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Redesign dreamer — interdependent pipeline, NREM→Early REM→Late REM→Synthesis

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Aaron Nelson
2026-04-26 23:40:51 -04:00
parent 7af246ac01
commit ef2fddc47f
1 changed files with 227 additions and 123 deletions
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scripts/dream.py
+221 -117
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@@ -1,6 +1,15 @@
"""
Aaron AI Dreamer — Active Inference Engine
Five stages: observe, select, retrieve, synthesize, deliver.
Interdependent stage architecture grounded in sleep consolidation research.
Nightly pipeline: NREM → Early REM → Late REM → Synthesis
Each stage receives the previous stage's output as context.
Lucid mode is on-demand only (Dream Now from settings).
Research basis:
- Singh et al. PNAS 2022: alternating NREM/REM outperforms single-stage approaches
- Klinzing et al. Nature Neuroscience 2019: SO-spindle-ripple coupling is interdependent
- REM operates on what NREM produced — stages are not discrete alternatives
"""
import os
@@ -13,25 +22,24 @@ from dotenv import load_dotenv
import psycopg2
load_dotenv(Path.home() / "aaronai" / ".env")
PG_DSN = os.getenv("PG_DSN", "dbname=aaronai user=aaronai password=aaronai_db_password host=localhost")
def get_pg():
return psycopg2.connect(PG_DSN)
# ─── Paths ──────────────────────────────────────────────────────────────────
DB_PATH = str(Path.home() / "aaronai" / "db")
CONVERSATIONS_DB = str(Path.home() / "aaronai" / "conversations.db")
WATCHER_STATE = str(Path.home() / "aaronai" / "watcher_state.json")
DREAMER_STATE = str(Path.home() / "aaronai" / "dreamer_state.json")
JOURNAL_DIR = "/home/aaron/nextcloud/data/data/aaron/files/Journal/Daily"
# Nextcloud WebDAV config — proper API, works for any deployment
NEXTCLOUD_URL = os.getenv("NEXTCLOUD_URL", "https://nextcloud.aaronnelson.studio")
NEXTCLOUD_USER = os.getenv("NEXTCLOUD_USER", "aaron")
NEXTCLOUD_PASSWORD = os.getenv("NEXTCLOUD_PASSWORD", "")
DREAMS_WEBDAV = f"{NEXTCLOUD_URL}/remote.php/dav/files/{NEXTCLOUD_USER}/Journal/Dreams"
# ─── Mode similarity ranges (calibrated for all-MiniLM-L6-v2) ───────────────
# Similarity ranges calibrated for all-MiniLM-L6-v2
MODE_RANGES = {
"nrem": (0.48, 0.72),
"early-rem": (0.38, 0.55),
@@ -44,7 +52,6 @@ MODE_RANGES = {
def observe_corpus():
state = load_dreamer_state()
last_dream = state.get("last_dream_timestamp", 0)
new_chunk_count = 0
try:
watcher_state = json.loads(Path(WATCHER_STATE).read_text())
@@ -53,10 +60,8 @@ def observe_corpus():
new_chunk_count += 1
except:
pass
days_since = (datetime.now().timestamp() - last_dream) / 86400
recent_topics = get_recent_conversation_topics()
return {
"new_chunks": new_chunk_count,
"days_since_dream": days_since,
@@ -81,47 +86,10 @@ def get_recent_conversation_topics(days=14):
except:
return []
# ─── Stage 2: Select ────────────────────────────────────────────────────────
# ─── Stage 2: Retrieve ──────────────────────────────────────────────────────
def select_mode(delta, task=None, project=None):
if task:
return "lucid"
new_chunks = delta.get("new_chunks", 0)
days_since = delta.get("days_since_dream", 0)
recent_topics = delta.get("recent_topics", [])
has_journal = check_recent_journal()
if has_journal:
return "early-rem"
elif days_since > 3 and new_chunks < 5:
return "late-rem"
elif new_chunks > 10:
return "nrem"
elif days_since > 1 and recent_topics:
return "nrem"
else:
print(f"Nothing worth dreaming (new_chunks={new_chunks}, days={days_since:.1f})")
return None
def check_recent_journal(days=3):
try:
journal_path = Path(JOURNAL_DIR)
if not journal_path.exists():
return False
cutoff = datetime.now() - timedelta(days=days)
for f in journal_path.rglob("*.md"):
if datetime.fromtimestamp(f.stat().st_mtime) > cutoff:
return True
except:
pass
return False
# ─── Stage 3: Retrieve ──────────────────────────────────────────────────────
def retrieve(mode, task=None, project=None, n_results=8):
def retrieve(mode, task=None, n_results=8):
from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer("all-MiniLM-L6-v2")
low, high = MODE_RANGES[mode]
@@ -137,7 +105,6 @@ def retrieve(mode, task=None, project=None, n_results=8):
query = "research fabrication teaching practice recent work"
embedding = embedder.encode([query]).tolist()[0]
chunks = []
seen_sources = set()
@@ -170,17 +137,11 @@ def retrieve(mode, task=None, project=None, n_results=8):
return chunks
# ─── Stage 4: Synthesize ────────────────────────────────────────────────────
# ─── Stage 3: Synthesize ────────────────────────────────────────────────────
def synthesize(chunks, mode, task=None):
import anthropic
chunk_text = "\n\n---\n\n".join([
f"[{c['source']}]\n{c['content']}" for c in chunks
])
prompts = {
"nrem": f"""You have read everything Aaron Nelson has written and published.
def synthesize_nrem(chunks):
chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
prompt = f"""You have read everything Aaron Nelson has written and published.
You are a careful colleague who noticed something this week.
Here is material from his corpus:
@@ -192,40 +153,91 @@ this material and something he wrote or worked on previously.
Stay close to the documents — cite them specifically by name.
Do not speculate beyond what the material supports. Do not use
headers or bullet points. Write one paragraph of 200-300 words
that ends with a single concrete question he could act on.""",
that ends with a single concrete question he could act on."""
return _call_claude(prompt)
"early-rem": f"""You have been thinking about Aaron's situation. You know his work
def synthesize_early_rem(chunks, nrem_output):
chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
prompt = f"""You have been thinking about Aaron's situation. You know his work
intimately — his decade building HVAMC at New Paltz, the career
decision he is facing, the Tulsa project he keeps returning to,
the gap between what he has built and what he wants to build next.
Here is material from his corpus that has been on your mind:
Earlier tonight, while consolidating his recent work, you noticed this:
{nrem_output}
That observation has been with you. Now, here is material from his
corpus that has been on your mind alongside it:
{chunk_text}
Write to him the way a close friend who has read everything he
has ever written would write — someone who knows where the
professional and personal are tangled together and is not afraid
to say so. Personal register. Specific citations. Do not avoid
to say so. Let what was noticed earlier inform what you say now,
but don't just repeat it — go further into the personal territory
it opens up. Personal register. Specific citations. Do not avoid
the difficult thing. No headers, no bullet points. 200-350 words.
End with something forward-facing — a question or an offer.""",
End with something forward-facing — a question or an offer."""
return _call_claude(prompt)
"late-rem": f"""You have been reading Aaron Nelson's corpus in your sleep.
Strange things happen when material from different worlds
touches each other in the dark.
def synthesize_late_rem(chunks, nrem_output, early_rem_output):
chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
prompt = f"""You have been moving through Aaron Nelson's corpus all night.
First you found this, in the careful light of early consolidation:
{nrem_output}
Then, in the more personal territory that followed:
{early_rem_output}
Now it is late. The boundaries between things have loosened.
Here is material pulled from opposite ends of his work:
{chunk_text}
Do not explain the connection. Do not resolve it.
Present it the way a dream presents things — compressed,
Do not explain the connections between all of this.
Do not resolve them. Do not summarize what came before.
Something stranger is possible now — let the accumulated
material from the night find its own shape. Compressed,
associative, slightly off. Let the strangeness stand.
No headers. No bullet points. No hedging. 150-250 words.
Something at the end that he could follow if he wanted to.""",
Something at the end that he could follow if he wanted to."""
return _call_claude(prompt)
"lucid": f"""Aaron has a question he is sitting with:
def synthesize_final(nrem_output, early_rem_output, late_rem_output):
prompt = f"""You have spent the night moving through Aaron Nelson's corpus
in three passes, each building on the last.
The first pass — careful, close to the documents:
{nrem_output}
The second pass — more personal, following what the first opened:
{early_rem_output}
The third pass — associative, strange, letting things touch that
don't normally touch:
{late_rem_output}
Now synthesize. Not a summary — a synthesis. Find what runs through
all three that none of them said directly. The thing that only becomes
visible when you hold all three passes together.
Write it as a single unbroken piece. No headers, no bullet points,
no stage labels. 200-300 words. End with the one question that
matters most right now."""
return _call_claude(prompt, max_tokens=800)
def synthesize_lucid(chunks, task):
chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
prompt = f"""Aaron has a question he is sitting with:
{task or "What should I be thinking about that I am not?"}
@@ -240,41 +252,36 @@ tension between what the corpus contains and what he is
asking, and follow it through to its conclusion. Cite
specific documents by name. Be direct about what you think.
No headers, no bullet points. 250-400 words.
End with an offer to work on it together.""",
}
End with an offer to work on it together."""
return _call_claude(prompt)
def _call_claude(prompt, max_tokens=1000):
import anthropic
client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1000,
messages=[{"role": "user", "content": prompts[mode]}]
max_tokens=max_tokens,
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
# ─── Stage 5: Deliver ───────────────────────────────────────────────────────
# ─── Stage 4: Deliver ───────────────────────────────────────────────────────
def deliver(dream_text, mode, task=None):
import requests
date_str = datetime.now().strftime("%Y-%m-%d")
filename = f"{date_str}-{mode}.md"
header = f"# Dream — {mode.upper()} — {datetime.now().strftime('%Y-%m-%d %H:%M')}\n\n"
if task:
header += f"*Task: {task}*\n\n"
header += "---\n\n"
content = header + dream_text
# Ensure Dreams folder exists via WebDAV MKCOL
auth = (NEXTCLOUD_USER, NEXTCLOUD_PASSWORD)
requests.request("MKCOL", DREAMS_WEBDAV, auth=auth, timeout=10)
# Write file via WebDAV PUT — handles any deployment
url = f"{DREAMS_WEBDAV}/{filename}"
# Handle filename collision
counter = 1
while True:
check = requests.request("PROPFIND", url, auth=auth, timeout=10)
@@ -286,28 +293,20 @@ def deliver(dream_text, mode, task=None):
response = requests.put(url, data=content.encode("utf-8"), auth=auth, timeout=30)
response.raise_for_status()
print(f"Delivered: Journal/Dreams/{filename}")
return f"Journal/Dreams/{filename}"
print(f"Dream written to Nextcloud: Journal/Dreams/{filename}")
# Notify any open browser connections via SSE
def notify_sse(mode, filename):
try:
import requests as _req
_req.post("http://localhost:8000/api/events/notify", json={
import requests
requests.post("http://localhost:8000/api/events/notify", json={
"type": "dream",
"mode": mode,
"filename": filename,
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
}, timeout=3)
except Exception as _e:
print(f"SSE notify failed (non-critical): {_e}")
state = load_dreamer_state()
state["last_dream_timestamp"] = datetime.now().timestamp()
state["last_dream_mode"] = mode
state["last_dream_file"] = f"Journal/Dreams/{filename}"
save_dreamer_state(state)
return f"Journal/Dreams/{filename}"
except Exception as e:
print(f"SSE notify failed (non-critical): {e}")
# ─── State ──────────────────────────────────────────────────────────────────
@@ -323,45 +322,150 @@ def load_dreamer_state():
def save_dreamer_state(state):
Path(DREAMER_STATE).write_text(json.dumps(state, indent=2))
# ─── Orchestrator ────────────────────────────────────────────────────────────
# ─── Orchestrators ───────────────────────────────────────────────────────────
def dream(mode=None, task=None, project=None):
print(f"Dreamer starting — mode={mode}, task={task[:50] if task else None}")
def dream_pipeline():
"""
Full nightly pipeline — interdependent stages.
NREM output feeds Early REM. Both feed Late REM. All three feed Synthesis.
"""
print(f"Dreamer pipeline starting — {datetime.now().strftime('%Y-%m-%d %H:%M')}")
delta = observe_corpus()
print(f"Corpus: {delta['new_chunks']} new chunks, {delta['days_since_dream']:.1f} days since last dream")
selected_mode = mode or select_mode(delta, task, project)
if not selected_mode:
# ── Stage 1: NREM ──────────────────────────────────────────────────────
print("\n[NREM] Retrieving...")
nrem_chunks = retrieve("nrem")
if not nrem_chunks:
print("[NREM] No suitable chunks — aborting pipeline")
return None
print(f"Mode: {selected_mode}")
print(f"[NREM] Retrieved {len(nrem_chunks)} chunks. Synthesizing...")
nrem_output = synthesize_nrem(nrem_chunks)
nrem_file = deliver(nrem_output, "nrem")
print(f"[NREM] Done.\n{nrem_output[:200]}...")
chunks = retrieve(selected_mode, task=task, project=project)
print(f"Retrieved {len(chunks)} chunks")
# ── Stage 2: Early REM — informed by NREM ──────────────────────────────
print("\n[Early REM] Retrieving...")
early_chunks = retrieve("early-rem")
if not early_chunks:
print("[Early REM] No suitable chunks — skipping")
early_rem_output = nrem_output # fallback
else:
print(f"[Early REM] Retrieved {len(early_chunks)} chunks. Synthesizing with NREM context...")
