# Efficiency Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Efficiency", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

Can You Make a Steady Profit by Blindly Following Polymarket's Pre-Game Win Probability to Bet on NBA Games?

**Can You Consistently Profit by Blindly Following Pre-Game Win Probabilities on Polymarket for NBA Games?** A backtest of the entire NBA 2025-26 regular season (1,096 games) was conducted to test the strategy of always betting $100 on the team with the higher pre-game win probability on Polymarket. The results show that this strategy is not profitable. The total amount wagered was $109,600, with a return of $107,545.20, resulting in a net loss of $2,054 and a Return on Investment (ROI) of -1.87%. This indicates that the market is highly efficient, and pre-game probabilities are accurately priced, leaving no simple arbitrage opportunity. In fact, blindly following the market would have been slightly less profitable than betting against it. However, a deeper analysis by team revealed significant differences. Certain teams consistently outperformed market expectations when they were favored to win: * Portland Trail Blazers (POR): 19% ROI * Philadelphia 76ers (PHI): 14% ROI * San Antonio Spurs (SAS): 12% ROI * Los Angeles Lakers (LAL): 11% ROI * Charlotte Hornets (CHA): 9% ROI In contrast, the market was highly efficient for the top-performing teams, offering minimal returns (e.g., Boston Celtics ROI: 4%, Denver Nuggets ROI: -5%). Results for the weakest teams were too inconsistent due to small sample sizes. The key finding is that team-specific factors, rather than the probability percentage itself, drive potential value, making a one-size-fits-all strategy ineffective.

Odaily星球日报Ieri 06:58

Can You Make a Steady Profit by Blindly Following Polymarket's Pre-Game Win Probability to Bet on NBA Games?

Odaily星球日报Ieri 06:58

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

The year 2026 marks the beginning of "computing power inflation." While AI inference costs have dropped by over 80% in 18 months globally, China's three major cloud providers—Alibaba Cloud, Baidu AI Cloud, and Tencent Cloud—simultaneously announced price hikes of 20–30%. This reflects a deeper structural shift driven by Jevons Paradox: as unit costs fall (e.g., via models like DeepSeek-R1), demand explodes, especially with the rise of reasoning models and AI agents that consume 10–50x more tokens per task. Although DeepSeek open-sourced its model weights, it did not release its inference optimization stack, leaving a significant engineering efficiency gap between cloud providers and smaller players. The big three are leveraging this advantage to reposition: Alibaba focuses on high-margin premium clients, Baidu filters out low-value users, and Tencent capitalizes on ecosystem lock-in. Meanwhile, ByteDance’s Volcano Engine adopts a more moderate pricing strategy to capture displaced customers. Unexpectedly, the price surge is pushing large enterprises toward self-built computing solutions once their cloud bills exceed a certain threshold. While cloud providers aim to boost profitability, they risk driving away innovative startups and accelerating competition from GPU leasing and domestic hardware providers like Huawei. The涨价 trend is expected to persist for 2–3 years, fueled by rising token consumption from reasoning models, AI agent adoption, and NVIDIA export restrictions. The inflection point depends on whether domestic chips can match NVIDIA’s efficiency, likely around 2027–2028. Until then, cloud providers will maintain pricing power, and the key for AI companies is to optimize token usage—the real moat in this era.

marsbit2 giorni fa 01:16

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

marsbit2 giorni fa 01:16

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

A Google engineer with 11 years of experience automated 80% of his work using Claude Code and a simple .NET application, reducing his daily work from 8 hours to just 2–3 hours while generating $28,000 in monthly passive income. The key to this transformation lies in three core elements: First, using a structured CLAUDE.md file based on Andrej Karpathy’s principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—reduces Claude’s rule violations from 40% to just 3%. Second, the "Everything Claude Code" system acts as a full AI engineering team, with 27 pre-built agents for planning, reviewing, and executing tasks across multiple AI platforms. Third, a hidden token consumption issue in Claude Code v2.1.100 was identified, where 20,000 extra tokens were silently added, diluting instructions and reducing output quality. A quick fix using npx downgrades the version to avoid this. The automated system enables code generation, testing, and review to run autonomously in 15-minute cycles. The engineer now only reviews output, saving 5–6 hours daily. The setup takes less than 20 minutes, and the return on time investment is significant—potentially saving $10,000–$12,000 monthly for those valuing their time at $100/hour. The article emphasizes that managing AI systems, not just using them, is the new critical skill, enabling a shift from doing work to overseeing automated processes.

