Dreamer, Writer. Media Observer [email protected] #StandWithUkraine

genesis
Joined July 2008
准备面试 AI 岗位的看看这个GitHub仓库! 有人把 35 家公司的面试题整理成了仓库,按公司分类,高频题还附了解答链接 GitHub:github.com/pallavi-shekhar/a… 想往大模型应用、Agent 方向投简历的,先把那 10 类高频题过一遍,收藏这个仓库
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Anthropic senior engineer: “Prompting is no longer the job. 99% of people still open one chat and call it agents. I run agents inside /loops + /graphs + /harness — same model, same tokens, 10x more completed work. The system plans, verifies, and keeps shipping while I’m offline.” Prompts → Agents → Loops → Graphs → Harness In this 1-hour course she shows exactly how Anthropic builds a team of self-improving agents that recover and improve without you. Worth more than every $1500 agent engineering course combined. Watch it today before it gets buried. Then read the full guide for Opus 5.5 loop + jev engineering ↓
Your Claude loops can burn money while nothing changes Use Jev to filter tasks and cut decision costs Fix the trigger before another overnight run The full Loop engineering + Jev engineering setup below ↓
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Transformer里面的简单数学 这篇思路不错 我也准备写一篇🤔
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太有诱惑力了😋这视频美透了✌️
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THIS IS A PURE F*CKING GOLD Anthropic made Agent Skills an open spec and GitHub filled up with entire Claude Code engineering teams for free one repo ships 380 skills, 30+ agents and 70+ commands. another one watches your spend in real time so the team you just installed doesn't quietly eat your weekly limit. nobody is charging for any of it 10 GitHub repos worth opening tonight: 01 wshobson/agents → a plugin marketplace that installs the same agents into Claude Code, Codex, Cursor and Copilot, so your setup survives switching tools github.com/wshobson/agents 02 subinium/awesome-claude-code → a curated list of tools, skills, plugins and MCP servers. the map before you start downloading github.com/subinium/awesome-… 03 rohitg00/awesome-claude-code-toolkit → 135 agents, 35 curated skills, 42 commands, 176+ plugins, 20 hooks and 14 MCP configs in one place github.com/rohitg00/awesome-… 04 anthropics/skills → the official Agent Skills repo. start here to see the shape of a working SKILL.md before you copy anyone else's github.com/anthropics/skills 05 alirezarezvani/claude-skills → 380+ skills, 30+ agents and 70+ commands, portable to Codex, Gemini CLI, Cursor and eight more agents github.com/alirezarezvani/cl… 06 disler/claude-code-hooks-mastery → the hooks reference. this is where you learn to make the agent stop on your rules instead of stopping to ask you github.com/disler/claude-cod… 07 muhammadusmangm/claude-code-best-practices → setup, CLAUDE.md templates, multi-agent patterns and cost optimization, written as a wiki instead of a dump github.com/muhammadusmangm/c… 08 Maciek-roboblog/Claude-Code-Usage-Monitor → real-time usage monitor with predictions and warnings, so you find the wall before you hit it github.com/Maciek-roboblog/C… 09 Scorpio3310/claude-code-usage-statusline → daily and monthly spend, 5h and weekly limit meters, per-model quotas, all local, no API key github.com/Scorpio3310/claud… 10 eugeniughelbur/obsidian-second-brain → persistent memory as plain markdown in an Obsidian vault, shared across Claude Code, Codex and six other agents github.com/eugeniughelbur/ob… install the statusline before you install the agents. you want to watch what the fleet costs while it runs, not find out on Sunday when the weekly meter is already flat then hand the first team one job with an obvious finish line: one reviewed PR, one brief, one red test turned green keep the pieces that changed the result and delete the rest the same day. a config you did not write is a config you cannot debug at 2am the repos cost nothing. four agents repeating the same investigation is where the money actually goes save this one, the list only gets longer from here the article below shows how to keep context small, reuse finished work and contain retries, so more of your budget reaches the finished result ↓
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现在最值得做的创业方向,很多其实已经明显到摆在桌面上了。 Greg Isenberg 一口气列出 39 个想法:围绕 AI Agent、数据、机器人和个人 AI,把已经验证过的商业模式重新做一遍。 完整清单👇 1. 企业数据版 Zestimate:估算一家公司数据对 AI 实验室值多少钱 2. Agent 版 Google Analytics:查看哪些 Agent 访问网站,以及它们想找什么 3. 一人公司版 Y Combinator:投资带着一支 Agent 团队创业的独立创始人 4. Agent 版 Okta:管理每个 Agent 的身份和权限 5. 企业数据版 Marie Kondo:清理数据,让 Agent 真正用起来 6. Agent 版 FICO:Agent 花钱前先查看信任评分 7. Agent 版 Geico:为 Agent 犯下的昂贵错误提供保险 8. Agent 版 Datadog:看清 Agent 执行任务的每一步 9. Agent 版 Twilio:为 Agent 提供电话号码和短信服务 10. Agent 版 Upwork:按任务雇用 Agent,而不是自由职业者 11. AI 版 ESPN:像报道大型比赛一样报道每次模型发布 12. 机器人训练版 Shutterstock:拍摄自己做家务的视频并获得报酬 13. AI 数据版 Credit Karma:查看、修改或删除 AI 掌握的个人信息 14. AI 诈骗版 Norton:保护父母免受伪造语音和邮件欺骗 15. Agent 购物版 PayPal Buyer Protection:Agent 买错东西时负责处理 16. AI 版 Geek Squad:为家庭和小企业安装、配置个人 Agent 17. 私有 AI 版 Dell:为不愿使用云服务的企业提供盒装 AI 18. 二手 GPU 版 Carfax:购买前先查看 GPU 健康报告 19. 闲置 GPU 版 Airbnb:出租没有使用的算力 20. AI 模型版 Rotten Tomatoes:用真实评价替代跑分榜 21. Skills 版 G2:帮助用户找到最适合 Agent 的 Skills 22. AI 记忆版 Dropbox:把一个 Agent 对你的了解迁移到其他 Agent 23. 实体产品版 Canva:用 AI 完成设计,并直接生产出来 24. 个人 Agent 版 ADT:当 Agent 做出越权行为时及时报警 25. 机器人版 Hertz:搬家、大扫除或办活动时临时租一台机器人 26. 互联网创业版 Harvard Business School:系统教授如何建立线上业务 27. Agent 版 Workday:入职、管理和停用数字员工 28. 个人肖像版 Getty Images:安全授权自己的脸和声音给 AI 使用 29. 老年人版 Meta Muse:为高 LTV 的垂直人群定制个人 AI 30. Agent 版 Google Ads:付费成为 Agent 优先推荐的答案 31. Agent 版 Loom:自动回放 Agent 完成任务的全过程 32. Agent 版 Plaid:让 Agent 获得银行、航空公司和商店的授权访问 33. Vibe Coding 应用版 Jiffy Lube:快速修复 AI 写出的故障应用 34. 数据集版 Ancestry:追踪训练数据到底来自哪里 35. 人类创作内容的“有机认证”:证明内容确实由真人制作 36. 创业者版 Strava:记录每周真正发布了什么 37. 会砍价的 Honey:让 Agent 直接替用户谈价格 38. 个人 Agent 平台版 SEMrush:分析 Muse 等平台上的流量与竞争 39. Agent 版航空里程:专门为 Agent 设计忠诚度和奖励计划
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This is f**king insane... Andrej Karpathy spent 8 years inside OpenAI and Tesla. Last week he compressed all of it into one free 2-hour lecture. Agents -> Harness -> Loops -> Graphs There are bootcamps charging $15K that teach less than this. You probably don't have 2 hours today. Bookmark it and watch it before the feed buries it. Then read the guide below and build your first agent harness ↓
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Google just dropped a free 2-hour masterclass on Loop and Graph Engineering From one prompt to 200+ agents that check and fix their own work: 0% → 00:15 - Learn graph engineering from scratch 30% → 30:13 - Build your first agent graph, step by step 45% → 42:41 - Spin up as many agents as you need, all in parallel 75% → 59:47 - Loop engineering: send bad work back until it passes 100% → 1:23:03 - Build a graph that fixes itself while you sleep Prompts → Agents → Loops → Graphs → Self-Improving Systems Most people still stuff every instruction into one agent and hope it listens Worth more than any $2,000 agentic engineering course Bookmark and watch it today Then read the full harness engineering guide below ↓
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卧槽,清华心理系学生这 9 分钟,简直把人性扒了个底朝天! 据说视频已经被抖音下架。 全程高能,越听越扎心…
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爆了!!!NVIDIA RTX 20、30、40 三代显卡的隐藏 AI 算力,居然都被民间大神破解了!!!🤯 老黄锁了这么多年的 Tensor Core 算力,居然被人直接解锁了,部分显卡的计算吞吐量甚至原地翻倍! 而且最离谱的是,不需要更换硬件,不用刷 BIOS,也不用魔改 NVIDIA 官方驱动。 直接从底层解除部分 Tensor Core 运算的性能限制。 目前社区已经公布了多个型号的测试结果: 🔹 RTX 4090:165.6 → 331.6 TFLOPS,直接翻倍! 🔹 RTX 3090 Ti:BF16 从 87.5 → 157.4 TFLOPS,暴涨 80%! 🔹 RTX 2080 Ti:特定 FP16 运算吞吐量接近翻倍! 🔹 RTX 4070 Ti SUPER:FP16/BF16 运算吞吐量也接近翻倍! 甚至连 Windows 11 上都已经有人成功复现了。 要知道,RTX 2080 Ti 已经是八年前的显卡了,结果到了 2026 年,居然还有人能从里面挖出这么多计算能力。 更魔幻的是,现在连开源项目和安装教程都有了。 不同系列有对应的解锁项目: 🔥 RTX 20 系列(Turing) github.com/nlzy/turing-unloc… 🔥 RTX 30 系列(Ampere) github.com/Zagitta/gpu-unloc… 🔥 RTX 40 系列(Ada) github.com/JustAResearcher/g… 🔥 Windows 11 实验方案(改装版 RTX 4080) huggingface.co/TOTORONG/AD10… 还有中文安装说明: gpu-unlock.com/zh Linux 用户按照对应仓库的教程,安装内核模块、配置启动参数,再通过冷启动触发解锁。 NVIDIA 靠着精准的产品刀法卖了这么多年显卡,结果 GitHub 上的民间大神,硬生生把老黄的刀给撅了。 谁能想到,一张用了七八年的老显卡,居然还能在 2026 年迎来这种级别的算力解锁。
