Results for “intelliprint”

Deployment runbook for IntelliVerse Kubernetes services. Use when deploying a new service, updating an existing deployment, rolling back, applying manifests, updating images, scaling, or managing namespaces in the aicart cluster.

Captures a baseline snapshot of a Jetson device's module model, L4T version, kernel, OS version, and power mode for performance testing or verification.

Run 250+ AI apps from the command line: generate images and videos, call LLMs, search the web, create 3D models, and automate Twitter posts.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Creates a print-friendly architecture or pipeline presentation deck with a blueprint-grid mask, rust-red callouts, and serif typography.

Dispatch coding work across the intelli-verse-x gastown swarm — pick the right rig (gastown, hermes-agent, content-factory, intelli-verse-kube-infra, intelliverse-x-games-platform-2, the SDKs, nakama, leantime, etc.), spawn or assign to a polecat with the right role (coder, reviewer, tester), monitor the convoy through to merge via the refinery. Use whenever the user says "have an agent fix X", "swarm on Y", "open PR for Z across all the SDK forks", "assign this work", or anything else that turns a beads task into a running async agent.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.