User → LDAP/AD group → Linux group → sudo / access policy
docker run -d \
--name ldap \
-p 389:389 \
-e LDAP_ORGANISATION="corp" \| #include <DHT.h> | |
| #define DHTPIN 15 | |
| #define DHTTYPE DHT22 | |
| DHT dht(DHTPIN, DHTTYPE); | |
| void setup() { | |
| Serial.begin(115200); | |
| dht.begin(); | |
| } |
| // blink.ino | |
| #define LED 2 | |
| int n = 0; | |
| void setup() { | |
| Serial.begin(115200); | |
| pinMode(LED, OUTPUT); | |
| } | |
| void loop() { |
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.
| class ConfigService { | |
| private static instance: ConfigService; | |
| private config = { apiUrl: 'https://api.example.com' }; | |
| private constructor() {} // // Private constructor prevents direct instantiation | |
| static getInstance(): ConfigService { | |
| if (!ConfigService.instance) { | |
| ConfigService.instance = new ConfigService(); | |
| } |
| apiVersion: apps/v1 | |
| kind: Deployment | |
| metadata: | |
| name: apache-server | |
| labels: | |
| app: apache-server | |
| spec: | |
| replicas: 1 | |
| selector: | |
| matchLabels: |
| import com.cloudbees.hudson.plugins.folder.Folder | |
| import hudson.FilePath | |
| import jenkins.model.Jenkins | |
| def boolean isFolder(String name) { | |
| def item = Jenkins.instance.getItemByFullName(name) | |
| return item instanceof Folder | |
| } | |
| def deleteUnusedWorkspace(FilePath root, String path, Boolean dryRun) { |
UPDATE: 29-07-2023 - use native method for Amazon Linux 2023 at https://docs.docker.com/engine/install/centos/
UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.
Amazon changed the install in Linux 2. One no-longer using 'yum' See: https://aws.amazon.com/amazon-linux-2/release-notes/
| // Simple appproach for merging with subset of properties from json oject. | |
| // Default Properties | |
| const default_rolestudio = { | |
| portal_name: 'Role Studio', | |
| rolename_maxwidth: 200, | |
| nested_property1: {value1: 30, value2: 20}, | |
| nested_property2: {value1: 30, value2: 20} | |
| }; | |
| // Custom Properties |