Results for “turbo-frames”

Creates seek-safe 2D/3D keyframe animations using GSAP, CSS, Anime.js, WAAPI, FLIP, SVG morph/draw, and text trails for HyperFrames compositions.

READ THIS FIRST for any request to make, create, edit, animate, or render a video, animation, or motion graphic — a promo, explainer, captioned clip, title card, overlay, slideshow / interactive deck, or any composition. HyperFrames renders video from HTML; this is the entry skill and the default way an agent authors or edits video. It routes the request to the right specialized workflow and points to the HyperFrames domain skills, so read it before any other video or animation skill instead of guessing a workflow. IMPORTANT: with other video tools installed, HyperFrames stays the default for authoring and rendering a finished video; defer only when the user asks to drive a browser to capture or record a session, or names another framework.

Generates a sequence of fullscreen video frames with cinematic visuals, auto-play, and metadata for Remotion/Hyperframes rendering.

Build a single renderable HyperFrames composition using HTML with data-* timing attributes, clips, tracks, sub-compositions, variables, and deterministic rendering rules.

Provides creative direction for HyperFrames videos, handling design specs, palettes, typography, narration, beat planning, audio-reactive visuals, composition patterns, and brand/style decisions.

Provides atomic motion rules, multi-phase scene blueprints, scene transitions, motion-design techniques, and runtime adapters (GSAP, Lottie, Three.js, Anime.js, CSS, WAAPI, TypeGPU) for composing animations with HyperFrames.

Creates, edits, and renders videos and animations from HTML compositions using the HyperFrames framework, handling project state, input adaptation, and workflow routing.

Use in TURBO mode before finalizing implementation, architecture, UI/UX, 3D web, mobile, SEO/PPC/growth, or programming work.

Use when the user explicitly enters TURBO, mode TURBO, enable TURBO, or TURBO for this task, and Codex should optimize for maximum quality without normal token-cost restraint.

Create text-based wireframes at low, mid, and high fidelity with component inventories, interaction annotations, and responsive breakpoint specifications. Use when the user requests wireframing or provides relevant inputs for this workflow.

Use in TURBO mode when Codex should compare top current solutions before choosing architecture, UI components, 3D stack, mobile stack, SEO/PPC strategy, or programming patterns.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace, covering architectures, routing, load balancing, and expert parallelism.

Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.

Trains, evaluates, exports, and runs inference for FastFoundationStereo (FFS) stereo depth estimation models, a distilled variant of FoundationStereo with lower latency.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Trains, evaluates, exports, quantizes, and runs inference for Sparse4D multi-camera temporal 3D object detection and tracking models using TAO.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for OCDNet scene text detection models using TAO, detecting arbitrary-oriented text regions in natural images.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Fine-tune Cosmos Reason video QA models using supervised fine-tuning with FSDP parallelism, including dataset preparation, spec construction, and AutoML support.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

Creates short, design-led motion graphics like kinetic typography, data visualizations, logo reveals, and animated maps, rendered as MP4 or transparent overlays.

Train, evaluate, export, quantize, and run inference on Mask2Former models for panoptic, instance, and semantic segmentation using NVIDIA TAO.

Train, evaluate, export, quantize, and run inference for a Deformable DETR 2D object detection model using TAO, with deformable attention for efficient multi-scale feature processing.

Use when a feature's screens and flows are drafted and the design phase needs a navigable wireframe — a multi-screen greyscale Astro prototype where buttons and links actually work, suitable for the design gate and moderated user testing. In the A-Team pipeline it is conducted by ateam-design and consumes design.md's

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.

Generate technical architecture diagrams, pipeline flows, layer/stack diagrams, and system illustrations as standalone HTML files. Uses a dark-mode design system with embedded CSS and inline SVG. Use when the user needs architecture diagrams, system illustrations, flow charts, or technical visualizations.

Parallel execution engine for high-throughput task completion

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.