Midlibrary
assistantsLibrary of Midjourney styles and SREF codes
code 16
AI code tools assist with writing, testing, reviewing, and debugging software across a broad range of programming languages and environments. The 344 tools here include IDE integrations, web-based coding environments, specialized tools for data pipelines, and platforms for non-developers building internal apps.
Realistic & Online
assistantsGenerate product backgrounds with one click
Automi AI
assistantsPrototypes and ships AI-powered applications
Mito
assistantsAI for Jupyter workflows, EDA, and data apps
Softgen
assistantsGenerate code from natural language descriptions
Codekidz
assistantsTechnical studio providing web, data, and workflow learning
Tomat.AI
assistantsVisual CSV analysis and data cleanup
Vidix
assistantsTask automation for macOS applications
Notewand
docsAutomate medical note-taking and documentation
Refraction
assistantsAI code assistant that understands your entire codebase
GeniusMindsAI
assistantsContent creation, voiceovers, and collaboration tools
Botnation AI
assistantsBuild chatbots for voice and messaging
Midjourney API
assistantsAPI for integrating Midjourney image generation
Vorsto
assistantsDigital platforms for mobility, logistics, and retail
AI-Powered Requirements Management Software for teams
docsAI requirements management for product development teams
Virtuoso QA
assistantsAI testing platform combining NLP and RPA for enterprise QA
The category is wide and includes tools that serve very different audiences. Experienced developers typically want tools that integrate into their existing editor and support their specific language stack well. Teams may prefer tools with collaboration features, shared context, and audit logging. Non-developers building internal tools are better served by visual or low-code platforms like Dynaboard AI. Bug-fixing tools like FixThisBug.de focus on a narrow but high-value task. Code review and quality tools like GitRoll and Relicx focus on testing and reliability rather than generation. When comparing tools, practical benchmarks on your own codebase outperform general capability claims. Also consider how the tool handles context: tools with larger context windows handle full-file and multi-file edits more reliably. Security considerations include whether your code leaves your environment and under what terms it may be used to train future models.