Alternatives
Products that do what Vera 1.6 does
UK Frontier AI model built for agentic systems
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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…
May 2026 · github.com
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Run multimodal AI locally with an encoder-free architecture
Jun 2026 · blog.google
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Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-trained to know when it's wrong. Every response comes with a confidence score between 0 and 1. Developers can accept the on-device when it's high, hand off to a bigger cloud model when it's low. By routing only 15-35% of queries to Gemini 3.1 Flash-Lite, Gemma-4-E2B matches Gemini 3.1 Flash-Lite on most benchmarks. - ChartQA: 15-20% - LibriSpeech: 25-30% - MMBench, GigaSpeech, MMAU: 30-35% -…
Jul 2026 · github.com
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0.8B-9B native multimodal w/ more intelligence, less compute
Mar 2026 · huggingface.co
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Hey it’s Hassaan & Quinn – co-founders of Tavus, an AI research company and developer platform for video APIs. We’ve been building AI video models for ‘digital twins’ or ‘avatars’ since 2020. We’re sharing some of the challenges we faced building an AI video interface that has realistic conversations with a human, including getting it to under 1 second of latency. To try it, talk to Hassaan’s digital twin: https://www.hassaanraza.com, or to our "demo twin" Carter: https://www.tavus.io We built this because until now, we've had to adapt communication to the limits of…
2024
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Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones. Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more. Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks…
2025 · github.com
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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…
27d ago · cactuscompute.com
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Related: https://simonwillison.net/2026/Jan/27/one-human-one-agent-on...
Jan 2026 · emsh.cat
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