Real

real

Surprise! “The Tortured Poets Department” does not appear to be about Joe Alwyn, but about Swift’s situationship with the problematic singer, at least according to alleged leaks.

If This Taylor Swift Album Leak Is Real, It’s All About Matty Healy

Fashion critic Cathy Horyn’s close read on a decade of Taylor Swift’s outfits including the Grammys, her Eras tour and just walking around with Travis Kelce.

The Real Reason Taylor Swift Dresses Like That

<strong>Real</strong> Madrid will face Bayern Munich in the Champions League semifinals after defeating Manchester City in a tense penalty shootout on Wednesday.

Real Madrid defeats Manchester City in penalty shootout to reach Champions League semifinals | CNN

Man City are one of the world&#x27;s elite teams, but <strong>Real</strong> Madrid showed why they are still the gold standard after a backs-against-the-wall win in the Champions League quarterfinals.

Real Madrid punish Man City's missed chances in Champions League epic

At this time, it would be unusual for an industry not to have a place in the world of decentralized ledgers and cryptocurrencies. Everyone can offer something here. Memecoins, for instance, offer basically nothing besides fun, but we also have the other side of the coin with Real-World Assets (RWA) tokens and platforms. Just as the name suggests, they offer assets that already exist in the real world (the world outside of crypto) through tokenization features. To go into detail, Real-World Assets (RWAs) refer to physical or traditional financial assets that can be represented as digital tokens on a crypto network. These assets, which may include real estate, commodities, bonds, and art, hold intrinsic value and are widely recognized in global financial markets. To go into detail, Real-World Assets (RWAs) refer to physical or traditional financial assets that can be represented as digital tokens on a crypto network. The process of tokenizing RWAs involves converting ownership rights into ledger-based tokens, allowing these assets to be traded and accessed in a decentralized manner. Now, for practical reasons, every time you see the term “RWA” inside crypto, they’ll likely be talking about the tokens and platforms related to this concept, and not exactly to the assets involved. The process of tokenizing The process of tokenizing One of the most significant advantages of RWA tokenization is its ability to improve liquidity. Unlike traditional asset markets, which often involve complex transactions and lengthy processing times, crypto-based RWAs enable instant and global trading. Additionally, tokenization allows for fractional ownership, lowering entry barriers for investors who might otherwise be excluded due to high capital requirements —not everyone can buy a whole house, for instance, but a lot of people could hold a fraction of it and enjoy the benefits. RWA Use Cases Real-World assets (RWAs) have the potential to revolutionize various industries by making traditionally illiquid assets more accessible and flexible. One major use case is in government and corporate bonds, where tokenization allows investors to purchase small portions of these financial instruments instead of needing large sums of money upfront. This fractional ownership makes bond investments more inclusive while maintaining the benefits of Distributed Ledger Technology (DLT), such as enhanced transparency and instant transactions. In real estate, RWAs enable fractional property ownership, lowering the barriers to entry. Instead of purchasing an entire building, investors can buy tokens representing a share of a property and earn rental income. This simplifies real estate investment, making it more transparent and accessible. Additionally, tokenized industrial properties like warehouses and factories provide businesses with easier capital-raising options while offering investors a diversified portfolio. In real estate, RWAs enable fractional property ownership, lowering the barriers to entry. Instead of purchasing an entire building, investors can buy tokens representing a share of a property and earn rental income. RWAs also apply to physical commodities and collectibles, allowing fractional ownership of precious metals, artwork, and machinery. Instead of buying a whole gold bar or a rare painting, individuals can invest in small shares, benefiting from potential appreciation. As the adoption of RWAs grows, industries will continue to explore how tokenization can optimize asset ownership, increase market efficiency, and create new economic opportunities. RWAs in Obyte Obyte provides a cost-effective and accessible way to create tokenized RWAs, making it an appealing platform for both beginners and experts. As a decentralized, DAG-based system, Obyte eliminates intermediaries while ensuring seamless transactions. Users can generate custom tokens either with coding for advanced configurations or without coding through a simple online tool. This flexibility allows businesses and individuals to tokenize a wide range of assets, from financial instruments to loyalty rewards, without incurring high costs. Obyte Obyte Users can generate custom tokens either with coding for advanced configurations or without coding through a simple online tool. Unlike other networks, where tokens require separate smart contracts for transactions, Obyte treats all tokens equally, just like its native currency, GBYTE. This means transfers happen smoothly within the core protocol, reducing complexity and potential errors. Transaction fees are minimal, often less than $0.01, making it ideal for frequent and small-value transfers. Additional token features include adjustable supply, privacy settings, and regulatory compliance options, like requiring verified users for transfers. all tokens all tokens A notable real-world application of Obyte’s tokenization is Aufort, a European investment firm that represents gold holdings using Obyte's DAG network. This enables instant and global transactions while maintaining a custodial model for simplicity. Beyond this, Obyte’s tokenization tools can be used for any kind of fractional ownership, internal business credits, or custom digital assets tailored to specific needs. Obyte gives you the power to decide—no middlemen, no high costs, just your vision made real on-chain. Obyte’s tokenization tools Obyte’s tokenization tools Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

Educational Byte: What are Real-World Assets (RWA) Tokens in Crypto?

Opvoedkundige Byte: Wat is Real-World Assets (RWA) Tokens in Crypto?

Адукацыйны байт: Што такое рэальныя сусветныя актывы (RWA) токены ў крыпта?

Uddannelsesbyte: Hvad er real-world assets (RWA) tokens i Crypto?

Byte educativo: ¿Qué son los tokens de activos del mundo real (RWA) en Crypto?

Byte educativo: cosa sono i token del mondo reale (RWA) in Crypto?

教育バイト:仮想通貨における現実世界の資産(RWA)トークンとは何ですか?

ការបណ្តុះបណ្តាលបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តុះបណ្តាល

Образовни бајтови: Што се реалниот свет средства (RWA) токени во крипто?

د زده کړې بیټ: په Crypto کې د حقیقي نړۍ د اټکلونو (RWA) ټوکنونه څه دي؟

Byte educacional: O que são tokens de ativos do mundo real (RWA) em criptografia?

Dubai, UAE, April 10th, 2025/Chainwire/--Zodor has announced the launch of its live, enterprise-grade tokenization infrastructure, developed to facilitate the secure and compliant issuance of real-world assets (RWAs). The platform will hold a fair token launch on April 15, with no pre-sale, providing access to tokenized assets for both institutional and retail participants. The blockchain platform, focused on advancing the tokenization of RWAs, introduces a full-stack infrastructure designed to support tokenized equity, debt, and renewable energy assets. Zodor’s end-to-end ecosystem emphasizes compliance, security, and accessibility for a broad range of users. The tokenization of RWAs is projected to become a $16 trillion market by 2030. However, the current ecosystem remains fragmented and largely conceptual. Zodor aims to address this gap with a production-ready infrastructure that prioritizes compliance, scalability, and privacy. “Zodor was built to bridge traditional finance with decentralized systems in a practical, regulation-friendly way,” said Allen C James, Co-Founder & CSO at Zodor. “We’ve spent years building a platform that is not only technically sound but also aligned with real-world legal and operational standards.” “Zodor was built to bridge traditional finance with decentralized systems in a practical, regulation-friendly way,” said Allen C James, Co-Founder & CSO at Zodor. “We’ve spent years building a platform that is not only technically sound but also aligned with real-world legal and operational standards.” Zodor’s Four Core Verticals Include: Zodor Infra: Secure, scalable infrastructure for compliant RWA issuance and trading.
Zodor Capital: A decentralized capital platform enabling startups and SMEs to raise funding directly from global investors.
Zodor Energy: A vertical focused on tokenizing renewable energy and climate-impact projects.
Zodor Infinity: A no-code platform that allows businesses to tokenize revenue, equity, or debt—similar to a decentralized version of Shopify for tokenized economies. Zodor Infra: Secure, scalable infrastructure for compliant RWA issuance and trading. Zodor Capital: A decentralized capital platform enabling startups and SMEs to raise funding directly from global investors. Zodor Energy: A vertical focused on tokenizing renewable energy and climate-impact projects. Zodor Infinity: A no-code platform that allows businesses to tokenize revenue, equity, or debt—similar to a decentralized version of Shopify for tokenized economies. A Unique Approach to Compliance and Privacy Zodor’s infrastructure incorporates advanced cryptographic tools, including Paillier homomorphic encryption, which enables privacy-preserving transactions while maintaining regulatory alignment. This focus on privacy and compliance makes Zodor a viable gateway for institutions seeking exposure to the crypto space. Key Launch Details: No pre-sale or insider allocations
Fair token launch on April 15
Products are live and accessible
Built for both institutional and retail users No pre-sale or insider allocations Fair token launch on April 15 Products are live and accessible Built for both institutional and retail users Targeted Use Cases and Global Focus Rather than attempting to tokenize all asset classes, Zodor concentrates on high-impact sectors, including: Revenue-generating startups seeking capital
Renewable energy projects in emerging markets
Institutional-grade assets with retail access pathways
Jurisdictions with clear regulatory frameworks Revenue-generating startups seeking capital Renewable energy projects in emerging markets Institutional-grade assets with retail access pathways Jurisdictions with clear regulatory frameworks Website | Whitepaper |  Twitter/X | Telegram Website Website Whitepaper Whitepaper Twitter/X Twitter/X Telegram Telegram About Zodor Zodor is a blockchain-based infrastructure provider focused on compliant and scalable tokenization of real-world assets. The platform integrates decentralized finance principles with traditional finance standards to unlock liquidity, transparency, 0and opportunity for both issuers and investors. Zodor Zodor Contact Principal founder Dinesh Kumar Zodor dinesh@zodor.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Zodor Launches Scalable Infrastructure For Real-World Asset Tokenization Ahead Of April 15 Launch

Zodor lanseer skaalbare infrastruktuur vir real-world asset tokenisasie voor 15 April lanseer

Zodor startet skalierbare Infrastruktur für Real-World-Asset-Tokenisierung vor dem Start am 15. April

Zodor lanza infraestructura escalable para tokenización de activos en el mundo real antes del lanzamiento del 15 de abril

Zodor מציגה תשתית מתוחכמת עבור טוקניזציה של נכסים בעולם האמיתי לפני ההשקה ב-15 באפריל

Zodor Luncurkan Infrastruktur Scalable untuk Tokenisasi Aset Dunia Real Sebelum Luncurkan 15 April

Zodor、4月15日の発売前にリアルな資産トークニケーションのためのスケーラブルインフラストラクチャを導入

„Zodor“ pristato skalbiamą realaus pasaulio turto tokenizacijos infrastruktūrą iki balandžio 15 d.

Zodor uzsāk skalējamu infrastruktūru reālās pasaules aktīvu tokenizācijai pirms 15. aprīļa uzsākšanas

Ny Zodor dia manomboka ny fanatsarana ny fanorenana ho an'ny tokenization ara-toekarena amin'ny alàlan'ny famoahana ny 15 Aprily

Zodor лансира скалабилна инфраструктура за реалниот свет токенизација на средства пред лансирањето на 15 април

Zodor, 15 Nisan'ın başlatılması öncesinde gerçek dünya varlık tokenizasyonu için ölçeklenebilir altyapı başlattı

San Francisco, United States/California, April 10th, 2025/Chainwire/--Huma Finance, the first PayFi Network, announced the launch of Huma 2.0, a permissionless, compliant, and composable real-yield platform built on Solana today. This launch opens global access to Huma's stable real yield from global payment financing related to everyday commerce and trade activities. This development broadens individual access to stable, real-world yield—historically more common in institutional finance—by leveraging payment-financing mechanisms. Simultaneously, the existing permissioned service has been rebranded as Huma Institutional to continue serving institutions and accredited investors. Users can begin depositing on the new Huma 2.0 platform immediately. Huma 2.0 offers users alternative ways to participate as liquidity providers. It introduces two primary modes to cater to different user preferences: Classic Mode, designed for those seeking stable, double-digit USDC yield (updated monthly) combined with rewards called Huma Feathers; and Maxi Mode, tailored for users aiming to maximize their accumulation of Huma Feathers, earning rewards at 5x the base rate, without receiving USDC yield. Users can switch the mode of their existing positions at any time. DeFi composability is also a core feature of Huma 2.0, enabled by the $PST, short for PayFi Strategy Token. This liquid, yield-bearing LP token allows holders to integrate their Huma positions with leading protocols on Solana. At launch, users can swap $PST for USDC via Jupiter, the first of several planned integrations with top Solana DeFi platforms. Support for using $PST as collateral on Kamino and trading future rewards via RateX are expected shortly after launch. While participation does not require capital lock-up, users can opt for 3-month or 6-month terms to significantly boost their Huma Feather rewards via multipliers. Notably, during the initial launch period, these multipliers are significantly elevated as part of a limited-time promotion, offering particularly high boosts in Maxi Mode. These features offer users enhanced flexibility and choice in managing their positions. In just two years, Huma Finance's PayFi Network has rapidly achieved significant scale, processing over $3.8 billion in transactions and generating $8 million in annualized revenue. Huma supports its partners to generate yield from their PayFi operations. Unlike DeFi yields often reliant on token incentives, market speculation, or typically low rates in traditional finance, PayFi yield originates directly from fees paid by businesses using the network for payment financing and settlement liquidity. Capital is recycled rapidly—often within days—compounding fees generated from tangible economic activity. This mechanism has enabled Huma to consistently deliver stable, double-digit USDC yields, showcasing a sustainable model further validated by backing from leading investors and recognition from industry analysts like Messari regarding PayFi's potential to address a $30 trillion market. Messari Messari “Huma 2.0 isn’t just another yield product — it’s a structural shift.” Said Erbil Karaman, Co-founder of Huma Finance. “By giving payments institutions a new source of liquidity that operates 24/7 with incredible capital efficiency, we are creating a new type of yield that is composable, transparent, and grounded in real economic activity. It finally gives DeFi access to a source of returns that institutions have kept to themselves for decades — and it does so without compromising on what makes DeFi powerful.” “Huma 2.0 isn’t just another yield product — it’s a structural shift.” Said Erbil Karaman, Co-founder of Huma Finance. “By giving payments institutions a new source of liquidity that operates 24/7 with incredible capital efficiency, we are creating a new type of yield that is composable, transparent, and grounded in real economic activity. It finally gives DeFi access to a source of returns that institutions have kept to themselves for decades — and it does so without compromising on what makes DeFi powerful.” The launch of Huma 2.0 comes during significant shifts in global finance and within the DeFi landscape itself. While legacy payment infrastructures like SWIFT face challenges with speed and transparency and trillions in capital remain inefficiently allocated, the demand for modern, blockchain-based solutions is clear, evidenced by stablecoin transaction volumes recently reaching a reported $35 trillion. Huma 2.0 leverages this shift, providing efficient settlement liquidity and broadening access for individuals worldwide to earn from foundational financial activities – an opportunity previously confined mainly to institutions. Crucially, the yield generated through PayFi is designed to be less dependent on crypto market cycles. Whether markets are booming or in a downturn, economic activities like payments and trade may continue, potentially offering a more stable foundation for Huma’s yield generation. This structure may make double-digit returns more relevant during bear markets, where speculative yields often decline. As DeFi matures, there is a readiness for this type of yield – one grounded in tangible commerce rather than token speculation. PayFi serves as a foundational layer, enabling new DeFi strategies like the recent Solmate product from Splyce, which combines Huma's $PST yield with SOL liquid staking, bridging decentralized finance with productive economic use. Huma 2.0 marks a key step in building a new financial future, fundamentally expanding participation by inviting everyone into a financial revolution where access is open and fair. Early participants can benefit from various reward multipliers, including boosts for prior Huma depositors and partner communities. As the platform expands with more DeFi integrations and plans to be the first major project on Jupiter's LFG 2.0 launchpad – Huma continues to build finance that is truly accessible. To learn more about Huma 2.0 or participate, users can visit https://huma.finance or follow @humafinance on X. https://huma.finance https://huma.finance @humafinance @humafinance For media inquiries, users can contact: Ejiro Oviri, ejiro@huma.finance. About Huma Finance Huma is the first PayFi (Payment Finance) network. It features an open-stack liquidity protocol with applications for key areas like cross-border payments, stablecoin-backed cards and trade finance. Huma Huma The network addresses a total market estimated at over $30 trillion. Its mission is to accelerate the movement of money for a world that’s always on. Huma is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact Director of Brand Marketing Ejiro Huma Finance ejiro@huma.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Huma Finance 2.0 Launches On Solana, Bringing Composable Real Yield To DeFi Users

