1 Day Build — AI Chat Interface

What can you do in a day?

Final Result

Introduction

In the world of AI and natural language processing, chat interfaces have become an integral part of our digital interactions. As a developer who frequently uses tools like Claude and ChatGPT, I often found myself bumping up against usage limits, especially when working on projects that required continuous refinement and iteration. This frustration sparked an idea: why not build my own chat interface that connects directly to an AI API?

The goal was simple yet ambitious — create a basic, functional chat app in just one day. I wanted something more than a terminal-based interface, but not so complex that I’d get lost in feature creep or design perfectionism. The key features I aimed for were:

1. A clean, intuitive user interface
2. Real-time streaming of AI responses
3. Proper formatting for code snippets with syntax highlighting
4. A way to track API usage costs

This project wasn’t just about creating a tool; it was about learning, pushing my limits, and seeing what could be accomplished in a short timeframe. Join me as I walk you through the process of building this chat interface, from conception to completion, in just 24 hours.

What We’ll Cover

In this post, we’ll take a deep dive into the entire process of building our chat interface. Here’s what you can expect:

1. Project Setup: We’ll discuss the tech stack chosen for this project, including Next.js, shadcn/ui, and the Anthropic API. You’ll learn how to set up the development environment and initialize the project.
2. Building the User Interface: We’ll explore the process of creating a clean, intuitive UI using shadcn/ui components. This includes designing the chat layout, implementing a dark mode toggle, and creating a welcoming initial screen.
3. Implementing Chat Functionality: We’ll dive into the core of our application — setting up the API route to communicate with the Anthropic API, handling message exchanges, and implementing real-time streaming of AI responses.
4. Adding Extra Features: We’ll enhance our basic chat interface with additional features like code block formatting with syntax highlighting, a copy button for code snippets, and API usage cost tracking.
5. Challenges and Solutions: We’ll discuss the obstacles encountered during the development process and how they were overcome. This includes managing streaming responses, handling long conversations, and implementing responsive design.
6. Future Improvements: While our one-day project resulted in a functional chat interface, we’ll explore potential future enhancements. This includes ideas like function calling, web search integration, and collaborative features.
7. Conclusion: We’ll reflect on what we’ve accomplished, the key takeaways from this project, and encourage you to embark on your own rapid development projects.
8. Resources: Finally, we’ll provide a curated list of resources for those who want to dive deeper into the technologies and concepts used in this project.

Whether you’re a seasoned developer looking to quickly prototype an AI-powered application, or a beginner curious about building chat interfaces, this post will provide valuable insights and practical knowledge. Let’s dive in and see what we can accomplish in just one day!

Project Setup

When it came to choosing the tech stack for this project, I gravitated towards tools I was familiar with and knew could deliver quick results. Here’s what I settled on:

• Next.js: A React framework that offers server-side rendering and an intuitive file-based routing system.
• shadcn/ui: A collection of re-usable components that makes building UIs with Tailwind CSS a breeze.
• Anthropic API: To power the AI responses in our chat interface.

To get started, I used the following commands to set up the project:

npx create-next-app@latest chat
cd chat
npx shadcn-ui@latest init
npm install next-themes

After the initial setup, I initialized a Git repository to track my changes:

git init
git add .
git commit -m "Initial commit from Create Next App"

Dark Mode

One of the first things I tackled was implementing dark mode. Not only is this a popular feature, but it’s also crucial for reducing eye strain during long coding sessions. I followed the shadcn/ui documentation to set up dark mode, which was surprisingly straightforward thanks to the `next-themes` package.

With the project structure in place and dark mode working, I had a solid foundation to start building the chat interface. The next step was to create the main navigation component and design the chat UI itself.

As I moved forward with the project, I made sure to commit my changes regularly. This not only helped me track my progress but also provided a safety net in case I needed to revert any changes.

In the next section, we’ll dive into how I built the user interface for the chat application, including the creation of a main navigation component and the design of the chat interface itself.

Building the User Interface

With our project set up, it was time to dive into creating the user interface for our chat application. This process involved crafting a main navigation component, designing the welcome chat page, and then refining our initial designs.

Creating the Main Navigation Component

To kickstart the UI development, I turned to v0, an AI-powered design tool that’s been making waves in the developer community. I prompted v0 to “create a main nav component for a SaaS company,” and it generated several options. I chose the design that best fit my vision for the chat interface.

The generated component included:

• A responsive design with a hamburger menu for mobile views
• A logo placement
• Navigation links for Home, Features, Pricing, and Contact
• Sign In and Sign Up buttons

Initial v0 design options for navigation

Here’s a snippet of the main navigation component code:

export default function Component() {
  return (
    <header className="flex h-20 w-full shrink-0 items-center px-4 md:px-6">
      <Sheet>
        <SheetTrigger asChild>
          <Button variant="outline" size="icon" className="lg:hidden">
            <MenuIcon className="h-6 w-6" />
            <span className="sr-only">Toggle navigation menu</span>
          </Button>
        </SheetTrigger>
        {/* ... rest of the navigation code ... */}
      </Sheet>
      {/* ... desktop navigation items ... */}
    </header>
  )
}

While this gave me a great starting point, I knew I’d need to customize it further to fit the specific needs of a chat application.

Designing the Welcome Chat Page

For the welcome chat page, I wanted to create an inviting interface that would immediately engage users and guide them into starting a conversation. I envisioned a page with:

1. A friendly greeting
2. An input area for users to start typing their messages
3. Some suggested prompts or recent chat history to help users get started

Again, I used v0 to generate an initial design. The AI came up with a layout that included:

• A large “Good morning” greeting at the top
• An input field with placeholder text “How can Claude help you today?”
• A section for recent chats
• A dropdown to select the AI model (Claude 3.5 Sonnet)

Here’s a simplified version of the initial welcome chat page component:

export default function WelcomeChat() {
  return (
    <div className="container mx-auto p-4">
      <h1 className="text-3xl font-bold mb-4">Good morning, Joe</h1>
      <div className="bg-gray-100 p-4 rounded-lg mb-4">
        <input 
          type="text" 
          placeholder="How can Claude help you today?"
          className="w-full p-2 rounded"
        />
      </div>
      <div>
        <h2 className="text-xl font-semibold mb-2">Your recent chats</h2>
        {/* Recent chat items would go here */}
      </div>
    </div>
  )
}

Revising and Refining

While the v0-generated designs provided a solid *cough* foundation, they needed some tweaking. Here are some of the key revisions I made:

1. Simplifying the Navigation: I removed unnecessary links like “Features” and “Pricing”, focusing the navigation on the chat functionality.
2. Enhancing the Welcome Page: Instead of showing recent chats (which wouldn’t exist for new users), I decided to display suggested prompts. This would help users get started quickly and showcase the capabilities of the chat interface.
3. Improving Responsiveness: I adjusted the layout to ensure it looked good on both desktop and mobile devices.
4. Integrating Dark Mode Toggle: I added a prominent dark mode toggle in the navigation bar for easy access.

revised v0 Welcome Chat

Here’s a snippet of the revised welcome chat component:

export default function WelcomeChat() {
  const suggestedPrompts = [
    "Explain how to use a React component",
    "Give me 5 ideas for a SaaS company",
    "Write a Python function to calculate Fibonacci numbers",
  ];

  return (
    <div className="container mx-auto p-4">
      <h1 className="text-4xl font-bold mb-6">Welcome to AI Chat</h1>
      <div className="bg-secondary p-4 rounded-lg mb-6">
        <input 
          type="text" 
          placeholder="Ask me anything..."
          className="w-full p-3 rounded text-lg"
        />
      </div>
      <div>
        <h2 className="text-2xl font-semibold mb-4">Try asking about:</h2>
        <ul className="space-y-2">
          {suggestedPrompts.map((prompt, index) => (
            <li key={index} className="bg-primary-foreground p-3 rounded-lg cursor-pointer hover:bg-primary hover:text-primary-foreground transition-colors">
              {prompt}
            </li>
          ))}
        </ul>
      </div>
    </div>
  )
}

These revisions transformed the initial AI-generated designs into a more focused, user-friendly interface that aligned perfectly with the goals of our one-day chat application project.

