Surging Ethereum (ETH) Scaling Solution Sees Rise in Network and Developer Activity Despite Crypto Bear Market

dailyhodlPubblicato 2022-07-30Pubblicato ultima volta 2022-07-30

Introduzione

Polygon (MATIC) is witnessing surging network and developer activity in the second quarter of 2022 despite the ongoing crypto bear market.

Polygon is a scaling solution that aims to enable the widescale adoption of decentralized apps on the Ethereum (ETH) blockchain.

The Polygon chain is now home to 5.34 million unique addresses, a 12% increase from the first quarter of the year, according to the project’s Twitter account. Second quarter transactions also increased by 4%, and the average cost of transactions fell by fell 49% to $0.018.

Additionally, the nonfungible token (NFT) marketplace OpenSea witnessed 1.2 million new non-fungible tokens (NFTs) come online on Polygon in Q2.

Developer activity is also skyrocketing: The network added an average of 1,000 new contract creators a day in Q2. That brings the project’s total to 90,514, “more than triple the pace of growth” of Q1, according to Polygon. Additionally, an average of 2,700 new contracts went live on the project’s chain each day.

Polygon was also recently chosen by the Walt Disney Company to participate in its new 2022 Disney Accelerator program.

The accelerator offers chosen companies “access to the range of creative expertise and resources of The Walt Disney Company to help them develop new entertainment experiences and products,” according to the program’s website.

Polygon’s native token, MATIC, is trading at $0.95 at time of writing. The 12th-ranked crypto asset by market cap is up nearly 10% in the past 24 hours.

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WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

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WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

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Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

Notion's growth from a niche note-taking tool to a platform with 100 million users is powered by three interconnected flywheels: Product-Led Growth (PLG), a Template Economy, and Community-Driven Growth. First, Notion's PLG strategy relies on a highly flexible, "plastic" product that users can adapt to countless personal and team workflows. Its freemium model lowers the barrier to entry, while features like page sharing and collaboration drive organic, usage-based viral growth as users naturally invite others. Second, the Template Economy solves the "blank page" problem. Templates, created by both Notion and its community, transform abstract product capabilities into concrete, copyable solutions for specific scenarios (e.g., project management, content calendars). This dramatically lowers activation costs for new users and fuels SEO-driven discovery. Third, a vibrant Community acts as a distributed growth engine. Users and official Ambassadors create tutorials, share use cases, and host local events. This community not only educates users but also fosters a sense of identity around pursuing "better ways of working," strengthening loyalty and enabling global, low-cost expansion. Together, these flywheels create a self-reinforcing ecosystem: a great product attracts users who create templates and community content, which in turn attracts more users and deepens engagement. This system allowed Notion to scale from individuals to teams and enterprises through a bottom-up adoption path. Looking ahead, AI integration promises to accelerate these flywheels further by making templates smarter and the platform a potential AI-native work operating system. Ultimately, Notion's defensible advantage is not just its features, but this deeply entrenched network of user assets, creators, and community trust.

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Deconstructing Notion's Growth: From a Note-taking Tool to 100 Million Users—How Notion Built a Triple Growth Flywheel Through Product, Templates, and Community

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$10 Billion, Qualcomm to Acquire Chip Legend Jim Keller's Company

Global mobile chip giant Qualcomm is in advanced talks to acquire AI chip startup Tenstorrent in a deal valued between $8-10 billion, according to media reports. This potential acquisition would be one of the largest in the AI chip sector in recent years. Tenstorrent, led by legendary chip architect Jim Keller, has gained prominence for its RISC-V architecture and AI accelerator designs. The move highlights Qualcomm's strategic push to diversify beyond its core smartphone chip business. As the smartphone market matures, Qualcomm is aggressively targeting growth in automotive, data center, and cloud AI. Acquiring Tenstorrent would allow Qualcomm to rapidly enter the high-end AI computing market, bypassing lengthy in-house development cycles. Tenstorrent's cost-effective system architecture, which avoids expensive HBM memory and relies on standard Ethernet for clustering, offers a potential alternative to Nvidia's costly solutions. Furthermore, Tenstorrent's high-performance RISC-V CPU technology and its focus on the automotive and edge computing segments align with Qualcomm's strategic goals, including its "Snapdragon Digital Chassis" platform. Despite the strategic rationale, the high valuation has sparked some investor caution. The successful integration of Tenstorrent's open-source culture and independent team into Qualcomm's organization, along with the commercialization of its technology, remains a key challenge.

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$10 Billion, Qualcomm to Acquire Chip Legend Jim Keller's Company

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CARDS' Brutal Truth of $535M FDV: Only $43M Net Revenue, Profit Margin Halved

The article deconstructs Collector Crypt (CC), a blockchain-based platform for trading tokenized collectible cards, revealing a significant disparity between its high volume and actual business fundamentals. Key findings include: * CC's cumulative revenue of $635M is misleading; 90.6% is instantly returned to users via card buybacks, leaving only $43M in net revenue (6.7% retention). * Trading activity is minimal, with real secondary market trading below $5M. eBay sales as a percentage of volume have declined for six consecutive quarters. * The platform's user base is highly concentrated, with a few dozen high-frequency wallets driving most of the volume, resembling a "casino" with ~420 daily active players. * As volume shifts to higher-priced card packs, the net profit margin has halved from 11.2% to 5.8%. * Token value capture (via burns and buybacks) totals only $1.4M, just 3.4% of net revenue. Meanwhile, operational wallets have off-ramped $45.7M in USDC. * With a Fully Diluted Valuation (FDV) of ~$535M, the token trades at 7.3x net revenue. The float is only 20.5%, with 72% of the supply allocated to insiders and locked until November 2027. The analysis concludes that while CC has found product-market fit as a high-speed gacha machine, it shows little evidence of evolving into a sustainable collector's marketplace, with minimal value accruing to its CARDS token.

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CARDS' Brutal Truth of $535M FDV: Only $43M Net Revenue, Profit Margin Halved

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Arthur Hayes-Linked Wallet And Whales Accumulate Tens Of Millions In ETH

On-chain tracker Lookonchain reports significant Ethereum accumulation by a wallet linked to Arthur Hayes, purchasing 1,400 ETH (worth ~$2.51 million). This follows a prior transaction of 3,000 ETH to a possibly Hayes-linked wallet. The activity highlights renewed whale interest as ETH faces market-wide pressure. While such large-scale buying can support market sentiment by suggesting stronger hands see value, analysts caution that wallet attribution is not a direct personal confirmation. The key for traders is whether this accumulation is part of a broader pattern that can help ETH defend key support levels. The signal is considered one input among many; price confirmation and broadening spot demand beyond isolated whale wallets are still needed for a convincing recovery setup. Observers are watching for sustained exchange withdrawals to gauge longer-term positioning.

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Arthur Hayes-Linked Wallet And Whales Accumulate Tens Of Millions In ETH

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