Vera 1.6
UK Frontier AI model built for agentic systems
What it does
Vera 1.6 is a frontier multimodal AI model designed for agentic systems and reasoning tasks. Built by Cortex Research, Vera 1.6 introduces a hybrid architecture with Sparse Mixture-of-Experts, enabling high reasoning performance while maintaining efficient inference at scale. The model supports text, image, and video inputs, processes extremely long documents with a 1M-token context window, and operates across 201+ languages, making it suitable for enterprise workflows and advanced AI agents.
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- NWNeedle: We Distilled Gemini Tool Calling into a 26M ModelMay 2026 · github.com · ▲776
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…
Google Gemma 4 12BJun 2026 · blog.google · ▲310Run multimodal AI locally with an encoder-free architecture
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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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…
AI · 27d ago · cactuscompute.com
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