cucumber_report.* HTML Conclusion Cucumber-js focuses mainly on making the collaboration between tech people and business people easy. But it doesn’t eliminate the coding part and still has a lot of code involved. When dealing with the main thing we have to keep in mind is how to properly use keywords GIVEN, WHEN, and THEN. behavior-driven automation, there are a lot of rules that you have to use as a guide; The testing industry has gone to the next step now with codeless testing integrated with AI and smart logic. Frameworks like allow you to create tests without using a single line of code using record and playback testing. But if you still need to use code for some reason, it also allows you to integrate your code into the test project. Keploy The bottom line is that you have three options to choose from for testing: Traditional testing. Code testing that’s easy for business people to understand. Codeless, AI-powered testing. What suits you best depends on your use case. But it’s important to know about all these options to decide what suits you best. So good luck exploring! Also published . here

BDD with Cucumber-js: Bridging the Gap between Developers and Business

Navigating New Financial Horizons: Chiel Ruiter on Bridging DeFi with Real World Assets

The information technology (IT) skills gap is a significant challenge for companies worldwide. Technological advancements continue rapidly, leaving organizations struggling to fill crucial IT roles. According to a CompTIA report, acknowledge a noticeable shortage of tech skills in their IT staff. 93% of employers To combat this, businesses and IT professionals must adopt potent training strategies to address the current gap while future-proofing the workforce against emerging technologies. The pressing need for effective training strategies is vital to nurturing the next generation of IT professionals, especially as the demand for tech talent steadily rises. The Looming Gap The tech skills gap bears substantial economic consequences. Companies find it challenging to keep up with technological advancements, leading to unoccupied IT positions that result in lost productivity. Moreover, this situation can potentially trigger cybersecurity issues and downtime. For employees, insufficient training results in frustration and burnout. Often, individuals find themselves stuck in roles that don't align with their qualifications, impeding productivity and compromising quality. As technology permeates every business facet, closing this skills gap is necessary for organizations to maintain competitiveness. Mapping the Needs Addressing the gap mandates a clear recognition of the critical skills in short supply. This includes technical expertise in cybersecurity data analytics and soft skills such as problem-solving and effective communication. The rise of soft skills gains significance as they complement technical prowess, enabling IT professionals to adapt and excel in diverse roles. Given the swift evolution of technology, training initiatives must emphasize versatility and the future-proofing of skills. Regular skills assessments, aligned with business objectives, aid in identifying talent needs, while various strategies can be employed to keep pace with these requirements. Effective Strategies Companies prioritizing learning and development are better equipped to cultivate a pipeline of skilled IT talent. Key strategies to achieve this include: Curating educational ecosystems with varied learning pathways like online courses, boot camps, mentoring programs, and stretch assignments cater to professionals' diverse learning preferences. Learning Ecosystems: Employing bite-sized learning segments enhances knowledge retention, enabling professionals to integrate new concepts into their routines seamlessly. : Microlearning Making training interactive through gamification, social features, and friendly competition mirrors real-life challenges, keeping learners invested in their educational journey. Engaging Learners: Offering individualized development plans aligned with personal strengths and career goals empowers professionals to track progress effectively. Personalized Pathways: : Incorporating badges, leaderboards, and celebrations into training programs motivates learners, while tangible rewards like a symbolic crystal trophy signify milestones in their learning journey. Fun & Rewards Providing interdisciplinary training fosters adaptable talent capable of understanding and contributing to various business facets, encouraging innovation and collaboration. Cross-training: Closing the IT skills gap necessitates a collaborative effort across the IT ecosystem. Investing in the future demands allocating resources to training and development while fostering collaborations with educational institutions, industry bodies, and tech leaders. Recognizing and nurturing leadership potential within the IT workforce is another pivotal step toward closing this gap. Leaders well-versed in technology can effectively guide teams and drive innovation. Strategic investment in workforce development enables companies to build agile, skilled IT teams poised for driving business success. Ultimately, investing in training yields invaluable dividends, both in the present and the future. The IT skills gap remains a challenge, but with collective commitment towards nurturing tech talent, it's surmountable.

