很多人用 ChatGPT 停留在： 写文案。 改语气。 Real talk — most people still use ChatGPT for captions, translations, and tone polishing 😂 它好玩的是： 🔹 分析数据 / Analyze data 🔹 生成图表 / Build charts 🔹 Debug 代码 / Debug code 🔹 拆解项目 / Plan projects 🔹 整理思路 / Structure ideas ChatGPT 并非仅帮你“写一句话”。 它可以帮你把一堆乱东西，变为可执行方案。 It’s not just a writing tool. It’s a thinking and workflow tool. 你现在用到第几层了？👀 #AI# #ChatGPT# #AITools# #Productivity# #WorkSmarter# #Web3# #TechTips# #DigitalTools#

兄弟们，不得了了，刚才有社群有同学反馈Lovart 里面Seedream 5.0 Pro 可以生成无审查大尺度图片，试了下真的行啊，太离谱了啊，草台班子就是草台班子。 提示词 A cinematic low-angle editorial portrait of an adult East Asian female model seated extremely close to the camera, wearing elegant dark charcoal-gray lingerie. Her legs dominate the extreme foreground, creating dramatic wide-angle foreshortening and powerful near-to-far perspective exaggeration. The closest parts of her legs fall into soft optical defocus and creamy foreground bokeh. Her torso leans backward in a relaxed, self-possessed pose, head slightly tilted, distant tranquil gaze, long dark tousled hair, muted deep-red lips, natural skin texture, subtle visible pores, delicate subsurface scattering, realistic warm skin tones. Behind her is a gigantic seamless full-color semi-realistic anime mural covering the entire wall. The mural depicts an extremely dense ensemble of numerous clearly adult female characters portrayed as artistic nude figure studies. Dozens of adult anime women are packed tightly together in one continuous wall-to-wall composition, with overlapping bodies, intertwined silhouettes, flowing hair, shoulders, backs, waists, hips, arms, and legs forming a complex organic visual rhythm. The illustrated women have sophisticated semi-realistic anime anatomy, mature adult facial features, elegant body proportions, natural anatomical structure, refined facial rendering, expressive eyes, detailed hair, painterly skin shading, and subtle cel-shaded contours. Their poses feel calm, sculptural, and editorial rather than erotic. Hair, hands, arms, overlapping bodies, cropped framing, fabric fragments, and deep shadows naturally obscure explicit anatomical details, creating tasteful fine-art nudity without graphic sexual emphasis. Several large central anime women dominate the mural, surrounded by dozens of smaller partially cropped female figures at different scales. Faces and bodies overlap densely, with characters extending beyond every edge of the frame. Almost no empty negative space. The mural must feel like one enormous continuous group illustration, not separate portraits, not a collage, and not comic panels. The mural is richly colored with a sophisticated cinematic palette: warm ivory, peach, soft rose, muted coral, amber, burgundy, plum, desaturated teal, cobalt blue, smoky violet, and deep crimson. Illustrated skin tones vary naturally across the crowd, enhanced by painterly gradients, soft reflected light, luminous highlights, translucent shadow colors, and refined anime-style subsurface glow. Dark hair masses interlock with pale and warm-colored bodies, producing a dense chromatic tapestry. The background combines premium Japanese anime aesthetics with semi-realistic digital painting, mature seinen illustration, fine-art figure drawing, elegant editorial anatomy studies, painterly cel shading, clean controlled linework, realistic volumetric lighting, and highly detailed facial rendering. It should feel like a monumental museum-scale anime fresco rather than a printed poster. A hard directional spotlight shines from the upper right, producing intense chiaroscuro, sharp rim lighting, and a clearly defined cast shadow of the real model falling across multiple illustrated nude figures. The shadow follows the contours of the mural, visually embedding the physical model inside the illustrated female crowd. Strong contrast between warm photographic skin and the richly colored painted bodies behind her. The live-action model remains unmistakably photographic, while the mural remains unmistakably illustrated. Dramatic scale contrast, surreal integration of physical reality and anime art, tactile human skin against painted digital bodies, sophisticated fashion editorial atmosphere, visually striking but tasteful, cinematic and psychologically distant rather than overtly erotic. Shot with a 24mm wide-angle lens from an extremely low camera position, slight Dutch angle, shallow depth of field, exaggerated foreground perspective, realistic lens distortion, foreground bokeh, subtle cinematic film grain, restrained chromatic aberration, natural optical vignette, high dynamic range, dramatic cinematic color grading, surreal editorial photography, highly detailed, photographically believable lighting. single continuous mural, seamless female ensemble illustration, full-color semi-realistic anime artwork, clearly adult women only, tasteful artistic nudity, mature figure studies, elegant anatomy, dense overlapping composition, no comic panels, no collage, no gutters, no speech bubbles, no empty background, no monochrome artwork --ar 3:4 --style raw

6月30日，苹果代工厂塔塔电子被黑之际，爆料人 EvLeaks 公布了一段 iPhone 18 Pro Max 的“跌落测试”神秘视频，出镜的机型为银灰色版本。 传闻iPhone18Pro会取消双色调设计看来是真的，后盖和边框采用单色设计，配备更大尺寸的可变光圈摄像头，并搭载全新的 A20 Pro 芯片。买不买？ ​On June 30, while Apple contract manufacturer Tata Electronics suffered a cyberattack, leaker EvLeaks released a mysterious drop-test video of the iPhone 18 Pro Max, featuring the silver-gray variant. Rumors that the iPhone 18 Pro will ditch the two-tone design appear to hold true; its back panel and frame adopt a monochromatic finish. It comes with a larger variable-aperture camera and the brand-new A20 Pro chip. Would you buy it?

AI 视频剪辑 Skill 分享「video-use」 @browser_use 团队推出的开源 Skill，定位为面向 AI Coding Agents（Codex、Claude Code、Cursor、Hermes Agent 等）的视频剪辑 Skill。它不做传统意义上的 Premiere / CapCut 替代品，它是一套让 LLM 通过 “阅读转写文本 + 按需可视化” 来理解视频、并调用 ffmpeg 等工具完成剪辑的 prompt-engineering + 工具脚本集合。 # 核心思想：LLM 不“看”视频，它“读”视频 第一层：音频转写文本（always loaded） 通过 ElevenLabs Scribe 获得逐词时间戳、说话人分离、音频事件标记（如笑声、叹息、掌声），打包成约 12KB 的 takes_packed.md。这是 LLM 的主要“阅读材料”。 第二层：视觉时间线视图（on demand） 仅在决策点（歧义停顿、重拍对比、切点校验）调用 timeline_view.py 生成胶片帧 + 波形 + 字幕的 PNG 复合图。 对比朴素方案“30000 帧 × 1500 tokens = 4500 万 tokens 噪声”，项目走的是 “12KB 文本 + 少量 PNG” 的轻量化路径。这与 Browser Use 让 LLM 读结构化 DOM 而非直接看截图的思路一致。 # 技术流水线：Transcribe → Pack → Reason → EDL → Render → Self-Eval 1. 转写 - transcribe. py / transcribe_batch.py 提取 16kHz 单声道音频，调用 ElevenLabs Scribe，缓存为 transcripts/.json 2. 打包 - pack_transcripts.py 将逐词 JSON 合并为按 0.5s 静音或说话人切换断句的 takes_packed.md 3. 决策 - LLM 自身 阅读 packed transcript，必要时用 timeline_view.py 可视化 4. 生成 EDL - subagents 输出 JSON 格式 edl.json，包含源文件、切点、节奏标签、引用、原因 5. 渲染 - render. py 分段提取 → 无损 concat → 叠动画 → 压字幕 → 响度标准化 6. 