Coincheck and Thunder Bridge Capital filed their registration statement with the SEC, bringing their merger and Nasdaq listing closer to completion.

First Mover Americas: Bitcoin Hovers Above $62K. The latest price moves in crypto markets in context for May 8, 2024. By Jamie Crawley, Omkar Godbole.

... Bitcoin and Ethereum. In contrast, Bitcoin remained flat over the past 24 hours, trading at $63,618, while Ethereum has dipped slightly by 0.84 ...

Among the mandatory documents to be submitted by bidders is a confirmation that the funds for which they plan to purchase bitcoin were not ...

Coinbase's Base has emerged as the most attractive option for investors right now.

US Arms Delay to Israel Brings Anxiety and Talk of Self-Reliance  Bloomberg

2024 has been a challenging year so far for Apple’s embattled stock, but the launch of the iPhone 16 could change everything.

In this article, we walk you through how to set up Heroku Flow for CI/CD using a dev and main branch and a staging and production app.

**PHILADELPHIA, Pennsylvania, May 8th, 2024/CyberNewsWire/--**Security Risk Advisors (SRA) announces the launch of their OT/XIoT Detection Selection Workshop, a complimentary offering designed to assist organizations in selecting the most suitable operational technology (OT) and Extended Internet of Things (XIoT) security tools for their unique environments. \ Led by seasoned OT/XIoT security consultants, the workshop provides participants with an invaluable opportunity to gain insights into both best-in-class and novel solutions and to identify those closest aligned to their specific needs .In today’s increasingly interconnected digital landscape, the importance of choosing the right OT/XIoT security tools cannot be overstated. These tools serve as the first line of risk reduction and defense against cyber threats targeting critical industrial processes and infrastructure. \ Making informed decisions, whether adding a new solution or replacing an incumbent, significantly impacts an organization’s ability to mitigate threats and protect its assets. During the half-day consultation, participants will delve deep into their OT/XIoT security environments, examining current tools and analyzing their infrastructure. The free workshop will result in personalized recommendations of the best-fit solutions from industry vendors. \ “We recognize the importance of selecting the right security tools for cyber-physical environments,” says Jason Rivera, Director of OT/XIoT Security at SRA. “Our workshop empowers organizations to make informed decisions, giving confidence that their selection is fit for purpose.” Submit your application here. About Security Risk Advisors Security Risk Advisors offers Purple Teams, Cloud Security, Penetration Testing, OT Security, and 24x7x365 Cybersecurity Operations. Based in Philadelphia, SRA operates across the USA, Ireland and Australia. For more information, visit SRA’s website at https://sra.io. Contact Marketing Manager Douglas Webster [email protected] 215-867-9051 :::tip This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. ::: \

Foundation has announced the Grants program to support the growing ecosystem of Bahamut and foster the expansion of decentralized projects and applications. This initiative provides multi-faceted support for a wide range of projects based on categories mainly revolving but not limited to DeFi, gaming, and NFTs. \ Bahamut Blockchain has already witnessed the successful results of the grants program with the latest launch of 3 DeFi projects - Lolik, Mutuari, and SilkSwap. \ These platforms have successfully passed all the qualification stages and, after an extensive development process, are now live on the Bahamut blockchain. The appearance of new DeFi projects will surely increase the scalability and adaptability of the native coin of Bahamut, FTN, and expand its usability and relevance. \ Mutuari is a lending protocol made for decentralized lending and borrowing of assets directly on Bahamut. Mutuari gives the users access to diverse crypto assets, including FTN, USTD, USDC, stFTN, and is accessible through all EVM-supporting non-custodial wallets, among them Fastex Wallet, Metamask, and WalletConnect. The project has been selected and successfully completed the task under the category of “Lending Protocol” with a prize of 60.000 FTN. \ Lolik is the next project that has received a grant from Bahamut Foundation. It is a liquid staking platform that supports staking on leading blockchains such as Bahamut, with other blockchains, including Ethereum and Polygon, coming soon. With Lolik, each user can earn rewards by participating in the validation processes of the blockchain directly with their Web3 wallet. \And the last one is the DeFi trading platform, SilkSwapthat allows users to enjoy peer-to-peer trading and easy and fast transactions. The application stays true to a pure Web3 concept, totally eliminating any third-party interactions. \n \n The application process continues, accepting more and more applications. The step-by-step description of the application process and information on preferable categories and their funding are available on Bahamut.io/grants. The process is transparent and fair, keeping it real with web3. \ Bahamut Foundation is currently announcing the list of approved applications that will start building their projects on Bahamut through the Bahamut Arena contest and wishes them the best of luck and successful completion of their projects. Gotbit Labs Category - DEX aggregator Category Prize fund - 40,000 FTN Delivery deadline - 2 months Lost Lore Category - NFT Marketplace Category Prize fund - 40,000 FTN \n Delivery deadline- 4 months Blockstars (Greedy Lottery) Category - Decentralized Game Category Prize fund - 13,000 FTN Delivery deadline - 1 month Erinaceus RNG Oracle Category - RNG Oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months Erinaceus Price Feed Oracle Category - Price Feed oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months \n Additionally, the Bahamut Foundation is hosting a series of meetups at theftNFT Phygital Spaces located in Dubai, Venice, and Yerevan. These events are designed to promote community engagement by bringing together Web3  developers, newcomers, and enthusiasts. The meetups aim to foster growth and development within the global and local Web3 sector through knowledge exchange and collaboration. \ Moreover,  a partnership with Yerevan State University has been announced to support blockchain education and community development in Armenia's growing Web3 industry. This collaboration--formalized through a memorandum of cooperation--represents a significant step towards integrating educational and research programs focused on blockchain and virtual assets, underscoring a shared commitment to promoting blockchain education and community development. About Bahamut Foundation The Switzerland-based Bahamut Foundation stays at the forefront of innovation, offering support and funding to the projects that foster advancements in the field, generate value for the blockchain ecosystem, and bring new users into the world of Web3, laying the foundations for future generations to learn and build upon. :::tip This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. ::: \

Whether you're a seasoned Elasticsearch user or just beginning your journey, understanding reindexing is important for maintaining an efficient Elasticsearch cluster.

DOVER, DE, USA, May 8, 2024 —ChainGPT, the AI-powered Web3 infrastructure providing a diverse suite of tools and services, will exclusively launch the IDO of its latest launchpad project, Engines of Fury: a free-to-play top-down extraction shooter designed for Web3. Developed by talents from AAA titles, Blizzard, Activision, Ubisoft, and Unity, its top-down characteristic makes it a first within Web3's free-to-play shooter arena. Set for May 8, Engines of Fury’s IDO leverages ChainGPT Pad’s acceleration program, enabling the game to perfect its product systems, action plans, and roadmaps while receiving expert marketing and promotional support. \ As a premier decentralized fundraising and incubation platform for Web3 projects of all types, the ChainGPT Pad has been recognized as the most popular launchpad of 2023. The incubation program promotes emerging startups strategically hand-picked by ChainGPT based on their disruptive potential, transforming their ideas into viable business solutions. The program extensively assists in product development, smart contract implementation, and user engagement. \ Through expert mentorship and access to an extensive network of influential partners and investors, the ChainGPT Pad’s incubation provides Web3 startups with the tools to navigate inevitable challenges and thrive. Engines of Fury immerses players in a dystopian world ravaged by a mutagenic virus from a meteor. Gamers navigate a perilous universe—scavenging for resources, enhancing hideouts, and upgrading gear while honing their abilities through single-player and cooperative modes. \ With a robust in-game economy driven by its native $FURY token, the game emphasizes a journey of survival, craftsmanship, and community. Engines of Fury’s goal is to create a game that introduces mainstream players to the distinct advantages of blockchain without forcing Web3 elements into core aspects of gameplay. Making it simple for all players to jump into the game, Engines of Fury’s crypto aspects serve as a bonus, not a must. The game includes classic gaming elements with an easy onboarding process for non-crypto natives, with features such as: \ NFT Mining and Trading: Players discover resources and forge unique NFTS to enhance their character's abilities and appearance. Gamers can trade these NFTs freely on the Engines of Fury marketplace and other compatible platforms. Play-And-Earn Mechanics: Players can earn in-game virtual currency that can potentially convert to real-world income. Earned crypto assets and NFTs remain fully under the player’s ownership. Skill-Based Rewards: Top players earn $FURY tokens based on leaderboard rankings, achievements, and in-game activities. Council Crates: Special, limited-quantity NFTs will be available during the Engine of Fury NFT Presale event. Cross-Game Interoperability: Engines of Fury collaborates with exciting projects to enable asset interoperability, expanding gameplay possibilities and community engagement. \ With an overwhelming response, Engines of Fury attracted over 80,000 applications for its Private Alpha Test, extending the game’s original playtest deadline. The enthusiasm following the private alpha test underscores the demand for high-quality Web3 gaming experiences. Engines of Fury IDO information: \ Token price: $.20 IDO date: May 13th Token ticker: $FURY Allocation size: 250,00 Max supply: 120,000,000 Network: Binance Smart Chain IMC (without liquidity): $416,667 IMC (with liquidity): $1,376,667 Circulating supply at TGE: 6,883,333 Fully Diluted Valuation at TGE: $24 million Vesting schedule: 15 percent TGE, 2-month cliff, 12 months linear vesting \ “It has been a pleasure collaborating with Engines of Fury, who brilliantly blend blockchain-based assets with a high-quality and engaging gaming experience,” says Ilan Rakhmanov, CEO and Founder of ChainGPT. \ “Engines of Fury exemplifies the unique and inclusive gaming experiences that resonate with a global audience. Their IDO represented an exciting opportunity for the community to support pioneering projects that reshape the gaming landscape for more than 3.3 billion players worldwide.” \ “With the help of ChainGPT, we’ve crafted a game that serves as a prime example of how gamers and developers can collaborate to build immersive experiences that rival traditional gaming structures,” says Saulius Aleksa, Co-Founder and CEO of Engines of Fury. We are grateful for the collaboration with the talented team at ChainGPT, whose expertise and invaluable guidance have been instrumental throughout the development of our game.” About ChainGPT: ChainGPT is the leading provider of AI-powered infrastructure for crypto, blockchain, and Web3. From a next-generation IDO launchpad and incubator to automated smart contract generation and auditing, as well as an advanced Web3 AI chatbot to AI-powered news aggregation, an AI training assistant, cross-chain swap, and an NFT generator, ChainGPT is a sophisticated, end-to-end solution for AI Web3 infrastructure. \ By integrating large language models (LLM) with blockchain, the company builds advanced, accessible, and user-friendly tools for all users. Further solidifying its position at the forefront of technological innovation, ChainGPT has established strategic partnerships and received recognition from notable tech leaders such as Google, Nvidia, BNBChain, and Chainlink. ChainGPT aims to advance the use of AI within the blockchain space. For more information, visit: About Engine Fury: Built by a talented team from top Web3 projects, AAA titles, Blizzard, Activision, Ubisoft, and Unity, and backed by industry leaders including Animoca Brands, Metavest Capital, Maven Capital, and Double Peek Group, Engine of Fury is an online survival game that immerses players in a post-apocalyptic world. \ The game is a free-to-play top-down extraction shooter that combines the thrill of player versus player (PVP) and player versus environment (PVE) gameplay. Engine of Fury incorporates non-fungible tokens (NFTs) into its gameplay, allowing players to own unique digital assets that can be used within the game. These NFTs include unique weapons, armor, and champions that players can use in battle. The game’s deflationary token, $FURY, is used for various in-game actions, including participating in arenas and forging new NFTs. For more information, visit: :::tip This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. ::: \

SEOUL, South Korea, May 7, 2024—CoinNess, one of the largest cryptocurrency investment information providers in Asia, today launched its English service with a Live Feed feature for global audiences. CoinNess's Live Feed service provides real-time updates for investors in the blockchain and cryptocurrency industry. \ Founded in 2018, CoinNess has quickly established a strong presence, amassing an extensive user base of over 1 million across Asia, extending beyond South Korea. \ Renowned for its rapid distribution of the latest cryptocurrency updates, CoinNess aims to deliver breaking news within five minutes of new industry developments, ensuring continuous, around-the-clock coverage. Since the platform’s launch, the Live Feed feature has generated over 200,000 news briefs for investors. \ CoinNess is also the largest provider of cryptocurrency investment information for institutions in South Korea. Some 40 crypto-related platforms have integrated CoinNess's Live Feed API into their services. \n In 2023, CoinNess gained significant attention when it was selected by Naver, South Korea's largest search engine, to be the exclusive provider of live cryptocurrency updates as the internet giant made its first foray into displaying this type of content. In its latest development, CoinNess has forged a partnership with Yonhap Infomax, the financial news provider of the national news agency Yonhap News, to exclusively provide a live crypto feed. \ On May 7, CoinNess introduced the English Live Feed service for retail investors, along with a business-to-business (B2B) application programming interface (API) that its partners can implement into their services. In addition to Live Feed, CoinNess offers a comprehensive suite of information offerings related to crypto investment, including Asian market analysis, market data, on-chain data, and personalized curation of social media posts. \ Regarding the company's latest service introduction, CoinNess CEO Kim Jungho said, "Renowned Asian crypto exchanges, legacy media outlets, wallet providers, and online communities have implemented our Live Feed API to offer a fuller user experience for their customers." \ In preparation for the English Live Feed launch, CoinNess hired over ten professional content writers with extensive backgrounds in the crypto sector. This acquisition of new talent has raised the company's workforce to 50, covering both its Korean and English services. \ CoinNess boasts a seasoned content team and its own advanced algorithm that monitors and collects real-time data from a wide range of sources, including global media outlets, government agencies, social media platforms, crypto exchanges, and on-chain data providers. This comprehensive approach enables the CoinNess team to publish news briefs within five minutes of an issue breaking out. The platforms that have partnered with CoinNess recognize the value that the timely delivery of professional, concisely-written crypto sector news briefs brings to their users. \ CEO Kim expressed confidence in the new service, saying, “Our crypto Live Feed service is now accessible to institutional investors, including South Korean brokerage firms, through a terminal. This marks the first time in the world such an integration has been offered, demonstrating our service’s content quality.” \ Lastly, in celebration of its English Live Feed service launch, CoinNess has decided to provide its Live Feed API to partners free of charge for a limited time. If you would like to avail of this value addition for the benefit of your enterprise, don't hesitate to reach out to the CoinNess business team today at [email protected]. :::tip This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. ::: \

:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA [email protected]; (2) Eric Bier, Palo Alto Research Center, USA [email protected]; (3) Lester Nelson, Palo Alto Research Center, USA [email protected]; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA [email protected]; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA [email protected]. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 5 Conclusion & Future work In summary, our new approach for generating floor plans from triangle mesh data collected by augmented reality (AR) headsets produces two styles: a detailed pen-and-ink style and a simplified drafting style. Our algorithms align the mesh data with primary coordinate axes to produce tidy floor plans with vertical and horizontal walls, while also allowing for the removal of ceilings and floors and the separation of multi-story buildings into individual stories. Our approach integrates with AR, supporting the addition of synthetic objects to physical geometry and providing a detailed 3D model and floor plan. \ Potential applications include navigation, interior design, furniture placement, facility management, building construction, and HVAC design. Moving forward, we plan to enable support for sloping ceilings, automate wall and door detection, and integrate with other tools such as energy simulators. Finally, we plan to compare our approach with existing state-of-the-art methods in terms of accuracy and computational time. We also plan to explore the applicability of block-based DBScan for 3D reconstruction from incomplete scans. Our approach has the potential to revolutionize the way we generate and visualize floor plans. References Adan, A., Huber, D.: 3d reconstruction of interior wall surfaces under occlusion and clutter. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. pp. 275–281 (2011). https://doi.org/10.1109/3DIMPVT.2011.42 \ Arikan, M., Schwärzler, M., Flöry, S., Wimmer, M., Maierhofer, S.: O-snap: Optimization-based snapping for modeling architecture. ACM Trans. Graph. 32(1) (feb 2013). https://doi.org/10.1145/2421636.2421642 \ Budroni, A., Boehm, J.: Automated 3d reconstruction of interiors from point clouds. International Journal of Architectural Computing 8(1), 55–73 (2010). https://doi.org/10.1260/1478-0771.8.1.55 \ Cabral, R.S., Furukawa, Y.: Piecewise planar and compact floorplan reconstruction from images. 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 628–635 (2014) Cai, R., Li, H., Xie, J., Jin, X.: Accurate floorplan reconstruction using geometric priors. Computers & Graphics 102, 360-369 (2022). https://doi.org/10.1016/j.cag.2021.10.011 \ Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-sp: Inverse cad for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision (ICCV) (2019) \ Chen, N., Lu, Z., Yu, X., Yang, L., Xu, P., Fan, Y.: Augmented reality-based home interaction layout and evaluation. In: Computer Graphics International Conference. pp. 395–406. Springer (2022) \ Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: Robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 616–624 (2016). https://doi.org/10.1109/CVPR.2016.73 \ Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 80–87 (2009). https://doi.org/10.1109/ICCV.2009.5459145 \ Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. p. 249–260. MobiCom ’14, Association for Computing Machinery, New York, NY, USA (2014). https://doi.org/10.1145/2639108.2639134 \ Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: Flat representation for estimating layout of general room types. ArXiv abs/1905.12571 (2019) \ Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1323–1331 (2015). https://doi.org/10.1109/ICCV.2015.156 \ Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. (eds.) Pattern Recognition. pp. 557–568. Springer International Publishing, Cham (2020) \ Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: Endto-end room layout estimation. 2017 IEEE International Conference on Computer Vision (ICCV) pp. 4875–4884 (2017) \ Liu, C., Wu, J., Furukawa, Y.: Floornet: A unified framework for floorplan reconstruction from 3d scans. In: ECCV (2018) \ Liu, H., Yang, Y.L., AlHalawani, S., Mitra, N.J.: Constraint-aware interior layout exploration for precast concrete-based buildings. Visual Computer (CGI Special Issue) (2013) \ McNeel, R., et al.: Rhinoceros 3d, version 6.0. Robert McNeel & Associates, Seattle, WA (2010) \ Microsoft: Spatial mapping. https://docs.microsoft.com/en-us/windows/mixed-reality/spatial-mapping (2022) \ Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: Rapter: Rebuilding manmade scenes with regular arrangements of planes. ACM Trans. Graph. 34(4) (jul 2015). https://doi.org/10.1145/2766995 \ Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multiroom interiors with arbitrary wall arrangements. Computer Graphics Forum 35(7), 179–188 (2016). https://doi.org/https://doi.org/10.1111/cgf.13015 \ Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor scan2bim: Building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 6126–6133 (2017). https://doi. org/10.1109/IROS.2017.8206513 \ Okorn, B., Xiong, X., Akinci, B.: Toward automated modeling of floor plans. In: In Proceedings of the symposium on 3D data processing, visualization and transmission. vol. 2 (2010) \ Pintore, G., Gobbetti, E.: Effective mobile mapping of multi-room indoor structures. The visual computer 30(6-8), 707–716 (2014) \ Pintore, G., Mura, C., Ganovelli, F., Fuentes-Perez, L.J., Pajarola, R., Gobbetti, E.: State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments. Computer Graphics Forum (2020). https://doi.org/10.1111/cgf.14021 \ Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J.M., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., Savva, M., Zhao, Y., Batra, D.: Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021), https://openreview.net/forum?id=-v4OuqNs5P \ Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. pp. 316–323 (2012). https: //doi.org/10.1109/3DIMPVT.2012.80 \ Weinmann, M., Wursthorn, S., Weinmann, M., Hübner, P.: Efficient 3d mapping and modelling of indoor scenes with the microsoft hololens: A survey. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 89(4), 319–333 (2021) \ Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3d building models from laser scanner data. Automation in Construction 31, 325–337 (2013). https://doi.org/10.1016/j.autcon.2012.10.006 \ Zhang, J., Kan, C., Schwing, A.G., Urtasun, R.: Estimating the 3d layout of indoor scenes and its clutter from depth sensors. In: 2013 IEEE International Conference on Computer Vision. pp. 1273–1280 (2013). https://doi.org/10.1109/ICCV.2013.161 \ Zou, C., Colburn, A., Shan, Q., Hoiem, D.: Layoutnet: Reconstructing the 3d room layout from a single rgb image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2051–2059. IEEE Computer Society, Los Alamitos, CA, USA (jun 2018). https://doi.org/10.1109/CVPR.2018.00219 \ \ :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. ::: \

:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA [email protected]; (2) Eric Bier, Palo Alto Research Center, USA [email protected]; (3) Lester Nelson, Palo Alto Research Center, USA [email protected]; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA [email protected]; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA [email protected]. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 4 Experiments We evaluated our approach to capturing 3D scans using AR headsets. We compared the floor plan dimensions with actual building dimensions and provided intermediate results: floor plans and 3D models. We also calculated the time taken for our algorithm steps. To demonstrate the approach robustness, we evaluated using multiple building types, including commercial buildings B1 and B3 and a residential building B3. Additionally, we validated our floor plan generation on the Matterport2D dataset [25]. \ \ Scanned Data Analysis We evaluated the precision of floor plan generation by comparing the actual dimensions of the rooms with the computed floor plan. Figure 10(a) illustrates the measurement of the building, which was scanned twice with our AR. We call these scans S1 and S2 (see figure 10(b) and figure 10(c)) For each scan, floor plans are computed and the dimensions are computed using geometric modeling software, Rhino [17]. We then compared the computed dimensions with the actual room dimensions as shown in Table 1. These results show the applicability of our approach to many building types. \ \ \ Our method can compute a floor plan even from relatively incomplete mesh data. With a higher quality HoloLens scan, the resulting floor plan is more precise. Figure 11 displays S1 results: both types of floor plan and the 3D model. \ Orienting floor and walls We must orient the mesh properly. Spherical k-means is compute intensive so we optimize it to get good performance. In Figure 4, we see the mesh of B1 before and after alignment, which took 12.4 seconds, of which 10.6 were spent aligning walls using spherical k-means. \ Partitioning into stories We can detect a multi-story building and divide it into stories with an additional step. The algorithm projects triangles onto the positive y-axis and creates a histogram showing horizontal peaks. By analyzing the peaks in the histogram, we can determine the number of stories. Figures 5 and 12 show a 2-story residential building and a multi-story model from the Matterport3D dataset [25] that were partitioned into stories. \ \ Finding planar walls To generate a drafting-style floor plan, we eliminate details and identify planar walls. The modified DBSCAN algorithm is the most time-consuming step. In the model of Figure 13, with 79,931 vertices and 134,235 faces, it took 27.4 seconds to prepare the data and run DBSCAN and an additional 3.79 seconds to construct flat walls from the generated clusters. For the residential building of Figure 14, with 173,941 vertices and 285,840 faces, it took 76 seconds to prepare and run DBSCAN and 23.36 seconds to compute flat walls. The results for a Matterport3D model appear in Figure 15. \ \ Generating the floor plan The final step of our floor plan generation is to slice the mesh at different heights and superimpose the slices. Figures 13, 14, and 15 show floor plans generated using our approach. \ We conducted experiments to evaluate the effects of changing graphical settings when rendering pen-and-ink floor plans. Each setting consists of a different combination of line segment opacity and slice count. We found that an opacity setting of 0.5 produced a floor plan that met our expectations. We also found that a floor plan with 100 slices provided a good balance between level of detail and clutter reduction. Optimal numbers will depend on use case. \ \ \ \ :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. ::: \

:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA [email protected]; (2) Eric Bier, Palo Alto Research Center, USA [email protected]; (3) Lester Nelson, Palo Alto Research Center, USA [email protected]; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA [email protected]; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA [email protected]. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 3 Methodology We compute floor plans in four main steps (see Algorithm 1). First, a user captures the interior of the building as a triangle mesh using an augmented reality headset. The mesh is oriented to align with primary axes, and the building is divided into stories. Floors and ceilings are removed, and flat walls are detected if desired. Finally, one of two floor plan styles is generated by slicing and projecting the resulting 3D model. Next we describe these steps in detail. \ Data collection Indoor environments can be captured in various formats using different devices. We use a Microsoft HoloLens 2 headset to capture triangle mesh data, annotating the mesh using voice commands. \ Capturing the triangle mesh The HoloLens provides hardware and software to create a 3D representation of the indoor environment using triangles, as shown in Figure 1-left. The headset overlays the triangles on the user’s view of the building interior. Although the headset captures most of the walls, floors, and ceilings, data may be missing from some regions, as in the figure. \ \ \ Annotating the mesh To capture object positions such as sensors, thermostats, windows, and doors, we developed an augmented reality (AR) user interface. This interface uses eye gaze detection and voice commands to enable users to place synthetic objects at desired locations, as shown in Figure 1-right where a synthetic sensor object is added to the immersive environment, superimposed on a physical sensor. 3.1 Mesh orientation After the annotated triangle mesh is captured, geometric processing is performed. Initially, the mesh’s orientation is based on the user’s starting position and gaze direction. To generate a floor plan, we must determine the floor’s position and facing direction. The AR headset provides a rough estimation of gravity direction, but additional computation improves precision. \ Orienting the floor To determine the mesh orientation, we tested two methods: (1) compute the shortest edges of the mesh bounding box, and (2) cluster the facing directions of mesh triangles using spherical k-means. Method (1) works for buildings with constant altitude and large floor area, but it fails on others, so we mainly use Method (2), described in Algorithm 2. \ Algorithm 2 applies to a broad range of meshes, including multi-story buildings with vertical dominance. It uses the surface normal vector of each triangle ∆ in the mesh M and filters out triangles deviating significantly from the positive y direction, preserving those likely to represent the floor (∆ ′). \ We use a spherical coordinates k-means algorithm with k = 1 to find the dominant direction gm of these triangles. We discard triangles that are more han an angle ϕ from the dominant direction and repeat the k-means algorithm until ϕmin is reached (e.g., start with ϕ = 30 degrees and end with ϕmin = 3). This gives an estimate of the true gravity direction gt. \ To orient the mesh, we compute the angle θ between gt and the negative yaxis and determine the rotation axis Y by taking their cross product. We rotate the mesh by θ around the Y axis, ensuring a horizontal floor. Further details on this method for floor orientation are in Algorithm 2. \ Figure 2 shows a model where the floor is not level, but tilts down from near to far and from right to left. After Algorithm 2, the floor is horizontal. \ Finding the height of the floor After orienting the mesh to have a horizontal floor, we find the altitude of the floor in the y direction: we take the centroid of each mesh triangle whose facing direction is within a small angle of the positive y axis. We create a histogram of the y coordinates of these centroids, with each bucket representing a vertical range, such as 2 inches. We consider adjacent pairs of buckets and look for the pair with the highest number of points, such as (0, 1), (1, 2), etc. For a single-story building, we search for two large bucket pairs representing the floor (near the bottom) and the ceiling (near the top). \ \ \ If the building has sunken floors or raised ceilings, the histogram will show spikes at similar but not identical altitudes. To ensure that we locate true ceilings and floors, we search for a gap of several feet (such as the expected floor-to-ceiling height of a room) between the low and high histogram spikes. The spikes below this gap are probably floors, and those above are probably ceilings. \ To generate the floor plan, we choose the highest of the floor levels and the lowest of the ceiling levels as the computed floor and ceiling levels, respectively. Pairing the buckets rather than taking them individually ensures that we do not overlook spikes in the histogram if the mesh triangles are distributed evenly across two adjacent buckets. \ Rotate mesh and associated annotations Our next goal is to align the mesh model’s primary wall directions with the axes of Euclidean coordinates. \ One optional step is to eliminate mesh triangles whose surface normals are within a small angle from the positive or negative y directions, as these are probably ceiling or floor triangles. This step is not mandatory, but decreases the number of triangles to be processed. Additionally, we eliminate all triangles below the computed floor altitude and all above the computed ceiling altitude. \ We then examine the surface normals of the remaining triangles. We express each normal in spherical coordinates and use spherical k-means clustering to identify the dominant wall directions. Assuming the building has mainly perpendicular walls, there will be four primary wall directions, so we can set k = 4 for k-means clustering. If the model still has floor and ceiling triangles, we can set k = 6 to account for the two additional primary directions. Figure 3-left illustrates a heat map of surface normal directions in spherical coordinates from an office building mesh. \ \ Figure 3-left contains many light blue rectangles that are far from any cluster center (e.g., far from the buckets that are red, orange, and white). These represent triangles whose facing directions do not line up with any of the primary walls, floors, or ceilings. Such triangles exist for two reasons: (1) Building interiors contain many objects that are not walls, floors, or ceilings, such as furniture, documents, office equipment, artwork, etc. These objects may be placed at any angle. (2) The AR headset generates