CIC Chain is a disruptive blockchain software company bridging the gap between Web2 and Web3

CIC Chain

The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

वर्ष के चैंपियन स्टार्टअप के लिए खड़े रहें!

今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

Встаньте на защиту стартапов-чемпионов года!

Yılın Şampiyon Startup'ları için Ayağa Kalkın!

Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

为年度冠军初创企业挺身而出！

Discussing the Russia-Ukraine Cyber War with Eran Fine on The HackerNoon Podcast

Discutiendo la Guerra Cibernética Rusia-Ucrania con Eran Fine en The HackerNoon Podcast

Discuter de la cyberguerre russo-ukrainienne avec Eran Fine sur le podcast The HackerNoon

हैकरनून पॉडकास्ट पर एरान फाइन के साथ रूस-यूक्रेन साइबर युद्ध पर चर्चा

HackerNoon ポッドキャストで Eran Fine とロシアとウクライナのサイバー戦争について語る

Discutindo a Guerra Cibernética Rússia-Ucrânia com Eran Fine no The HackerNoon Podcast

Thảo luận về Chiến tranh mạng Nga-Ukraine với Eran Fine trên The HackerNoon Podcast

在 HackerNoon 播客上与 Eran Fine 讨论俄罗斯-乌克兰网络战争

The Story of Machine Proofs - Part II

In the quest to simulate cosmic expanses and the intricate dance of celestial bodies, algorithms such as Particle-Mesh (PM) and its more nuanced counterpart, Particle-Particle-Particle-Mesh (P3M), have revolutionized our computational approach. These methods, which are deeply rooted in the seminal work of Hockney and Eastwood, provide a powerful framework for simulations, which I've explored and implemented with the help of modern GPU capabilities. Based on my peer-reviewed paper " ," this blog post provides a condensed exploration of these algorithms and their GPU implementations. Computational simulation using particles on GPU and GLSL language PM and P3M Algorithms: A Primer The PM algorithm works by assigning masses to a regular grid, solving the Poisson equation for the potential, and then differentiating the potential to obtain forces. This method simplifies the n-body problem, making it computationally feasible. The P3M algorithm extends PM by incorporating short-range corrections via direct particle-particle interactions, resulting in improved accuracy in force computations without significantly increasing computational load. GPU Implementation: A Leap in Performance Going into greater detail, my implementation of the PM algorithm makes use of the versatility and power of the GLSL language and Vulkan API, ensuring compatibility across a wide range of graphics cards, from integrated Intel GPUs to the robust Apple M1 chips. Here is how I customized the PM algorithm for the GPU: First Stage: Point Partitioning The first stage involves partitioning points within parallelepipeds, laying the groundwork for the subsequent density assignment. We build 32x32 linked lists, each of which corresponds to a region in space. The goal is for each list to contain the indices of points in the corresponding parallelepiped. We start by defining shared lists for each partition in GLSL using the compute shader: // particles3_parts.comp - List Construction
const int nl = 32; // number of linked lists along one dimension
shared int lists[nl][nl];

// Initialize lists to -1
for (uint i = from; i < to; i++) {
    list[i] = -1;
}

lists[gl_LocalInvocationID.y][gl_LocalInvocationID.x] = -1;
barrier();

float h32 = l/32.0; // grid size divided by number of lists

// Build the linked lists by x, y coordinates
for (uint i = from; i < to; i++) {
    vec2 x = vec2(Position[i]) - vec2(origin);
    ivec2 index = clamp(ivec2(floor(x/h32)), 0, 31);

    // The head of the list for the area (index.y, index.x) is 
    // in lists[index.y][index.x]
    // The previous list element is saved in list[i]
    // Each thread updates a unique element, so no synchronization is needed here
    list[i] = atomicExchange(lists[index.y][index.x], int(i));
}
... After populating the shared lists, we have a structured overview of where each point lies within our space-partitioning grid. The GPU's atomic operations ensure that this process is both fast and accurate, laying the groundwork for efficient mass distribution in the PM algorithm's subsequent steps. Take a look at this illustrative image to get a better idea of how these 2x2 linked lists are built and function within the larger context of our partitioning strategy: This visualization depicts the process on a simplified 2x2 grid. In practice, a 32x32 grid is used, but the principles remain the same, albeit on a larger scale. With our 32x32 linked lists ready, we move on to the mass distribution phase. We traverse the lists in a chessboard pattern here, which is not only systematic but also strategically designed to avoid race conditions when writing density values to our density array. This traversal is depicted in the following image, where the active lists being processed at any given time are highlighted in blue: As we cycle through the particles in each list, we distribute their mass across the grid nodes. The chessboard-like processing pattern ensures that simultaneous writes to the density array from multiple threads do not interfere with each other, avoiding the classic problem of race conditions. This method improves the algorithm's efficiency while also ensuring data integrity. The compute shader, which serves as the foundation of this initial setup, orchestrates the creation of linked lists within the GPU. For those who are interested in the actual code and its nuances, the compute shader responsible for this can be found on my GitHub repository: . particles3_parts.comp - Constructing Linked Lists on the GPU Once our lists are established, we will traverse them in a chessboard pattern to avoid data races during mass distribution. The corresponding shader, which handles this traversal and density array construction meticulously, can also be viewed in detail here: . particles3_mass.comp - Traversal and Density Distribution Shader For the mass distribution, we use linear interpolation, leveraging barycentric coordinates to weigh the mass across the grid nodes. This technique ensures a smooth and continuous mass transition, which is critical for simulating physical systems. Consider the image below, which depicts the mass distribution using the Cloud-in-Cell (CIC) method: The use of linear interpolation for mass distribution is a technique that mirrors physical reality by ensuring that the influence of a particle is felt across its immediate vicinity in a manner proportional to its position within a cell. Poisson's Equation and Fast Fourier Transform Solving Poisson's equation to calculate gravitational potential from mass distribution is a critical component of the PM algorithm. In my implementation, I use variable separation and a Fast Discrete Real Fourier Transform (FDRFT), adapting the algorithm described in A. A. Samarskii and E. S. Nikolaev's book "Numerical Methods for Grid Equations." This adaptation was specifically designed for GPU execution, leveraging its parallel processing capabilities to improve computational performance. The shader code responsible for this part of the computation is complex, reflecting both the complexities of the mathematical procedure and the optimizations required for GPU use. The shader is proof of modern computing platforms' power and ability to handle calculations that were previously the exclusive domain of supercomputers. For a more in-depth look at this process, please see the shader that handles the Poisson's equation using FDRFT on the GPU: . particles3_poisson2.comp shader The P3M Algorithm and GPU Acceleration The P3M (Particle-Particle Particle-Mesh) algorithm improves on the PM (Particle-Mesh) algorithm, particularly in how it computes local forces using Newtonian mechanics. While both algorithms share common ground, the P3M algorithm adds an extra layer of precision by partitioning points into cubes rather than parallelepipeds, improving the granularity of the mass distribution and force computation. Advancing to Cubic Partitioning The P3M implementation involves additional partitioning using lists, which is similar to the PM algorithm but takes a step further to accommodate cubic volumes. This additional step, which closely follows the classic Newtonian formulation, allows for a more precise calculation of local forces. Computing Newtonian Forces in Parallel The calculation of these local forces in parallel employs a classic parallel algorithm, which is finely detailed in the paper " " by L. Nyland, M. Harris, and J. Prins. This seminal work underpins the parallel computational approach adapted for the GPU, enabling significantly faster computations required for large-scale simulations. Fast N-Body Simulation with CUDA The shader codes that power these computations can be examined in greater depth by visiting the following links: For the sorting process that prepares the data for force calculations: . particles3_pp_sort.comp shader For the parallel force computation itself: . particles3_pp.comp shader Final Steps with Vertex Shader The calculation of velocities and positions is the culmination of the P3M algorithm on the GPU. This is accomplished deftly with the Velocity Verlet algorithm, an integration method that provides a symplectic solution—ideal for conserving energy over long simulations, which is critical for accurate astrophysical results. The shader responsible for these final calculations can be found here: . particles3.vert shader The Velocity Verlet algorithm is a pillar of computational physics, providing stable and efficient computations of particle trajectories. For those who are unfamiliar with the Velocity Verlet algorithm, a primer can be found on its . Wikipedia page Performance Benchmarks Our implementations have resulted in significant performance improvements. The following table summarizes the benchmarks: Stage M1 (CPU) M1 (GPU) Nvidia 3060 Density 9.45 11.61 2.26 Potential 8.87 5.69 0.86 Force 313.93 103.09 28.87 Positions 82.74 8.38 33.54 In the performance comparison table, we look at the execution time of each stage of the algorithm, measured in milliseconds across different processors: Apple M1 (CPU), Apple M2 (GPU), and Nvidia 3060. For the CPU version of the algorithm, OpenMP was utilized. The data from this table reveals a significant performance difference: the algorithm on the Nvidia 3060 runs 13 times faster compared to its execution on the Apple M1 CPU. These calculations were conducted with one million particles and a grid size of 64x64x64. The P3M Algorithm and GPU Acceleration: A Note on Performance When it comes to GPU acceleration, the P3M and PM algorithms present some computational challenges. Atomic operations and the management of linked lists, in particular, are not inherently optimal for GPU architectures. Despite this, the bottlenecks of these algorithms are located elsewhere. Bottlenecks in Performance The primary bottleneck in the PM algorithm is updating the density matrix, which requires significant memory bandwidth and synchronization between GPU threads. This limitation is vividly highlighted in a performance analysis using NVIDIA Nsight Graphics, as shown in this screenshot: The P3M algorithm, on the other hand, has a bottleneck in the computation of local forces, which requires intensive floating-point operations and data shuffling between GPU threads and memory. This performance barrier is also visible in a detailed Nsight Graphics analysis, as shown in this screenshot: Future Optimizations Recognizing these performance bottlenecks, the program's next iterations will concentrate on addressing these issues. The efficiency of these calculations is hoped to be significantly improved through careful optimization and possibly leveraging new GPU features or architecture improvements. While these challenges are not trivial, the solutions will not only improve the performance of the P3M and PM algorithms but may also contribute to the broader field of computational physics, where such algorithms are widely used. Further Insights For those interested in learning more about these simulations, check out these YouTube videos that show the calculation of a galaxy's evolution and the dynamics of the early universe: Galaxy Simulation Early Universe Evolution In the simulation video linked above, the Friedman universe model has been used to account for the expansion of the universe. This model describes a universe filled with homogeneously distributed fluid with pressure and energy, which governs the observed dynamics of cosmic expansion. For a more in-depth understanding of this model, see the . Friedman Universe on Wikipedia Finally, for enthusiasts and fellow developers, the source code for these algorithms, written in GLSL and using the Vulkan API to ensure compatibility across various GPUs, including Intel integrated graphics and Apple's M1, is available on GitHub: . Access the Source Code Repository Conclusion In conclusion, modern mid-range gaming graphics cards and integrated GPUs are proving to be quite capable of accelerating numerical computations. The algorithms presented are implemented efficiently, with GPU computation times comparable to cluster processing across multiple nodes. The findings are consistent with those of other researchers, confirming the accuracy of this approach. The developed program can serve as a solid foundation for particle-based computational physics problems. Subroutines like the one for solving Poisson's equation could be applied to more complex hydrodynamic problems. One key advantage of the approach described here is the ability to use a personal computer for rapid, parameter-varying numerical experiments with the luxury of real-time observation of evolving changes without the need for intermediate file storage or separate visualization software. While not all aspects of the algorithm perform optimally on GPUs (for example, mass distribution on the grid), careful optimization of GPU parameters such as computation partitioning, stream counts, and shared memory usage within thread groups has the potential for significant performance gains.

Unveiling the Power of PM and P3M Algorithms on GPUs

INTRODUCTION.

SECONDARY SEXUAL CHARACTERS OF INSECTS

As in all elections of great importance, tallying the official results of Hacker Noon 's annual awards took us somewhat longer than expected. That's because this year’s were bigger, better and greener than anything, ever before: Noonies Awards of you were nominated for one (or more) of the 2,587 Hacker Noon awards up for grabs this year, 221 across award categories: , , , , and . 5 Technology Decentralization Software Development Future Heroes Back to the Internet weeks later: 8 over 200 Noonie Nominee Interviews , AMAs , and Podcasts are live on Hacker Noon , and a whopping votes have been cast over at which means only one thing: 232,444 noonies.tech , The Winners of the Internet for 2020 can now be officially declared! 🥳 Head over to noonies.tech to check out all 221 Noonies Winners (and 442 Runner-Ups!) right now. With very special thanks to who made the 2020 Noonies possible: , , , , , , and ! the Hacker Noon Partners Sustany Capital .TECH Domains Grant for the Web Skillsoft Flipside Crypto Udacity Beyondskills Scroll down to view a selection of top Noonies awards and winners from this year's Hacker Noon awards, or view all on NOONIES.TECH 🤖 🏆 Best Web Monetization Innovator —presented in partnership with GRANT FOR THE WEB A huge thanks to for this award's prize, which includes: Grant for the Web A $1000 award for the eventual winner (!!!) The winner will also receive a 1 year registration for a .tech domain. And the winner is... 1,774 votes) 🥇 Jasmin Virdi ( An open-source contributor who innovated around the Ideas of Web Monetization by submitting 3 project ideas that involve a web component, a Vue plugin, and a starter kit for the Electron App using the Web monetization technology. Runner-ups: 936 votes) 🥈 gFam.live ( gFam is a project that allows content consumers to tip content creators instantly. Creators also receive micropayments when Coil subscribers view their content 🥉 Sharon Wang Co-creator of the Akita web monetization browser extension. The extension makes it easy to learn about and try out Web Monetization! Also has posted some informative articles on dev.to. She's part of the movement making Web Monetization super fun to get involved with, is genuinely friendly and supportive, and cares about the cause. 🏆 Top Programming Guru —presented in partnership with .TECH Domains A huge thanks to for this award's prizes, which include: .TECH Domains $ 3000 Cash Prize + A Free 10-yr domain; 1st Runner-up gets Free 5-yr domain; 2nd Runner-up gets a 3-yr domain And the winner is... ,695 votes) 🥇 Programming with Mosh (30 He trains novice and professional software engineers to increase their skills through his courses, make more money and ultimately change their lives for the better. Runner-ups: ,810 votes) 🥈 Traversy Media (17 Traversy Media features the best online web development and programming tutorials for all of the latest web technologies including Node.js, Angular 2, React.js, PHP, Rails, HTML, CSS and much more 🥉 Corey Schafer He creates a wide variety of videos on topics that include: Python, Git, Development Environments, Terminal Commands, SQL, Programming Terms, JavaScript, Computer Science Fundamentals, and plenty of other tips and tricks which will help you in your career. 🏆 Most Outstanding Educator in Technology —presented in partnership with Skillsoft A huge thanks to for this award's prizes, which include: Skillsoft 1 Year FREE Access to Percipio to the Winner; and, 6 months Free Access to the runners-up; The winner will also receive a 1 year registration for a .tech domain! And the winner is... 533 votes) 🥇 Sajeetharan Sinnathurai ( Mentor and open source contributor who is empowering and helping every developer in their pretty journey. He works at Microsoft as a Cloud Solution Architect. He is also the top contributor on Stack Overflow from SriLanka and He ranks among the top 10 contributors in the world on Angular, Azure. He contributes his knowledge into open source projects and sharing it with the community by mentoring, creating POCS, running workshops, writing blogs to help make the world a better and more developed place. Runner-ups: 🥈 shishir (339 votes) Through his succinct yet informative pieces, he is able to pack a lot of knowledge into a diverse set of topics. Even in his blog, he covers more such consequential technology and its intersection with society. 🥉 Paul Graham Founder of Y Combinator and author of numerous essential startup essays. 🏆 Top Tech YouTuber —presented in partnership with .TECH Domains A huge thanks to for this award's prizes, which include: .TECH Domains A Free 10-yr domain;1st Runner-up gets Free 5-yr domain ;2nd Runner-up gets a 3-yr domain And the winner is... 5,575 votes) 🥇 Traversy Media ( Traversy Media features the best online web development and programming tutorials for all of the latest web technologies including Node.js, Angular 2, React.js, PHP, Rails, HTML, CSS and much more Runner-ups: 2,524 votes) 🥈 The Net Ninja ( If you're looking to learn Javascript, Flutter, NodeJS, Typescript, or a host of other such cool languages, the Net Ninja's tutorials have you covered 🥉 Ready Dev The best content about python, javascript, django and bots with python. 🏆 Innovative Company of the Year: Artificial Intelligence —presented in partnership with SKILLSOFT A huge thanks to for this award's prize, which includes: Skillsoft 1 Year FREE Access to Percipio to the Winner and 6 months Free Access to the runners-up The winner will also receive a 1 year registration for a .tech domain. And the winner is... (359 votes) 🥇 Behavioral Signals Behavioral Signals' Emotion AI technology proved actual ROI in a commercial deployment, with a large bank, garnering them recognition by Gartner as an example for AI adoption by industry managers. Their AI technology has the ability to gather emotions and behaviors from voice intonation without having to convert speech to text, which is crucial when it comes to user privacy. It's all about HOW you say something and not WHAT you say. Runner-ups: 🥈 Raven Protocol (278 votes) Raven Protocol is working on speeding up AI training. it typically takes about 2-3 weeks to train a 1M image dataset on say AWS. But Raven is building a distribution framework from the ground up that takes this down to 2-3 hours. 🥉 LionbridgeAI Lionbridge provides human-labeled data for hundreds of use cases. 🏆 The Best Analytical Mind in Blockchain —presented in partnership with FLIPSIDE CRYPTO A huge thanks to for this award's prize, which includes: Flipside Crypto Winner shall be featured on 's newsletter, gets an interview, and a blog post. Flipside Crypto The winner will also receive a 1 year registration for a .tech domain. And the winner is... (1,599 votes) 🥇 Akshay Kumar KANDHI Akshay is a blockchain and biometrics researcher and mathematician based out of Paris. He is the Director of R&D at Uniris. Runner-ups: (255 votes) 🥈 Laurent Benayoun With just 1.2k USD as a personal seed investment into the company, Laurent co-founded HedgeTech, a quantitative trading firm focusing on market making for digital assets. 🥉 Paul Payam Almasi Infrastructure Solutions Strategist at Alchemy. The founder of two blockchain analytics platforms. Recognized as a top Bitcoin writer on Medium.com and has served as a cryptocurrency writer for HackerNoon. 🏆 Best Defi Initiative (NeoBanks) —presented in partnership with SUSTANY CAPITAL The winner will receive a 1 year registration for a .tech domain. And the winner is... (44 votes) 🥇 Benjamin Jones Ben is the CEO/CTO and co-founder of Bitwala. Bitwala is the first provider of the crypto bank account of the world. It is a great DeFi initiative to bridge the traditional finance with the DeFi space and offer people a way to reach out to DeFi products with the ease of using bank account. Runner-ups: 🥈 Hassan Khan TodaQ CEO (37 votes) From traditional finance to leading one of the most advanced decentralized platform in the space having the ability to represent any asset cryptographically and most importantly enable its circulation without any dependencies on third parties, no ledger, no miners, no cloud, no thing in the middle means true p2p and subsequently unprecedented economical and technical scaling. All because they are building on TODA protocol with several variations. 🥉 Aviv Gadot, current VP, Engineering 🏆 Hacker Noon Contributor of the Year - BLOCKCHAIN The winner will receive a 1 year registration for a .tech domain. And the winner is... (1,215 votes) 🥇 Vaibhav Saini Entrepreneur | Co-founder @tbc_inc, an MIT CIC incubated startup | Speaker |https://vaibhavsaini.com Runner-ups: 1,011 votes) 🥈 Ishan Pandey ( Lawyer in Making | Contributing in the Blockchain Industry 🥉 Sergey Baloyan Good articles on blockchain 🏆 Hacker Noon Contributor of the Year - DECENTRALIZATION —presented in partnership with SUSTANY CAPITAL The winner will receive a 1 year registration for a .tech domain. And the winner is... (227 votes) 🥇 Adam Boudjemaa An accomplished, results-oriented professional with a comprehensive knowledge of the Crypto / Blockchain industry at a strategic and operational level, and a demonstrable record of success in executing incisive market analyses that inform consistently profitable investments in the space. Runner-ups: 🥈 Erik P.M. Vermeulen (39 votes) Business & finance professor, digital lawyer, restaurant owner, board member & traveler. 🥉 Daniel Jeffries I'm an author, futurist, engineer, systems architect, public speaker and pro blogger. During my twenty years in the tech industry, I’ve designed and implemented a broad range of solutions with Linux, virtualization and containers, first with my own consulting company and now with open source pioneer Red Hat. 🏆 Hacker Noon Contributor of the Year - PROGRAMMING —presented in partnership with UDACITY A huge thanks to for this award's prize, which includes: Udacity A 100% Free Udacity Course on Programming The winner will also receive a 1 year registration for a .tech domain. And the winner is... (72 votes) 🥇 Gajesh S. Naik Allegedly the youngest Programmer with a YouTube channel (Gajesh S Naik) Runner-ups: 🥈 Iman Tumorang (32 votes) Experienced Software Engineer with a demonstrated history of working in the computer software industry more than +3 years experience. Blogger and love to contribute to open-source. Passionate on Startup culture move fast and fix things, with expertise in Software Architecture, Microservices Architecture and Design, API Development and Go (Golang). 🥉 Raphaël Huchet Plus d'infos : https://raph.site 🏆 Hacker Noon Contributor of the Year - WOMEN IN TECH —presented in partnership with Skillsoft A huge thanks to for this award's prizes, which include: Skillsoft 1 Year FREE Access to Percipio to the Winner; and, 6 months Free Access to the runners-up; The winner will also receive a 1 year registration for a .tech domain! And the winner is... (217 votes) 🥇 Lina Survila I am tech PR with a passion for cybersecurity and... fashion. Runner-ups: 143 votes) 🥈 L.B. Lewis ( Selected out of 12k contributors as an "insightful technologist" by Hacker Noon in July 2020. Melinda Lewis is an experienced, strategic marketing professional and best-selling author. She has over eight years' experience in digital marketing and has worked with startups and big brands alike. 🥉 Janani Ravi Ex-google, Janani Ravi is a co-founder at loonycorn, a studio for high quality tech videos. She has created a lot of courses on python, Google cloud platform and machine learning. 🏆 Best Web Monetized Digital Artist —presented in partnership with GRANT FOR THE WEB A huge thanks to for this award's prize, which includes: Grant for the Web A $1000 award for the eventual winner (!!!) The winner will also receive a 1 year registration for a .tech domain. And the winner is... (355 votes) 🥇 Kassjan Smyczek An amazing artist on Coil and Cinnamon Video Runner-ups: 🥈 Connie Digital (133 votes) Connie Digital has produced a wide variety of monetized content, including digital art, music, virtual reality installations, as well as a unique non-fungible crypto token. Over a dozen media outlets have featured his innovative use of digital media and his presence in the virtual space collective known as Metaverse, making him a premier example of what is possible in the new arts economy. 🥉 VESA Mixed media artist VESA innovated his own bodypainting based art form Artevo in 2008, which reached global impact via a Bollywood star project, which altered the foundation of India and Pakistan in 2012. 🏆 VISIT NOONIES.TECH TO VIEW ALL 221 OF HACKER NOON'S 2020 NOONIES AWARD WINNERS Including, but by no means limited to: Startup Blogger of the Year Trending Tech Product of the Year Product Manager of the Year Hacker Noon Contributor of the Year - TECH Hacker Noon Contributor of the Year - PRODUCT MANAGEMENT Hacker Noon Contributor of The Year: CYBERSECURITY NEW Hacker Noon Contributor To Watch Most Exciting Data Storage And Sharing Project Hacker Noon Contributor of the Year - ETHEREUM Hacker Noon Contributor of the Year - CRYPTO Programming Language You Love the Most Hacker Noon Contributor of the Year - RUBY ON RAILS The Most Controversial Writer of the Year Best Use of Tech for Good Mental Health Advocate of the Year Hacker Noon Contributor of the Year - PRIVACY Hacker Noon Contributor of the Year - FUTURE Hacker Noon Contributor of the Year - POLITICS Hacker Noon Contributor of the Year - COVID-19 Hacker Noon Contributor of the Year - ENTREPRENEURSHIP Independent Tech Journalist of the Year Hacker Noon Contributor of the Year - WRITING Slack, Telegram, and/or Discord Channel of the Year Hacker Noon Contributor of the Year - SOCIAL MEDIA Meme of The Pandemic AND . SO MANY MORE A Parting Note RE: Next Year's Noonies The 2021 Awards Nomination period is now less than one year away. Hacker Noon to to ensure you're in the running for one of 🚀 Start submitting your best tech stories the top tag pages on Hacker Noon TODAY these prestigious internet awards next year ! 🤖 / 👾 CREATE YOUR HACKER NOON PROFILE OPEN A NEW DRAFT

