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Steven AI Talk(English)

  1. 1D AGO

    Building a Personal Knowledge Management (PKM) system

    Stop just reading—start compounding your knowledge. Here is how LLMs are transforming the "Second Brain." Building a Personal Knowledge Management (PKM) system is no longer about manual filing. By leveraging frontier LLMs, we can now automate the ingestion of articles, papers, and datasets into a structured Markdown wiki. Using Obsidian as the frontend, research becomes a continuous process of autonomous indexing, summarization, and connection-finding. Key Workflow Insights: Data Ingest: Automated indexing of raw documents and images.AI-Managed Wiki: Incremental compilation with zero manual intervention.Complex Queries: Autonomous research agents that query hundreds of articles.Visual Output: Auto-generated slide presentations (Marp) and data visualizations.As repositories grow, the future lies in synthetic data and fine-tuning to internalize this knowledge directly into model weights. #LearnByDoingWithSteven #SecondBrain #Obsidian #PersonalKnowledgeManagement #AI #LLM #Productivity #KnowledgeBase #DataScience #AIAgents All my links: https://linktr.ee/learnbydoingwithsteven

    4 min

  2. 5D AGO

    LLM Teams   as Distributed Systems

    This research explores the design of Large Language Model (LLM) teams by applying the formal principles of distributed computing. The authors argue that multi-agent systems face the same challenges as networked computers, including communication overhead, consistency conflicts, and task synchronization. By using Amdahl’s Law, the study demonstrates that a team's efficiency is strictly limited by how much of a task can be performed in parallel. Experimental results show that while decentralized teams are more resilient to individual "straggler" agents, they often suffer from higher token costs and coordination errors. Ultimately, the paper provides a principled framework to move beyond trial-and-error when building robust and cost-effective AI collectives.linktree: https://linktr.ee/learnbydoingwithstevenarvix: https://arxiv.org/pdf/2603.12229

    7 min

  3. 6D AGO

    Continual Learning in Large Language Models: A Comprehensive Survey

    This research survey explores continual learning (CL) as a vital strategy for allowing large language models to acquire new knowledge without suffering from catastrophic forgetting. The authors organize the field into three primary stages: continual pre-training, continual fine-tuning, and continual alignment, detailing how models adapt to new data while maintaining prior expertise. By examining rehearsal-based, regularization-based, and architecture-based methodologies, the text illustrates how traditional machine learning techniques are evolved to meet the massive scale of modern AI. The paper also provides a rigorous overview of evaluation metrics like forgetting rates and introduces essential benchmarks for measuring long-term performance. Ultimately, the sources identify persistent challenges and future opportunities in achieving lifelong learning for more autonomous and dynamic language systems.linktree: https://linktr.ee/learnbydoingwithstevenarvix: https://arxiv.org/pdf/2603.12658

    8 min

  4. 6D AGO

    Large Language Model-Enhanced Relational Operators Taxonomy and Benchmark Analysis

    大语言模型增强的关系算子体系与基准测试分析 Large Language Model-Enhanced Relational Operators Taxonomy and Benchmark Analysis 随着大语言模型的发展，诸多研究通过类似算子的组件将大语言模型集成到关系型数据处理任务中，例如带有语义谓词的过滤器、知识增强的表格插补、数据实体匹配以及复杂的语义查询处理。这些组件在调用大语言模型的同时保留了关系型的输入和输出接口，因此被称为大语言模型增强的关系算子。然而，现有的此类算子存在定义碎片化、实现策略各异以及缺乏全面评估基准等问题。 https://arxiv.org/pdf/2603.02537 https://linktr.ee/learnbydoingwithsteven #LargeLanguageModels #DatabaseManagement #DataScience #RelationalDatabases #ArtificialIntelligence #TechPodcast #StevenDataTalk #数能生智 #数据漫谈

