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Open-source library and desktop app for fast, memory-efficient local fine-tuning and inference of open LLMs.
Kimi is Moonshot AI's conversational assistant known for long-context chat, coding help, and agentic tasks.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Optimized LoRA/FFT/PT training kernels for 500+ models
- ✦Local offline model runner for Mac and Windows
- ✦No-code dataset creation from PDFs, CSVs, and JSON
- ✦Unlimited tool-calling and web search inside model runs
- ✦Data Recipes workflow to turn documents into training datasets
- ✦Export to safetensors or GGUF for llama.cpp, vLLM, Ollama
- ✦Multi-GPU support on paid tiers
- ✦Conversational AI assistant
- ✦Long-context document understanding
- ✦Coding assistance
- ✦Agent and plugin capabilities
- ✦Web and mobile app access
- ✦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
- ✦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
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- →ML engineers fine-tuning open models on a single GPU for free
- →Teams building custom datasets from unstructured documents
- →Developers wanting to run and compare LLMs fully offline
- →Enterprises needing faster, more accurate multi-node training
- →Answering questions and research
- →Summarizing long documents
- →Writing and editing help
- →Coding support
- →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
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work