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A Complete Guide to IPLD: Its Significance, How it Works and Practical Examples This post is a continuation(part 2) in a new “ ” series which will help anybody to understand the underlying concepts of IPFS. If you want an overview of what is IPFS and how it works, then you should check out the first part too 😊 Understanding IPFS in Depth Understanding IPFS in Depth(1/6): A Beginner to Advanced Guide In our first part of the series we briefly discussed IPLD. We saw that IPLD deals with “defining the data” in IPFS(well, not just in IPFS). In this part, we will dive fully into IPLD and discuss: : What is the philosophy behind it, Why do we need it and Where does it fit into IPFS? The Significance of IPLD : An Explanation of its specification and how does it coordinate with other components of IPFS? How does IPLD Work? : Well, It’s always fun to play 😊 Playing with IPLD You can follow me on twitter @vasa_develop If you like high-tech Web3 concepts like IPLD explained in simple words with interactive tutorials, then . Hope you learn a lot about IPFS from this series. Let’s start! head here The Significance of IPLD IPLD is not just a part of the IPFS project, but a . To understand its significance in the decentralized world we have to understand the concept of . separate project in itself Linked Data: The Semantic Web What is Linked Data & Why do we Need it? The Semantic Web or Linked Data is a term coined by Sir Tim Berners Lee in a seminal article published in Scientific American in 2001. Berners Lee articulated a vision of a World Wide Web of data that machines could process independently of humans, enabling a host of new services transforming our everyday lives. While the paper’s vision of most web pages containing structured data that could be analyzed and acted upon by software agents , the Semantic Web has emerged as a platform of increasing importance for data interchange and integration through the growing community implementing data sharing using international , called . has NOT materialized semantic web standards Linked Data There are many current examples of the use of semantic web technologies and Linked Data to share valuable structured information in a flexible and extensible manner across the web. Semantic Web technologies are used extensively in the life sciences to facilitate drug discovery by finding paths across multiple datasets showing via genes linked to each. associations between drugs and side effects The New York Times has published its vocabulary of approximately 10,000 subject headings developed over 150 years as Linked Data and will expand coverage to approximately 30,000 topic tags; they encourage the development of services consuming these vocabularies and linking them with other online resources. The to make content more findable by search engines and more linkable through social media; to add additional context from supplemental resources in domains like music or sports, and to propagate linkages and editorial annotations beyond their original entry target to bring relevant information forward in additional contexts. British Broadcasting Corporation uses Linked Data The home page of the United States site states, “As the web of linked documents evolves to include the Web of linked data, we’re working to maximize the potential of Semantic Web technologies to realize the promise of Linked Open Government Data.” And also all the social media websites use Linked Data to create a web of People to make their platform as engaging as possible. data.gov So we do have some Linked Data but we still have a long way to go in order to harness the true power of Linked Data. Now imagine if you could refer your latest commits in a git branch to a bitcoin transaction to timestamp your work. So, by linking your git commit, you can view the commit from your blockchain explorer. Or, if you could link your Ethereum contract to media on IPFS, perhaps modifying it and tracking its changes on each function execution. All this is possible using IPLD. IPLD is the data model of the content-addressable web(as discussed in ). part1 It allows us to treat all hash-linked data structures as subsets of a unified information space, unifying all data models that link data with hashes as instances of IPLD. Or in other words, IPLD is a set of standards and implementations for creating decentralized data-structures that are universally addressable and linkable. These structures allow us to do for data what URLs and links did for HTML web pages. Content addressing through hashes has become a widely-used means of connecting data in distributed systems, from the blockchains that run your favorite cryptocurrencies, to the commits that back your code, to the web’s content at large. Yet, whilst all of these tools rely on some common primitives, their specific underlying data structures are (as right now I can’t connect my git commit to a blockchain transaction). NOT interoperable IPLD is a single namespace for . Through IPLD, . all hash-inspired protocols links can be traversed across protocols, allowing you to explore data regardless of the underlying protocol How does IPLD Work? Before diving deeper into IPLD, Let’s see the properties of IPLD. Properties of IPLD The sky’s the limit as IPLD allows you to work across protocol boundaries. The point is that IPLD provides that make the underlying data interoperable across tools and across protocols by default. libraries A canonical data model A that uniquely identifies any hash-based data structure and ensures the same logical object always maps to the exact same sequence of bits. self-contained descriptive model Protocol independent resolution IPLD brings isolated systems together(like connecting Bitcoin, Ethereum and git), making integration with existing protocols simple. Upgradeable With Multiformats(we will dive more into this in part 4) support, IPLD is easily upgradeable and will grow with your favorite protocols. Operates across formats Express your IPLD objects in various serializable formats like JSON, CBOR, YAML, XML and many more, making IPLD a cinch to use with any framework. Backward compatible Non-intrusive resolvers make IPLD easy to integrate within your existing work. A namespace for all protocols IPLD allows you to explore data across protocols seamlessly, binding hash-based data structures together through a common namespace. Now, let’s dive deeper into the IPLD. Diving Deeper into IPLD Specs IPLD is not a single specification, it is a set of specifications. (IPLD Stack) The goal of this stack is to enable decentralized data-structures which in turn will enable more decentralized applications. Many of the specifications in this stack are inter-dependent. (Dependency Graph) These diagrams show a high level of project specification of IPLD. Even if you don’t understand it fully it’s totally OK. To learn more about IPLD, here is a by great talk Juan Benet . OK. Enough of theoretical stuff. Let’s dive into the most fun part of this post 😊 Playing with IPLD In IPFS, IPLD helps to structure and link all the data chunks/objects. So, as we saw in , IPLD was responsible for organizing all the data chunks that constituted the image of the kitty🐱. part 1 In this part, we will create a medium.com like publication system, and link the tags, articles, and authors using IPLD. This will help you to get a more intuitive understanding of IPLD. You can also find the complete tutorial on . Github Let’s get started! Before creating the publication system, we’ll be exploring the IPFS DAG API, which lets us store data objects in IPFS in IPLD format. (You can store more exciting things in IPFS, like your favorite cat GIF, but we will stick to simpler things for now.) If you are not familiar with Merkle tries and DAGs then head over If you have an understanding of what these terms mean then continue… here . Create a folder name . Run and press for all the questions. ipld-blogs npm init Enter Now install the dependencies using: npm install ipfs-http-client cids --save After installing the module your project structure will look like this: Creating an IPLD format node You can create a new node by passing a data object into the method, which returns a Content Identifier (CID) for the newly created node. ipfs.dag.put ipfs.dag.put({name: ‘vasa’}) A CID is an address for a block of data in IPFS that is derived from its content. Every time someone puts the same data into IPFS, they'll get back an identical CID to the one you got. If they put in instead, the CID will be different. {name: 'vasa'} {name: 'vAsa'} Paste this code in and run tut.js node tut.js ipfsClient = ( ); CID = ( ); ipfs = ipfsClient({ : , : , : }); ipfs.dag.put({ : }, { : , : }, (err, cid)=>{ (err){ .log( , err);
  } multihash = cid.multihash; cids = CID( , , multihash); .log(cids.toBaseEncodedString()); }); //Initiate ipfs and CID instance const require 'ipfs-http-client' const require 'cids' //Connecting ipfs instance to infura node. You can also use your local node. const new host 'ipfs.infura.io' port '5001' protocol 'https' /*
Creating an IPLD format node:
ipfs.dag.put(dagNode, [options], [callback])

For more information see: 
https://github.com/ipfs/interface-js-ipfs-core/blob/master/SPEC/DAG.md#ipfsdagputdagnode-options-callback
*/ name "vasa" format 'dag-cbor' hashAlg 'sha2-256' if console "ERR\n" //featching multihash buffer from cid object. const //passing multihash buffer to CID object to convert multihash to a readable format const new 1 'dag-cbor' //Printing out the cid in a readable format console //zdpuAujL3noEMamveLPQWJPY6CYZHhHoskYQaZBvRbAfVwR8S You will get this CID . We have successfully created an IPLD format node. zdpuAujL3noEMamveLPQWJPY6CYZHhHoskYQaZBvRbAfVwR8S Connecting IPLD objects One important feature of Directed Acyclic Graphs (DAGs) is the ability to link them together. The way you express links in the DAG store is with the of another node. ipfs CID For example, if we wanted one node to have a link called “foo” pointed to another CID instance previously saved as , it might look like: barCid {
  foo: barCid
} When we give a field a name and make its value a link to a CID, we call this a named link. Below is the code showing how to create a named link. ipfsClient = ( ); CID = ( ); ipfs = ipfsClient({ : , : , : }); { vasa = ipfs.dag.put({ : }); secondNode = ipfs.dag.put({ : vasa});
}

