neurospice
An executive function prosthetic
What it does
What makes NeuroSpice unique isn't just that it breaks down tasks (which many AI tools do), but that it is designed as a momentum engine rather than just a list maker. While other tools give you information, NeuroSpice is built to handle the "Action Gap"—the space between knowing what to do and actually doing it
Does the same job
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- NTNeuro Tools, a collection of tools to help neurodivergent people2025 · neurotools.app · ▲22
As someone with ADHD, depression and on the autism spectrum, I get in my head A LOT. Every time I run into an issue I create a tool for it. - I should post on Hacker News, but my app is not good enough > challenge my inner critic. - I want to binge eat instead of working > use the catch the urge feature and follow a strategy - Task is too overwhelming > task breakdown and copy the tasks to my notepad - I really don't want to get started on a task > procrastination solver Would love your feedback and any questions are welcome :)
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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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Launched alongside, May 2026
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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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