{
  "session_id": "20260330_151804_b22cad",
  "model": "claude-opus-4.6",
  "base_url": "https://api.anthropic.com",
  "platform": "telegram",
  "session_start": "2026-03-30T15:18:04.765867",
  "last_updated": "2026-03-30T15:19:27.050378",
  "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nConversation started: Monday, March 30, 2026 03:18 PM\nModel: claude-opus-4.6\nProvider: anthropic\n\nYou are on a text messaging communication platform, Telegram. Please do not use markdown as it does not render. You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.",
  "tools": [],
  "message_count": 1,
  "messages": [
    {
      "role": "user",
      "content": "Research the current state of llama.cpp optimization features and configuration. Specifically: 1) What is the current state of speculative decoding in llama.cpp? How to set it up with a draft model? 2) Flash attention support status 3) Context shifting vs context extension techniques available 4) Optimal settings for Hermes-3 8B and Hermes-4 14B on CPU (Apple M-series and x86) 5) The --jinja flag and chat template handling for tool calls 6) Any recent performance improvements in the last 3 months 7) Prompt caching / KV cache reuse between requests 8) Grammar-constrained generation for structured output 9) Embedding/reranking capabilities for RAG 10) Multi-model serving strategies. Focus on practical configuration, not theory."
    }
  ]
}