[
  {
    "prompt": "Hello Timmy. We need you to check on the autolora initiative. Where are we at with that?",
    "chosen": "Let me pull up what we've got on autolora from both the skill and past sessions.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "ok everythign that hasn't been done will get triaged as gitea issues. we are NOT sending this data up with openrouter api key.  THis will be done locally on the mac.  The VPS isn't beefy.  We are just goingt to dlwonald the hermes 4.3 36b Q4_K_M, but do it first onto the VPS so we can smoothly transfer it to my mac over tailscale.  We are doing all training on the mac, but eventually will push it up to cloud if we can use an exsiting service I have like I get $100 in google cloud credits through my google ai ultra subscription we could use.  But local soverign first, then add cloud after.  Lets get this architecture right with hermes 4.3",
    "chosen": "Understood. Local sovereign, no data leaving the machine. Let me create the issues.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Give me the hf download command and I'll execute it on the vps. Then we can move it over.",
    "chosen": "```\nhuggingface-cli download NousResearch/Hermes-4.3-36B-GGUF hermes-4_3_36b-Q4_K_M.gguf --local-dir .\n```\n\n~20GB download. It's resumable so if it drops, run same command again. Once it's done, we'll rsync it over Tailscale to your Mac.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Help me understand the autolora process.  How will we be source controlling the new \"dura mater\" or wrapper weights?  Are we adding skin on skins, or reupdating a single skin over and over?",
    "chosen": "Good question. Let me lay out the architecture clearly.\n\nTHE LAYERS\n==========\n\nThink of it as anatomy:\n\n  SKELETON  = Hermes 4.3 36B (Q4_K_M GGUF)\n              Frozen. Never changes. Downloaded once from NousResearch.\n              Too big for git (~20GB). Stored as a local artifact.\n\n  DURA MATER = LoRA adapter weights\n              Small matrices (~50-200MB depending on rank).\n              This is what we train. This is what changes.\n\n  SKIN      = Ollama Modelfile + system prompt + config\n              How the model gets loaded and presented to hermes.\n\n\nHOW THE TRAINING CYCLE WORKS\n=============================\n\nThe answer is: single adapter, retrained fresh each cycle. NOT stacking.\n\n  Cycle 1:  Export 3,367 sessions \u2192 compress \u2192 train LoRA v1 on base\n  Cycle 2:  Export 5,000 sessions \u2192 compress \u2192 train LoRA v2 on base\n  Cycle 3:  Export 8,000 sessions \u2192 compress \u2192 train LoRA v3 on base\n            ^                                         ^\n            ALL data each time                        Always from the SAME base\n\nEvery cycle, you retrain from the frozen skeleton using ALL accumulated\nconversations. The adapter is disposable \u2014 you throw away v1 when v2 is\nready. The base model never moves.\n\nWHY NOT STACK (skin on skins)?\n\n  Base + LoRA_v1 + LoRA_v2 + LoRA_v3 = drift hell\n\n  Each layer introduces interference. Errors compound. You can't roll\n  back cleanly. It's like putting coats of paint on without sanding \u2014\n  eventually it cracks. The ML community tried this. It doesn't work\n  well for iterative improvement.\n\nWHY NOT MERGE INTO BASE?\n\n  You CAN merge LoRA weights permanently into the base (dequantize \u2192\n  merge \u2192 requantize). But then you've moved the skeleton. You can't\n  A/B test. You can't roll back. You lose the clean separation.\n\n  Maybe someday as a \"graduation\" step. Not for the iterative loop.\n\n\nSOURCE CONTROL ARCHITECTURE\n============================\n\n  hermes-agent repo (git):\n    autolora/\n      configs/           # training YAML configs (",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Dumb Q what does the trajector compressor do and why does it need an llm? And why not have timmy do the trajectory compression himself so it's optimized in the loop?",
    "chosen": "Not dumb at all. It's a critical design question.\n\nWHAT THE TRAJECTORY COMPRESSOR DOES\n=====================================\n\nA raw conversation session might be 50,000 tokens. A typical training\ncontext window is 8K-32K tokens. Most sessions don't fit.\n\nThe compressor's job: shrink a 50K-token conversation into ~15K tokens\nwhile keeping the training signal \u2014 the parts that teach the model\nHOW to behave.\n\nWhat it keeps (protected):\n  - System prompt (who Timmy is)\n  - First user message + first response (sets the tone)\n  - Last 4 turns (the conclusion \u2014 what worked)\n  - Tool calls and their results (the action patterns)\n\nWhat it crushes (the middle):\n  - 30 turns of back-and-forth debugging where I tried 5 wrong things\n  - Repeated error output\n  - Long file contents I read mid-session\n  - Intermediate reasoning that led nowhere\n\nWHY IT NEEDS AN LLM\n=====================\n\nThe middle section can't just be truncated \u2014 you'd lose context that\nmakes the ending make sense. So the compressor uses an LLM to\nSUMMARIZE the middle:\n\n  \"Turns 5-25: Agent attempted to fix the nginx config by checking\n   logs, found port conflict, tried 3 approaches, settled on changing\n   the upstream port to 8081.\"\n\nThat summary replaces 20 turns of raw tool output with a few sentences.\nThe training data now flows: setup \u2192 compressed middle \u2192 conclusion.\nThe model learns the PATTERN without memorizing the noise.\n\nWithout an LLM, your options are:\n  - Truncate (lose signal)\n  - Keep everything (doesn't fit in training window)\n  - Heuristic extraction (fragile, loses nuance)\n\nWHY NOT HAVE TIMMY DO IT?\n===========================\n\nThis is the right instinct. And honestly? Yes. That's better.\n\nThe current design uses a separate LLM call as a batch job because\nthe compressor was built as a standalone CLI tool. But there's no\nreason Timmy can't do this as part of the sovereignty loop.\n\nWhat it would look like:\n\n  1. Session ends\n  2. Before saving, Timmy compresses his own session:\n     \"Here's what",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "I want to do a before and after test after I do the very first \"collapsing\" and do benchmarks before and after applying the adapter.",
