⛏️创客淘金
B2B SaaS/数据服务B2B订阅落地可行性

遭LinkedIn起诉卖千万ARR业务,2周重启新工具半年月入1.5万美元

Hitting $15k/mo 6 Months After Getting Sued And Acquired

NinjaPear · B2B数据服务 · 2周上线 · $15K+/月

收入规模
$15K–$20K / 月
团队规模
1人独立开发者
启动速度
2周
复刻难度
★★★★☆
访谈亮点
  • 千万ARR业务遭巨头起诉后,2周快速重启新项目
  • 🎯自研LLM+深度网页数据体系,彻底规避平台法律风险
  • 🚀依托2万私域用户+SEO冷启动,半年突破1.5万美元月收入
  • 💡验证独立开发者年入百万美元的可落地路径

资深连续创业者遭LinkedIn起诉后出售千万级ARR业务,2周搭建新B2B数据平台,半年达1.5万月收入,核心挑战为用户流失与数据延迟优化。

NinjaPear

诉讼、伤病与新产品上线

Lawsuits, injuries, and launches

我是一名拥有20多年编程经验的软件工程师、连续创业者,已经出售过两家公司。此前我做的Proxycurl是市场上最大的LinkedIn爬虫API服务,年营收诉讼事件达1000万美元。但2025年1月LinkedIn起诉了Proxycurl,我们庭外和解后我把公司卖给了竞争对手。收购2周上线完成后我右脚跖骨骨折行动不便,期间我借助Claude Code工具,仅用两周就原型开发并上线了月入1.5万NinjaPear,一款B2B企业情报和数据 enrichment 平台。那是6个月前的事了,上个月我们的总营收超过了1.5万美元。

I’m a software engineer and serial entrepreneur — 20+ years behind the keyboard. I've sold two companies. Proxycurl was by far the largest LinkedIn scraping API on the market, with $10M ARR. But then, LinkedIn sued Proxycurl in January 2025. We settled out of court, and I sold Proxycurl to a competitor. After the acquisition, I fractured the metatarsal bone in my right foot and found myself immobile — with Claude Code. Two weeks later, I prototyped and launched NinjaPear, a B2B company intelligence and data enrichment platform. That was six months ago. We did over $15k in gross revenue last month.

📅

关键里程碑

2025年1月 · LinkedIn起诉Proxycurl,和解后出售业务

休养期 · 脚骨骨折,2周完成NinjaPear开发上线

上线6个月 · 月营收突破1.5万美元,正向盈利

赛道选择

Picking the idea

我做NinjaPear是因为我对B2B数据 enrichment 市场极其熟悉,我想打造一款完全使用自有数据、不依自研数据体系赖LinkedIn的产品,这样未来十年都不用担心微软的法律追责。我用LLM+深度网页研究搭建了API服务,提供丰富完整的B2B数据,这些数据完全不从LinkedIn获取,还包含很多LinkedIn没有的维度,比如客户列表数据、竞品研究数据、产品列表数据。我作为全职独立开发者运营NinjaPear,整个开发过程只花了两周时间,成本就是一个Claude 20x Max订阅。我本身是加密货币早期投资者,还出售过两家公司,过去的积累让我可以无限期运营这个项目不需要盈利,而且它现在已经盈利了。

I built NinjaPear because I knew the B2B enrichment data market so well. I wanted to build a Proxycurl with my own data instead of LinkedIn's. I wanted to build something that could scale for the next ten years without fear of the Microsoft legal war machine. So, I prototyped an API with LLM + Deep Web Research that provided rich and complete B2B data. The data is not sourced from LinkedIn, and it includes many data points that LinkedIn simply does not have, such as customer list data, competitor research, and product lists. I work on NinjaPear as a solo, full-time founder. All it took was two weeks of time and a Claude 20x Max subscription. But money isn't really an issue anyway. I’m an early investor in cryptocurrency, and I have sold two companies. My past successes allow me to work on NinjaPear indefinitely without needing to turn a profit — and like I said, it's already profitable.

谁适合参考

  • 有B2B数据服务行业积累的资深开发者
  • 拥有私域流量的连续创业者
  • 熟练使用AI开发工具的独立开发者

Not for

  • 无相关行业资源的新手创业者
  • 无法承担前期算力成本的小团队
  • 对合规风险无应对能力的开发者

技术栈选择

The tech doesn't matter

AI时代技术本身不再是门槛,我的技术栈非常成熟:Python(用mypy做类型检查减少AI生成代码的bug)、Typescript/Tail成熟技术栈wind做前端、Kubernetes、Redash做BI、Grafana做监控。我所有的服务器都托管在家里,用太阳能供电。

In the post-AI world, the tech does not matter. My stack is boring: Python (type hints enable free bug catching via mypy, which you want because of AI), Typescript/Tailwind (frontend), Kubernetes, Redash for business intelligence, Grafana. I also own all the servers and co-locate them at my house, powering them with solar power.

⚙️

技术思路

放弃追逐前沿新技术,用成熟稳定的技术栈降低AI生成代码的出错概率,自建太阳能服务器大幅压缩运维成本。

应对用户流失

Wrestling with churn

NinjaPear的用户按数据调用量购买点数计费模式点数,可以选择更优惠的订阅制,也可以按量充值。我们的增长逻辑是让用户把NinjaPear的数据集成到自己的工作流里,和用户共同成长。当前我遇到的最大挑战是用户流失,我推测原因有两个:一是LLM实时做全网研究的处理速度比LinkedIn的缓存数据慢,二是产品的定位文案还不够清晰,我一直在迭代优化这两个问题。创业永远会有挑战,这正是创业的乐趣所在,就像现实世界的游戏,每一次失败都是靠近成功的一步。

NinjaPear users pay for data with credits. Users purchase credits through subscriptions (which are cheaper) or pay-as-you-go top-ups. We rely on users integrating NinjaPear data into their workflow. We grow with them. The biggest challenge I'm experiencing right now is churn, and I have hypotheses about why it is happening. Our data processing is being compared to LinkedIn's, and it's slower because LLMs perform web-scale research in real-time. Product messaging is also an issue. I'm iterating constantly and working to fix both. There will always be a challenge. That’s what makes venture building fun! It’s the “IRL” game that I play. Every failure is a step closer to success.

📈

留存策略

通过让用户深度嵌入自身业务流程实现长期留存,当前核心优化点为降低用户对实时数据延迟的感知。

双轨GTM策略

A two-pronged GTM strategy

营销方面我主要聚焦三个方向:给我2万多名newsletter订阅用户发邮件,运营出售Proxycurl后保留的域名做SEO,最近也开始在Re三大增长渠道ddit发帖。接下来6个月我还会亲自负责所有增长相关的工作。

As far as marketing, I focus my attention on: Emailing my 20k+ newsletter subscribers, SEO efforts on the domain I retained despite selling Proxycurl, Posting on reddit — though this is more recent. I'll continue wearing the GTM hat for the next six months.

🔍

SEO策略

出售原有业务时保留高权重域名,直接复用多年积累的SEO权重,省去新站冷启动的漫长周期。

接受失败

Getting okay with failure

现在我完全不会把失败和负面评价放在心上,我经常告诉自己要主动尝试10次失败,不断试错校准。输出的频率比单次结果重要,坚持做、不断试错就好。比如前几天我花了两小时在Reddit发的帖子几乎没有流量,换做年轻时的我会非常沮丧,但现在我知道只要不断尝试,总有内容会爆火。

These days, I take nothing personally — including failures and haters. In fact, I frequently tell myself, "Let’s set out to fail 10x." Then, I keep failing and recalibrating as I go. The cadence of the output is what matters. Just keep going. And keep failing. For example, a few days ago, I spent a couple of hours knocking out this article on Reddit, and I barely got any traction on it. Younger Steven would be very upset. Now, I know it means to try again and again, and eventually something will go viral.

🧠

核心心态

提出「主动尝试10次失败」的量化试错理念,把失败当成校准方向的常规数据点,而非打击信心的负面事件。

用户评论与创作者回复

已过滤 spam、低价值附和与重复内容 · 原始 62 条 · 展示 10 条 · 创作者回复 5

10
网友1
👍 2
风险分析

The shift from relying heavily on platform scraping to building an LLM + Deep Web Research engine is a masterclass in risk mitigation. Question on the technical side: How do you handle the latency when combining LLMs with live deep web research?

从依赖平台爬取转向LLM+深度网页自研数据是极佳的风险规避方案,好奇你是如何处理实时深度网页研究带来的延迟问题的?

笔记:点出了项目最大的战略价值:彻底摆脱单一平台的法律风险,同时提出了核心技术痛点。

网友2
👍 2
资产洞察

The most instructive detail here is buried: he kept the domain and the 20k newsletter when he sold Proxycurl. The two-week rebuild gets the attention, but the years-old email list is doing the work.

最被忽略的关键细节是他出售Proxycurl时保留了域名和2万订阅用户,两周开发的噱头很吸睛,但真正推动增长的是积累多年的邮件列表。

笔记:指出了创始人快速起量的隐藏核心资产,给所有创业者的退出谈判提供了重要参考。

S
Steven of Proxycurl · 创作者
回复 网友2
创作者回复

You're correct :) This was a non-negotiable for me.

你说得完全对,这是我当时谈判时的不可让步条款。

笔记:确认了保留私域资产是退出时的核心谈判要点。

网友3
👍 2
增长追问

What was the single most important decision you made post-acquisition that accelerated growth?

收购完成后你做的哪一个决定最有效地加速了增长?

笔记:引导创始人输出可复用的增长实操经验,避免神话单一成功要素。

S
Steven of Proxycurl · 创作者
回复 网友3
创作者回复

I'm still figuring it out. But there isn't ONE thing. It's really a constant iteration of the product, marketing, product messaging, ICP, etc.

我也还在摸索,不存在单一的制胜点,完全是产品、营销、定位、目标用户画像各环节持续迭代的结果。

笔记:破除了创业神话,说明成功是多环节持续优化的复利结果。

网友4
👍 2
合规探讨

LinkedIn scraping has always been a tough space... We moved away from relying on LinkedIn data and started combining information from multiple sources instead. It's far more stable in the long run.

LinkedIn爬取赛道一直风险极高,我们也放弃了单一依赖LinkedIn数据的方案,改用多源数据整合,长期稳定性高很多。

笔记:用自身团队的实操经验验证了创始人选择多源自研数据方向的正确性。

S
Steven of Proxycurl · 创作者
回复 网友4
创作者回复

You are right. But more importantly, you won't get sued or have LinkedIn shut off your supply; which means you can scale without fear.

你说得对,更重要的是你再也不会被起诉,也不会被LinkedIn切断数据源,可以毫无顾虑地规模化。

笔记:点明了自研数据体系带来的最大好处是消除了规模化的心理负担。

网友5
👍 1
流量追问

Wow, how did you grow a newsletter to 20K? And isn't that enough to help you go viral?

你是怎么把newsletter做到2万订阅的?有这么多用户难道还不够让内容爆火吗?

笔记:打破了很多新手对私域流量的错误认知,明确了私域打开率的真实转化水平。

S
Steven of Proxycurl · 创作者
回复 网友5
创作者回复

I write a lot, see nubela.co/blog, and also past users. Nah, 20K readers won't get you viral. Assuming 10% open rate.

我长期坚持输出博客内容,订阅者大多是过往的老用户,2万订阅根本带不来爆火效果,算上10%的打开率实际触达人数很少。

笔记:给出了真实的私域流量转化数据,避免新手产生不切实际的预期。

网友6
👍 1
渠道提问

What is the most effective distribution channel so far?

到目前为止最有效的获客渠道是什么?

笔记:直接获取创始人验证过的最高效增长渠道,可直接复用。

S
Steven of Proxycurl · 创作者
回复 网友6
创作者回复

SEO

SEO搜索流量。

笔记:验证了高权重域名SEO是B2B数据服务的最高效获客渠道。

网友7
👍 1
心态探讨

What struck me most wasn't the two-week build — it was the line about money not really being an issue. That's probably a big reason you can say "let's fail 10x" and actually mean it.

最让我印象深刻的不是两周开发,而是你说资金不是问题的那句话,这才是你能坦然接受10次失败的核心底气。

笔记:点出了试错心态背后的现实基础,避免新手盲目模仿忽略自身现金流安全。

网友8
👍 1
经验总结

The real advantage wasn't building the product in two weeks. It was the years of industry experience, plus the distribution he had already built through SEO, his domain, and newsletter.

真正的优势不是两周做完产品,而是他积累多年的行业经验,以及通过SEO、域名、newsletter搭建好的分发体系。

笔记:破除了AI神话,说明工具只是加速手段,长期积累的行业认知和分发资产才是核心竞争力。

网友9
👍 1
优化建议

Have you experimented with showing users a partial result fast while the deeper research finishes in the background? Might help the "waiting" feel less like slowness.

你有没有试过先快速返回部分初步结果,后台再完成深度研究?这样用户就不会觉得是卡顿,而是感受到产品在深度处理数据。

笔记:给出了解决延迟感知问题的低成本落地方案,可直接应用到同类数据产品中。

网友10
👍 1
合规追问

If NinjaPear's "deep web research" is pulling data that originated on LinkedIn profiles even indirectly, that could still be in contested territory. Have you had legal counsel specifically review the data sourcing chain?

如果NinjaPear的深度网页研究间接获取了来自LinkedIn的数据,依然可能存在合规风险,你有没有让律师专门审核过数据溯源链路?

笔记:提醒所有做数据服务的创业者,即使切换到LLM方案,依然要做好全链路合规审核。

Localization Notes

  • 国内可替换为企查查/天眼查公开数据源+大模型处理方案,完全规避LinkedIn相关合规风险
  • 放弃自建太阳能服务器方案,采用阿里云/腾讯云等国内主流云服务大幅降低运维门槛
  • 适配微信支付/支付宝等本土支付体系,面向国内中小B端销售点数制API服务
  • 依托国内企服类私域社群、公众号、知乎SEO获取首批种子客户,复用高权重域名快速起量
  • 严格遵守《数据安全法》《个人信息保护法》,所有采集数据需做合规脱敏处理
  • 可优先面向出海SaaS、跨境电商客户提供海外企业数据 enrichment 服务,避开国内头部数据服务商的竞争