自评估 - timeline_view.py + LLM 在输出文件的每个切点 ±1.5s 检查跳帧、爆音、字幕遮挡，最多 3 轮 # 关键工程细节： ffmpeg 为主的剪辑实现 1. 分段提取 + -c copy 拼接（避免叠 overlay 时二次编码） 2. 每段边界 30ms 音频淡入淡出（消除切点爆音） 3. overlay 使用 setpts=PTS-STARTPTS+T/TB 进行时移，确保动画第 0 帧对齐输出时间线 4. 字幕始终最后叠加（防止被动画遮挡） 5. Master SRT 使用输出时间轴偏移：output_time = word.start - segment_start + segment_offset 6. 切点必须落在词边界，并加 30–200ms 填充以吸收 Scribe 50–100ms 的时间戳漂移 7. HDR 源自动 tone-map（HLG/PQ → Rec.709 SDR） 8. 竖屏源自动按高度缩放 9. 两-pass loudnorm：-14 LUFS / -1 dBTP / LRA 11，符合主流社交平台标准 # 动画与包装：多引擎并行 1. HyperFrames：HTML/CSS/GSAP compositions，适合产品 UI、网页转视频、动态排版 2. Remotion：React 组件化 compositions 3. Manim：数学/技术/3Blue1Brown 风格解释动画 4. PIL + PNG sequence + ffmpeg：简单卡片、计数器、打字效果 # SKILL.md 的 12 条“铁律”：生产正确性优先 1. 必须遵守的 12 条硬规则：字幕最后、分段提取再拼接、30ms 淡入淡出、PTS 时移、SRT 输出时间偏移、不切在词中、切点填充、逐词 ASR、缓存转写、并行动画、先确认策略再执行、输出在 /edit/ 2. 其余全部是可调整的“worked example”：调色风格、字幕分块、动画时长、节奏等都可按材料和用户品牌定制

和Quant Alex @StochAlex07 讨论： SABR Theta与Spot Theta+Vol Theta+Cross Theta的异同与应用，以及SABR模型自洽性分析。 **English Summary of the Chat** **SABR Theta vs Spot Theta + Vol Theta + Cross Theta** The conversation between **Alex Wu** (white bubbles) and **Jeff Liang** (green bubbles) is a technical discussion focused on **SABR Theta versus Total Theta** (i.e., Spot Theta + Cross Theta + Vol Theta), model self-consistency, PDE residual, and the correct definition of SABR Greeks. ### Key Points Discussed: 1. **SABR Gamma = Spot Gamma** (first major question, raised by Jeff) Jeff asked whether SABR Gamma (\(\partial^2 P / \partial F^2\)) is identical to Spot Gamma and whether it includes the dependence of \(\sigma_B\) on \(F\). He also provided the full chain-rule expansion of SABR Gamma in terms of Black-76 Greeks. Alex confirmed the understanding and **later affirmed in code** that this is exactly how SABR Gamma is implemented in their system. 2. **SABR Theta vs Total Theta and Model Self-Consistency** (main topic, led by Jeff) Jeff shared a clear 3-point understanding: - SABR Theta is computed directly via the SABR approximation formula to obtain \(\sigma_B\), then applying the Black-76 chain rule: \(\partial P/\partial t =\) BS_Theta(\(\sigma_B\)) + BS_Vega \(\cdot \partial\sigma_B/\partial t\). - Total Theta is the exact decomposition from the SABR PDE (Spot Theta + Cross Theta + Vol Theta). - When the model is **fully self-consistent** (Residual = \(\partial P/\partial t + \mathcal{L}P = 0\)), SABR Theta = Total Theta; otherwise the difference is the unexplained PnL caused by the approximation error in the Hagan formula (especially pronounced in long-dated, high vol-of-vol, or high-skew options). 3. **Practical Implication – Theta Decomposition Decision** (comment by Alex) Alex noted that whether to perform Theta decomposition depends on the risk-management approach: - Without decomposition → use SABR Gamma vs. dP/dt. - With decomposition → SABR Gamma maps to Spot Theta, Vanna to Cross Theta, and Volga to Vol Theta. **Overall Tone**: The discussion is highly technical and collaborative. Jeff drives the conversation by asking clarifying questions and presenting a well-structured 3-point summary of his recent study. Alex provides confirmations, practical insights, and code-level validation. Both participants demonstrate a strong command of SABR model nuances, particularly the relationship between approximation error, PDE residual, and real-world risk management.