Performance Archaeology, Access Patterns, Memory Hierarchies, Optimization Forensics

QUIC! Jump to User Space!
hackaday.com·7h
🌐Network Protocols
LLM Optimization Notes: Memory, Compute and Inference Techniques
gaurigupta19.github.io·4d·
Discuss: Hacker News
💻Local LLMs
Is the End of Detection-Based Security Here?
thenewstack.io·7h
🎯Threat Hunting
Activation Alchemist: Sculpting Stability with Functional Signatures
dev.to·3h·
Discuss: DEV
🔍Concolic Testing
Multi-Core By Default
rfleury.com·22h·
🔩Systems Programming
Speed Matters: How We Achieve the Fastest Web Agent
browser-use.com·1d·
Discuss: Hacker News
🎬WebCodecs
I Built the Perfect Workflow and attracted some friends in the process
graemefawcett.ca·24m·
Discuss: Hacker News
Proof Automation
10 Data + AI Observations for Fall 2025
towardsdatascience.com·9h
🌊Stream Processing
Beating the L1 cache with value speculation (2021)
mazzo.li·4d·
CPU Microarchitecture
InferenceMAX – open-source Inference Frequent Benchmarking
github.com·3h·
Discuss: Hacker News
Performance Mythology
N8n vs. Windmill vs. Temporal
blog.arcbjorn.com·1d·
Discuss: Hacker News
🌊Stream Processing
We built a CUDA emulator that profiles GPU code with zero hardware
rightnowai.co·3d·
Discuss: Hacker News
🎯Emulator Accuracy
H1B-KV: Hybrid One-Bit Caches for Memory-Efficient Large Language Model Inference
arxiv.org·2d
💨Cache Optimization
Latency vs. Accuracy for LLM Apps — How to Choose and How a Memory Layer Lets You Win Both
dev.to·3d·
Discuss: DEV
Performance Mythology
The Bit Shift Paradox: How "Optimizing" Can Make Code 6× Slower
hackernoon.com·2d
🧮Compute Optimization
Parameterized Complexity of s-Club Cluster Edge Deletion
arxiv.org·1d
🧮Kolmogorov Complexity
Just shipped Shimmy v1.7.0: Run 42B models on your gaming GPU!
reddit.com·1d·
Discuss: r/rust
🖥️Terminal Renaissance
How View Caching in Rails Works (2020)
honeybadger.io·9h·
Discuss: Hacker News
💨Cache Optimization
Progress being made in porting AMD OpenSIL Turin PoC to Coreboot in a Gigabyte MZ33-AR1
blog.3mdeb.com·3h·
🖥️Terminal Renaissance
TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
arxiv.org·1d
🔨Compilers