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AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
AI coding platform routing many agents and models through one encrypted, usage-based endpoint with CLI, IDE and multi-agent execution.
Headless CMS and page builder where marketing teams and AI agents assemble on-brand landing pages from reusable, developer-built sections.
No public pricing
No public pricing
Free trial available
No public pricing
Free trial available
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Cross-session project memory recalling prior decisions and state
- ✦Autonomous execution of long, multi-step notebook tasks
- ✦Reads cell outputs (plots, tables, metrics), not just code
- ✦In-notebook cell-level assistance and error fixing
- ✦Installs directly into existing JupyterLab via pip, no new editor
- ✦Concept explanations with runnable example cells
- ✦Unified encrypted inference endpoint
- ✦Multi-agent parallel execution
- ✦CLI, IDE, and API access
- ✦App builder and remote coding agents
- ✦Chairman LLM output evaluation
- ✦35+ IDE integrations
- ✦Visual page builder for assembling pre-built sections
- ✦Slice Machine developer tooling for reusable components
- ✦AI-assisted page and copy generation
- ✦AI translation across locales
- ✦ChatGPT/Claude integration for bulk content edits
- ✦API-driven content delivery via CDN
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Data scientists running multi-week model iteration projects
- →Domain experts (e.g. risk/fintech) who know the problem but not deep Python
- →Researchers wanting an agent that remembers project context across days
- →Analysts needing help understanding unfamiliar algorithms or libraries
- →Automating refactors, tests, and migrations
- →Running competing AI coding agents
- →Building apps from prompts
- →Integrating agents into CI/CD
- →Marketing teams launching SEO or ABM landing pages quickly
- →Developers building a component library for non-technical editors
- →Multilingual sites needing synced translations
- →Agencies managing many client sites on one CMS