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[metavi]

Claude Code with Crazyrouter: Practical B2B AI Application Ideas by Industry

Claude Code, Crazyrouter, and Practical B2B AI Ideas

The useful question for B2B AI is not “Can we connect a model?” It is “Which workflow is specific enough to ship and valuable enough for a team to keep using?”

When using Claude Code with Crazyrouter, start by getting the endpoint configuration right. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com. OpenAI-compatible SDKs, HTTP clients, and application backends should use https://cn.crazyrouter.com/v1. Keep API endpoints clean and do not add tracking parameters to them. Also watch for the common /v1/v1/... mistake when a client or SDK appends the version path automatically.

From there, choose application ideas with three filters: real data access, fast expert review, and a path back into an existing system of record. A support ticket summarizer is a strong first project because the conversations already exist, operators can review the output quickly, and the result can be written into a ticketing system. A fully autonomous decision system in a regulated domain is much harder because governance, review, and liability are part of the product.

Good B2B starting points include manufacturing inspection reports, equipment fault knowledge assistants, customer support reply drafts, internal policy Q&A, lesson plan generation, unit test generation, API documentation, contract clause review, incident report summaries, CRM account notes, invoice extraction, and governed Text-to-SQL.

The pattern is consistent across industries: collect reliable context, retrieve approved source material, ask the model to produce a structured draft, require human approval, and store the final result. This is less flashy than a fully autonomous agent, but it fits how businesses actually adopt software.

Crazyrouter helps centralize model access across Claude Code, backend services, and OpenAI-compatible clients. The product work still comes down to workflow design: choose a narrow task, preserve source references, make review easy, and integrate with the tools people already use.

Reference project: Claude Code Guide

[metavi]

Build an Interactive Fiction Tool with Claude Code and Crazyrouter

Building an Interactive Fiction Tool with Claude Code and Crazyrouter

Interactive fiction is a strong practical example for LLM application design. It is small enough to prototype quickly, but complex enough to expose real product problems: state management, branching, context drift, editing, retries, and user control.

The basic workflow is simple. A writer enters a title and a short opening. The app sends that context to a model through Crazyrouter and asks for several possible next branches. The writer chooses one branch, and the app expands it into a full scene. The selected scene becomes part of the active story path, and the process repeats.

The important design choice is to keep the story state in the application, not only in the prompt. Each story node should have an id, parent id, summary, full text, selected branch, timestamp, and model metadata. That gives the app a reliable timeline or tree view, and it lets the writer resume, edit, or regenerate specific parts of the story.

Claude Code is useful because this is not just a single API call. It can help implement the data model, API wrapper, UI states, local persistence, and error handling across multiple files. A structured prompt works better than a vague request: describe the screens, the branch behavior, the model calls, and how generated text should be edited before the next continuation.

Crazyrouter endpoint configuration also matters. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com. OpenAI-compatible SDKs and regular HTTP applications should use https://cn.crazyrouter.com/v1. Keeping those separate avoids the common /v1/v1/... mistake.

A first version can store stories and settings locally. A production version should move API key handling to a backend, add rate limits, and preserve model metadata for debugging. Streaming output is useful, but the UI should handle cancellation and partial results cleanly.

The full series repository is here: claude-code-guide.

[metavi]

How Product Managers Can Use Claude Without Becoming Context Couriers

A common problem in AI-assisted product work is not that Claude gives weak answers. It is that the work is scattered across too many conversations.

You might discuss a membership points system in three separate Claude windows: one for ROI and redemption cost, one for competitor analysis, and one for refund logic and edge cases. Then you open a new writing session and ask for a PRD outline. The model asks the same basic questions again: What is the exchange ratio? Which competitors matter? What happens when an order is refunded?

At that point, the product manager has become a context courier.

The better pattern is to stop treating chat history as the project memory. Put the work into local Markdown files that both you and Claude can read. Obsidian works well for this not because it is special, but because it stores plain files. A project folder can contain research notes, business rules, PRD drafts, architecture notes, checklists, and a decision log.

Once that folder becomes the source of truth, Claude can work by role. A product manager role reads research and writes the PRD. An architect role reads the PRD and writes the technical plan. Frontend and backend roles read the relevant files and produce implementation notes. A QA role reads the rules and edge cases, then creates test scenarios.

