
tldt — Too Long, Didn't Tokenize
Protect your data and agents while saving tokens
What it does
Too Long; Didn't Tokenize (tl;dt) is a CLI and library that uses machine learning to summarize long texts with context. It targets API calls, document uploads and crawled sites with excess tokens, prompt injection, side instructions. - LexRank and TextRank - OWASP LLM Top 10 support - Unicode confusables protection - Converts from HTML to markdown - Text sanitization - PII and API Keys cleaning - No API keys required - A Go Library for agents that call AI APIs directly - A skill for coding
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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Dev tools · May 2026 · github.com