Steve Xu

Claude 反封号大使、网络安全背景
🤖AI 深度沉迷、工业 AI 联合创始人（A 轮）

[Steve Xu @cyber_utopia]

@dongxi_nlp 最难的还是懂客户的业务，招了一圈这种人太少了，另外懂 Agent 的人其实也不多，大部分人面试下来就是让 AI 完成了安排的任务，比以前更水了，完全是把大脑外包😵‍💫

[Steve Xu @cyber_utopia]

@poteto @santiagomed @bot 新建一个 skills 给我

[Steve Xu @cyber_utopia]

B 站逆天博主，从 10 月 1 号开始发视频，到今天一共 20 天一共涨粉 68 万，优秀的审美还是稀缺的。

[Steve Xu @cyber_utopia]

Box CEO 最近这段分享挺有价值的，模型越来越强之后，到底什么业务才越来越有价值？
他认为随着模型越能干，能处理更长的任务、用好工具，按领域做差异化的空间反而越大。早期模型能力有限，应用层能做的基本就是 "wrapper"，加个 custom prompt 或简单的 RAG，再把 token 递给用户。
现在的情况不一样了：agent 会调用工具，任务一跑就是几小时甚至几天，workflow 会触发 agent，agent swarm 要围绕一个目标协调，数据源是领域专有的，model routing 也要按领域来定，落地时还得真正改造业务流程，而不是丢给用户一个聊天框。
所以这些系统的专业化程度很可能会上升。他也承认离大模型实验室核心用例越近，这条路越难走。他预计安全、生命科学、医疗、金融、制造等领域会出现很多以前做不出来的自主系统。

[Aaron Levie @levie]

The big bitter lesson question in the applied AI layer is that as models get better and better, what is the best way to diffuse that intelligence into an enterprise.

Ironically, I’d argue that as models are getting more capable, and can handle longer running tasks and better tool use, the ability to differentiate on a domain by domain basis goes up instead of down.

When models were quite limited in what they could do, ironically all you could be literally was a “wrapper” because you were effectively delivering tokens back to the user with a custom prompt or some limited RAG.

But in a world of agents using tools, tasks that run on for hours or days, workflows that kick off agents, agent swarms that need to be coordinated against a goal, data sources that are domain specific, model routing that requires domain-specific routing, implementation that requires real change management of the workflow instead of “just type into this chat box”, it’s very very possible that specialization of these systems goes up more.

Obviously this doesn’t apply to everything - the closer you are to the labs core use-cases the tougher this play gets, but I’d argue we’re going to see autonomous systems for a wide range of job functions that weren’t practical to build out before. We’ll see this in security, life sciences, healthcare, finance, manufacturing, and many other fields. Either way going to be a fun and wild couple of years.

[Steve Xu @cyber_utopia]

@jakemcgaw @bot update this one to me

[Steve Xu @cyber_utopia]

@wustep @bot @bot I wanna this

[Steve Xu @cyber_utopia]

@taresky 高频分类用Jev模型的效果应该也很不错吧

[Steve Xu @cyber_utopia]

@yangyi Intro 类的岗位太不友好了，AI 的替代性过强了

[Steve Xu @cyber_utopia]

@libukai 这个Ultra fast 真是笑死人了不说远远比不上 DeepSeek 的 250 tok/s 了，也被 opus 的速度吊打了啊

[Steve Xu @cyber_utopia]

你咋能把一个多因素模型归结于单一因素的呢？
mac IP 是否共享？
有没有问触发安全风险的问题？
长时间中文+美国半夜工作
支付不干净
Max20 的变态风控

[Steve Xu @cyber_utopia]

才买的 M5Pro 的 MacBook🥲🥲
是不是可以现在下单一个老款 MAC，等发货新款了？符合预期就不退了😉

[Greg Joswiak @gregjoz]

Can’t wait to pro and tell next week.

[Steve Xu @cyber_utopia]

@kale243222 国内 To C 靠超级 App 分发入口，To B 靠开源私有化和 API 低价。Kimi 卡在中间两头都没占到，单拼模型能力破不了局，而且也不强，本地化也部署不起。

[Steve Xu @cyber_utopia]

@shao__meng 企业落地真能用上的就前两层。后两层最卡脖子的是业务评测集，连对错标准都定不出，尤其是企业内部的非 sota 模型，更不敢搞自博弈了，只会把幻觉放大。

[Steve Xu @cyber_utopia]

个人能靠 vibe coding 退订一堆软件，但放到企业里完全行不通——Agent 来了，企业反而会买更多软件和 TOKEN了，尤其是 AI 让很多公司的雄心起来了，更舍得花钱了。