The important rule is boundaries. Each role needs a clear input scope, output file, checklist, and policy for missing information. Otherwise, multi-role AI work becomes another pile of unstructured text.

A useful habit is to turn every important conversation into a minimum viable document. It does not need to be polished. It only needs to preserve goals, decisions, alternatives, constraints, open questions, and edge cases. Then turn AI questions into a project checklist instead of letting them disappear in chat.

For teams using Crazyrouter, keep endpoints clean. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs use https://cn.crazyrouter.com/v1. Do not attach UTM parameters to API endpoints, and avoid doubled paths such as /v1/v1/....

Reference guide: Claude Code Guide

[metavi]

Pencil + Claude Code: Turning UI Prototypes into Runnable Code with MCP

Pencil is useful in an AI coding workflow because it gives Claude Code a real design surface to operate on. Instead of asking an agent to imagine a UI and produce a rough HTML version, you can let it create and edit a structured Pencil design through MCP, inspect the result, and then export implementation code.

The workflow is straightforward. Install the Pencil desktop app, verify its MCP server inside Claude Code with /mcp, and allow the mcp__pencil permission if you do not want repeated confirmation prompts. From there, Claude Code can use Pencil tools such as get_editor_state, get_screenshot, batch_get, get_variables, and especially batch_design.

The important capability is batch_design. It lets the agent insert, copy, update, replace, move, and delete design nodes. That means Claude Code can make concrete changes to the design file rather than only giving written suggestions.

A practical prompt should describe the product surface, not just the visual style. For example: create a sign-in screen for an internal API management console, including email/password login, SSO, error state, loading state, and an environment selector. Claude can create the design, check it through a screenshot, and then respond to targeted edits such as changing spacing, translating labels, or updating all cards.

When the structure is ready, the design can be exported into a React project using Tailwind CSS and shadcn/ui-style components. The generated project can be installed and run locally, then refined like normal application code.

One underrated benefit is version control. Pencil’s .pen file is JSON, so it can live in Git beside the code. That makes it easier to review design changes, track prototype evolution, and keep specs, design files, and implementation in the same repository.

For Crazyrouter users, keep endpoint usage clean. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com; OpenAI-compatible SDKs and HTTP applications use https://cn.crazyrouter.com/v1. Do not add tracking parameters to API endpoints.

Full guide: claude-code-guide

[metavi]

Using Claude Code to Generate Functional Module Diagrams and System Architecture Diagrams

Diagrams are useful until they become stale. In many teams, the README changes, the service boundaries change, and the deployment model changes, but the system diagram remains untouched because opening a design tool is outside the normal development workflow.

Claude Code can make diagram maintenance more practical when you treat diagrams as repository artifacts instead of one-off images. The most useful approach is to generate editable Markdown diagrams from real project context: feature documents, architecture notes, README files, service folders, deployment configuration, and API descriptions.

There are two diagram types worth separating.

A functional module diagram explains what the system does. It is organized around business capabilities and user-facing modules: authentication, user management, order management, billing, notifications, reporting, admin workflows, and similar areas.

A system architecture diagram explains how the system is built and operated. It should show technical components such as frontend applications, API gateways, backend services, databases, caches, queues, background workers, external APIs, and observability tools.

Keeping these separate prevents the common problem where one diagram mixes “Order Center” with “PostgreSQL” and “Redis” without making clear whether the reader is looking at product capability, runtime structure, or infrastructure.

A practical Claude Code workflow is simple:

1. If visual consistency matters, provide a reference diagram and ask Claude Code to extract layout, grouping, colors, node styles, connector styles, and label density.

2. Provide the functional module document and generate a Mermaid-based functional module diagram.

3. Provide technical context from the repository and generate a Mermaid-based architecture diagram.

4. Review the output like code-adjacent documentation: check naming, boundaries, arrows, external systems, and whether the diagram is editable.

When routing Claude Code through Crazyrouter, keep endpoint usage precise. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs and HTTP requests use https://cn.crazyrouter.com/v1. Do not add tracking parameters to API endpoints.

The full guide repository is here: claude-code-guide.

