初一｜AI工具×X运营 @ChuyiFirst
记录X小号从0到1｜实测AI工具怎么帮X小号写帖、配图和复盘｜分享可复制模板与真实数据｜不做工具百科 smarttools.cc 香港 Joined September 2021-
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@shadouyoua 你说的对，就是需要明确分工，每个角色都有不同的技能然后分工协同工作，但是需要清晰的边界，否则工作中也会混乱
@tom_doerr People often focus on the model output, but in real products, a lot of time goes into these “boring” parts 😂 Data flow, tracking, and edge cases are what often make the biggest difference in user experience.
@SimplyAnnisa @LoovaAI Honestly, the most disruptive part of AI video isn’t generating clips. It’s rapidly removing production cost as a barrier. When everyone can create cinematic content, the advantage may shift from technology to taste.
@simplifyinAI The biggest advantage for AI learning tools may not be the model itself, but memory. An AI that understands your level and where you struggle will feel completely different from one that starts from zero every time.
仓库： github.com/addyosmani/age… Codex CLI 0.122+安装： codex plugin marketplace add addyosmani/agent-skills 不建议一开始全用，先选需求、测试和代码审查这3类即可。
刚翻到一个81.3k Star的Agent Skills仓库。 它最有价值的不是“24个Skill”，而是把AI Coding拆成了完整交付链： 需求 → 计划 → 实现 → 测试 → 审查 → 发布 支持Codex，8个命令可以触发对应流程。 我的看法：Skill解决的是让Agent少跳步骤，不是让模型突然变聪明。
@RoundtableSpace Anyone who has built an e-commerce site probably feels this 😂 The page is just the beginning. Conversion still depends on creatives, positioning, trust, and constant testing. AI lowers the barrier to building, but operating skills become even more important.
最近研究AI 内容工作台，我发现角色越多，不等于自动化越强。 AI的价值不是自动化，而是能稳定交付结果。
@dotey 这个变化其实挺明显的😂 以前测试 LLM 更多是看它会不会生成一个结果，现在开始看它能不能理解一个完整目标并把事情做出来。 从“回答问题”到“完成任务”，这中间的差距才是最有意思的地方。
@XFreeze This is starting to look more like an AI employee 😂 Not opening a chat every time you need help, but giving it responsibilities and triggers to run workflows by itself.
The biggest change in AI video is not just quality, but control 😂 Moving from text generation to image references, voice, and character consistency feels much closer to real production workflows. But the question is: when everyone can make decent videos, what becomes the real differentiator?
Open-sourcing this kind of infrastructure is actually important. People focus a lot on model capability, but post-training, data generation, and evaluation also determine the final performance. Future model competition may not just be about parameters, but who can build the better training loop.
@teslaownersSV Honestly, many people probably aren’t just buying a plushie 😂 They’re buying a feeling of connection and being part of the space journey.
@sama This connects with a trend I’ve been watching: AI is turning individuals from executors into orchestrators. Coding ability still matters, but the bigger skill may be organizing AI to achieve goals.
@thsottiaux If AI starts contributing to scientific discovery, that could be much bigger than generating images or writing code 😂 The interesting part isn’t just getting answers, but whether AI can ask questions humans haven’t thought of.
@viktoroddy This is actually a really interesting shift 😂 AI is allowing people without design backgrounds to create surprisingly good work. But the question is: when production becomes cheap, will taste, judgment, and expression become the real scarce skills?
这次主要对比出片速度、人工参与、单条成本和成片效果。 两个项目源码跑通后分享完整测试结果。
我决定先不换24GB显卡。 直接拿手里的RTX 5060 Ti 8GB，测试一次AI自动出片。 目标不是硬跑最强的视频模型，而是先打通完整流程： 选题 → 脚本 → 分镜 → 配音 → 字幕 → 素材 → 剪辑 → 成片 轻任务尽量放本地，真正吃显存的视频生成再考虑API或云算力。 我准备先装Toonflow和MoneyPrinterTurbo，用同一个选题对比： 自动化程度、出片时间、单条成本、成片质量和人工修改量。 跑不动、效果差、哪里花钱，我都会真实记录。 8GB显卡到底能不能做AI自动出片，不看配置表，直接跑一次再说。
@ollama Tools like Claude Code combined with more model choices feel like an early version of future development environments. Instead of one fixed model, the system could choose based on the task.
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