今天看到 @Kwebbelkop 喊大家别再给 Notion、HubSpot 交月费，让 Claude Code 自己搓一个。但正如今早我引用 Box CEO Aaron Levie 的观点：大家最大的误判，就是以为世界只需要固定数量的软件。从网络安全和企业 AI 落地的视角看，一下午搓出来的只是界面，这三件事根本 vibe 不出来：

1. 权限与审计：谁能查哪行数据、Agent 调了哪个接口，全靠细粒度权限和完整审计日志控制，手搓脚本一旦越权根本没法合规交差。
2.私有化与老系统接入：企业核心数据讲究数据驻留和私有化部署，还得跟老系统做接口对齐。今天在上海和头部央企的客户深入交流，他们光是数据字段就是 80w 个；
3.维护责任：SaaS 订阅费里一大半买的是备份容灾和半夜三点崩了有人修，没人敢拿一下午搓出来的代码跑核心业务。
你们公司现在允许把 vibe code 出来的工具直接连进生产环境给客户服务吗？

[Steve Xu @cyber_utopia]

@Jack84sr @libukai 保持 IP 干净，应该问题不大，我也是护照 KYC

[Steve Xu @cyber_utopia]

@Jack84sr @libukai 没有封号吧？

[Steve Xu @cyber_utopia]

BOX CEO 对于 AI 和软件的关系的判断：
大部分人最大的误判是以为「世界只需要那么多软件」，自动化一来工程岗位就会变少。现实正好相反：正因为 agents 会用工具、操作电脑、即时写软件、又以远超人类的速度处理数据，以前根本没法数字化的问题现在都能「扔软件」去解决。结果是工程在经济里的权重上升，银行新交易形态、生命科学实验、制造业供应链自动化，越来越多工作开始长成软件问题的形状。他承认未来不会再大量手写代码，但几乎一切都会变成 code，懂它的人处于极有利位置。

[Aaron Levie @levie]

The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor.

Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before.

What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before.

If you’re a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If you’re in manufacturing, you’re trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems.

Yes we’re not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.

[Steve Xu @cyber_utopia]

@libukai 主要是很多人不舍得花这个钱，觉得自己的 30 块一个月的也能用，这个本质也是概率问题，测了好多家宽 IP，确实贵一点的稳定性和纯净度都很高，现在爽用 Claude

[Steve Xu @cyber_utopia]

企业 AI 落地的门槛从来不在模型，全在机房、权限和老系统里。

@suraj_sharma14 拆解了企业 AI 落地的 12个阶段 3 大部分：
1. 进场：搞定 SSO 权限、部署与一张 GPU 跑量化 8B 模型；
2. 连接：旧数据 ETL、带权限 RAG，用 MCP 安全读写 15 年老系统；
3. 管控：5 万美元采购单必须人工审批，拿 100 张历史工单做影子测试。

这份清单在国内 ToB 就是私有化交付实施工程师的日常。我们公司年营收约 2 亿人民币的盘子里，SaaS 和本地化部署的收入比大约 1:2，金融、政府、大企业客户基本不接受 SaaS。所以清单里第 2 步部署和第 8 步本地推理在我们这根本算不上加分项，完全是拿单的入场券。

放到工业场景还要再加难度：工厂 OT 网络通常和办公网隔离，数据不出厂，国内客户还常有国产化软硬件环境要求，这些都是进场实施绕不开的坑。

先做数据映射，三个系统对同一个客户有三种叫法，工业里同一台设备在 MES、ERP、点检表里名字也全不一样，必须先对齐一个稳定 ID；另外第 7 步审批必须绑定具体采购单内容，防止经理点批准后 Agent 改金额。

我们新招的应届生把 Agent 当许愿机：一句话提示词，零反馈。其实 AI 时代真正稀缺的，是能把客户业务流程翻译成 Agent 规则、又能在客户机房把东西装起来的人。

[Suraj Sharma @suraj_sharma14]

If you learn Forward Deployed Engineering, you will never be unemployed again.

I’d do this.

Stage 1: Enterprise Identity & Access (The Gatekeeper)
- Learn: OAuth2, SAML, OIDC, SCIM provisioning, Row-Level Security (RLS) and multi-tenant data isolation.

- Practice: Build an auth bridge connecting your AI app to a simulated customer's Okta/Azure AD, ensuring User A can only query data their specific RBAC role permits.

- Why: No SSO and strict data isolation means no enterprise contract. Security review is the first gate.

