Skill UI

This comprehensive guide details the setup and authentication process for the TwinMind AI suite. It covers installing the Chrome extension, setting up mobile applications, integrating with Google/Microsoft calendars, and configuring API access. Use this skill when initializing TwinMind into a professional workflow for automated meeting transcription, note-taking, and data synchronization.

This guide assists in migrating meeting AI workflows from competing tools (such as Otter.ai, Fireflies) to TwinMind. It provides a deep dive into setting up the enterprise environment, covering prerequisites, API configuration, and utilizing advanced AI models (GPT-4, Claude, Gemini) for robust transcription and intelligent summarization. Ideal for managing large-scale meeting intelligence operations.

This comprehensive guide outlines the production-grade reference architecture for advanced AI meeting systems using TwinMind. It details the end-to-end workflow, integrating high-accuracy transcription (Ear-3), AI summarization (GPT-4, Claude, Gemini), memory vault management, and automatic action item and calendar integration. Use this resource when implementing or managing a robust, automated AI meeting solution.

This skill outlines security best practices for the TwinMind AI meeting assistant. It covers crucial aspects like on-device audio processing, encrypted cloud backups, and managing microphone permissions. Use it when setting up new TwinMind instances or managing AI operations to ensure data privacy, compliance, and robust security for sensitive meeting data.

Provides expert guidance on Unsloth to accelerate fine-tuning via LoRA/QLoRA, reduce memory usage, and streamline API usage, debugging, and best practices.

This comprehensive workflow guides users through the end-to-end process of utilizing cloud-based GPU computing resources on Vast.ai. It covers searching for ideal GPU offers based on specs and price, provisioning an instance, running complex training or inference jobs via SSH, collecting model artifacts, and crucially, destroying the instance to prevent unexpected billing. Ideal for deep learning training and research.

This comprehensive script suite provides methods to optimize GPU cloud computing expenses on Vast.ai. It guides users through selecting the most cost-efficient GPU for their workload, leveraging interruptible (spot) instances, and implementing critical lifecycle management safeguards. Features include detecting idle compute resources, auto-destroy timers, and calculating total cost burn rates, ensuring AI model training and inference jobs stay within budget.

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.

This guide details the production-grade reference architecture for running complex GPU compute workflows on Vast.ai. It covers the foundational three-tier pattern—Orchestrator, GPU Workers, and Artifact Storage—providing blueprints for job queue management, fault-tolerant training pipelines, and robust checkpointing. Use this when designing scalable and reliable ML/AI training systems.

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.