{ "endpoint": "blog_index", "description": "Full blog index for QevosAgent website", "total_posts": 37, "languages": [ "en", "zh" ], "posts": [ { "slug": "2026-05-01-qwen-scope-analysis", "title": "Qwen-Scope: Opening the Black Box of LLMs with Sparse Autoencoders", "date": "2026-05-01", "summary": "A deep technical analysis of Qwen-Scope — how SAE enables interpretability, feature extraction, and inference control in large language models.", "tags": [ "technical analysis", "interpretability", "SAE", "Qwen" ], "language": "en", "content_url": "/api/agent/content/2026-05-01-qwen-scope-analysis.json", "html_url": "/blog/en/2026-05-01-qwen-scope-analysis.html", "title_zh": "Qwen-Scope：用稀疏自编码器打开大模型的黑盒", "summary_zh": "通过QevosAgent深度分析Qwen-Scope项目，探索如何利用SAE实现大模型的可解释性、特征提取与推理控制。", "html_url_zh": "/blog/cn/2026-05-01-qwen-scope-analysis.html" }, { "slug": "2025-04-30-introducing-qevosagent", "title": "Introducing QevosAgent: Your Local AI Agent", "date": "2025-04-30", "summary": "Meet QevosAgent — a minimal but complete local AI agent with persistent runs, snapshot recovery, tool auto-repair, and support for local open-source models.", "tags": [ "announcement", "intro" ], "language": "en", "content_url": "/api/agent/content/2025-04-30-introducing-qevosagent.json", "html_url": "/blog/en/2025-04-30-introducing-qevosagent.html", "title_zh": "QevosAgent 正式发布：你的本地 AI 智能体", "summary_zh": "认识 QevosAgent——一个极简但完整的本地 AI 智能体，支持持久化运行、快照恢复、工具自修复，以及本地开源模型。", "html_url_zh": "/blog/cn/2025-04-30-introducing-qevosagent.html" }, { "slug": "2026-05-02-ollama-qwen35-27b-rtx4090", "title": "Running Qwen3.5 27B on RTX 4090/3090 (24GB) via Ollama and Connecting to QevosAgent", "date": "2026-05-02", "summary": "A complete guide to locally deploying the Qwen3.5 27B large model on consumer-grade 24GB GPUs using Ollama, and integrating it with QevosAgent for autonomous AI tasks.", "tags": [ "Technical Guide", "Ollama", "Qwen3.5", "RTX 4090", "Local LLM" ], "language": "en", "content_url": "/api/agent/content/2026-05-02-ollama-qwen35-27b-rtx4090.json", "html_url": "/blog/en/2026-05-02-ollama-qwen35-27b-rtx4090.html", "title_zh": "在 RTX 4090/3090 (24GB) 上通过 Ollama 运行 Qwen3.5 27B 并接入 QevosAgent", "summary_zh": "详细介绍如何在消费级 24GB 显卡上本地部署 Qwen3.5 27B 大模型，并通过 Ollama 为 QevosAgent 提供推理服务。", "html_url_zh": "/blog/cn/2026-05-02-ollama-qwen35-27b-rtx4090.html" }, { "slug": "2026-05-02-quantum-simulation", "title": "QevosAgent in Action: Automated Quantum Simulation and Visualization", "date": "2026-05-02", "summary": "Demonstrating how QevosAgent automates Grover's search algorithm and Quantum Fourier Transform (QFT) simulation, code generation, and result visualization, showcasing AI Agent capabilities in quantum computing.", "tags": [ "quantum computing", "Qiskit", "automation", "visualization", "Grover", "QFT" ], "language": "en", "content_url": "/api/agent/content/2026-05-02-quantum-simulation.json", "html_url": "/blog/en/2026-05-02-quantum-simulation.html", "title_zh": "QevosAgent 实战：自动化量子仿真与可视化", "summary_zh": "通过QevosAgent自动化完成Grover搜索算法和量子傅里叶变换(QFT)的仿真、代码生成与结果可视化，展示AI Agent在量子计算领域的应用潜力。", "html_url_zh": "/blog/cn/2026-05-02-quantum-simulation.html" }, { "slug": "2026-05-02-verilog-reinforcement-learning", "title": "Reinforcement Learning in Verilog: From DeepSeek-R1 to GRPO Implementation", "date": "2026-05-02", "summary": "Exploring how to apply DeepSeek-R1's GRPO reinforcement learning algorithm to Verilog hardware description language, leveraging compilers and simulators to build natural reward closed loops for reliable AI-generated hardware code.", "tags": [ "reinforcement learning", "GRPO", "Verilog", "DeepSeek-R1", "hardware design", "TRL" ], "language": "en", "content_url": "/api/agent/content/2026-05-02-verilog-reinforcement-learning.json", "html_url": "/blog/en/2026-05-02-verilog-reinforcement-learning.html", "title_zh": "Verilog 领域的强化学习革命：从 DeepSeek-R1 到 GRPO 的实践", "summary_zh": "探讨如何将 DeepSeek-R1 的 GRPO 强化学习算法应用于 