የ Huma Finance 2.0 በ Solana ላይ ይጀምራል, የ DeFi ተጠቃሚዎች የ komposable Real Return ያቀርባል

Huma Finance 2.0 spouští na Solana, přináší kompozitní reálný výnos pro uživatele DeFi

Huma Finance 2.0 lanza en Solana, trayendo rendimiento real compuesto a los usuarios de DeFi

Huma Finance 2.0 Launches On Solana, Bringing Composable Real Yield To DeFi Users ソラナで実現

Huma Finance 2.0 იწყება Solana- ში, რათა DeFi- ის მომხმარებელს კომპიუტერული რეალური მოგება

Huma Finance 2.0은 Solana를 출시하여 DeFi 사용자에게 복합 가능한 실제 수익을 가져옵니다.

Huma Finance 2.0 dia manomboka amin'ny Solana, mitondra tombontsoa ara-toekarena ho an'ny mpampiasa DeFi

Huma Finance 2.0 wprowadza na Solana, przynosząc kompozytowy realny zysk użytkownikom DeFi

Huma Finance 2.0 Solana හි ප්රදර්ශනය, DeFi පරිශීලකයින්ට සංකීර්ණ සැබෑ ආදායමක් ලබා දීම

Huma Finance 2.0 இன் Solana இல் தொடங்கியது, DeFi பயனர்களுக்கு Composable Real Return கொண்டு வருகிறது

Blockchain introduces the promise of a decentralized video games landscape where players can earn rewards, own in-game assets and have a real say in the direction of their future development. \It’s a compelling promise, and yet it’s one that many gamers strongly resist. Gamers have become some of the most vocal naysayers against crypto, labeling digital assets as worthless, with many arguing that the entire industry is a scam. Given the number of hacks and ill-intentioned social media influencers around, it’s understandable that many might think like this. Web3 Gaming Is Far From Dead In a recent editorial in Cointelegraph, NPC Labs co-founder and CEO Daryl Xu argues that Web3 games have failed to achieve mainstream adoption because they’ve ended up recreating many of the problems they set out to fix, creating walled gardens that aim to keep players trapped in their gaming ecosystems. Moreover, he claims developers tend to place too much emphasis on blockchain and tokenomics, at the expense of fun gameplay. editorial editorial But there are many reasons to believe that decentralization can be better for gaming. One of the most compelling is the idea that gamers can exercise control over their development. Indeed, Vitalik Buterin says that a decision by the creators of World of Warcraft to nerf his favorite character was one of the main inspirations that drove him to create the Ethereum blockchain. Then there was the infamous incident with Madden NFL 23, where a data storage issue on one of EA’s servers caused the progress of thousands of gamers to be permanently erased.  With blockchain’s decentralized governance and distributed network, neither of these things can happen. nerf his favorite character nerf his favorite character permanently erased permanently erased Web3 gaming has its share of success stories too. The rise of so-called “tap-to-earn” games on Telegram such as Hamster Combat and Notcoin, which boast millions of players, speaks to the appeal of crypto rewards in gaming. Then there’s the case of Off The Grid, the hit battle royale shooting game that gathered 9.12 million users and processed more than 100 million transactions within the first month of its launch. Hamster Combat Hamster Combat gathered 9.12 million users gathered 9.12 million users A Showcase Of Token Stability A recent spate of token delistings on platforms like Binance stoked apprehension for blockchain gamers. A number of native gaming tokens were included among them, but this is not so much a criticism of the value of these assets, but rather a reaction to the rise of numerous worthless memecoins, which has resulted in many exchanges tightening their listing requirements to protect customers. token delistings token delistings While there may be a few bad apples among blockchain games, most Web3 gaming assets boast sound tokenomics and provide genuine utility. Web3 games developers have put a lot of thought and effort into strengthening their tokenomics models in the wake of what happened to Axie Infinity’s, whose token price crashed once the game’s popularity peaked. But that’s unlikely to happen with newer games like My Neighbor Alice and Alien Worlds. The price of MNA’s ALICE token has been stable since the summer of 2022, propped up by the growing popularity of its metaverse-based farming and social adventure, ample liquidity with more than 50 exchanges listing the asset, and its adherence to industry compliance standards. My Neighbor Alice My Neighbor Alice Alien Worlds Alien Worlds ALICE ALICE It’s a similar story for Alien Worlds, widely regarded as one of the most successful blockchain games. Its native token TLM has a market cap of around $25 million, with its price hovering around the $0.004 mark for more than three years, in stark contrast to the usual volatility of crypto assets. TLM TLM A closer look at ALICE highlights some strong fundamentals. More than 90% of its total supply is already in circulation, reducing any chances of inflation. It provides vast utility, serving as the main rewards token and currency (for buying NFT-based virtual assets and acquiring skills). Players can also unlock exclusive content by paying with ALICE, and they can even start an in-game business and earn ALICE tokens. ALICE also powers MNA’s decentralized governance. By holding ALICE tokens, players are eligible to make proposals to the community and vote on those ideas, with their votes proportional to the amount of tokens they hold. They can thus play an active role in the game’s development and help shape its future. ALICE’s time-tested tokenomics model is based on the idea that transaction fees are returned to the game’s treasury, then fed back to players as rewards for fulfilling various in-game tasks. The game’s economy is rounded out by DeFi features such as staking, lending and borrowing, providing further utility. Continuous Development and Community Engagement Games must evolve if they don’t want to die, and MNA has been transforming itself rapidly with new features such as the launch of its race-inspired NFT Collection, which fuels an ongoing event called “From Land to Track”. In the event, players are encouraged to design their very own race track, on land they own, with big ALICE prizes for those that attract the most community votes. MNA is also celebrating its birthday with its second ALICE airdrop, rewarding its loyal gamers for sticking around, and has many new gaming features planned, such as an innovative new guest mode for new players and WebGPU to ensure smoother gameplay. race-inspired NFT Collection race-inspired NFT Collection celebrating its birthday celebrating its birthday new gaming features new gaming features The pace of innovation in Alien Worlds is just as frenetic, with recent examples being the launch of its Battlefleet Armageddon and Starblind mini-games. It has also introduced community grants to help fund developers who want to create new experiences within the Alien Worlds universe, and launched a new, 10-week community engagement program to help kick start its expansion into China. community engagement program community engagement program Stability: The Key To Success Mainstream adoption remains a distant dream for blockchain gaming, for the industry continues to be dogged by misplaced narratives and broader market challenges. Nonetheless, it’s encouraging to see the resilience, the ongoing innovation and the never-ending community engagement with newer Web3 games like MNA and Alien Worlds. With their strong token fundamentals and loyal player bases, these games have established a strong foundation for the future that won’t easily crumble. With their unwavering commitment to continuous development, they showcase just the kind of sustainability needed by the Web3 gaming industry to further its progress. 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. #DYO 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. #DYO Vested Interest Disclosure: 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. #DYO business blogging program business blogging program

Ignore The Noise: Web3 Gaming Is Making Real Progress

Jan yäqañaw Ruido: Web3 Juegos ukax Chiqpach nayrar sartaski

Ignore el soroll: Web3 Gaming està fent progrés real

Ignore el ruido: los juegos Web3 están logrando un progreso real

ERROR: invalid target_lang (fa-AF)

शोर को अनदेखा करें: Web3 गेमिंग वास्तविक प्रगति कर रहा है

Abaikan Kebisingan: Web3 Gaming Membuat Kemajuan Nyata

התעלם מהרעש: משחקי Web3 מתקדמים ממש

雑音は無視：Web3ゲームは真の進歩を遂げている

소음을 무시하십시오 : Web3 Gaming은 진정한 진전을 이루고 있습니다.

Ignore Udhaifu: Web3 Gaming inafanya maendeleo ya kweli

Gürültüyi görmezden gelin: Web3 Gaming Gerçek Gelişme Yaptı

Honisa Mpfumawulo: Web3 Gaming Yi Endla Nhluvuko Wa Xiviri

Authors:
(1) Sanchit Sinha, University of Virginia (sanchit@virginia.edu);
(2) Guangzhi Xiong, University of Virginia (hhu4zu@virginia.edu);
(3) Aidong Zhang, University of Virginia (aidong@virginia.edu). Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology and 3.1 Representative Concept Extraction 3.2 Self-supervised Contrastive Concept Learning 3.3 Prototype-based Concept Grounding 3.4 End-to-end Composite Training 4 Experiments and 4.1 Datasets and Networks 4.2 Hyperparameter Settings 4.3 Evaluation Metrics and 4.4 Generalization Results 4.5 Concept Fidelity and 4.6 Qualitative Visualization 5 Conclusion and References Appendix 4.3 Evaluation Metrics We consider the following evaluation metrics to evaluate each component of the concept discovery framework. • Generalization: We start by quantitatively evaluating the quality of concepts learned by measuring how well the learned concepts can generalize to new domains. To achieve this, we compare our proposed method against the aforementioned baselines on domain adaptation settings. • Concept Fidelity: To evaluate consistency in the learned concepts, we compute the intersection over union of the concept sets associated with for two data points xi and xj from same class as defined in Equation 10: 4.4 Genenralization Results Tables 2, 3, and 4 report the domain adaptation results on the OfficeHome, DomainNet, VisDA and the Digit datasets, respectively. The notation X→Y represents models trained on X as the source domain (with abundant data) and Y as the target domain (with limited data) and evaluated on the test set of domain Y . The best statistically significant accuracy is reported in bold. The last three rows in all the tables list the performance of the RCE framework, RCE trained with regularization (RCE+PCG), and RCE trained with both regularization and contrastive learning paradigm (RCE+PCG+CCL). Comparision with baselines. The first row in each table lists the performance of a standard Neural Network trained using the setting described in [Yu and Lin, 2023] (S+T). As a standard NN is not inherently explainable, we consider this setting as a baseline to understand the upper bound of the performance-explainability tradeoff. The second and third rows in each table lists the performance of SENN and DiSENN respectively. SENN performs worse than S+T setting in almost all settings, except in a handful of settings where the performance matches S+T. This is expected, as SENN is formulated as an overparameterized version of a standard NN with regularization. Recall that DiSENN replaces the autoencoder in SENN with a VAE, and as such is not generalizable to bigger datasets without domain engineering. DiSENN performs the worst among all approaches for all datasets due to poor VAE generalization. Recall that UnsupervisedCBM is an improved version of SENN architecture with a discriminator in addition to the aggregation function. In most cases, it performs slightly better than SENN and is at par with S+T. However, in particular cases in OfficeHome data (R→A) and DomainNet (S→P), UnsupCBM performs the best. We attribute this result to two factors: first, the Art (A) and Sketch (S) domains are significantly different from Real (R) and Picture (P) domains due to both of the former being hand-drawn while the latter being photographed as mentioned in [Yu and Lin, 2023]. Second, the use of a discriminator as proposed in UnsupervisedCBM helps enforce domain invariance in those particular cases. BotCL explicitly attempts to improve concept fidelity and applies contrastive learning to discover concepts. However, the contrastive loss formulation is rather basic and they never focuses on domain invariance. BotCL’s performance is similar to S+T for the most part except in OfficeHome data (C→A), where it just outperforms all other approaches. One possible reason is that Clipart domain is significantly less noisy, and hence basic transformations in BotCL work well. As the last row demonstrates, our proposed framework RCE+PCG+CCL outperforms all baselines on a vast majority of the settings across all four datasets and is comparable to SOTA baselines in the other settings. Ablation studies. We also report the performance corresponding to various components of our proposed approach. We observe that the performance of RCE is almost identical to SENN, which is expected as there is very weak regularization in both cases. In almost all cases, adding prototype-based grounding regularization (RCE+PCG) improves performance over RCE while models trained with both PCG regularization and contrastive learning (RCE+PCG+CCL) outperform all approaches on a vast majority of settings across all datasets. Note that the setting RCE+CCL is not reported, as it defeats the fundamental motivation of maintaining concept fidelity. Effect of the number of concepts and dimensions. We observe that there are no significant differences in performance over a varying number of concepts or dimensions. For all results reported, the number of concepts is set to a number of classes in the dataset, and their dimension is set to 1. For results on varying numbers of concepts and dimensions - refer to Appendix. 4.5 Concept Fidelity As the RCE framework is explicitly regularized with a concept fidelity regularizer and grounded using prototypes, we would expect high fidelity scores. Table 5 lists the fidelity scores for the aforementioned baselines and our proposed method. Fidelity scores are averaged for each domain when taken as a target (e.g. for the domain (A) in DomainNet, the score is an average of C→A, P→A and R→A). As expected, our method and BotCL, both with specific fidelity regularization outperform all other baseline approaches. Our method outperforms BotCL in most settings, except when the target domains are Art in DomainNet and Clipart in OfficHome due to significant domain dissonance. 4.6 Qualitative Visualization Domain Alignment. We consider the extent to which the models trained using both concept grounding and contrastive learning maintain concept consistency not only within the source domain but also across the target domain as well. To understand what discriminative information is captured by a particular concept, Figure 5 shows the most important prototypes selected from the training set of both the source and target domains corresponding to five randomly selected concepts. We observe that prototypes explaining each concept are visually similar. For more results, refer Appendix. Explanation using prototypes. For a given input sample, we also plot the prototypes associated with the highest activated concept, i.e., the important concept. Figure 6 shows the prototypes associated with the concepts most responsible for prediction (highest relevance scores). As can be seen, the prototypes possess distinct features, for eg., they capture the round face of an alarm clock. More results are reported in Appendix. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Sanchit Sinha, University of Virginia (sanchit@virginia.edu); (2) Guangzhi Xiong, University of Virginia (hhu4zu@virginia.edu); (3) Aidong Zhang, University of Virginia (aidong@virginia.edu). Authors: Authors: (1) Sanchit Sinha, University of Virginia (sanchit@virginia.edu); (2) Guangzhi Xiong, University of Virginia (hhu4zu@virginia.edu); (3) Aidong Zhang, University of Virginia (aidong@virginia.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology and 3.1 Representative Concept Extraction 3 Methodology and 3.1 Representative Concept Extraction 3.2 Self-supervised Contrastive Concept Learning 3.2 Self-supervised Contrastive Concept Learning 3.3 Prototype-based Concept Grounding 3.3 Prototype-based Concept Grounding 3.4 End-to-end Composite Training 3.4 End-to-end Composite Training 4 Experiments and 4.1 Datasets and Networks 4 Experiments and 4.1 Datasets and Networks 4.2 Hyperparameter Settings 4.2 Hyperparameter Settings 4.3 Evaluation Metrics and 4.4 Generalization Results 4.3 Evaluation Metrics and 4.4 Generalization Results 4.5 Concept Fidelity and 4.6 Qualitative Visualization 4.5 Concept Fidelity and 4.6 Qualitative Visualization 5 Conclusion and References 5 Conclusion and References Appendix Appendix 4.3 Evaluation Metrics We consider the following evaluation metrics to evaluate each component of the concept discovery framework. • Generalization : We start by quantitatively evaluating the quality of concepts learned by measuring how well the learned concepts can generalize to new domains. To achieve this, we compare our proposed method against the aforementioned baselines on domain adaptation settings. Generalization • Concept Fidelity: To evaluate consistency in the learned concepts, we compute the intersection over union of the concept sets associated with for two data points xi and xj from same class as defined in Equation 10: • Concept Fidelity: 4.4 Genenralization Results Tables 2, 3, and 4 report the domain adaptation results on the OfficeHome, DomainNet, VisDA and the Digit datasets, respectively. The notation X→Y represents models trained on X as the source domain (with abundant data) and Y as the target domain (with limited data) and evaluated on the test set of domain Y . The best statistically significant accuracy is reported in bold. The last three rows in all the tables list the performance of the RCE framework, RCE trained with regularization (RCE+PCG), and RCE trained with both regularization and contrastive learning paradigm (RCE+PCG+CCL) . RCE RCE (RCE+PCG), RCE (RCE+PCG+CCL) Comparision with baselines. The first row in each table lists the performance of a standard Neural Network trained using the setting described in [Yu and Lin, 2023] (S+T). As a standard NN is not inherently explainable, we consider this setting as a baseline to understand the upper bound of the performance-explainability tradeoff. Comparision with baselines. The second and third rows in each table lists the performance of SENN and DiSENN respectively. SENN performs worse than S+T setting in almost all settings, except in a handful of settings where the performance matches S+T. This is expected, as SENN is formulated as an overparameterized version of a standard NN with regularization. Recall that DiSENN replaces the autoencoder in SENN with a VAE, and as such is not generalizable to bigger datasets without domain engineering. DiSENN performs the worst among all approaches for all datasets due to poor VAE generalization. Recall that UnsupervisedCBM is an improved version of SENN architecture with a discriminator in addition to the aggregation function. In most cases, it performs slightly better than SENN and is at par with S+T. However, in particular cases in OfficeHome data (R→A) and DomainNet (S→P), UnsupCBM performs the best. We attribute this result to two factors: first, the Art (A) and Sketch (S) domains are significantly different from Real (R) and Picture (P) domains due to both of the former being hand-drawn while the latter being photographed as mentioned in [Yu and Lin, 2023]. Second, the use of a discriminator as proposed in UnsupervisedCBM helps enforce domain invariance in those particular cases. BotCL explicitly attempts to improve concept fidelity and applies contrastive learning to discover concepts. However, the contrastive loss formulation is rather basic and they never focuses on domain invariance. BotCL’s performance is similar to S+T for the most part except in OfficeHome data (C→A), where it just outperforms all other approaches. One possible reason is that Clipart domain is significantly less noisy, and hence basic transformations in BotCL work well. As the last row demonstrates, our proposed framework RCE+PCG+CCL outperforms all baselines on a vast majority of the settings across all four datasets and is comparable to SOTA baselines in the other settings. Ablation studies. We also report the performance corresponding to various components of our proposed approach. We observe that the performance of RCE is almost identical to SENN, which is expected as there is very weak regularization in both cases. In almost all cases, adding prototype-based grounding regularization (RCE+PCG) improves performance over RCE while models trained with both PCG regularization and contrastive learning (RCE+PCG+CCL) outperform all approaches on a vast majority of settings across all datasets. Note that the setting RCE+CCL is not reported, as it defeats the fundamental motivation of maintaining concept fidelity. Ablation studies. Effect of the number of concepts and dimensions. We observe that there are no significant differences in performance over a varying number of concepts or dimensions. For all results reported, the number of concepts is set to a number of classes in the dataset, and their dimension is set to 1. For results on varying numbers of concepts and dimensions - refer to Appendix. Effect of the number of concepts and dimensions. 4.5 Concept Fidelity As the RCE framework is explicitly regularized with a concept fidelity regularizer and grounded using prototypes, we would expect high fidelity scores. Table 5 lists the fidelity scores for the aforementioned baselines and our proposed method. Fidelity scores are averaged for each domain when taken as a target (e.g. for the domain (A) in DomainNet, the score is an average of C→A, P→A and R→A). As expected, our method and BotCL, both with specific fidelity regularization outperform all other baseline approaches. Our method outperforms BotCL in most settings, except when the target domains are Art in DomainNet and Clipart in OfficHome due to significant domain dissonance. 4.6 Qualitative Visualization Domain Alignment. We consider the extent to which the models trained using both concept grounding and contrastive learning maintain concept consistency not only within the source domain but also across the target domain as well. To understand what discriminative information is captured by a particular concept, Figure 5 shows the most important prototypes selected from the training set of both the source and target domains corresponding to five randomly selected concepts. We observe that prototypes explaining each concept are visually similar. For more results, refer Appendix. Domain Alignment. Explanation using prototypes. For a given input sample, we also plot the prototypes associated with the highest activated concept, i.e., the important concept. Figure 6 shows the prototypes associated with the concepts most responsible for prediction (highest relevance scores). As can be seen, the prototypes possess distinct features, for eg., they capture the round face of an alarm clock. More results are reported in Appendix. Explanation using prototypes. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Reliable Are AI Concepts in Real-World Domain Adaptation Tasks?

Today, we are excited to announce the support of continuous updates for long-running pipelines in CocoIndex. This powerful feature automatically applies incremental source changes to keep your index up-to-date with minimal latency. With continuous updates, your indexes remain synchronized with your source data in real time, ensuring that your applications always have access to the most current information without the performance overhead of full reindexing. If you like our work, it would mean a lot to us if you could support Cocoindex on Github with a star. Thank you so much with a warm coconut hug 🥥🤗. Cocoindex on Github When to use it? It fits into situations that you need to access the fresh target data continuously in most of the time. How does it work? It continuously captures changes from the source data and updates the target data accordingly. It's long-running and only stops when being aborted explicitly. A data source may enable one or multiple change capture mechanisms: CocoIndex supports two main categories of change detection mechanisms: General Mechanism:

Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state.


Source-Specific Mechanisms:

Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. General Mechanism:

Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. General Mechanism Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. Refresh Interval Source-Specific Mechanisms:

Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Source-Specific Mechanisms Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!) Push Change Notification Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Recent Changes Poll These mechanisms work together to ensure CocoIndex can detect and process changes as they happen, maintaining your index in perfect sync with source data with minimal latency and resource usage. Under the hood, after the change is detected, CocoIndex will use its incremental processing mechanism to update the target data. incremental processing mechanism How to enable continuous updates? Here is an example of how to enable continuous updates for Google Drive. It is pretty simple: example 1. Configure the change capture mechanisms for the source @cocoindex.flow_def(name="GoogleDriveIndex")
def my_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.GoogleDrive(
            service_account_credential_path=credential_path,
            root_folder_ids=root_folder_ids,
            recent_changes_poll_interval=datetime.timedelta(seconds=10)),
        refresh_interval=datetime.timedelta(minutes=1)) @cocoindex.flow_def(name="GoogleDriveIndex")
def my_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.GoogleDrive(
            service_account_credential_path=credential_path,
            root_folder_ids=root_folder_ids,
            recent_changes_poll_interval=datetime.timedelta(seconds=10)),
        refresh_interval=datetime.timedelta(minutes=1)) In this example, we've configured two change detection mechanisms: recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured.


refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured.
The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by:

Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured. recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured. recent_changes_poll_interval=datetime.timedelta(seconds=10) refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured.
The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by:

Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured. refresh_interval=datetime.timedelta(minutes=1) The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by: refresh_interval Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval Comparing the current list with the previously known list Identifying any differences (additions, modifications, deletions) Triggering updates only for the changed items While source-specific mechanisms like recent_changes_poll_interval are more efficient for near real-time updates, the refresh_interval provides comprehensive coverage. We recommend setting it to a reasonable value based on your freshness requirements and resource constraints - shorter intervals provide fresher data but consume more resources. recent_changes_poll_interval refresh_interval You can read the full documentation: for source specific change detection mechanisms here. Different sources have different mechanisms supported.
for the universal fallback mechanism here. for source specific change detection mechanisms here. Different sources have different mechanisms supported. here for the universal fallback mechanism here. here 2. Run the flow in live update mode Option 1: CLI Add a @cocoindex.main_fn() decorator to your main function, so CocoIndex CLI will take over the control (when cocoindex is the first command line argument). @cocoindex.main_fn() @cocoindex.main_fn()
def main():
    pass

if __name__ == "__main__":
  main() @cocoindex.main_fn()
def main():
    pass

if __name__ == "__main__":
  main() To run the CLI with live update mode, you can use the following command: python main.py cocoindex update -L python main.py cocoindex update -L This will start the flow in live update mode, which will continuously capture changes from the source and update the target data accordingly. Option 2: Python API You can create cocoindex.FlowLiveUpdater. For example, cocoindex.FlowLiveUpdater @cocoindex.main_fn()
async def main():
    my_updater = cocoindex.FlowLiveUpdater(demo_flow)
    await my_updater.wait()
    ...

if __name__ == "__main__":
    asyncio.run(main()) @cocoindex.main_fn()
async def main():
    my_updater = cocoindex.FlowLiveUpdater(demo_flow)
    await my_updater.wait()
    ...

if __name__ == "__main__":
    asyncio.run(main()) And you run with the flow with python main.py python main.py You can also use the updater as a context manager. It will abort and wait for the updater to finish automatically when the context is exited. See full documentation here. here Now you are all set! It is super simple to get started and have continuous updates for your data. Get started now quickstart guide 🚀 quickstart guide It would mean a lot to us if you could support Cocoindex on Github with a star if you like our work. Thank you so much with a warm coconut hug 🥥🤗. Cocoindex on Github

