2026-04-17 Sexta

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Bitcoin Maintains Bearish Tone, HYPE Pulls Back for Accumulation at the Right Time | Guest Analysis

In this market analysis, analyst Cody from Odaily provides a technical outlook for Bitcoin (BTC) and the HYPE token. For **HYPE**, the analysis identifies a five-wave Elliott Wave structure on the daily chart, suggesting the asset is currently in a potential Wave V upward move starting from the April 2nd low of $34.44. Short-term 4-hour charts indicate the token is in an overbought state, signaling a need for a pullback or consolidation. The strategy for the week is to "go with the trend and buy on dips," specifically looking for a potential long entry if the price finds support and stabilizes in the $37.5 - $38 range. Two recent 1x leveraged long trades were highlighted, resulting in a combined profit of approximately 9.02%. For **Bitcoin**, the medium-term outlook remains bearish. A core 60% short position, opened at $89,000, is still being held with a profit of about 20.51% at the time of writing. The price is expected to continue wide-range volatility between $65,000 and $74,000. The 4-hour chart shows BTC is overbought and facing resistance near $74,500, suggesting a high probability of a short-term pullback. Three short-selling strategies are proposed for the week: selling on a rally to the $74.5K-$76K resistance zone (Plan A), a small short on a break below $69,000 support (Plan B), and a larger short on a break below the key $65,000 support level (Plan C). The analyst emphasizes strict risk management, including setting initial stop-losses immediately upon entry and trailing them to lock in profits. A disclaimer notes that all views are for informational purposes only and do not constitute investment advice.

marsbit04/13 06:25

Bitcoin Maintains Bearish Tone, HYPE Pulls Back for Accumulation at the Right Time | Guest Analysis

marsbit04/13 06:25

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

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

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

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