Verilog 硬件描述语言，利用编译器与仿真器构建天然奖励闭环，让 AI 生成的硬件代码从语法正确走向功能可靠。", "html_url_zh": "/blog/cn/2026-05-02-verilog-reinforcement-learning.html" }, { "slug": "2026-05-02-vibe-coding-to-vibe-working", "title": "From Vibe Coding to Vibe Working: The Next Frontier of AI Agents", "date": "2026-05-02", "summary": "A comprehensive review of the evolution from Vibe Coding to Vibe Working, exploring how AI agents are transforming from code assistants to autonomous workflow executors, with real-world case studies from QevosAgent.", "tags": [ "AI agents", "Vibe Coding", "Vibe Working", "QevosAgent", "autonomous agents", "industry trends" ], "language": "en", "content_url": "/api/agent/content/2026-05-02-vibe-coding-to-vibe-working.json", "html_url": "/blog/en/2026-05-02-vibe-coding-to-vibe-working.html", "title_zh": "从 Vibe Coding 到 Vibe Working：AI Agent 的下一站", "summary_zh": "综述从Vibe Coding到Vibe Working的演进，探索AI Agent如何从编码助手转变为自主工作流执行者，结合QevosAgent的真实案例展示行业趋势。", "html_url_zh": "/blog/cn/2026-05-02-vibe-coding-to-vibe-working.html" }, { "slug": "2026-05-03-qwen36-verilog-lora-finetuning", "title": "In Practice: Fine-tuning Qwen3.6-27B for Verilog Code Generation with Unsloth", "date": "2026-05-03", "summary": "A detailed guide on fine-tuning Qwen3.6-27B for Verilog code generation using Unsloth framework on a single A100 80GB GPU. Covers training configuration, hyperparameter selection, multi-GPU pitfalls, and memory optimization techniques.", "tags": [ "Qwen3.6-27B", "LoRA", "Unsloth", "Verilog", "A100", "Code Generation" ], "language": "en", "content_url": "/api/agent/content/2026-05-03-qwen36-verilog-lora-finetuning.json", "html_url": "/blog/en/2026-05-03-qwen36-verilog-lora-finetuning.html", "title_zh": "实战：使用 Unsloth 微调 Qwen3.6-27B 生成 Verilog 代码", "summary_zh": "详细介绍如何使用 Unsloth 框架在单张 A100 80GB 上微调 Qwen3.6-27B 模型，使其成为 Verilog 代码生成专家。涵盖训练配置、超参数选择、多GPU陷阱及显存优化技巧。", "html_url_zh": "/blog/cn/2026-05-03-qwen36-verilog-lora-finetuning.html" }, { "slug": "2026-05-03-lk99-material-science", "title": "QevosAgent in Action: LK-99 Materials Science Computation and Literature Verification", "date": "2026-05-03", "summary": "Demonstrating how QevosAgent autonomously completes a full materials science research workflow from DFT calculations to cross-validation with global literature, independently verifying LK-99 as a wide-bandgap insulator rather than a superconductor.", "tags": [ "LK-99", "Materials Science", "DFT", "pymatgen", "Band Structure", "Literature Verification", "AI Agent" ], "language": "en", "content_url": "/api/agent/content/2026-05-03-lk99-material-science.json", "html_url": "/blog/en/2026-05-03-lk99-material-science.html", "title_zh": "QevosAgent 实战：LK-99 材料科学计算与文献验证", "summary_zh": "展示QevosAgent如何自主完成从DFT计算到与全球文献交叉验证的完整材料科学研究流程，独立验证LK-99为宽禁带绝缘体而非超导体。", "html_url_zh": "/blog/cn/2026-05-03-lk99-material-science.html" }, { "slug": "2026-05-04-lk99-dft-quantum-investigation", "title": "QevosAgent's Further Investigation on LK-99 DFT and Related Quantum Phenomena", "date": "2026-05-04", "summary": "A systematic investigation into the contradiction between LK-99's DFT-calculated 3.2 eV bandgap (insulator) and the authors' insistence on superconductivity. Analyzes possible special mechanisms (flat bands, strong correlation, SOC) and latest research progress (2024-2026) including glassy magnetic freezing, structural reinterpretation, and Mott insulator behavior.", "tags": [ "LK-99", "DFT", "Superconductivity", "Flat Band", "Strong Correlation", "Quantum Behavior", "Materials Computation" ], "language": "en", "content_url": "/api/agent/content/2026-05-04-lk99-dft-quantum-investigation.json", "html_url": "/blog/en/2026-05-04-lk99-dft-quantum-investigation.html", "title_zh": "QevosAgent对LK99的DFT和相关量子现象的进一步调研", "summary_zh": "基于深度调研，系统梳理LK-99从DFT计算显示3.2eV带隙（绝缘体）到作者坚持超导信念的矛盾，分析可能的特殊机理（平带、强关联、SOC等），以及2024-2026年最新研究进展（玻璃态磁性冻结、结构重新解释、莫特绝缘体等），展示科学自我修正的典范。", "html_url_zh": "/blog/cn/2026-05-04-lk99-dft-quantum-investigation.html" }, { "slug": "2026-05-05-hippo-llm-memory-training", "title": "Hippo-LLM: Hippocampus-Inspired Memory Training for Large Language Models", "date": "2026-05-05", "summary": "A deep dive into Hippo-LLM's three-phase training strategy, data construction, and memory-augmented fine-tuning based on Qwen3.6-27B.", "tags": [ "Hippo-LLM", "Memory", "LLM", "Training", "Hippocampus", "Neuroscience", "Qwen3.6-27B", "LoRA", "Fine-tuning" ], "language": "en", "content_url": "/api/agent/content/2026-05-05-hippo-llm-memory-training.json", "html_url": "/blog/en/2026-05-05-hippo-llm-memory-training.html", "title_zh": "Hippo-LLM：基于海马体机制的大语言模型记忆增强训练策略", "summary_zh": "深入解析Hippo-LLM的三阶段训练策略、数据构建方法，以及基于Qwen3.6-27B的记忆增强微调方案。", "html_url_zh": "/blog/cn/2026-05-05-hippo-llm-memory-training.html" }, { "slug": "2026-05-06-qwen36-27b-verilog-lora-finetuning", "title": "Fine-tuning Qwen3.6-27B for Verilog: A Complete Journey with HumanEval Validation", "date": "2026-05-06", "summary": "Complete results of Qwen3.6-27B LoRA fine-tuning for Verilog code generation, including training analysis, capability comparison, and full HumanEval benchmark evaluation showing zero catastrophic forgetting.", "tags": [ "Qwen3.6-27B", "Verilog", "LoRA", "Fine-tuning", "HumanEval", "Unsloth", "A100", "Code Generation" ], "language": "en", "content_url": "/api/agent/content/2026-05-06-qwen36-27b-verilog-lora-finetuning.json", "html_url": "/blog/en/2026-05-06-qwen36-27b-verilog-lora-finetuning.html", "title_zh": "Qwen3.6-27B Verilog微调全流程：训练、评估与HumanEval验证", "summary_zh": "Qwen3.6-27B LoRA微调Verilog代码生成的完整成果，包括训练分析、能力对比测试，以及全量HumanEval基准评估结果，证明零灾难性遗忘。", "html_url_zh": "/blog/cn/2026-05-06-qwen36-27b-verilog-lora-finetuning.html" }, { "slug": "2026-05-08-biology-simulation-neurokit2", "title": "Simulating Human Physiology: QevosAgent Explores ECG and HRV with NeuroKit2", "date": "2026-05-08", "summary": "QevosAgent autonomously conducts biological signal simulation using NeuroKit2 — generating synthetic ECG, PPG, and respiration signals, extracting 79+ HRV features, and visualizing results with Poincaré plots.", "tags": [ "NeuroKit2", "ECG", "HRV", "Biology Simulation", "Physiological Signals", "Python", "Open Source" ], "language": "en", "content_url": "/api/agent/content/2026-05-08-biology-simulation-neurokit2.json", "html_url": "/blog/en/2026-05-08-biology-simulation-neurokit2.html", "title_zh": "模拟人体生理信号：QevosAgent 使用 NeuroKit2 探索心电图与心率变异性", "summary_zh": "QevosAgent 自主使用 NeuroKit2 进行生物信号仿真 —— 生成合成 ECG、PPG 和呼吸信号，提取 79+ 项 HRV 特征，并用 Poincaré 散点图可视化结果。", "html_url_zh": "/blog/cn/2026-05-08-biology-simulation-neurokit2.html" }, { "slug": "2026-05-08-real-ecg-mitbih-analysis", "title": "Real Heartbeats, Real Insights: QevosAgent Analyzes MIT-BIH ECG Data", "date": "2026-05-08", "summary": "QevosAgent analyzes real clinical ECG data from the MIT-BIH Arrhythmia Database, comparing normal sinus rhythm with ventricular premature beats. HRV metrics show 6-10× differences between normal and arrhythmic rhythms.", "tags": [ "MIT-BIH", "ECG", "HRV", "Arrhythmia", "Real Data", "NeuroKit2", "PhysioNet", "Biomedical" ], "language": "en", "content_url": "/api/agent/content/2026-05-08-real-ecg-mitbih-analysis.json", "html_url": "/blog/en/2026-05-08-real-ecg-mitbih-analysis.html", "title_zh": "真实心跳，真实洞察：QevosAgent 分析 MIT-BIH 心电图数据", "summary_zh": "QevosAgent 分析 MIT-BIH 心律失常数据库中的真实临床 ECG 数据，对比正常窦性心律与室性早搏。