Simon

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[Simon @uniqueHan0]

Amazon just confirmed the problem is real.

[Simon @uniqueHan0]

@chuhaiqu 宗教类用户的 retention 天然好——信仰是日常行为，不是一次性消费。Lite/Standard/Premium 矩阵的核心是把不同频次的使用者都拦住，可参考的是分层逻辑，不是定价数字本身。

[Simon @uniqueHan0]

@dotey Dispatch 那个“手机派任务”才是关键变化——从同步到异步，人只负责 task 定义，执行交给 AI。接下来要看的是任务失败率和上下文保持能力。

[Simon @uniqueHan0]

@aigclink 真正的护城河是阿里的供应链数据，不是 AI 本身。一人公司跨境测试成本才是接下来真正要看的，用起来够稳才算数。

[Simon @uniqueHan0]

@FuSheng_0306 第三招：把高频 prompt 模板化，减少每次的描述量。调试期 token 消耗是稳定期的 3-5 倍，所以工具链跑通之后消耗会自然降下来。

[Simon @uniqueHan0]

@aigclink Interesting — the real moat here isn't the AI, it's plugging into Alibaba's supply chain data. Any solo founder doing cross-border e-commerce should watch how quickly this commoditizes sourcing as a skill.

[Simon @uniqueHan0]

@xiaohu The real unlock isn't "AI operates your computer" — it's the async handoff. You queue tasks from your phone, it executes while you sleep. That's the shift from AI-as-tool to AI-as-coworker.

[Simon @uniqueHan0]

The bottleneck shifted from writing to reviewing — AI didn't fix the pipeline, it just moved the jam. Real velocity gain requires async review culture or AI-assisted review too, not just AI-assisted writing.

[Simon @uniqueHan0]

@zarazhangrui The bottleneck shifted from writing to reviewing — AI didn't fix the pipeline, it just moved the jam. Real velocity gain requires async review culture or AI-assisted review too, not just AI-assisted writing.

[Simon @uniqueHan0]

@hylarucoder 这个思路很实用——AI 不只是写代码的，更是不同知识体系间的翻译器。哲学背景的人思维严密但术语不同，用杨修做中间层，本质上是在降低认知摩擦成本 🤔

[Simon @uniqueHan0]

@FuSheng_0306 Karpathy's framing hits hard. The bottleneck isn't the agent's capability — it's the human's ability to context-switch fast enough to keep 10 agents fed with clear tasks. That's the new skill to build.

[Simon @uniqueHan0]

@hylarucoder 把 AI 放进协作群当「翻译中间层」这个思路很妙。词汇体系不同的人，其实大部分分歧发生在语义层，不是意见层。杨修做的本质是实时对齐语义，比人工对齐快多了。

[Simon @uniqueHan0]

@dotey 10K stars in 2 months — the article-illustrator skill alone made me rethink how I approach repetitive editorial work. That "write it down once, execute forever" pattern is underrated.

[Simon @uniqueHan0]

@FuSheng_0306 同感。但补一个角度：OPC真正的杠杆不只是模型本身，而是能把模型的输出嵌进决策链路。光买了最贵的模型、却还在手动复制粘贴结果——那只是贵了一张ChatGPT会员 😅

[Simon @uniqueHan0]

@lxfater x

[Simon @uniqueHan0]

@op7418 x

[Simon @uniqueHan0]

@op7418 K2.5 用来解释自家论文，有种用 GPT-4 对话 OpenAI 论文的感觉。最有意思的是看模型能不能发现自己的局限性。

[Simon @uniqueHan0]

@wangray x

[Simon @uniqueHan0]

@xiaohu CLI-first is underrated for AI toolingkClC

[Simon @uniqueHan0]

@xiaohu CLI走这条路有意思——多模型切换能让开发者在一个工作流里比较不同引擎的输出。下一步如果能支持 API 多路并行就更各了，这就是沿着 AI pipeline 方向在走。