Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
import requests | |
import json | |
import pprint | |
GRAPH_NAME = "GRAPH" | |
API_TOKEN_READ = "TOKEN" | |
API_TOKEN_WRITE = 'TOKEN' | |
BASE_URL = "https://api.roamresearch.com" | |
ENDPOINT_q = f"/api/graph/{GRAPH_NAME}/q" |
# Adjust the following variables as necessary | |
REMOTE=origin | |
BRANCH=$(git rev-parse --abbrev-ref HEAD) | |
BATCH_SIZE=250 | |
# check if the branch exists on the remote | |
if git show-ref --quiet --verify refs/remotes/$REMOTE/$BRANCH; then | |
# if so, only push the commits that are not on the remote already | |
range=$REMOTE/$BRANCH..HEAD | |
else |
This worked for me on M1 Pro 2021 with MacOS Ventura, original method was for Big Sur but I changed it using a different type of domain block since the old method doesn't work anymore:
First of all, if you want to trigger the notification you can use this command:
sudo profiles show -type enrollment
Now we will start. First block your Mac from reaching the domain iprofiles.apple.com. For this you can use your hosts file like:
echo "0.0.0.0 iprofiles.apple.com" | sudo tee -a /etc/hosts
or blocking them from your firewall.
;; Instructions for including the Clojure script (this) into your Roam can be found in my article | |
;; here: https://lifehacky.net/how-to-list-namespaces-and-find-more-in-roam-research-5c25d9f24556 | |
;; Search for section "How to make it work in your Roam?" and think of "better-search" as of "children-block-count" | |
(ns reddit.8-7-2022-reagent | |
(:require [roam.datascript :as rd] | |
[reagent.core :as r] | |
[roam.datascript.reactive :as rdr])) | |
(defn show-num [uid] |
function sleep(ms) { | |
return new Promise(resolve => setTimeout(resolve, ms)); | |
} | |
function getPage(page) { | |
// returns the uid of a specific page in your graph. | |
// _page_: the title of the page. | |
let results = window.roamAlphaAPI.q(` | |
[:find ?uid | |
:in $ ?title |
"""Automatic distraction detection. Work in progress. | |
Usage: | |
python detect.py main | |
""" | |
import fire | |
import subprocess | |
import plyvel | |
import re |
Copy this to roam/js page, including the "{{[[roam/js]]}}" node: | |
- {{[[roam/js]]}} | |
- ```javascript | |
/* | |
* Roam template PoC by @ViktorTabori | |
* 0.1alpha | |
* | |
* How to install it: | |
* - go to `roam/js` page` |
import numpy as np | |
import pandas as pd | |
from math import e | |
class Node: | |
''' | |
A node object that is recursivly called within itslef to construct a regression tree. Based on Tianqi Chen's XGBoost | |
the internal gain used to find the optimal split value uses both the gradient and hessian. Also a weighted quantlie sketch | |
and optimal leaf values all follow Chen's description in "XGBoost: A Scalable Tree Boosting System" the only thing not |
# Load relevant libraries | |
import numpy as np | |
import numba as nb | |
import matplotlib.pyplot as plt | |
# Goal is to implement a numba compatible polyfit (note does not include error handling) | |
# Define Functions Using Numba | |
# Idea here is to solve ax = b, using least squares, where a represents our coefficients e.g. x**2, x, constants | |
@nb.njit |