[
  {
    "prompt": "Build and run a Python script that creates an audio-reactive ASCII visualizer for ~/Downloads/Lunacy.mp3. Save to ~/ascii-video-showcase/mode2-audio-reactive/lunacy-viz.mp4.\n\nApproach:\n1. Extract audio features: Use scipy to read the mp3 (convert to wav first via ffmpeg), compute FFT per frame, extract: RMS energy, frequency bands (sub, bass, mid, hi), spectral centroid, beat detection via onset/flux\n2. For each frame (24fps), render multiple layers:\n   - Background: fBM noise field modulated by RMS, colored with time-cycling hue\n   - Mid layer: concentric rings count driven by bass, interference patterns\n   - Foreground: spectrum bar visualization (mirrored around center), particle explosions on beats\n3. Composite layers with screen blend\n4. Apply shaders: chromatic aberration on beats, bloom, grain\n5. Mux final video with original audio\n\nKey details:\n- Resolution: 1920x1080, 24fps\n- Duration: match audio length (or cap at 60s)\n- Grid: 'sm' (10pt) for background, 'md' (16pt) for content\n- Palette: PAL_KATA (katakana halfwidth) for background, PAL_BLOCKS for spectrum\n- Colors: NEON style - cycling through cyan, magenta, yellow, with centroid-driven hue shifts\n- Beat detection: use spectral flux peak detection\n- Font: /System/Library/Fonts/Menlo.ttc\n- Cell height from font.getmetrics()\n- ffmpeg stderr to file\n- Convert mp3 to wav first: ffmpeg -i input.mp3 -f wav -acodec pcm_s16le output.wav\n- Read wav with scipy.io.wavfile\n- Final mux: ffmpeg -i video.mp4 -i audio.mp3 -c:v copy -c:a aac output.mp4\n\nPrint progress every 30 frames. Cap at 30 seconds if the song is longer.",
    "chosen": "",
    "session": "session_20260320_183756_a85351.json"
  }
]