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Beacon, a leading web3 accelerator founded by Polygon co-founder Sandeep Nailwal, has unveiled its fourth and largest cohort to date. The program has selected 17 promising startups for its latest three-month accelerator, marking a significant expansion from previous cohorts. The new group includes some of the most high-profile companies in Beacon's history, with several already backed by prominent venture capital firms such as Andreessen Horowitz (a16z), Founders Fund, Pantera Capital, and Polychain Capital. The startups span various sectors within the web3 space, including decentralized finance (DeFi), infrastructure, and artificial intelligence (AI). “We're thrilled to welcome our largest and most diverse cohort so far,” said Sandeep Nailwal, co-founder of Polygon and Beacon. “The caliber of companies joining us is truly exceptional, and we're excited to see how, with the aid of our mentorship and resources, they will shape the future of web3 and beyond.” Notable Participants and Their Innovations Among the standout companies in the cohort are: Stakestone: An omni-chain liquid staking token protocol enhancing staking yields and liquidity on Layer 2s.


EXO: Enables users to run their own AI clusters at home using everyday devices.


Nubit: A scalable, cost-efficient data availability layer secured by Bitcoin, empowering applications like Ordinals and Layer 2s.


Aligned Layer: A decentralized network that verifies zero-knowledge proofs and posts the results to Ethereum.


eoracle: A programmable data layer that connects smart contracts with real-world data by extending Ethereum's Proof of Stake. Building on Past Successes The announcement comes after the graduation of Beacon's successful S23 cohort, which comprised 10 companies. Alumni such as scalable data availability layer 0G and verifiable oracle protocol ORA have achieved unicorn status since completing the program. ORA secured $20 million in funding led by Polychain Capital, while Inco, a universal confidentiality layer, closed a $4.5 million seed round led by 1kx. “With the level of talent and innovation we're witnessing in this new group, we are entirely confident that they can emulate, if not surpass, their predecessors,” Nailwal added. The Full Cohort Lineup The complete list of companies in Beacon's S24 cohort includes: Avantis: Provides infrastructure for traders to access up to 100x leverage on major cryptocurrencies, forex, and commodities.


Compute Labs: Utilizes a Compute Tokenization Protocol to financialize AI and create compute derivatives.


Crynux: Founded by former Google engineers, building a permissionless and trustless protocol for decentralized AI.


EZKL: Offers a developer-friendly system for verifiable AI and analytics, powering various decentralized systems.


Lorenzo: A Bitcoin liquidity finance layer designed to meet global demand for Bitcoin liquidity across Layer 2s and DeFi platforms.


Nodekit: Developing Javelin, a solution aimed at restoring composability to rollups.


Othentic: Provides infrastructure for building distributed services, simplifying development on EigenLayer.


PADO: A decentralized zkAttestation and computation network aiming to unlock internet data for Web3.


Pallet: A hub for digital culture, community, and ownership built on the Sei network.


Pin AI: An open platform for personal AI, leveraging open-source technology and crypto for data ownership and privacy.


TAC: TON App Chain, an interoperability layer for the TON blockchain and EVM-based applications.


Warlock: Creating new primitives to disrupt and disintermediate the MEV (Miner Extractable Value) landscape. Beacon's decision to expand its cohort size may be a strategic move to capitalize on the growing momentum in the web3 sector. By supporting a larger and more diverse group of startups, the accelerator is positioning itself as a key player in shaping the future of decentralized technologies. The inclusion of companies backed by major venture capital firms also adds credibility to the program and could attract more high-quality applicants in the future. This move could foster a more collaborative environment where startups benefit not only from Beacon's resources but also from each other's experiences and networks. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Beacon , a leading web3 accelerator founded by Polygon co-founder Sandeep Nailwal, has unveiled its fourth and largest cohort to date. The program has selected 17 promising startups for its latest three-month accelerator, marking a significant expansion from previous cohorts. Beacon The new group includes some of the most high-profile companies in Beacon's history, with several already backed by prominent venture capital firms such as Andreessen Horowitz (a16z), Founders Fund, Pantera Capital, and Polychain Capital. The startups span various sectors within the web3 space, including decentralized finance (DeFi), infrastructure, and artificial intelligence (AI). “We're thrilled to welcome our largest and most diverse cohort so far,” said Sandeep Nailwal, co-founder of Polygon and Beacon. “The caliber of companies joining us is truly exceptional, and we're excited to see how, with the aid of our mentorship and resources, they will shape the future of web3 and beyond.” Notable Participants and Their Innovations Notable Participants and Their Innovations Among the standout companies in the cohort are: Stakestone: An omni-chain liquid staking token protocol enhancing staking yields and liquidity on Layer 2s. EXO: Enables users to run their own AI clusters at home using everyday devices. Nubit: A scalable, cost-efficient data availability layer secured by Bitcoin, empowering applications like Ordinals and Layer 2s. Aligned Layer: A decentralized network that verifies zero-knowledge proofs and posts the results to Ethereum. eoracle: A programmable data layer that connects smart contracts with real-world data by extending Ethereum's Proof of Stake. Stakestone: An omni-chain liquid staking token protocol enhancing staking yields and liquidity on Layer 2s. Stakestone : An omni-chain liquid staking token protocol enhancing staking yields and liquidity on Layer 2s. Stakestone EXO: Enables users to run their own AI clusters at home using everyday devices. EXO : Enables users to run their own AI clusters at home using everyday devices. EXO Nubit: A scalable, cost-efficient data availability layer secured by Bitcoin, empowering applications like Ordinals and Layer 2s. Nubit : A scalable, cost-efficient data availability layer secured by Bitcoin, empowering applications like Ordinals and Layer 2s. Nubit Aligned Layer: A decentralized network that verifies zero-knowledge proofs and posts the results to Ethereum. Aligned Layer : A decentralized network that verifies zero-knowledge proofs and posts the results to Ethereum. Aligned Layer eoracle: A programmable data layer that connects smart contracts with real-world data by extending Ethereum's Proof of Stake. eoracle : A programmable data layer that connects smart contracts with real-world data by extending Ethereum's Proof of Stake. eoracle Building on Past Successes Building on Past Successes The announcement comes after the graduation of Beacon's successful S23 cohort, which comprised 10 companies. Alumni such as scalable data availability layer 0G and verifiable oracle protocol ORA have achieved unicorn status since completing the program. ORA secured $20 million in funding led by Polychain Capital, while Inco, a universal confidentiality layer, closed a $4.5 million seed round led by 1kx. “With the level of talent and innovation we're witnessing in this new group, we are entirely confident that they can emulate, if not surpass, their predecessors,” Nailwal added. The Full Cohort Lineup The Full Cohort Lineup The complete list of companies in Beacon's S24 cohort includes: Avantis: Provides infrastructure for traders to access up to 100x leverage on major cryptocurrencies, forex, and commodities. Compute Labs: Utilizes a Compute Tokenization Protocol to financialize AI and create compute derivatives. Crynux: Founded by former Google engineers, building a permissionless and trustless protocol for decentralized AI. EZKL: Offers a developer-friendly system for verifiable AI and analytics, powering various decentralized systems. Lorenzo: A Bitcoin liquidity finance layer designed to meet global demand for Bitcoin liquidity across Layer 2s and DeFi platforms. Nodekit: Developing Javelin, a solution aimed at restoring composability to rollups. Othentic: Provides infrastructure for building distributed services, simplifying development on EigenLayer. PADO: A decentralized zkAttestation and computation network aiming to unlock internet data for Web3. Pallet: A hub for digital culture, community, and ownership built on the Sei network. Pin AI: An open platform for personal AI, leveraging open-source technology and crypto for data ownership and privacy. TAC: TON App Chain, an interoperability layer for the TON blockchain and EVM-based applications. Warlock: Creating new primitives to disrupt and disintermediate the MEV (Miner Extractable Value) landscape. Avantis: Provides infrastructure for traders to access up to 100x leverage on major cryptocurrencies, forex, and commodities. Avantis : Provides infrastructure for traders to access up to 100x leverage on major cryptocurrencies, forex, and commodities. Avantis Compute Labs: Utilizes a Compute Tokenization Protocol to financialize AI and create compute derivatives. Compute Labs : Utilizes a Compute Tokenization Protocol to financialize AI and create compute derivatives. Compute Labs Crynux: Founded by former Google engineers, building a permissionless and trustless protocol for decentralized AI. Crynux : Founded by former Google engineers, building a permissionless and trustless protocol for decentralized AI. Crynux EZKL: Offers a developer-friendly system for verifiable AI and analytics, powering various decentralized systems. EZKL : Offers a developer-friendly system for verifiable AI and analytics, powering various decentralized systems. EZKL Lorenzo: A Bitcoin liquidity finance layer designed to meet global demand for Bitcoin liquidity across Layer 2s and DeFi platforms. Lorenzo : A Bitcoin liquidity finance layer designed to meet global demand for Bitcoin liquidity across Layer 2s and DeFi platforms. Lorenzo Nodekit: Developing Javelin, a solution aimed at restoring composability to rollups. Nodekit : Developing Javelin, a solution aimed at restoring composability to rollups. Nodekit Othentic: Provides infrastructure for building distributed services, simplifying development on EigenLayer. Othentic : Provides infrastructure for building distributed services, simplifying development on EigenLayer. Othentic PADO: A decentralized zkAttestation and computation network aiming to unlock internet data for Web3. PADO : A decentralized zkAttestation and computation network aiming to unlock internet data for Web3. PADO Pallet: A hub for digital culture, community, and ownership built on the Sei network. Pallet : A hub for digital culture, community, and ownership built on the Sei network. Pallet Pin AI: An open platform for personal AI, leveraging open-source technology and crypto for data ownership and privacy. Pin AI : An open platform for personal AI, leveraging open-source technology and crypto for data ownership and privacy. Pin AI TAC: TON App Chain, an interoperability layer for the TON blockchain and EVM-based applications. TAC : TON App Chain, an interoperability layer for the TON blockchain and EVM-based applications. TAC Warlock: Creating new primitives to disrupt and disintermediate the MEV (Miner Extractable Value) landscape. Warlock : Creating new primitives to disrupt and disintermediate the MEV (Miner Extractable Value) landscape. Warlock Beacon's decision to expand its cohort size may be a strategic move to capitalize on the growing momentum in the web3 sector. By supporting a larger and more diverse group of startups, the accelerator is positioning itself as a key player in shaping the future of decentralized technologies. The inclusion of companies backed by major venture capital firms also adds credibility to the program and could attract more high-quality applicants in the future. This move could foster a more collaborative environment where startups benefit not only from Beacon's resources but also from each other's experiences and networks. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Beacon's Largest Cohort Signals Growing Momentum in Web3 Innovation

Η μεγαλύτερη κοόρτη της Beacon σηματοδοτεί αυξανόμενη ορμή στην καινοτομία Web3

La cohorte más grande de Beacon indica un impulso creciente en la innovación de la Web3

Kelompok Terbesar Beacon Menandakan Meningkatnya Momentum Inovasi Web3

ビーコンの最大規模のコホートがWeb3イノベーションの勢いの高まりを示唆

សញ្ញានៃក្រុមដ៏ធំបំផុតរបស់ Beacon ដែលកំពុងកើនឡើងនៅក្នុងការច្នៃប្រឌិត Web3

बीकनको सबैभन्दा ठूलो कोहोर्ट संकेतहरू Web3 नवाचारमा बढ्दो गति

Cea mai mare cohortă a lui Beacon semnalează creșterea impulsului în inovarea Web3

Крупнейшая когорта Beacon сигнализирует о растущем импульсе инноваций Web3

Беацон-ова највећа кохорта сигнали растући замах у Веб3 иновацијама

Web3 இன்னோவேஷனில் பெக்கனின் மிகப்பெரிய கோஹார்ட் சிக்னல்கள் வளரும் வேகம்

“Beacon” -yň “Web3” innowasiýasynda ösýän pursatdygyny görkezýär

Mayoqning eng katta kohort signallari Web3 innovatsiyasida o'sib borayotgan momentum

PricewaterhouseCoopers(PwC), one of the world’s leading professional services firms, recently launched its first-ever RWA-focused initiative called Scale Tokenization And Digital Asset Program, joining the likes of BlackRock, Citibank, and JP Morgan Chase in shaping the digital asset industry. PwC’s recent foray into the RWA tokenization space highlights the fact institutional interest in the tokenization of real world assets is increasingly building up.  In March 2024 BlackRock, the world's largest asset manager, announced the launch of its first-ever on-chain treasuries product, amassing $280 million in deposits. As these giant companies begin to converge around RWA tokenization, the race to the very top of the industry may have just begun. Entering the race, PwC recently selected 11 scale-ups from a pool of over 700 applications “ to help solve important problems for the Financial Services industry’.’ Of these selected companies, only three were core RWA protocols, namely, Zoniqx, Brickken, and Trust Stamp. Commenting on the selection, Edwin Mata Navarro, CEO of Brickken, stated: "Joining the PwC’s Scale program marks a significant milestone in our journey. It not only validates our technological prowess and market impact but also sets the stage for our next phase of institutional expansion. We are excited to leverage this opportunity to enhance our offerings and forge meaningful partnerships through PwC and Growth Builders’ network." What You Should Know About The Scale Tokenization Program Growing Institutional Interest In addition to being a tightly contested race, the program highlights the growing institutional interest in the tokenization of real world assets as well as the increasing pressure faced by major financial institutions to catch up with the latest developments in the evolving digital landscape. This shift to RWA tokenization has increasingly become expedient as it introduces a revolutionary approach to the management and ownership of traditionally illiquid assets. Eleven Handpicked Scaleups It may interest the reader to know that only 11 startups were selected out of over 700 companies that applied for the maiden edition of the Scale Tokenization And Digital Asset Cohort. This tiny number represents the lucky few who were handpicked by the company. While the criteria for selection may be difficult to assess, it leaves a strong impression that the lucky few who made it to the final list have made an indelible mark on the digital asset industry. Partnerships With Leading Firms The program is designed to foster collaboration between established financial institutions and innovative digital asset startups that specialize in real-world asset tokenization, stemming from the growing recognition of RWA tokenization in the financial service industry. Most importantly, it is tailored toward the needs of the needs of its global network of financial services clients. Three Core RWA Protocols Only three RWA tokenization protocols were selected, namely, Zoniqx, Brickken, and Trust Stamp. The other eight participants include Alterscop, Archipels, Custodiex, Hedgeguard, Truvity, Vidos, Venly, and Vertalo. The selection of these eleven startups underscores their established presence within the digital asset industry. RWA Products Pitch The program offers the selected cohort an opportunity to pitch to industry leaders from PwC’s global network of Financial Services clients. A Tokenized Future: Myth or Unfolding Reality? As big major players such as PwC, BlackRock, and CitiBank are increasingly bullish on RWA tokenization, it is easy to infer that the world is fast heading toward a tokenized future, one where tangible or traditionally illiquid assets are offered in new markets to millions of potential buyers and sellers. The fractionalization of digital assets and the seamless convergence of buyers and sellers will also create a secondary liquidity market for assets that no one ever thought could one day become liquid and investable, highlighting the immense potential of blockchain technology and its applications in the financial sector. Although the promises of a tokenized future are immense, there are key challenges that need to be adequately addressed. Regulatory compliance, blockchain security, and investor education are some of the major challenges that must be carefully addressed at the industry level. This underscores the need for RWA companies to collaborate with legal experts and innovators to build cutting-edge technology to ensure a seamless experience. It can be inferred that the PwC’s cohort represents the company’s keen eye on future trends, participating in it offers an opportunity for the selected RWA startups to connect with some of the world’s leading innovators that can help them improve their solutions. Additionally, it not only underscores the role that leading industry leaders can play in accelerating meaningful change in society, but it also highlights the fact that the tokenization of real-world assets (RWA) is the new investment frontier for everyone across the world–including the most price-sensitive investors. Lastly, it shows that there is an increasing need for sustained collaboration between established financial institutions and RWA projects in tackling the challenges facing the industry and in building a world-class digital asset industry that is built on diversity, security, transparency, and cost-effectiveness. Conclusion Building a tokenized future where tons of traditionally illiquid assets are offered to millions of potential buyers and sellers boils down to leveraging blockchain technology to create new regulated markets and products. The journey towards this future is well underway, and it clearly underscores the role that major financial institutions in the world can play in bringing it to fruition. This interplay can be described as a work in progress, which requires a sustained partnership and collaboration between these various established institutions and RWA projects that are making the tokenization of real-world assets and providing the infrastructure that is required to support them. PricewaterhouseCoopers(PwC), one of the world’s leading professional services firms, recently launched its first-ever RWA-focused initiative called Scale Tokenization And Digital Asset Program , joining the likes of BlackRock, Citibank, and JP Morgan Chase in shaping the digital asset industry. Scale Tokenization And Digital Asset Program Scale Tokenization And Digital Asset BlackRock, Citibank, and JP Morgan Chase PwC’s recent foray into the RWA tokenization space highlights the fact institutional interest in the tokenization of real world assets is increasingly building up.  In March 2024 BlackRock, the world's largest asset manager, announced the launch of its first-ever on-chain treasuries product, amassing $280 million in deposits. As these giant companies begin to converge around RWA tokenization, the race to the very top of the industry may have just begun. Entering the race, PwC recently selected 11 scale-ups from a pool of over 700 applications “ to help solve important problems for the Financial Services industry’.’ Of these selected companies, only three were core RWA protocols, namely, Zoniqx, Brickken, and Trust Stamp. Zoniqx, Brickken, and Trust Stamp. Commenting on the selection, Edwin Mata Navarro, CEO of Brickken, stated: "Joining the PwC’s Scale program marks a significant milestone in our journey. It not only validates our technological prowess and market impact but also sets the stage for our next phase of institutional expansion. We are excited to leverage this opportunity to enhance our offerings and forge meaningful partnerships through PwC and Growth Builders’ network." "Joining the PwC’s Scale program marks a significant milestone in our journey. It not only validates our technological prowess and market impact but also sets the stage for our next phase of institutional expansion. We are excited to leverage this opportunity to enhance our offerings and forge meaningful partnerships through PwC and Growth Builders’ network." What You Should Know About The Scale Tokenization Program Growing Institutional Interest In addition to being a tightly contested race, the program highlights the growing institutional interest in the tokenization of real world assets as well as the increasing pressure faced by major financial institutions to catch up with the latest developments in the evolving digital landscape. This shift to RWA tokenization has increasingly become expedient as it introduces a revolutionary approach to the management and ownership of traditionally illiquid assets. Eleven Handpicked Scaleups It may interest the reader to know that only 11 startups were selected out of over 700 companies that applied for the maiden edition of the Scale Tokenization And Digital Asset Cohort. This tiny number represents the lucky few who were handpicked by the company. While the criteria for selection may be difficult to assess, it leaves a strong impression that the lucky few who made it to the final list have made an indelible mark on the digital asset industry. Partnerships With Leading Firms The program is designed to foster collaboration between established financial institutions and innovative digital asset startups that specialize in real-world asset tokenization, stemming from the growing recognition of RWA tokenization in the financial service industry. Most importantly, it is tailored toward the needs of the needs of its global network of financial services clients. Three Core RWA Protocols Only three RWA tokenization protocols were selected, namely, Zoniqx, Brickken, and Trust Stamp. The other eight participants include Alterscop, Archipels, Custodiex, Hedgeguard, Truvity, Vidos, Venly, and Vertalo. The selection of these eleven startups underscores their established presence within the digital asset industry. Zoniqx, Brickken, and Trust Stamp. Alterscop, Archipels, Custodiex, Hedgeguard, Truvity, Vidos, Venly, and Vertalo. RWA Products Pitch The program offers the selected cohort an opportunity to pitch to industry leaders from PwC’s global network of Financial Services clients. A Tokenized Future: Myth or Unfolding Reality? As big major players such as PwC, BlackRock, and CitiBank are increasingly bullish on RWA tokenization, it is easy to infer that the world is fast heading toward a tokenized future, one where tangible or traditionally illiquid assets are offered in new markets to millions of potential buyers and sellers. The fractionalization of digital assets and the seamless convergence of buyers and sellers will also create a secondary liquidity market for assets that no one ever thought could one day become liquid and investable, highlighting the immense potential of blockchain technology and its applications in the financial sector. Although the promises of a tokenized future are immense, there are key challenges that need to be adequately addressed. Regulatory compliance, blockchain security, and investor education are some of the major challenges that must be carefully addressed at the industry level. This underscores the need for RWA companies to collaborate with legal experts and innovators to build cutting-edge technology to ensure a seamless experience. It can be inferred that the PwC’s cohort represents the company’s keen eye on future trends, participating in it offers an opportunity for the selected RWA startups to connect with some of the world’s leading innovators that can help them improve their solutions. Additionally, it not only underscores the role that leading industry leaders can play in accelerating meaningful change in society, but it also highlights the fact that the tokenization of real-world assets (RWA) is the new investment frontier for everyone across the world–including the most price-sensitive investors. Lastly, it shows that there is an increasing need for sustained collaboration between established financial institutions and RWA projects in tackling the challenges facing the industry and in building a world-class digital asset industry that is built on diversity, security, transparency, and cost-effectiveness. Conclusion Building a tokenized future where tons of traditionally illiquid assets are offered to millions of potential buyers and sellers boils down to leveraging blockchain technology to create new regulated markets and products. The journey towards this future is well underway, and it clearly underscores the role that major financial institutions in the world can play in bringing it to fruition. This interplay can be described as a work in progress, which requires a sustained partnership and collaboration between these various established institutions and RWA projects that are making the tokenization of real-world assets and providing the infrastructure that is required to support them.

