Features
CUDA kernel editor with AI autocomplete
Multi-DSL support: CUDA, Triton, CUTE, TileLang, PyTorch, Numba, Mojo
GPU emulator for 86+ GPU architectures
Real-time profiling with NCU integration
Automated kernel benchmarking and comparison
PTX/SASS assembly inspection
Register pressure and race detection
Multi-GPU performance comparison (up to 6 GPUs in Pro)
Custom agents with skills and MCP integrations
Natural language profiling queries
Forge CLI for automatic kernel optimization (CUDA/Triton generation)
Local LLM support (Ollama, vLLM, LM Studio)
Stable SSH remote GPU workflows
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
