织界 WorldLoom
An AI-powered multi-world narrative engine
What it does
织界 WorldLoom 是一款 AI 驱动的多世界文字冒险 RPG。创建角色进入修仙、科幻、废土等世界，每次选择写入存档，驱动 NPC 记忆、属性变化与剧情分支。Android 版与网页端开放体验。
Does the same job
all alternatives →- ADAI Dungeon 2 – AI-generated text adventure built with 1.5B param GPT-22019 · aidungeon.io · ▲584
- YWYuanzai World – LLM RPGs with branching world-linesJan 2026 · yuanzai.world · ▲31
Hi HN, I'm Kai Wang, one of the creators of Yuanzai World. We built a simulation engine (currently on iOS & Android) that allows the community to create and share text adventures populated by multiple LLM-based agents. Unlike standard chatbots, our focus is on community co-creation—users define the worldviews, and our agents (with persistent memory and social relationships) bring them to life. The cool part: We implemented a system we call "World-Line Divergence" (inspired by visual novels like Steins;Gate). Usually, AI RPGs feel random or infinite loop. We built a state machine that tracks…
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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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