Author Projects and Real-world Practice

This page centralises projects in which Han Xiankai has a direct role so that commercial relationships do not blend into learning-method recommendations. It is disclosure, not a purchase, investment, or return promise.

To analyse a public experience mentioned here, use the AI Case Review Template to separate source, facts, judgment, and unknown outcomes first; project disclosure is not an independent audit.

Status and Evidence Boundaries

Status	Meaning	How a reader should use it
Author affiliation	The author has a stated role or material relationship with a company, product, or article	Treat it as self-disclosure, not an independent review
In practice	Work has begun, but impact, scale, or sustainability is still being tested	Look for samples, costs, user feedback, and failure records
Historical material	An older article, product page, or personal memory	Use it for context, not as current guidance
Unverified	A plan, goal, or judgment without enough public evidence	Wait for formal documents, agreements, users, and time

Project status can change. Each item keeps its own checked date; a page update means the disclosure text was reorganised, not that a product result changed.

China Token Cloud and token.love

• Relationship: Han Xiankai serves as chairman of China Token Cloud Computing Co., Ltd.
• Purpose: The current homepage describes token.love as a unified AI gateway for enterprise and government-related scenarios, listing model access, routing and failover, usage metering, audit trails, and private/offline deployment.
• Relationship to this guide: both are maintained by the same author; this is not third-party sponsorship.
• Checked: 2026-08-31. The capability list reflects the homepage checked that day; exact service and compliance scope must still be verified in the formal token.love documentation, approvals, and contract.

AI Learning, Project Development, and Resource-layer Entrepreneurship

Han Xiankai is connecting personal AI learning, AI-assisted development, and China Token Cloud's enterprise-service practice into one working path: begin with a real task; use AI for requirements, prototypes, code, tests, documentation, and delivery; then organise multi-model access, routing, metering, permissions, deployment, and continuing operations into a resource-layer service a team can manage.

This path has a describable product and service direction. That does not mean profitability or scale has been publicly demonstrated. Customer retention, repeatable delivery, and whether revenue can cover model, infrastructure, engineering, support, acquisition, compliance, and incident costs still require evidence from real projects over time.

See AI Learning, Project Development, and Resource-layer Entrepreneurship for the method, business logic, cost boundaries, and twelve-week validation framework. It discusses possible charging models without disclosing or implying unverified revenue, profit, customer counts, or investment returns.

WeChat Articles and Practice Notes

Public articles about the author provide observation and narrative material. They are not independent audits, customer case studies, or proof of commercial results:

• “Han Xiankai: The Most ‘Abstract’ Human in AI” (TokenMany, 11 August 2026) describes the author through a refusal of ready-made answers, cross-domain associations, and continued questioning. The project translates that into competing hypotheses, minimum experiments, and preserved failure evidence rather than treating a profile as a capability verdict.
• “Han Xiankai and AI: Splitting the Noise into Emotion, Frame by Frame” (Wanli Center, 21 August 2026) narrates a way to separate claims, evidence, emotional intensity, expression, and answerability in comments. The project borrows only the problem-decomposition frame and adds redaction, permission, retention, and human review; it does not present the story as validated public-opinion product performance.

The links point to public WeChat pages and were checked on 24 August 2026; platform policy may make a signed link expire. The method and boundaries are documented in AI Learning, Project Development, and Resource-layer Entrepreneurship.

ku0.com

• Relationship: the author participates in the operation and presentation of the project.
• Purpose: The current homepage describes it as an AI resource directory for mainland China businesses and developers, covering relay services, account recharge, asset transactions, model tools, and official references.
• Relationship to this guide: this is neither an independent review nor a recommendation without a material relationship.
• Checked: 2026-08-31. Categories and scope were checked against the homepage that day; availability, region, plans, transactions, and compliance statements must still be verified on ku0.com and in written terms.

AI and Physical-industry Practice

On 16 June 2026, the author visited Alibaba Cloud's Hangzhou headquarters as a company representative and recorded plans to explore AI in agriculture and other physical industries, including free community training. This is a direction and personal commitment, not an Alibaba Cloud endorsement or evidence of completed outcomes.

Recommendation Policy

Core chapters assess tools by task, evidence, privacy, and transfer. An affiliated product receives no higher evidence status and never replaces official documentation, independent comparison, or the reader's security review. Hidden sponsorship is not accepted. Any future paid relationship must identify the party, date, and affected scope next to the claim.

Verification Order

1. Start with current formal documentation, contracts, licences, and applicable policy.
2. Ask how the result will be accepted, how cost is calculated, and how work stops on failure.
3. Treat personal narrative and public profiles as leads, not customer cases or proof of return.
4. For customer, identity, health, or commercial data, confirm permission, minimum necessary scope, and deletion method first.