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Google just released free 2-hour course on full Harness engineering from 0% to 100%: 0% → 0:29 - Harness engineering from scratch 15% → 11:08 - build your first agent right way 30% → 28:08 - Eval engineering: build Chief of Staff agent 65% → 1:00:13 - Loop engineering 100% → 1:23:19 - self-improving graphs system that works 24/7 most people keep trusting agent that "looks right" - the real upgrade is a Harness that tests, judges and rewrite your agent until it works learn Harness from Google, ship your first - then unlock the full Agent systems below:
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《从零构建大模型》作者 Sebastian Raschka 推出了一套免费视频课,教你从零写一个推理模型,8 集共 11 小时,赶紧收藏🔖 这套课是配合他的新书《从零构建推理模型》录的,每集对应书里一章。 用 Qwen3 0.6B 预训练模型,拿 PyTorch 一行行把推理模型的关键部分写出来: 1️⃣:加载模型,写 KV cache,用 torch.compile 加速 2️⃣:写一个数学答案验证器,跑 MATH-500 评测 3️⃣:推理时扩展,先采样,再用 self-consistency 做多数投票 4️⃣:给答案打分,让模型自我修正 5️⃣:从零实现简化版 GRPO(RLVR) 6️⃣:看训练指标,加 clip 和 KL 项,讲 reward hacking 7️⃣:拿 DeepSeek R1 当老师做蒸馏 跟着学的几个提示: 前几集 CPU 就能跑,Mac 的 Apple 芯片也支持,到训练那几集再上 GPU。 不用先看《从零构建大模型》,会 Python 和 PyTorch 就能直接开始练。 视频合集:magazine.sebastianraschka.co… 代码:github.com/rasbt/reasoning-f…
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我他妈真的被笑死😂
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川普和马斯克对中期选举都没什么好办法了,只剩公开贿选。
JUST IN: Elon Musk says he will give one of his personal SpaceX shares to every American kid next year
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谷歌刚刚放出一门 60 分钟的官方课,手把手教你打造一个7 * 24小时完全自动化的生产级 Agent 。 很多人学 Agent 看了几十个小时零散教程依然搭不出可用的东西,核心是没有搞清玩具 Demo 与工业级架构的边界。 谷歌这门 1 小时课程没有任何水分,只聚焦 5 个真正的工程核心: 1. 从 0 跑通首个 Agent:拆解模型调用、工具绑定与执行环境的基础闭环。 2. 状态持久化(Persistent Memory):告别单次对话即失忆,构建跨会话的记忆索引与上下文回填。 3. 长时自主循环(Agentic Loops):摆脱线性问答,让 Agent 具备自我反思、容错重试与多轮状态流转能力。 4. 吃透 MCP 核心架构:彻底理清 Model Context Protocol 与传统 API 的本质区别,学会构建标准化的上下文协议。 5. 多智能体协同系统(Multi-Agent):把分散的专职 Agent 串联成高协作度的自动化系统。 完全免费。比市面上价值700刀的课还要干货。 建议收藏,今晚就观看 。🔖
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#独家 美国官员和知情人士说,马斯克下令对乌克兰依赖的军用级星链服务实施限速,这一决定令基辅方面陷入恐慌,也让特朗普政府内部感到困惑。cn.wsj.com/articles/WP-WSJS-…
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马斯克亲自下令乌克兰星链限速,马斯克这个侵略者的帮凶。
華爾街日報剛剛獨家報導:上週下令限制烏克蘭星鏈速度的正是馬斯克本人。上週馬斯克稱,是軍方、川普有權決定;川普本人、白宮否認下令。今天有美國官員告訴華爾街日報,是馬斯克幹的。目前,馬斯克本人還未回應。
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据《华尔街日报》援引美国官员和知情人士的消息称,埃隆·马斯克下令降低了乌克兰赖以生存的军用版星链(Starlink)的传输速度。
Elon Musk ordered the slowdown of the military version of Starlink that Ukraine relies on, according to U.S. officials and people familiar with the matter who spoke to The Wall Street Journal. Musk wrote on X after it became public that “any limits on speed or usage are decided by the US military and ultimately POTUS.” Trump told reporters Monday he didn’t know who was responsible, “but I would like to find out.” The Journal reports Musk and Trump spoke by phone soon after the slowdown became public, according to two people, and one said they also discussed it before. Some U.S. officials told the Journal Musk acted unilaterally and Trump did not order retribution against Ukraine. That conflicts with earlier reports. Reuters reported the order came from inside the White House, and The Telegraph reported Musk proposed it to Trump. A senior U.S. official told Reuters the president made no such request. Source: wsj.com/world/ukraine-starli…
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