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How to deploy quantized LLMs on Apple Neural Engine with Core ML and ExecuTorch in 2026
AI & ML

How to deploy quantized LLMs on Apple Neural Engine with Core ML and ExecuTorch in 2026

20 min read · Apr 26, 2026, 2:06 AM · 10 views

Apple’s official Core ML on-device Llama walkthrough shows Llama-3.1-8B-Instruct running locally on an M1 Max at about ~33 tokens/s after Core ML conversion and optimization — but the model must be carefully shaped around fixed input sizes and memory-bandwidth limits, so the practical bottleneck is not just quantization, it is getting the export and runtime path to fit Apple silicon constraints.

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How much does attic insulation cost in the US, and what R-value do you need by climate zone?
Lifestyle & Home Improvement

How much does attic insulation cost in the US, and what R-value do you need by climate zone?

25 min read · Apr 25, 2026, 9:37 AM · 7 views

Most attic insulation upgrades can qualify for a 30% federal tax credit up to $1,200, and ENERGY STAR notes that adding insulation first can keep you from oversizing other efficiency upgrades — but the right R-value still depends on your climate zone and attic assembly, so the cheapest quote is not always the right one.

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Cost Calculator: LVP vs. Engineered Hardwood Installation (2026 Price Estimates)
Lifestyle & Home Improvement

Cost Calculator: LVP vs. Engineered Hardwood Installation (2026 Price Estimates)

29 min read · Apr 25, 2026, 9:22 AM · 7 views

While material costs for LVP and engineered hardwood appear comparable at $6–$12 per square foot, the 20-year total cost of ownership favors LVP by $5,000–$10,000 for a standard 1,500 sq ft home due to the high-frequency maintenance and refinishing requirements of wood — but LVP loses on resale impact, where engineered hardwood typically adds 2–3% to total home value.

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AI & ML

Implementing Machine Unlearning for NIST AI 100-2e Compliance

14 min read · Apr 25, 2026, 12:02 AM · 3 views

By utilizing gradient-based unlearning (e.g., SISA or Gradient Ascent) to explicitly modify model parameter-sets rather than relying on output suppression, firms can achieve (epsilon, delta)-differential privacy, though they must balance the 'onion effect' where unlearning one point risks compromising the security of the retain-set.

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