triangles that bridge across multiple surfaces (e.g., that touch multiple walls) and hence point in an intermediate direction. To compensate, we use a modified version of spherical coordinates k-means clustering that ignores triangle directions that are outliers as follows: \ After computing spherical k-means in the usual way, we look for all triangles in each cluster whose facing direction is more than a threshold θ1 from the cluster center. We discard all such triangles. Then we run k-means again, computing updated cluster centers. Then we discard all triangles that are more than θ2 from each cluster center where θ2 < θ1. We repeat this process several times until we achieve the desired accuracy. For example, in our current implementation, we use this sequence of angles θi in degrees: [50, 40, 30, 20, 10, 5, 3]. Once our modified k-means algorithm completes, we have 4 (or 6) cluster centers. Figure 3-right shows sample results for k = 6. \ \ Once the primary wall directions are computed, we pick the cluster with the largest number of triangles, take its direction (cluster center), project that direction onto the x−z plane, and call it θwall. We rotate the mesh by the angle between θwall and the x axis. Now the primary walls will be pointing along the x axis. Figure 4-left shows a building that is not aligned with the axes. Figure 4- right shows the same building after wall rotation. Adding in the x axis (in red) and z axis (in blue), we see that the walls are now well-aligned with the axes. \ Dividing a mesh into separate levels The HoloLens can digitize multi-story buildings. Given a multi-story model, we can compute a floor plan for each story. The process is similar to the one used in 3.1 to find the height of the floor. First, our system computes a histogram, as shown in figure 5 and segments the building into multiple levels, as shown in figure 5-middle and right. \ 3.2 Floor plan computation Our floor plan computation depends on the type of floor plan desired and whether the mesh is oriented with respect to the global axes. If we desire a pen-and-ink style floor plan and the mesh is oriented, we can simply pass the mesh M to the ComputeAndSuperimposeSlices() function, as in line 14 of Algorithm 3.2. \ \ \ However, if the mesh is not properly oriented, we align it with the global axes before computing the floor plan. If a drafting-style floor plan is desired, we utilize lines 2-13 of Algorithm 3.2 to compute flat walls. \ Computing flat walls To generate a drafting-style floor plan, we compute flat walls and separate them from other building contents using these steps: \ \ DBSCAN For each wall direction, we perform a modified DBSCAN algorithm: We compute the centroid C of each triangle ∆i . For each centroid point Ci during DBSCAN, we count the number of other centroid points that are near enough to be considered neighbors. However, instead of looking for neighbors in a sphere around each point as for traditional DBSCAN in 3D, we look for neighbors in a rectangular block of length l, width w and height h centered on the point. This block is tall enough to reach from floor to ceiling in the y direction, a little less wide than a door in the direction parallel to the proposed wall (e.g., 1.5 feet), and a few inches in the wall direction (to allow for walls that deviate slightly from being perfectly flat). Relying on the National Building Code, the wall’s minimum height is set at 8 feet, with a thickness of 8 inches. After DBSCAN, the mesh triangles are grouped into wall segments W S. \ Filtering We discard wall segments that are not good candidates, such as walls that are too small, that aren’t near the floor, or that aren’t near the ceiling. \ Plane fitting For each wall segment, we find a plane that has the same facing direction as the wall direction and that is a good fit to the triangle centroids in that wall segment. Given that the points are tightly collected in this direction, simply having the plane go through any centroid works surprisingly well. However, it is also possible to choose a point more carefully, such as by finding a point at a median position in the wall direction. \ Rectangle construction For each remaining wall segment, we construct rectangles R that lie in the fitted plane and are as wide as the wall segment triangles in width and as tall as the wall segment triangles in height. \ Mesh replacement For floor plan construction, we discard the original mesh triangles and replace them with the new planar wall rectangles to serve as a de-cluttered mesh. If the subsequent steps use libraries that expect a triangle mesh, we use two adjacent right triangles in place of each rectangle. \ \ \ for each i such that 0 ≤ i ≤ n. For each yi , we compute the intersection of the mesh with the plane y = yi . We end up with a stack of slices (see Figure 7). \ \ We can use the same method to produce a drafting-style floor plan. In this case, we begin with the flat wall model instead of the full mesh. This model has fewer details to capture by slicing at multiple altitudes, so we may choose to slice at a single intermediate altitude. \ Drawing the floor plan For either style of floor plan, we can project the slices to a plane by ignoring the y coordinates of the resulting line segments and plotting the resulting (x, z) coordinates as a two-dimensional image. We have also found it informative and aesthetically pleasing to draw the lines of each slice in a partially-transparent color, so that features that occur at multiple altitudes appear darker than features that occur only at a single altitude. \ As an example, Figure 8-left shows a mesh gathered from a commercial building. Figure 8-center shows the result of our DBSCAN on that data. Figure 8-right shows the flat walls that result after mesh replacement. Figure 9-right shows the drafting-style floor plan that results from slicing the flat walls. Figure 9-left shows the pen-and-ink floor plan made by slicing the oriented mesh at multiple altitudes. \ \ Drawing synthetic objects Because our data comes from an AR headset, we can add synthetic objects to mark the positions of objects in a room, such as sensors and windows. We can display these objects in our 3D models and floor plans. For the steps of mesh processing described above, we note the geometric transformations applied to the mesh and apply the same transformations to the synthetic objects, which then appear in the correct places in the 3D views and in the floor plans. For example, the black objects in Figure 8-left represent the objects placed by the user to show the positions of sensors and windows. Likewise, the red objects in the floor plan of Figure 9-left are those same objects, projected onto the same plane as the mesh slices. \ :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. ::: \

:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA [email protected]; (2) Eric Bier, Palo Alto Research Center, USA [email protected]; (3) Lester Nelson, Palo Alto Research Center, USA [email protected]; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA [email protected]; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA [email protected]. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. \ Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. \ Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. \ There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. \ Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. \ Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. \ Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. \ In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. \ Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. \ :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. ::: \

:::info Authors: (1) Ritesh Sharma, University Of California, Merced, USA [email protected]; (2) Eric Bier, Palo Alto Research Center, USA [email protected]; (3) Lester Nelson, Palo Alto Research Center, USA [email protected]; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA [email protected]; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA [email protected]. ::: Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. \ Keywords: Clustering based methods · Floor plans · Augmented Reality· 3D Models. 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. \ To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. \ Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: \ – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. \ – An orientation-based clustering method that finds walls at arbitrary angles. \ – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. \ – Generating two kinds of precise floor plans from incomplete mesh data. \ :::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. ::: \

ONE of the most prominent firms in crypto recently sent shockwaves through the industry by announcing it is suing the U.S. Securities and Exchange Commission (SEC). Here's why the lawsuit is doomed.

\ 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. \n \n 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. \n 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. \n 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. \n \n 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. \n The future of DePINs relies heavily on collaboration among stakeholders and ongoing technological innovation to address these challenges and unlock their full potential.

In this blog article, we’ll explore the principles of software craftsmanship, the benefits of becoming a software craftsperson, and how we can improve our skills. We’ll look at a growth mindset and some resources to help us on our journey. Let’s dive in! Witnessing Craftsmanship Let’s say we enter a home and face this beautifully crafted staircase. Why do we even think this is beautiful? What comes to mind is the skill and work that has gone into it. The craftsperson has had to think about how to ensure that it’s only connected at the top and the bottom, it can support the weight and doesn’t fall under its weight, or when there are people on it, climbing up and down. There is also the craftsmanship of the handrail and the curved wall. Why does this craftsmanship strike us or cause us to take notice? Is it because we can see the care taken in creating the stairs? Or maybe we can see that a lot of skill went into it? Or perhaps it’s because the knowledge of physics has been used to make it appear that it defies gravity? What Is Craftsmanship? From Collins dictionary, we can see the definition is: \ Craftsmanship is the quality that something has when it is beautiful and has been very carefully made. What Is Software Craftsmanship? The Manifesto for Software Craftsmanship describes it as follows: \ Not only working software, \n but also well-crafted software. Not only responding to change, \n but also steadily adding value. Not only individuals and interactions, \n but also a community of professionals. Not only customer collaboration, \n but also productive partnerships. If we simplify it, a software craftsperson cares about all aspects of their work. What Separates a Software Developer From a Software Craftsperson? While software developers are primarily concerned with the code they write, software craftspeople take a broader approach. They manage the code, its maintainability, deployability, and application monitoring. This results in robust applications that meet user needs and bring joy to users. Software craftspeople continually hone their skills to create better applications that perform well in production without constant supervision. Quality applications are made through thorough testing and proactive monitoring that alerts the team to potential issues. Why Choose to Embark on the Path of a Software Craftsperson? For anyone who connects with the principles of software craftsmanship — well-crafted software, steadily adding value, being part of a community, and having productive partnerships with their users — the path of software craftsperson is a good fit. It’s a journey where we continually learn the craft of building software in an evolving landscape. As software craftspeople, we’re not happy just throwing things out the door but instead focusing on quality and stability. We also want to build up a community of people who can create high-quality software so that we all can learn from each other and build on what others are learning. Why Did I Make the Transition to Software Craftsperson? Different people have different journeys, motivations, and experiences regarding craftsmanship. Let me tell you my story. I had worked in software development for over ten years when I joined a software craftsmanship dojo. At the start, I didn’t understand the impact that the dojo would have. I thought I was only there to learn Test Driven Development (TDD). Previously, I had learned TDD by participating in code retreats. Still, I needed help incorporating the new working method into day-to-day coding outside of fixing defects or working on straightforward features. The dojo allowed me to learn hands-on each week, developing the skills that drive my development through testing. This mindset progressed to the point where I now find it hard to think about developing without using TDD. \ The move to software craftsmanship made sense as a path for my career since I had worked on many projects where we were fighting the storm of trying to develop the application, dealing with production issues, and managing our technical debt. This storm led me to burnout and disillusionment in the software developer career. Having an opportunity in the weekly