By leveraging v0 for initial designs and then refining them based on project-specific needs, I was able to quickly create a polished UI that set the stage for implementing the chat functionality. This approach significantly sped up the design process, allowing me to focus more time on the core chat features in our limited one-day timeframe.

Implementing Chat Functionality

With a shell of a user interface in place, it was time to bring our chat application to life by implementing the core chat functionality. This involved setting up the Anthropic API integration, handling message exchanges, and implementing real-time streaming of AI responses.

Setting up the Anthropic API Integration

The first step was to set up our backend to communicate with the Anthropic API. We created an API route in our Next.js application to handle this. Here’s a simplified version of our API route:

import { NextRequest, NextResponse } from 'next/server';
import Anthropic from "@anthropic-ai/sdk";

export const runtime = "edge";

export async function POST(req: NextRequest) {
  const body = await req.json();
  const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

  const anthropicMessages = body.messages.map((msg: any) => ({
    role: msg.role,
    content: [{ type: "text", text: msg.content }],
  }));

  const stream = await anthropic.messages.create({
    model: "claude-3-5-sonnet-20240620",
    max_tokens: 3000,
    temperature: 0,
    messages: anthropicMessages,
    stream: true,
  });

  // ... streaming logic here ...

  return new Response(customReadable, {
    headers: {
      "Content-Type": "text/event-stream",
      "Cache-Control": "no-cache",
      Connection: "keep-alive",
    },
  });
}

This API route receives messages from the frontend, formats them for the Anthropic API, sends them to the API, and then streams the response back to the client.

Handling Messages and Displaying Responses

On the frontend, we implemented a `Chat` component to handle sending messages and displaying responses. Here’s a simplified version of this component:

export default function Chat() {
  const [inputMessage, setInputMessage] = useState("");
  const [messages, setMessages] = useState<Message[]>([]);
  const [isLoading, setIsLoading] = useState(false);

  const handleSubmit = async (e: React.FormEvent) => {
    e.preventDefault();
    if (!inputMessage.trim()) return;

    setIsLoading(true);
    const userMessage: Message = { role: "user", content: inputMessage };
    setMessages(prevMessages => [...prevMessages, userMessage]);
    setInputMessage("");

    try {
      const res = await fetch("/api/claude", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ messages: [...messages, userMessage] }),
      });

      if (!res.ok) throw new Error("Failed to send message");

      // ... handle streaming response ...

    } catch (error) {
      console.error("Error:", error);
      setMessages(prevMessages => [
        ...prevMessages,
        { role: "assistant", content: "An error occurred while sending the message." },
      ]);
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <div className="flex flex-col h-[calc(100vh-5rem)] p-4 max-w-3xl mx-auto">
      <div className="flex-grow overflow-y-auto mb-4 pb-4">
        {messages.map((message, index) => (
          <div key={index} className={`mb-4 ${message.role === "user" ? "text-right" : "text-left"}`}>
            <div className={`inline-block rounded-lg ${
              message.role === "user" 
                ? "bg-primary text-primary-foreground p-2"
                : "py-4 px-4 bg-secondary text-secondary-foreground relative"
            }`}>
              {message.content}
            </div>
          </div>
        ))}
      </div>
      <form onSubmit={handleSubmit} className="flex space-x-2">
        <Textarea
          value={inputMessage}
          onChange={(e) => setInputMessage(e.target.value)}
          placeholder="Type your message here"
          disabled={isLoading}
          className="flex-grow"
        />
        <Button type="submit" disabled={isLoading}>
          {isLoading ? "Sending..." : "Send"}
        </Button>
      </form>
    </div>
  );
}

This component manages the state of the conversation, sends user messages to our API route, and displays both user messages and AI responses.

