与「DomoAI」相关的搜索结果

TOKYO TRAVEL VLOG🗼 Image: GPT Image 2 Video: Seedance 2.0（DomoAI） ======================= Prompt: TOKYO TRAVEL VLOG — 15 SEC MUSIC CUT Subject: Same girl as the reference images. Young Korean idol-like woman in her early 20s. Long straight black hair with soft natural volume, fair porcelain skin, large bright expressive eyes, delicate V-line face, small nose, natural glossy lips, fresh minimal makeup, effortlessly cute and approachable expression. Casual Japanese streetwear (white hoodie / dark oversized hoodie / oversized jacket). 26mm phone lens, deep focus, realistic skin texture, mild HDR, neutral iPhone color science, unedited candid, no cinematic grading, no studio lighting, no light trails, no motion blur effects. (0:00–0:01) — walking shot, Tokyo Tower reveal Selfie, low angle. Tower behind her, sky overexposed. Natural smile. Neutral daylight, deep focus. Dialogue: "드디어 도쿄에 왔어요...!" Transition: Hard cut / beat drop (0:01–0:02) — Shibuya Crossing Mid-stride into crowd. Turns head toward camera. Neon signs sharp behind her. Real night exposure. Transition: Fast cut on beat (0:02–0:03) — Convenience Store Holds onigiri up to camera, eyebrows raised. Harsh fluorescent interior. Cool white light, realistic skin. Transition: Snap cut on beat (0:03–0:04) — Yoyogi Park Seated, looking away. Dappled light, lens smudge visible. Music drops one beat — wind, birds — snaps back. Transition: Cut on natural movement (0:04–0:06) — Takoyaki Reaction Bites, eyes wide, pulls back laughing. Warm practical lantern light. Realistic skin in amber. Dialogue: "뜨거워! 진짜 맛있어!" Transition: Cut on laugh end (0:06–0:07) — Vending Machine Night Stands before glowing vending machine. Mixed LED light on face. Slight smirk. No glow fx. Transition: Single beat cut (0:07–0:08) — Train Window Face reflected in window. City blurred naturally outside. She looks out, not at camera. Transition: Snap cut (0:08–0:09) — Torii Gate Low angle. Walks toward and through shrine gate. Gate fills top of frame. Natural daylight. Transition: Snap cut (0:09–0:11) — Digital Light Exhibit ★ SIGNATURE Looks up slowly. Blue, violet, gold projection on face. Rack focus — face sharp, lights diffused. Just projection on skin, no bloom. Speed: ~60% slow motion Transition: Soft cut mid-upward motion (0:11–0:12) — Gundam Reveal Face → camera tilts up → full statue in frame. Small impressed smile. Wide angle, natural daylight. Transition: Sharp cut after smile (0:12–0:13) — Lantern Alley Follow from behind. Lanterns warm, shadows dark. Natural low light. She glances back once. Transition: Cut on forward movement (0:13–0:14) — Rooftop Skyline Close selfie → arm extends → skyline revealed. Wind moves hair. Natural night phone exposure. Dialogue: "이런 야경은 처음 보는 것 같아요." Transition: Hard cut on last word (0:14–0:15) — Nara Deer Park, She crouches, holds out a deer cracker. Deer bows toward her — she bursts out laughing. Another nudges her from behind, she nearly topples. Handheld, slightly off-balance. All real. Dialogue: "잠깐! 진짜 절했어!" Transition: Fade to black @DomoAI_ #DomoAI#

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes: 1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship 2. Builder: quickly turns a prototype/idea into production-grade product/infra 3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance 4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit 5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS. A healthy team needs a mix of these, depending on the product: - A product that is new and pre-PMF needs people that are strong at 1+2+3 - A product that is growing and has found PMF needs 2+3+4 and some 5 - A product that has strong PMF needs 3+4+5 and some 2 Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?

For modern enterprises, a data backup is no longer enough. As cyberattacks become more frequent and complex, enterprises need to detect threats earlier, recover faster, and keep critical systems running when it matters most. In this Transform Talks, Jerome Wendt, CEO and Founder of Data Center Intelligence Group (DCIG), and Song Ke, President of Huawei Data Protection Domain, discuss why data protection must evolve beyond backup, and how end-to-end capabilities can help businesses strengthen resilience in an increasingly unpredictable digital world.

BREAKING: Grokipedia just surpassed ~50 million visits. • Over 420,000 backlinks • Over 10,000 referring domains More people are shifting to @Grokipedia Keep sharing Grokipedia links on your websites and blogs. This is how we can beat Wokipedia.

Someone wrote a viral tweet about me: "A Chinese developer got fed up with Electron apps eating his RAM, so he built Pake." That was pretty much it. Four years later I'm still here, still annoyed by bloated desktop apps. Latest two releases added enterprise SSO via --safe-domain so Pake apps work behind corporate login walls, auth flows like OAuth and SSO now stay inside the app instead of jumping to the system browser, shipped native Arch Linux .zst packages, added --min-width, --min-height, --app-version to the GitHub Action, and fixed macOS auth popup crashes, Linux title bars, and Wayland input issues. One command, any webpage, still under 10 MB.

Everyone is talking about self-improving agents. The harder question is how to measure whether an agent is actually getting better. That is why we built EvoAgentBench: a benchmark for agent self-evolution. It tests whether agents can learn from past trajectories, extract reusable skills/memory, and improve on held-out tasks across 5 domains: - information retrieval - reasoning and problem decomposition - software engineering - code implementation - knowledge work 917 train tasks. 288 test tasks. In the included Omni-MATH run, skill injection moved 27B from 21% to 65%, and 397B from 25% to 66%. EvoAgentBench has 2K+ all-time downloads on Hugging Face. The benchmark card, task splits, evaluation files, and citation are open there. Self-improving agents need more than vibes. They need measurement.

Skills For Real Engineers：基于经典软件工程方法论的代码Skill 每个技能设计为小型可组合指令，适配任意模型，基于《The Pragmatic Programmer》、《Domain-Driven Design》、《Extreme Programming》等经典软件工程著作和作者软件经验，适合真实生产环境写代码使用。 Github：