ThiagoEvoa

Model Configuration Reference: qwen3.8:27b-mlx (Pi Agent & OpenCode)

A comprehensive reference and side-by-side configuration guide for deploying the qwen3.8:27b-mlx model across both Pi Agent (~/.pi/agent/models.json) and OpenCode (~/.config/opencode/opencode.jsonc) agent harnesses.

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1. Quick Harness Configuration Snippets

A. Pi Agent Harness (~/.pi/agent/models.json)

sts10

A curated list of command-line utilities written in Rust

Note: I have moved this list to a proper repository. I'll leave this gist up, but it won't be updated. To submit an idea, open a PR on the repo.

Note that I have not tried all of these personally, and cannot and do not vouch for all of the tools listed here. In most cases, the descriptions here are copied directly from their code repos. Some may have been abandoned. Investigate before installing/using.

The ones I use regularly include: bat, dust, fd, fend, hyperfine, miniserve, ripgrep, just, cargo-audit and cargo-wipe.

• atuin: "Magical shell history"
• bandwhich: Terminal bandwidth utilization tool

stanislaw

Open source requirements management tools

• Almirah

Almirah is an Application Lifecycle Management framework.

• BASIL

A tool developed to support Software Specification analysis and Testable Requirements definition.

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.

tykurtz

GROKKING NOTES

I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.

So below I made a list of leetcode problems that are as close to grokking problems as possible.

Pattern: Sliding Window

• https://leetcode.com/problems/maximum-subarray/ # Close enough
• https://leetcode.com/problems/minimum-size-subarray-sum/

djangofan

#!/bin/bash

#brew install socat
# socat TCP-LISTEN:2377,reuseaddr,fork UNIX-CONNECT:/var/run/docker.sock

#file: docker.socat_listener
# <?xml version="1.0" encoding="UTF-8"?>
# <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
# <plist version="1.0">
#     <dict>

zachbrowne

#!/bin/bash
iatest=$(expr index "$-" i)

#######################################################
# SOURCED ALIAS'S AND SCRIPTS BY zachbrowne.me
#######################################################

# Source global definitions
if [ -f /etc/bashrc ]; then
	 . /etc/bashrc

AD1993

import android.util.Log;
import org.json.JSONObject;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import okhttp3.Call;
import okhttp3.Callback;
import okhttp3.Headers;
import okhttp3.MediaType;

lbe

HTMX Reference for AI Context

This work is marked CC0 1.0

Overview

HTMX extends HTML with attributes that enable AJAX requests, CSS transitions, WebSockets, and Server-Sent Events directly in markup. Responses are HTML fragments, not JSON.

Core principle: Server returns HTML, HTMX swaps it into the DOM.