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Native macOS app that unifies 300+ AI models in one private workspace with agents, MCP tools, and one-time licensing.
Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
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- ✦Switch across 300+ hosted and local AI models
- ✦Native macOS app with global shortcut and screenshot-to-answer
- ✦Reusable agents, projects, and forked chats
- ✦Multimodal analysis of PDFs, images, and code
- ✦MCP tools and code execution
- ✦Local chat storage with encryptable API keys
- ✦Conversational writing and editing
- ✦Code generation and debugging (Claude Code)
- ✦Data analysis and visualization
- ✦Web search plus memory across chats
- ✦Connectors and remote MCP integrations
- ✦Extended thinking for complex tasks
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦Visual workflow studio for agents
- ✦RAG knowledge pipelines
- ✦Agent runtime with tools and memory
- ✦Marketplace of models and plugins
- ✦Publish as app, API or MCP tool
- ✦Logging, analytics and monitoring
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Using multiple AI providers in one place
- →Explaining or fixing on-screen content instantly
- →Building reusable task-specific agents
- →Analyzing documents and screenshots privately
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Building AI agents and chatbots
- →Creating RAG-based knowledge apps
- →Deploying LLM apps at enterprise scale
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects