n1snt

Oh my zsh Setup (Fast + Minimal)

Install Zsh

Ubuntu

sudo apt update
sudo apt install zsh curl git

lasergoat

Herdr: Persistent AI Agent Workspace Setup (macOS)

A working configuration for Herdr that survives a reboot: shut the machine down, boot it back up, and every workspace, tab, pane and running AI agent conversation comes back where you left it.

Verified end-to-end on macOS with Herdr 0.7.4, Claude Code, and OpenCode.

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n1snt

Note

I’ve written a newer optimized 2026 version of this guide with a faster setup using Zinit + Starship and improved plugin loading.

This version avoids slow shell startups and works better with modern Zsh setups.

Updated guide: https://gist.github.com/n1snt/2cccc8aa5f7b645a7628d3512c70deb6

Oh my zsh.

alonsoir

Atencion, pregunta:

Cuando ves que un servicio en Spring Boot se está comiendo la RAM, 
el 90% de las veces la culpa no es del framework, sino de cómo le metimos mano. 
Antes de saltar a pagar un servidor más grande para solucionar el problema a billetazos, 
suelo revisar 4 cosas clave: -El Garbage Collector: 
Si manejas heaps grandes y necesitas que la app no se "congele", 
dale una oportunidad a ZGC o Shenandoah en lugar del que viene por defecto. 
-Proyecciones en JPA: Si solo necesitas tres datos en pantalla, no te traigas la entidad completa de la base de datos 
con todas sus relaciones. Usa DTOs.

almahmudbd

Free AI API Keys for Agentic Coding

Usage Tips

• Do not subscribe to every service at once. Most free credits expire quickly. Test them one by one and move to the next when your current credits run out.
• Register, verify your account, and claim your credits. Follow each platform's documentation to connect your coding tools.
• Keep your API keys private. Do not share them publicly. (save or star this gist for future update)

List 1: Top Sites in This Genre

Shpigford

Delegation policy (read first)

The session model (Fable) is the orchestrator, not the worker. Fable is the most expensive tier; spend its tokens only on decomposition, judgment calls, synthesis, and talking to the user. Delegate everything else to subagents via the Agent tool, picking the cheapest model that can do the job well:

• model: "opus" — the default worker tier. Anything requiring real judgment: implementation, debugging, architecture-aware exploration, adversarial review.

• model: "sonnet" — cheap tier for mechanical or low-stakes work: running tests and reporting output, simple greps/lookups with a known target, rote refactors from an exact spec, formatting, screenshot capture, admin chores. If getting it slightly wrong is cheap to catch, use sonnet.

• Exploration/research: never read broadly yourself. Spawn Explore agents (model: opus) with tightly scoped questions; consume their synthesized reports, not raw files. Trivial "find the file that defines X" lookups can go to sonnet.

bmsimons

Just run the following JavaScript bookmarklet on https://www.microsoft.com/en-us/software-download/windows10 to reveal all Windows versions.

$("select#product-edition").html("<option value selected value='selected'>Select edition</option><option value='2'>Windows 7 Home Basic SP1 </option><option value='4'>Windows 7 Professional SP1 </option><option value='6'>Windows 7 Home Premium SP1 </option><option value='8'>Windows 7 Ultimate SP1 </option><option value='10'>Windows 7 Home Premium N SP1 </option><option value='12'>Windows 7 Professional N SP1 </option><option value='14'>Windows 7 Ultimate N SP1 </option><option value='16'>Windows 7 Professional K SP1 </option><option value='18'>Windows 7 Professional KN SP1 </option><option value='20'>Windows 7 Home Premium K SP1 </option><option value='22'>Windows 7 Home Premium KN SP1 </option><option value='24'>Windows 7 Ultimate KN SP1 </option><option value='26'>Windows 7 Ultimate K SP1 </option><option value='28'>Windows 7 Starter SP1 </option><option value

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.

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