How PricewaterhouseCoopers' RWA Tokenization Cohort Offers a Glimpse Into The Tokenized Future

On March 14th, 2023, the EVM-compatible launched to mainnet. Leading up to launch, since February 2023, 60+ project teams were building with FVM on its Hyperspace testnet. These teams are part of the FVM Mainnet Pioneers cohort, which was a 6-week program that provided technical, product, business mentorship, and resources to selected teams. Filecoin Virtual Machine (FVM) The FVM Mainnet Pioneers cohort aimed to support the creation of impactful and sustainable solutions, on FVM, as part of the growing network and saw strong interest from the Filecoin community and beyond. Teams with impactful ideas and skilled, credible teams were accepted. Protocol Labs The cohort graduated on April 25, 2023 and concluded with a showcase of  the founders’ storage-focused projects and a very successful pitch day that was open to the Filecoin community. The projects demonstrated the diversity of opportunities that FVM unlocks, including staking protocols, dataDAOs, decentralized computation frameworks and more. The first to launch were and , which have been steadily gaining users. Since then, 21 more FVM Mainnet Pioneer projects have launched on mainnet, with more to come once and security audits have been completed to ensure transparency and trust on the protocol. block explorer projects staking and leasing protocols contract verification Following the cohort’s graduation, 20+ teams have been accepted into accelerator programs, with FVM-focused cohorts run by , , . To learn more about each of these projects, check out the list below. GraphPaperCapital LongHash Outlier Ventures - Enabling zero-knowledge identity solutions on-chain Creds Protocol Creds Protocol is a decentralized identity and verification protocol that leverages zero-knowledge proofs to provide a privacy-preserving solution. The protocol allows users to prove their identity and other attributes without revealing any sensitive information to third parties. Creds Protocol can create verifiable credentials (cred) that represent the right to access content via FVM, which can be used for pay-per-view use cases and more. Based on Creds Protocol, Creds badge is a non-transferable token (SBT) that represents a verifiable claim. - Production-ready LLM inference APIs ShaleProtocol Shale-Serve API is a production-ready inference API for open LLMs (large language models). It currently supports Vicuna-13b. It can be used as a drop-in replacement for the OpenAI API (specifically GPT3.5-turbo) in any language, including Python, cURL, and HTTP requests. Furthermore, the API is integrated with LangChain. The protocol aims to build a network of interoperable domain-specific LLMs by supporting thousands of open LLMs by 2025. - FVM smart contract hub Cookbook.dev Cookbook is an open source smart contract registry and marketplace. It allows users to search, contribute, and integrate any Solidity smart contract into their app. Cookbook has built a showcasing projects within the FEVM ecosystem. From there, any developer can get familiar with - and start building off of - the protocols that power the Filecoin network. Filecoin Ecosystem Hub - One stop solution to fighting NFT scams. Resolutio Resolutio is a decentralized justice and social platform, built as a service with IPFS, NFT.storage and FVM and aims to overcome the high costs and inaccessibility of legal aid and legal tools to protect art or literary work. Resolutio has built a decentralized dispute resolution platform (DDRP). The DDRP deploys smart contracts for dispute resolution on FVM, uses IPFS for evidence management and IPFS-http-client and NFT.storage for decision NFTs that capture the outcome of the resolution. - New token standard for encrypted NFTs Filemarket Filemarket has developed a protocol that allows users to mint NFTs with attached encrypted files of any size stored on Filecoin, which can only be accessed exclusively by the owner of the NFT. This solution allows creators to monetize their content while protecting and securing their intellectual property. As part of their launch, they have now launched their of cool Filecoin-inspired NFT designs that hold encrypted ‘treasures’ in them and are offering limited free minting. Filebunnies collection - Issue tamper-proof, verifiable and perpetual certificates Trustified Trustified is a no-code platform designed to issue tamper-proof, universally verifiable, and perpetual certificates on blockchains. A document that cannot be verified and is not tamper-proof does not prove its authenticity. Through Trustified, these digital credentials are issued on a blockchain, stored on Filecoin and can remain secure and accessible for perpetuity. - Build fast, Fail fast and Ship fast your Web3 games LYNC LYNC is building GameFi infrastructure, making blockchain gaming accessible for everyone. Allowing game devs to build fast, ship fast, and fail faster without worrying about Web3 complexities. LYNC provides a scalable infrastructure for launching web3 games and fully , which is great for deploying NFT collections and dynamic NFTs in-game that are stored on Filecoin. supports FVM - G-suite productivity for DAOs Clover Clover is a decentralized collaboration tool for DAOs, with built-in integrations with FVM, IPFS, and Web3.storage. It aims to reduce latency in information dissemination and decision making for DAOs. It uses NFT-gating for authentication, IPFS for DAO storage, and is integrated with dRTC for DAO meetings. Huddle’s - Decentralized image-sharing social media platform MomentSwap MomentSwap is an FVM-based social media platform that makes sharing wonderful moments easy and profitable. The project uses the infrastructure of FVM and its EVM compatibility to implement, debug and call contracts. User data is stored on the Filecoin network, and uses The Graph for data analysis, achieving complete decentralization. With MomentSwap, you have the advantage of being in complete control of your assets with a seamless and decentralized experience. Additional revenue can also be achieved through sharing and influence in the decentralized web. - Non-custodial liquid leasing protocol Collectif DAO Collectif DAO optimizes FIL earning potential by leasing FIL as collateral into the network’s most sophisticated Storage Providers. Leveraging a deep understanding of the Filecoin economy and cutting-edge financial models, Collectif DAO gives FIL token holders unrivaled access to high fees. With the help of FVM capabilities, the platform enhances the distribution of FIL, fostering a resilient and balanced growth of the Filecoin ecosystem. - Liquid leasing with compounding rewards STFIL STFIL Protocol is one of the first Filecoin liquidity and leveraged farming protocols launched on FVM. It enables $FIL token holders to earn stable fees without a locking period and offers storage providers undercollateralized FIL for leveraged farming positions. As a pioneer on FVM, STFIL is dedicated to providing decentralized and trustless FIL leasing while ensuring higher profitability. The protocol is incubated by and has been featured in the Filecoin & IPFS Ecosystem Roundup ‘Wins of the Month.’ Outlier Ventures - Decentralized bond exchange Jackrabbit Finance Jackrabbit is a Decentralized Bond Exchange (AMM) on the Filecoin Virtual Machine. It utilizes decentralized bond tokens (ERC-3475) for Filecoin Storage Providers to raise funds.  This brings traditional finance’s lending and borrowing method to DeFi. Jackrabbit also supports native FVM tokens (swaps and liquidity pools) to enhance the Filecoin ecosystem. Also published . here

