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AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Agentic terminal and cloud agent platform (Warp Terminal, Warp Agent, Oz) for developers orchestrating Claude Code, Codex, and other agents.
No public pricing
No public pricing
Free trial available
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
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Modern terminal rebuilt for agentic coding workflows
- ✦Warp Agent with multi-agent orchestration and model routing
- ✦Oz platform for launching agents into the cloud via SDK, CLI, or terminal
- ✦Codebase indexing and granular permission controls
- ✦Team-wide usage visibility and spend/credit caps
- ✦Open-source terminal core
- →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
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Developers who want an AI-assisted terminal for daily coding
- →Teams orchestrating multiple coding agents (Claude Code, Codex) together
- →Engineering orgs needing governance over agent-driven development
- →Companies moving agent workflows from local machines to the cloud