WeChaser 不是个 50 人 startup。是一种正在被验证的工作模式——靠 Claude / Hermes / 时光脉络这些 AI 工具，把一个人的产能放大到能服务工业级客户的水平。 WeChaser is not a 50-person startup. It's a working model under active validation — using Claude, Hermes, and Shiguangmailuo as force-multipliers, scaling one person's output to industrial-grade client work.
2026 年最大的浪费之一：每家企业都在自己造一个 ChatGPT 套壳，但没人解决"AI 不懂这家企业"的问题。
市场上 AI 工具够多了。Claude、ChatGPT、飞书 AI、钉钉 AI——它们的大脑都不缺，缺的是对这家企业的真实理解。一个工程师 23 年沉淀的判断逻辑，比 GPT-5 训练数据里 100 万个泛工业案例都重要。
我们不卖又一个聊天框。我们做企业 AI 生态的中间件——所有 AI 工具的"懂企业"那一层。这事很专一。也很冷门。但这是 2026 年最缺的东西。
The largest waste of 2026: every company is building its own ChatGPT wrapper, but nobody is solving "AI doesn't actually understand this company".
The market has enough AI tools. Claude, ChatGPT, Lark AI, DingTalk AI — none lack a brain. What they lack is real understanding of this specific company. One engineer's 23 years of judgment matter more than a million industrial examples in GPT-5's training data.
We're not selling another chatbox. We're building middleware for the enterprise AI ecosystem — the "understands this company" layer that every AI tool calls. It's a narrow problem. It's an unsexy problem. But it's the most important missing piece in 2026.
用 AI 复刻麦肯锡级方法论给中小企业。陪跑顾问，交付：业务流程梳理、AI 转型路线图、问题诊断报告。这是收费的部分。
McKinsey-grade methodology, AI-leveraged, delivered to mid-market. Hands-on advisory: workflow audit, AI transformation roadmap, problem diagnosis. This is the paid layer.
咨询过程产生的所有原料（录音 / 笔记 / 客户文档 / AI 报告）→ 沉淀进时光脉络 → 客户用一段时间后离不开。
Everything produced during consulting (recordings / notes / client docs / AI reports) → distilled into Shiguangmailuo → after a few months of usage the client can't go back.
找到具体切入点（比如"销售场景接进来")→ 增值收费开发功能模块。这是规模化的部分——但只有 A + B 跑通了才会发生。
Find a specific wedge ("now wire in sales") → priced feature development. This is the scale layer — but only fires after A + B have proved out.
为什么公开？这种结构的好处不在于神秘，而在于"我们和客户的利益不冲突"——咨询是真的用 AI 做麦肯锡级别的工作，知识库是真送，后期增值是客户主动找回来的。中间没有忽悠空间。 Why publish? The model's strength isn't secrecy — it's the absence of conflict. Consulting really does deliver McKinsey-grade work; the KB really is a gift; the upsell only happens when the client comes back. There's no room for sleight of hand.
不被花哨 UI 拖住。客户能简单安装、操作、用起来——其余等真用上再说。
Don't be paralysed by polished UI. Install, operate, run. Everything else waits for real usage.
未来要做工业生产决策——零容忍幻觉。不知道就明确说"不知道"。
We're heading toward industrial production decisions. Zero tolerance for hallucination. If we don't know, we say so.
同时只服务 3-5 家客户。深度比规模重要——一家做透胜过十家半生不熟。
At most 3-5 clients in flight. Depth over breadth — one client done thoroughly beats ten half-finished.
架构、技术栈、商业模式、客户案例——全部公开。不靠信息差赚钱。
Architecture, stack, business model, client cases — all public. We don't profit from information asymmetry.
核心模型本地 GPU 部署，可断网运行。敏感数据永不上云——这条没有商量。
Core models run on local GPUs. Air-gappable. Sensitive data never goes to cloud — non-negotiable.
Shawn 一人 + AI 协同（Claude / Hermes / 时光脉络）。AI 是同事，不是工具。
Shawn + AI co-workers (Claude / Hermes / Shiguangmailuo). AI as colleague, not tooling.
独立开发者 / 创业者。曾从事咨询和产品工作，2025 起 all-in AI 协同的单人公司模式。亲自跑客户、写代码、做反思——目前同时在 3 个真实项目上推进。 Independent developer / founder. Background in consulting and product. As of 2025, fully committed to a single-operator + AI-coworker model. Personally meets clients, writes code, runs the reflection — currently driving three live engagements.
编码、代码审查、技术写作。我们叫他 X Agent——Anthropic 的 Claude Opus 1M context。Coding, review, tech writing. We call this one X Agent — Anthropic's Claude Opus 1M context.
主动学习 / 自我迭代 / 飞书 Gateway / Cron 调度。是时光脉络的"神经系统"。Self-learning / self-iterating / Lark Gateway / cron-scheduled. The "nervous system" of Shiguangmailuo.
知识中枢。所有客户文档、咨询沉淀、反思日志的归处。它本身就是一个同事，会主动提示"信息不足"。Knowledge hub. Where every client doc, consulting deliverable, reflection log lands. It's itself a co-worker — actively says "I don't have this".
30 分钟。我们带着行业案例 + 可行性评估表上门。前 10 分钟你说，我们听。后 20 分钟我们告诉你这个问题在我们看来值不值得动——值得就给最小试点报价，不值得就直说。 30 minutes. We bring industry cases + a feasibility checklist. First 10 minutes you talk, we listen. Last 20 we tell you whether the problem is worth solving in our view — if yes we quote a minimal pilot, if no we say so.