linkNodes(); //Initiate ipfs and CID instance const require 'ipfs-http-client' const require 'cids' //Connecting ipfs instance to infura node. You can also use your local node. const new host 'ipfs.infura.io' port '5001' protocol 'https' /*
Creating an IPLD format node:
ipfs.dag.put(dagNode, [options], [callback])
For more information see: 
https://github.com/ipfs/interface-js-ipfs-core/blob/master/SPEC/DAG.md#ipfsdagputdagnode-options-callback
*/ async ( ) function linkNodes let await name 'vasa' //Linking secondNode to vasa using named link. let await linkToVasa Reading Nested data using Links You can read data from deeply nested objects using path queries. allows queries using IPFS paths. These queries return an object containing the value of the query and any remaining path that was unresolved. ipfs.dag.get The cool thing about this API is that it can also traverse through links. Here is an example of how you can read nested data using links. ipfsClient = ( ); CID = ( ); ipfs = ipfsClient({ : , : , : }); { (err) { .error( + err)
    } { .log(result)
    }
} { vasa = ipfs.dag.put({ : }); secondNode = ipfs.dag.put({ : { : { : vasa
            }
        }
    }); multihash = secondNode.multihash; cids = CID( , , multihash); ipfs.dag.get(cids.toBaseEncodedString()+ , errOrLog); }
createAndFeatchNodes(); //Initiate ipfs and CID instance const require 'ipfs-http-client' const require 'cids' //Connecting ipfs instance to infura node. You can also use your local node. const new host 'ipfs.infura.io' port '5001' protocol 'https' ( ) function errOrLog err, result if console 'error: ' else console async ( ) function createAndFeatchNodes let await name 'vasa' //Linking secondNode to vasa using named link. let await publication authors authorName //featching multihash buffer from cid object. const //passing multihash buffer to CID object to convert multihash to a readable format const new 1 'dag-cbor' //Featching the value using links '/publication/authors/authorName/name' /* prints { value: 'vasa', remainderPath: '' } */ You can also explore your IPLD nodes using this cool IPLD explorer. Like, if I want to see this CID: , I will go to this link: zdpuAujL3noEMamveLPQWJPY6CYZHhHoskYQaZBvRbAfVwR8S Now, as we have explored IPFS DAG API we are ready to work with IPLD and create our publication system. ttps://explore.ipld.io/#/explore/zdpuAujL3noEMamveLPQWJPY6CYZHhHoskYQaZBvRbAfVwR8S Creating a Publication System We will create a simple blogging application. This blogging application can: Add a new Author IPLD object. An author will have 2 fields: name and profile(a tag line for your profile). Create a new Post IPLD object. A post will have 4 fields: author, content, tags and publication date-time. Read a Post using post CID. Below is the code implementation for the above goals. ipfsClient = ( ); CID = ( ); ipfs = ipfsClient({ : , : , : }); { newAuthor = ipfs.dag.put({ : name, : }); .log( +name+ + newAuthor); newAuthor;
} { post = ipfs.dag.put({ : author, : content, : tags, : ()
    }); .log( + author + + post); post;
} {
    ipfs.dag.get(postCID + , (err, post) => { (err) { .error( + err)
        } { .log( , post);
            ipfs.dag.get(postCID + , (err, author) => { (err) { .error( + err)
                } { .log( , author);
                }
              });
        }
    });
} {
    addNewAuthor( ).then( {
        createBlog(newAuthor, , [ , , , ]).then( readBlog(postCID))
      });
}