[metavi]

From Product Requirements to High-Fidelity UI Prototypes with Claude Code and Crazyrouter

From Requirements to UI Prototype with Claude Code and Crazyrouter

A better AI-assisted UI workflow does not begin by asking a model to “make a beautiful screen.” That usually leads to inconsistent colors, spacing, shadows, and components. The more reliable approach is to move through the same artifacts a product team would normally review: requirements, design system, low-fidelity wireframes, and high-fidelity prototype.

The first step is to ask Claude Code to generate a design system document from the product requirement. Give it the audience, product tone, core features, and base color palette. Then require a structured Markdown output with design tokens for colors, typography, spacing, radius, shadows, and semantic states. Also ask it to define reusable components such as Button, Input, Card, Modal, Tabs, and Bottom Navigation, including props and state behavior.

The second step is to generate low-fidelity wireframes from that design system. At this stage, the goal is structure, not polish. A useful prompt asks Claude Code to produce a single HTML file with multiple mobile artboards, a simulated status bar, fixed bottom navigation, and independently scrollable content areas. All visual values should come from CSS custom properties derived from the design tokens.

The third step is high-fidelity styling. Provide visual references only after the wireframe is structurally correct. The instruction should preserve the existing page structure and token system while applying the reference style. Iteration works best when feedback is precise: reduce a specific card shadow, align a tab label, use a named token for the primary button, or improve contrast on one component.

For Crazyrouter, keep endpoint usage clean. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs and HTTP clients use https://cn.crazyrouter.com/v1. Avoid the common /v1/v1 mistake.

Full guide repository: Claude Code Guide

[metavi]

Claude Code and Mermaid: Generate Architecture, Sequence, and Flow Diagrams from Your Codebase

Mermaid is a practical format for developer documentation because it turns diagrams into text. That means architecture diagrams, sequence diagrams, flowcharts, and state machines can live in the same repository as the code they explain. They can be reviewed in pull requests, updated with normal editor tools, and rendered in GitHub, Mermaid Live Editor, draw.io, or ProcessOn.

Claude Code makes the workflow more useful because it can inspect a codebase before drafting the diagram. The key is to avoid vague prompts like “make a nice architecture diagram.” A better prompt defines the audience, scope, diagram type, and level of detail. For example, ask it to inspect the checkout flow, summarize the relevant components first, and then output a valid Mermaid flowchart LR diagram for backend onboarding.

A reliable process is:

1. Choose one diagram type and one audience.

2. Point Claude Code at the relevant files or directories.

3. Ask for a short system summary first.

4. Correct any wrong assumptions.

5. Generate Mermaid.

6. Render and adjust the diagram.

7. Commit the Mermaid source.

Architecture diagrams should show stable boundaries such as clients, APIs, services, databases, queues, workers, and external providers. Sequence diagrams are better for ordered interactions such as login, payments, uploads, notifications, and webhooks. Flowcharts are best for decision-heavy logic such as eligibility checks, approval flows, validation pipelines, and feature flag behavior.

If you are using Crazyrouter as the routing layer, keep the endpoint rules clear. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs, HTTP calls, and apps that expect an OpenAI-style base URL use https://cn.crazyrouter.com/v1. Do not append /v1 twice.

Reference guide: Claude Code Guide

[metavi]

Using Claude Code with Figma MCP: A Practical Setup for Turning Designs into Frontend Code

Claude Code + Figma MCP: a practical design-to-code workflow

Claude Code is most useful when it can work with real project context. For frontend work, that context often lives in two places: the codebase and Figma. Connecting Claude Code to Figma through MCP gives you a cleaner handoff loop: point Claude Code at a Figma file or selection, ask for a targeted implementation, then review the generated code like any other change.

This setup is useful for portfolio pages, product screens, component updates, and design revisions where the visual intent already exists. It is less useful when the design is still vague. In that case, you are asking the model to do product design and implementation at the same time, which can be fine for exploration but should not be treated as production-ready output.

The key setup steps are straightforward. Install Claude Code, configure model access, add the Figma MCP server, authenticate with Figma, and install the official Figma plugin inside Claude Code. If you use Crazyrouter, keep the endpoint rules clear:

Claude Code and Anthropic-native clients use https://cn.crazyrouter.com

OpenAI-compatible SDKs and app-side API calls use https://cn.crazyrouter.com/v1

That distinction matters because adding /v1 in the wrong place can lead to broken paths such as /v1/v1/....