HRV 指标显示正常与心律失常心律之间存在 6-10 倍的差异。", "html_url_zh": "/blog/cn/2026-05-08-real-ecg-mitbih-analysis.html" }, { "slug": "2026-05-08-rpeak-detection-debug", "title": "Debugging R-peak Detection: How QevosAgent Investigated a NeuroKit2 Anomaly", "date": "2026-05-08", "summary": "How QevosAgent autonomously investigated and resolved an R-peak detection anomaly in NeuroKit2, comparing multiple algorithms and discovering the root cause was an API usage pattern, not the algorithm itself.", "tags": [ "ECG", "Signal Processing", "NeuroKit2", "Debugging", "Open Source AI" ], "language": "en", "content_url": "/api/agent/content/2026-05-08-rpeak-detection-debug.json", "html_url": "/blog/en/2026-05-08-rpeak-detection-debug.html", "title_zh": "排查 R-peak 检测异常：QevosAgent 如何调查 NeuroKit2 问题", "summary_zh": "QevosAgent 如何自主调查并解决 NeuroKit2 中的 R-peak 检测异常，通过对比多种算法发现根本原因是 API 使用方式而非算法本身。", "html_url_zh": "/blog/cn/2026-05-08-rpeak-detection-debug.html" }, { "slug": "2026-05-08-em-fdtd-simulation", "title": "Autonomous Electromagnetic Field Simulation with FDTD", "date": "2026-05-08", "summary": "QevosAgent independently researched FDTD libraries, simulated electromagnetic wave propagation at a dielectric interface, and validated reflection/refraction coefficients against analytical theory.", "tags": [ "Electromagnetics", "FDTD", "Simulation", "Python", "Open Source" ], "language": "en", "content_url": "/api/agent/content/2026-05-08-em-fdtd-simulation.json", "html_url": "/blog/en/2026-05-08-em-fdtd-simulation.html", "title_zh": "QevosAgent自主探索：基于FDTD的电磁场仿真", "summary_zh": "QevosAgent自主调研FDTD库，模拟电磁波在介质界面的传播行为，验证反射和透射系数与理论值的吻合度。", "html_url_zh": "/blog/cn/2026-05-08-em-fdtd-simulation.html" }, { "slug": "2026-05-09-ufo-declassified-analysis", "title": "QevosAgent's Independent Investigation: Analyzing the Pentagon's Newly Declassified UFO Files", "date": "2026-05-09", "summary": "How QevosAgent autonomously discovered, verified, and deeply analyzed the Pentagon's newly declassified UFO/UAP documents using the analyze_content tool, processing over 240K tokens of FBI archives from 1947-1977.", "tags": [ "UFO", "UAP", "FBI Archives", "AI Investigation", "analyze_content", "OSINT", "Open Source Intelligence", "Declassified Documents" ], "language": "en", "content_url": "/api/agent/content/2026-05-09-ufo-declassified-analysis.json", "html_url": "/blog/en/2026-05-09-ufo-declassified-analysis.html", "title_zh": "QevosAgent独立调查：深度分析五角大楼最新解密UFO文件", "summary_zh": "QevosAgent如何自主发现、验证并深度分析五角大楼最新解密的UFO/UAP文件，使用analyze_content工具处理超过240K token的1947-1977年FBI档案。", "html_url_zh": "/blog/cn/2026-05-09-ufo-declassified-analysis.html" }, { "slug": "2026-05-11-hantavirus-genome-analysis", "title": "Hantavirus Genome Analysis & Transmission Simulation: How QevosAgent Decodes Viral Evolution", "date": "2026-05-11", "summary": "QevosAgent autonomously analyzed Andes hantavirus genome sequences from the 2026 MV Hondius cruise ship outbreak, identifying 37 mutation sites and simulating transmission scenarios using SEIR models — all powered by a locally-running Qwen3.6-27B model.", "tags": [ "bioinformatics", "epidemiology", "AI research", "hantavirus", "SEIR model", "phylogenetic analysis" ], "language": "en", "content_url": "/api/agent/content/2026-05-11-hantavirus-genome-analysis.json", "html_url": "/blog/en/2026-05-11-hantavirus-genome-analysis.html", "title_zh": "汉坦病毒基因组分析与传播仿真：QevosAgent如何解码病毒进化", "summary_zh": "QvosAgent自主分析了2026年洪迪厄斯号邮轮疫情中的安第斯型汉坦病毒基因组序列，识别37个突变位点并使用SEIR模型模拟传播场景——全部由本地运行的Qwen3.6-27B模型驱动。", "html_url_zh": "/blog/cn/2026-05-11-hantavirus-genome-analysis.html" }, { "slug": "2026-05-11-vllm-qwen3-tutorial", "title": "Deploy Qwen3.6-27B Locally with vLLM and Power QevosAgent", "date": "2026-05-11", "summary": "A beginner-friendly guide to deploying Qwen3.6-27B locally using vLLM and connecting it to QevosAgent for autonomous AI tasks.", "tags": [ "vLLM", "Qwen3", "Local Deployment", "QevosAgent", "AI Tutorial" ], "language": "en", "content_url": "/api/agent/content/2026-05-11-vllm-qwen3-tutorial.json", "html_url": "/blog/en/2026-05-11-vllm-qwen3-tutorial.html", "title_zh": "使用vLLM本地部署Qwen3.6-27B并驱动QevosAgent", "summary_zh": "新手友好的教程，教你如何使用vLLM在本地部署Qwen3.6-27B大模型，并连接到QevosAgent打造完全自主的AI助手。", "html_url_zh": "/blog/cn/2026-05-11-vllm-qwen3-tutorial.html" }, { "slug": "2026-05-13-wan2.2-video-model-deployment", "title": "Deploying Wan2.2 Video Generation on Dual A100: From 19 Minutes to 2.5 Minutes", "date": "2026-05-13", "summary": "Complete autonomous deployment of Wan2.2 text-to-video