startPublication(); /*
PUBLICATION SYSTEM
Adding new Author
An author will have
-> name
-> profile
Creating A Blog
A Blog will have a:
-> author
-> content
-> tags
-> timeOfPublish
List all Blogs for an author
Read a Blog
*/ //Initiate ipfs and CID instance const require 'ipfs-http-client' const require 'cids' //Connecting ipfs instance to infura node. You can also use your local node. const new host 'ipfs.infura.io' port '5001' protocol 'https' //Create an Author async ( ) function addNewAuthor name //creating blog author object var await name profile "Entrepreneur | Co-founder/Developer @TowardsBlockChain, an MIT CIC incubated startup | Speaker | https://vaibhavsaini.com" console "Added new Author " ": " return //Creating a Blog async ( ) function createBlog author, content, tags //creating blog object var await author content tags timeOfPublish Date console "Published a new Post by " ": " return //Read a blog async ( ) function readBlog postCID "" if console 'Error while reading post: ' else console "Post Details\n" "/author" if console 'Error while reading post author: ' else console "Post Author Details\n" ( ) function startPublication "vasa" ( ) => newAuthor "my first post" "ipfs" "ipld" "vasa" "towardsblockchain" ( ) => postCID On running this code and it will first create an author via , which will return the authors CID. Then this CID will be passed to function which will return the . This will be used by function to fetch the post details. addNewAuthor createBlog postCID postCID readBlog You can create more complex applications using IPLD… Ok. that’s it for this part. If you have any question, then you can shoot them in the comments. I hope you have learned a lot of thing from this post. In the next post, we will dive into the naming System of the distributed web, IPNS. So stay tuned… Thanks to for the excellent explanation on DAGs. proto.school Thanks for reading ;) Learned something? Share this article and help others to find it. Hold down the clap button if you liked the content! It helps me gain exposure. About the Author is a Co-Founder of Vaibhav Saini TowardsBlockchain , an MIT Cambridge Innovation Center incubated startup. He works as Senior blockchain developer and has worked on several blockchain platforms including Ethereum, Quorum, EOS, Nano, Hashgraph, IOTA etc. He is a Speaker, Writer and a drop-out from . IIT Delhi Want to learn more? Check out my previous articles. ConsensusPedia: An Encyclopedia of 30+ Consensus Algorithms Getting Deep Into Ethereum: How Data Is Stored In Ethereum? ContractPedia: An Encyclopedia of 40+ Smart Contract Platforms A Beginner’s Ultimate Guide To DAGs Follow me on Twitter: @vasa_develop

Understanding IPFS in Depth(2/6): What is InterPlanetary Linked Data(IPLD)?

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