ForgetMeNot AI ‐ Poster - Poppenspel-Advies/forget-me-not-mobile-app-ai GitHub Wiki
🌌 ForgetMeNot AI™
Predict the Omission. Prepare Before the Ripple.
From Multimodal Perception to Predictive Preparedness.
🧠 About ForgetMeNot AI™
ForgetMeNot AI™ is a multimodal predictive-awareness application designed to transform everyday user signals into structured contextual intelligence.
The application allows users to capture information through different modalities and then uses AI analysis to understand the captured context.
The core concept is simple:
CAPTURE
↓
UNDERSTAND
↓
ANALYZE
↓
REMEMBER
↓
PREDICT
↓
PREPARE
ForgetMeNot AI is designed around the idea that many real-world omissions are not simply failures of memory — they can emerge from context, timing, dependencies, intentions, and changing circumstances.
The application therefore creates a contextual layer between what the user observes and what the user may need to remember or prepare for.
🌐 Multimodal Signal Capture
ForgetMeNot AI provides multiple ways for a user to introduce information into the application.
📝 Note / Text
Users can provide written notes, observations, reminders, intentions, tasks, or other contextual information.
🖼️ Image
Users can provide visual information that can be interpreted as a contextual signal.
🎙️ Voice-Over
Coming Soon
The planned Voice-Over capability will provide another natural way for users to introduce contextual signals through speech.
🏷️ Three Contextual Signal Types
ForgetMeNot AI organizes captured information around three primary signal dimensions:
USER SIGNAL
│
┌─────────┼─────────┐
▼ ▼ ▼
👤 PEOPLE 📍 PLACES 📦 THINGS
👤 PEOPLE
Person-related information, relationships, meetings, contacts, or people connected with a user's context.
📍 PLACES
Locations, destinations, venues, journeys, or place-related context.
📦 THINGS
Objects, belongings, tasks, activities, documents, or other contextual items.
These signal types can be captured through the supported multimodal input methods.
✨ Gemini Intelligence Layer
Captured information enters the application's AI analysis pipeline.
┌──────────────────────────┐
│ USER SIGNAL │
│ │
│ 📝 Text │
│ 🖼️ Image │
│ 🎙️ Voice — Coming Soon │
└────────────┬─────────────┘
↓
┌──────────────────────────┐
│ GEMINI AI │
│ ANALYSIS │
└────────────┬─────────────┘
↓
┌──────────────────────────┐
│ STRUCTURED CONTEXT │
│ │
│ Signal │
│ Category │
│ Context │
│ Relevant information │
└────────────┬─────────────┘
↓
┌──────────────────────────┐
│ FIREBASE DB │
└──────────────────────────┘
Gemini provides the analysis layer that transforms the captured information into structured contextual information for the application.
☁️ Contextual Memory
After AI analysis, the resulting signals are stored in the user's Firebase database.
The stored information becomes part of the user's application memory.
USER
↓
CAPTURE
↓
GEMINI ANALYSIS
↓
STRUCTURED SIGNAL
↓
FIREBASE
↓
MEMORY
The user can subsequently access stored signals through the application's Memory experience.
Stored signals can be:
- 👁️ Viewed
- ✏️ Edited
- 🗑️ Deleted
This creates a user-controlled memory lifecycle.
🛡️ From Memory to Prevention
ForgetMeNot AI extends beyond simple information storage.
The application uses contextual signals as part of a broader predictive-awareness experience.
OBSERVATION
↓
CONTEXT
↓
MEMORY
↓
POTENTIAL OMISSION
↓
AWARENESS
↓
PREVENTION
The conceptual goal is to help the user identify information that may become important before an omission creates a larger downstream effect.
This is represented by the application's Ripple Shield and prevention concepts.
🎯 The ForgetMeNot AI Experience
The application brings multiple capabilities together:
| Capability | Purpose |
|---|---|
| 📡 Capture | Introduce new contextual signals |
| 🧠 Gemini Analysis | Analyze captured information |
| ☁️ Firebase Memory | Persist analyzed signals |
| 🔮 Omission Intelligence | Identify potential contextual omissions |
| ⚓ Intent Anchor | Connect information with user intention |
| 🛡️ Ripple Shield | Surface contextual risk and prevention awareness |
| 🧠 Memory | Review, edit, and delete stored signals |
| 📅 Events | Connect contextual awareness with calendar information |
| 💬 Coffee Chat AI | Interact conversationally with the application |
| 👤 Profile | Personal profile, discovery, planning, and account controls |
🗺️ Context Meets the Real World
ForgetMeNot AI also connects contextual intelligence with the user's surrounding environment.
The application can combine recent signals with location and map information to provide contextual discovery experiences, including:
- 📍 Nearby places
- 🍽️ Restaurant discovery
- ⭐ Ratings
- 💰 Price information
- 🗺️ Location context
- 📅 Dynamic short-trip planning
This creates a bridge between digital contextual memory and real-world activity.
🔄 The ForgetMeNot AI Intelligence Loop
┌───────────────┐
│ USER │
└───────┬───────┘
↓
┌───────────────┐
│ CAPTURE │
└───────┬───────┘
↓
┌───────────────┐
│ GEMINI AI │
│ ANALYSIS │
└───────┬───────┘
↓
┌───────────────┐
│ CONTEXT │
└───────┬───────┘
↓
┌───────────────┐
│ MEMORY │
└───────┬───────┘
↓
┌───────────────┐
│ PREDICT │
└───────┬───────┘
↓
┌───────────────┐
│ PREPARE │
└───────┬───────┘
│
└───────────────┐
↓
NEW CONTEXT
│
└──────→ CAPTURE
This creates a continuous contextual cycle between the user's observations, AI analysis, memory, prediction, and preparation.
🌌 The Philosophy
ForgetMeNot AI is built around a simple question:
What if technology could help us recognize what we might forget before the omission happens?
Rather than treating memory as a static list of reminders, ForgetMeNot AI explores a more contextual model:
What happened? What matters? What is connected? What might be missed? What can be prepared for now?
💫 ForgetMeNot AI™
FROM MULTIMODAL PERCEPTION TO PREDICTIVE PREPAREDNESS
Predict the Omission. Prepare Before the Ripple.
Official Project Poster
The official ForgetMeNot AI™ Poster is maintained within the project's repository assets and can be displayed as the visual centerpiece of this Wiki page.
Poster asset: ForgetMeNotAI_Poster.png
View the official ForgetMeNot AI™ poster in the GitHub repository
