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# 2023-11-27 MIT LICENSE | |
Here's the open source version of my ChatGPT game MonkeyIslandAmsterdam.com. | |
It's an unofficial image+text-based adventure game edition of Monkey Island in Amsterdam, my home town. | |
Please use it however you want. It'd be nice to see more ChatGPT-based games appear from this. If you get inspired by it, please link back to my X https://x.com/levelsio or this Gist so more people can do the same! | |
Send me your ChatGPT text adventure game on X, I'd love to try it! |
ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?
I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.
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Rank | Type | Prefix/Suffix | Length | |
---|---|---|---|---|
1 | Prefix | my+ | 2 | |
2 | Suffix | +online | 6 | |
3 | Prefix | the+ | 3 | |
4 | Suffix | +web | 3 | |
5 | Suffix | +media | 5 | |
6 | Prefix | web+ | 3 | |
7 | Suffix | +world | 5 | |
8 | Suffix | +net | 3 | |
9 | Prefix | go+ | 2 |
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import org.apache.spark.sql.DataFrame | |
def dataFrameToDDL(dataFrame: DataFrame, tableName: String): String = { | |
val columns = dataFrame.schema.map { field => | |
" " + field.name + " " + field.dataType.simpleString.toUpperCase | |
} | |
s"CREATE TABLE $tableName (\n${columns.mkString(",\n")}\n)" | |
} | |
import spark.sqlContext.implicits._ |
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import cv2 | |
import io | |
import socket | |
import struct | |
import time | |
import pickle | |
import zlib | |
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | |
client_socket.connect(('192.168.1.124', 8485)) |
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#!/bin/bash | |
# install git | |
sudo yum install git | |
# maven | |
sudo wget http://repos.fedorapeople.org/repos/dchen/apache-maven/epel-apache-maven.repo -O /etc/yum.repos.d/epel-apache-maven.repo | |
sudo sed -i s/\$releasever/6/g /etc/yum.repos.d/epel-apache-maven.repo | |
sudo yum install -y apache-maven | |
mvn --version |
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import { BrowserModule } from '@angular/platform-browser'; | |
import { NgModule } from '@angular/core'; | |
import { AppComponent } from './app.component'; | |
//Angular Material Components | |
import {BrowserAnimationsModule} from '@angular/platform-browser/animations'; | |
import {MatCheckboxModule} from '@angular/material'; | |
import {MatButtonModule} from '@angular/material'; |
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def build_hive_ddl( | |
table_name, object_schema, location, file_format, partition_schema=None, verbose=False): | |
""" | |
:param table_name: the name of the table you want to register in the Hive metastore | |
:param object_schema: an instance of pyspark.sql.Dataframe.schema | |
:param location: the storage location for this data (and S3 or HDFS filepath) | |
:param file_format: a string compatible with the 'STORED AS <format>' Hive DDL syntax | |
:param partition_schema: an optional instance of pyspark.sql.Dataframe.schema that stores the | |
columns that are used for partitioning on disk | |
:param verbose: |
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CREATE TRIGGER person_notify AFTER INSERT OR UPDATE OR DELETE ON income | |
FOR EACH ROW EXECUTE PROCEDURE notify_trigger( | |
'id', | |
'email', | |
'username' | |
); | |
CREATE TRIGGER income_notify AFTER INSERT OR UPDATE OR DELETE ON income | |
FOR EACH ROW EXECUTE PROCEDURE notify_trigger( | |
'id', |
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