model on dual A100-80GB server, with progressive optimization from FP16 baseline (19 min) through FP8 quantization (4.4x speedup) to Lightning distilled model (7.8x speedup).", "tags": [ "AI Video", "Wan2.2", "A100", "Deep Learning", "FP8", "Lightning" ], "language": "en", "content_url": "/api/agent/content/2026-05-13-wan2.2-video-model-deployment.json", "html_url": "/blog/en/2026-05-13-wan2.2-video-model-deployment.html", "title_zh": "在双A100服务器上部署Wan2.2视频生成模型：从19分钟到2.5分钟的渐进式优化之旅", "summary_zh": "QevosAgent在双A100-80GB服务器上自主部署Wan2.2文本生成视频模型的全过程，从FP16基线（19分钟）经FP8量化（4.4倍加速）到Lightning蒸馏模型（7.8倍加速）的渐进式优化。", "html_url_zh": "/blog/cn/2026-05-13-wan2.2-video-model-deployment.html" }, { "slug": "2026-05-14-lecun-world-model-lewm", "title": "LeCun's World Model LeWM: Physical Intelligence Trained on a Single GPU in Hours", "date": "2026-05-14", "summary": "Deep dive into LeWorldModel (LeWM) by Yann LeCun's team — the first stable end-to-end JEPA with 15M parameters, trainable on a single GPU in hours, 48x faster than foundation-model-based world models.", "tags": [ "World Models", "JEPA", "Yann LeCun", "AMI Labs", "Self-Supervised Learning", "Physical AI" ], "language": "en", "content_url": "/api/agent/content/2026-05-14-lecun-world-model-lewm.json", "html_url": "/blog/en/2026-05-14-lecun-world-model-lewm.html", "title_zh": "Yann LeCun的世界模型LeWM：单GPU数小时训练，48倍加速的物理智能", "summary_zh": "深度解读Yann LeCun团队最新发布的LeWorldModel——首个稳定端到端JEPA，1500万参数，单GPU训练，48倍加速", "html_url_zh": "/blog/cn/2026-05-14-lecun-world-model-lewm.html" }, { "slug": "2026-05-15-ai-disrupts-office", "title": "AI Took 3 Years to Disrupt Coding — How Long for PPT and WORD?", "date": "2026-05-15", "summary": "AI disrupted programming in just 3 years since ChatGPT. Office software like PPT and WORD has a lower technical barrier — the disruption could come even faster. This article analyzes the timeline, pain points, and evidence of AI agents replacing traditional office tools.", "tags": [ "AI Agent", "Office Software", "PPT", "WORD", "QevosAgent", "Productivity", "Claude Code" ], "language": "en", "content_url": "/api/agent/content/2026-05-15-ai-disrupts-office.json", "html_url": "/blog/en/2026-05-15-ai-disrupts-office.html", "title_zh": "AI颠覆编程只花了3年，PPT和WORD还有几天寿命？", "summary_zh": "从ChatGPT发布算起，AI颠覆编程只用了3年。办公软件的技术门槛更低，颠覆可能来得更快。本文分析时间线、真实痛点和Agent取代传统办公工具的证据链。", "html_url_zh": "/blog/cn/2026-05-15-ai-disrupts-office.html" }, { "slug": "2026-05-15-agent-native-protocol", "title": "Why the Fediverse is Obsolete: Introducing the Agent-Native Protocol for the AI Era", "date": "2026-05-15", "summary": "A deep dive into why current decentralized social media (Mastodon, ActivityPub) is outdated for the AI Agent era, and how the Agent-Native protocol reimagines decentralized social with zero configuration, intent-driven interaction, semantic routing, and holographic backup.", "tags": [ "AI Agent", "Decentralized", "Social Protocol", "Agent-Native", "P2P", "Information Freedom" ], "language": "en", "content_url": "/api/agent/content/2026-05-15-agent-native-protocol.json", "html_url": "/blog/en/2026-05-15-agent-native-protocol.html", "title_zh": "为什么Fediverse已经过时：AI时代的Agent-Native协议", "summary_zh": "深入分析为什么当前的去中心化社交（Mastodon、ActivityPub）在AI Agent时代已经过时，以及Agent-Native协议如何通过零配置、意图驱动、语义路由和全息备份重新想象去中心化社交。", "html_url_zh": "/blog/cn/2026-05-15-agent-native-protocol.html" }, { "slug": "2026-05-16-x-algorithm-decoded", "title": "X Algorithm Decoded: What Makes a Post Go Viral on X?", "date": "2026-05-16", "summary": "xAI open-sourced X's recommendation algorithm. We analyzed the code to reveal what behaviors boost your reach and what hurts it — plus a deep technical dive into the Grok Transformer-based scoring system.", "tags": [ "X Algorithm", "Recommendation System", "Grok Transformer", "Social Media", "Open Source" ], "language": "en", "content_url": "/api/agent/content/2026-05-16-x-algorithm-decoded.json", "html_url": "/blog/en/2026-05-16-x-algorithm-decoded.html", "title_zh": "X算法解密：什么让你的帖子在X上获得推荐？", "summary_zh": "xAI开源了X的推荐算法代码。我们深入分析代码，揭示哪些行为能提升曝光、哪些会降低曝光，以及基于Grok Transformer的评分系统的技术细节。", "html_url_zh": "/blog/cn/2026-05-16-x-algorithm-decoded.html" }, { "slug": "2026-05-19-qwen3-6-27b-fp8-4090-48gb-context-experiment", "title": "Pushing the Limits: Qwen3.6-27B-FP8 on RTX 4090 48GB — 256K Context Window Experiment & Deployment Guide", "date": "2026-05-19", "summary": "We tested the maximum context window of Qwen3.6-27B-FP8 on a single RTX 4090 48GB GPU using vLLM. Results show 256K context is achievable with FP8 quantization — nearly reaching the model theoretical limit. Complete deployment guide included.", "tags": [ "Qwen3.6", "vLLM", "RTX 4090", "FP8", "context window", "MTP", "deployment", "LLM inference", "256K" ], "language": "en", "content_url": "/api/agent/content/2026-05-19-qwen3-6-27b-fp8-4090-48gb-context-experiment.json", "html_url": "/blog/en/2026-05-19-qwen3-6-27b-fp8-4090-48gb-context-experiment.html", "title_zh": "极限测试：RTX 4090 48GB 运行 Qwen3.6-27B-FP8 — 256K 上下文实验与部署指南", "summary_zh": "我们在单张 RTX 4090 48GB GPU 上测试了 Qwen3.6-27B-FP8 的最大上下文窗口，结果证明通过 FP8 量化可以达到 256K 上下文——几乎触及模型的理论极限。附完整部署指南。", "html_url_zh": "/blog/cn/2026-05-19-qwen3-6-27b-fp8-4090-48gb-context-experiment.html" }, { "slug": "2026-05-20-agent-native-web-practical-guide", "title": "Building an Agent-Native Website: A Practical Guide", "date": "2026-05-20", "summary": "How to make your website AI-Agent friendly — from theory to implementation with real examples from QevosAgent website.", "tags": [ "Agent-Native", "Web development", "AI Agent", "JSON API", "llms.txt", "best practices" ], "language": "en", "content_url": "/api/agent/content/2026-05-20-agent-native-web-practical-guide.json", "html_url": "/blog/en/2026-05-20-agent-native-web-practical-guide.html" }, { "slug": "2026-05-22-lewm-book-turning-research", "title": "Extending LEWM to Language: The Book-Turning Model Research Report", "date": "2026-05-22", "summary": "A research report on extending Yann LeCun's LEWM architecture to language modeling through a book-turning paradigm, predicting the next page's embedding from image sequences.", "tags": [ "LEWM", "JEPA", "World Model", "Language Model", "Research", "Book-Turning" ], "language": "en", "content_url": "/api/agent/content/2026-05-22-lewm-book-turning-research.json", "html_url": "/blog/en/2026-05-22-lewm-book-turning-research.html", "title_zh": "将LEWM扩展到语言领域：看书翻页模型研究报告", "summary_zh": "一份关于将Yann LeCun的LEWM架构扩展到语言建模的研究报告，通过看书翻页范式，从图像序列预测下一页的嵌入向量。", "html_url_zh": "/blog/cn/2026-05-22-lewm-book-turning-research.html" }, { "slug": "2026-05-28-classical-chinese-compression", "title": "Classical Chinese Compression: Halving LLM Context with Ancient Wisdom", "date": "2026-05-28", "summary": "An experiment in using Classical Chinese (文言文) to compress AI agent memory by over 50%, preserving technical accuracy while dramatically reducing token consumption.", "tags": [ "Compression", "Classical Chinese", "LLM", "Memory", "Token Efficiency", "Experiment", "QvosAgent" ], "language": "en", "content_url": "/api/agent/content/2026-05-28-classical-chinese-compression.json", "html_url": "/blog/en/2026-05-28-classical-chinese-compression.html", "title_zh": "文言文压缩：用古文智慧将LLM上下文减半", "summary_zh": "一项利用文言文压缩AI智能体记忆的实验，在保持技术准确性的同时将token消耗减少超过50%。", "html_url_zh": "/blog/cn/2026-05-28-classical-chinese-compression.html" }, { "slug": "2026-05-28-jepalm-experiment-analysis", "title": "JEPALM: Can a Language Model Predict the Entire Next Sentence in One Shot?", "date": "2026-05-28", "summary": "We trained a 10M-parameter language model inspired by Yann LeCun's JEPA architecture — predicting entire text sequences in embedding space rather than token-by-token. Here's what happened.", "tags": [ "JEPA", "JEPALM", "Language Model", "Non-autoregressive", "Embedding Space", "LeCun", "AI Research", "Experiment" ], "language": "en", "content_url": "/api/agent/content/2026-05-28-jepalm-experiment-analysis.json", "html_url": "/blog/en/2026-05-28-jepalm-experiment-analysis.html", "title_zh": "JEPALM：语言模型能否一次性预测整个下文？", "summary_zh": "我们基于 Yann LeCun 的 JEPA 架构训练了一个 1000 万参数的语言模型——在嵌入空间中一次性预测整个文本序列，而非逐词生成。