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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

jeroavf

My setup for running tensorman (https://github.com/pop-os/tensorman) with opencv and jupyter :

On host command line:

mkdir ambiente 
cd ./ambiente 
tensorman run -p 8888:8888 --gpu --python3 --jupyter --root --name container_name bash

Inside running container:

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How to install Ubuntu 14.04.05 on Alienware 15 R3

Tiny guide to install Ubuntu 14.04.05 on a brand new Alienware 15 R3.

Let windows 10 install

Just next, next, next filling up your data.

You should get a BIOS update alert from the Alienware Update widget. If not, right click on the Down arrow icon in the bottom right extra icons ^ thing and right click, then click Check for Updates.

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minikube stop; minikube delete &&
docker stop $(docker ps -aq) &&
rm -rf ~/.kube ~/.minikube &&
sudo rm -rf /usr/local/bin/localkube /usr/local/bin/minikube &&
launchctl stop '*kubelet*.mount' &&
launchctl stop localkube.service &&
launchctl disable localkube.service &&
sudo rm -rf /etc/kubernetes/ &&
docker system prune -af --volumes

jeroavf

Procedures used to configure a new fresh installed Ubuntu 16.04.5 LTS with support for OpenCV4 + Python 3.6 + Nvidia GTX 1060 + Tensorflow + Keras.

Installing GPU drivers

Open the link https://www.geforce.com/drivers and under the Manual Driver Search select the appropriated values, make sure to select Recommended/Certified option, and click search.

Search for the latest version in the list. While writing this gist the latest version was 410.78.

jeroavf

# coding: utf-8

# #### Teste de conexão ao elasticsearch via python
# 
# Ativar o docker container do elasticsearch :
# docker pull docker.elastic.co/elasticsearch/elasticsearch:6.4.2
# docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node"  docker.elastic.co/elasticsearch/elasticsearch:6.4.2
# 

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class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

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import sqlite3 

# Define find_hotels()
def find_hotels(params):
    # Create the base query
    query = 'SELECT * FROM hotels'
    # Add filter clauses for each of the parameters
    if len(params) > 0:
        filters = ["{}=?".format(k) for k in params]
        query += " WHERE " + " and ".join(filters)

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license: mit

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sudo apt-get install -y supervisor

sudo mkdir /usr/share/elasticsearch
cd /usr/share/elasticsearch

sudo wget https://download.elasticsearch.org/kibana/kibana/kibana-4.0.1-linux-x64.tar.gz
sudo wget https://download.elasticsearch.org/elasticsearch/elasticsearch/elasticsearch-1.4.4.tar.gz
sudo wget https://download.elasticsearch.org/logstash/logstash/logstash-1.4.2.tar.gz

sudo tar -zxvf elasticsearch-0.90.0.tar.gz