In many tech conferences, attendees are invited to rate the talk and/or the speaker from 1 to 5 stars. This type of ratings is interesting but has a few drawbacks.
The discussion started as a twitter thread with this french proposition.
| mvn -Dmaven.test.skip | |
| mvn dependency:resolve | |
| mvn dependency:tree : arbre | |
| mvn install:install-file -Dfile=as3corelib.swc -DgroupId=com.adobe -DartifactId=as3corelib -Dversion=0.93 -Dpackaging=swc -DgeneratePom=true -DcreateChecksum=true | |
| #compiler src/test/java car compile tout court ne fait que src/main/java | |
| mvn test-compile | |
| mvn versions:set -DnewVersion=1.2.3-SNAPSHOT |
| #notepad++ | |
| indent XML : CRLT + ALT + SHIFT + B | |
| #eclipse | |
| ##code | |
| CTRL + espace : completion | |
| CTRL + 1 : correcton auto | |
| CTRL + SHIFT + O : organize import | |
| CTRL + SHIFT + F : formatter le code | |
| CTRL + I : indenter |
| const fs = require('fs'); | |
| function walk(root) { | |
| const files = []; | |
| const dirs = [root]; | |
| while (dirs.length) { | |
| const dir = dirs.shift(); | |
| const dircontent = fs.readdirSync(dir).map(file => path.join(dir, file)); | |
| const [newDirs, newFiles] = partition(dircontent, file => fs.statSync(file).isDirectory()); | |
| files.push(...newFiles); |
In many tech conferences, attendees are invited to rate the talk and/or the speaker from 1 to 5 stars. This type of ratings is interesting but has a few drawbacks.
The discussion started as a twitter thread with this french proposition.
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.