Caveman cuts ~75% of Claude's output tokens without losing technical accuracy. One-line install for Claude Code, Cursor, Windsurf, Copilot, and more. Four grunt levels, terse commits, one-line PR reviews, and input compression built in. 24.9K stars.
Julius taught Claude to talk like a caveman. 24.9K stars later, it's the most useful meme in developer tooling.
LLMs are verbose by default. Phrases like "I'd be happy to help you with that" and "Let me summarize what I just did" contribute nothing — but burn tokens, slow responses, and push you into usage limits faster. Caveman makes Claude skip the throat-clearing and go straight to the answer. Same fix. 75% less word. Brain still big.
What stands out: 🪨 ~75% output token reduction: Benchmark average 65%, range 22–87% across real coding tasks ⚡ ~3x faster responses: Less token to generate = speed go brrr 🎚️ Four intensity levels: Lite, Full, Ultra, and 文言文 (Classical Chinese) mode 📝 Caveman-commit: Terse commit messages, ≤50 char subject, why over what 🔍 Caveman-review: One-line PR comments: L42: 🔴 bug: user null. Add guard. 🗜️ Caveman-compress: Rewrites your CLAUDE.md into caveman-speak, cutting ~46% of input tokens every session 🔌 Works everywhere: Claude Code, Codex, Gemini CLI, Cursor, Windsurf, Cline, Copilot, and 40+ more 🆓 Free, MIT, one-line install
Before and after: 🗣️ Normal Claude (69 tokens): "The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle..." 🪨 Caveman Claude (19 tokens): "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo."
Note: works best for coding tasks. Nuanced responses still need full Claude, and the system prompt loads as input tokens, so net savings vary per use case. A March 2026 paper found brevity constraints improved accuracy by 26 percentage points on certain benchmarks. Verbose not always better.
Perfect for developers hitting usage limits and anyone who wants their AI agent to do the work and shut up about it.
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified →@rohanrecommends
About Caveman on Product Hunt
“Why use so many token when few do trick?”
Caveman launched on Product Hunt on April 14th, 2026 and earned 237 upvotes and 8 comments, placing #6 on the daily leaderboard. Caveman cuts ~75% of Claude's output tokens without losing technical accuracy. One-line install for Claude Code, Cursor, Windsurf, Copilot, and more. Four grunt levels, terse commits, one-line PR reviews, and input compression built in. 24.9K stars.
On the analytics side, Caveman competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Caveman performed against the three products that launched closest to it on the same day.
Who hunted Caveman?
Caveman was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Caveman including community comment highlights and product details, visit the product overview.
Rohan Chaubey
Julius taught Claude to talk like a caveman. 24.9K stars later, it's the most useful meme in developer tooling.
LLMs are verbose by default. Phrases like "I'd be happy to help you with that" and "Let me summarize what I just did" contribute nothing — but burn tokens, slow responses, and push you into usage limits faster. Caveman makes Claude skip the throat-clearing and go straight to the answer. Same fix. 75% less word. Brain still big.
What stands out:
🪨 ~75% output token reduction: Benchmark average 65%, range 22–87% across real coding tasks
⚡ ~3x faster responses: Less token to generate = speed go brrr
🎚️ Four intensity levels: Lite, Full, Ultra, and 文言文 (Classical Chinese) mode
📝 Caveman-commit: Terse commit messages, ≤50 char subject, why over what
🔍 Caveman-review: One-line PR comments: L42: 🔴 bug: user null. Add guard.
🗜️ Caveman-compress: Rewrites your CLAUDE.md into caveman-speak, cutting ~46% of input tokens every session
🔌 Works everywhere: Claude Code, Codex, Gemini CLI, Cursor, Windsurf, Cline, Copilot, and 40+ more
🆓 Free, MIT, one-line install
Before and after:
🗣️ Normal Claude (69 tokens): "The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle..."
🪨 Caveman Claude (19 tokens): "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo."
Note: works best for coding tasks. Nuanced responses still need full Claude, and the system prompt loads as input tokens, so net savings vary per use case. A March 2026 paper found brevity constraints improved accuracy by 26 percentage points on certain benchmarks. Verbose not always better.
Perfect for developers hitting usage limits and anyone who wants their AI agent to do the work and shut up about it.
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends