| #!/usr/bin/env python | |
| """Simple HTTP Server With Upload. | |
| This module builds on BaseHTTPServer by implementing the standard GET | |
| and HEAD requests in a fairly straightforward manner. | |
| """ |
This installation is going to require 2 servers one acts as kerberos KDC server
and the other machine is going to be client. Lets assume the FQDN's are (here
cw.com is the domain name, make a note of the domain name here):
Important: Make sure that both systems have their hostnames properly set and both systems have the hostnames and IP addresses of both systems in
With autofs you can easily mount network volumes upon first access to the folder where you want to mount the volume. Autofs is available for many OS and is preinstalled on Mac OS X so I show you how I mounted my iTunes library folder using this method.
autofs needs to be configured so that it knows where to gets its configuration. Edit the file /etc/auto_master and add the last line:
#
# Automounter master map
#
+auto_master # Use directory service
| package main | |
| import ( | |
| "crypto/tls" | |
| "golang.org/x/crypto/acme/autocert" | |
| "log" | |
| "net" | |
| "net/http" | |
| ) |
| import numpy as np | |
| import scipy.linalg | |
| import scipy.stats | |
| def ks_key(X): | |
| '''Estimate the key from a pitch class distribution | |
| Parameters | |
| ---------- | |
| X : np.ndarray, shape=(12,) |
| package main | |
| import ( | |
| "context" | |
| "flag" | |
| "fmt" | |
| "log" | |
| "net/http" | |
| "os" | |
| "os/signal" |
See the new site: https://postgresisenough.dev
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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.
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.