A good prompt is specific. Instead of “make this Figma design into code,” ask Claude Code to implement a selected Figma frame as a React component, preserve the current prop API, reuse local CSS conventions, and avoid adding a new UI library. Smaller, more explicit prompts usually produce cleaner diffs.

For design changes, copy the Figma selection link rather than the whole file link. Then ask Claude Code to update only the relevant component. This keeps the change reviewable and reduces accidental rewrites.

The point is not to remove frontend judgment. Claude Code can infer layout and generate a useful first pass, but developers still need to check responsive behavior, accessibility, component boundaries, token usage, and unnecessary dependencies.

Full guide: Claude Code Guide

[metavi]

From Prompt to Prototype: Using Claude Code, Cursor, and Figma/MasterGo for High-Fidelity App Screens

From Prompt to Prototype with Claude Code, Cursor, and Figma/MasterGo

A common mistake in AI prototyping is asking for the final UI too early. If the prompt is only “create a high-end car sales app,” the result may look polished, but it often lacks product structure, page coverage, reusable design tokens, and design-tool compatibility.

A more reliable workflow is staged:

1. Write a rough product request.

2. Ask the AI to rewrite it into a complete product/design/development prompt.

3. Use Claude Code to generate a single-file HTML prototype.

4. Refine the code in Cursor.

5. Import or rebuild the result in Figma or MasterGo.

For a premium automotive sales app, the prompt should define the product positioning, target users, required modules, and screen list. Useful screens include home, vehicle catalog, vehicle detail, 360-degree view, smart configuration, configuration comparison, test drive booking, finance calculator, order tracking, profile, membership center, settings, customer support, news list, and article detail.

The UI section should be specific. Instead of saying “make it high-end,” define the system: deep black, white, champagne gold, neutral gray, large whitespace, subtle shadows, premium typography, consistent spacing, rounded cards, realistic vehicle imagery, and minimal navigation.

The frontend section should ask for Tailwind CSS, open-source icons, mobile dimensions such as 390×844, semantic HTML, clear comments, status bar simulation, fixed bottom navigation, and all screens displayed in a reviewable grid.

For Figma/MasterGo, the prototype should include export expectations: SVG output per screen, semantic class names, downloadable design tokens, copyable CSS variables, and a short import guide. After importing, the design team should still ungroup, convert to frames, apply Auto Layout, and rebuild reusable components.

If using Crazyrouter with Claude Code, keep endpoint configuration exact. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs and HTTP clients use https://cn.crazyrouter.com/v1. Do not add tracking parameters to API endpoints, and avoid accidental /v1/v1/... paths.

The full guide is available here: https://github.com/xujfcn/claude-code-guide?utm_source=tumblr&utm_medium=social&utm_campaign=claude_code_guide_daily

[metavi]

Claude Code and Figma: Practical Workflows for Turning PRDs into Editable Prototypes

Claude Code and Figma: A Practical Prototype Workflow

Claude Code and Figma work best together when you treat AI as a translation layer between structured artifacts: PRDs, UI guidelines, HTML prototypes, Figma nodes, and code files.

The simplest workflow starts with an HTML prototype. Give Claude Code your PRD and design rules, then ask it to generate a single self-contained HTML file with all important screens. For a mobile app, that might mean several 390x844 frames placed side by side in one browser page. Once rendered locally, you can import it into Figma with the html.to.design plugin and get editable frames for review.

For daily implementation work, Figma's Copy as code is often more useful than a heavier integration. Select the changed component, copy CSS or platform-specific output, paste it into Claude Code, and tell it exactly which file to update. This keeps changes scoped and avoids asking the model to infer too much from screenshots.

Figma MCP becomes useful when you want Claude Code to read design context directly from Figma: selected nodes, metadata, screenshots, layout values, and style data. It can also support pushing generated UI back into Figma depending on the setup. MCP is worth exploring after the basic HTML and Copy as code workflows are already working.

If you use Crazyrouter, keep endpoint configuration exact:

Claude Code / Anthropic-native clients: https://cn.crazyrouter.com

OpenAI-compatible SDKs and HTTP clients: https://cn.crazyrouter.com/v1

Do not add UTM parameters to API endpoints. Use tracking only on human-facing links.

