{
  "session_id": "20260330_151804_4ab02d",
  "model": "claude-opus-4.6",
  "base_url": "https://api.anthropic.com",
  "platform": "telegram",
  "session_start": "2026-03-30T15:18:04.659801",
  "last_updated": "2026-03-30T15:19:27.059438",
  "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 practical techniques for making small local LLMs (8B-14B parameter) smarter and faster WITHOUT more hardware. Focus on concrete, implementable approaches: 1) Prompt engineering techniques that improve tool-call accuracy (few-shot examples, chain-of-thought, structured output formatting) 2) Speculative decoding and draft models for faster generation 3) KV cache optimization and context management strategies 4) Retrieval-augmented generation for grounding responses 5) Self-improvement loops: model generates data, scores it, learns from it 6) Quantization-aware fine-tuning vs post-training quantization tradeoffs 7) Mixture of experts at the application layer (routing tasks to specialized prompts) 8) Continuous batching and scheduling optimizations for llama.cpp specifically 9) Tool-call fine-tuning datasets and approaches 10) Any 'auto-ingest' patterns where a local model can absorb new capabilities from papers/docs without retraining. Give concrete implementation details, not just theory."
    }
  ]
}