Chunking Text: AI Coding Tools that Make it Easy vs. Writing from Scratch

As indie hackers and side project builders, we often find ourselves in a tug-of-war between efficiency and creativity. When it comes to coding, especially with text chunking—a technique for breaking text into manageable pieces—AI coding tools have emerged as game-changers. But is using these tools really more efficient than writing code from scratch? Let’s dive into the details and weigh the pros and cons of both approaches in 2026.

The Challenge of Text Chunking

Text chunking can be a tedious process, especially when dealing with large datasets or complex texts. Manually writing scripts to parse and analyze text can take hours, while AI coding tools promise to streamline this process. However, the effectiveness of these tools can vary widely, and knowing when to use them versus traditional coding methods is crucial.

Key AI Coding Tools for Text Chunking

Here’s a breakdown of some popular AI coding tools that can help with text chunking, along with their pricing, use cases, limitations, and our take on each.

| Tool Name | Pricing | What It Does | Best For | Limitations | Our Take | |------------------|----------------------------|-----------------------------------------------------|---------------------------------|------------------------------------------|--------------------------------| | OpenAI Codex | $20/mo | Generates code snippets based on natural language. | Quick prototyping | Can produce incorrect code | We use this for brainstorming. | | Tabnine | $12/mo | AI-powered code completion tool. | Enhancing coding speed | Limited to supported languages | We love it for JavaScript. | | GitHub Copilot | $10/mo | Suggests code as you type, optimized for GitHub. | Integrating with GitHub projects| Sometimes lacks context | Essential for our workflow. | | Replit | Free tier + $7/mo for Pro | Online IDE with collaborative coding features. | Team projects | Limited offline capabilities | Great for quick demos. | | Snorkel | Free | Tool for programmatically labeling training data. | Preparing datasets for ML | Requires ML knowledge | We don’t use it often. | | DeepAI | $29/mo, no free tier | Offers various AI APIs, including text processing. | Integrating AI into projects | Can get expensive for heavy usage | We’ve tried it, but pricey. | | Hugging Face | Free, with paid tiers | Access to pre-trained NLP models for text processing.| NLP tasks | Steeper learning curve | We prefer simpler tools. | | TextRazor | $0-50/mo depending on usage| Extracts entities and relationships from text. | Analyzing large text corpora | Limited free tier | We use this for data analysis. | | MonkeyLearn | $0-299/mo based on usage | No-code platform for text analysis and classification.| Non-technical users | Can be limiting for advanced users | Useful for simple tasks. | | Aylien | $49/mo | Provides news and text analysis tools. | Business intelligence | Not suitable for custom NLP tasks | We don’t find it necessary. |

What We Actually Use

In our experience, we rely heavily on GitHub Copilot and OpenAI Codex for text chunking tasks. They allow us to quickly generate code snippets and enhance our coding speed without getting bogged down by syntax errors. However, we still write custom code when we need specific functionality that these tools can't provide.

Writing from Scratch: The Old School Way

While AI tools offer efficiency, writing code from scratch has its merits. It allows for complete control over the logic and structure of your code. Here are some pros and cons of this approach:

Advantages of Writing from Scratch

• Full Control: You dictate the logic and flow of the code.
• Customization: Tailor your code to fit specific needs without relying on AI interpretations.
• Learning Opportunity: Writing code enhances your skills and understanding of the language.

Disadvantages of Writing from Scratch

• Time-Consuming: It can take significantly longer, especially for complex tasks.
• Potential for Errors: Without the assistance of AI, you might overlook syntax issues or logical errors.
• Lack of Collaboration: If you're working in a team, it can be harder to maintain consistency without shared tools.

Making the Decision: When to Use AI Tools vs. Writing from Scratch

Choosing between AI coding tools and writing from scratch depends on your specific needs. Here’s a quick framework to help you decide:

• Use AI Tools If:

  • You need to prototype quickly.
  • You're working on repetitive tasks.
  • You're collaborating in a team environment.

• Write from Scratch If:

  • You require specific customizations.
  • You want to deepen your coding skills.
  • You’re tackling a one-off task that doesn’t justify tool investment.

Conclusion: Start with AI Tools, But Don’t Forget the Basics

If you're just starting out or looking for efficiency in your coding workflow, I recommend experimenting with AI coding tools like GitHub Copilot and OpenAI Codex. They can significantly speed up your development process and help you learn along the way. However, don’t shy away from writing code from scratch when the situation calls for it—there’s no substitute for understanding the fundamentals.