Some of my IT-projects
*Mark-up a route on GoogleMap, then distances and elevations for the route points are calculated, and you can generate a gpx file for the route
*Create a gpx file for a OSM(OpenStreetMap) path or from a (lat,lng)-set, elevation profile is also calculated
*Show map witn colored height levels, user interactive scales
gpx is Garmin’s geographical file format, you can download the gpx file to a phone or smartwatch.
You can easily convert a gpx file to i.e. a kml file, which can be opened in GoogleEarth.
https://openlayers.org/en/latest/examples/ shows more than 200 examples, based on javascript using the open source OpenLayer package. It can bet used without installation of gis programs on user pc. Impressing possibilities! incredible number of different data sources can be used. It is easy to make user interactive gis programs.
I have tried, tweaked, investigated most of these examples.
Some of the more useful, interesting, impressing examples are shown in the link
AI
In the last weeks I have worked with a rather impressing data-flow-programme n&n.
It facilitates construction of data flows by composing nodes on a visual platforme, thus making it easier to
automate data flows with inclusion of AI and to visualize flows to colleagues.
In May, I spent three weeks using ChatGPT for code generation and developing user agents, using OpenAI’s Python
SDK from spring 2025. According to experienced developers, this SDK makes many development tasks significantly easier than before —and
I agree that many tasks now feel rather more accessible.´
Based on web searches, helpful YouTube videos, and many good examples from OpenAI, I have implemented an orchestrator agent
that delegates tasks to other user agents, GuardRails to check user input/output, and tested the latest, highly
accessible OpenAI RAG tool to create a user agent that incorporates data from a file directory.
I have also experimented with agents that automatically perform web searches —quite impressive.
3 short online courses about Machine-learning(ML)/AI, offered by Stanford University<
Supplied by a course in Copilot-code-generation, where I worked with implementation of different ML algorithms in Python:
I have a good theoretical and practical understanding of neurale network, super/unsuper-vised ML,
linear models, cluster determination, anomaly detection and more.
Fra online kursus, hos SAS-institute, har jeg en rimelig forståelse for SAP