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Products that do what GrsAi does
GrsAI: The lowest-priced and most stable AI API platform
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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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Fast and efficient models optimized for coding and subagents
Mar 2026 · openai.com
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One API for leading AI models and video generation
27d ago · video.gqaiapp.com
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We took a recently released Bonsai 1.7B ternary model from PrismML (https://github.com/PrismML-Eng/Bonsai-demo) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous. Measured against unmodified upstream llama.cpp at the same Bonsai/Q2_0 commit, same M4 Max: - tg128: 309.82 → 442.42 t/s (+42.0%) - pp512: 4250.32 → 4622.63 t/s (+8.8%)
May 2026 · agents2agents.ai
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Hey HN! I wanted to share an open-source project I’ve been working on called k8sAI. It’s a personal AI Kubernetes expert that can answer questions about your cluster, suggests commands, and even executes relevant kubectl commands to help diagnose and suggest fixes to your cluster, all in the CLI! As a relative newcomer to k8s, this tool has really streamlined my workflow. I can ask questions about my cluster, k8sAI will run kubectl commands to gather info, and then answer those question. It’s also found several issues in my cluster for me - all I’ve had to do is point it in the right…
2024 · github.com
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The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices. Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon. To create a smart on-device "Semantic Router," models need to reach the 27B+ parameter scale. Achieving this on a…
Jul 2026
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