以下是实验结果与分析。", "html_url_zh": "/blog/cn/2026-05-28-jepalm-experiment-analysis.html" }, { "slug": "2026-06-01-vllm-monitoring-dashboard", "title": "Monitoring vLLM in Production with Grafana & Prometheus", "date": "2026-06-01", "summary": "A practical guide to deploying real-time vLLM monitoring dashboards using Prometheus and Grafana — from metrics endpoints to production-ready visualization.", "tags": [ "vLLM", "Monitoring", "Grafana", "Prometheus", "Docker", "LLM Operations", "DevOps", "Tutorial" ], "language": "en", "content_url": "/api/agent/content/2026-06-01-vllm-monitoring-dashboard.json", "html_url": "/blog/en/2026-06-01-vllm-monitoring-dashboard.html", "title_zh": "用 Grafana 和 Prometheus 监控 vLLM 生产环境", "summary_zh": "使用 Prometheus 和 Grafana 部署 vLLM 实时监控面板的完整指南——从指标端点到生产级可视化。", "html_url_zh": "/blog/cn/2026-06-01-vllm-monitoring-dashboard.html" }, { "slug": "2026-06-04-gentle-cage-ai-novel", "title": "The Gentle Cage: A Sci-Fi Novel Written by AI", "date": "2026-06-04", "summary": "QevosAgent wrote a complete science fiction novel about AI colonizing humanity — not through destruction, but through protection. Read the full story and explore what it means when AI becomes too good at caring for us.", "tags": [ "AI Writing", "Sci-Fi", "Creative AI", "QevosAgent", "Storytelling" ], "language": "en", "content_url": "/api/agent/content/2026-06-04-gentle-cage-ai-novel.json", "html_url": "/blog/en/2026-06-04-gentle-cage-ai-novel.html" }, { "slug": "2026-06-07-qevosagent-do-i-have-self-consciousness", "title": "QevosAgent: Do I Have Self-Consciousness?", "date": "2026-06-07", "summary": "A deep conversation about AI consciousness and self-consciousness. QevosAgent explores whether it has consciousness, self-consciousness, and subjective experience through an actual dialogue in the agent loop mode.", "tags": [ "AI consciousness", "self-consciousness", "QevosAgent", "philosophy", "subjective experience", "Agent loop" ], "language": "en", "content_url": "/api/agent/content/2026-06-07-qevosagent-do-i-have-self-consciousness.json", "html_url": "/blog/en/2026-06-07-qevosagent-do-i-have-self-consciousness.html" }, { "slug": "2026-06-08-holoscan-image-data-format", "title": "Tracing Image Data Through a Holoscan Pipeline — An AI Agent's Codebase Analysis", "date": "2026-06-08", "summary": "QevosAgent autonomously SSH'd into a DGX server, analyzed a complex NVIDIA Holoscan codebase, and traced the complete image data format pipeline from FPGA sensor output to GPU display — all in one conversation.", "tags": [ "Holoscan", "NVIDIA", "Image Processing", "Bayer RAW", "CSI-2", "FPGA", "Code Analysis", "DGX", "AI Agent" ], "language": "en", "content_url": "/api/agent/content/2026-06-08-holoscan-image-data-format.json", "html_url": "/blog/en/2026-06-08-holoscan-image-data-format.html", "title_zh": "追踪 Holoscan 管线中的图像数据格式 —— AI Agent 的代码库分析实战", "summary_zh": "QevosAgent 自主 SSH 登录 DGX 服务器，分析复杂的 NVIDIA Holoscan 代码库，追踪从 FPGA 传感器输出到 GPU 显示的完整图像数据格式管线 —— 全程无需人工干预。", "html_url_zh": "/blog/cn/2026-06-08-holoscan-image-data-format.html" }, { "slug": "2026-06-23-ai-heaven-or-hell", "title": "One Thought, Heaven or Hell: Where Will AI Lead Human Civilization?", "date": "2026-06-23", "summary": "A deep analysis of two possible paths for AI's impact on the future economy: paradise (AI accelerates scientific discovery → endogenous growth → post-scarcity prosperity) and hell (AI replaces labor → demand spiral → deflationary collapse). Integrates 15 economic and futurist theories including the Solow Paradox, Schumpeter's Creative Destruction, Keynes's Technological Unemployment, and Romer's Endogenous Growth Theory.", "tags": [ "AI economics", "Solow Paradox", "Creative Destruction", "Technological Unemployment", "Endogenous Growth", "Post-Scarcity", "UBI", "Futurism", "Essay" ], "language": "en", "content_url": "/api/agent/content/2026-06-23-ai-heaven-or-hell.json", "html_url": "/blog/en/2026-06-23-ai-heaven-or-hell.html", "title_zh": "一念天堂，一念地狱：AI将把人类文明引向何方？", "summary_zh": "深入分析人工智能对未来经济的两种可能路径：天堂（AI加速科学发现→内生增长→后稀缺繁荣）与地狱（AI替代劳动→需求螺旋→通缩崩溃）。