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New: The Local LLM Clinic — Describe your use-case and get tailored answers drawn from our articles. Try it →

Recent Posts

• What Breaks When AI Agent Frameworks Are Forced Into <1MB RAM and Sub-ms Startup 25 February 2026
  #agent-architecture#agent-systems#agents#constraints#edge-ai-deployment#edge-deployment#memory-optimization#model-optimization#performance-optimization#quantisation#resource-constrained-agents#startup-optimization

  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.

• How AI is Redefining Price and Performance in Modern Laptops 25 February 2026
  #ai-accelerators#ai-in-laptops#economic-impact-local-ai#hardware#hardware-acceleration#laptop-ai-accelerators#laptops#llama#llama-cpp#local-inference#local-inference-frameworks#mlx#npu#performance-benchmark#power-efficient-inference#privacy#privacy-first-ai-applications#quantisation#quantized-llms#quantized-models

  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.

• Show HN: A Human-Curated, CLI-Driven Context Layer for AI Agents 25 February 2026
  #agent-reliability#agents#cli-tools#context-management#cost-saving#data-curation#knowledge-retrieval#local-ai-deployment#local-deployment#performance-optimization#privacy#privacy-compliance#self-hosted

  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.

• Advanced Quantization Techniques Show Surprising Performance Gains Over Standard Methods 25 February 2026
  #advanced-quantization#benchmarks#dynamic-bit-allocation#dynamic-quantization#llama-cpp#memory-optimization#model-performance#optimization#quantisation#quantization-benchmarking#quantization-techniques#quantization-tradeoffs

  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.

• Show HN: 100% LLM Accuracy–No Fine-Tuning, JSON Only 25 February 2026
  #benchmarks#computational-overhead-reduction#edge-deployment#fine-tuning#fine-tuning-alternative#hallucination-elimination#hallucination-reduction#inference-optimization#json#json-schema-constraints#llm-accuracy#local-llms#model-efficiency#quantisation#structured-output#training

  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.

• Show HN: MCP-Enabled File Storage for AI Agents, Auth via Ethereum Wallet 25 February 2026
  #agents#ai-agents#blockchain-authentication#decentralized-infrastructure#decentralized-storage#edge-deployment#local-deployment#mcp#multi-modal-context-processing#offline-capability#on-device-agents#on-device-inference#privacy#privacy-compliance#storage#verifiable-storage

  A Model Context Protocol implementation providing decentralized file storage for AI agents using blockchain-based authentication, enabling local agents to access persistent, verifiable storage.

• Mirai Announces $10M to Advance On-Device AI Performance for Consumer Devices 25 February 2026
  #consumer-devices#consumer-hardware-optimization#edge-deployment#funding#hardware-software-co-optimization#inference-optimization#llama#llama-cpp#local-inference-latency#local-llms#mlx#model-compression#ollama#on-device-ai#open-source#optimization#privacy#privacy-first-ai#privacy-preserving-ai#quantisation#quantization

  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.

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