two-hour dojo to learn new skills and have hands-on experience meant that it was two hours that I looked forward to the most in the week. \ Outside the dojo, I practice a daily coding exercise, use what I learned in my work, and consider new ways of doing things. This practice has led me to develop skills to quickly deploy new, well-tested applications with testing, monitoring, and scanning toolchains, improving my DORA and DASA scores. Growth Mindset Moving to become a software craftsperson will mean that we can see that there are ways that we can grow. Rather than seeing our skills as something that can’t be changed, we realise we can improve incrementally over time. So, rather than having a fixed mindset where we think our skills limit our growth, we have a growth mindset. Referring to the previous post, building habits and working on getting 1% better is fundamental to creating a growth mindset. This growth mindset doesn’t just stop with us; it should also include growing the people around us. Having a growth mindset is vital to building a community of software craftspeople. Benchmarking Our Skills To understand where we are with our software craftsmanship skills, we can use the DevOps Agile Skills Association (DASA) DevOps quick scan to know where we are with our skill levels. Then, we can work on improving the areas that need addressing. The quick scan looks at 12 different areas: \ Business Value Optimisation Business Analysis Architecture and Design Test Specification Programming Continuous Delivery Infrastructure Engineering Security, Risk, Compliance Courage Team Building DevOps Leadership Continuous Improvement \ Each area will receive a score from one (novice) to five (master). The report will help us understand what is required at the next level and how to improve. Methodology for Developing Quality Cloud Applications A methodology called the twelve-factor app is used to build software-as-a-service applications that can scale without significant changes to tooling, architecture, or development practices. The created app uses declarative formats for setup automation, has a clean contract with the underlying operating system, and minimizes divergence between development and production. The methodology can be applied to apps in any programming language and can use any combination of backend services. We can build the best software-as-service application possible by following the twelve factors. Getting Started on Our Journey as Software Craftspeople Understanding more about software craftsmanship can always be helpful. There is a link to further reading on the Manifesto for Software Craftsmanship. There you will see, among others: \ Apprenticeship Patterns: Guidance for the Aspiring Software Craftsman by Dave Hoover and Adewale Oshineye Software Craftsmanship by Pete McBreen The Pragmatic Programmer by David Thomas and Andrew Hunt A title missing from that list is: The Software Craftsman by Sandro Mancuso \ These titles help us further understand software craftsmanship and what we must look at in our journey. We should improve ourselves and those around us to build well-crafted software using the lessons learned. Conclusion Changing our identity from a developer to a software craftsperson leads us to build well-crafted applications. The key to the change is treating it as a journey, and as with any journey, we can take many different routes. We’ve talked about some of the resources that might be useful, and we can use the resources that entice us and keep us going along the journey. Transforming 1% daily will mean we will have significantly impacted how we work for a year and beyond. References Craftsmanship Definition — https://www.collinsdictionary.com/dictionary/english/craftsmanship Manifesto for Software Craftsmanship — https://manifesto.softwarecraftsmanship.org DASA Quick Scan — https://scan.devopsagileskills.org BriX Software Craftsmanship Dojo — https://swcraftsmanshipdojo.com DORA Quick Check — https://dora.dev/quickcheck The Twelve-Factor App — https://12factor.net Apprenticeship Patterns: Guidance for the Aspiring Software Craftsman by Dave Hoover and Adewale Oshineye — https://www.amazon.com/Apprenticeship-Patterns-Guidance-Aspiring-Craftsman/dp/0596518382/ Software Craftsmanship by Pete McBreen — https://www.amazon.com/Software-Craftsmanship-Imperative-Pete-McBreen/dp/0201733862 The Pragmatic Programmer by David Thomas and Andrew Hunt — https://www.amazon.com/Pragmatic-Programmer-journey-mastery-Anniversary/dp/0135957052 The Software Craftsman by Sandro Mancuso — https://www.amazon.com/Software-Craftsman-Professionalism-Pragmatism-Robert/dp/0134052501 Credits The title image is from Dreamstudio AI.

Tether has announced its expansion into Eastern Europe and will invest in a Georgian payment service.

Take a closer look at the latest crypto events with U.Today's news digest!

This campaign seeks to replicate Bitcoin Beach's success in more communities worldwide.

SINGAPORE, SINGAPORE / ACCESSWIRE / May 8, 2024 / In today's digital era, blockchain technology has become a focal point across various industries.

CNW/ -- The leading digital assets exchange HTX has collaborated with Astar Network, a blockchain that aims to become Polkadot's "smart contract ...

Bangkok, Thailand, May 8th, 2024, Chainwire Southeast Asia Blockchain Week (SEABW), a premier blockchain conference exploring the evolving ...

Digital asset manager Grayscale has withdrawn its filing for an Ethereum futures exchange-traded fund (ETF), prompting traders to bet against Ether’s price performance in the [...]

The presale of Dogeverse sparks excitement in the crypto community as a single whale purchases tokens worth $180,000, equivalent to 62.3 Ethereum. Enthusiasm for the [...]

The DOG•GO•TO•THE•MOON price soared 29% in the last 24 hours to trade at $0.002301 as of 03:11 a.m. EST on trading volume that skyrocketed 81% [...]

In a recent public statement, Binance CEO Richard Teng has voiced his concerns regarding the ongoing detention of Tigran Gambaryan, the crypto exchange’s compliance officer, [...]

While most of the top cryptocurrencies are trading sideways or slightly down, Solana (SOL) has surged nearly 2.28% in the last 24 hours. Its price is $155. [...]

This article covers several cryptocurrency contenders worth considering in your watchlist. These coins have shown promise and could be valuable additions to your investment radar. [...]

Magic Eden, one of the biggest multi-chain NFT marketplaces and Launchpads in the Solana ecosystem, has partnered with APhone, a device-agnostic web-based virtual phone app [...]

In today’s global market, innovative solutions in the fintech sector have surged, particularly in cryptocurrency. As digital assets gain mainstream acceptance, consumers seek convenient and [...]