FAL.ai (Seedance 2.0)
assistantsAccess 1000+ generative media models
code 15
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.
UI Bakery
assistantsAI app builder for CRUD apps and admin panels
DoubleO.ai
assistantsAutomate business processes like scheduling and data entry
Cubeo. Your AI Assistant For Business Automation
assistantsAutomate business workflows with AI
Ellipsis
assistantsCode review and bug detection
Online stores for small businesses using Stripe
assistantsOnline store for local businesses
Cradl AI
assistantsAI document parsing and data extraction
Decisions
assistantsNo-code software for business processes
Belva
assistantsAI tools for simplifying tasks and boosting efficiency
GenAI No-code Agentic Platform
assistantsNo-code platform to build and deploy Gen AI agents
ML Clever
assistantsGenerate dashboards and reports from data and documents
Ishi
assistantsLocal AI agent interface and workflow builder
CielChan
assistantsOffline desktop AI that remembers you and learns context
CodeScope Studio
assistantsBuild websites with plain English and visual editing
JaxoAI
assistantsFree AI content creation for articles, emails, images, and blog ideas
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.