Results for “persistence”

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

NATS cloud-native messaging system. Covers Core NATS, JetStream persistence, and request/reply patterns. Use for lightweight, high-performance microservices communication. USE WHEN: user mentions "nats", "jetstream", "cloud-native messaging", "request/reply", "subject wildcards", asks about "lightweight messaging", "microservices communication", "nats streaming" DO NOT USE FOR: complex routing - use `rabbitmq`; AWS-native - use `sqs`; Azure-native - use `azure-service-bus`; JMS compliance - use `activemq`; persistent queues only - use dedicated broker

Post-exploitation workflow. Covers privilege escalation (Linux SUID/sudo/kernel, Windows UAC/service/token), persistence assessment, local enumeration, credential harvesting, and pivot preparation. Structured workflows for Linux and Windows targets using impacket, netexec, john, linpeas/winpeas, and standard Kali tools. Includes kernel exploit reference tables, GTFOBins exploitation chains, Potato attack selection, Docker/container escapes, DLL hijacking, SSH key harvesting, credential recovery from memory, and Windows token manipulation. Chains from /pentester or /credential-audit when access is obtained.

Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replicated caches, Private Link access, persistence, CLI/PowerShell automation, or ARM/Bicep deployments, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.