Deep Research How to Evaluate Agentic Coding Tools in a Real Repository A practical evaluation rubric for agentic coding tools, covering context, tests, reviewability, approval gates, cost, and production risk.
Deep Research Evidence-Aware Handwriting-to-Report Systems A design argument for handwriting-to-report systems that preserve evidence, provenance, uncertainty, abstention, and human review instead of acting like simple OCR.
Deep Research Agent-First Computing and the Fate of Beginner CLI Literacy A research-grounded argument that beginner programming literacy is shifting from command production toward agent supervision, verification, and recovery.
Deep Research How to Evaluate LLM Trading Agents Without Backtest Theater A practical evaluation checklist for LLM trading agents: decision records, leakage controls, closed-loop tests, cost models, live tracks, and portfolio-level risk metrics.
Deep Research 当代“UFO热潮”的跨学科神学研视与基督徒的应对范式 一份围绕当代 UFO/UAP 热潮的中文深度研究：从官方披露、社会心理、天主教与福音派神学、青年地球创造论、开放外星神学，到基督徒的属灵与理性应对范式。
Deep Research Outcome-Based Pricing for Consumer Agentic AI: What’s Contractible, Verifiable, and Defensible
Deep Research Property Tax Appeals Is Becoming Software: An AI-Enabled Competitive Map (and Where the Moat Lives)
Deep Research Physics-informed machine learning & inverse design for automotive metallic and pearlescent coatings
Deep Research U.S. vs China Sleep Products Market: Competitive Landscape (2025) Sleep has become a high-frequency consumer-health topic in both China and the United States. This report synthesizes public sources and market materials to map (1) demand drivers, (2) product categories, (3) key brands and competitive dynamics, and (4) near-term opportunities across the two markets. Key takeaways * Demand
Deep Research 中美睡眠产品市场竞争分析报告（2025） 市场概览 睡眠健康已成为大众关注的焦点问题之一。在中国，最新数据估计超过3亿人存在睡眠障碍，约占总人口的38.2%[1]。美国同样面临睡眠不足的问题，调查显示约30%的美国劳动者每晚睡眠少于6小时，导致每年经济损失相当于GDP的3%[2]。睡眠需求激增催生了庞大的“睡眠经济”。根据市场研究，2020年全球助眠市场规模约为812亿美元，预计2025年将增长至915亿美元，年均增速约6.3%，其中北美市场占据全球约一半份额[3]。美国的睡眠健康产业规模估计达到300400亿美元，并以每年8%左右的速度增长[[3]](https://mjzj.com/article/bgtn3nu4jrwh#::text=%E8%B0%83%E6%9F%A5%E6%98%BE%E7%A4%BA%EF%BC%8C%E7%BE%8E%E5%
Deep Research Top 10 Generative AI Application Startups (2020–2025) — Ranked by Revenue Growth Generative AI apps went from novelty to real revenue shockingly fast. In this list, I focus on application-layer companies founded in (roughly) 2020–2025 that show unusually strong revenue growth (or credible ARR run-rates), and I highlight the underlying product wedges that made the growth possible. Executive summary
Deep Research B2C（网页端）AI 心理健康产品：10 个最值得做的机会方向 全球心理健康需求持续攀升，数字化解决方案的供给也在加速。精神健康类应用市场在 2023 年约为 62 亿美元，预计到 2030 年以 约 15% 的 CAGR 增长。 [1] 同时，AI 聊天机器人正在成为“非典型入口”：不少用户已经开始把通用 LLM（例如 ChatGPT）当作情绪支持/建议渠道。 [2][3] 这意味着一个清晰机会：做“更专业、更安全、更可验证”的 消费级（B2C）网页产品，把 AI Agent 嵌入到可重复的行为改变流程里，而不是把 AI 伪装成“万能治疗师”。 下面是 10 个增长快、仍有空位 的方向，
Deep Research Top 10 AI-Driven Mental Health Opportunities (B2C Web-Based) Mental health needs are surging worldwide, and digital solutions are rapidly expanding to meet demand. The global mental health app market was valued at $6.2B in 2023 and is projected to grow at ~15% CAGR through 2030. [1] In parallel, AI chatbots have emerged as a disruptive force—surveys
Deep Research 算法交易中的深度学习与强化学习（2018–2025）：哪些有效、哪些会失效，以及如何更稳健地落地 2018–2025 年，深度学习（DL）和深度强化学习（DRL）在量化交易里的位置发生了变化：从“论文里很强”逐步走向“可以进入生产体系的工具箱”。但真正被反复验证的，不是“神奇的 Alpha”，而是一些更朴素、也更残酷的结论： * 预测不等于交易：方向准确率提升，并不必然转化为扣除成本后的净收益。 * 风险调整目标更可迁移：Sharpe、回撤等目标往往比“最大化收益”更接近真实约束。 * 泛化才是难点：很多失败来自市场状态切换（regime shift）、数据泄漏、以及过于理想化的回测假设。 下面按模型家族（LSTM/CNN/Transformer）与 DRL 家族（DQN、PPO/A2C/DDPG/SAC）总结关键经验，并给出一个更“可落地”的实践清单。 1）深度学习真正擅长的地方
Deep Research Deep Learning and Reinforcement Learning in Algorithmic Trading (2018–2025): What Worked, What Broke, and How to Deploy Safely Deep learning (DL) and deep reinforcement learning (DRL) have moved from “interesting papers” to real, production-adjacent toolkits for systematic trading. From 2018–2025, the literature converged on a few uncomfortable truths: * Prediction ≠ trading: higher directional accuracy doesn’t automatically translate into net performance after costs. * Risk-adjusted objectives matter:
Deep Research 进阶/高级量化交易学习：Top 25 算法交易 YouTube 频道清单 目录 1. 执行摘要 2. 如何使用这份清单 3. 频道清单 4. 反套路评分表 5. 两周计划 6. References 更新说明（2026 年 7 月）： 这份清单最初整理于 2025–2026 年初，聚焦进阶/高级算法交易学习资源。随着 2026 年量化生态的发展——Python 生态更成熟、自动化回测平台普及、AI/ML 深度融入交易策略开发——大部分推荐频道仍然高质量，但新增了一些值得关注的创作者和社区。建议在学习过程中结合最新的平台更新（如 QuantConnect 的新功能、TradingView 的 Pine Script V6 等）一起使用。 如果你在 YouTube 上搜"
Deep Research Top 25 Algorithmic Trading YouTube Channels (Intermediate/Advanced) Most “algo trading YouTube” content is either (a) platform tutorials with no rigor, or (b) strategy marketing without evidence. The channels below skew toward intermediate/advanced learners who care about process: research hygiene, backtesting methodology, risk controls, and implementation details. Executive summary * You’ll learn fastest by mixing (1) research
