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Ndamulelo Nemakhavhani ndamulelonemakh

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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.

@ndamulelonemakh
ndamulelonemakh / api_gateway.conf
Created December 24, 2024 11:47 — forked from nginx-gists/api_gateway.conf
Deploying NGINX Plus as an API Gateway, Part 2: Protecting Backend Services
include api_backends.conf;
include api_keys.conf;
limit_req_zone $binary_remote_addr zone=client_ip_10rs:1m rate=1r/s;
limit_req_zone $http_apikey zone=apikey_200rs:1m rate=200r/s;
server {
access_log /var/log/nginx/api_access.log main; # Each API may also log to a
# separate file
@ndamulelonemakh
ndamulelonemakh / data_split.R
Created June 29, 2021 16:36 — forked from duttashi/data_split.R
splitting a given dataset into train and test
# Method 1: Easiest and does not require any library
data(mtcars)
## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))
## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)