daemonhorn

Cloudflare WARP tunnel via Wireguard client

This example provides a simple configuration for a Debian client to have a Cloudflare tunnel while not installing the official Cloudflare WARP client. Note: Tunnel transport outbound to engage.cloudlflare.com on udp/2408 is default, with a dynamic listening udp port and a fwmark for packet matching by wireguard. Any applicable firewall rules may need to be adjusted.

• Top-level GitHub project to convert cloudflare endpoint to generic wireguard configuration file: https://github.com/ViRb3/wgcf

Install dependancies

sudo apt install openresolv wireguard-tools golang git

Get the latest client from Github and build using go

git clone https://github.com/ViRb3/wgcf.git

tejainece

https://marketplace.visualstudio.com/items
https://marketplace.visualstudio.com/_apis/public/gallery

mary-ext

Bluesky's age assurance sucks, here's how to work around it.

Bluesky has implemented age verification measures in response to regional laws that restrict access, prompting users to verify their age through Epic Games' Kids Web Services before they can access adult content.

This sucks, but thankfully there are ways to work around it.

Before diving in: I encourage you to read this entire document, including the

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.

grentank

Полезные ссылки с доклада

Телеграм канал

✉️ Написать в личку

Ссылка на презу

Youtube канал

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

pkpahuja

AI Invoice Data Extractor, free n8n workflow

Drop an invoice into a Google Drive folder. A minute later the vendor, invoice number, dates, subtotal, tax, total, currency and line items are sitting in a Google Sheet row. PDFs, scanned PDFs and phone photos of paper bills all work.

Built by Pankaj Pahuja. Free to use, change, and pull apart.

The full build, including the part where it invented an invoice that did not exist: https://youtu.be/oSlq1rAV8XA

The thing that makes this different

illuminatianon

Z‑Image Turbo is a bit different from “classic” Stable Diffusion, so a lot of old prompting habits don’t quite apply. I’ll walk through how to prompt it deeply and safely, with special focus on controlling content (nudity, stereotypes, unwanted artifacts) even though the model does not support traditional negative prompts at all. ([Hugging Face][1])

---

1. How Z‑Image Turbo thinks (and why “negative” prompts don’t work)

Key facts that matter for prompting:

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections: