pog5

VMware Workstation Pro Serial Keys

Updated: 2022-11-18

NOTE: I DON'T GUARANTEE THE KEYS WORK, DON'T CONTACT ME IF ONE IS NOT WORKING, JUST TRY ANOTHER ONE OR QUIT THE PAGE

VMware Workstation 4.x.x

ZHDH1-UR90N-W844G-4PTN6

G1NP0-T88AL-M016F-4P8N2

ZC14J-4U16A-0A04G-4MEZP

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

DrH3lix

Training microWakeWord locally on a Blackwell GPU (RTX 50-series / sm_120)

A working recipe for training custom ESPHome/HA wake words on consumer Blackwell hardware — locally, free, sovereign — when the standard trainer can't.

By DJ Oetken · June 2026

Why

Custom wake-word detection (microWakeWord, the on-device engine for ESPHome Voice) is normally trained either on a paid web service or a rented cloud GPU. The goal here was sovereign local training: your voice, your hardware, your electricity, nothing leaving the building — on a consumer RTX 50-series card.

The catch: the otherwise-excellent TaterTotterson microWakeWord-Trainer-Nvidia-Docker does not work on Blackwell out of the box. This documents why, and the fix.

Zbizu

Here is the documentation for defining the style of an image in OTUI using the properties handled in uiwidgetimage.cpp.

---

Defining Image Styles in OTUI

The UIWidget class supports various properties for styling images. These properties allow you to define the source, size, position, appearance, and behavior of images in your OTUI files.

---

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.

anshulxyz

Privacy Policy for [Bot Name]

This Privacy Policy governs the collection, use, and sharing of personal information by [Bot Name], a Discord bot developed by [Developer Name]. By using [Bot Name], you agree to the terms of this Privacy Policy.

Information We Collect

We collect information that you provide to us through your use of the bot, such as your Discord user ID and username, server and channel information, and message content. We may also collect usage data, such as the frequency and duration of your use of the bot.

How We Use Your Information

CodeLeom

## Workflow Orchestration

### 1. Plan Mode Default
- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
- If something goes sideways, STOP and re-plan immediately 
- Don't keep pushing.
- Use plan mode for verification steps, not just building
- Write detailed specs upfront to reduce ambiguity

### 2. Subagent Strategy