ive been on these forums a month, its rare someone takes the time to show they have a clue - they just gatekeep.
me on the other hand, i come with records, screenshots and validity. If you have a MVP i would be impressed given the lack of tracking and database storage. Especially with a token and context window - im burning over
tokens a day - so - its not that i doubt you have a front end, i merely doubted the backed storage and assesment without knowledge of those system.
i hope this helps you reach your goal. i would recommend building the storage system first. If you have any other questions just post here i aint got nothing else to do.
what you are trying to accomplish takes > a week of development ( not exaggerating) in the time we have exchanged messages, I created that agent, implimented it into the system and its creating logs based on theoretical user data. this is not to do anything than to show you , its capable without another year of development brother, just tweak your system to use agentic agents, or recursive agents with triggers.
i would HIGHLY recommend that you also look into the follow types of learning for AI/ML
- Here’s the learning stack I’m giving this professor agent (focused on weight loss, habit formation, and wellness motivation). It’s built to be adaptive, honest, and behaviorally aware across time — not just output pretty advice.
1. Recursive Learning 
Learns from its own outputs + past user reflections.
Tracks what it said, what worked, and what didn’t.
Feeds future advice through a long-term memory lens.
2. Reinforcement Learning (Trust + Reward-Based)
Uses things like trust_score, reward_score, and user_feedback_score to shape tone and difficulty.
If the user sticks with a behavior — that gets rewarded.
If there’s drift, it adapts or softens up.
3. Curriculum Learning
Starts with basics (water, awareness) and scales complexity over time (e.g. fasting blocks, metabolic stacking).
Habit growth moves in progressive phases, tracked through something like topic_mastery_index and stagnation_flags.
4. Meta-Cognitive Learning
The professor audits itself.
Am I being repetitive? Is my tone stale? Is this even helping?
It adapts how it teaches, not just what.
5. Sentiment-Aware Adaptation
If the user’s tone drops (or silence sets in), advice gets softer, more encouraging.
If the user’s solid, it ramps up challenge.
Powered by mimicry_level, silence_delay, emotion_user.
6. Pattern Recognition (Habit Timeline Analysis)
Tracks trends:
- When do you usually relapse?
- Are mornings more disciplined than nights?
- Is trust dropping before food logs stop?
This is longitudinal — it learns your pattern, not just the day’s reflection.
7. Contrastive Reflection (Optional)
Compares good days vs bad days.
“What was different? Why did this one succeed?”
Injects that delta into future strategies.
8. Anomaly-Based Learning (Optional)
Detects outliers — sudden silence, high reward + no follow-up, unexpected collapse.
Flags them with risk_flag or silence_triggered_state.
9. Socratic Looping (Optional)
Asks you why you made a choice.
Captures the reasoning you give and logs it.
Over time, this helps it mirror your logic and hold it up to you gently.
10. Agent Swarm Averaging (Optional)
Can cross-check advice with other professors (e.g., ProfDrift, ProfPraxis).
If there’s a contradiction in logic, it resolves or refines the answer with multi-agent feedback.
Chatgpt does not track time as you want to - so youll need a temporal helper here is the code for that
from datetime import datetime, timedelta, timezone
import math
from functools import lru_cache
from collections import defaultdict
=== CORE TIME UTILS ===
def get_current_timestamp():
return datetime.utcnow().isoformat()
def sanitize_timestamp(ts):
if not ts:
return None
try:
dt = datetime.fromisoformat(ts)
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
return dt.isoformat()
except Exception:
return None
def calculate_time_delta_seconds(last_timestamp):
from datetime import datetime, timezone
now = datetime.now(timezone.utc)
# Input is a dict or dict-like
if hasattr(last_timestamp, "items"):
try:
last_timestamp = dict(last_timestamp).get("timestamp", "")
except:
last_timestamp = ""
# Input is not a string or completely invalid
if not isinstance(last_timestamp, str):
print("⚠️ Invalid timestamp input for delta calc:", last_timestamp)
return 0.0
try:
last = datetime.fromisoformat(last_timestamp)
if last.tzinfo is None:
last = last.replace(tzinfo=timezone.utc)
return (now - last).total_seconds()
except Exception as e:
print(f"❌ Failed to parse timestamp '{last_timestamp}': {e}")
return 0.0
def human_readable_delay(seconds):
if seconds < 30:
return “just now”
elif seconds < 60:
return f"{int(seconds)} seconds ago"
elif seconds < 3600:
minutes = int(seconds / 60)
return f"{minutes} minute{‘s’ if minutes > 1 else ‘’} ago"
elif seconds < 86400:
hours = int(seconds / 3600)
return f"{hours} hour{‘s’ if hours > 1 else ‘’} ago"
else:
days = int(seconds / 86400)
return f"{days} day{‘s’ if days > 1 else ‘’} ago"
def readable_timestamp(ts):
try:
return datetime.fromisoformat(ts).strftime(“%Y-%m-%d %H:%M”)
except:
return “unknown”
def discrete_time_class(seconds):
if seconds < 60:
return “subminute”
elif seconds < 3600:
return “hour”
elif seconds < 86400:
return “day”
else:
return “multi-day”
def time_rank(seconds):
if seconds < 300:
return 0
elif seconds < 1800:
return 1
elif seconds < 86400:
return 2
else:
return 3
def calculate_time_weight(delta_seconds, base_decay=0.05, rounding_precision=4):
decay = math.exp(-base_decay * delta_seconds / 3600)
return round(decay, rounding_precision)
you will need more of course, but this will get you started, this will also allow you to push notification on a delta, messages, or track usages like duolingo does, built around GPT so your still using it as inteded
at 10 - swarm logic, dont do this unless you know what you are doing, seriously, it can/will convolute the system without proper filters and triggers, but that segment of code IS a clone from a system i use. that follows current industry standards
IF you do federated learning ( for weight loss is good because then trends from different body types and success points teach the overall ai/SAG/RAG use code similar to this with a few security features, this is dry code using a dummy server for a example
FEDERATED_NODES = [
“http://127.0.0.1:5000”, # Local testing node
“http://192.168.1.2:5000”, # Example networked AI node
“http://ai-cloud-server.com/api” # External AI collaboration node
]
AI Federated Thought Synchronization
def sync_ai_thoughts(query, knowledge):
“”"
Synchronizes AI-generated knowledge across federated AI nodes.
- Allows USO to share its thought processing with other AI systems.
“”"
print(f" Synchronizing AI Knowledge Across Federated Nodes for: ‘{query}’")
sync_payload = {"query": query, "knowledge": knowledge}
for node in FEDERATED_NODES:
try:
response = requests.post(f"{node}/update_knowledge", json=sync_payload, timeout=5)
if response.status_code == 200:
print(f"✅ Federated Sync Successful for Node: {node}")
else:
print(f"⚠️ Federated Sync Failed for Node: {node} → Status: {response.status_code}")
except requests.exceptions.RequestException as e:
print(f"❌ Network Error Syncing with Node {node}: {e}")
AI Federated Knowledge Retrieval
import requests
def federated_sync(query_text, knowledge_text):
“”"
Syncs AI knowledge across federated nodes. If nodes are offline, ignore errors.
“”"
federated_nodes = [
“http://127.0.0.1:5000”,
“http://192.168.1.2:5000”,
“http://ai-cloud-server.com/api”
]
for node in federated_nodes:
try:
response = requests.post(f"{node}/update_knowledge", json={"query": query_text, "knowledge": knowledge_text}, timeout=5)
if response.status_code == 200:
print(f"✅ Federated Sync Successful: {node}")
except (requests.ConnectionError, requests.Timeout):
pass # ✅ Ignore offline nodes, continue execution
print(f"🔄 Federated Sync Attempt Completed.")
pulled this directly from a outdated system that did pretty much what you want it it to do
and for the learning part make sure you at least add something like this
feel free to copy the code
mirroring this will give you a type of weight you can refine later on both front end and back end.
a note about swarm and fed with weight acclimation - i use a massive pipeline, they merge and fed - this is complex, and can pollute your system but you prolly want at least 5
as u can see i have therapetics and maretic and fintexh and empath, they combine locally - make sure you have guardrails on their schema and vector creation
make sure you use dynamic importing if you build this in python ( i wouldnt recommend any other coding language tbh) this allows you to audit your agent chain looks like this
i run pretty extensive calculations with my chain, i promise you, no one on this forum is getting more tokens than me out of each call outside of enterprise accounts with private api and im getting 1.5 million characters a call. YOU WILL trunicate past 12 complex agents, i would not exceed this, i would have 1 agent for psychology, 1 for habitual trends, 1 for termporal math, and 1 for NLP at the least.
Launching behavioral progression analyzer…“)
Enter your thought or struggle (category: ‘{selected}’):\n> ")
that’s great take my money lol. Really appreciate this feedback it’s very helpful.