Keep Your Indexes Fresh With This Real-time Pipeline

Singapore, Singapore, April 4th, 2025/Chainwire/--Yala, the Bitcoin-native liquidity layer enabling cross-ecosystem financial access, today announced the upcoming launch of Yala RealYield, a curated marketplace for real-world asset (RWA) yield opportunities powered by Bitcoin. The new platform will enable BTC holders to earn regulated, risk-adjusted yields by allocating capital into tokenized financial products, including U.S. Treasury bills, private credit, corporate bonds, and real estate-backed assets. Yala RealYield is designed as a unified access point to all of Yala’s RWA-related integrations. Rather than operating as a standalone product, it consolidates partnerships and investment opportunities into a structured platform where users can explore, compare, and combine diverse RWA yield sources, each offering distinct risk profiles, durations, and APYs. By enabling global, 24/7 access to high-quality, compliant RWA opportunities, Yala RealYield democratizes investment strategies previously limited to institutional investors and high-net-worth individuals. “Real-world assets are rapidly becoming the next frontier in blockchain finance. With RealYield, we’re offering Bitcoin holders a reliable, transparent way to earn real returns without leaving the decentralized economy,” said Kaitai Chang, COO at Yala. “This is a foundational step toward integrating BTC with the broader financial system.” “Real-world assets are rapidly becoming the next frontier in blockchain finance. With RealYield, we’re offering Bitcoin holders a reliable, transparent way to earn real returns without leaving the decentralized economy,” said Kaitai Chang, COO at Yala. “This is a foundational step toward integrating BTC with the broader financial system.” Key Features of Yala RealYield: Curated RWA Marketplace: Investors can access a wide selection of tokenized real-world yield opportunities, including sovereign debt, real estate, and private credit—all with transparent terms regarding risk, maturity, and return. Curated RWA Marketplace: Investors can access a wide selection of tokenized real-world yield opportunities, including sovereign debt, real estate, and private credit—all with transparent terms regarding risk, maturity, and return. Customizable Yield Strategies: Users can mix and match products to create bespoke portfolios tailored to individual preferences. Customizable Yield Strategies: Users can mix and match products to create bespoke portfolios tailored to individual preferences. Seamless Bitcoin Integration: All investments begin with BTC, ensuring on-chain transparency, verifiability, and security. Seamless Bitcoin Integration: All investments begin with BTC, ensuring on-chain transparency, verifiability, and security. Compliance-First Design: Robust legal and regulatory reviews to ensure real-world assets can be securely and legally onboarded. Compliance-First Design: Robust legal and regulatory reviews to ensure real-world assets can be securely and legally onboarded. Unified Experience: Yala offers a single dashboard for minting, trading, and managing RWA and DeFi positions—eliminating the need for fragmented platforms. Unified Experience: Yala offers a single dashboard for minting, trading, and managing RWA and DeFi positions—eliminating the need for fragmented platforms. Institutional-Grade Access: Multi-signature custody, on-chain governance, and permissioned access provide a secure foundation for institutional participation. Institutional-Grade Access: Multi-signature custody, on-chain governance, and permissioned access provide a secure foundation for institutional participation. Incentivized Participation: Yield farming and staking mechanisms will be introduced to drive engagement across both retail and institutional segments. Incentivized Participation: Yield farming and staking mechanisms will be introduced to drive engagement across both retail and institutional segments. Personalized Yield Through a Marketplace Model Yala RealYield operates as a marketplace, enabling users to browse and select from a range of yield products across categories such as: Risk Profile – From low-risk instruments like U.S. Treasuries to higher-yield private credit vehicles. Risk Profile – From low-risk instruments like U.S. Treasuries to higher-yield private credit vehicles. Time Horizon – Options range from short-duration liquidity to long-term fixed-income strategies. Time Horizon – Options range from short-duration liquidity to long-term fixed-income strategies. Target Returns – Each product includes APY benchmarks to help users align returns with personal investment objectives. Target Returns – Each product includes APY benchmarks to help users align returns with personal investment objectives. Yala will also introduce the RWA Vault Allocation Framework, a model that packages diversified real-world assets into unified yield vaults, each with defined allocation weights. This mirrors traditional asset allocation strategies but with the added composability and efficiency of Web3 infrastructure. Strategic Vision and Market Opportunity Yala RealYield addresses a long-standing gap in crypto: unlocking Bitcoin’s immense liquidity for productive, low-risk yield generation. The launch comes as institutional interest in tokenized real-world assets accelerates, with the total addressable market for RWAs projected to reach trillions of dollars in the coming years. By combining compliance-first infrastructure, seamless BTC access, and a growing roster of RWA partnerships, Yala is well-positioned to become the leading yield gateway for Bitcoin in traditional financial markets. “Bitcoin remains the most underutilized source of liquidity in the global financial system,” said Kaitai. “RealYield allows us to connect that capital to compliant, income-generating products, building a future where BTC earns real-world returns.” “Bitcoin remains the most underutilized source of liquidity in the global financial system,” said Kaitai. “RealYield allows us to connect that capital to compliant, income-generating products, building a future where BTC earns real-world returns.” Looking Ahead Yala plans to expand the RealYield marketplace over the coming months through additional integrations, refined yield packaging, and improved tooling for developers and institutional users. Long-term, Yala will support the use of RWA-backed positions as collateral for stablecoin minting, creating deeper synergies between DeFi and traditional finance. About Yala Yala is building a liquidity layer to unlock Bitcoin’s untapped yield across DeFi and RWAs. Users can deposit BTC and access seamless liquidity, enabling efficient capital movement and yield opportunities across ecosystems, chains, and protocols. Yala Yala To learn more, users can visit www.yala.org or follow Yala on X at @yalaorg. www.yala.org www.yala.org @yalaorg @yalaorg Contact Yala media@yala.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Yala Announces RealYield: A Marketplace For Bitcoin-Powered Real-World Asset Yields

Yala aangekondig RealYield: 'N Marketplace vir Bitcoin-aangedrewe werklike-wêreld asset opbrengs

Yalax RealYeld ukar uñt’ayi: Mä qhathuw Bitcoin-Powered Real-World Asset Yields ukataki

Yala обявява RealYield: Пазар за реални приходи от активи, задвижвани от Bitcoin

Yala anuncia RealYield: un mercado para rendimientos de activos reales basados en Bitcoin

Yala julkistaa RealYieldin: Bitcoin-pohjaisten reaaliaikaisten varojen tuottojen markkinapaikka

Yala annonce RealYield: un marché pour les rendements des actifs du monde réel alimentés par Bitcoin

Yala が RealYield を発表: ビットコインを利用した現実世界の資産利回りのマーケットプレイス

Yala Asakoli RealYield: Zando Ya Ba Rendements Ya Biloko Ya Mokili Ya Solo oyo Esalelaka Bitcoin

Ny Yala dia mampahafantatra ny RealYield: Marketplace ho an'ny Bitcoin-Powered Real-World Asset Yields

यालाले रियलइल्डको घोषणा गर्‍यो: बिटकॉइन-संचालित वास्तविक-विश्व सम्पत्ति उपजको लागि बजार

Yala ප්රකාශ කරයි RealYield: Bitcoins-Powered Real-World Asset Yields සඳහා වෙළෙඳපොළ

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It finally happened: I consumed AI content that I could not detect was AI-generated. This was embarrassing for me, a person who has made something of a sport out of spotting AI content in the wild. I’ve told clients when their landing pages looked too AI-ish - and they were genuinely surprised I could tell. I’ve nudged friends and pointed out an AI-generated voice narrating a video or the subtle turns of phrase that appear in AI-generated reviews of a restaurant. I’ve been proud of my digital literacy, a way to navigate the synthetic-content universe that we find ourselves in. I’ve been proud of my digital literacy, a way to navigate the synthetic-content universe that we find ourselves in. So when I came across a song - well-produced, emotionally-coherent, with catchy lyrics - and I later found out it was made by a new AI-music platform, I had to take a breath. I replayed it over a few times and sought out the signs: robotic weirdness, offbeat rhythms, or lyrical nonsense. Nope. Just a good song. We’ve crossed a line. The Evolution of AI-Generated Music AI-generated music has been simmering in the background of the tech world for years now—just another genre in the ever-expanding playlist of artificial creativity. At first, it felt like a novelty. Tools like OpenAI’s Jukebox or Google’s Magenta were fascinating but more proof-of-concept than production-ready. You’d get a warped Beatles-esque melody with garbled lyrics, or loops that felt stuck. Fun to play with, but nothing serious. Then came the lawsuits. Artists and rights holders pushed back against the data these models were trained on, and for good reason. Copyright law hadn’t anticipated an algorithm that could convincingly mimic Drake’s vocal tone or write a passable country ballad. The ethics were murky. The legality of generative AI for authors and artists was murkier. And for a while, it seemed like the technology might just stall under the weight of all that uncertainty. legality of generative AI for authors and artists legality of generative AI for authors and artists But the world turns and we, broadly, kind of stopped caring because as impressive as these tools were in theory, the output wasn’t good enough to take seriously. good enough It was interesting. Not threatening. You might use it to score a YouTube video or mess around, but it wasn’t going to replace a producer, or a songwriter, or a recording artist. But naturally, people kept building. It wasn’t long before a group of developers became Mureka, they developed something new, and it ended up in my ears. Inside the Machine Leveling-Up AI-Generated Music Inside the Machine Leveling-Up AI-Generated Music Hearing a demo track that sounded polished and too emotionally attuned to be machine-made sent me down a rabbit hole, eager to figure out how I was duped. There’s not a lot out there about this specific startup (as they’re technically pre-launch) so I reached out to the founding team to try and get a sense of what exactly they’ve done. exactly In developing a music generation tool, Mureka built an O1 Fine-Tune model that initially enabled minor changes to underlying instrumentals. The team continued to iterate, working on vocal tracks and ultimately building a chain-of-thought (or MusiCoT) model which constructs a full structural outline of the musical piece prior to generating individual audio segments. This enables a more complex and advanced structural coherence and arrangement. a chain-of-thought (or MusiCoT) model Previous models we had all become accustomed to generated audio sequentially, which lead to repetitive behaviors without an eye on the full picture, and lead to an unrealistic vocal tract that missed on important things like inflection and pacing. These developments mean you have an intelligent music studio assistant in your pocket, one which can use style reference to draw notes and inspiration from your previous tracks to match its mood, tempo, genre, and instrumentation. This works with fully-baked tracks and can even work with something as simple as humming a melody into Mureka - the AI will analyze the melody and can complete an entire song based on the input. This means you can upload your catalog, demos, or sonic references, and teach the AI to reflect your sound. For artists, producers, and even game studios, this is more than customization—it’s control over the creative engine itself. your The result: voice that sounds like voice and music that sounds like music, and can tell a comprehensive story that aligns with the rest of your musical work. It’s clear to me that this is a tool you could use to actually create an album - and with an open API that allows developers to build apps and experiences beyond what Mureka offers, it seems like this is the kind of AI engine labels were (rightfully) scared of not long ago. What Happens When the Tool Becomes the Artist? What Happens When the Tool Becomes the Artist? I’ve been the person in the room telling creatives not to panic. I’ve been the person in the room telling creatives not to panic. Whenever conversations about AI come up—whether with musicians, writers, or designers—I’d use the same analogy: AI is like the calculator. The old-school accountant might have resisted it, clinging to the abacus and the ledger book. But the modern accountant learned to use it and got faster, sharper, better. I was quick to reason that the same would be true for artists. The AI wouldn’t replace you, I said. It would enhance you. enhance AI would handle the grunt work, the repetitive drafts, the uninspired filler. The soul, the emotion, the originality? That would always be human territory. But hearing that Mureka-generated song shook that belief because this wasn’t AI playing backup, stitching loops, or writing placeholder lyrics. It was front and center, leading the creative and conveying emotion. And it did it without any human artist in the seat.. In almost every way, this is a case where the tool becomes the artist. becomes Now I’m asking questions I didn’t think I’d have to ask. If AI can convincingly replicate the emotional arc of a breakup ballad, what’s left for the person who actually lived through one? If you can train a model on your back catalog, is it still your music? What happens to the value of voice, style, even memory, when they can be cloned and recombined in seconds? your This isn’t a calculator moment. This feels like something else entirely. What’s Next in AI-Generated Music? What’s Next in AI-Generated Music? I don’t have a clean answer here. I’m not personally prepared to fully embrace a future where algorithms write our love songs and breakup ballads better than we can. But I know we’re not in the same place we were a year ago, and Mureka isn’t just another AI music tool. Mureka Mureka This is a turning point that raises questions without easy answers—about creativity, ownership, and what we actually value in human expression. The optimist in me has settled on this being the uncertain and strange beginnings of a new chapter where artists learn to co-create with these tools in ways we haven’t yet imagined. Maybe this will push the creative boundaries further. Maybe they’ll find new ways to use the machine to create. Or maybe we won’t even know which parts were real and which weren’t. And maybe we won’t care.

It Finally Happened—I Consumed AI Content Without Realizing It

Can a Decentralized Internet Make Free Speech and Global Access a Reality?

The AI-Driven Marketing Revolution The AI-Driven Marketing Revolution Artificial intelligence (AI) has been a key player in digital marketing for years. In 2015, Google first introduced RankBrain, which used machine learning to better interpret search queries and understand a query’s full intent. However, the more recent mainstream adoption of large language models (LLMs) has amplified AI’s influence across the industry, sometimes in negative ways. Suddenly, businesses were writing AI-generated blog posts, had AI-enabled live chatbots for their customer support, and were deploying search engine optimization (SEO) strategies targeting Google’s Search Generative Experience (SGE). While these advancements enhance efficiency, they have also highlighted the enduring value of human interaction. Customers increasingly seek authentic engagement, underscoring the role of live video chat sales solutions in fostering trust. Are We Over-Automating Digital Marketing? AI technologies have transformed digital marketing, yet their impact is not universally positive. Some consumers criticize AI-generated content for its lack of originality and emotional intelligence, perceiving it as generic and impersonal (Zhang & Gosline, 2023). AI-written blogs and chatbot interactions often fail to replicate the nuanced communication that fosters strong brand relationships. (Zhang & Gosline, 2023) (Zhang & Gosline, 2023) Modern marketers advocate for AI-powered chatbots as an essential tool for websites, yet research highlights that consumers frequently express frustration with their limitations when it comes to more advanced tasks (Vachon & Marcoux, 2025). Unlike human representatives, AI lacks the ability to provide dynamic, emotionally intelligent responses, which can lead to customer dissatisfaction. In contrast, live video chat sales tools allow businesses to engage with visitors in real-time, offering personalized interactions that drive higher trust and conversion rates. (Vachon & Marcoux, 2025) (Vachon & Marcoux, 2025) Real Conversations = Real Conversions As businesses rely more on automation, consumers increasingly seek real human interaction. AI chatbots can handle simple tasks, but they can’t replace emotional intelligence. Studies show that direct human communication enhances consumer trust and positively influences purchasing decisions (Jussupow et al., 2020). (Jussupow et al., 2020) (Jussupow et al., 2020) Live video chat provides immediate reassurance, reducing hesitation in the customer journey. A study by Campaign Monitor (2021) found that live chat solutions can increase conversion rates by up to 20%, as real-time responses mitigate uncertainty and encourage commitment. When consumers have the opportunity to interact with a representative through a live video chat sales platform, they are more likely to finalize transactions and develop brand loyalty. study by Campaign Monitor (2021) study by Campaign Monitor (2021) How Brands Can Leverage Live Video Chat Communication methods vary in their effectiveness, with AI chatbots ranking lowest and in-person meetings highest in terms of persuasiveness. The scale of customer engagement effectiveness follows this order: AI chatbot → human chatbot → phone call → video chat → in-person meeting. Businesses that rely solely on automated chat solutions may struggle to convert high-intent leads effectively. Delays in customer response time significantly reduce engagement. A study by Foster-McBride (2024) explored the "AI trust paradox," highlighting that while AI tools enhance efficiency, they often lack the relational depth required for high-stakes decision-making. Implementing live video chat sales solutions enables companies to address this gap by providing immediate, interactive, and trustworthy engagement. Several software providers have stepped up to meet this need, including Face2Face, Consulto, and Cardina. study by Foster-McBride (2024) study by Foster-McBride (2024) Face2Face Face2Face Consulto Consulto Cardina Cardina The Future is Human-First AI will always be part of digital marketing, but real human connection is irreplaceable. It’s  important to balance automation with real-time human interaction, as trust plays a central role in consumer decision-making. As companies adopt AI-driven tools, they must also prioritize strategies that maintain authentic engagement with their audiences. Businesses that integrate live video chat sales technology will be better positioned to build trust, enhance customer experience, and drive long-term growth. In a digital landscape dominated by automation, brands that emphasize real conversations will gain a competitive edge in consumer engagement. Sources: Campaign Monitor. (2021). How much does live chat increase conversions? Read more
Vachon, A., & Marcoux, B. (2025). The impact of AI chatbots on customer experience and loyalty. Read more
Zhang, Y., & Gosline, R. (2023). Human favoritism, not AI aversion: People’s perceptions and bias toward AI and human-generated content. Read more
Jussupow, E., Benbasat, I., & Heinzl, A. (2020). Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. Read more
Foster-McBride, C. (2024). The AI trust paradox: Navigating verisimilitude in advanced language models. Read more Campaign Monitor. (2021). How much does live chat increase conversions? Read more Read more Read more Vachon, A., & Marcoux, B. (2025). The impact of AI chatbots on customer experience and loyalty. Read more Read more Read more Zhang, Y., & Gosline, R. (2023). Human favoritism, not AI aversion: People’s perceptions and bias toward AI and human-generated content. Read more Read more Read more Jussupow, E., Benbasat, I., & Heinzl, A. (2020). Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. Read more Read more Read more Foster-McBride, C. (2024). The AI trust paradox: Navigating verisimilitude in advanced language models. Read more Read more Read more Originally published on HEC Digital. Originally published on HEC Digital. HEC Digital HEC Digital

Despite Advancements in AI, Real Human Conversations Are Still Key to Winning Customers

The tech industry is at the forefront of cutting-edge innovation. Everywhere you turn, someone's talking about disruption, transformation, or unlocking new possibilities – but the truth is, not every new tech trend aligns with actual user needs. And as product managers, we would do well to remember that the innovation should always serve the customer, not the trend. Last week, I was invited to share my experiences of leading product at companies working with emerging technologies like artificial intelligence (AI) and the new iteration of the internet focussed on data ownership (web3) on a Product People webinar. Using a few examples from my career, I argued for ignoring the hype and focussing on solving real user needs – aligned, of course, to business objectives – when introducing AI or web3 into the product. Product People webinar Understanding the temptation Understanding the temptation Of course, it's tempting to jump on the bandwagon. After all, if everyone is hyped up about something, shouting about how it's going to change the world, that energy can be contagious. Even more so in the tech sector, where we're all afraid of missing out on the next big thing. So whether it's accepting crypto or integrating AI tools, there's often pressure – from consumers, from leaders, from investors, hell, even from ourselves – to "do something with the trend". But here's the catch – just because we have access to a new technology doesn't mean it makes sense for our users. A cautionary example is the way businesses have been introducing AI chatbots. Sure, you can now say that you're AI-powered, but how is the experience for your typical user? Far too often, poorly executed chatbots frustrate users rather than helping. Instead of enhancing their interaction with your product, it can lead to friction as they struggle to get access to human support agents. In other words, adding AI just for the sake of it can end up alienating the very users it's meant to serve. It's not all about the tech It's not all about the tech This tends to happen because we get so caught up in the possibilities of the technology that we forget the most important thing, what problem are we solving? This is especially relevant when we talk about driving adoption of new technologies. technology what problem are we solving I was once on a panel with a woman who'd been an early adopter of Bitcoin and blockchain-based currencies more generally, and she drew a comparison with the success of Skype and no one caring about Voice over Internet Protocol (VoIP). The underlying technology is secondary. Its main purpose is helping you achieve your goals quickly and easily. Skype helped connect you to relatives across the world without paying exorbitant fees, that's the value. helping you achieve your goals quickly and easily that's The other interesting thing to highlight from my fellow panellist's observation is that, of course, it benefits from hindsight. We know (as we knew then) what problems Skype was solving, but we didn't know at the time whether the Skype protocol would win over the other variations of protocols being developed. I must admit that I don't know the history in detail, but I would hazard a guess that user experience had something to do with Skype's ultimate success. hindsight didn't In the same way, blockchain is just the facilitator[1]. Of course, there are significant advantages in terms of security and permanence, but ultimately, it's about the problem it's solving for users. And right now, it's lacking on the user experience side, though we have made great strides in improving this. I'm personally a firm believer that blockchain, crypto, NFTs, and especially AI do add value, but my overarching point is that we as product managers need to look past the technology and solve for users. have do Uncovering the "why" Uncovering the "why" As such, when it comes to our users, we need to get to know them so well that we can anticipate their needs and build solutions without worrying about the underlying technology. There's the often-quoted Henry Ford statement that if he had asked user what they wanted, they would've said a faster horse. While it's unlikely that he actually said this, not the least because he didn't actually invent the car, the quote conveys an important point about the user need state to (a) get somewhere faster and (b) to be able to afford it. Ford's invention, therefore, becomes the solution to a very specific problem – getting from point A to point B more efficiently. without worrying about the underlying technology Therefore, it's always a good idea to take a step back and ask a simple question: why? Why are we adding [insert feature here]? If you can't answer in a way that addresses a key business objective, whether that's increased revenue, improved retention, better engagement, or whatever else, through a better customer experience, it's time to reconsider. It's easy to get caught up in the hype and think that blockchain or AI or some other new technology is the magical silver bullet solution, but you can't lose sight of what you're trying to achieve – both as a business and as the product with the customer top-of-mind. why The benefits of web3 and AI The benefits of web3 and AI So what benefits are there in web3 and AI? Of course, we haven't yet reached the mass adoption of, say, Skype circa 2013, and it remains to be seen whether we will from a consumer side. From the consumer side, there's a disconnect between the promise (store of value) and the reality (speculation) of crypto, as well as the promise (owning IP) as well as the reality (again, speculation) of NFTs – so while the technology is innovative, the why hasn’t been fully addressed, at least not for consumers. But blockchain is already being used in government and business applications. Estonian and American government bodies, for example, are using the KSI blockchain for protecting privacy and security. Governments and big companies store value in bitcoin. are consumer side why is And then when we think about AI, it's more of the same. Maybe we haven't quite identified the overarching why of consumer AI, though in my personal opinion, it's just a matter of time before mass adoption for the simplification of mundane tasks (my mother regularly uses DeepL to check her English, neurodiverse acquaintances swear by ChatGPT for tackling simple emails that would otherwise take hours, etc). From a business perspective, however, AI is already embedded into our meeting software, into collaboration tools, and even into the core of software development through tools like GitHub Copilot, with more use cases added daily. why Knowing when to pivot Knowing when to pivot A few years ago, I was working with a company that encountered this very problem – it failed to find product market fit among consumers, but found it for businesses. This was a mission-driven crypto startup focussed on bringing Bitcoin to emerging markets as a hedge against inflation. We gamified the experience, rewarding users with small amounts of bitcoin for engaging with the platform. On paper, this seemed like a great way to build user engagement, but the reality was that this didn't support our business goals long term. consumers businesses Given the market and the industry, we had to do everything by the book – complying with regulation, receiving the necessary licenses, partnering with banks and authorities. This was not without its challenges, often financial. It quickly became apparent that our target audience wasn't able to sustain our growth ambitions, and while target users were using us to earn bitcoin, we weren't seeing enough traction in terms of transactions to make any money off the exchange. Now, I was young, a bit naïve, and so committed to the mission as central to the product – in other words, the technology and the mission became inextricably entwined for me – that I left when the CEO decided to pivot, leveraging all our banking relationships and licenses to actually improve the experience of transferring money to more complicated regions using blockchain rails and our user-friendly UI. But with the wisdom of hindsight, I can admit that this use case actually solves a problem and adds value for users, and, from a business perspective, it's a clever repackaging of our existing tech and partner stack to solve a different problem for a different set of users. These new users don't care that the money transfers are cheap and convenient because of blockchain, they just care that they're cheap, secure, and get the funds to a heavily regulated market compliantly. because When technology becomes the solution to a problem that doesn't exist When technology becomes the solution to a problem that doesn't exist But it doesn't always work that way. Sometimes, we want to work the technology into the solution so much that the problem we're solving for users is forgotten. In another company I worked for, the founding mission was the ability to register Certificates of Authenticity for physical artworks on the blockchain as a permanent record, which matters to artists who want to track provenance. Blockchain was seen as a solution to the question of permanence, as well as security. The issue was adoption – this is an issue for the art world generally, why there isn't a single database of Certificates of Authenticity is that each artist, gallery, museum or whatever it may be issues their own, which makes it difficult to track and authenticate. So while there was a use case, the same issue pursued the startup, because in order for these Certificates to continue to exist as intended to, they needed to be transferred alongside the artwork. This was then complicated by the emergence of NFTs and our company's entry into the space, because NFTs were intended, first and foremost, to serve as proof of ownership in and of themselves – you own the NFT, therefore you own the underlying art, and any transactions are visible on the blockchain, therefore establishing provenance. However, instead of doubling down on the use case and pursuing the vision of establishing an immutable repository of Certificates, we focussed on the technology, and tried to certify NFTs alongside physical works. The logic being that NFTs can be faked, because it's easy to make an NFT with an underlying image that isn't your artwork – though, of course, NFTs track which address created the artwork, so the artists for whom this matters can prove or disprove authorship anyway. So how does this all reflect on the problem we were trying to solve? Well, the issue is that the decisions we were making (pursuing NFTs in parallel to certification) were contradicting the why of what we were doing (establishing authenticity of certification). And, of course, the big one – we were solving a very niche problem in a very complicated way, which prevented us from driving adoption by focussing on simplifying user needs. The main takeaway being, of course, that before getting too carried away with how to build the solution, determine whether the problem being solved matches your business objectives. simplifying Build or buy? Build or buy? To top it all off, there's the additional consideration of whether to build the solution yourself or to delegate the work to a third party. This becomes especially relevant when it concerns AI, because ownership of the underlying data model is a competitive advantage. In my current organisation, we have a number of products that are introducing AI-powered solutions that address specific user need states, such as manual and labour-intensive purchase invoicing. This kind of repetitive process with clear parameters is ripe for AI automation. Why AI? Because it needs to account for nuance in business operations, which is why attempting to address it with rule-based processing is not sufficient. But this kind of solution comes with additional considerations of tradeoffs (time-to-market versus long-term value, as well as who builds it and who handles the legal questions like data processing), all within the context of the breakneck pace of AI. nuance In Silverbucket, the product I am currently working on, there are many areas in which we can improve usability through AI, both internally and customer-facing. The important thing is to recognise what is actually relevant for us – for example, there's no need for us to screen "easy" customer service queries trained on our knowledge base, because we don't typically get any queries that don't require engineering. This then paves the way for a more comprehensive question of how to build AI assistants for our customers, because a simple AI chatbot for the sake of it is unnecessary. The main question that we need to answer when we think about how our solution addresses user needs is, how do we facilitate the user's intended result in the easiest way possible? As you can see, that last question doesn't say anything about AI – but in our day and age, this can very well end up being the answer, which is something that we're therefore obligated to explore as a potential (but not only) solution. how do we facilitate the user's intended result in the easiest way possible? Conclusion Conclusion The main takeaway is that companies need to solve real pain points, and the technology is just there as a facilitator. If we use it correctly, our users benefit; if we don't, we risk alienating them. The only thing we can really rely on is continuous discovery – watching users, speaking to them, looking at product analytics, exploring their need states, and understanding how to solve their problems, and this can be with or without web3, blockchain, AI or whatever new technology appears on the horizon. [1] I do want to sound a note of caution here, because AI is a bit of a different beast, the inputs do matter, because they can replicate existing inequalities or spread misinformation, but that's somewhat of a different conversation. do