marsbit04/15 04:10

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

marsbit04/15 04:10

5 Minutes to Make AI Your Second Brain

This article introduces a powerful personal knowledge management system combining Claude Code and Obsidian, designed to function as an "AI second brain." Unlike traditional RAG systems that perform temporary, one-off retrievals, this system enables AI to continuously build and maintain an evolving knowledge wiki. The architecture consists of three layers: a raw data layer (notes, articles, transcripts), an AI-maintained structured knowledge base that builds cross-references, and a schema layer that governs organization and system logic. Core operations are Ingest (bringing in external information), Query (instant knowledge access), and Lint (checking consistency and fixing issues). The system's power lies in creating a "compound interest" effect for knowledge: it reduces cognitive load by offloading the tasks of connecting, organizing, and understanding information to AI, while simultaneously improving the accuracy and contextual consistency of the AI's outputs. The setup process is quick, requiring users to download Obsidian, create a vault (knowledge repository), configure Claude Code to access that vault, and apply a specific system prompt. Advanced tips include using a browser extension to easily add web content, maintaining separate vaults for work and personal life, and utilizing the "Orphans" feature to identify unlinked ideas. The main drawbacks are the need for visual thinking, a commitment to ongoing maintenance, and local storage usage. Ultimately, the system transforms scattered information into a reusable, interconnected network of knowledge.

marsbit04/11 12:46

5 Minutes to Make AI Your Second Brain

marsbit04/11 12:46

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit

Here is an English summary of the article "10 Must-Know Claude Code Tips: The Sooner You Know, The Sooner You Benefit": This article shares essential tips for using Claude Code, an AI coding assistant, to significantly boost productivity. It is divided into three main sections. First, it covers three ways to launch Claude: 1) A simple GUI desktop app for non-programmers, 2) A command-line method with a key tip (`claude -c`) to resume from a specific point in the chat history, avoiding restarting context, and 3) A headless mode (`-p` flag) for automation tasks using a subscription token. Second, it details three crucial in-session techniques: 1) Using `Esc` to gracefully interrupt a response and `Esc+Esc` to revert to a previous checkpoint, 2) Using the `!` syntax (e.g., `!ls`) to run shell commands without leaving the chat, and 3) Managing context with `/clear` to remove history or `/compact` to optimize it when performance slows down. Finally, the article recommends companion software to solve human-AI collaboration bottlenecks: 1) **Superpowers**, a structured workflow methodology for higher-quality code output. 2) Voice input tools like **Typeless** and **Douban Input法** to overcome typing speed limit. 3) Tools like **Cmux** (a terminal for managing multiple AI agent instances) and **Vibe Island** (for seamless context switching between tasks) to solve the problem of lost focus when multitasking. The overall goal is to help users focus more deeply on their programming work by streamlining their interaction with Claude Code.

marsbit04/08 07:05

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit

marsbit04/08 07:05

Will Middle Management Be Replaced by AI? What Will the Future Company Structure Look Like

The article explores whether AI will eliminate middle management and reshape future corporate structures. It traces the historical evolution of organizations—from Roman military units to modern corporations—showing how hierarchical systems emerged to manage information flow under the constraint of limited "span of control." Middle management, matrix structures, and bureaucratic systems were all solutions to coordination challenges in information-scarce environments. AI, however, challenges this foundational premise. By enabling real-time modeling, understanding, and distribution of information, AI could replace human-centric coordination mechanisms. Examples like the AI firm "Moon Dark Side" illustrate radical experiments: no departments, titles, or traditional KPIs, with co-founders directly managing large teams and AI agents handling tasks from data processing to code generation. Block (founded by Jack Dorsey) is presented as a case study in building an "intelligent company." This model relies on two core components: a "company world model" (a real-time understanding of internal operations via digital traces) and a "customer world model" (built from real behavioral data, especially financial transactions). An intelligence layer uses these models to dynamically combine capabilities (e.g., payments, lending) to serve customers proactively, without pre-defined product roadmaps. In this structure, traditional roles shift. Middle managers are replaced by a system that handles coordination, while humans focus on individual contributions (ICs), direct responsibility (DRIs), or player-coach roles. The organization becomes flatter, faster, and more adaptive. The article concludes that AI is not just a tool for efficiency but a transformative force that could redefine organizational design, moving companies from human-led hierarchies to system-driven intelligence.

marsbit04/01 08:11

Will Middle Management Be Replaced by AI? What Will the Future Company Structure Look Like

marsbit04/01 08:11

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