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AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
AI Pine Script generator for TradingView strategies and indicators.
Connects Git repos to answer plain-English questions about your code with file references and dependency context.
An AI lab building compact Liquid Foundation Models that run on-device on phones, laptops and cars rather than in the cloud.
Side-by-side arena to compare AI coding models and build multi-file apps, with a public leaderboard and battle mode.
No public pricing
Free trial available
No public pricing
No public pricing
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦AI-powered Pine Script code generation
- ✦Custom strategy and indicator creation
- ✦Error correction and code optimization
- ✦TradingView integration
- ✦Natural-language search across a codebase
- ✦Architecture explanations and dependency graphs
- ✦Bug hunter that traces issues across files
- ✦AI code review before opening a PR
- ✦Automatic documentation generation
- ✦Multi-repo support via OAuth
- ✦Liquid Foundation Models (LFMs) for on-device use
- ✦Variants sized to run on phones, laptops and cars
- ✦Broad runtime support (llama.cpp, MLX, ONNX, CoreML, vLLM)
- ✦On-device reasoning, vision and retrieval models
- ✦Enterprise and embedded deployment partnerships
- ✦Head-to-head model comparison
- ✦Battle mode matchups
- ✦Public model leaderboard
- ✦Multi-file app generation
- ✦File uploads as input
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Generating custom trading strategies for backtesting on TradingView
- →Creating custom indicators for technical analysis
- →Automating the process of writing Pine Script code
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Run private AI locally on consumer hardware
- →Embed intelligence in cars and edge devices
- →Deploy tool-calling agents without the cloud
- →Choosing the best coding model
- →Benchmarking AI code quality
- →Prototyping small apps