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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 […]

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,

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

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']

Optimal Dynamic Pricing of Inventories over Finite Horizons

The nose flute The Duo A’Reng, a primitive talkbox!

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.

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

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

The GADM database contains geographical data for administrative regions, e.g. countries, regions and municipalities. As always, once you have the data in the right format, it is easy to import it into a database. The data is available from GADM in several formats. All data has the coordinate reference system in longitude/latitude and theWGS84 datum.