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Recent Posts
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What Breaks When AI Agent Frameworks Are Forced Into <1MB RAM and Sub-ms Startup
A deep dive into the fundamental constraints and trade-offs when deploying AI agent frameworks on severely resource-limited devices, exploring what architectural patterns fail and what succeeds at the edge.
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How AI is Redefining Price and Performance in Modern Laptops
Modern laptops are increasingly optimized for local AI inference through improved hardware accelerators, specialized chips, and software frameworks. This shift is creating more capable platforms for running quantized language models without cloud dependency.
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Show HN: A Human-Curated, CLI-Driven Context Layer for AI Agents
A new framework for managing context and knowledge retrieval for local AI agents through a command-line interface, emphasizing human curation and local-first operation.
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Advanced Quantization Techniques Show Surprising Performance Gains Over Standard Methods
Recent benchmarking reveals that specialized quantization strategies like Unsloth Q3 dynamic quantization can outperform standard Q4 and MXFP4 quantizations in specific scenarios, challenging conventional wisdom about quantization trade-offs.
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Show HN: 100% LLM Accuracy–No Fine-Tuning, JSON Only
A technique for achieving perfect LLM accuracy on structured outputs using JSON schema constraints rather than model fine-tuning, reducing computational overhead for local deployments.
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Show HN: MCP-Enabled File Storage for AI Agents, Auth via Ethereum Wallet
A Model Context Protocol implementation providing decentralized file storage for AI agents using blockchain-based authentication, enabling local agents to access persistent, verifiable storage.
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Mirai Announces $10M to Advance On-Device AI Performance for Consumer Devices
Mirai has secured $10 million in funding to optimize AI model performance specifically for on-device deployment on consumer hardware. The investment reflects growing market demand for privacy-preserving, latency-free local LLM inference.