Deepnote Copilot
assistantsData science notebook with AI assistance
code 344
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.
Emergent
assistantsAI coding assistant for faster development
bolt.new
assistantsLow-code AI app builder with templates
Devin
assistantsAI software engineer for development tasks
C3 AI
assistantsEnterprise platform for rapid AI application development
Raygun
debuggingApplication error tracking and monitoring
Qdrant
assistantsOpen-source vector search engine built in Rust
Kite
assistantsArchived code completion tool, no longer supported
Oopsie, by Zipy
testingAutomated bug detection for mobile apps
Codeanywhere
assistantsCloud IDE with real-time collaboration
Rocket
assistantsAI development assistant with market research
Marblism
assistantsAI-powered app builder for coding projects
SourceAI
assistantsPenetration testing for web apps and cloud
GoCodeo
assistantsAI test automation across languages and frameworks
GitHub Spark
assistantsAI command-line assistant for shell commands and debugging
JDoodle
assistantsOnline IDE and compiler for 50+ programming languages
Testim
testingAutomated testing platform with AI-powered stable tests
Phala Cloud
assistantsConfidential AI inference on TEE-protected GPUs
YouWare
assistantsAI code generation tool to accelerate software development
Mocha
assistantsWriting and content creation assistant
Decipher
debuggingAI tool for finding and fixing code errors
Traycer
assistantsAI code generation integrated into IDEs
Zzzcode
assistantsWrite code in any language with ChatGPT
IDX
assistantsWeb-based workspace for full-stack app development
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.