AI Development Tools
Free online tools for AI developers: token counter with visual analysis, prompt template builder with variables. All client-side, privacy-first.
Document Extraction
Extract structured content from any document
Token Counter
Count & visualize AI tokens
Prompt Builder
Build & test AI prompt templates
About AI Development Tools
Building with LLMs means constantly managing tokens and iterating on prompts. These tools are built specifically for AI developers: count and analyze tokens instantly, build and test prompt templates with variables, all without sending your data to any server. Everything runs in your browser, making these tools safe for proprietary prompts and sensitive data.
Frequently Asked Questions
How does the token counter work without an API?
The Token Counter uses a BPE (Byte Pair Encoding) tokenizer that runs entirely in your browser. It provides approximate token counts along with type distribution and frequency analysis. No text is sent to any server.
How accurate is the token count?
The count is approximate. Different LLM providers use different tokenizers, so actual counts may vary by up to ~10% depending on the model. For exact counts in production, use the official tokenizer libraries provided by each vendor.
What are prompt template variables?
Prompt template variables are placeholders like {{user_name}} or {{context}} that get replaced with actual values at runtime. The Prompt Builder lets you define these variables, fill them with test values, preview the rendered prompt, and export to Python f-strings, JavaScript template literals, or Jinja2 syntax.
Is my prompt data safe?
Yes. All tools run 100% client-side in your browser. Your prompts, templates, and test data never leave your device. There are no API calls, no analytics on your content, and no server-side storage. You can verify this in your browser's Network tab.
Do these tools call any AI API?
No. These are utilities for AI developers, not AI-powered tools. They perform deterministic operations like tokenization, template rendering, and data validation. No LLM inference is involved.