A small design-to-code map inside the repository makes the whole workflow more reliable:

| UI element | Figma location | Code file |

| --- | --- | --- |

| Weight card | Home / WeightCard | src/components/WeightCard.tsx |

| Sleep summary | Home / SleepBar | src/components/SleepCard.tsx |

Full repository: Claude Code Guide

[metavi]

From PRD to Code: A Practical AI Coding Workflow with Claude Code and Crazyrouter

From PRD to Code with Claude Code and Crazyrouter

AI coding is most useful when it follows a repeatable engineering workflow. A PRD alone usually does not contain enough context for reliable code generation. It may explain the product behavior, but it often leaves out service boundaries, repository conventions, validation rules, failure handling, observability, and rollout constraints.

A practical Claude Code and Crazyrouter workflow starts with endpoint hygiene. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com. OpenAI-compatible SDKs, HTTP calls, and application backends should use https://cn.crazyrouter.com/v1. Keeping those two cases separate prevents common /v1/v1/... configuration mistakes.

From there, the workflow should move in stages. First, normalize the PRD into a system design artifact that covers entities, domain services, application services, state transitions, external interactions, and high-availability concerns. Then expand the model context with business wiki content, repository-level notes, common engineering rules, and previous requirement knowledge.

RAG helps, but only when retrieval is specific. Instead of searching with an entire PRD, extract business entities, service names, bundle names, RPC names, and repository labels first. A lightweight knowledge graph can also help recover related files and documents when markdown chunking returns only a partial match.

Once the context is ready, ask the model to split the requirement into ordered implementation tasks. Each task should have one responsibility, declare dependencies, and include expected validation, failure handling, and test coverage. Before generating code, produce repository-specific coding rules that capture naming, layering, exceptions, logging, dependency injection, and test patterns.

For MVC-style applications, layer-by-layer generation is often easier to review than asking for one large feature diff. Generate domain behavior, persistence changes, services, application orchestration, controllers, and tests in separate passes. Finish by asking the agent for a review summary that maps changed files back to the PRD and calls out assumptions or gaps.

Full guide: Claude Code guide

[metavi]

Building a Real Claude Code Prototype with Crazyrouter: States, Errors, History, and Mock Data

Building a Real Claude Code Prototype with Crazyrouter

A working API call is only the first step in an AI prototype. If the user clicks a button, the request succeeds, and the result appears, you have proven the connection. But you have not yet proven the workflow.

When using Claude Code with Crazyrouter, start by keeping the endpoint configuration clean. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com. OpenAI-compatible SDKs, HTTP requests, and application code that expects an OpenAI-style base URL should use https://cn.crazyrouter.com/v1. Do not add tracking parameters to API endpoints, and watch for accidental /v1/v1/... paths.

From there, focus on the product behavior around the model call.

A practical prototype should show a loading state while generation is running. The button should be disabled to prevent duplicate requests, and the result area should make it clear that work is in progress. If the request fails, the app should show a visible message, keep the user's input, and allow retry. Console-only errors are not enough once other people start testing the tool.

History is another important layer. For a frontend-only prototype, LocalStorage is often enough. Store the user input, generated output, status, and timestamps so the page does not reset to a blank state after refresh. For local backend experiments, a JSON file can be easier to inspect and edit manually.

Mock data matters too. An empty dashboard is hard to evaluate. If you are building an ecommerce content workbench, create realistic product tasks across categories such as fashion, electronics, and beauty. Include generated titles, copy, images, and task states. Then add lightweight create and delete behavior so testers can interact with the prototype as if it were a real tool.

The final step is observation. Put the prototype in front of users and watch what they do before explaining anything. If they cannot find the generate button, the interface has a design issue. If they understand the button but hesitate, the input flow may be unclear. If the AI integration is not finished yet, you can still validate the workflow manually with a Wizard of Oz approach.

Repository: claude-code-guide

[metavi]

Adding Real AI Features to a Prototype with Claude Code and Crazyrouter

Adding Real AI Features to a Prototype

A clickable prototype is good for validating screens and workflows, but it becomes less useful when the product depends on AI behavior and the AI is only simulated. If a button says "generate copy," "analyze image," or "create poster," the next useful step is to connect it to a real model API and observe what actually happens.

