rixz-dev

# USER-CONFIG: export dari browser setelah login ke chat.qwen.ai
QWEN_COOKIE=paste_cookie_string_disini
QWEN_BX_UMIDTOKEN=paste_bx_umidtoken_disini

# USER-CONFIG: copy dari URL browser (chat.qwen.ai/c/{ID_INI}), atau kosongkan untuk auto-create
QWEN_CHAT_ID=

# Optional
QWEN_MODEL=qwen3.7-plus
QWEN_DEBUG=0

dennisameling

DDEV ❤ WSL2: getting started

Now that WSL2 is about to become generally available as part of the Windows 10 May 2020 (2004) update, it's time to look into running DDEV on WSL2!

All Windows 10 editions (including Windows 10 Home) support WSL2. Docker Toolbox support for DDEV will be deprecated, as we'll move testing capacity towards WSL2. If you're already familiar with DDEV on Windows, you might have been using NFS for better filesystem performance. You won't need NFS anymore once you switch to WSL2, since it provides awesome filesystem performance out of the box 🚀

Outline

• Install WSL2
• Install Docker
• Install DDEV

cpursley

PostgreSQL is Enough

See the new site: https://postgresisenough.dev

---

• Simplify: move code into database functions
• Just Use Postgres for Everything
• Just use Postgres
• PostgreSQL is the worlds’ best database

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.

antoinebrl

Preparation of ImageNet (ILSVRC2012)

The dataset can be found on the official website if you are affiliated with a research organization. It is also available on Academic torrents.

This script extracts all the images and group them so that folders contain images that belong to the same class.

1. Download the ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar
2. Download the script wget https://gist.githubusercontent.com/antoinebrl/7d00d5cb6c95ef194c737392ef7e476a/raw/dc53ad5fcb69dcde2b3e0b9d6f8f99d000ead696/prepare.sh
3. Run it ./prepare.sh

wobedi

import type { Stripe } from 'stripe';

export type Replace<T, K extends object> = Omit<T, keyof K> & K;

/** https://stripe.com/docs/expand#with-webhooks */
export interface StripeSubscriptionUnexpanded extends Stripe.Subscription {
  customer: string;
}

export interface StripeSubscriptionEvent extends Stripe.Event {