Bridging the IT Skills Gap: Effective Training Strategies

In this week’s episode of the What's AI podcast, I, Louis-François Bouchard, engage in a super informative discussion with Jerry Liu, CEO and co-founder of LlamaIndex. We explore the focus on the innovative (and somewhat new) world of Retrieval Augmented Generation (RAG), the crucial role of effective documentation in technology, the potential of new multimodal models like Gemini, and much more. All related to AI! Our conversation begins with Jerry shedding light on the challenges associated with processing diverse data formats, particularly PDFs. He offers insights into how emerging multimodal models, such as Gemini recently released, could revolutionize this area. We then went into the importance of clear and comprehensive documentation in tech ventures. Jerry, drawing from his experiences at LlamaIndex, emphasizes the need for documentation to be more than just informative – it should guide developers on a journey from understanding basic concepts to mastering advanced applications in AI. A significant part of our discussion is devoted to the advantages of Retrieval Augmented Generation. Jerry explains why RAG is gaining popularity in the enterprise sector due to its simplicity, efficiency, and practicality. He also explains the differences between RAG and fine-tuning models, providing valuable insights on when each method is preferable, detailing the unique benefits of RAG in reducing costs and setup time, and minimizing hallucinations often seen in AI models. We also tackle the technical side of things, discussing the importance of chunking strategies and data quality in RAG systems. Jerry's expertise shines through as he discusses embedding models, fine-tuning for specific domains, and optimizing retrieval processes. This part of our conversation is especially valuable for those interested in implementing or enhancing RAG systems in their own projects. Don't forget to leave a like or a 5-star review to support the podcast! If you find this episode as enlightening as I did, please share it with your friends and colleagues who are keen on staying updated with the latest developments in AI. Let's dive into this episode on , , or : Spotify Apple Podcasts YouTube https://youtu.be/kZxl4gpe3OM?embedable=true&transcript=true

The AI Landscape With Jerry Liu: Bridging RAG Systems, Documentation, and Multimodal Models