Implementing Streaming Responses

To provide a more responsive user experience, we implemented streaming for the AI’s responses. This allows the response to appear gradually, similar to how a human would type.

Here’s how we handled the streaming response in our frontend:

const reader = res.body?.getReader();
if (!reader) throw new Error("No reader available");

let aiResponse = "";
setMessages(prevMessages => [...prevMessages, { role: "assistant", content: "" }]);

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  const chunk = new TextDecoder().decode(value);
  const lines = chunk.split("\n\n");
  for (const line of lines) {
    if (line.startsWith("data: ")) {
      const data = line.slice(6);
      if (data === "[DONE]") {
        setIsLoading(false);
        break;
      }
      try {
        const parsedData = JSON.parse(data);
        if (typeof parsedData === "string") {
          aiResponse += parsedData;
          setMessages(prevMessages => {
            const updatedMessages = [...prevMessages];
            updatedMessages[updatedMessages.length - 1].content += parsedData;
            return updatedMessages;
          });
        }
      } catch (error) {
        console.error("Error parsing data:", error);
      }
    }
  }
}

This code reads the streaming response chunk by chunk, updates the UI in real-time as new parts of the response arrive, and handles the end of the stream.

Challenges and Solutions

Implementing the chat functionality wasn’t without its challenges. Some key issues we faced and solved included:

1. Rate Limiting: We implemented a simple cooldown system to prevent users from overwhelming the API with requests.
2. Error Handling: We added comprehensive error handling to gracefully manage API failures or network issues.
3. Message History: We stored the conversation history in the component’s state, allowing for context-aware responses from the AI.
4. Responsive Design: We ensured that the chat interface remained usable on both desktop and mobile devices, adjusting the layout as necessary.

By tackling these challenges, we were able to create a robust, user-friendly chat interface that effectively leverages the power of the Anthropic API. In the next section, we’ll explore some of the extra features we added to enhance the user experience.

Our initial chat interface

Adding Extra Features

With the core chat functionality in place, I decided to enhance the user experience by adding some extra features. These additions not only made the chat interface more useful but also more polished and professional-looking.

Code Block Formatting and Syntax Highlighting

One of the key features I wanted to implement was proper formatting for code blocks with syntax highlighting. This is especially important for a chat interface that might be used for programming-related queries.

To achieve this, I used the `react-markdown` library for parsing markdown and `react-syntax-highlighter` for code highlighting. Here’s how I implemented it:

import ReactMarkdown from "react-markdown";
import { Prism as SyntaxHighlighter } from "react-syntax-highlighter";
import { oneDark } from "react-syntax-highlighter/dist/esm/styles/prism";
import gfm from "remark-gfm";
import raw from "rehype-raw";

// ... inside the component ...

<ReactMarkdown
  remarkPlugins={[gfm as any]}
  rehypePlugins={[raw as any]}
  components={{
    code: ({ node, inline, className, children, ...props }) => {
      const match = /language-(\w+)/.exec(className || "");
      return !inline && match ? (
        <CodeBlock
          language={match[1]}
          value={String(children).replace(/\n$/, "")}
        />
      ) : (
        <code className="bg-secondary-foreground text-secondary px-1 rounded-sm" {...props}>
          {children}
        </code>
      );
    },
    // ... other component overrides ...
  }}
>
  {message.content}
</ReactMarkdown>

The `CodeBlock` component is a custom component I created to handle the display of code blocks:

const CodeBlock: React.FC<CodeBlockProps> = ({ language, value }) => {
  return (
    <div className="relative flex flex-col rounded-lg my-2 bg-primary-foreground border max-w-2xl overflow-x-auto">
      <div className="text-text-300 absolute pl-3 pt-2.5 text-xs ">
        {language}
      </div>
      <SyntaxHighlighter
        language={language}
        style={oneDark}
        customStyle={{
          margin: "0",
          borderRadius: "0.5rem",
          fontSize: "0.875rem",
          lineHeight: "1.5",
        }}
      >
        {value}
      </SyntaxHighlighter>
    </div>
  );
};

This setup allows for beautiful, syntax-highlighted code blocks within the chat interface.

