🔥 Hot Repo: Adobe Proved This Meme Works — 110K Stars and Counting

One-liner — Caveman is a Claude Code skill and LLM proxy that forces your AI agent to respond in minimal, caveman-style English — no fluff, one idea per sentence, answer first — cutting token costs 1.4–2.4x with zero measurable quality loss, as independently verified by Adobe Research, JetBrains, and Elasticsearch Labs. - Repo: JuliusBrussee/cavemanhttps://github.com/JuliusBrussee/caveman - Stars: ⭐ 110,110 updated October 6, 2026 - Language: Go - License: Apache-2.0 --- What It Does Caveman is a two-part system. The /caveman skill changes how your AI agent replies: no greeting, no recap, answer first, one idea per sentence, maximum 20 words per sentence. A 63-token React explanation becomes 20 tokens — same fix, 68% smaller. The second part is an optional proxy that compresses what the agent reads — CSVs, logs, JSON, YAML — before they enter the context window, cutting input tokens by 33.2% across whole sessions. Code, commands, paths, and error messages are left byte-for-byte intact. Why It's Blowing Up The project hit 1 on Hacker News and 1 on GitHub Trending — but the real credibility spike came from three independent validations. Adobe Research published the CAVEWOMAN paper arXiv 2606.24083 confirming 1.4–2.4x cost reduction across eight models and five datasets. Elasticsearch Labs remade the approach for MCP scenarios and measured 63.6% fewer response tokens with "zero information loss." JetBrains ran 86 paired A/B coding tasks and found no measurable quality difference p = 0.82 with 8.5% fewer output tokens. The timing is sharp. As Claude Code, Codex, and Gemini CLI sessions grow longer, context burn is the silent tax on every agentic workflow. Caveman attacks that problem from both sides: smaller outputs and compressed inputs. The proxy benchmark puts the combined saving at 33.2% fewer input tokens on a six-file suite — with 18/18 answers correct, compared to competitor Headroom's 15/18 on the same suite. The meme framing "why many token when few