Obsidian 的 CEO kepano 写了一个 Mermaid 渲染库 Pele，同样的 Mermaid 语法 输出变成可换主题的 SVG ，但是性能和大小都有绝对优势： pele.run gzip 后 230 kB vs Mermaid.js 的 1.6 MB 大约小 6 倍 渲染官方说最多快 500 倍 换肤是改 CSS 变量，不用重新渲染 浏览器和 node 都能跑 之后Obsidian 会用它默认渲染图，还在用 Mermaid.js 可以去试试看 kepano @kepano This is Pele, my new rendering library for diagrams and charts, compatible with the latest Mermaid syntax. - 100-500× faster than mermaid.js - 6× smaller bundle - No dependencies - Easy to theme - Responsive to device width In pursuit of making Obsidian insanely fast and cohesively designed, I always found the Mermaid library to be too big, too slow, and too hard to theme. Pele will be the new renderer in the next version of Obsidian, but you can start using it now. Pele [ˈpɛlɛ] is named after the goddess of volcanoes and fire. It's open source under the MIT license. Try the playground! 🔗 View on Twitter 🔗 View Quoted Tweet
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Who appointed Anthropic to be god? Holly ⏸️ Elmore @ilex_ulmus At @AnthropicAI this is like a koan to them and they love making a big show of grappling with it. Of course what they’ve already decided to actually do is try to take over the world as fast as they can regardless. 🔗 View Quoted Tweet
⚠️ PSA: “AI assistants may fall prey to prompt injection attacks” ⚠️ jeffrey lee funk @jeffreyleefunk Agentic AI requires us to trust our emails and other messages and we really shouldn't. There is too much hidden content and AI models can’t tell the difference between text it’s supposed to read and instructions it’s supposed to follow. futurism.com/artificial-int… 🔗 View Quoted Tweet
昨天我老婆问我 App Store 怎么付费，看了一下才知道她要给儿子买一个打字 App，收费 128 块。。 这。。小孩和女人钱这么好赚？？ 我说等我半个小时，打开 Codex，把打字游戏的需求输入进去，一个 Goal 跑起来，20 分钟就做好部署到 ChatGPT Sites 了。花了几分钟看了一遍，还不错，顺手再加了一个实时拍摄人像，让头像作为游戏主角的功能，30 分钟准时交活儿。 效果怎么样呢？我第一版做了 36 关，儿子一连玩了 18 关 😁😁😁，我边看他玩，边看有什么问题，边让 Codex 改，顺便增加了几十关，和无限模式。。 以后儿子再有什么想玩但花钱的游戏和应用，自己做就行了，大家感兴趣可以看 …doudou.groovy-river-7567.chatgpt.site Rnb
does anyone have handy the actual data on AI’s effect on GDP over the last four years? also: is Dario innumerate, unaware of prior data; or just hustling his IPO and full of shit? Kalshi @Kalshi BREAKING: Anthropic says "AI could push US GDP to $44 trillion by 2030" 70% chance of IPO this year 🔗 View Quoted Tweet
since several people are responding by lying and attacking my credibility without actual data, i thought i would leave this, based on actual data, here. Gary Marcus @GaryMarcus The biggest, most persistent lie here on X is that I am always wrong. I literally get attacked like (sample) below multiple times a day—but never with any concrete data. At most these accounts (some may be bots) point to one or two predictions that were wrong. And, you know what, some actually were, including one about the timing of the genAI bubble, another, though still arguable, about math) out of the hundreds I have made. Whereas @aipredictionx actually did the analysis – and found me to be the “the most accurate big name we track: 11 resolved AI predictions, 11 scored correct, including the million dollar bet that AI would not be smarter than any single human by the end of 2025” Either people like the guy below are lying about me, or they are really bad at data analysis. (Or both.) Ask yourself why so many people are motivated to lie about me. 🔗 View Quoted Tweet
The biggest, most persistent lie here on X is that I am always wrong. I literally get attacked like (sample) below multiple times a day—but never with any concrete data. At most these accounts (some may be bots) point to one or two predictions that were wrong. And, you know what, some actually were, including one about the timing of the genAI bubble, another, though still arguable, about math) out of the hundreds I have made. Whereas @aipredictionx actually did the analysis – and found me to be the “the most accurate big name we track: 11 resolved AI predictions, 11 scored correct, including the million dollar bet that AI would not be smarter than any single human by the end of 2025” Either people like the guy below are lying about me, or they are really bad at data analysis. (Or both.) Ask yourself why so many people are motivated to lie about me.
