Объём транзакций в сети Base достиг рекордного уровня

investing.ruPublicado a 2024-09-15Actualizado a 2024-09-15

Happycoin.club - По данным аналитической компании Nansen, по количеству активных пользователей сеть второго уровня Base опередила большинство своих конкурентов, в частности, блокчейны Avalanche, Polygon и Cronos.

В настоящее время число активных адресов в сети Base подскочило до рекордно высокого уровня более 1,964 млн. Для сравнения, зафиксированный в прошлом году минимум составлял всего 196000.

Другой рекордный показатель Base — объём транзакций. Число обрабатываемых сетью операций превысило 4,8 млн, по сравнению с менее чем 300 000, зарегистрированными в январе этого года.

Прирост показателей Base особенно впечатляют на фоне потерь других блокчейнов. К примеру, количество активных адресов и транзакций в Avalanche сократилось более чем на 50% по сравнению с самым высоким уровнем 2024 года.

Запущенная в 2023 году Base набирает популярность благодаря скорости и низкой платы за газ. Так, по мере увеличения количества транзакций общий объём комиссий упал с более чем $2,3 млн в марте до текущих $50 425. В этом году сеть заработала всего $57 млн в виде комиссий, в то время, как Ethereum и Tron получили свыше $1 млрд.

По данным платформы DeFiLlama, в блокчейне Base развёрнуто около 348 децентрализованных приложений, а её общая заблокированная стоимость превышает $1,57 млрд, что делает её шестой по величине сетью.

Читайте оригинальную статью на сайте Happycoin.club

Lecturas Relacionadas

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

In just 11 days, Bun's founder Jarred Sumner used Anthropic's Claude AI models to rewrite its million lines of code from Zig to Rust. This move sparked significant controversy, particularly from Zig's creator, Andrew Kelley, who publicly criticized Sumner's engineering practices and the decision to use AI for such a massive rewrite. Bun, a high-performance JavaScript/TypeScript runtime and rival to Node.js, was originally written in Zig. After Anthropic acquired Bun, the team encountered persistent stability and memory safety bugs in the Zig codebase. These issues, combined with Zig's strict policy against LLM-generated code, led to the decision to rewrite in Rust. The rewrite was executed using Claude AI tools at an estimated API cost of $165,000, dramatically reducing the expected time and financial cost. Andrew Kelley's response was scathing. He blamed the original bugs on poor engineering habits, calling Bun's Zig code a collection of "hacks on top of hacks." He expressed relief that Bun was no longer associated with Zig, fearing it would misrepresent the language and attract low-quality, AI-generated contributions. The tech community is divided; some view Kelley's critique as unprofessional, while others see it as a defense of engineering integrity. A major concern about the AI-driven rewrite is the resulting code quality. The translation from Zig left approximately 27,000 lines of unsafe Rust code, raising fears about long-term maintainability and technical debt. The debate centers on whether this project is a milestone in AI-assisted development or a future maintenance nightmare.

marsbitHace 53 min(s)

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

marsbitHace 53 min(s)

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

From Auto Finance to Bitcoin and Now AI: Cango's "What Not to Do" Strategy Cango, a Chinese auto finance platform that went public on the NYSE in 2018, is undergoing its third major transformation. After selling its entire auto business in 2024, it pivoted to become a large-scale Bitcoin miner, acquiring 50 exahash of mining rigs from Bitmain. However, its true goal was never Bitcoin, but owning and controlling energy infrastructure. Now, Cango is pivoting again. While most listed Bitcoin miners are leasing power to giant hyperscalers for AI training clusters, Cango is taking the opposite path. It has launched an AI inference subsidiary called EcoHash, focusing not on training but on distributed inference. The company's strategy hinges on the insight that over 70% of mining industry power is controlled by small, independent sites (10-50 MW), which are too small for hyperscalers but ideal for low-latency AI inference. Cango aims to partner with these small operators, providing the AI technology, customers, and financing through its EcoLink software layer, which can distribute workloads across sites for reliability. Cango maintains a hybrid model, running roughly 31.7 EH/s of Bitcoin mining for cash flow while aggressively cleaning its balance sheet—slashing long-term debt by 94.5% to $30.6 million and raising $75 million for its AI venture. Its first AI deployment will be at a 50 MW site in Georgia. The strategy faces skepticism, given the high costs of converting mining sites and the potential for an AI bubble. However, Cango's leadership believes discipline around "what not to do"—avoiding direct competition with hyperscalers in training—positions it to capture the long-tail demand for distributed AI inference power.

