komiya-atsushi

import org.apache.commons.math3.distribution.ChiSquaredDistribution;
import org.apache.commons.math3.stat.inference.ChiSquareTest;
import org.hamcrest.BaseMatcher;
import org.hamcrest.Description;

import java.util.Arrays;
import java.util.List;

/**
 * カイ二乗検定を用いた検証を行う BaseMatcher 継承クラスです。

oza

Presto source code reading #1

• This document is created for Presto Source Code Reading #1.
  • http://atnd.org/events/47149
• Target: trunk code, Feb 10th, 2014
  • Main Topic: Coordinator and Executor ./presto-main/src/main/java/com/facebook/presto/executions
  • http://www.slideshare.net/frsyuki/hadoop-source-code-reading-15-in-japan-presto/10
• NOTE
  1. All source code noted here is quoted from https://github.com/facebook/presto. License bases on https://github.com/facebook/presto/blob/master/LICENSE.
  2. This document can include something wrong. Pull requests to fix them are always welcome :-)

jonkrohn

frsyuki

Presto connector development 1

One of the very good design decisions Presto designers made is that it's loosely coupled from storages.

Presto is a distributed SQL executor engine, and doesn't manager schema or metadata of tables by itself. It doesn't manage read data from storage by itself. Those businesses are done by plugins called Connector. Presto comes with Hive connector built-in, which connects Hive's metastore and HDFS to Presto.

We can connect any storages into Presto by writing connector plugins.

Plugin Architecture

karpathy

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.