City Memory
Your cities. Your stories. Recalled in seconds.
What it does
City Memory is a private atlas of every place that has mattered to you. That ramen shop in Fukuoka. The unmarked wine bar in Porto. The coffee roaster on the third floor in Tokyo. Capture by text, voice, or photo, in any language. Ask it years later in plain English and it finds it instantly. Built for people who collect cities, not just passport stamps.
Does the same job
all alternatives →- ATA tool to help you remember shit you are interested in2022 · recall-app.com · ▲493
I've been working on Recall for a while now, it had some initial traction in the beginning which has since died down now. I am facing the inevitable question of whether to continue with the project. I just put out a new release and it would be helpful to get advice from the community on what they think of the idea and my implementation.
- ALA local memory of all you have seen, said or heard while you workJul 2026 · github.com · ▲11
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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
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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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