Foresight NewsHace 1 hora(s)

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

Foresight NewsHace 1 hora(s)

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

The article discusses how MicroStrategy's potential Bitcoin sales go far beyond the announced $1.25 billion "reserve-building capacity." It clarifies a key distinction in the company's "BTC Monetization Program": selling Bitcoin to *build* a new dollar reserve (the $1.25B cap) versus selling to *replenish* the existing USD Reserve after it's used for expenses like preferred share dividends. The recent $216M BTC sale for dividend payments was a "replenishment," leaving the headline $1.25B building quota untouched. The plan actually outlines three potential funding pools from BTC sales: 1) Building the reserve ($1.25B cap), 2) Covering preferred share/ debt costs (no specified cap), and 3) Funding buyback programs (up to $20B). This means the structured sales potential exceeds $30 billion, not including uncapped replenishment sales. The piece argues this marks MicroStrategy's shift from a passive "buy-and-hold" Bitcoin proxy to an actively managed entity using BTC as a balance-sheet tool to manage its complex capital structure (common stock, preferred shares, debt, reserve). This creates new dynamics and potential conflicts, as actions benefiting one part (e.g., selling BTC to pay dividends) may pressure another (e.g., undermining the "never sell" narrative). Investors must now parse the company's specific terminology ("build" vs. "replenish") to understand the true scope of future BTC sales, which is significantly larger than the market initially perceived.

marsbitHace 1 hora(s)

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

marsbitHace 1 hora(s)

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

Goldman Sachs analyzes China's AI large language model (LLM) landscape, identifying key players and a strategic shift towards efficiency and global expansion. The report highlights that Chinese open-source/open-weight models are closing the performance gap with top global proprietary models at significantly lower cost, driven by architectural innovations like MoE. This enables a "two-tier" market: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with pricing at ~$1 per million tokens, and a low-end, price-sensitive global segment. Open-source strategies aid adoption but limit monetization, as deployments via third-party platforms (e.g., AWS Bedrock, Alibaba Cloud) may not generate direct revenue for model creators. The industry is thus moving towards "open-weight + community license" models with revenue-sharing to improve unit economics. Internationally, the focus is shifting from "token maximization" to ROI-driven enterprise adoption, particularly in non-U.S. markets. Major cloud platforms are integrating Chinese models (e.g., DeepSeek, MiniMax). Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman Sachs identifies **Zhipu AI** and **DeepSeek** as leaders in foundational text models, and **ByteDance** (with Seedance) leading in multimodal/video generation. **MiniMax** and **Kuaishou** are also rated favorably. The firm forecasts China's AI model API/subscription revenue growing from ~RMB 35bn (2026E) to RMB 879bn by 2030.

marsbitHace 1 hora(s)

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

marsbitHace 1 hora(s)

All about CleanSpark’s addition of 454 Bitcoin and what it means

CleanSpark, a publicly traded company, added 454 Bitcoin to its holdings, bringing its total treasury to 13,924 BTC worth $897.1 million. In 2026, it has sold 258 BTC while adding 1,171 BTC. The news follows a 28% rise in its stock price this year. Other major firms also increased their Bitcoin reserves in June and July. Public companies collectively added nearly 9,000 BTC in June, led by purchases from Strategy and Strive. Notable buyers included DDC Enterprise, MARA Holdings, and CIMG. In July, Strive, American Bitcoin Corp., Boyaa Interactive, and OranjeBTC also expanded their holdings. However, Strategy, the world's largest Bitcoin holder, sold 3,588 BTC for approximately $216 million, citing active balance sheet management rather than a bearish outlook. Meanwhile, Bitcoin ETFs saw net inflows of $35.4 million in July. Overall, corporate Bitcoin accumulation remains strong, with CleanSpark among the key players.

ambcryptoHace 3 hora(s)

All about CleanSpark’s addition of 454 Bitcoin and what it means

ambcryptoHace 3 hora(s)

Trading

Spot
活动图片