-
Convert Maven build to gradle
gradle init
-
Initialise new project folder structure (Java example)
gradle init --type java-library
/* | |
Serve is a very simple static file server in go | |
Usage: | |
-p="8100": port to serve on | |
-d=".": the directory of static files to host | |
Navigating to http://localhost:8100 will display the index.html or directory | |
listing file. | |
*/ | |
package main |
Go to onedrive folder in your pc and create a folder for git repository. | |
Usually onedrive resides in `C:\Users\%username%\OneDrive` | |
Open GitBash | |
cd ~/OneDrive | |
mkdir git | |
cd git | |
mkdir myproject | |
cd myproject |
- Download Miniconda windows from here: https://repo.anaconda.com/miniconda/ (taken from https://docs.conda.io/en/latest/miniconda.html)
- Extract the installer with 7zip. or
conda-installer.exe /InstallationType=JustMe /AddToPath=1 /S /RegisterPython=1 /D=%cd%\conda_install
- Rename the conda installation directory.
Download postgresql from https://www.enterprisedb.com/download-postgresql-binaries;
Unzip it;
Open a cmd;
Enter into the ‘bin’ directory;
# From https://stackoverflow.com/a/68876046/1319998, which is itself inspired by https://stackoverflow.com/a/68814418/1319998 | |
from contextlib import contextmanager | |
from collections import namedtuple | |
from ctypes import cdll, byref, string_at, c_char_p, c_int, c_double, c_int64, c_void_p | |
from ctypes.util import find_library | |
from sys import platform | |
def query(db_file, sql, params=()): |
#!/usr/bin/env python3 | |
import sys | |
import json | |
import os | |
import os.path | |
import shutil | |
import logging | |
import tempfile | |
import glob | |
import argparse |
It is possible to compile Go programs for a different OS, even though go build
says otherwise.
You'll need:
- a valid toolchain for the platform/os you're targetting
- Go Tip (works on 1.1 and 1.2rc1 but you might as well be on tip)
golang-crosscompile
helper script https://github.com/davecheney/golang-crosscompile- the patch provided
Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
package utils | |
import "crypto/cipher" | |
type ecb struct { | |
b cipher.Block | |
blockSize int | |
} | |
func newECB(b cipher.Block) *ecb { |