- Remove generate-memory-blocks.md documentation file (#362)

generate-memory-blocks.md

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-## Step 1: Data Pre-processing & Filtering 🧹
-
-The goal here is to remove data points that are not actual, intentional visits.
-
-    Remove Accommodation Stays: The first step is to filter out all visits to the known accommodation. This location serves as your base reference point, not a tourist activity.
-
-    Filter by Duration: Remove very short visits. Stops under 10-15 minutes are often just traffic lights, brief errands (like an ATM), or GPS drift. Set a minimum duration threshold to focus on meaningful stays.
-
-    Consolidate Micro-Visits: If your app generates multiple separate "visits" for wandering around a single large area (e.g., a park or a market), you may need to merge these into one continuous visit before proceeding.
- 
-## Step 2: Data Enrichment with Context 🗺️
-
-Raw coordinates are not useful for a travel log. You need to understand what these places are.
-
-    Reverse Geocoding: Convert each visit's latitude and longitude into a human-readable address.
-
-    Point of Interest (POI) Matching: This is the most crucial step. Use a service like the Google Places API, Foursquare API, or OpenStreetMap to match the coordinates to a named place. This will give you a name (e.g., "Louvre Museum"), a category (e.g., "museum"), and other details.
-
-Your data will transform from this:
-{lat: 48.8606, lon: 2.3376, start: '14:30', end: '17:00'}
-
-To this:
-{name: 'Louvre Museum', category: 'museum', address: 'Rue de Rivoli, 75001 Paris', ...}
-
-## Step 3: Scoring & Identifying "Interesting" Visits ✨
-
-Now you can define what makes a visit "interesting" by calculating an interest score. This helps prioritize the highlights of the day.
-
-Combine several factors into a weighted score:
-
-    Duration: Longer stays are generally more significant. A 3-hour museum visit is more important than a 20-minute coffee stop.
-
-    Distance from Accommodation: Visits far from where you're staying are likely planned day trips or major excursions and should be scored higher. This is a very strong signal of intent.
-
-    Place Category: This is key. Use the POI data from Step 2 to assign a weight to each category.
-
-        High Interest: museum, landmark, park, tourist_attraction, historic_site.
-
-        Medium Interest: restaurant, cafe, shopping_mall.
-
-        Low Interest: grocery_store, pharmacy, gas_station.
-
-    Novelty: A place visited only once on the trip is typically more notable for a travel log than a coffee shop visited every morning.
-
-You can create a simple scoring formula, for instance:
-Score=(wd​⋅Duration)+(wx​⋅Distance)+(wc​⋅CategoryWeight)
-
-
-Where wd​, wx​, and wc​ are the weights you assign to duration, distance, and category, respectively.
-
-## Step 4: Clustering & Creating a Narrative ✍️
-
-A simple list of interesting places is good, but a great travel log groups them into a story.
-
-    Spatio-Temporal Clustering: Group visits that are close in both location and time. For example, a visit to a museum, followed by a visit to a café next door 15 minutes later, should be part of the same event.
-
-    Algorithm Choice: An algorithm like DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is excellent for this. You can define a "neighborhood" in terms of time (e.g., within 2 hours of each other) and space (e.g., within 500 meters of each other) to automatically find these groups.
-
-    Summarize the Cluster: Once you have a cluster of visits, create a single travel log entry for it.
-
-        Title: Name the event after the highest-scoring visit within the cluster (e.g., "Visit to the Eiffel Tower and Champ de Mars").
-
-        Timeframe: Use the start time of the first visit and the end time of the last visit in the cluster.
-
-        Content: List the significant places visited within that cluster.