    8 min

  5. APR 2

    How LLMs Distort Our Written Language

    大型语言模型如何扭曲我们的书面语言How LLMs Distort Our Written Language本文介绍了一项由加州大学伯克利分校、加州大学圣地亚哥分校、华盛顿大学、宰图纳学院以及谷歌DeepMind的研究人员于2026年3月联合发表的研究。该研究指出，全球每周有超过十亿人使用大型语言模型辅助写作，但这些模型不仅改变了人类写作的语气和语调，还持续地改变了文本的预期含义。研究表明，人工智能的广泛使用可能会引导人类的写作和决策偏离其最初的意图，从而对文化和科学机构产生深远的影响。This article introduces a study jointly published in March 2026 by researchers from UC Berkeley, UC San Diego, the University of Washington, Zaytuna College, and Google DeepMind. The research points out that over a billion people globally use large language models weekly to assist with writing, but these models not only alter the voice and tone of human writing, but also consistently alter the intended meaning. The study indicates that the widespread use of artificial intelligence may subtly steer human writing and decision-making away from original intentions, thereby having profound impacts on cultural and scientific institutions.感谢您阅读本期内容。我们全面介绍了大型语言模型对书面语言影响的最新研究成果。如果您对数据分析、人工智能以及更多前沿科技内容感兴趣，诚邀您关注Learn By Doing With Steven 数能生智、steven data talk、steven数据漫谈等系列节目。相关内容已同步在小红书、微信公众号、YouTube、Spotify等平台更新。Steven Data Talk播客也可在YouTube Music和Spotify收听。欢迎访问节目描述区域的Linktree链接，获取我们所有社交媒体平台的完整列表。Thank you for reading this issue. We have comprehensively introduced the latest research findings on the impact of large language models on written language. If you are interested in data analysis, artificial intelligence, and more cutting-edge technology content, we sincerely invite you to follow Learn By Doing With Steven, steven data talk, steven数据漫谈, and other related programs. Relevant content is updated across platforms including Xiaohongshu, WeChat Public Account, YouTube, and Spotify. The Steven Data Talk podcast is also available on YouTube Music and Spotify. Please visit the Linktree link in the show notes area to access the complete list of our social media platforms.https://arxiv.org/pdf/2603.18161https://linktr.ee/learnbydoingwithsteven#人工智能 #大型语言模型 #科学研究 #写作助手 #数据漫谈 #LearnByDoingWithSteven #StevenDataTalk

    5 min

  6. MAR 31

    Dynamic World Modeling Dataset and Benchmark for Generative Action Role-Playing Games

    Training AI for interactive gaming just got a massive upgrade! 🦖🚀 WildWorld Dataset: ✅ 108 Million Frames (Monster Hunter: Wilds) ✅ 450+ semantically rich actions ✅ Synchronized skeletons & world states ✅ WildBench for interactive evaluation The road to AI-native games. https://arxiv.org/pdf/2603.23497 All my links: https://linktr.ee/learnbydoingwithsteven #WildWorld #WorldModels #AI #DeepLearning #MonsterHunter #GameDev #learnbydoingwithsteven #MachineLearning

    9 min

  7. MAR 30

    Accelerating LLM Reasoning

    Accelerating LLM Reasoning: Slicing RL Training Gaps from 74% to 3% Scaling reinforcement learning is critical for long chain-of-thought (CoH) reasoning in LLMs, but hardware efficiency has remained a massive hurdle. In traditional on-policy RL, the rollout phase—dominated by slow autoregressive generation—can consume 70% of total training time, creating massive "computational bubbles" as systems wait for the longest samples to finish. SortedRL addresses this through an online length-aware scheduling strategy. By dynamically batching samples with similar generation lengths and implementing oversubscription mechanisms, it minimizes hardware idle time while maintaining near-perfect policy alignment. Key Results: Bubble Ratio: Reduced from 74.0% to as low as 3.37%.Throughput: Boosted rollout speed by up to 39.48%.Performance: LLaMA-3.1-8B-Instruct achieved high baseline scores using 40.74% fewer samples.Accuracy: 18.4% improvement on competition-level math benchmarks (AIME 2024).This research proves that scheduling optimizations are as vital as algorithm architecture for scalable AI reasoning. https://arxiv.org/pdf/2603.23414 All my links: https://linktr.ee/learnbydoingwithsteven #SortedRL #ReinforcementLearning #LLMs #AIResearch #LargeLanguageModels #MachineLearning #AIOptimization #DataScience #MathReasoning #learnbydoingwithsteven

    5 min

  8. MAR 29

    Beyond Tokens: The Rise of Context Engineering in Agentic AI

    As we move from simple apps to autonomous agentic workflows, traditional authorization models (user-app-resource) are failing. The non-deterministic nature of AI agents requires a new discipline: Context Engineering. Strategic Pillars: Situational Context: Analyzing the environment state across orchestrators, agents, and servers.Resource & User Context: Integrating identity and capability boundaries into dynamic decision-making.Model Context & History: Leveraging task history to improve response quality over time.Prompt engineering was the start. Context engineering is the future of reliable autonomy. All my links: https://linktr.ee/learnbydoingwithsteven #AI #AgenticAI #ContextEngineering #Innovation #Engineering #DataScience #learnbydoingwithsteven

    5 min
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