September 11, 2025
As developers build AI-powered applications, having reliable utilities for text manipulation, data validation, and credential management is essential. Our suite of Text, Data, and Crypto tools helps you cleanly prepare input, verify structured data, and securely manage secrets—without leaving your workflow.
Automation, data integrity, and security are foundational to modern development. When you build LLM-powered features, you often juggle raw text, JSON, XML, and credentials. Small quality issues can lead to failed prompts, broken pipelines, or security risks. These utilities provide fast, dependable building blocks to keep data clean and workflows smooth.
Keep lists, logs, or user inputs ordered consistently. Sorting is a common preprocessing step before comparison, deduplication, or generating deterministic prompts.
Input: "banana, apple, cherry"
Output: "apple, banana, cherry"Encode arbitrary text for safe transport or embedding in prompts. This is useful when you need to embed complex strings in a single token-limited prompt or log payload.
Input: Hello, World!
Output: SGVsbG8sIFdvcmxkIQ==Reverse the encoding to recover the original text.
Input: SGVsbG8sIFdvcmxkIQ==
Output: Hello, World!Generate seeds, session IDs, or test data nondeterministically or with a fixed seed for reproducibility.
Seeded (example): 12345
Random number (0-999): 482Validate and format JSON to ensure your prompts or API payloads are well-formed. This reduces syntax errors and helps with tooling that expects pretty-printed JSON.
Input: {"name":"Alice","age":30,"active":true}
Formatted:
{
"name": "Alice",
"age": 30,
"active": true
}Similarly, validate and pretty-print XML to avoid malformed payloads and to aid readability during debugging or prompt design.
Input: <user><name>Alice</name><age>30</age></user>
Formatted:
<user>
<name>Alice</name>
<age>30</age>
</user>Create strong, unique passwords for accounts or credentials used in demos or tooling. We recommend long, random passwords and safe storage practices.
Example (16 characters): zX3$9nA!pQ7rLm2dCompute an MD5 hash for non-cryptographic checks or legacy compatibility. Note: MD5 is not suitable for password hashing or secure integrity checks in modern systems.
Input: password
Output: 5f4dcc3b5aa765d61d8327deb882cf99Generate a password hash in the HTpasswd format for basic authentication files used by many web servers. This is handy when you need quick credentials for testing or demos.
Username: admin
Hashed password (htpasswd format): $apr1$3n8bcH0$F3GQ6k7kX9d2Pq2Hri7dMUse these tools as building blocks in your data pipelines and prompts:
In practice, you can call these tools from your API, CLI automation, or within your CI/CD pipelines. Pair an input validator with a formatter to ensure consistent prompts, then attach a cryptographic tool to manage credentials in a controlled way.
Always treat credentials with care. Use Password Generator with a high entropy length (16+ characters), store hashes rather than plaintext, and prefer modern hashing schemes (bcrypt, scrypt, Argon2) for real credentials. MD5 should be avoided for security purposes but can be useful for legacy checks or data integrity testing. Base64 is for transport-friendly encoding, not encryption, so keep sensitive data encrypted at rest and in transit.
LLMs benefit from clean, validated input. When your prompts rely on structured data, validating JSON/XML, normalizing text, and ensuring that any secret references are resolved prior to prompt dispatch can reduce token waste and improve reliability. These utilities act as a lightweight, developer-friendly layer between raw data and AI models—helping you ship more predictable, secure, and efficient AI features.