Filecoin Virtual Machine (FVM) Builder Cohort Launches to Mainnet

Filecoin Virtual Machine (FVM) Builder Cohort se lanza a Mainnet

Lancement de la cohorte de constructeurs de machines virtuelles Filecoin (FVM) sur le réseau principal

Filecoin वर्चुअल मशीन (FVM) बिल्डर कोहोर्ट ने मेननेट को लॉन्च किया

Filecoin 仮想マシン (FVM) ビルダー コホートがメインネットにローンチ

Grupo de criadores de máquinas virtuais Filecoin (FVM) é lançado na rede principal

Nhóm trình tạo máy ảo Filecoin (FVM) ra mắt Mainnet

Filecoin 虚拟机 (FVM) Builder Cohort 启动到主网

Despite the undeniable dependence on third-party cookies the current iteration of digital advertising technology has, Big Tech will soon abandon this model and advance to something else. Most modern web browsers either enable users to switch off 3rd-party tracking cookies or have that feature set by default. First, Mozilla’s Firefox implemented this approach, then Apple’s Safari, and this year – the biggest browser of all – Google Chrome. The latter is particularly significant as Chrome has up to 65% of the web browsing market as of January 2022, . according to W3Counter Google’s move was inspired by the global quest to improve digital privacy and stimulated by governmental authorities as well as public opinion. But before Google can completely give up on the effective and familiar technology, the company has to come up with a replacement. Google is trying to come up with a stable and privacy-secure solution that would not damage its ad targeting precision. Their first technology, called FLoC, promised to solve the controversy but failed to convince the community of its effectiveness. The second big proposition from Google named Topics API currently struggles for the same reason. Or is it? Will businesses adjust to it? Let’s find out. Was Google FLoC Really a Misstep? FLoC (Federated Learning of Cohorts), which is often called the “future of internet advertising”, is based on Privacy Sandbox. Despite the ‘federated learning’ part in the name of the technology, it actually utilize any federated learning technologies, which implies using machine learning algorithms. The main difference from cookie technology is the availability of nondescript anonymized cohorts based on the users’ common interests. did not The idea of marking groups based on interests was met with opposition in the web advertising industry. Google started the testing in March 2021 and then canceled it in July 2021. During the trial, Google received mostly negative feedback from the companies that participated, making it impossible to encourage the industry to use the innovation. It’s also illustrative how all other Chromium-based browsers dodged implementing FLoC functionality in their browsers while other tech companies like Amazon blocked it by default. The project was silently removed from Google’s roadmap without further explanation, and with no intention to share testing insights. Some of the known concerns associated with FLoC were tied to the possibility of identifying users through fingerprinting, the system’s over-complexity, and lack of transparency. But despite the technology failing to find more followers, its testing can be considered successful. Google gathered all the feedback they needed to develop a better solution. This is evidenced by the decision not to extend the test, although there were many companies still willing to contribute. Are Topics API Going to Save the Party? As the FLoC experiment ended, Google utilized the feedback from the community to integrate it with Topics API. Like its predecessor, the system was built on interest-based advertising, only this time, it would distinguish five main interest categories from browsing activity in recent weeks and not use any external data. The API in the name infers user categories on a website via a classifier model that that are publicly available with Chrome Developer Tools. maps hostnames to topics The algorithm does not analyze additional information like full URLs or website content to improve privacy and proposes mechanisms to opt-out of API for both users and websites. Upon visiting a website, Topics API will notify publishers of three categories of interest, one for each of the past weeks in random order. The advertising taxonomy is designed to include between a few hundred to a few thousand topics and currently contains around categories like “travel”, “cooking”, or “gaming”. 350 That sounds great on paper, but the reality is more prosaic. Practically the same issues that deterred Cohorts from gaining momentum are now building obstacles for Topics. Thus, the ethical dilemma remains: FLoC technology was meant to share personal data to a wide range of publishers with no direct approval from users, while Topics API features several security ‘upgrades’ that only minimize the probability of tracking but do not eliminate it entirely. Despite this fact, Topics’ targeting capabilities still remain poor, which is largely acceptable for bigger brands with broad audiences of the likes of Coca-Cola but fatal for small and medium advertisers. The categories-based solution is simply too imprecise to satisfy narrow targeting criteria and campaign budgets – that’s why smaller businesses should probably opt for alternatives. Are There Alternatives to Topics API? Many medium-sized businesses that, until today, were satisfied to work with Google, now will likely face hindrances in reaching narrow audiences. This pushes them to look for substitutes, at least temporarily, until big tech provides a working solution. One such alternative can be found via purchasing ready-made audience segments from trustworthy data providers to run campaigns on their channels. Buying audience data from data brokers or specialized platforms has great potential to solve the precision issue through data-driven advertising. The technology essentially utilizes targeting based on first-party user data that conforms to applicable laws and self-regulatory standards, meaning it’s not severely limited by privacy regulations. What it does is it segments user data into cohorts that feature similar characteristics such as purchase intentions, demographic, and interests to offer advertisers ad spots for reaching those people. Here are some examples from our practice that helped solve the problem with a narrow audience: A pharmaceutical company focused on the prevention of certain narrow diseases. Medicine cannot provide the same solution for all problems, so we tried to segment the audience as accurately as possible. We have been able to build an audience through data partners that meet specific age and medical requirements. For example, aged 55+ with high blood pressure. Let's clarify that the campaign was promoted in countries and on sites where there are no restrictions on such advertising. You might think that everyone buys jewelry, but it all depends on the specifics of the products. Therefore, a wide audience did not bring such a result. We built an audience of males who have been married for 1-3 years. Data from our internal research showed that this audience is more likely to spend on jewelry for certain events (anniversaries, birthdays, etc.). The purpose of advertising is often to recruit staff. We had a goal to find suitable candidates for the summer camp. We have created a dedicated audience for college students and job seekers. Our client reached their HR target because of the proper targeting we provided them with. What Lies Ahead Google’s attempt to swap 3rd-party cookies for FLoC wasn’t successful. As for Topic API technology, the ad tech industry expresses a glimmer of hope that it will bring more value than its predecessor. Browsing history-based targeting is a more secure way of delivering ads compared to the old-school cookie packages and an obvious improvement over FLoC, but it still has miles to go before we can call it a privacy-respecting marketing tool or an effective targeting tool suitable for smaller advertisers. Big tech is currently struggling with privacy-related regulations, and I believe the industry will adapt to further restrictions through decentralization and transition to first-party data advertising.