Stop Jumping on Every Tech Trend—Real Innovation Always Puts Users First

The buzz around blockchain was once deafening. Every headline screamed disruption. Every startup had "decentralized" in its pitch deck. And behind the scenes? APIs were supposed to be the unsung heroes making it all possible. But here we are, several years and millions of dollars later, asking a very real question: Did blockchain APIs actually deliver? Did blockchain APIs actually deliver? Let’s talk. The Promise: Seamless Access to the Decentralized World Blockchain APIs were supposed to be the bridges between real-world applications and decentralized networks. They promised to simplify what was once seen as impossibly complex. Developers could skip writing raw smart contracts or learning obscure protocols. Just plug in an API and, boom, you’re in the blockchain game. It sounded great. On paper. They were sold as the keys to unlocking new types of apps: decentralized finance (DeFi) tools, supply chain systems with instant audit trails, NFT marketplaces, and secure identity verification platforms. Some APIs would offer one-click wallet integration; others gave access to transaction data, token standards, or even governance frameworks. The pitch was: "Focus on your app. We'll handle the blockchain." But that pitch has some cracks now. The Reality Check: What Actually Happened? A handful of major players like Alchemy, Infura, and Moralis did rise to prominence. They provided the infrastructure so developers didn’t have to run their own nodes. These services were fast, accessible, and easy to work with. For a while, everything looked on track. But then came the bottlenecks. The limitations. The creeping realization that a lot of what blockchain APIs promised — scale, cost-efficiency, reliability — wasn't panning out in the real world. 1. Centralization Creep Centralization Creep Here’s the irony: the whole point of blockchain was decentralization. But most blockchain APIs are run by a few centralized providers. This means if Infura or Alchemy goes down, entire swathes of Web3 apps go offline. That's not hypothetical. It's happened. When Infura had an outage, MetaMask (a widely used crypto wallet) stopped working properly. The decentralized dream was suddenly bottlenecked by one API provider. 2. Opaque Pricing and High Costs Opaque Pricing and High Costs Another pain point? Pricing. Blockchain API services can get expensive, especially as your app scales. What begins as a "generous free tier" often becomes a slippery slope into mounting operational costs. Developers have expressed frustration about vague pricing models, rate limits, and unexpected throttling. For startups especially, this unpredictability can be a deal breaker. 3. Incomplete or Inconsistent Data Incomplete or Inconsistent Data Blockchain data isn’t exactly easy to work with. APIs try to abstract the mess, but they don’t always succeed. Some APIs return inconsistent or incomplete data depending on the chain, the network status, or the provider’s own infrastructure. Imagine building a financial tool on top of unreliable data. Not great. 4. Security & Trust Gaps Security & Trust Gaps Using a third-party API means introducing a potential point of failure. If the API is compromised, or the data is manipulated, your app is vulnerable. This isn’t just theoretical — it's happened in the wild. Remember, blockchain is supposed to be trustless. Relying on an API provider brings that trust back into the equation. Real Use Cases: A Mixed Bag To be fair, there have been successes. Crypto exchanges use APIs to pull real-time prices and on-chain data. NFT platforms use them to verify ownership and display metadata. DAOs rely on APIs for governance participation tracking. Some gaming platforms have integrated blockchain APIs for asset ownership verification. But for every working use case, there are ten that hit a wall. Either due to infrastructure limits, cost constraints, or complexity in user experience. Even more telling: many big financial institutions and enterprises that flirted with blockchain quietly backed away. Not because the tech had no potential, but because the tooling — including APIs — wasn't mature or stable enough. Developer Sentiment: Excitement Fading? Once the most hyped crowd in the blockchain ecosystem, developers are now increasingly skeptical. Sure, there are still hackathons, grant programs, and flashy VC-backed ecosystems. But go on forums, Discords, or Reddit threads, and you'll see a pattern: "Why is this API returning stale data?"
"Why does this keep failing during high traffic?"
"Is anyone else paying way too much for what should be a basic call?" "Why is this API returning stale data?" "Why does this keep failing during high traffic?" "Is anyone else paying way too much for what should be a basic call?" The dev community is still interested in blockchain, but they’re no longer enchanted by it. They want tools that just work. Reliable, affordable, scalable. Blockchain APIs, in their current form, often fall short. work So, What Went Wrong? It’s not that blockchain APIs are useless. It's that the expectations were outsized, and the tech hasn’t caught up. The dream was real, but the execution still feels like beta. A few core issues: Too many blockchains, not enough standards. Interoperability is a nightmare. APIs are often chain-specific and require juggling multiple tools.
Lack of true decentralization. We replaced centralized databases with centralized API providers. That defeats a lot of the original purpose.
Poor onboarding experience. For new devs, getting started with blockchain APIs often feels like being dropped into a maze with no map. Too many blockchains, not enough standards. Interoperability is a nightmare. APIs are often chain-specific and require juggling multiple tools. Too many blockchains, not enough standards. Lack of true decentralization. We replaced centralized databases with centralized API providers. That defeats a lot of the original purpose. Lack of true decentralization. Poor onboarding experience. For new devs, getting started with blockchain APIs often feels like being dropped into a maze with no map. Poor onboarding experience. The Bigger Picture: Blockchain vs Real-World Impact Zooming out, the bigger issue is this: blockchain as a whole is still struggling to prove its value to everyday users. Yes, there are wins in cross-border payments, identity systems, and digital collectibles. But when was the last time you used a blockchain-powered app that felt easier or better than a traditional one? This is where software development statistics help ground the conversation. While blockchain adoption has grown, traditional cloud-based platforms still dominate by a massive margin. According to recent software development statistics, over 90% of enterprise applications are still built using conventional web technologies, while blockchain-based applications make up only a tiny fraction. 90% of enterprise applications And when blockchain apps are built, they still often rely on centralized API providers. The decentralization loop remains incomplete. are The Way Forward: Can Blockchain APIs Bounce Back? Yes, but it’ll take some work. Here's what needs to change: Open Standards: We need unified APIs that can work across chains, much like REST did for the web.
More Decentralized Infra: Projects like Pocket Network are experimenting with decentralized API nodes. That’s a promising direction.
Transparent Pricing: Devs need clarity on what they’re paying for. Usage-based pricing with no surprises.
Better Docs and Tooling: Let’s make onboarding easier, not harder. Open Standards: We need unified APIs that can work across chains, much like REST did for the web. Open Standards: More Decentralized Infra: Projects like Pocket Network are experimenting with decentralized API nodes. That’s a promising direction. More Decentralized Infra: Transparent Pricing: Devs need clarity on what they’re paying for. Usage-based pricing with no surprises. Transparent Pricing: Better Docs and Tooling: Let’s make onboarding easier, not harder. Better Docs and Tooling: And maybe most importantly: let’s stop selling hype and start selling honest value. Blockchain APIs don’t need to change the world overnight. They just need to work well, reliably, and at scale. Final Thoughts: Time to Get Real The promise of blockchain APIs was exciting. But excitement doesn’t ship products. Execution does. We’re past the point of empty buzzwords. The devs are watching. The users are waiting. And the market is quietly separating the hype from the helpful. Blockchain still has potential. APIs can still be the key. But only if they evolve from shiny demos into dependable infrastructure. Until then? The hype train has left the station. It’s time to build something real.

Blockchain APIs Are Failing to Deliver Real-World Impact

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Seberapa Baik Perkongsian Skrin WebRTC, Betulkah? Saya Menguji 4 Codec

WebRTC स्क्रिन सेयरिङ कति राम्रो छ, साँच्चै? मैले ४ वटा कोडेक्स परीक्षण गरें

Yakanaka Sei WebRTC Screen Kugovera, Chaizvoizvo? Ndakaisa 4 Codecs kuMuedzo

Мубодилаи экрани WebRTC то чӣ андоза хуб аст, воқеан? Ман 4 кодекро ба озмоиш гузоштам

Regarding trading buzzwords, the word “liquidity” is very popular to throw around. Most price action trading strategies nowadays put high emphasis on concepts of liquidity and constant attempts of markets “hunting stops” of retail traders in order to initiate larger moves. Is this really the case? Is there really someone sitting behind the curtain hunting your stop-loss and can you gain a trading edge from using different tools that display “liquidity” in markets? This is what we are going to find out in this article where I cover everything you need to know about liquidity and how to implement some of the popular concepts in your trading. What is liquidity? Although you very often hear liquidity described as “stop losses of other traders” this is actually not a very accurate description of what liquidity actually is. I will talk about stops later on, but at its core, liquidity simply represents resting limit orders in any given market. The picture above shows available liquidity for BTCUSDT on Binance. For simplicity and better understanding, I increased grouping to $50 for each price point. As you can see from the image there are 70.827BTC willing to buy at 28900 and 16.608 BTC willing to sell at 28950. You can think about these levels as floors and ceilings below and above price as markets won’t be able to advance higher or lower by $50 as long as someone is going to either buy into those 16.608 BTC at the offer or sell into those 70.827 BTC at the bid. In the order book (often also called ladder, or depth of market (DOM)), you can only see limit orders. In other words, if I want to enter the new position and place a limit order in the order book, or if I want to exit the existing position via a limit order for profit. What you cannot see are stop orders (stop-losses) or liquidation prices as these are executed via market orders as default on all exchanges. Looking at the order books and heatmaps is a popular way for a lot of traders as they try to see if there is a large supply or demand coming to the market. What a lot of traders do is look at significant orders resting under or above price and think about them as “whales” creating support or resistance. This can be very tricky in practice as you should ask yourself a question why someone with a large size would want to let everyone know their intention. Many times these large outstanding orders are spoof orders, which are meant to create a false narrative about supply/demand coming into markets just to be pulled when price reach close proximity of the orders. What often ends up happening is that those who put the large orders in the market in the first place, were filling the opposite side of the trade as a lot of smaller traders were providing liquidity in an attempt to front-run the large order. Although sometimes large order is genuine, large orders are very often executed OTC, or just spread into smaller limit orders across more prices. You might have experienced an iceberg order which is a large filled position out of nowhere that is caused by spreading a lot of small limit orders into near proximity by one entity. So is there any reason to pay attention to available liquidity in markets? I would say yes, but it will highly differ based on the market you trade. If you trade forex and CFDs, these markets are traded over the counter and liquidity is gained from liquidity providers which are firms specializing in providing liquidity. In this case, there is no way for you to see the depth of the market and everything in regards to liquidity will be just pure speculation. For classic futures traded on CME or Eurex, you can see available liquidity for any exchange just by looking at DOM. Futures markets are much less liquid and fast-paced than they used to be just a few years ago and it is harder and harder to gain an edge from watching DOM alone due to the large amount of HFT (high-frequency trading) firms participating in the markets. That being said I think looking at DOM can still provide you with some decent edge, but it will take a long time and a large amount of focus to develop. Crypto is very interesting in this regard because of how much data and tools are available. It is still a very thin and fast-paced market so looking at DOM on single price points won’t give you much, same as trying to base your decision on outstanding orders as I previously mentioned they are often not genuine. One of the things I found interesting is looking at how order books are skewed to either the bid or offer side mostly on spot exchanges. Although once again these pending orders are just the “advertisement” if the supply or demand comes from more participants at the same time, the chances of it being genuine increase. Below you can see BTCUSDT on Binance and how price action reacted when the order book got significantly skewed to one side. How does liquidity determine the volatility in different markets The main thing that liquidity does is it determines how much is given market going to move. We can look at BTC and ETH or ES and NQ as an example. These markets are very highly correlated meaning that in most cases when one moves to the upside or downside, the second one is doing exactly the same. The only difference is the magnitude of how much they will move % wise. The image above shows ES and NQ on a weekly timeframe. As you can see, after March 2020 covid crash, ES rallied 120% followed by a 27% correction. At the same time, NQ rallied 150% followed by a 37% correction. There is not a huge difference between ES and NQ from the fundamental point of view as they both track the US stock market, the main difference is the fact that ES is a much more liquid market. In other words, it takes a much bigger effort to move ES compared to NQ. It is then up to you if want to trade a thicker market that moves less (has lower volatility) or a thinner market that moves more (has higher volatility). Price action and liquidity pools You have likely heard about liquidity pools and different price action strategies that are focusing on hunting liquidity (stops). This is a little funny as I already mentioned the stop orders are not technically liquidity as these orders are not visible to anyone. This makes it in regards of price action more of a buzzword to make trading look more interesting and magical. With that being said, you have probably seen many times markets trade past certain swing points just to completely reverse. The truth behind these moves is not hidden in some secret manipulation, but rather in core essence of the markets and the behavior of traders. All markets work in two-sided auctions, in other words for every buyer there has to be a seller and vice versa. Although moves past certain swing points trigger some stop-losses as they tend to accumulate in the same areas, what you will often find is that these moves trigger just a larger amount of participation than usual from both mean-reverting and trend-following traders. Markets very often tend to range rather than a trend, but retail traders tend to feel FOMO during the quick moves and very often try to participate in hopes of capturing large trends in price. This provides a great opportunity for large traders to fill their, in this case, shorts and trade the mean reversion side. If markets would be all about just “hunting stops” these moves would be often rather hectic as a large number of stops triggered at the same price points cause markets to slip several points at a time. Just ask yourself if this move in Gold was just a quick stop hunt before going back down, why would the market spend over a day in the same area before selling off? Although without a doubt some stops got triggered at the high, what came next was this two-sided auction between buyers and sellers that got resolved to the downside during the macroeconomic release. Are market makers hunting your stops? Another buzzword that goes along with liquidity is market makers. These are often described as evil entities chasing your stops. I covered this topic in more detail previously on the blog, so I will keep it short and simple here. blog Market making is delta neutral strategy that solely relies on providing liquidity to any given market. Market makers quote both bid and offer at the same time and their main goal is to not have directional exposure to the market, they don’t make money by trying to capture where markets are going to go next, but by capturing the spread between bid and the ask. Because of that the sharp “stop-hunts” are actually the least favorable scenario for market makers as they often end up having the directional exposure in the underlying asset. The image above shows the ideal scenario where market makers provide liquidity in lower volatility environments and make money on both sides of the market. Does this mean there is no manipulation in financial markets? Of course not. Every market whether if its forex, crypto, or futures has its fair share of manipulation and it is always done by different actors. There are many cases of manipulation in Forex by large banks. In crypto things get even worse and the overall market is much less liquid and therefore easier to manipulate there are many cases of firms creating a false demand for coins only to dump them on retail later on. With that being said, thinking that someone is hunting your 1 contract with your stop loss placed above a swing high on a 5-minute timeframe is more than silly. Liquidations and seeing stops In crypto, you can always see your liquidation price. This is a price point where the exchange will automatically close your position as you do not have enough margin to maintain the position opened. Liquidation prices, same as stop losses, are not available to see in the order books. The only way for you to see liquidations or large amounts of stops getting hit is after the facts in these called liquidation cascades. These are characterized by sharp moves in price action and large decreases of open interest (positions getting forcefully closed) and in some software like Coinalyze which you can see on the chart below, liquidations are also plotted as indicator (below the open interest). Coinalyze Without naming any, there are some software in crypto that sell users access to liquidation heatmaps that basically plot levels of liquidations on your chart. The indicator above shows “liquidation” levels and is made by Leviathan, it is completely free so you can test it out yourself. Indicators and software like these only plot liquidation levels as estimates derived from changes in the open interest, and you can see on the chart that most of these levels tend to be at very logical places like above resistance or below the support. In my opinion, it is much smarter to watch how markets behave at these levels in regard to changes in open interest, order flow, and relative volume rather than trying to pre-emptively fade every single move because someone will get liquidated there. If anything, after large spikes in price that cause bigger amounts of stops/liquidations getting triggered, markets tend to slow down and form accumulation/distribution rather than moving in straight lines up and down. Conclusion Throughout the years liquidity has been used as a huge buzzword in the trading community. For most people, it is largely misunderstood and often presented as a pure marketing trick to make you buy anything that will give you an “insider” look into financial markets. In many cases, it takes only common sense to find areas where other traders might be stopped out and even if you are not able to do that, you will very often have plenty of time to position yourself after the event by utilizing simple concepts of price action, volume, and orderflow.

