Prof Paul Stewart – University of Derby UK – Intelligent Control and Systems Engineering Applications
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مدونة الحقيقة – القران الكريم + فقرات دينية
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WAVES OF EXPRESSION – Inspiration and projects to help you get back into creating.
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Unknown
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LA NOTA digital – Entre Ríos
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Lilith's Den – by Runa, The Wild Elf – A virtual Character in Second Life
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Bindi lubi to.
Hennap Wellness Center – Promoting Healing and Holistic Care for a Healthier You. At Hennap Wellness Center, we are dedicated to providing comprehensive health and wellness services that promote healing and holistic care. Our team of experienced professionals is here to support you on your journey to better health, resilience, and gratitude.
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LIGA "LUIS CHIRIBOGA PARRA" – "Mas Que Una Liga, Somos Una Familia"
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vankaditavroom | Just another WordPress.com site
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Le Refoulons – Recréer la ligne de chemin de fer Enghien – Montmorency
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The Galactic Angel – "LOVE is the answer, no matter the question"
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My Blog | Just another WordPress.com site
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Chefiori Roma – Johnson James Chin
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TiH eadingtle
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An Insatiable Foodie – Eating My Way Through Kolkata & the World
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playhtofficialstudioclonevoice
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主页 | WeLinux
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Mein Teneriffa - Mi Tenerife | Willkommen – Bienvenidos
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murlog - murlog.org
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Chiraag's Musings
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Michael W Hale | Comedy, Improv, Musician
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EJ Says — AI, Ethics & Society
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GetLibre – Get Liberated from bigtech
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Tales Lucas — TalesMd.Tech
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Herd Mercadeo – AgHerd
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Neil Mercer – cryptocurrencies
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I was interested in finding walkable areas in a city I had never visited before. After using OpenClaw bot to summarize my JSON files, I thought I could do the same for OSM-based metrics.
I started by generating an OSM extract with a 5km radius from my hotel. I then extracted the geometry and tags for every way, park, building, and tourist attraction in this area. I normalized the raw data into a handful of generic classes like “Food & Café” and “Nature / Quiet.”
I then assigned each way and point of interest to an H3 hexagon. I calculated aggregate metrics for each hexagon, like the length of roads and the area taken up by parks and water. Then I simply fed the metrics for the hexagon to Ollama with Mistral Nemo, asking it to generate a short one-sentence vibe of a place based on the collected metrics and label it as positive, negative, or mixed.
To visualize the results, I created a KML file and imported it into Google My Maps. I had to iterate on the LLM prompts, as there are a lot of fields in the generated JSON and the LLM struggles to interpret what the numbers mean.
I also discovered a number of bugs in how I calculate features per H3 hexagon, but I eventually arrived at a reasonable overlay showing how walkable each area is. It’s not perfect — partly because OSM data is incomplete for the area I picked, and partly because I need to make my prompts more specific.
The recommendations are generic, but they add an extra dimension to the map. I think this is really exciting because you can create any perspective you like on OSM data with your own overlays for driving, finding a house, or finding a place to eat.
I started by generating an OSM extract with a 5km radius from my hotel. I then extracted the geometry and tags for every way, park, building, and tourist attraction in this area. I normalized the raw data into a handful of generic classes like “Food & Café” and “Nature / Quiet.”
I then assigned each way and point of interest to an H3 hexagon. I calculated aggregate metrics for each hexagon, like the length of roads and the area taken up by parks and water. Then I simply fed the metrics for the hexagon to Ollama with Mistral Nemo, asking it to generate a short one-sentence vibe of a place based on the collected metrics and label it as positive, negative, or mixed.
To visualize the results, I created a KML file and imported it into Google My Maps. I had to iterate on the LLM prompts, as there are a lot of fields in the generated JSON and the LLM struggles to interpret what the numbers mean.
I also discovered a number of bugs in how I calculate features per H3 hexagon, but I eventually arrived at a reasonable overlay showing how walkable each area is. It’s not perfect — partly because OSM data is incomplete for the area I picked, and partly because I need to make my prompts more specific.
The recommendations are generic, but they add an extra dimension to the map. I think this is really exciting because you can create any perspective you like on OSM data with your own overlays for driving, finding a house, or finding a place to eat.
Wayfaring – An eclectic mix of poetry, stories and musings
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My Beautiful Laundrette
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