An Update to Sir Shortoken: Introducing LELP-S+ (Less English, Less Prose)

A small update to Sir Shortoken. Sir Shortoken already had Quick, Balanced, Deep, Bullets, and Aggressive Bullets. I wanted something between Bullets and normal prose. So I added LELP-S+ (Less English. Less Prose.). The idea is simple: keep complete grammatical sentences, but remove as much English as possible without removing information. Every sentence should introduce a new fact. LELP-S+ also uses simple symbols like → where they reduce words without losing meaning. I tested it on five technical topics (TCP congestion control, virtual memory, B-trees, Raft consensus, and Redis persistence) across GPT, Claude, Gemini, and DeepSeek. Average token savings versus each model's normal prose: 🥇 GPT — 44% 🥈 Gemini — 36% 🥉 Claude — 32% 4️⃣ DeepSeek — 30% The interesting part wasn't accuracy:all four models stayed factually correct across the evaluation. The real differentiator was compression discipline. GPT consistently removed the most prose while preserving the technical explanation. DeepSeek's lower score mostly came from adding extra sections that weren't requested, making answers longer than necessary. LELP-S+ is now part of Sir Shortoken. The goal isn't shorter answers instead it's more information per token. https://github.com/shouvik12/sir-shortoken submitted by /u/Substantial_Load_690 [link] [comments]

Source: r/ollama | 2026-08-06