Upload experiment_notes.json with huggingface_hub

experiment_notes.json

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+[
+  {
+    "id": "onboarding__note__Users_rs2020_Blog_workspace_notes_experiments_onboarding_EXPERIMENT_README_md",
+    "experiment_id": "onboarding",
+    "title": "EXPERIMENT_README.md",
+    "filename": "EXPERIMENT_README.md",
+    "relative_path": "/Users/rs2020/Blog/workspace/notes/experiments/onboarding/EXPERIMENT_README.md",
+    "content_md": "# Welcome to RACA\n\nThis is a sample experiment to show you how the dashboard works. You're looking at the **Overview** tab right now \u2014 it displays the experiment's README (this file).\n\nEverything you see here is generated from plain files in `notes/experiments/onboarding/`. You can browse them in your editor anytime.\n\n## How This Dashboard Works\n\nEach experiment has several tabs at the top. Here's what they do:\n\n### Overview (you are here)\n\nDisplays the experiment's README and any notes you've written in the `user/` folder. This is the main landing page for each experiment \u2014 a summary of what the experiment is, what you're investigating, and what you found.\n\n### Red Team Brief\n\nBefore any experiment runs, RACA reviews the design for potential problems \u2014 wrong evaluation metrics, truncated outputs, missing baselines, wasted compute. The brief lives at `red_team_brief.md`. This tab will be empty until you run your first real experiment.\n\n### Timeline\n\nA chronological log of everything that happened: when jobs were submitted, when artifacts were uploaded, when bugs were found and fixed. This is auto-generated from `activity_log.jsonl` \u2014 RACA writes to it as events happen.\n\n### Runs\n\nTracks each job submission \u2014 which model, which cluster, what status (pending, running, completed, failed), and links to the HuggingFace dataset with the results. Empty until you run something.\n\n### Artifacts\n\nLinks to all HuggingFace datasets produced by this experiment \u2014 canary runs, partial results, final data. Each artifact has metadata about what generated it. Empty until artifacts are uploaded.\n\n### Files\n\nAll the markdown and YAML files in the experiment folder. Click any file to read it. This is a quick way to browse the experiment's configuration and notes without leaving the dashboard.\n\n## Folder Structure\n\n```\nnotes/experiments/onboarding/\n  EXPERIMENT_README.md    \u2190 this file (shows in Overview tab)\n  experiment.yaml         \u2190 config: hypothesis, models, tasks\n  flow_state.json         \u2190 current phase (design/running/complete)\n  HUGGINGFACE_REPOS.md    \u2190 links to all uploaded datasets\n  questions.md            \u2190 research questions (read-only)\n  red_team_brief.md       \u2190 created during preflight review\n  activity_log.jsonl      \u2190 timeline entries (auto-generated)\n  user/                   \u2190 YOUR notes \u2014 RACA doesn't touch these\n    README.md             \u2190 your interpretation and observations\n    FINDINGS.md           \u2190 key results and surprises\n    DECISIONS.md          \u2190 design decisions and rationale\n    summary.md            \u2190 one-paragraph summary when done\n```\n\n**Most of this is automated.** RACA creates and updates the experiment files, uploads artifacts, and keeps the timeline current. The only files you write are in `user/` \u2014 that's your space for notes, findings, and decisions.\n\n## What's Next\n\nThis sample experiment hasn't been run yet \u2014 it's just here to show you the structure. When you're ready to run a real experiment, just tell RACA:\n\n> *I want to test whether Qwen3-8B follows complex instructions better than Llama-3.1-8B*\n\nOr try the full guided tutorial:\n\n> */raca:experiment-tutorial*\n",
+    "created": "",
+    "updated": ""
+  },
+  {
+    "id": "onboarding__note__Users_rs2020_Blog_workspace_notes_experiments_onboarding_HUGGINGFACE_REPOS_md",
+    "experiment_id": "onboarding",
+    "title": "HUGGINGFACE_REPOS.md",
+    "filename": "HUGGINGFACE_REPOS.md",
+    "relative_path": "/Users/rs2020/Blog/workspace/notes/experiments/onboarding/HUGGINGFACE_REPOS.md",
+    "content_md": "# HuggingFace Repositories\n\n| Dataset | Date | Rows | Purpose |\n|---------|------|------|---------|\n",
+    "created": "",
+    "updated": ""
+  },
+  {
+    "id": "onboarding__note__Users_rs2020_Blog_workspace_notes_experiments_onboarding_questions_md",
+    "experiment_id": "onboarding",
+    "title": "questions.md",
+    "filename": "questions.md",
+    "relative_path": "/Users/rs2020/Blog/workspace/notes/experiments/onboarding/questions.md",
+    "content_md": "# Research Questions\n\n1. Can Qwen3-1.7B solve basic Countdown problems (4 numbers, targets < 100)?\n2. What reasoning strategies does the model use (trial-and-error, systematic search, pattern matching)?\n3. Where does the model fail \u2014 wrong arithmetic, giving up, or invalid expressions?\n",
+    "created": "",
+    "updated": ""
+  }
+]