Der er ingen spoilers i denne vejledning. Den er udelukkende skrevet så du kan starte FE’s øvelsesoperation på en Mac med Apple Silicon hardware. Den officielle vejledning virker nemlig ikke til den type computer. Her er hvordan jeg fik det til at virke. Læs mere om øvelsen her: https://hackerakademi.dk/challenge Start VM på en Mac med […]
Hackerakademi Øvelsesoperation på Mac med Apple Silicon hardware Read More »
Today we will try running an LLM locally on a Macbook Pro with Apple Silicon. To do this we will use ollama, since it is one of the easiest ways. First, download the right installer for your OS from ollamma.com. This will also install the CLI command ollama, which you can use to run LLMs
How to run an LLM locally with Ollama Read More »
I read an article by a guy who used ChatGPT4 as the dungeon master for a solo compaign for Dungeons & Dragons. Just him and ChatGPT. The chatbot fully played the role of a human dungeon master! It sounded really fun and I decided to try it out, but since I love Cyberpunk very much,
Playing Shadowrun with ChatGPT Read More »
Taken from lecture 1 of Statistical Rethinking course (around the 44 minute mark). The course material is also on Github. How to draw an "owl" version 1: Create generative simulation (GS) Write an estimator Validate estimator using simulated data Analyze real data: … Reuse 1 to compute hypothetical interventions How to draw an "owl" version
How to Draw an Owl Read More »
Tested in Databricks import pyspark.sql.functions as F import requests # create dataframe pokenumbers = [(i,) for i in range(100)] cols = ["pokenum"] df_pokenums = spark.createDataFrame(data=pokenumbers, schema=cols) # call API def get_name(rows): # take the first item in list (API doesn't support batch) first = rows[0] url = f'https://pokeapi.co/api/v2/pokemon-form/{first.pokenum}' try: resp = requests.get(url) name = resp.json()['pokemon']['name']
How to call an API from PySpark (in workers) Read More »
The nose flute The Duo A’Reng, a primitive talkbox!
Unusual instruments in Vietnam Read More »
For the last five years, I have been fascinated with machine learning techniques, but that fascination is slowly running out. I increasingly consider ML a tool in my tool box among others, not a panacea to all problems. In particular, I’d like to return to other algorithmic techniques from my educational back, i.e. computer science.
Getting back into operations research Read More »
Programming is like writing a book. Both programs and books are written in a language, e.g. Datalog or Tagalog, but the similarity goes deeper than that. Imagine that you must write a book with the subject "a man walks his dog". There are endless ways to write that book, but only a few of them
Good Book, Bad Program Read More »
Datasets: – Urban morphological zones 2000 (EU): https://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2000-2 – Population count (World): http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10/ – Administrative regions (World): http://gadm.org/ The map is European since the “urban” data from the European Environmental Agency (EEA) only covers Europe. Caveats The UMZ data ended up in PostGIS with srid 900914. You can use prj2epsg.org to convert the contents of
Create a European city map with population density Read More »