Windows 10 or Windows 11 โ€“ which system is really faster?
igorslab.deยท12h
โš™๏ธSystems Programming
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xb.sql and xbi.sql - Explain Oracle Execution Plans Better! (Part 3 - Cardinality Misestimates)
tanelpoder.comยท1d
๐Ÿš€Compiler Optimization
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How KVM and QEMU run VMs in Linux
popovicu.comยท2dยท
Discuss: r/linux
โš™๏ธSystems Programming
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On Designing Low-Latency Systems for High-Traffic Environments
hackernoon.comยท1d
๐ŸŒDistributed Computing
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Troubleshooting multi-GPU with 2 RTX PRO 6000 Workstation Edition
reddit.comยท1dยท
Discuss: r/LocalLLaMA
โฑ๏ธCUDA Events
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Thoughts on "Static Retrival Revisited"
curiouscoding.nlยท18h
๐Ÿ”Type Checkers
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Can-t stop till you get enough
cant.bearblog.devยท1dยท
Discuss: Hacker News
๐Ÿ“œTorchScript
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Why stop at 1 million tokens when you can have 10? My journey to extreme context on a gaming GPU. [P]
reddit.comยท5hยท
๐ŸŽ๏ธTensorRT
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I want to run 8x 5060 ti to run gpt-oss 120b
reddit.comยท1dยท
Discuss: r/LocalLLaMA
๐ŸŽฏGPU Kernels
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Creating a Linux Application Using VSCodium, Cline, OpenRouter, and Claude
taosecurity.blogspot.comยท16hยท
๐Ÿ—๏ธBuild Systems
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Linux 6.19 To Optimize Exiting To User-Space For Restartable Sequences
phoronix.comยท18h
โš™๏ธSystems Programming
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DGX Spark UMA can trick you
bartusiak.aiยท4dยท
Discuss: Hacker News
๐Ÿง CUDA Memory Management
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Deep Integration and the Convergence of Model Architecture and Hardware in AI
dev.toยท1dยท
Discuss: DEV
๐ŸŽฏTensor Cores
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Running MiniMax-M2 locally - Existing Hardware Advice
reddit.comยท33mยท
Discuss: r/LocalLLaMA
๐Ÿ”งPTX
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Intel's killed-off BMG-X3/X4 GPUs: 3D stacked die, up to 40 GPU cores, 512MB Adamantine cache
tweaktown.comยท1d
๐Ÿ”งPTX
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We found embedding indexing bottleneck in the least expected place: JSON parsing
nixiesearch.substack.comยท1dยท
Discuss: Substack
๐Ÿ•Ruff
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Cycle-accurate 6502 emulator as coroutine in Rust
github.comยท3dยท
๐Ÿ“ŠProfiling Tools
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How to optimise huge rust backend build time
reddit.comยท20hยท
Discuss: r/rust
๐Ÿ—๏ธBuild Optimization
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Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
arxiv.orgยท12h
๐Ÿ”„ONNX
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