No, the Market Isn’t ‘Hunting’ Your Stop-Loss—Here’s What’s Really Happening Instead

አይ፣ ገበያው የማቆሚያ-ኪሳራህን ‘ማደን’ አይደለም - ይልቁንስ በእውነቱ ምን እየሆነ ነው

Janiwa, Markasax janiw ‘Hunting’ Stop-Loss ukar uñt’aykiti —Akanx Kunatix Chiqpachans Uka lantix luraski ukawa

No, el mercat no està "caçant" el vostre stop-loss; aquí teniu el que realment està passant

No, el mercado no está “cazando” su stop loss: esto es lo que realmente está sucediendo

Non, o mercado non está "cazando" o seu stop-loss; aquí está o que realmente está a suceder.

いいえ、市場はあなたのストップロスを「狩っている」わけではありません。代わりに実際に何が起きているのか、ここにあります。

ບໍ່, ຕະຫຼາດບໍ່ແມ່ນ 'ການລ່າສັດ' ການສູນເສຍຢຸດຂອງເຈົ້າ - ນີ້ແມ່ນສິ່ງທີ່ເກີດຂື້ນແທ້ໆແທນ

Ne, rinka „nemedžioja“ jūsų „stop-loss“ – štai kas iš tikrųjų vyksta

نه، بازار ستاسو د تمځای له لاسه ورکولو 'ښکار' نه کوي — دلته هغه څه دي چې واقعیا پیښیږي پرځای یې

නැහැ, වෙළඳපොළ ඔබේ නැවතුම් පාඩුව 'දඩයම්' කරන්නේ නැහැ - ඒ වෙනුවට ඇත්තටම සිදුවන්නේ කුමක්ද යන්න මෙන්න

Nej, marknaden "jagar" inte din stop-loss - här är vad som verkligen händer istället

Không, thị trường không ‘săn’ lệnh dừng lỗ của bạn—Đây là những gì thực sự đang xảy ra

**NEW YORK, United States, March 19th, 2025/Chainwire/--**Arda, a company developing a universal operating system for real estate, has raised $3 million in a pre-seed funding round led by Lightshift. The investment supports Arda’s initiative to enhance efficiency in the $380 trillion real estate sector by addressing its fragmented and opaque infrastructure. Arda is building a digital framework designed to unify real estate assets, data, and financial services into a single, interoperable system. Leveraging AI and blockchain technology, the platform aims to reduce transaction times and enhance transparency by enabling real-time ownership and financial processes. A Purpose-Built Digital Infrastructure for Real Estate Arda’s platform introduces a programmable execution layer for real estate, integrating digital rights management, AI-driven automation, and seamless data interoperability. Key features include: Digital Asset Profiles: A verifiable record of property history to streamline due diligence.
Real-Time Money Movement: Payments directly linked to transactions for improved settlement efficiency.
Integrated Data and Automation: Automated workflows reducing administrative friction in asset management. Digital Asset Profiles: A verifiable record of property history to streamline due diligence. Real-Time Money Movement: Payments directly linked to transactions for improved settlement efficiency. Integrated Data and Automation: Automated workflows reducing administrative friction in asset management. Industry Leadership and Institutional Backing Arda was founded by Oli Harris, former Managing Director in Digital Assets at Goldman Sachs and Head of Crypto Assets Strategy at JPMorgan Chase. Harris played a key role in the development and commercialization of Quorum, an enterprise Ethereum platform, and contributed to institutional digital asset adoption. His experience includes board positions at Anchorage, BitGo, Blockdaemon, and TRM Labs. “Real estate remains one of the most valuable yet operationally fragmented asset classes,” said Oli Harris, Founder & CEO of Arda. “By creating a programmable, trust-based system, we aim to bring real estate transactions in line with modern digital financial infrastructure.” “Real estate remains one of the most valuable yet operationally fragmented asset classes,” said Oli Harris, Founder & CEO of Arda. “By creating a programmable, trust-based system, we aim to bring real estate transactions in line with modern digital financial infrastructure.” A Market Evolving Toward Digitization As institutional investment in digital assets grows, governments and industry leaders are exploring frameworks for secure, verifiable digital property transactions. Regions such as the Gulf are advancing adoption, while U.S. policymakers are shaping regulations to integrate digital assets into mainstream financial systems. “We invest in founders who are building the foundations of next-generation industries,” said Simao Cruz, Founding Partner at Lightshift. “Arda is developing purpose-built infrastructure that could redefine how real estate is owned, transacted, and financed.” “We invest in founders who are building the foundations of next-generation industries,” said Simao Cruz, Founding Partner at Lightshift. “Arda is developing purpose-built infrastructure that could redefine how real estate is owned, transacted, and financed.” About Arda Arda is developing a real estate operating system designed for institutions, enterprises, and governments. Its platform enables digital interoperability, automated transactions, and real-time execution in the global real estate sector. Founded by Oli Harris, Arda is backed by investors supporting the evolution of digital real estate infrastructure. For more information, users can follow @arda_labs on X (Twitter) or contact contact@arda.xyz. About Lightshift Lightshift is a venture firm investing in early-stage companies focused on digital infrastructure and programmable assets. The firm partners with founders to provide capital, technical expertise, and strategic insights for market-defining innovation. Lightshift Lightshift For more information, users can visit www.lightshift.xyz or contact katy.campbell@lightshift.xyz. www.lightshift.xyz www.lightshift.xyz Contact CEO and Founder Oliver Harris Arda oli@arda.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Arda Raises Pre-Seed To Build Real Estate’s Operating System,Led By Ex-Goldman MD & JPMC Crypto Head

Arda verhoog Pre-Seed om Real Estate se bedryfstelsel te bou, gelei deur oud-Goldman MD & JPMC Crypto Head

Arda zvyšuje předběžnou přípravu k vybudování operačního systému Real Estate, vedené bývalým Goldmanem MD a krypto hlavou JPMC

Arda recauda capital inicial para construir el sistema operativo inmobiliario, liderado por el exdirector general de Goldman Sachs y el director de criptomonedas de JPMC.

Arda levanta pre-seed para construír o sistema operativo de inmobles, liderado polo ex-Goldman MD e JPMC Crypto Head

Arda、元ゴールドマンMD兼JPMC暗号資産責任者が率いる不動産のオペレーティング システム構築のためにプレシード資金を調達

Arda ຍົກສູງບົດບາດເບື້ອງຕົ້ນເພື່ອສ້າງລະບົບປະຕິບັດງານຂອງອະສັງຫາລິມະສັບ, ນໍາໂດຍ Ex-Goldman MD & JPMC Crypto Head

Арда экс Голдман MD & JPMC Crypto Head тэргүүтэй үл хөдлөх хөрөнгийн үйлдлийн системийг бий болгохын тулд урьдчилсан үрийг өсгөв.

Arda e Godiša Peu ya Pele Go Aga Real Estate’s Operating System,E eteletšwe pele ke Ex-Goldman MD & JPMC Crypto Head

முன்னாள் கோல்ட்மேன் எம்.டி & ஜே.பி.எம்.சி கிரிப்டோ தலைவரால் வழிநடத்தப்படும் ரியல் எஸ்டேட்டின் இயக்க முறைமையை உருவாக்க ஆர்டா முன் விதைகளை திரட்டுகிறது.

ኣርዳ ቅድመ ዘርኢ ኣዕቢዩ ሪል እስቴት’ስ ኦፐሬቲንግ ሲስተም ንምህናጽ፣ብ Ex-Goldman MD & JPMC Crypto Head ዝምራሕ

Arda huy động vốn trước để xây dựng hệ điều hành bất động sản, do cựu giám đốc điều hành Goldman và giám đốc tiền điện tử JPMC dẫn đầu

I-Arda iphakamisa iMbewu yangaphambili ukuze kwakhiwe iNkqubo yokuSebenza ye-Real Estate, ekhokelwa ngu-Ex-Goldman MD kunye ne-JPMC Crypto Head

Hello AI Enthusiasts! Welcome to the tenth edition of "This Week in AI Engineering"! "This Week in AI Engineering" Google released Gemma 3 with a powerful 27B parameter version, AI21 Labs launched Jamba 1.6 outperforming Mistral and Llama on long-context tasks, Manus AI emerged as a breakthrough autonomous agent with 95% task completion, and Sesame AI just made AI girlfriends real by achieving human-level speech quality with emotional intelligence. With this, we’ll also be talking about some must-know tools to make developing AI agents and apps easier. AI Girlfriends Just Got Real AI Girlfriends Just Got Real Sesame AI has introduced a voice-based AI companion focused on achieving genuine "voice presence" through emotional intelligence and contextual adaptation. The system moves beyond traditional text-to-speech by incorporating conversational dynamics to create more engaging and natural interactions. Seems like AI girlfriends just got real! Sesame AI Sesame AI Technical Architecture: Technical Architecture: Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness
Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output
Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M)
Training Scale: Processed approximately one million hours of predominantly English audio Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness Conversational Speech Model (CSM): Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output Split Tokenization System: Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M) Three-Tier Model Range: Training Scale: Processed approximately one million hours of predominantly English audio Training Scale: Performance Metrics: Performance Metrics: Word Error Rate: Small model matches human ground truth at 2.9% WER
Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline)
Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI
Pronunciation Consistency: 90% for the Medium model, outperforming all competitors
Subjective Testing: 52.9% win rate against human references in blind no-context tests Word Error Rate: Small model matches human ground truth at 2.9% WER Word Error Rate: Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline) Speaker Similarity: Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI Homograph Disambiguation: Pronunciation Consistency: 90% for the Medium model, outperforming all competitors Pronunciation Consistency: Subjective Testing: 52.9% win rate against human references in blind no-context tests Subjective Testing: Key Innovations: Key Innovations: Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency
Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context
Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency
Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency Compute Amortization: Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context Contextual Adaptation: Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency Novel Benchmarks: Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Multi-Stage Processing: The technology fundamentally reimagines voice assistants by focusing on the emotional and contextual elements that make human communication meaningful, addressing the "emotional flatness" problem that limits user engagement with current systems. Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI is an autonomous agent system that executes complex tasks end-to-end and achieves 95% task completion without continuous user supervision. The China-based platform is gaining significant attention as the "second DeepSeek moment" for AI, moving beyond conversation to focus on end-to-end task automation. Manus AI Manus AI Technical Architecture: Technical Architecture: Asynchronous Processing: Cloud-based task execution without requiring active user monitoring
Preference Learning: Adaptive system that optimizes outputs based on previous user interactions
File Management: Native capabilities for working with complex document types and compression formats
Output Customization: Flexible delivery formats including documents and spreadsheets Asynchronous Processing: Cloud-based task execution without requiring active user monitoring Asynchronous Processing: Preference Learning: Adaptive system that optimizes outputs based on previous user interactions Preference Learning: File Management: Native capabilities for working with complex document types and compression formats File Management: Output Customization: Flexible delivery formats including documents and spreadsheets Output Customization: Performance Metrics: Performance Metrics: GAIA Score: >65% benchmark rating on general AI agent intelligence assessments
Task Completion: 95% success rate across varied task types
Response Time: 2.5s average response latency during interactions
User Satisfaction: 92% positive rating from current users
Integration Capacity: 100+ external tool connections for expanded functionality GAIA Score: >65% benchmark rating on general AI agent intelligence assessments GAIA Score: Task Completion: 95% success rate across varied task types Task Completion: Response Time: 2.5s average response latency during interactions Response Time: User Satisfaction: 92% positive rating from current users User Satisfaction: Integration Capacity: 100+ external tool connections for expanded functionality Integration Capacity: Comparative Analysis: Comparative Analysis: Task Performance: Consistently outperforms competitors by 10-15% across key metrics
User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025
Use Cases: Excels in data analysis, content creation, document processing, and decision support
Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Task Performance: Consistently outperforms competitors by 10-15% across key metrics Task Performance: User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025 User Growth: Use Cases: Excels in data analysis, content creation, document processing, and decision support Use Cases: Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Accuracy Rating: The platform bridges the gap between conception and execution, enabling users to delegate tasks like resume screening, stock analysis, and educational content creation while receiving notification upon completion, without requiring continuous monitoring. Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google has introduced Gemma 3, a collection of open-source AI models designed for efficient operation on consumer hardware while delivering frontier-level performance. The family includes four parameter sizes (1B, 4B, 12B, and 27B) with multimodal capabilities, 128K token context window, and support for over 140 languages. Gemma 3 Gemma 3 Technical Architecture: Technical Architecture: Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions
Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage
Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions Vision Integration: Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage Context Management: Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Token Processing: Performance Metrics: Performance Metrics: Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304)
MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%)
HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B
MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304) Chatbot Arena: MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%) MATH Benchmark: HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B HumanEval: MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation MMLU: Key Innovations: Key Innovations: Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements
Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens
Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization
Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements Architecture Optimization: Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens Multimodal Processing: Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization Language Coverage: Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Knowledge Distillation: Google has also released ShieldGemma 2, a 4B parameter image safety classifier built on the Gemma 3 foundation, providing developers with ready-made solutions for content moderation across dangerous content, and violence categories. Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks AI21 Labs has released Jamba 1.6, a new family of open-source models designed for enterprise deployments where data privacy and performance are critical requirements. The lineup includes Jamba Large 1.6 and Jamba Mini 1.6, both optimized for quality, speed, and extended context handling. Jamba 1. Jamba 1. 6 Technical Architecture: Technical Architecture: Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing
Context Window: 256K tokens supported without performance degradation
Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini
Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing Hybrid Design: Context Window: 256K tokens supported without performance degradation Context Window: Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini Inference Speed: Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Deployment Options: Performance Metrics: Performance Metrics: Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67
CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+
HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2
LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+
HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67 Arena Hard: CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+ CRAG: HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2 HELMET RAG: LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+ LongBench: HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 HELMET LongQA: Enterprise Implementations: Enterprise Implementations: Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini
Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots
Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality
E-commerce: Automated conversion of inventory databases to structured product descriptions Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini Retail (Fnac): Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots Education (Educa Edtech): Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality Digital Banking: E-commerce: Automated conversion of inventory databases to structured product descriptions E-commerce: The new Batch API allows enterprises to handle large volumes of requests asynchronously, supporting high-volume data processing with faster turnaround times than traditional batch systems. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems.
The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments.
Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time.
Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems. N8n N8n The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments. The Eclipse Theia IDE The Eclipse Theia IDE Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time. Adrenaline Adrenaline Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. Wren AI Wren AI And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this newsletter with your fellow AI enthusiasts and follow for more weekly updates. Until next time, happy building!