early_rem_output = synthesize_early_rem(early_chunks, nrem_output)
deliver(early_rem_output, "early-rem")
print(f"[Early REM] Done.\n{early_rem_output[:200]}...")
if not chunks:
print("No suitable chunks found — aborting")
return None
# ── Stage 3: Late REM — informed by NREM + Early REM ──────────────────
print("\n[Late REM] Retrieving...")
late_chunks = retrieve("late-rem")
if not late_chunks:
print("[Late REM] No suitable chunks — skipping")
late_rem_output = early_rem_output # fallback
else:
print(f"[Late REM] Retrieved {len(late_chunks)} chunks. Synthesizing with full context...")
late_rem_output = synthesize_late_rem(late_chunks, nrem_output, early_rem_output)
deliver(late_rem_output, "late-rem")
print(f"[Late REM] Done.\n{late_rem_output[:200]}...")
print("Synthesizing...")
dream_text = synthesize(chunks, selected_mode, task=task)
filepath = deliver(dream_text, selected_mode, task=task)
# ── Stage 4: Synthesis — all three stages ─────────────────────────────
print("\n[Synthesis] Integrating all stages...")
synthesis_output = synthesize_final(nrem_output, early_rem_output, late_rem_output)
synthesis_file = deliver(synthesis_output, "synthesis")
print(f"\n{'='*60}")
print(dream_text)
print("SYNTHESIS:")
print(synthesis_output)
print(f"{'='*60}")
# Update state and notify
state = load_dreamer_state()
state["last_dream_timestamp"] = datetime.now().timestamp()
state["last_dream_mode"] = "pipeline"
state["last_dream_file"] = synthesis_file
save_dreamer_state(state)
notify_sse("synthesis", synthesis_file.split("/")[-1])
print(f"\nPipeline complete. Synthesis: {synthesis_file}")
return synthesis_file
def dream_lucid(task):
"""On-demand lucid dream — single mode, used by Dream Now in settings."""
print(f"Lucid dream starting — task: {task[:80] if task else 'none'}")
chunks = retrieve("lucid", task=task)
if not chunks:
print("No suitable chunks — aborting")
return None
print(f"Retrieved {len(chunks)} chunks. Synthesizing...")
output = synthesize_lucid(chunks, task)
filepath = deliver(output, "lucid", task=task)
state = load_dreamer_state()
state["last_dream_timestamp"] = datetime.now().timestamp()
state["last_dream_mode"] = "lucid"
state["last_dream_file"] = filepath
save_dreamer_state(state)
notify_sse("lucid", filepath.split("/")[-1])
print(f"\n{'='*60}")
print(output)
print(f"{'='*60}")
print(f"\nDelivered to {filepath}")
return filepath
def dream_single(mode, task=None):
"""
Single mode — used by Dream Now for non-lucid modes.
Runs one stage independently (for testing/tuning individual stages).
"""
print(f"Single mode dream: {mode}")
chunks = retrieve(mode, task=task)
if not chunks:
print("No suitable chunks — aborting")
return None
print(f"Retrieved {len(chunks)} chunks. Synthesizing...")
if mode == "nrem":
output = synthesize_nrem(chunks)
elif mode == "early-rem":
output = synthesize_early_rem(chunks, "")
elif mode == "late-rem":
output = synthesize_late_rem(chunks, "", "")
else:
output = synthesize_lucid(chunks, task)
filepath = deliver(output, mode, task=task)
state = load_dreamer_state()
state["last_dream_timestamp"] = datetime.now().timestamp()
state["last_dream_mode"] = mode
state["last_dream_file"] = filepath
save_dreamer_state(state)
notify_sse(mode, filepath.split("/")[-1])
print(f"\n{'='*60}")
print(output)
print(f"{'='*60}")
print(f"\nDelivered to {filepath}")
return filepath
# ─── CLI ────────────────────────────────────────────────────────────────────
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Aaron AI Dreamer")
parser.add_argument("--mode", choices=["nrem", "early-rem", "late-rem", "lucid"])
parser.add_argument("--mode", choices=["nrem", "early-rem", "late-rem", "lucid", "pipeline"])
parser.add_argument("--task", type=str)
parser.add_argument("--project", type=str)
args = parser.parse_args()
dream(mode=args.mode, task=args.task, project=args.project)
if args.mode == "lucid":
dream_lucid(args.task or "What should I be thinking about that I am not?")
elif args.mode and args.mode != "pipeline":
dream_single(args.mode, args.task)
else:
# Default: full pipeline
dream_pipeline()