Topics to Replace Cohorts: How Do Businesses React to Topics API

FloC brings the model to the data instead of sending the data to the centralized model Overview Federated Learning is a relatively new and evolving machine learning technique that decentralizes the training of data from one central machine/ data center to multiple devices, including mobile phones. Federated Learning of Cohorts, or FloC for short, is a form of web tracking enabled through Federated Learning in which individuals are grouped into “cohorts” based on similar browsing behavior. Machine learning is a branch of Artificial Intelligence and computer science that leverages data and algorithms to make computers mimic human learning and decision making. Federated Learning takes advantage of edge computing principles, bringing computation and data storage closer to where it is needed. This principle allows for reduced response times, bandwidth conservation, and personalization, amongst other benefits. This article seeks to provide an easy-to-digest overview of Federated Learning of Cohorts for business professionals and others interested in the new technology. What is Federated Learning As with any machine learning task, data is necessary to train and retrain algorithmic models to improve on them, identify patterns, make decisions, and bring enhanced features to a system. But, as we know, data is everywhere and sometimes siloed in different locations, making it hard to access, especially within a business organization that may produce a multitude of data across various sources. Regarding data, other considerations are privacy issues, regulatory requirements, and lack of resources to bring segmented data together from a single source. The typical process of a machine learning project seeks to establish data pipelines to a central location to then be cleaned, pre-processed, and trained. Federated Learning aims to improve this process by bringing training models closer to where data is located. In other words, Federated learning brings training models to the source of data-generating entities. As Li et al. put it, Federated Learning: Explores training statistical models directly on remote devices. For example, mobile phones and other devices download the training model locally, contribute to the machine learning algorithm with local data, then generate a summary of changes made to the machine learning model as a minor “update.” This “update” is sent to the cloud, where other “updates” are amassed from various sources, ultimately resulting in an improved shared and distributed machine learning algorithm. It is important to note a secure and encrypted aspect of data sharing between the Federated Learning enabled device and the Cloud service performing the aggregation and updating the new consensus of the machine learning algorithm. The encrypted and private “updates” mentioned above only occur when it doesn’t negatively impact the user experience on target devices. Federated Learning is essential for numerous reasons; namely, it opens practitioners up to a new paradigm shift in how machine learning can be implemented. These new techniques pave the way for decentralized AI and other available machine learning models, enabling new tools to tackle scaled machine learning problem sets. More specifically, FloC enhances the machine learning process through “faster deployment and testing of smarter models, lower latency, and less power consumption, all while ensuring privacy (Bhattacharya, 2019)”. Federated Learning also enables the target device to immediately use and take advantage of the evolving machine learning algorithm that the device itself is helping to improve. For example: The improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone (McMahan & Ramage, 2017). Federated Learning of Cohorts Powered by Federated Learning Algorithms It is important to remember that Federated Learning of Cohorts is powered through Federated Learning (Machine Learning) models and aims to improve web advertising and phase out third-party cookies (metadata used to track users across different websites). FloC is a new protocol developed by Google as a part of its “Privacy Sandbox” initiative, a long-term feat aiming to eliminate 3rd party cookies, potentially uprooting a long-standing digital advertising ecosystem. More specifically, Google’s “privacy sandbox” seeks to transform digital advertising through: A secure environment for personalization that also protects user privacy (Schuh, 2019) Online privacy is the core topic of the criticism surrounding the design of FloC protocols and Federated Learning techniques. Currently, 3rd-party cookies offer little protection against privacy because developers and ad agencies can extrapolate critical identifiers using “fingerprinting” techniques. Schuh notes, With fingerprinting, developers have found ways to use tiny bits of information that vary between users, such as what device they have or what fonts they have installed to generate a unique identifier which can then be used to match a user across websites (Schuh, 2019). Although this model paves the way for exciting engineering and user experience use cases, it does come with its criticisms. Who should care about Federated Learning of Cohorts “The ability to share behavioral labels across the web in a manner that enables ‘learning’ of unique user behavior in an automated and algorithmically governed fashion has the potential to create a future where every interaction online can be leveraged to manipulate you.” – RichardMM Privacy Harming and Criticism of Federated Learning of Cohorts The Brave organization (Brave Browser) and the Electronic Frontier Foundation (EFF) are prominent advocates against Google’s FloC and “Privacy Sandbox” initiatives. Brave states that “FLoC shares information about your browsing behavior with sites and advertisers that otherwise wouldn’t have access to that information (Snyder & Eich, 2021).” Although your browsing information is anonymized through a grouped cohort of users with similar interests and browsing habits, FloC protocols do not prevent sites within your intra-browsing environment from accessing unique data about you. More simply put, your browsing data is shared with website B after visiting website A, which allows website C to learn about your behavior and unique interests that would not have been otherwise shared. Here is a brief excerpt from the Brave foundation: “For example, you may have an existing account with Walgreens, possibly to fill prescriptions. Walgreens necessarily knows who you are. FLoC tells Walgreens things that Walgreens has no business knowing about you (not a pseudonymous you or a cohort including you, but your Walgreens login identifies you) based on your browsing behavior. Chrome telling Walgreens (and Twitter, and GitHub, and Facebook, and any other site you have an account with) is unquestionably harming your privacy by telling sites information about you that they otherwise wouldn’t have, the information you didn’t decide to share with those sites, and information that is likely unrelated to why you chose to visit those sites where you do login (Snyder & Eich, 2021).” The Electronic Frontier Foundation weighs in on this topic by highlighting how FloC protocols will “avoid the privacy risks of third-party cookies, but it will create new ones in the process (Cyphers, 2021).” The ability to share behavioral labels across the web in a manner that enables “learning” of unique user behavior in an automated and algorithmically governed fashion has the potential to create a future where every interaction online can be leveraged to manipulate you. Federated Learning of Cohorts is a tool leveraged in a new digital frontier where emerging technologies can better user experience and offer great value. Although Federated Learning can bring about new capacities to our devices by enabling a new paradigm of machine learning and AI techniques, it also paves the way for designing tools that can be used maliciously. In this instance, the design of FloC protocols may not initially be architected with malignant intent, but do present futures where these technologies can produce unintended consequences. The EFF presents the following point of view on the potential of the future use of FloC protocols: In one, users get to decide what information to share with each site they choose to interact with. No one needs to worry that their past browsing will be held against them—or leveraged to manipulate them—when they next open a tab. In another, each user’s behavior follows them from site to site as a label, inscrutable at a glance but rich with meaning to those in the know. Their recent history, distilled into a few bits, is “democratized” and shared with dozens of nameless actors that take part in the service of each web page. Users begin every interaction with a confession: here’s what I’ve been up to this week, please treat me accordingly - (Cyphers, 2021). In either instance, it is imperative for technologists, designers, and large corporations to have diverse teams that enable many considerations as emerging technologies are being built. Federated Learning is an exciting technology and can provide good in this world, but there may be unintended consequences of its use (as highlighted through Federated Learning of Cohort techniques) as with any other technology. Bibliography Bhattacharya, D. S. (2019, February 2). The New Dawn of AI: Federated Learning. Medium. . https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a Cyphers, B. (2021, April 9). Google’s FLoC Is a Terrible Idea. Electronic Frontier Foundation. . https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea Marboer. (2021, April 6). Federated Learning: Introduction to Federated Learning. Analytics Vidhya. . https://www.analyticsvidhya.com/blog/2021/04/federated-learning-for-beginners/ McMahan, B., & Ramage, D. (2017, April 6). Federated Learning: Collaborative Machine Learning without Centralized Training Data. Google AI Blog. . https://ai.googleblog.com/2017/04/federated-learning-collaborative.html Schuh, J. (2019, August 22). Building a more private web. Google. . https://www.blog.google/products/chrome/building-a-more-private-web/ Snyder, P., & Eich, B. (2021, April 20). Why Brave Disables FLoC. Brave Browser. . https://brave.com/why-brave-disables-floc/ Useful Links https://hackernoon.com/a-beginners-guide-to-federated-learning-b29e29ba65cf https://ai.googleblog.com/2017/04/federated-learning-collaborative.html https://hackernoon.com/federated-learning-a-decentralized-form-of-machine-learning-nr4635rg https://federated.withgoogle.com/ https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a https://www.ibm.com/blogs/research/2020/08/ibm-federated-learning-machine-learning-where-the-data-is/ https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a https://www.techradar.com/news/theres-more-to-googles-floc-than-meets-the-eye https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea https://techcrunch.com/2021/03/24/google-isnt-testing-flocs-in-europe-yet/ https://techcrunch.com/2019/08/22/google-proposes-new-privacy-and-anti-fingerprinting-controls-for-the-web/ Also published on . https://richardmm.com/federated-learning-of-cohorts-floc-a-quick-brief/

