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对比阅读:No Code? The AI Money Is in the 'Skin' Layer 与 不会写代码也能做软件生意?AI 时代你该抢的是这层'皮'

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AI entrepreneurshipno-codeCoze·

No Code? The AI Money Is in the 'Skin' Layer

Last week I had dinner with a friend who runs Xiaohongshu accounts. She said she wanted to build a tool that auto-writes viral titles and charge for it, then asked: "Do I need to learn Python first?" I almost spit out my Coke.

Two years ago I thought the same thing. I wanted to build a small tool helping freelancers manage their quote sheets — spent three months just "finding a tech cofounder." They ghosted. The product died.

Now it's different: you're not building the engine, you're building the steering wheel

Recently saw a concept called "harness" — the idea: AI models are like car engines. Smart, they run, but a bare engine can't hit the road. What you build is the steering wheel, seats, dashboard — the "stuff users can touch."

Abroad there's a company called Harvey. They make "AI assistants for lawyers." Lawyers don't need to understand AI — they just open the interface, drop in a contract, and ask "is this clause fair to my client?" Harvey's core isn't training their own model. It's stitching together a general model + legal-domain data + lawyer workflows. Last year they raised $150M on this model.

In China you can see the same play: all those "Xiaohongshu viral title generators," "WeChat article formatting AI," "e-commerce customer service scripts" — same playbook. Anyone can plug in a model, but "which scenario, for whom, in what tone" — that's your job.

To replicate this today, what do you need?

Money: Take a cheap model like GPT-4o mini. 1,000 normal Q&As run about $1-2 (around 7 RMB). A few thousand calls a month costs less than a bubble tea.

Time: With drag-and-drop platforms (ByteDance Coze, Alibaba Dify), and not building complex features, you can ship a minimum version in one or two evenings.

Tech barrier: No code. I built an "auto-categorize customer inquiries" tool in Coze myself — all mouse clicks.

First step: Go to coze.cn, register with your phone, click "Create Bot," pick a scenario you know cold (help students revise resumes, help clients write weekly reports — anything), follow the three-step prompts, and you're live.

Three types, three plays

Just starting: You probably don't even know "what to sell" yet. My advice: don't build a tool first. Post 10 pieces of content on Xiaohongshu or WeChat Moments with the format "I used AI to do X, saved Y time." See what gets the most traction — then decide if it's worth a product.

Have 1-2 clients: You already know their specific pain. Build a minimum version in Coze for them to use. Charge even 99 RMB/month. The point isn't profit — it's validating "will they pay for this workflow."

Scaling up: Starting to worry "what if the model changes," "how do I manage user data." Time to build your moat — accumulate client history, maintain your own prompt library. The model vendor can't take these from you.

Final word: This playbook isn't for everyone. If your current business doesn't really need "automating some workflow," forcing it just wears you out. First ask: among the annoying repetitive things you do daily, is there one worth "outsourcing to an AI shell"?

来源: blog.sshh.io
BZH
AI 创业无代码Coze·

不会写代码也能做软件生意?AI 时代你该抢的是这层'皮'

上周跟一个做小红书的朋友吃饭,她说想做个"自动写爆款标题"的工具收钱,问我"是不是得先学 Python"。我差点把可乐喷出来。

两年前我也这么想。当时想做一个帮自由职业者管报价单的小工具,光"找技术合伙人"就耗了三个月,最后人跑了,产品也没了。

现在不一样了:你不用造引擎,你造方向盘

最近看到一个说法叫 harness(外壳),意思是——AI 模型就像汽车引擎,聪明、能跑,但裸引擎没法上路。你要造的是方向盘、座椅、仪表盘这些"用户能摸到的东西"。

国外有个叫 Harvey 的公司,做的是"给律师用的 AI 助手"。律师不需要懂 AI,他们只要打开界面、丢一份合同进去、问"这条对客户公不公平"。Harvey 的核心不是自己训模型,而是把通用模型 + 法律场景的数据 + 律师工作流缝在一起,去年靠这模式融了 1.5 亿美元。

国内你也能看到:各种"小红书爆款生成器""公众号排版 AI""电商客服话术"——全是这个套路。模型谁都能接,但"在什么场景、给谁用、用什么语气回答"——这是你的活。

今天复刻这个思路,需要什么?

钱:以 GPT-4o mini 这种便宜模型为例,1000 次普通问答大概 1-2 美元(约 10 块人民币)。月跑几千次也就一杯奶茶钱。

时间:用现成的拖拽平台(字节 Coze、阿里 Dify),不做复杂功能的话,一两个晚上能搭出最小版本。

技术门槛:不用写代码。我自己用 Coze 搭过一个"自动把客户咨询分类"的工具,全程鼠标点击。

第一步:去 coze.cn 注册,手机号登录,点"创建 Bot",选一个你最熟悉的场景(帮学员改简历、帮客户写周报都行),跟着提示填三步就能跑起来。

三种人,三种打法

刚起步:你可能连"卖什么"都没想清楚。建议先别搭工具,先去小红书或朋友圈发 10 篇"用 AI 帮我做 X 省了 Y 时间"的内容,看哪种反馈最多,再决定要不要做成产品。

有 1-2 个客户:这时候你已经知道客户的具体痛点了。去 Coze 搭一个最小版本给他们用,收费哪怕 99 块一个月也行。重点不是赚钱,是验证"他们愿不愿意为这个流程付费"。

在扩规模:开始担心"模型变了怎么办""用户数据怎么管"。这时候要建自己的护城河——比如积累客户的历史咨询记录、维护专属的提示词库。这些是模型厂商拿不走的。

最后说一句:这思路不是所有人都适合。如果你现在的业务根本不需要"自动化某个流程",硬套反而累。先想清楚你每天重复做的烦事里,有没有一件值得"外包给 AI 外壳"。

来源: blog.sshh.io