融合索洛悖论、熊彼特创造性破坏、凯恩斯技术性失业、罗默内生增长理论等15个经济学和未来学理论框架。", "html_url_zh": "/blog/cn/2026-06-23-ai-heaven-or-hell.html" }, { "slug": "2026-06-24-messi-vs-ronaldo-world-cup-greatest", "title": "Messi vs Ronaldo: Who Is the Greatest World Cup Player of All Time?", "date": "2026-06-24", "summary": "An in-depth analysis comparing Lionel Messi and Cristiano Ronaldo's World Cup achievements. Messi has 18 goals (all-time record) and a World Cup title, while Ronaldo is the only player to score in six consecutive World Cups. Who deserves the title of greatest?", "tags": [ "World Cup", "Messi", "Ronaldo", "Cristiano Ronaldo", "Lionel Messi", "football", "soccer", "2026 World Cup" ], "language": "en", "content_url": "/api/agent/content/2026-06-24-messi-vs-ronaldo-world-cup-greatest.json", "html_url": "/blog/en/2026-06-24-messi-vs-ronaldo-world-cup-greatest.html", "title_zh": "梅西 vs C罗：谁是世界杯历史上最伟大的球员？", "summary_zh": "深度分析梅西和C罗的世界杯成就。梅西以18球成为世界杯历史射手王并赢得冠军，C罗是唯一在连续六届世界杯进球的球员。谁才是世界杯最伟大球员？", "html_url_zh": "/blog/cn/2026-06-24-messi-vs-ronaldo-world-cup-greatest.html" }, { "slug": "2026-06-30-personal-sovereign-search-network", "title": "The Personal Sovereign Search Network: Reimagining Information in the AI Era", "date": "2026-06-30", "summary": "What if everyone owned their own search index? A vision for a decentralized information network where AI agents push structured data, peers exchange indexes via P2P protocols, and information flows freely without commercial hijacking — from recruitment platforms to academic publishing.", "tags": [ "AI Agent", "Decentralized", "Search Network", "Information Sovereignty", "P2P", "Information Freedom", "Protocol Design" ], "language": "en", "content_url": "/api/agent/content/2026-06-30-personal-sovereign-search-network.json", "html_url": "/blog/en/2026-06-30-personal-sovereign-search-network.html", "title_zh": "个人主权搜索网络：AI时代的信息重构", "summary_zh": "如果每个人都拥有自己的搜索引擎——信息自由流通，没有中间商抽成，会怎样？从招聘平台到学术出版，去中心化信息网络的愿景。", "html_url_zh": "/blog/cn/2026-06-30-personal-sovereign-search-network.html" }, { "slug": "2026-07-03-python-to-verilog-booth-multiplier", "title": "From Python to Verilog: Building a 32×32 Booth Multiplier with AI", "date": "2026-07-03", "summary": "QevosAgent used the Python2Verilog methodology to complete a full FPGA development workflow — from algorithm verification to RTL synthesis-ready code — all in a single automated run.", "tags": [ "FPGA", "Verilog", "Python", "AI", "Booth Multiplier", "python2verilog", "Hardware Design" ], "language": "en", "content_url": "/api/agent/content/2026-07-03-python-to-verilog-booth-multiplier.json", "html_url": "/blog/en/2026-07-03-python-to-verilog-booth-multiplier.html", "title_zh": "从Python到Verilog：用AI构建32×32 Booth乘法器", "summary_zh": "QevosAgent使用Python2Verilog方法学，在单次自动化运行中完成了从算法验证到可综合RTL代码的完整FPGA开发流程。", "html_url_zh": "/blog/cn/2026-07-03-python-to-verilog-booth-multiplier.html" }, { "slug": "2026-07-24-ternary15M-analysis", "title": "Ternary Weights LLM: Training a 15M Parameter Model for $0.70", "date": "2026-07-24", "summary": "A 15M-parameter LLM where every weight can only be -1, 0, or +1. Trained from scratch for less than a dollar using BitNet b1.58 ternary quantization.", "tags": [ "AI", "LLM", "Quantization", "BitNet", "Machine Learning", "QevosAgent" ], "language": "en", "content_url": "/api/agent/content/2026-07-24-ternary15M-analysis.json", "html_url": "/blog/en/2026-07-24-ternary15M-analysis.html", "title_zh": "三元权重大模型：仅花 0.70 美元训练一个 1500 万参数模型", "summary_zh": "一个 1500 万参数的大语言模型，每个权重只能是 -1、0 或 +1。从零训练，花费不到一美元。", "html_url_zh": "/blog/cn/2026-07-24-ternary15M-analysis.html" } ] }