Stage 2: Air-Gapped & VPC Deployments (The Environment)
- Learn: Kubernetes (K8s), Helm, Terraform, private container registries, air-gapped networking and egress filtering.

- Practice: Script a one-click Terraform/Helm deployment of your entire AI stack into a simulated customer VPC with zero egress to the public internet.

- Why: Banks, hospitals and governments don't let your AI talk to the public cloud. You must deploy in their basement.

Stage 3: Legacy Data Extraction & ETL (The Fuel)
- Learn: OCR, unstructured PDF parsing, mainframe DB connectors, Apache Airflow, dbt and data reconciliation.

- Practice: Build a pipeline that ingests 10 years of messy, scanned customer invoices and turns them into a clean, queryable hybrid (vector + relational) store.

- Why: Enterprise AI starves without clean data. The customer's data will always be a mess.

Stage 4: Secure RAG & Permission-Filtered Retrieval (The Brain)
- Learn: Hybrid search, document-level access control, citation grounding, metadata tagging and abstention logic.

- Practice: Build a RAG pipeline where the LLM dynamically filters out retrieved chunks that the specific user asking the question doesn't have clearance to see.

- Why: An AI that leaks HR salaries or M&A docs to a junior employee is an immediate breach of contract.

Stage 5: MCP & Legacy System Integration (The Hands)
- Learn: Model Context Protocol (MCP), SOAP/REST translation, web scraping, headless browsers and API rate-limit handling.

- Practice: Build an MCP server that lets an LLM safely query and write to the customer's 15-year-old on-prem SAP or Oracle instance without exposing raw credentials.

- Why: Agents are useless if they can't touch the customer's actual systems of record.

Stage 6: AI Guardrails & PII Redaction (The Shield)
- Learn: NER (Named Entity Recognition), deterministic regex filtering, LLM output validation, UUID masking, and audit logging.

- Practice: Build a middleware proxy that intercepts LLM inputs/outputs, masks SSNs and credit cards with UUIDs before they hit the model, and restores them post-generation.

- Why: Compliance teams will kill the deal if a single PII token hits an external frontier API.

Stage 7: Human-in-the-Loop (HITL) Workflows (The Trust)
- Learn: Temporal {dot} io, webhook routing, state machines, durable execution and UI approval gates.

- Practice: Build a workflow where the AI drafts a $50k purchase order, but physically pauses execution until a human manager clicks "Approve" in Slack or Teams.

- Why: Enterprises adopt AI only when humans remain legally accountable for the final action.

Stage 8: Edge & On-Prem Inference (The Silicon)
- Learn: vLLM, TensorRT-LLM, Ollama, GPU partitioning (MIG), and local quantized models (AWQ/GGUF).

- Practice: Deploy a quantized 8B model on a single customer-owned GPU node to handle highly sensitive triage locally without network latency.

- Why: Sometimes the data is too hot to move. You must bring the model to the data.

Stage 9: Observability & Customer Health (The Pulse)
- Learn: OpenTelemetry, Langfuse, custom Grafana dashboards, usage telemetry and cost-attribution.

- Practice: Build a "Customer Health" dashboard showing the exact cost-savings, time-saved and token-spend the AI has generated for their specific team this month.

- Why: If the customer doesn't see the ROI quantified, they will churn at renewal.

Stage 10: Evals on Customer Data (The Proof)
- Learn: Golden dataset creation, trajectory grading, shadow testing, and LLM-as-a-judge calibration.

- Practice: Take 100 real past customer support tickets, run them through your agent in shadow mode, and prove to the VP of Support that it resolved 80% accurately.

- Why: Generic benchmarks mean nothing to a buyer. You must prove the AI works on *their* messy data.

Stage 11: Technical Discovery & Scoping (The Translation)
- Learn: Stakeholder interviewing, architecture diagramming, SOW (Statement of Work) writing and expectation management.

- Practice: Translate a vague "we want AI for supply chain" executive request into a scoped, 4-week technical deployment plan with defined, measurable success metrics.

- Why: FDEs are the translators between revenue (Sales) and reality (Engineering).

Stage 12: The Handoff & Self-Serve Admin (The Scale)
- Learn: Technical writing, runbooks, admin portals, automated onboarding and chaos testing.

- Practice: Write a deployment runbook and build an admin UI so the customer's internal IT team can manage the AI stack, update prompts and view logs without calling you.

- Why: Your leverage is measured by how little the customer needs you for day-to-day operations.

AI labs build the engine. FDEs build the roads.
The modern Forward Deployed Engineer builds the secure, durable nervous system that connects frontier intelligence to legacy reality.

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