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AI Coding Plan Comparison 2026 — CodingPlan.org

Side-by-side comparison of the best AI coding subscriptions: Claude Code, GLM, MiniMax, Kimi, and Qwen

🔗 Full comparison: https://codingplan.org/en

🎯 Why This Comparison?

TIZEN-HOWTO: Deploying a Custom App to Samsung Commercial Displays

Step-by-step guide for deploying a signed Tizen web app (.wgt) to a Samsung commercial (B2B) display. Written from hard-won experience with a Samsung OH46DX (Tizen 8.0 / SSSP 10). Most of this applies to any Samsung SMART Signage display running Tizen 6.5+.

Key fact: This is a commercial signage display, not a consumer TV. Most Tizen tutorials online target consumer TVs and will not work. On the OH46DX, installing a Custom App via the signage App Management /

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

ICE Out; Abolish ICE

Basic Snake HTML and JavaScript Game

Snake is a fun game to make as it doesn't require a lot of code (less than 100 lines with all comments removed). This is a basic implementation of the snake game, but it's missing a few things intentionally and they're left as further exploration for the reader.

Further Exploration

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

Go map: устройство под капотом

Шпаргалка для подготовки к собеседованиям. В Go 1.24 реализация map была полностью переписана: bucket-based hash table заменили на Swiss Table. Ниже разобраны оба варианта — старый (Go < 1.24) и новый (Go ≥ 1.24).

Вариант 1. Bucket-based (Go < 1.24)

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.

The Production Engineer’s Study Guide

A Synthesis on Performance, Data, and Distributed Systems.

📘 FAIR USE NOTICE

This document is intended for educational and research purposes only. It contains a structured study outline and technical review of foundational systems and infrastructure concepts that are publicly documented and broadly relevant to practitioners in DevOps, SRE, and systems engineering roles.

It does not contain any proprietary information, internal tools, or confidential interview questions or processes from any specific employer. All materials herein are based on publicly available sources, common industry practices, and original synthesis for peer learning.

My Claude Code Setup Guide (macOS)

A step-by-step guide to installing and configuring Claude Code on macOS, the way I use it. This isn't meant to be the definitive reference -- it's just my personal setup that's been working well for me. Things change fast, so if something looks off, check the official docs or let me know.

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