Copy Button for Code Blocks

To make it easier for users to use the code snippets provided by the AI, I added a copy button to each code block. Here’s how I implemented this feature:

const CodeBlock: React.FC<CodeBlockProps> = ({ language, value }) => {
  const [copied, setCopied] = useState(false);

  const handleCopy = () => {
    navigator.clipboard.writeText(value);
    setCopied(true);
    setTimeout(() => setCopied(false), 2000);
  };

  return (
    <div className="relative flex flex-col rounded-lg my-2 bg-primary-foreground border max-w-2xl overflow-x-auto">
      {/* ... language display ... */}
      <div className="pointer-events-none sticky z-20 my-0.5 ml-0.5 flex items-center justify-end px-1.5 py-1 mix-blend-luminosity top-0">
        <div className="from-bg-300/90 to-bg-300/70 pointer-events-auto rounded-md bg-gradient-to-b p-0.5 backdrop-blur-md">
          <button
            onClick={handleCopy}
            className="flex flex-row items-center gap-1 rounded-md p-1 py-0.5 text-xs transition-opacity delay-100 hover:bg-bg-200"
          >
            <CopyIcon size={14} className="text-text-500 mr-px -translate-y-[0.5px]" />
            <span className="text-text-200 pr-0.5">
              {copied ? "Copied!" : "Copy"}
            </span>
          </button>
        </div>
      </div>
      {/* ... SyntaxHighlighter component ... */}
    </div>
  );
};

This adds a sleek, non-intrusive copy button to each code block, improving the usability of the chat interface for developers.

Cost Tracking for API Usage

To help users keep track of their API usage, I implemented a simple cost tracking feature. This required modifications to both the backend and frontend.

On the backend, I added code to calculate the cost based on the number of input and output tokens:

const INPUT_TOKEN_COST = 3; // Cost per 1,000,000 input tokens
const OUTPUT_TOKEN_COST = 15; // Cost per 1,000,000 output tokens

// ... inside the POST function ...

const inputCost = (totalInputTokens / 1_000_000) * INPUT_TOKEN_COST;
const outputCost = (totalOutputTokens / 1_000_000) * OUTPUT_TOKEN_COST;

controller.enqueue(
  encoder.encode(
    `data: ${JSON.stringify({
      inputTokens: totalInputTokens,
      outputTokens: totalOutputTokens,
      inputCost: inputCost.toFixed(6),
      outputCost: outputCost.toFixed(6),
    })}\n\n`
  )
);

On the frontend, I updated the Chat component to display this information:

const [messages, setMessages] = useState<Message[]>([]);

// ... inside the component ...

const totalChatCost = useMemo(() => {
  return messages.reduce((total, message) => {
    const messageCost =
      (Number(message.inputCost) || 0) + (Number(message.outputCost) || 0);
    return total + messageCost;
  }, 0);
}, [messages]);

// ... in the return statement ...

<div className="text-right mb-1 text-xs text-muted-foreground">
  Total Chat Cost: ${totalChatCost.toFixed(4)}
</div>

// ... for each message ...

<TooltipProvider>
  <Tooltip>
    <TooltipTrigger className="">
      <div className="text-xs text-muted-foreground">
        Message Cost: $
        {(
          (Number(message.inputCost) || 0) +
          (Number(message.outputCost) || 0)
        ).toFixed(4)}
      </div>
    </TooltipTrigger>
    <TooltipContent>
      <div className="">
        Input cost: ${Number(message.inputCost || 0).toFixed(5)} | Output
        cost: ${Number(message.outputCost || 0).toFixed(5)}
      </div>
    </TooltipContent>
  </Tooltip>
</TooltipProvider>This feature provides users with transparency about the cost of their API usage, both per message and for the entire conversation.

Markdown Formatting for Better Readability

To improve the readability of AI responses, I implemented markdown formatting for elements like lists and inline code. This was achieved through the `ReactMarkdown` component:

<ReactMarkdown
  remarkPlugins={[gfm as any]}
  rehypePlugins={[raw as any]}
  components={{
    p: ({ node, ...props }) => (
      <p className="whitespace-pre-wrap" {...props} />
    ),
    ul: ({ node, ...props }) => (
      <ul className="-mt-1 list-disc space-y-2 pl-8" {...props} />
    ),
    ol: ({ node, ...props }) => (
      <ol className="-mt-1 list-decimal space-y-2 pl-8" {...props} />
    ),
    li: ({ node, ...props }) => (
      <li className="whitespace-normal break-words" {...props} />
    ),
    // ... other component overrides ...
  }}
>
  {message.content}
</ReactMarkdown>

These extra features significantly enhanced the functionality and user experience of our chat interface. They transform it from a basic chat application into a more comprehensive tool for interacting with AI, especially for tasks related to programming and development.

Final Chat Interface

Future Improvements

While our one-day project resulted in a functional and feature-rich chat interface, there’s always room for improvement. Here are some ideas for future enhancements that could take this application to the next level:

Function Calling

One powerful feature we could implement is function calling. This would allow the AI to not just respond with text, but also to trigger specific actions or retrieve particular data. For example:

• The AI could query a database to provide real-time data in its responses.
• It could interact with external APIs to fetch weather information, stock prices, or other dynamic data.
• The AI could trigger actions within the application, like setting reminders or creating tasks.

Implementing this would involve creating a set of predefined functions that the AI can call, and modifying our API route to handle these function calls.

Web Search Integration

Currently, our AI’s knowledge is limited to its training data. By integrating web search capabilities, we could significantly expand its ability to provide up-to-date information. This could work as follows:

1. When the AI encounters a question it can’t answer confidently, it could trigger a web search.
2. The search results could be summarized and incorporated into the AI’s response.
3. The response would include citations or links to the original sources.

This feature would make our chat interface much more versatile and capable of handling a wider range of queries.

Code Execution in a Sandbox Environment

For programming-related queries, it would be incredibly useful to allow users to run code directly within the chat interface. This could be implemented as follows:

1. Create a secure, sandboxed environment for code execution.
2. When the AI provides a code snippet, add a “Run” button alongside the existing “Copy” button.
3. When clicked, the code would be sent to the backend, executed in the sandbox, and the results would be displayed in the chat.

This feature would allow users to immediately test and experiment with the code provided by the AI, making the application an even more powerful tool for developers.

Conversation Memory and Context Management

While our current implementation maintains context within a single session, we could enhance this further:

• Implement session storage or a database to allow conversations to persist across page reloads or even multiple visits.
• Add a feature to name and save specific conversations for future reference.
• Implement a system to manage and switch between multiple conversation contexts.

These improvements would make the chat interface more useful for ongoing projects or recurring topics.

User Authentication and Personalization

Adding user authentication would open up a range of new possibilities:

• Personalized conversation history for each user.
• User-specific API usage tracking and quotas.
• The ability to tailor the AI’s responses based on user preferences or past interactions.

This would transform our application from a general-purpose tool into a personalized AI assistant.

Multi-Modal Interactions

Expanding beyond text-only interactions could greatly enhance the user experience:

• Implement image upload capabilities, allowing the AI to analyze and comment on images.
• Add speech-to-text and text-to-speech features for voice interactions.
• Enable the AI to generate and display simple diagrams or charts to illustrate its explanations.

These features would make the chat interface more accessible and versatile, catering to different learning and interaction styles.

Collaborative Features

Introducing collaborative features could make our chat interface useful for team scenarios:

• Implement shared conversations that multiple users can view and contribute to.
• Add the ability to invite other users to a conversation.
• Create a system for saving and sharing useful AI responses within an organization.

This would extend the utility of our application beyond individual use to team and organizational contexts.

Advanced Analytics and Insights

To provide more value to users, we could implement advanced analytics:

• Track common queries and provide insights on frequently asked questions.
• Analyze conversation patterns to suggest improvements in how users interact with the AI.
• Provide visualizations of API usage over time to help users optimize their interaction with the AI.

These analytics could help users get more value out of the AI and use it more effectively.

While implementing all these features would certainly extend beyond a one-day project, each of them represents a direction in which our chat interface could evolve. By gradually adding these enhancements, we could transform our simple chat application into a powerful, multi-functional AI assistant platform.

The beauty of our current implementation is that it provides a solid foundation upon which all of these features could be built. The modular nature of our React components and the flexibility of our API route make it relatively straightforward to extend the functionality in any of these directions.

As with any project, the key would be to prioritize these potential improvements based on user needs and feedback. By continually iterating and enhancing our chat interface, we can create an increasingly valuable tool for interacting with AI.

Conclusion

As we wrap up this journey of building a chat interface in just one day, it’s worth taking a moment to reflect on what we’ve accomplished and what we’ve learned along the way.

Key Takeaways

1. Rapid Prototyping is Powerful: In just 24 hours, we managed to create a functional, feature-rich chat interface that leverages the capabilities of a sophisticated AI model. This demonstrates the power of modern web development tools and AI APIs in enabling rapid prototyping and development.
2. Balancing Features and Simplicity: Throughout this project, we had to make constant decisions about what features to include and what to leave out. We learned that it’s crucial to focus on core functionality first and then carefully add features that genuinely enhance the user experience.
3. The Importance of User Experience: Even in a quick project like this, we paid attention to details like code syntax highlighting, cost tracking, and responsive design. These elements significantly improve the usability of the application, showing that good UX doesn’t necessarily require a lot of time, just thoughtful consideration.
4. AI Integration is Becoming Accessible: This project demonstrates how relatively straightforward it has become to integrate advanced AI capabilities into web applications. This opens up exciting possibilities for developers to create increasingly intelligent and helpful tools.
5. Continuous Improvement is Key: While we created a solid foundation in one day, we also identified numerous areas for future improvement. This highlights the iterative nature of software development and the importance of viewing our work as a continual process of refinement and enhancement.

Reflections on the Process

Building this chat interface in a day was an exhilarating experience. It pushed me to make quick decisions, prioritize effectively, and focus on delivering a functional product within a tight timeframe. This process reinforced the value of setting clear goals and working within constraints — sometimes, limitations can spark creativity and force us to focus on what’s truly important.

The use of modern tools and libraries like Next.js, shadcn/ui, and the Anthropic API was crucial in allowing us to move quickly without sacrificing quality. It’s a testament to how far web development has come that we can create such sophisticated applications in such a short time.

Looking Forward

While our one-day project resulted in a functional and useful chat interface, it’s clear that there’s potential for so much more. The future improvements we discussed — from function calling and web search integration to collaborative features and advanced analytics — hint at the exciting possibilities that lie ahead.

This project serves as a starting point, a foundation upon which we can build increasingly sophisticated AI-powered tools. As AI technology continues to advance at a rapid pace, the potential applications for interfaces like this one will only grow.

Final Thoughts

Building a chat interface in a day was not just about creating a product; it was about learning, exploring, and pushing the boundaries of what’s possible with modern web development and AI technologies. It’s a reminder of how much we can accomplish when we set our minds to it and focus our efforts.

I hope this blog post has been informative and perhaps even inspiring. Whether you’re a seasoned developer or just starting out, I encourage you to try building your own chat interfaces or AI-powered applications. The barrier to entry is lower than ever, and the possibilities are endless.

Remember, every great application starts with a simple idea and a willingness to start building. So why not start your own one-day project? You might be surprised at what you can create in just 24 hours.

Thank you for joining me on this journey. Happy coding, and may your future projects be as exciting and rewarding as this one has been!