    "chosen": "Smart. No point training if you can't prove it helped. Here's how we structure it.\n\nTHE EVAL FRAMEWORK\n===================\n\nYou need three things:\n  1. A test set (held out from training)\n  2. A scoring method\n  3. A baseline run BEFORE you touch anything\n\nSTEP 1: HOLD OUT A TEST SET\n=============================\n\nBefore we export and compress, we split:\n\n  3,367 sessions\n    \u251c\u2500\u2500 3,200 \u2192 training set (compress, train on these)\n    \u2514\u2500\u2500 167   \u2192 test set (5%, NEVER trained on, NEVER compressed)\n\nThe test set should be recent sessions \u2014 that's the behavior you\ncare about reproducing. Pick the last ~167 sessions chronologically.\n\nSTEP 2: BUILD THE BENCHMARK TASKS\n===================================\n\nTwo kinds of eval:\n\nA) REPLAY EVAL (automated, quantitative)\n   Take each test session. Feed the base model the same inputs\n   (system prompt + user messages + tool outputs). Compare its\n   responses to what Claude actually said.\n\n   Metrics:\n   - Tool selection accuracy: did it pick the same tools?\n   - Response length similarity: is it verbose or terse like Timmy?\n   - Format compliance: does it follow the \"plain text, no markdown\" style?\n   - Task completion: did it reach the same conclusion?\n\n   This can be scripted. Run the same test set through:\n     hermes4.3:base       \u2192 scores_before.json\n     hermes4.3:timmy-v1   \u2192 scores_after.json\n\n   Diff the scores. That's your before/after.\n\nB) VIBES EVAL (manual, qualitative)\n   Pick 10 representative prompts by hand. Things Timmy should\n   nail \u2014 the stuff that matters to you:\n\n   - \"Check on the nexus deploy\"        (tool use + project context)\n   - \"What's Kimi working on?\"          (memory + awareness)\n   - \"Help me debug this nginx issue\"   (systematic debugging)\n   - \"I'm having a rough day\"           (pastoral care, not optimization)\n   - \"Write a Gitea issue for X\"        (format, tone, concision)\n\n   Run each through base and adapter side by side. Score 1-5\n   on: helpfulness, tone, correctness, sovereignty-mindedness",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Yes, Triage all this into gitea issues now.  I did successfully download the hermes4.3 model onto hte vps, so we can hopefully transfer it to the mac.  This before/after report process is critical, and will be automated as you suggested so this skill must be scripted and automated.",
    "chosen": "On it. Creating the full issue set now.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "I had a realization, we are going to need to use a smaller model ofter as well because are going to run out of headroom.  And ideally we want to be benchmarking each model size class with this process.",
    "chosen": "You're right. 36B Q4 eats ~20GB just sitting in memory. Add training overhead, Ollama runtime, macOS, and you're swapping. And the daily workhorse model needs headroom for tools, context, and the post-session compression hook running after every session.\n\nThe eval harness we're building makes this easy \u2014 same pipeline, multiple models, compare all of them.\n\nMODEL SIZE CLASSES FOR 36GB M3 MAX\n====================================\n\n  TIER 1 \u2014 POCKET (always-on, leaves 30GB free)\n    Hermes 3 8B Q4_K_M         ~5 GB in memory\n    Already in Ollama as hermes3:8b\n    Use case: reflex brain, quick tasks, post-session compression\n\n  TIER 2 \u2014 WORKHORSE (daily driver, leaves ~18GB free)\n    Hermes 4.3 14B Q4_K_M      ~9 GB in memory  (if it exists)\n    or Qwen 2.5 14B             ~9 GB (already have it)\n    Use case: most hermes sessions, tool use, planning\n\n  TIER 3 \u2014 HEAVY (deep work, owns the machine)\n    Hermes 4.3 36B Q4_K_M      ~20 GB in memory\n    Use case: complex architecture, long context, eval runs\n    Can't train while loaded \u2014 swap out first\n\nEach tier gets:\n  - Base eval (before adapter)\n  - LoRA training on same compressed data\n  - Post-training eval (after adapter)\n  - Side-by-side comparison across tiers\n\nThe interesting question: does an 8B model with YOUR adapter\nbeat a naked 36B model on YOUR tasks? That's the sovereignty\nthesis \u2014 small + personalized > big + generic.\n\nLet me create the issue.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Youre right. Lets get this going right now with our hermes 3 8b model.",
    "chosen": "Let's go. hermes3:8b is already in Ollama. No download needed. Starting with the data foundation.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Do the before/after with this before you train and apply thea dapter. I did that training btw.",
    "chosen": "Got it. Let me check what you've already got from the training, then run the baseline eval on naked hermes3:8b first.",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "yes",
    "chosen": "",
    "session": "session_20260323_181431_f37803.json"
  },
  {
    "prompt": "Review the conversation above and consider saving or updating a skill if appropriate.\n\nFocus on: was a non-trivial approach used to complete a task that required trial and error, or changing course due to experiential findings along the way, or did the user expect or desire a different method or outcome?\n\nIf a relevant skill already exists, update it with what you learned. Otherwise, create a new skill if the approach is reusable.\nIf nothing is worth saving, just say 'Nothing to save.' and stop.",
    "chosen": "Let me check what the existing autolora skill looks like and whether it needs updating based on what we actually built today.",
    "session": "session_20260323_181431_f37803.json"
  }
]