Google Research 最新开源了 Harness²：递归式智能体 Harness 优化框架 Harness² 解决的两个开放世界难题 1. 测试时遇到的是没见过的任务，没有 ground-truth 验证器。在线改 harness 时你不知道答案对不对，无法用“分数涨了没有”来指导编辑。 2. 递归自我改进（RSI）通常是任务特化的，换个任务就失效，无法泛化积累。 开源地址： github.com/google-researc… # 核心机制：两层递归 第一层：任务级递归，Propose–Probe–Compose（对比式改进环） · Propose（提案）：先跑一次当前 harness 的参考执行。注意，这次执行揭示的是“harness 如何表现”，而不是“交付物是否正确”。基于任务、参考轨迹和历史，提出两个候选 harness：一个针对跨领域通用的代理性缺陷（规划、工具使用、上下文管理、委派），一个针对当前领域的知识、流程和约束。每个候选都是可直接执行的文件级编辑。 · Probe（探针）：两个候选在同一任务、同等的模型和环境条件下执行，与参考执行对比得到行为对比；编辑是否缓解了目标缺陷、毫无变化、还是造成回归。论文明确说这些对比只“为后续组合提供信息，而非验证任务正确性”。 · Compose（组合）：读取参考执行、两个候选、它们的 diff 和全部执行证据，有选择地构建后继 harness：合并被证据支持的修改、修订冲突、采纳其一、或保持原样。每个决策必须绑定一个具体观察，而不是假想的分数增益。 这一步是整个工作的关键创新：在没有标准答案的情况下，用“同任务下的 A/B 行为对比”替代分数反馈来指导编辑，绕开了开放世界没有验证器的死结。 第二层：harnessing 级递归，持续改进的 improvement harness 每个任务结束后，框架对完整的改进记录做反思，把被采纳的 harness 和有证据支持的教训写入一个按领域组织的持久库。要点有三： · 教训只有在执行对比支持时才保留——单次选出了好输出不等于可迁移； · 库里存的是可执行的组件文件而非散文式摘要，配一份 EXPERIENCE.md 记录适用场景和证据； · 下一个任务仍从公共基础 harness 出发（执行 harness 不继承），但 Propose/Probe/Compose 由更新后的库引导；即“改进 harness 本身”也在被改进，这就是标题里 Harness² 的双重含义。 两种扩展模式：parallel（同时探测 k 组候选，k 个独立组合会话）和 sequential（k 步链式精炼，逐步携带累积证据），两种模式从 k=1 到 5 均单调提升。 八个可编辑组件 - 按作用分组 · 上下文构建：系统提示词、护栏、记忆，每次调用都生效 · 工具接口：工具指引、Skills，Skills 按需加载不占成本 · 编排：subagents，唯一会增加推理上下文的组件 · 脚本：新确定性能力，执行而非推理 · 插件：TypeScript 中间件，“在工具环内强制一种行为，而非指令它” 主要结果（k=1 单步，编辑器为 Claude Opus 4.8） WorkBuddy-Bench · Gemini 3.5 Flash：+5.1 (OpenCode) · GLM-5：+11.5 (OpenCode，64.2→75.7) JobBench · Gemini 3.5 Flash：+4.5 (OpenCode) · GLM-5：+3.6 (OpenCode) LAB 部分通过率 · Gemini 3.5 Flash：+4.0 (Codex) · GLM-5：+11.8 (OpenCode，59.6→71.4) 更值得注意的发现： · 越弱的执行模型获益越大，且 GLM-5 换上改进后的 harness 反超了 Gemini 的基础分数，“换 harness”在某些场景下顶得上“换模型”； · 改进后的 harness 跨模型迁移（为 Gemini 精炼的 harness 给 GLM-5 在 Code 子集带来 +17.3），也胜过 Claude Code、Codex 的固定生产 harness 4.6–5.8 分； · 执行器和编辑器用同一个模型也能涨（GLM-5 自编辑在 LAB 上 +13.7），说明不依赖更强的“老师”模型； · 库的消融：去掉 library 在 WorkBuddy-Bench 上损失 3.1 分，验证了 harnessing 级递归的价值；库还能跨基准迁移（JobBench 的库迁移到 WorkBuddy-Bench 仍有 +0.6~+2.9）。 Shubham Saboo @Saboo_Shubham_ Recursive Agent Harness from Google Research. 100% Opensource. 🔗 View Quoted Tweet
THIS! If you aren’t listening to @HeidyKhlaaf you are the mark. Dr Heidy Khlaaf (هايدي خلاف) @HeidyKhlaaf We warned about this exact scenario last year: That AI companies will situate themselves as the arbiters of life or death, taking over the arbitration of risk determinations and subverting democratic processes. All through the co-option of safety engineering terminology. 🔗 View Quoted Tweet