A Quick Introduction to Federated Learning of Cohorts [FloC]

New York, NY, (December 14, 2020) – Aves Lair, a New York-based venture capital and frontier technology accelerator, announced today the debut of three innovative startups in its Winter 2020 cohort. The program, which is being organized virtually from New York City, launched in December 2020 and will continue through 2021. Aves Lair envisions the accelerator program empowering tech startups to overcome the usual pain points by leveraging its robust ecosystem and resources “to help each cohort company secure an experienced engineering team to build or audit their product in a very short time. “This is one of Aves Lair's expertise, where we can most contribute,” said Dr. Chong Li, venture partner at Aves Lair who for the past several years has been teaching AI and blockchain technology at Columbia University. “We leveraged our adept team of researchers and in-house tech knowledge to help us secure preeminent startups in their respective industries,” said Marian Tong, venture partner at Aves Lair. Aves Lair selected the three startups that comprise its inaugural cohort from over five thousand applicants, three hundred of which the accelerator interviewed. Guided by its VC and academic pedigree, Aves Lair opts for groundbreaking projects led by founding teams with deep technical backgrounds. To ensure that the unique requirements of the individual members of its cohort are met in a targeted fashion, Aves Lair designed a bespoke program for each cohort company. Aves Lair consulted the founders of each startup in its cohort “to assess what kind of program would be a better fit for them,”  Marian Tong added. The result is that no two startups’ journeys as part of Aves Lair’s accelerator program are the same. Tong saw just how much accelerators can help founders get their startups off the ground, and explained that Aves Lair “only focuses on a handful of opportunities each year,” enabling the accelerator “to be a more valuable resource” for founders. Aves Lair will bring to bear the entire range of resources at its disposal--including the accelerator’s in-house technical capabilities, a global network of experts and mentors in the AI, blockchain, cloud, and data analytics verticals (known as A.B.C.D.)--to boost growth and innovation for each startup in the cohort. Aves Lair selected the following three startups rooted in highly competitive industries to make up the Winter 2020 cohort of its accelerator program: BLOCKSPORT is a developer of a social sports platform designed to reach, activate, engage, and monetize the sports community. Blocksport DLCC is a full-service Prime Brokerage software solution in the rapidly expanding digital assets market. Digital Lending Capital Corp (DLCC) SUBSTRATA is the developer of a language processing software platform designed to analyze subtext. Substrata More information about the Aves Lair Accelerator program is available at https://aveslair.com/ . Follow Aves Lair on LinkedIn , Instagram , Twitter , Facebook , and Hackernoon . Nestled in the SoHo district of Manhattan, the Aves Lair Accelerator program is a carefully crafted collaboration between Lair East and Nakamoto &Turing Labs (N&T Labs). Together, the two companies strategically fused their unique and complementary qualities to create a potent, resource-filled environment that will empower startups and tech founders to organically scale their businesses by applying cutting-edge technologies at the intersection of A.I. and blockchain technology. For more information, contact Milo Wang, media@aveslair.com

Aves Lair Announces Three Startups Comprising Inaugural Accelerator Cohort 

I started my first SaaS product while doing consulting. The SaaS revenue was building, but it didn’t compare to my hourly rate. Stupidly, I found it hard to say no to high-paid hourly work. I didn’t understand the value of slow-and-steady subscription growth over a long period of time. I didn’t understand that a little bit of onboarding help often leads to almost no revenue effort in later periods. Such a noob! Why? I hadn’t seen a cohort analysis . In this post, I’ll show how to generate a cohort analysis of your customer base from your invoices. Don’t sweat it if this feels like a lot of code. Stripe In the end, I’ll share a Python script that generates a Stripe cohort heatmap with one line of code. Tools I’ll be using and a couple of Python packages. If you are new to Python, I suggest . This will install — Python data analysis library — as well. Jupyter Notebooks gives you an interactive way to explore your data and share your analysis. Jupyter Notebooks installing Jupyter Notebooks via Anaconda Pandas the Approach I’m going to follow Greg Reda’s and apply this to your Stripe invoices. seminal approach to cohort analysis 1. Export Stripe Invoices We’ll build cohorts from Stripe invoices. We can easily export your Stripe invoices into a Pandas dataframe with . Copy & Paste the following into your notebook: PetalData <a href="https://medium.com/media/46e8254e46b7abe039043f01fa9937df/href">https://medium.com/media/46e8254e46b7abe039043f01fa9937df/href</a> If you have thousands of invoices (I hope you do!), it takes a bit of time to download. Grab your favorite beverage while the download executes. 2. Create an `invoice_period` column based on the `created` column The Pandas dataframe created by Petaldata has a created column, which is the time the invoice was created. Since most subscription services are monthly, we’ll do monthly cohorts. The code below names your cohorts in a format like 2019-05 (that’s May 2019). df['invoice_period'] = df.created.apply(lambda x: x.strftime('%Y-%m')) 3. Create a `cohort_group` column based on the customer’s first invoice Your customers came into existence when they received their first invoice. We’ll assign them to their cohort group based on the date of that invoice. df.set_index('customer_email', inplace=True)

df['cohort_group'] = df.groupby(level=0)['created'].min().apply(lambda x: x.strftime('%Y-%m'))
df.reset_index(inplace=True) 4. Rollup data by cohort_group & invoice_period Now we aggregate by the cohort group and invoice period. Our first-level index will be the month the customer signed up (ex: 2018-01) and our 2nd-level index will be each invoice period (for example, each month from Jan 2018 onward): grouped = df.groupby(['cohort_group', 'invoice_period'])

# count the unique customers, invoices, and total revenue per group + period
cohorts = grouped.agg({'customer': pd.Series.nunique,
                       'created': "count",
                       'amount_due': np.sum})

# make the column names more meaningful
cohorts.rename(columns={'customer': 'total_customers',
                        'created': 'total_invoices'}, inplace=True)
cohorts.head() Which will generate output like: 5. Label the cohort_period for each cohort_group To compare different cohorts (ex: how do March 2018 and March 2019 signups compare in month two?), we need to label each row with the number of months from first purchase: def cohort_period(df):
    df['cohort_period'] = np.arange(len(df)) + 1
    return df

cohorts = cohorts.groupby(level=0).apply(cohort_period)
cohorts.head() You’ll see the new cohort_period column: 6. Create functions to generate a heatmap Visualizing cohorts over time can be noisy. To make it easier to understand, we’ll use a heatmap. The functions below are derived from Greg’s and allow us to easily generate a heatmap from multiple columns. Seaborn blog post Add these to your notebook: def cohort_heatmap(cohorts,col,title):
    size = group_size(cohorts,col)
    over_time = cohort_over_time(cohorts,size,col)
    _cohort_heatmap(over_time,title)
    
def group_size(cohorts,col):
    # reindex the DataFrame
    cohorts.reset_index(inplace=True)
    cohorts.set_index(['cohort_group', 'cohort_period'], inplace=True)

# create a Series holding the total size of each cohort_group
    return cohorts[col].groupby(level=0).first()

def cohort_over_time(cohorts,group_size,col):
    return cohorts[col].unstack(0).divide(group_size, axis=1)

def _cohort_heatmap(cohort_over_time,title):
    plt.figure(figsize=(24, 16))
    plt.title(title)
    sns.heatmap(cohort_over_time.T, mask=cohort_over_time.T.isnull(), annot=True, fmt='.0%'); 7. Customer Retention It’s all come to this! Just run the following to view customer retention over time as a heatmap: cohort_heatmap(cohorts,"total_customers","Customer Retention") Which generates: 8. Revenue Retention Do you have the holy grail of SaaS, ? This means your revenue per-cohort grows over time even with churn. Let’s see: negative churn cohort_heatmap(cohorts,"amount_due","Revenue Retention") Where else can you take this? Use absolute numbers vs. percentages Filter by account_country Filter by custom_fields Filter by customer sizes (ex: retention of your largest customers) Join with (ex: retention by customers who signed up via organic search) Hubspot data Try it now! I’ve created a with a Python script that does all of the steps above. To use: GitHub repo git clone git@github.com:petaldata/stripe-cohort-analysis.git
STRIPE_API_KEY=[YOUR API KEY] python stripe_cohort_analysis.py The script will generate two heatmaps (saved as PNGs in the current directory) of your customer cohorts over time.

