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name = 'server'

# the logstash agent instance
logstash_instance name do
  action            :create
end

# the service that manages the instance
logstash_service name do

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# Encoding: UTF-8
# Cookbook Name::logstash-wolfe
# Attribute:: default
#
node.override[:logstash][:server][:inputs] = [
                               :file => {
              :path => [
                "/var/log/*.log"
              ],
              :tags => [

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Keybase proof

I hereby claim:

• I am dr3s on github.
• I am dr3s (https://keybase.io/dr3s) on keybase.
• I have a public key ASAr_jA7eYmtKL0emQW7F7Ed_GiebkQp3pWmviNFB5LNTgo

To claim this, I am signing this object:

dr3s

Keybase proof

I hereby claim:

• I am dr3s on github.
• I am dr3s (https://keybase.io/dr3s) on keybase.
• I have a public key ASC6C_y2ZfAEDM3a37sGqOKEt9x62K2F2XRLzN7DB1R4Ggo

To claim this, I am signing this object:

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---
version: "2.1"
services:
  unifi-controller:
    image: lscr.io/linuxserver/unifi-controller:7.2.94
    container_name: unifi-controller
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=America/New_York

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DeepSeek R1 Distill: Complete Tutorial for Deployment & Fine-Tuning

This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:

1. Environment Setup
2. FastAPI Inference Server
3. Docker Configuration
4. Google Cloud Run Deployment
5. Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)

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A tutorial on fine-tuning DeepSeek R1 for medical applications and integrating DSPy for reinforcement learning.

This tutorial will be structured for AI/ML engineers and medical professionals, covering:

• Introduction: Overview of DeepSeek R1 and DSPy in medical AI.
• Features & Benefits: Key advantages of this approach.
• Warnings & Considerations: Potential risks and limitations.
• Installation & Setup: Environment configuration and dependencies.
• Dataset Preparation: Selecting and formatting medical datasets.
• Fine-tuning DeepSeek R1: Using LoRA with Unsloth for efficient training.

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# SPARC Agentic Development Rules

Core Philosophy

1. Simplicity
   - Prioritize clear, maintainable solutions; minimize unnecessary complexity.

2. Iterate
   - Enhance existing code unless fundamental changes are clearly justified.

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Claude-SPARC Automated Development System For Claude Code

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.

Features