How to Build a Software MVP with Claude Haiku 4.5

TL;DR

Start by planning a tightly scoped MVP, then use Claude Haiku 4.5 in an AI coding editor to generate the code and resolve errors. The demonstrated workflow combines React Flow, a simple Node.js backend, SQLite persistence, and an Anthropic-powered processing node, while tracking every model request to keep the claimed development cost below $5.

Transcript

10 years ago, you needed tens of thousands of dollars and a team of developers to build a software startup. Today, you can do it for less than $5. And that's thanks to this new AI model from Enthropic called Claude Haiku 4.5. And this model is not only better than GPT5, it's also multiple times cheaper. And this is how we can build a software start... Read More

Key Insights

• Claude Haiku 4.5 is presented as a low-cost coding model priced at $1 per million input tokens and $5 per million output tokens. The demonstration uses its speed and pricing to support the claim that a software MVP can be created for less than $5.
• Claude Code can be launched from a terminal after installing it with the command supplied on Anthropic's page. Typing /model exposes Sonnet 4.5, Opus 4.1, and Haiku as available choices, while /usage displays the user's limits within Claude Code.
• Cost tracking is essential when testing whether an AI-built startup stays within a small budget. Kilo Code displays the token window and cost of each API request, while Cursor's dashboard provides another way to review Claude Haiku 4.5 usage and spending.
• React Flow supplies the wiring needed for a drag-and-drop workflow builder. Using an established library reduces the amount of custom interface infrastructure required and allows a solo builder to concentrate on the workflow nodes, AI processing, backend, and persistence needed for the MVP.
• Planning is the key distinction the presenter draws between rushed AI coding and serious software development. The workflow researches official documentation, records it in the project, uses an architecture mode, answers implementation questions, and saves a written plan before asking the model to build.
• The MVP scope is deliberately simple and explicit. It uses a minimal node setup, adds a Claude Haiku 4.5 node powered by the Anthropic API, processes data received from the preceding node, runs a simple Node.js backend, and stores workflows in SQLite.
• Cursor's plan mode turns a broad product idea into implementation decisions. It asks what node types the workflow needs, what the AI should do, which backend is appropriate, and whether workflows require persistence, then consolidates the answers into a saved Markdown architecture file.
• Claude Haiku 4.5 is described as three times cheaper and two times faster than Sonnet, with performance presented as equal to Sonnet 4. The transcript also reports generation speeds around 120 tokens per second, sometimes reaching approximately 130 tokens per second.

Questions & Answers

Q: How can you build a software MVP with Claude Haiku 4.5?

Begin with an empty project and define the smallest useful version of the product before generating code. Research the official documentation for important libraries, save those findings in the workspace, and use a planning mode to settle the node types, AI behavior, backend, and persistence. After saving the architecture, let Claude Haiku 4.5 build the tasks, inspect errors, and refine the generated frontend and backend.

Q: How do you install and select Claude Haiku 4.5 in Claude Code?

Search for the Claude Code page from Anthropic, copy the installation command shown there, and paste it into a global terminal. Launch Claude Code by typing the command demonstrated in the transcript, then enter /model to open the model choices. Select Haiku from the listed options, which also include Sonnet 4.5 and Opus 4.1. The presenter chooses Haiku because it is the fastest option shown.

Q: How do you track Claude Haiku 4.5 development costs?

Use a coding tool that exposes request-level spending or review the usage dashboard provided by the editor. Kilo Code shows the token window and the cost associated with each API request, while Cursor records Claude Haiku 4.5 requests in its dashboard. During the demonstration, several requests cost less than 1 cent, one costs about 2 cents, and some display zero, allowing the budget to be checked continuously.

Q: Why use React Flow for an AI workflow builder?

React Flow provides the basic wiring for a drag-and-drop workflow canvas, so the builder does not need to create that interaction system from the beginning. The presenter first asks the coding model to research the official React Flow documentation and explain its setup. Those findings are stored in a project documentation file, referenced during planning, and used as the foundation for the workflow builder's frontend.

Q: What architecture is used for the workflow builder MVP?

The planned application uses React Flow for the workflow interface, a simple Node.js backend, and SQLite for saving workflows. Its node configuration is intentionally minimal and includes a Claude Haiku 4.5 node connected to the Anthropic API. The AI component has one focused responsibility: process data coming from the previous node. This scope keeps the product understandable and suitable for an initial implementation.

Q: Why should AI coding projects be planned before code generation?

Planning forces the builder to decide what the application will and will not include before the model creates files. In the demonstration, plan mode asks about supported workflow nodes, the AI integration level, backend requirements, and persistence. The answers are converted into a simple architecture using a proven technology stack, saved as a Markdown file, and retained in the workspace so later coding requests can reference the same decisions.

Q: How is OpenRouter used with Claude Haiku 4.5?

OpenRouter is used inside the coding extension as a way to choose among different available models. The presenter selects Claude Haiku 4.5 for the current project but notes that the model could be switched later if another option becomes available. This setup separates the coding workflow from dependence on one model choice while still allowing the selected model's request costs and token usage to be observed.

Q: What cost and speed does Claude Haiku 4.5 offer?

The transcript lists Claude Haiku 4.5 at $1 per million input tokens and $5 per million output tokens, with generation around 120 tokens per second. It also cites a coding extension's comparison describing the model as three times cheaper and two times faster than Sonnet, sometimes reaching about 130 tokens per second, while presenting its performance as equal to Sonnet 4. These figures support the video's low-cost development argument.

Summary & Key Takeaways

• The project begins with an empty codebase and a narrowly defined goal: create an AI-powered workflow builder. React Flow supplies the drag-and-drop canvas and connections, while the planned MVP includes the simplest node setup, an Anthropic-powered AI node, a Node.js backend, and SQLite storage for saved workflows.

• The presenter emphasizes architecture before implementation. Official React Flow documentation is researched and saved inside the workspace, then Cursor's plan mode asks clarifying questions about supported nodes, AI behavior, backend requirements, and persistence. The resulting plan is saved as a reusable Markdown file before the model begins generating the application.

• Cost and speed are monitored throughout development. Claude Haiku 4.5 is selected through coding tools such as Claude Code, Kilo Code, OpenRouter, and Cursor. Individual requests are shown costing less than 1 cent or about 2 cents, and the generated project quickly expands into frontend and backend folders with 12 tracked tasks.