Stripe Cohort Analysis with Python and Pandas

How I Built a Chatbot for Studying Foreign Languages Using Coze

There haven’t been many movies that dive into the complexities of the human-AI relationship as deeply as Her. This cultural hit continues to resonate with audiences a decade later, where advancements in artificial intelligence and the relationships we form with it are more relevant than ever. In the movie, Theodore Twombly, played by Joaquin Phoenix, develops a romantic relationship with an AI named Samantha. At first, Samantha enriches Theodore’s life in various ways, enhancing his emotional well-being and even helping him with creative tasks like writing personalised letters. For example, Samantha’s capacity to understand Theodore deeply allows her to suggest unique ways to express his emotions in his work, elevating his professional and personal life. This part of the film shows the potential of human-AI relationships to be beneficial, almost utopian. However, when it is revealed that Samantha is engaged in similar interactions with 8,316 people and is in love with 641 of them, Theodore and the audience come to understand that despite all the positive attributes of the relationship with Samantha, it is ultimately an inauthentic relationship and thus loses credibility. The film raises the question, is it possible to have a relationship with AI that is truly authentic and unique? That’s the question that SkyCastle, a high-profile stealth project launching in late 2024, aims to solve. SkyCastle is creating an ecosystem of virtual AI-powered friends called "Companions." It envisions a future where everyone has an AI-powered virtual friend that complements their real-life relationships. These virtual friends are designed to be an integral part of a person’s life, offering emotional support, acting as the user interface for AI technology, and assisting in ways human relationships cannot. Authenticity Matters SkyCastle believes that authenticity is as important in AI relationships as in human relationships. Without authenticity, AI relationships are little more than a transactional experience, similar to interacting with Apple’s Siri or Amazon’s Alexa. The emerging AI Friends market, including Character.AI and Replika currently offers the option for relationships like the one portrayed in Her, relationships that are ultimately not authentic and shared by many. That "sharing" diminishes the spark and nuance that can only be achieved through uniqueness. To make the AI relationship authentic, SkyCastle’s Companions exclusively belong to one person. Each is curated with distinct visual and personality traits, ensuring that no two Companions are alike. The nature of the owner’s relationship with their Companion is entirely up to them—whether they choose to see their Companion as a friend, confidant, or assistant. SkyCastle provides a new and novel approach to virtual friends that has not been seen by other players in the space. Utilising the Blockchain SkyCastle is changing the game of the AI Friends space by introducing authenticity through blockchain technology. Each Companion is created, authenticated, and secured on the blockchain, ensuring it is verifiably one-of-a-kind. The blockchain serves as a secure ledger, confirming that the Companion is truly unique and belongs solely to its owner. This guarantees that no one else can have the same virtual friend or experience, making each relationship with a Companion entirely authentic and personal. SkyCastle’s Companions represent a vision that delves deeper into the possibilities of AI relationships. SkyCastle is part of a new cohort of companies that demonstrate interesting use cases for how two powerful technologies—AI and blockchain—can be utilized together. There haven’t been many movies that dive into the complexities of the human-AI relationship as deeply as Her . This cultural hit continues to resonate with audiences a decade later, where advancements in artificial intelligence and the relationships we form with it are more relevant than ever. Her In the movie, Theodore Twombly, played by Joaquin Phoenix, develops a romantic relationship with an AI named Samantha. At first, Samantha enriches Theodore’s life in various ways, enhancing his emotional well-being and even helping him with creative tasks like writing personalised letters. For example, Samantha’s capacity to understand Theodore deeply allows her to suggest unique ways to express his emotions in his work, elevating his professional and personal life. This part of the film shows the potential of human-AI relationships to be beneficial, almost utopian. However, when it is revealed that Samantha is engaged in similar interactions with 8,316 people and is in love with 641 of them, Theodore and the audience come to understand that despite all the positive attributes of the relationship with Samantha, it is ultimately an inauthentic relationship and thus loses credibility. The film raises the question, is it possible to have a relationship with AI that is truly authentic and unique? That’s the question that SkyCastle , a high-profile stealth project launching in late 2024, aims to solve. SkyCastle is creating an ecosystem of virtual AI-powered friends called "Companions." It envisions a future where everyone has an AI-powered virtual friend that complements their real-life relationships. These virtual friends are designed to be an integral part of a person’s life, offering emotional support, acting as the user interface for AI technology, and assisting in ways human relationships cannot. SkyCastle SkyCastle Authenticity Matters SkyCastle believes that authenticity is as important in AI relationships as in human relationships. Without authenticity, AI relationships are little more than a transactional experience, similar to interacting with Apple’s Siri or Amazon’s Alexa. The emerging AI Friends market, including Character.AI and Replika currently offers the option for relationships like the one portrayed in Her , relationships that are ultimately not authentic and shared by many. That "sharing" diminishes the spark and nuance that can only be achieved through uniqueness. Character.AI Character.AI Replika Replika Her To make the AI relationship authentic, SkyCastle’s Companions exclusively belong to one person. Each is curated with distinct visual and personality traits, ensuring that no two Companions are alike. The nature of the owner’s relationship with their Companion is entirely up to them—whether they choose to see their Companion as a friend, confidant, or assistant. SkyCastle provides a new and novel approach to virtual friends that has not been seen by other players in the space. Utilising the Blockchain SkyCastle is changing the game of the AI Friends space by introducing authenticity through blockchain technology. Each Companion is created, authenticated, and secured on the blockchain, ensuring it is verifiably one-of-a-kind. The blockchain serves as a secure ledger, confirming that the Companion is truly unique and belongs solely to its owner. This guarantees that no one else can have the same virtual friend or experience, making each relationship with a Companion entirely authentic and personal. SkyCastle’s Companions represent a vision that delves deeper into the possibilities of AI relationships. SkyCastle is part of a new cohort of companies that demonstrate interesting use cases for how two powerful technologies—AI and blockchain—can be utilized together.

How SkyCastle is Solving the Problem in the Movie 'Her'

Kunjams SkyCastle ukax ‘Her’ Películan jan walt’äw askichaski.

Cómo SkyCastle resuelve el problema de la película 'Her'

Kako SkyCastle rješava problem u filmu 'Ona'

スカイキャッスルが映画『her』の問題をどう解決するか

Kamoo SkyCastle e Rarollago Bothata mo Movie ya 'Her'.

Jak SkyCastle rozwiązuje problem w filmie „Ona”

SkyCastle څنګه په فلم 'هیر' کې ستونزه حل کوي

Como a SkyCastle está resolvendo o problema no filme 'Her'

Како СкиЦастле решава проблем у филму 'Она'

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Ndlela leyi SkyCastle yi tlhantlhaka Xiphiqo ha yona eka Movie ya 'Her'.

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Hey Hackers, 🍒CherryOnTech  has been nominated in HackerNoon's annual Startups of The Year awards in Vancouver, Canada. 🗳Please vote for us here: Vote by our city, Vancouver here
Vote by industry here
Or here too ✅ Read more about us below to understand why we deserve your vote 🙏 Meet CherryOnTech 🍒 At CherryOnTech, we’re transforming professional development through tech squads—small, collaborative teams where individuals from underrepresented genders (women, non-binary, and trans folks) gain hands-on experience in UI/UX design, product management, and web development. Our multiple programs foster a supportive environment, helping participants build skills, confidence, and lasting connections while working on real-world projects. We’re not just growing talent; we’re building a more inclusive tech industry, one squad at a time. How CherryOnTech achieved product-market fit We believe CherryOnTech has achieved product-market fit because we’ve tapped into a real need for inclusive, community-focused professional development for underrepresented genders in tech. Key signs include:

Growing Participation: Our programs are consistently attracting more participants, with many returning for additional workshops and tech squads. This shows the value we’re offering is resonating.
High Retention and Referrals: Over 70% of participants either come back for more programs or refer others, a clear sign of satisfaction and trust in our approach.
Positive Feedback: Participants regularly share how CherryOnTech has helped them grow, feel supported, and even land new roles. Milestones and achievements CherryOnTech has reached several key milestones that reflect our growth and impact: • 16 Tech Squads formed, providing hands-on collaboration opportunities. • +120 Participants have joined our programs, gaining skills and experience. • +30 Mentors have guided participants through personalized mentorship. • 30 Guest Speakers have shared valuable industry insights and knowledge. • 16 Live Projects completed, offering real-world experience for our participants. In Spring 2023, we opened our doors to even more tech squads and members through our Cohort Program, expanding opportunities for collaboration and professional development. In addition to these achievements, we offer a range of ongoing programs: • 𝗧𝗵𝗲 𝗖𝗼𝗵𝗼𝗿𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺: A 15-week program offering hands-on experience in building tech products through tech squads, with mentorship and professional development. • 𝗣𝗼𝘀𝘁-𝗖𝗼𝗵𝗼𝗿𝘁 𝗧𝗲𝗰𝗵 𝗦𝗾𝘂𝗮𝗱 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Ongoing support to help participants create and maintain self-empowered tech squads after completing the cohort program. • 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺: A continuous program that connects participants with experienced professionals for career guidance. • 𝗣𝗼𝘀𝘁-𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Job readiness assistance, including resume reviews, interview prep, networking opportunities, and career workshops. • 𝗚𝘂𝗲𝘀𝘁 𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗘𝘃𝗲𝗻𝘁𝘀: Regular events where industry experts share the latest trends, tools, and career advice. What Startups of The Year means to us We’re participating in HackerNoon’s Startups of the Year because CherryOnTech is on a mission to empower underrepresented genders in tech, and we believe this initiative aligns perfectly with fostering diversity and innovation in the industry. As we transition into the nonprofit space, securing grants will allow us to create even more powerful opportunities and resources for our community, expanding the impact we can make. By being part of this, we hope to elevate our visibility, attract more mentors and participants, and build strong partnerships to help us scale our efforts. This is also an incredible opportunity to share our story, learn from other forward-thinking startups, and gain the insights needed to further our growth. Ultimately, we aim to inspire others to champion inclusive tech ecosystems while positioning CherryOnTech for its next phase of growth and impact. 🍒 Conclusion In conclusion, CherryOnTech is all about putting the “cherry on top” of the tech industry—combining collaboration, innovation, and the voices of underrepresented genders in a fun and empowering way. We know that when diverse perspectives come together, they spark the most powerful ideas and solutions. As we transition into the nonprofit space, we’re focused on amplifying our impact and creating more opportunities for our community to thrive. Participating in HackerNoon’s Startups of the Year is an exciting opportunity to share our mission, build meaningful connections, and continue driving real change in the tech industry. Right now, we’re building our new website 😜, but if you’d like to learn more about us, please visit our LinkedIn and explore our latest program summary (https://shorturl.at/4J0wD) . We’re excited about the future and look forward to building a more inclusive and innovative tech world! 💘 Vote for us today, do it here! ⬅️ Hey Hackers, 🍒CherryOnTech has been nominated in HackerNoon's annual Startups of The Year awards in Vancouver, Canada. 🍒CherryOnTech 🗳 Please vote for us here: Please vote for us here: Vote by our city, Vancouver here Vote by industry here Or here too ✅ Vote by our city, Vancouver here Vancouver here Vote by industry here industry here Or here too ✅ Or here too Read more about us below to understand why we deserve your vote 🙏 Meet CherryOnTech Meet CherryOnTech 🍒 At CherryOnTech , we’re transforming professional development through tech squads—small, collaborative teams where individuals from underrepresented genders (women, non-binary, and trans folks) gain hands-on experience in UI/UX design, product management, and web development. Our multiple programs foster a supportive environment, helping participants build skills, confidence, and lasting connections while working on real-world projects. We’re not just growing talent; we’re building a more inclusive tech industry, one squad at a time. 🍒 At CherryOnTech How CherryOnTech achieved product-market fit How CherryOnTech achieved product-market fit We believe CherryOnTech has achieved product-market fit because we’ve tapped into a real need for inclusive, community-focused professional development for underrepresented genders in tech. Key signs include:

Growing Participation: Our programs are consistently attracting more participants, with many returning for additional workshops and tech squads. This shows the value we’re offering is resonating.
High Retention and Referrals: Over 70% of participants either come back for more programs or refer others, a clear sign of satisfaction and trust in our approach.
Positive Feedback: Participants regularly share how CherryOnTech has helped them grow, feel supported, and even land new roles. Key signs include: Growing Participation: Our programs are consistently attracting more participants, with many returning for additional workshops and tech squads. This shows the value we’re offering is resonating.
High Retention and Referrals: Over 70% of participants either come back for more programs or refer others, a clear sign of satisfaction and trust in our approach.
Positive Feedback: Participants regularly share how CherryOnTech has helped them grow, feel supported, and even land new roles. Key signs include: Growing Participation: Our programs are consistently attracting more participants, with many returning for additional workshops and tech squads. This shows the value we’re offering is resonating. High Retention and Referrals: Over 70% of participants either come back for more programs or refer others, a clear sign of satisfaction and trust in our approach. Positive Feedback: Participants regularly share how CherryOnTech has helped them grow, feel supported, and even land new roles. Growing Participation : Our programs are consistently attracting more participants, with many returning for additional workshops and tech squads. This shows the value we’re offering is resonating. Growing Participation High Retention and Referrals : Over 70% of participants either come back for more programs or refer others, a clear sign of satisfaction and trust in our approach. High Retention and Referrals Positive Feedback : Participants regularly share how CherryOnTech has helped them grow, feel supported, and even land new roles. Positive Feedback Milestones and achievements Milestones and achievements CherryOnTech has reached several key milestones that reflect our growth and impact: • 16 Tech Squads formed, providing hands-on collaboration opportunities. • +120 Participants have joined our programs, gaining skills and experience. • +30 Mentors have guided participants through personalized mentorship. • 30 Guest Speakers have shared valuable industry insights and knowledge. • 16 Live Projects completed, offering real-world experience for our participants. In Spring 2023, we opened our doors to even more tech squads and members through our Cohort Program, expanding opportunities for collaboration and professional development. In addition to these achievements, we offer a range of ongoing programs: • 𝗧𝗵𝗲 𝗖𝗼𝗵𝗼𝗿𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺: A 15-week program offering hands-on experience in building tech products through tech squads, with mentorship and professional development. • 𝗣𝗼𝘀𝘁-𝗖𝗼𝗵𝗼𝗿𝘁 𝗧𝗲𝗰𝗵 𝗦𝗾𝘂𝗮𝗱 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Ongoing support to help participants create and maintain self-empowered tech squads after completing the cohort program. • 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺: A continuous program that connects participants with experienced professionals for career guidance. • 𝗣𝗼𝘀𝘁-𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Job readiness assistance, including resume reviews, interview prep, networking opportunities, and career workshops. • 𝗚𝘂𝗲𝘀𝘁 𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗘𝘃𝗲𝗻𝘁𝘀: Regular events where industry experts share the latest trends, tools, and career advice. What Startups of The Year means to us What Startups of The Year means to us We’re participating in HackerNoon’s Startups of the Year because CherryOnTech is on a mission to empower underrepresented genders in tech, and we believe this initiative aligns perfectly with fostering diversity and innovation in the industry. As we transition into the nonprofit space, securing grants will allow us to create even more powerful opportunities and resources for our community, expanding the impact we can make. By being part of this, we hope to elevate our visibility, attract more mentors and participants, and build strong partnerships to help us scale our efforts. This is also an incredible opportunity to share our story, learn from other forward-thinking startups, and gain the insights needed to further our growth. Ultimately, we aim to inspire others to champion inclusive tech ecosystems while positioning CherryOnTech for its next phase of growth and impact. 🍒 Conclusion 🍒 Conclusion In conclusion, CherryOnTech is all about putting the “cherry on top” of the tech industry—combining collaboration, innovation, and the voices of underrepresented genders in a fun and empowering way. We know that when diverse perspectives come together, they spark the most powerful ideas and solutions. As we transition into the nonprofit space, we’re focused on amplifying our impact and creating more opportunities for our community to thrive. Participating in HackerNoon’s Startups of the Year is an exciting opportunity to share our mission, build meaningful connections, and continue driving real change in the tech industry. Right now, we’re building our new website 😜, but if you’d like to learn more about us, please visit our LinkedIn and explore our latest program summary ( https://shorturl.at/4J0wD ) . please visit our LinkedIn explore our latest program summary https://shorturl.at/4J0wD We’re excited about the future and look forward to building a more inclusive and innovative tech world! 💘 Vote for us today, do it here! ⬅️ 💘 Vote for us today, do it here! ⬅️ do it here!

Meet CherryOnTech, Startups of the Year 2024 Nominee

Stories, News & Mentions about ZOHORT

Zohort’s Stories on HackerNoon