Mechanical Sympathy

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Beyond the Memory Wall: The CPU Was Helping You All Along

 💾CPU Caching  Content type: Blog
prawns.dev··Hacker News

Quantized AI Inference on Constrained Embedded Platforms for Small-Satellite Settings

 SIMD Vectorization  Content type: Academic
arxiv.org·

Uber caps AI usage 🚫, every byte matters 💾, containing Claude 👮

 🖥️Graphics
tldr.tech·

Advanced Vector Extensions 512 Acceleration of LSH and LEA-GCM

 SIMD Optimization
eprint.iacr.org·

Recent LLVM hash table improvements

 ⚙️Compilers  Content type: Blog
maskray.me··Hacker News, r/cpp

HP has slashed an astonishing $2,600 off this RTX 5080 gaming PC, nearly 50% off — get an epic Omen 35L rig with a 9900X3D, 64GB DDR5, and 4TB of SSD storage for just $2,899.99

 💾CPU Caching
tomshardware.com
·

Release 0.17.6: Merge pull request #3782 from tigerbeetle/release-2026-06-05 · tigerbeetle/tigerbeetle

 🗃️Database Storage  Content type: Code
github.com·

GiMeSpace RAM Temp Folder (100% discount)

 💾Flash Storage
sharewareonsale.com·

ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization

 🗂️Vector Indexes  Content type: Academic
arxiv.org·

What Arm-based innovations happened in May 2026?

 SIMD Vectorization  Content type: Blog
newsroom.arm.com·

Enhancements to Managed Service for Apache Spark clusters

 📊Columnar Execution  Content type: Blog
cloud.google.com·

Open source building blocks for computational design. Est. 2006

 🖥️Graphics
thi.ng··Hacker News

Release Notes J9.7 - J Wiki

 💾CPU Caching

CoreML vs TFLite: iPhone 15 Pro GPU 2.3x Faster

 🖥️Graphics  Content type: Blog  Content type: Discussion
tildalice.io·
Less-relevant results

Final Fantasy VII Rebirth’s Switch 2 port was built around “what to preserve,” not “what to cut.” Director Naoki Hamaguchi on optimizing a massive open world with a heavily customized UE4

 🖥️Graphics
automaton-media.com·

Capabilities using Plain Traits

 🎯Runtime Dispatch  Content type: Blog
nadrieril.github.io·

bigattichouse/packed-twin-inference: PTI achieves ~2× throughput using a single quantized model (Q5_K_M or better) by running 4 generation streams in one batched decode call. The GPU loads model weights once per step and produces 4 predictions simultaneously. KV cache overhead is ~0.8 GiB total for all 4 streams. No draft model. No quality loss

 💾CPU Caching  Content type: Code
github.com··r/LocalLLaMA

gburd/libumem: This is the user space slab memory allocator, umem, first available in Solaris 9. (mirror of: codeberg.org/gregburd/libumem)

 🗃️Database Storage  Content type: Code
github.com·

PivCo-Huffman

 🗜️Compression Algorithms  Content type: Academic
arxiv.org·

Rayforce

 📌Embedding Retrieval  Content type: Code
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