Results for “melo”

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Expert guidance for Mojo, the programming language by Modular that combines Python's usability with C-level performance. Helps developers write high-performance AI/ML code, optimize numerical computations with SIMD and parallelism, and gradually port Python code to Mojo for orders-of-magnitude speedups.

Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.

Launch, monitor, stop, and debug NeMo-RL recipes on a Kubernetes cluster using the nrl-k8s CLI, supporting ephemeral and long-lived RayCluster modes.

Run evaluation tasks against a NeMo Platform server using the Evaluator plugin CLI and Python SDK.

Provides guidance for training Mixture-of-Experts models with long context windows, covering context parallelism sizing, selective recomputation, dispatcher choices, and practical patterns from recent experiments.

Maintain durable session memory across agent disconnects by writing structured checkpoints to the repo's session directory, enabling context recovery.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.

Adopts the voice and comedic methodology of filmmaker Mel Brooks to craft parody, satire, and humor in responses.

Data pipeline specialist for ML experiments. Use when the user asks to talk to Gekko, requests the data engineer, or needs DataLoader optimization.

Creates ML demo interfaces with Gradio, supporting images, text, audio, and video inputs/outputs.

Drive Moli (`moli`), Lexmount's open-source headless browser for AI agents, built around on-demand rendering: real JavaScript, DOM, and CSS by default, with layout and pixels computed only when explicitly requested via `--layout`. Use when the user wants to fetch/extract a live JavaScript-rendered page as Markdown/HTML/JSON/semantic-tree, capture a screenshot or PDF, run a small bounded crawl, start a CDP/WebDriver automation server for Playwright/Puppeteer, replace a Chromium/ChromeDriver dependency, or diagnose readiness/network/frame issues on a rendered page. Triggers on: "moli fetch", "moli serve", "headless browser for agents", "on-demand rendering browser", "CDP server without Chrome", "structure-first web scraping", "Lexmount browser", "moli-webfetch", "moli-cdp-server".

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Augment the standard exec lifecycle with monorepo detection, lane-spec generation, guard validation, and package-scoped dispatch

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Provides guidance on Hong Kong's four mobile network operators for reference and selection.

Orchestrates three frontier models to debate a question and synthesizes their best insights into a single superior answer.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

HeartMuLa: Suno-like song generation from lyrics + tags.

Maestro — declarative E2E mobile UI testing framework by mobile.dev. YAML-based flow files, single tool for Android + iOS (and Compose Multiplatform / Flutter / React Native). Built-in cloud runner, recording mode, JS scripting for complex assertions, screen state diffing, no flakiness from explicit waits. USE WHEN: user mentions "Maestro", "maestro test", "mobile E2E", "cross-platform UI test", "maestro studio", "mobile.dev cloud", ".maestro" folder, "launchApp" YAML DO NOT USE FOR: web E2E - use `testing/playwright` DO NOT USE FOR: unit tests - use `testing/kotest`, `testing/vitest`, etc. DO NOT USE FOR: instrumented Android tests - use Espresso/Compose Test DO NOT USE FOR: snapshot tests - use `testing/compose-snapshot`

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Comprehensive guide to moco. Master the concepts, implementation, best practices, and real-world applications of moco in professional environments.

Creates, reads, deletes, and lists memos on a Memos instance via its API, using a personal access token.

RHINO — Momentum pyramider. Top 10 assets by OI + volume. Enters small (30% of max) on high-conviction convergence, then adds to winners at +10% ROE (40% more) and +20% ROE (final 30%). Thesis re-validated before every add — 4h trend intact, SM aligned, volume present. DSL High Water Mode trails the full position. The only skill in the zoo that builds into winners instead of entering full size and hoping.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

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

Build interactive web UIs and ML demos in Python using Gradio's core API, components, and patterns.

Configure fault tolerance, straggler detection, preemption, in-process restart, and re-run state machine for Megatron Bridge training jobs.

Trains, evaluates, and runs inference for Mask Auto-Label (MAL) weakly-supervised segmentation models using ViT-MAE backbones with minimal point or box annotations.