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Bitcoin Liquidity Rotation Turns Bullish Again As Stablecoin Shelter Starts To Unwind

Bitcoin's recent surge above $70,000 signals a potential shift in market dynamics, as on-chain data indicates capital is beginning to rotate out of stablecoins and back into Bitcoin. This movement, though still modest, suggests a reduction in the previously defensive investor posture. The Bitcoin realized cap—a measure of the market's aggregate cost basis—has improved from a steep negative of -$28.7 billion to around -$3 billion, while stablecoin market capitalization has declined. This shift coincides with significant inflows into US Spot Bitcoin ETFs, including a $471 million net inflow on April 6, and comes despite ongoing geopolitical tensions. If this trend continues, it may support further recovery in Bitcoin's price.

bitcoinist04/13 06:17

Bitcoin Liquidity Rotation Turns Bullish Again As Stablecoin Shelter Starts To Unwind

bitcoinist04/13 06:17

Bitcoin Maintains Bearish Tone, HYPE Pulls Back to Accumulate Momentum | Exclusive Analysis

Analysis by Cody,特邀分析师 for Odaily. **Core Market View:** Bitcoin (BTC) maintains its primary bearish trend, expected to trade within a wide 65,000–74,000 USD range this week. Key resistance is at 74,500–76,000 USD, with critical support levels at 69,500 USD and 65,000–66,000 USD. The medium-term strategy remains short (60% short position from 89,000 USD). Short-term tactics involve selling on rallies near resistance or selling breakouts below support, with strict stop-losses. **HYPE Analysis:** HYPE is identified as being in a potential Wave V rally, starting from its April 2nd low of 34.44 USD. After a 10-day rally approaching its Wave III high of 43.78 USD, short-term indicators show overbought conditions, suggesting a consolidation is due. The strategy is to "go long on dips," waiting for a pullback to the 37.5–38 USD support area for a confirmed long entry. Two recent long trades using a 30% position size yielded a total profit of 9.02%. **Key Takeaways:** * BTC: Bearish, range-bound. Short on rallies or breakdowns. * HYPE: Bullish trend, expect a short-term pullback. Buy the dip near support. * All strategies use 1x leverage with dynamic stop-loss management to lock in profits. *Disclaimer: This is a personal trading analysis for informational purposes only, not investment advice. The market is high-risk; invest cautiously.*

Odaily星球日报04/13 06:17

Bitcoin Maintains Bearish Tone, HYPE Pulls Back to Accumulate Momentum | Exclusive Analysis

Odaily星球日报04/13 06:17

A Collective Consensus Called Decentralization

The article "A Collective Consensus Called Decentralization" critiques the gap between the ideological promise of decentralization in the crypto industry and its practical reality. While Bitcoin’s original vision proposed a trusted, peer-to-peer financial system free from central control, the author argues that the industry has largely evolved into a performance of decentralization" rather than a true commitment to it. Key examples illustrate this shift: Bitcoin mining is now dominated by a few large mining pools; stablecoins like USDT and USDC—which are centralized, bank-reliant, and allow wallet freezes—account for over 90% of the market; platforms like Hyperliquid operate with centralized decision-making despite decentralized branding; and prediction markets like Polymarket prioritize utility over ideology. The author distinguishes between two layers: the infrastructure layer (prioritizing efficiency and often openly centralized) and the protocol layer (like Bitcoin and Ethereum, which retain structural decentralization). Ultimately, economic incentives drive centralization, as users prefer functional products over pure principles. The piece concludes by calling for honesty: if a project is centralized, it should say so—users can handle the truth but will eventually reject the hypocrisy.

marsbit04/13 06:00

A Collective Consensus Called Decentralization

marsbit04/13 06:00

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

The article discusses the rise of HappyHorse-1.0, an AI video generation model developed by Alibaba, which topped the Artificial Analysis leaderboard in both text-to-video and image-to-video categories in April 2026. The model was created under the leadership of Zhang Di, who returned to Alibaba in November 2025 after working at Kuaishou, where he led the development of the Kling model. HappyHorse is open-source and commercially available, similar to Alibaba's Qwen model. Zhang Di's background includes extensive experience in large-scale data systems and machine learning at Alibaba and Kuaishou, which contributed to the rapid development of HappyHorse within just five months. The model uses a 15-billion-parameter transformer architecture with native multimodal training, supporting multiple languages and lip-sync capabilities. It also focuses on reducing inference time and cost, making it practical for commercial use. The primary application of HappyHorse is in e-commerce, where it can generate product videos to enhance user engagement and conversion rates by creating contextual and personalized content. This aligns with Alibaba's strengths in commerce, advertising, and data feedback loops. The model's success with open-source approach contrasts with challenges faced by closed-source models like OpenAI's Sora (shut down due to high costs) and ByteDance's Seedance 2.0 (paused over copyright issues). HappyHorse represents a strategic move for Alibaba to integrate AI video generation into its core business ecosystems.

marsbit04/13 05:10

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

marsbit04/13 05:10

Giants Collectively Raise Prices, Is the AI Price Hike Wave Coming? Can We Still Afford Lobster Employees?

Major AI companies, including Alibaba Cloud, Baidu Intelligent Cloud, Tencent Cloud, and Zhipu, have recently announced significant price increases for AI computing and storage services, with hikes ranging from 5% to over 460% in some models. This trend follows similar moves by global giants like Amazon AWS and Google Cloud earlier this year. The price surge is driven by explosive demand for computing power, fueled by the rapid adoption of AI agents like OpenClaw (referred to as "Lobster" in the article), which consume tokens at rates dozens or even hundreds of times higher than traditional AI applications. This has created a severe supply-demand imbalance. Additionally, shortages in high-end hardware—such as AI chips and high-bandwidth memory (HBM)—have constrained computing capacity and raised operational costs. The industry is shifting away from loss-leading pricing strategies toward value-based models, prioritizing sustainable development over market-share competition. A new "token economy" is emerging, where pricing is increasingly based on token usage, complexity, and speed rather than flat fees. This reflects AI computing's evolution from a generic service to a specialized, high-value resource. Some companies are even considering token allowances as part of employee benefits, highlighting its growing role as both a production tool and a cost factor. The article concludes by questioning whether AI services will remain affordable as compute costs continue to rise.

marsbit04/13 04:20

Giants Collectively Raise Prices, Is the AI Price Hike Wave Coming? Can We Still Afford Lobster Employees?

marsbit04/13 04:20

Thin Harness, Fat Skills: The True Source of 100x AI Productivity

The article "Thin Harness, Fat Skills: The True Source of 100x AI Productivity" argues that the key to massive productivity gains in AI is not more advanced models, but a superior system architecture. This framework, "fat skills + thin harness," decouples intelligence from execution. Core components are defined: 1. **Skill Files:** Reusable markdown documents that teach a model *how* to perform a process, acting like parameterized function calls. 2. **Harness:** A thin runtime layer that manages the model's execution loop, context, and security, staying minimal and fast. 3. **Resolver:** A context router that loads the correct documentation or skill at the right time, preventing context window pollution. 4. **Latent vs. Deterministic:** A strict separation between tasks requiring AI judgment (latent space) and those needing predictable, repeatable results (deterministic). 5. **Diarization:** The critical process where the model reads all materials on a topic and synthesizes a structured, one-page summary, capturing nuanced intelligence. The architecture prioritizes pushing intelligence into reusable skills and execution into deterministic tools, with a thin harness in between. This allows the system to learn and improve over time, as demonstrated by a YC system that matches startup founders. Skills like `/enrich-founder` and `/match` perform complex analysis and matching that pure embedding searches cannot. A learning loop allows skills to rewrite themselves based on feedback, creating a compound improvement effect without code changes. The conclusion is that 10x to 1000x efficiency gains come from this disciplined system design, not just smarter models. Skills represent permanent upgrades that automatically improve with each new model release.

marsbit04/13 04:19

Thin Harness, Fat Skills: The True Source of 100x AI Productivity

marsbit04/13 04:19

How Should Crypto VCs Survive? When Top Projects No Longer Need Institutional Funding

Cryptocurrency venture capital is at a watershed moment. Token exits, once the primary driver of outsized returns, are undergoing a major reset. The definition of token value is being rewritten in real-time, yet no standard valuation framework has emerged. Key market shifts include the rise of tokens with real, on-chain revenue (like HYPE), which exposed the weakness of governance tokens with no fundamentals; a supply shock from meme coins (e.g., PUMP) fragmenting liquidity; and competition from prediction markets, stock perps, and leveraged ETFs diverting retail speculative capital. This has compressed token lifecycles and cratered holding periods. VCs now face critical questions: Are they underwriting equity, tokens, or a hybrid? What is the best practice for on-chain value accrual beyond potentially toxic buybacks? Will the "crypto premium" vanish entirely, forcing valuations to align with public equities and crashing many Layer 1 tokens? The result is a divergence: early-stage investors are becoming more price-sensitive on token projects, while appetite for equity deals is growing. Later-stage crypto VCs are increasingly competing with traditional funds in "Web2.5" deals. To survive, crypto VCs must find their product-market fit with founders. Capital alone is no longer sufficient. Winning the best deals—from projects that may not even need institutional funding—requires providing unmatched brand value and non-capital advantages.