AI Girlfriends Just Got Real

Software development and programming, once regarded as endeavors that required deep expertise, can now be done by anyone using natural language. A feature that used to take days or months to develop can now be developed in minutes or hours thanks to code from an AI model. For example, OpenAI Codex and Google BERT are trained on programming web blogs, Stack overflow questions, etc. These intelligent models create code through mathematical probabilities while they are also known to hallucinate and present false information. A research done by academia claims that AI code generation is the leading cause for top 10 vulnerabilities and nearly 40% of code has security bugs. Many of the leading players, along with new SaaS providers are leveraging AI for making their offerings smarter. SaaS programmers too, must be more knowledgeable about AI-based SaaS tools. nearly 40% of code has security bugs What Makes AI-Generated Code Unsafe? Following programming standards and code quality dictates software safety. But, AI models are trained on every bit of information available on the internet. The code quality, reliability, security, and more might differ from that which is generated by human developers. A model trained on web development examples may contain poor data validation practices, for instance. This lack of validation can lead to security issues when the model generates code that adopts the same poor practices. 5 Indicators That Suggest Code Contains Security Weaknesses No matter the size (million or billion of parameters) the mоdels are known to hallucinate and make incorrect predictions. When a typical developer sees the code that an AI produces, they will miss the subtle but serious security vulnerabilities. However, for a developer who has complete knowledge of design and development patterns, flaws are a review away from being identified. Developers can leverage these patterns to discover vulnerabilities and align with SaaS security best practices. SaaS security best practices 1. Type Inference and Input Validations are Not Enforced Modern frameworks and libraries rely heavily on interface/enum for inference and validation. This guarantees that the code does its job accurately and imposes security. AI-generated code won't infer unless we direct it. Even after crafting a careful prompt, type mismatch and validation enforcement may not match the use case. To locate and amend code mismatches, developers must be well aware of the domain and business requirements. def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result 2. Non-Standard State and Context Sharing Between Classes/Objects Programs share objects in Public/Private/Protected ways. Higher order functions and classes inherit object state by accessing public/protected variables directly to perform computations. If something is done incorrectly in implementation or execution, security or performance bottlenecks may easily occur. SaaS developers must implement their state and context management logic appropriately and review it for correct and safe use. class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password 3. Weak Implementation of Data Handling and Sharing Techniques Services share and receive information over the network. These days, secure connectivity and data handling have been crucial to the success of cloud-based systems. When reading, processing, and sharing sensitive data on organizations through distributed data networks, strong protocols and security techniques must be in place to prevent data interception. Using AI, the SaaS developer has to implement every single aspect of the architecture in full-fledged applications. data interception #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) 4. Inadequate Secrets and Auth Handling 4. Inadequate Secrets and Auth Handling In today's cyber sensitive world, tight RBAC implementation is a must for access control to secure identity while maintaining privacy and compliance. When the code is generated by an LLM, by default there will be a mechanism that connects and authenticates with the auth provider with basic implementation. Simple solutions are not enough to stay secure with emerging cyber threats. While adding custom functionalities developers must test thoroughly to ensure security and auth handling is strongly implemented. RBAC implementation RBAC implementation # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) 5. Outdated Dependencies with Deprecated Functionality Usage AI programming is being directed by libraries and frameworks made by the community and open-source. People support new technology by using these promising tools and creating new ones. The data these models were trained on is not up to date and the model capabilities are frozen, and so is their knowledge. With the development of technology, a lot of features will become obsolete and some libraries won't be relevant to current needs. A SaaS developer is tasked with the review and usage of valid dependencies to ensure functionality and security. import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() Tips to Make AI-Generated Code Safe to Use The advanced coding capabilities of Large Language Models is due to their extensive mathematical calculations. No fancy techniques are needed to make it compliant with security and programming standards. We can use these simple checks to make AI-generated code safe and compliant with standards: Code review with security and architecture teams should be a standard part of your life cycle.
Integrate automated security testing and validation steps in version control tools.
Include dependency and compliance checks in testing KPIs.
Adopt Zero-Trust architecture with static and dynamic security testing tools.
Leverage DevSecOps practices and shadow AI. Code review with security and architecture teams should be a standard part of your life cycle. Integrate automated security testing and validation steps in version control tools. automated security testing Include dependency and compliance checks in testing KPIs. Adopt Zero-Trust architecture with static and dynamic security testing tools. Leverage DevSecOps practices and shadow AI. Handling Unsafe AI-Generated Code with a Simple Github Action No matter how carefully we review and audit the code, chances of human error are always there. Relying solely on manual audits is not enough as we need to have some predefined checks that can test and validate the code as soon as it enters the version control system. What better check than to add a Github action which automatically runs security and quality checks when a PR is raised. name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) Conclusion Large language models are quite useful tools for SaaS developers to generate code and information with natural prompts. However, they pose security risks and sometimes deliver non-performant code that doesn't suit enterprise needs. SaaS developers must be very careful when using these tools and implementing AI-generated code for real-life use cases. This practical guide focuses on the factors that arise and influence security posture while showing how to overcome these challenges.

Is Your AI-Generated Code Really Secure?

هل الكود الذي تم إنشاؤه بواسطة الذكاء الاصطناعي الخاص بك آمن حقًا؟

আপনার এআই-জেনারেটেড কোড কি সত্যিই নিরাপদ?

¿Es realmente seguro su código generado por IA?

Il tuo codice generato dall'intelligenza artificiale è davvero sicuro?

AI 生成コードは本当に安全ですか?

តើលេខកូដដែលបង្កើតដោយ AI របស់អ្នកពិតជាមានសុវត្ថិភាពមែនទេ?

Je váš kód vygenerovaný AI skutočne bezpečný?

Yako AI-Yakagadzirwa Kodhi Yakachengeteka Chaizvoizvo?

Да ли је ваш код генерисан вештачком интелигенцијом заиста безбедан?

Är din AI-genererade kod verkligen säker?

ብAI ዝፍጠር ኮድካ ብሓቂ ውሑስ ድዩ?

کیا آپ کا AI سے تیار کردہ کوڈ واقعی محفوظ ہے؟

Michael Vasilkovsky didn’t start his career aspiring to transform how millions of people interact with cutting-edge technology. Yet today, as a Technical Lead specializing in generative AI at Snap, he has established himself at the forefront of a rapidly expanding field that aims to redefine digital experiences—from the creation of intricate 3D assets to the real-time generation of video. “I’m fascinated by how technology can evolve from a purely research-driven endeavor into products that genuinely reshape human creativity,” Vasilkovsky says. His journey spans top-tier research, hands-on product development, and leadership roles, culminating in innovations that serve an expansive user base and push the boundaries of what generative AI can accomplish. Vasilkovsky’s trajectory began in academic research, where he first encountered computer vision challenges that demanded more than conventional machine learning solutions. He published in prominent forums like CVPR and SIGGRAPH, earning early recognition among the global research community. Over time, he noticed that while many researchers were producing impressive proofs of concept, only a fraction made it into actual products that people could use every day. “Translating research into something consumers find intuitive and valuable is a real challenge,” he reflects. “It’s not enough to create a brilliant model; you need to optimize its performance, build infrastructure around it, and then ensure it can withstand the real-world demands of millions, if not hundreds of millions, of users.” His professional leap came when he joined Snap in 2021, just as augmented reality was accelerating in popularity. Instead of focusing exclusively on AR filters or lenses, Vasilkovsky set his sights on foundational 3D models, exploring how generative AI could revolutionize asset creation. “A typical 3D model for a game or AR application can take over 40 hours to develop from scratch,” he explains. “We are now cutting that time down to minutes, unlocking entirely new levels of creative freedom.” Indeed, one of his hallmark achievements is leading a suite of generative AI tools that allow both novices and professionals to produce high-grade 3D assets using simple text prompts or images. This dovetails with the growing market appetite for AI-powered content creation; according to the IBM Global AI Adoption Index 2022, 35 percent of businesses today use AI in their operations (Source: IBM). While Snap’s platform is home to hundreds of millions of daily users, Vasilkovsky’s focus extends beyond the specifics of the company’s ecosystem. He emphasizes the broader implications of generative AI for anyone looking to streamline content production or develop immersive experiences. “We’re seeing a paradigm shift. An artist without extensive technical knowledge can now create a 3D asset or short animated clip by describing what they want in a single sentence,” he points out. “That empowerment fosters new types of creativity and drives economic opportunities for both individual creators and businesses.” Statistics highlight the accelerating demand for generative AI solutions. A 2022 report by Omdia predicts the generative AI market will surge from an estimated $1.2 billion in 2022 to $10.8 billion by 2027 (Source: Omdia). In part, that explosive growth reflects an increasing awareness across industries—from gaming to e-commerce—of AI’s potential to lower production costs and shorten go-to-market timelines. “If a fashion retailer wants to showcase a new clothing line in a virtual runway setting, historically they’d need specialized 3D artists, hours of rendering time, and complicated workflows,” Vasilkovsky notes. “Now, they can generate and animate digital garments in a fraction of the time and cost.” His unique vantage point allows him to see how generative AI can be applied to user-driven experiences, particularly those involving augmented or virtual reality. One of his notable projects involves integrating text-to-3D and image-to-3D pipelines into creation platforms, enabling creators to build more compelling visuals that can be rigged to human movements. “Anyone can place these 3D objects into an interactive environment,” he says. “We’ve proven that generative 3D assets can be effectively integrated into consumer-facing experiences with minimal friction. That’s the real breakthrough: bridging research with real-world application.” A hallmark of Vasilkovsky’s career is his holistic approach to development. While many AI specialists are accustomed to working within the silo of research labs, he manages projects through the entire lifecycle—from paper submissions to deploying a final product. “It’s not just about building the model or the algorithm. You have to think about performance optimization, server loads, user interface design, and the moral or ethical questions around AI-generated content,” he says. His track record of building infrastructure that can handle large-scale use is rooted in practical necessity: Snap’s augmented reality features alone engage a massive audience every day. “When you have a platform that sees hundreds of millions of daily active users, your innovations need to be robust and responsive,” Vasilkovsky adds. Part of that robustness comes from actively collaborating with cross-functional teams, including data scientists, software engineers, and product managers. He considers these collaborations critical not just for scaling AI models, but also for ensuring they align with human behavior and creative needs. “A specialized model might perform flawlessly in the lab, but if the average user finds it unintuitive or slow, it’s essentially useless,” he remarks. “We discovered that giving creators a text-prompt-based interface significantly lowered the entry barrier. That’s when generative AI starts to truly democratize creation.” His work has garnered attention in media circles, with outlets like Reuters, TechCrunch, and Maginative covering the launch of advanced generative AI tools and video generation models that incorporate camera control. While these achievements often reference Snap’s ecosystem, Vasilkovsky is cautious not to oversell the company itself. “I don’t view this purely as a Snap story,” he explains. “Yes, we have a large user base and a thriving augmented reality community, but the real narrative is about how generative AI can disrupt and improve content creation across industries.” Looking ahead, Vasilkovsky sees video as the next major frontier. Existing models often require significant time—sometimes minutes per second of content—to render passable outputs, which is hardly practical in an age of instant media. “Waiting five or six minutes for a short video clip might be acceptable in certain production environments, but it’s not going to spark mainstream adoption,” he asserts. His goal is to dramatically reduce these generation times while preserving and even improving quality. “We want to allow people to create longer, more engaging videos, but that means tackling a host of new challenges. Video generation is inherently more complex than still-image generation: motion dynamics, camera angles, transitions, and even user interactivity come into play.” To address these complexities, Vasilkovsky envisions new editing tools that go beyond typed prompts. “It’s one thing to describe a scene with words; it’s another to precisely control camera movement or the timing of certain actions,” he says. “I’m looking to merge generative AI with more intuitive user interfaces, so individuals can direct their video content much like a film director. That involves advanced rigging techniques, timeline editing, and real-time previews.” In discussing these aspirations, he underscores the economic impact. According to a 2022 McKinsey study, 44 percent of organizations surveyed reported cost savings from AI adoption in various business units (Source: McKinsey & Company). As AI-based video generation becomes more efficient, it stands to slash production budgets for marketing agencies, film studios, and freelancers. “The tools we’re building aren’t just flashy demos; they could shift how entire industries operate,” he notes. “That’s a compelling reason to invest heavily in both research and productization. We’re creating new efficiencies, new workflows, and ultimately new opportunities.” Part of Vasilkovsky’s vision includes democratizing these tools so that smaller businesses and individual creators also benefit. “Too often, innovations get locked behind large enterprises. I’d like to see generative AI-driven video creation become as commonplace as smartphone photography,” he says. “Widespread access is where you see real leaps in creativity and economic development. I want budding entrepreneurs, educators, and everyday social media users to tap into this potential.” When asked about the human element—inevitable discussions around AI ethics, data usage, and potential displacement of artists—he’s candid that these are meaningful conversations that require ongoing attention. “AI doesn’t replace human creativity; it augments it,” he says. “But there’s a learning curve. We need guidelines and best practices for how people use these generative tools. My hope is that it remains a catalyst for collaboration, not a cause for fear.” He adds that transparency and user education will be crucial. By making the mechanics of AI clearer and user-friendly, he believes that people will better embrace the technology and use it responsibly. Ultimately, Vasilkovsky measures success by how seamlessly generative AI integrates into people’s creative processes. “We’re at a point where AI can remove technical barriers and open up new forms of expression,” he says. “If our tools inspire someone who’s never touched 3D design or video production to create something original—and even share it with a global audience—then we’ve succeeded.” That democratizing ethos underpins his entire career. From publishing top-tier research to launching real-world products, he has consistently advocated for bridging the gap between cutting-edge technology and everyday experiences. In doing so, he has become one of the leading voices in generative AI, shaping the immediate future of content creation and carving out a blueprint for its long-term societal and economic impact. “Every new tool or model we build can reshape the creative landscape,” he says. “I want to keep pushing those boundaries—faster video generation, more immersive 3D content, easier editing tools. The end goal is to empower as many people as possible to tell their stories. That’s what excites me most about generative AI: it’s not just for big corporations or specialized studios. It’s for all of us.”

Michael Vasilkovsky Traded Research For Real-World GenAI and Is Now Transforming Content Creation

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