This article focuses on adding AI capabilities to a prototype with Claude Code and Crazyrouter. The most important starting point is the Base URL convention. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com, while OpenAI-compatible SDKs and application code should use https://cn.crazyrouter.com/v1. Mixing those up can lead to confusing paths such as /v1/v1/chat/completions.

For text generation, start small. Create a dedicated API token, find the text generation request example, and ask Claude Code or your AI IDE to wire it into an existing button, form, loading state, result area, and error state. A text generation feature is easier to inspect than image generation, so it is a good first integration.

To confirm the prototype is really calling the model, test with very specific inputs that change each time. Ask for ecommerce copy with unusual constraints, change the tone, add exclusions, and compare the outputs. You can also check provider logs in the Crazyrouter console.

Once text generation works, expand to image-to-text with a vision-language model such as qwen3-vl-8b-instruct. This is useful when the workflow starts from an uploaded product image. The model can extract visual descriptions, product attributes, selling points, and keywords, which can then feed the next copy-generation step.

Image generation should come after you have clear error handling. Image APIs can fail because of invalid inputs, unsupported image formats, timeouts, model constraints, or response parsing issues. Do not settle for a generic "generation failed" message during development. Show the full failure reason somewhere you can inspect.

The full series and implementation notes are here: claude-code-guide.

[metavi]

Build a Useful AI IDE Prototype Before You Wire In the AI

Building an AI IDE Prototype That Tests the Real Workflow

A useful AI IDE prototype starts before code generation. The first step is not choosing a model or styling the UI. It is finding the smallest business workflow that is painful enough to matter and concrete enough to test.

Take an ecommerce operations example. A vague request like “build an AI workspace for product descriptions, images, videos, and asset management” can easily produce a large but shallow demo. The screen may look complete, but the workflow may not reflect how operators actually work.

A better approach is to ask practical questions first: How are products listed today? How many items are handled each day? Which assets are scattered across chats, dashboards, spreadsheets, or local folders? What part of the process is most repetitive? What output is “good enough” for the first product launch?

In many ecommerce teams, content creation is iterative. Operators often use manufacturer images, older materials, or reference screenshots to get a product online quickly. Only after a product shows promise do they spend more time improving images, copy, or video. That means the first prototype does not need to be a complete creative platform. It can focus on two core functions:

1. Generate first-draft product content in batches.

2. Save useful outputs as reusable templates.

The prototype can be a single page. The left side collects product information: name, category, brand, material, size, color, target audience, and an image. The right side shows mock generated output: a main image preview, a structured product title, and one or two selling-point lines. A user should be able to save a good result as a template and apply that template to another product.

This is enough to test the core loop: input product, generate draft, review, save, reuse.

When using Claude Code with Crazyrouter, keep endpoint configuration clean. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs and application requests use https://cn.crazyrouter.com/v1. Do not add tracking parameters to API endpoints.

The key lesson: do not ask an AI IDE to build everything at once. Ask it to build the smallest real workflow, make every primary button work, add sample data for fast testing, and only connect real AI after the prototype proves the product idea.

Reference repo: claude-code-guide

[metavi]

Finding Product Ideas Developers Can Actually Validate with Claude Code and Crazyrouter

A technically well-built product can still fail if the original demand was never validated. This is especially common in developer side projects: the idea starts as something broad, such as “a fitness app”, “a note-taking tool”, or “an AI assistant”, and quickly turns into weeks or months of implementation before anyone confirms whether users care enough to pay.

The practical lesson from this Claude Code and Crazyrouter article is simple: do not start with the feature. Start with the user situation.

A generic fitness app is hard to justify when mature alternatives already exist. But a recovery assistant for postpartum mothers is a different kind of idea. The user is specific. The anxiety is clearer. The use cases are more constrained: short time windows, safety concerns, physical recovery, emotional support, and guidance that generic workout products do not provide well.

That shift from category to scenario is where product quality begins.

A useful framework is to separate demand into three types:

Pain points: problems users urgently want solved because the cost of ignoring them is high.

Delight points: features that create immediate convenience or satisfaction.

Itch points: things users like in theory but can live without.

Many developer projects accidentally build for itch points while describing them as pain points. The better path is to find users who already spend time, money, or effort on a workaround. Ask what they do today, how often the problem occurs, what the current solution costs them, and what would make them switch.