TL;DR: My recent interview on the Semaphore podcast Despite their common subject matter, the software industry and often operate on divergent assumptions and priorities, making collaboration difficult. In this episode, Maximiliano Contieri helps us understand the longstanding tension between academia and industry from an insider’s perspective. Read on for an insightful discussion on strengthening bridges, applying rigorous methodologies aimed at real-world relevance, and the central goal of building robust software simulations that capture the complexity of the world around us. computer science https://www.youtube.com/v/q0VtmkA6sVM?embedable=true Edited transcription from https://semaphoreci.com/blog/maximiliano-contieri?embedable=true As a result of his 30 and 25 years of experience in the software industry and computer science academia, respectively, Maximiliano Contieri has repeatedly witnessed the “tension” that arises between both realms. As industry often outpaces academia, students often hold outdated or inaccurate views (e.g., coding as a solitary activity).  “They think that coding is like Hollywood shows in the background, in a basement and alone, and we try to say ‘no, it’s a contact sport with a lot of people, with teamwork,’ ” he jokes. Conversely, the industry uses practices academics have deemed bad for years (e.g., nulls, singletons). For this reason, Maxi regards it as a still “very immature profession” that, for the greater good, needs to bridge the gaps between industry and academia. The vital interplay between academia and industry in software development ” Maxi says, emphasizing the value of both perspectives: While academia foundational knowledge and long-term insights prevent industry stagnation and keep developers away from reinventing the wheel. The value of academia comes from “standing on the shoulders of giants;” for this reason, it prioritizes the value of advancing knowledge instead of moving “to the next trendy thing.” In turn, the industry is where software’s practical experience takes place, and cutting-edge tools are demanded and developed. What’s more, it’s the industry’s real-world applications that keep development relevant and computer science in demand. “It’s important for everyone to have weight in both academia and industry; otherwise, you’re in a niche and miss seeing things around, To bridge the gap, Maxi stresses the need to be adaptable and context-aware. Academics and professionals must step outside their comfort zones by reading, learning, and engaging with the other side. To this end, both groups should stay informed about broader trends and advancements beyond their immediate niche and view the differences as opportunities for mutual learning and growth. Bridging the gap through test-driven development Maxi believes TDD can bridge the gap between the academic and real-world software development realms. Test-driven development (TDD) is a software development practice that emphasizes writing tests for your code before you write the actual code itself. It’s an iterative process that involves writing a test, making sure it fails (because there’s no code yet to pass it), writing just enough code to make the test pass, and then refactoring the code to improve its quality. Maxi defines TDD as to his understanding, In this way, he believes TDD is a powerful tool for: “a learning methodology, not a testing methodology;” “It’s not even a development methodology: It’s a methodology to discover how the world works.” Discovering and learning: Writing tests forces you to extract use cases and understand the problem space. Building clean code: Hard coding followed by refactoring leads to simpler, more maintainable code. Regression testing and knowledge extraction: Each test case captures specific knowledge about the system’s behavior. Building good scientific models: By reflecting real-world behavior, TDD helps you create accurate models of how the system should work. TDD emphasizes writing tests before code, gradually building understanding through test cases. This “baby steps” approach ensures development is driven by concrete evidence rather than speculation, which resonates with the academic emphasis on rigorous experimentation and data-driven conclusions and mirrors the scientific practice of formulating hypotheses, testing them through experiments, and refining the understanding based on results. As such, TDD forces programmers to confront their assumptions and uncover hidden complexities. Withal, Maxi points out that academics tend to favor abstract theories and anticipate future possibilities, while TDD thrives on concrete test cases and incremental learning. Likewise, the initial hard coding encouraged by TDD can clash with the academic ideal of clean code. This requires a shift in perspective, recognizing that initial “messy” code can be a stepping stone to cleaner, more maintainable solutions through refactoring guided by tests. Withal, Maxi makes it clear that TDD isn’t a silver bullet; it requires re-education and a significant learning curve for effective implementation. In this regard, While Maxi’s teams heavily utilize TDD, he acknowledges it’s not universally applicable. He echoes Software Engineer Kent Beck’s view that TDD may not suit everyone, particularly those prone to anxiety or over-engineering. To Maxi, a programmer’s hesitation to hard code and overgeneralization tendencies can also keep them away from TDD. Using TDD in legacy systems: A gradual approach In the case of legacy systems, Maxi acknowledges the difficulty of implementing TDD comprehensively in heavily coupled environments. However, he suggests a strategic approach: using TDD in isolated parts of the system and gradually decomposing the legacy system based on Michael Feathers’ approach to legacy code. “ ,” he affirms. You can’t write all code using TDD in a legacy system immediately, but you can use it in isolated parts to decompose the legacy The idea involves shipping the TDD solution to production and addressing new behaviors as they arise. Maxi emphasizes that isolating parts of the system for TDD requires breaking some coupling, indicating that TDD can be applied gradually as different parts of the system are rebuilt over time. Employing TDD in a legacy system is not an easy or quick fix; it requires dedication and perseverance, especially as it involves a process of education and rehabilitation for the team to adapt to this methodology in such an environment. Yet, Maxi encourages giving it a try despite the difficulty: “It pays off, it’s your choice. Try it. And try it hard. Don’t try it for a couple of days because it isn’t for anxious people. So it’s frustrating. Of course, it’s frustrating. You are educating. You are in rehab.” Clean Code Cookbook: Maxi’s guide to building better software During the COVID-19 pandemic, Maxi began regularly chronicling his experiences in both realms, culminating in the release of his latest book, Clean Code Cookbook. The book’s title draws inspiration from Robert C. Martin’s (Uncle Bob) definition of clean code. Maxi envisions the book as a collection of recipes, a repository of his own experience infused with Uncle Bob’s tenets but enriched by his own unique perspective and extensive experience, aimed at experienced programmers who might not realize they’re making certain mistakes. To this end, the book covers different aspects impacting software development, delving into concepts like primitive obsession, mutability versus immutability, declarative code, naming conventions, commenting practices, code standards, and complexity management, among many others. Each chapter addresses a specific aspect or problem commonly encountered in software development, offering insights and guidelines on how to approach these issues for better code quality, readability, maintainability, and security. As Maxi explains, the book aims to dispel the notion of “clean code” as a subjective opinion and empower programmers to understand the long-term consequences of their coding choices. He believes that good design decisions are not just about immediate functionality but also about future-proofing the code for long-term maintainability. While acknowledging the existence of numerous “clean code” definitions, Maxi’s book avoids getting bogged down in theory and, in turn, presents concrete recipes, showing how to transform “core snippets” into better solutions with tangible results. The book explains the reasons behind each recipe, providing theoretical and practical foundations for its recommendations. In like manner, the book attempts to be language agnostic, providing sources in 25 different languages. The bottom line Purchase Clean Code Cookbook and read Maxi’s latest blog post on . Stay connected with Maxi’s expertise and updates on Twitter/X via his handle . You can also catch glimpses of his recent conferences, podcasts, and upcoming engagements on his website. cleancodecookbook.com @mcsee1 You can also find it on: Apple Podcasts https://podcasts.apple.com/us/podcast/semaphore-uncut/id1463768812?embedable=true Google Podcasts https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8yNzYxMzMucnNz?embedable=true Spotify https://open.spotify.com/show/6Tqtzmt78HtlMeNROHAgzh?embedable=true Also published . here

An Interview With Maximiliano Contieri: Bridging the Industry-Academia Divide

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