marsbit04/13 04:08

How Should Crypto VCs Survive? When Top Projects No Longer Need Institutional Funding

marsbit04/13 04:08

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

A New York Times investigation, in collaboration with AI startup Oumi, reveals significant accuracy and reliability issues with Google's AI Overviews search feature. Testing over 4,300 queries showed the accuracy rate improved from 85% (powered by Gemini 2) to 91% (Gemini 3). However, given Google's scale of ~5 trillion annual searches, this 9% error rate translates to nearly 57 million incorrect answers generated hourly. A critical finding is the prevalence of "unsubstantiated citations." For correct answers, the rate of citations that do not support the AI's summary surged from 37% to 56% with the Gemini 3 upgrade, making it difficult for users to verify information. The AI heavily relies on low-quality sources, with Facebook and Reddit being among its top-cited websites. Furthermore, the system is highly manipulable. A BBC journalist successfully "poisoned" it by publishing a fabricated article; Google's AI began presenting the false information as fact within 24 hours. Google disputed the study's methodology, criticizing its use of the SimpleQA benchmark and an AI model (Oumi's own) to evaluate another AI. The company maintains its AI Overviews, combined with its search ranking systems, perform better than the underlying model alone. Critics note this defense does little to bolster user confidence in the feature's reliability.

marsbit04/13 02:09

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

marsbit04/13 02:09

Reflections and Confusions of a Crypto VC

An encrypted VC's reflection on the current crypto investment landscape, which is undergoing a significant reset. Token exits, once the primary driver of outsized returns, are being redefined in real-time, with no established valuation framework yet emerging. Key market shifts include: the rise of tokens like HYPE, which demonstrated that token prices can be backed by real, on-chain revenue, forcing a reassessment of governance tokens with weak fundamentals; a massive supply shock from meme coins (e.g., PUMP) fragmenting liquidity; and the diversion of retail speculative capital into prediction markets, stock perps, and leveraged ETFs. Major questions VCs are now grappling with: whether they are underwriting equity, tokens, or a hybrid; what constitutes best practices for on-chain value accrual beyond potentially toxic token buybacks; and whether the "crypto premium" will vanish entirely, compressing token valuations to traditional equity multiples and potentially crashing Layer 1 valuations by over 95%. The author argues the pendulum has swung too far towards quantitative DeFi metrics and that qualitative factors like culture, innovation, and security remain crucial for non-DeFi projects. The conclusion is that token return expectations have compressed significantly, pushing later-stage investors towards "Web2.5" companies with tangible revenue. Crypto VCs must now prove their value beyond capital by offering strong branding and value-add to founders to survive.

marsbit04/13 01:33

Reflections and Confusions of a Crypto VC

marsbit04/13 01:33

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

In 2026, the AI industry's primary bottleneck is no longer model capability but rather the encoding of domain knowledge, agent-world interfaces, and toolchain maturity. The open-source community is rapidly bridging this gap, evidenced by projects like OpenClaw and Claude Code experiencing explosive growth in their Skill ecosystems. Perseus Yang, a contributor to over a dozen AI open-source projects, argues that Skill systems are the most underestimated infrastructure of the AI agent era. They enable non-coders to program AI by writing natural language SKILL.md files, transferring power from engineers to all professionals. His project, GTM Engineer Skills, demonstrates this by automating go-to-market workflows, proving Skills can extend far beyond engineering into areas like product strategy and business analysis. He also identifies a critical blind spot: while browser automation thrives, agent operations are nearly absent from mobile apps, the world's dominant computing interface. His project, OpenPocket, is an open-source framework that allows agents to operate Android devices via ADB. It features human-in-the-loop security, agent isolation, and the ability for agents to autonomously create and save new reusable Skills. Yang believes the value of open source lies not in the code itself, but in defining the infrastructure standards during this formative period. His work validates the SKILL.md format as a portable unit for agent capability and pioneers new architectures for agent operation in API-less environments. His design philosophy prioritizes usability for non-technical users, ensuring the agent ecosystem can be expanded by practitioners from all fields, not just engineers.

marsbit04/13 01:29

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

marsbit04/13 01:29

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