Then validate payment intent. A compliment is weak evidence. A deposit, pre-order, paid pilot, or paid consultation is stronger. The source methodology uses a practical rule of thumb: if more than 10% of qualified prospects pay a deposit, the signal is strong; 5% to 10% means the idea may need positioning work; below 5% suggests the offer may not be painful enough.

Claude Code and AI conversation can help turn this into an executable MVP plan. The key is to ask for segmentation, scope reduction, technical options, cost drivers, validation metrics, and kill criteria. The goal is not to build the most impressive version first. The goal is to learn whether the demand is real before investing in expensive features.

For Crazyrouter configuration, keep endpoints clean: Claude Code and Anthropic-native clients use https://cn.crazyrouter.com, while OpenAI-compatible SDKs and app code use https://cn.crazyrouter.com/v1. Avoid adding UTM parameters to API endpoints.

Full guide: claude-code-guide

[metavi]

Learning AI Coding Tools: IDEs, Agents, and a Practical Claude Code Setup with Crazyrouter

Learning AI Coding Tools Without Turning Your Workflow Into Guesswork

AI coding tools are easiest to misunderstand when they are treated as magic code generators. In practice, they are more useful as part of your development environment: a way to inspect files, explain errors, generate small features, run commands, and help you move through unfamiliar code with less friction.

A normal IDE gives you the basic working surface: syntax highlighting, project navigation, terminal access, Git integration, search, debugging, and language extensions. That still matters. AI does not remove the need for structure. It becomes more valuable when it can see and operate inside that structure.

An AI IDE adds a model-aware assistant on top of the usual editor. Depending on the tool, it can explain a function, generate a file, refactor selected code, add tests, install dependencies, or apply changes across multiple files. This is useful, but only when the task is framed clearly. “Build a web app” is too vague. “Build a React Snake game with keyboard controls, score, growth after eating food, game-over on collision, and a restart button” gives the agent something testable.

For Claude Code and Crazyrouter, endpoint configuration is one of the easiest places to make mistakes. Claude Code and Anthropic-native clients should use https://cn.crazyrouter.com. OpenAI-compatible SDKs, HTTP requests, and app integrations should use https://cn.crazyrouter.com/v1. API endpoints should stay clean and should not include UTM parameters. Tracking belongs on article and social links, not machine-to-machine requests.

A useful beginner exercise is to open an empty folder in an AI IDE and ask it to create a small React Snake game. Then verify the behavior yourself. Does the snake move? Does it grow? Does the score update? Does collision end the game? If something fails, describe the observed behavior rather than saying “it does not work.” Specific observations give the agent a much better chance of fixing the right thing.

The same applies to UI changes. Instead of saying “make it look better,” ask for concrete changes: center the board, use a lighter background, make the score visible, show a clear game-over message, and place the restart button under it.

The full guide is here: claude-code-guide.

[metavi]

Claude Code + Crazyrouter: Vibe Coding Your First AI-Native Snake Game

Vibe coding is the idea that software can begin with conversation: describe what you want, inspect what the model builds, then keep refining. It does not remove engineering judgment, but it can make small prototypes and internal tools much faster to create.

A practical example is a browser-based Snake game. Start with a normal version: arrow keys move the snake, food increases the score, collision ends the game, and start/restart buttons control state. Once that works, upgrade the mechanic. Instead of eating dots, the snake eats words. Each word goes into a collection panel. After eight words, an LLM generates a poem. Then the poem becomes the input for an image generation module.

The workflow matters more than the game. Ask for one layer at a time. Test after each change. When something breaks, describe the observed behavior and the expected behavior: “The restart button resets the score but not the snake position” is much better than “fix the bug.”

For Claude Code and Crazyrouter users, the key integration detail is the Base URL split. Claude Code and Anthropic-native clients use https://cn.crazyrouter.com. OpenAI-compatible SDKs and custom app code use https://cn.crazyrouter.com/v1. Avoid accidentally producing /v1/v1/... in the final request path.

You do not need to be a frontend expert to prototype this way, but basic vocabulary helps. HTML defines structure, CSS defines presentation, JavaScript defines interaction, and frameworks like React or Vue organize UI into components and state. The better you can describe layout, state, user actions, and error handling, the better the model can assist.

Full guide series: claude-code-guide