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#profiling

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Hugging Face Blog · 2026-06-11 Cached

This blog post continues the profiling in PyTorch series, exploring nn.Linear, MLP blocks, and fusion techniques using Triton kernels to optimize performance.

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#profiling

Google is building a lifestyle profiling engine, not a "helpful assistant"

Reddit r/ArtificialInteligence · 2026-05-29

Google's AI strategy is criticized as a surveillance-based profiling engine that forces users into consent through mandatory login, circumventing GDPR. The article exposes Google's plan to replace traditional search with AI-generated answers and personalized tracking, calling it a legal loophole wrapped in AI hype.

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#profiling

Human Psychometric Questionnaires Mischaracterize LLM Behavior

Hugging Face Daily Papers · 2026-05-29 Cached

This paper finds that human psychometric questionnaires fail to reliably predict LLM behavior in real-world interactions, and proposes generation-based profiling as a more accurate alternative.

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#profiling

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Hugging Face Blog · 2026-05-29 Cached

A beginner-friendly guide to using PyTorch's torch.profiler for profiling and optimizing neural network operations, starting with matrix multiplication and bias addition. It explains how to read profiler traces and understand CPU/GPU interactions.

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#profiling

@RisingSayak: I realized that what I cannot profile, I cannot optimize. This is why I embarked on a little project in Diffusers, to t…

X AI KOLs Following · 2026-05-22 Cached

Sayak Paul describes a project to profile and optimize Diffusers pipelines using torch.compile, and announces a tutorial series by Ari G. on the topic.

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#profiling

Speeding Up the Back End with Graph Theory (2019)

Lobsters Hottest · 2026-05-21 Cached

Sensor Tower's engineering team used graph theory analysis and profiling tools to identify bottlenecks in a slow backend endpoint, achieving a fourfold speed improvement by optimizing Protobuf decoding and encoding steps.

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#profiling

@WEB3_furture: What did the world's most expensive financial teams open source on GitHub? How can ordinary people learn about quantitative trading? Directly getting hands-on is the fastest way. Top quantitative and high-frequency trading institutions like Jane Street, Goldman Sachs, J.P. Morgan, etc., have released representative financial/engineering tools to help ordinary quant...

X AI KOLs Timeline · 2026-05-21 Cached

This tweet introduces three financial/engineering tools open-sourced by top quantitative institutions such as Jane Street, Goldman Sachs, and J.P. Morgan: magic-trace (high-precision process tracing), gs-quant (Python package for derivatives pricing and risk management), and Perspective (real-time data visualization tool), helping quant enthusiasts gain institutional-level technical capabilities for free.

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#profiling

Skim: Speculative Execution for Fast and Efficient Web Agents

arXiv cs.AI · 2026-05-19 Cached

Accio is a speculative execution framework that reduces cost and latency for web agents by leveraging offline site-structure profiling and online selection of fast paths, achieving a 1.9x reduction in per-task cost and 33.4% latency reduction while maintaining accuracy.

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#profiling

@ariG23498: Made a simple utility to share your GPU profile traces. `uvx trace-tuil <local traces> -b <hf bucket name>`

X AI KOLs Following · 2026-05-14 Cached

A simple utility to share GPU profile traces using a command-line tool, designed for developers working with GPU profiling.

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#profiling

Profiling.sampling – Statistical Profiler

Hacker News Top · 2026-05-10 Cached

Python 3.15 introduces the profiling.sampling module, Tachyon, a statistical profiler that periodically samples stack snapshots with minimal overhead, suitable for development and production environments.

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#profiling

Journey in optimising Elixir application

Lobsters Hottest · 2026-04-20 Cached

A developer shares lessons learned while optimizing Elixir applications, particularly focusing on performance improvements to a Postgres connection pooler (Ultravisor). The article covers profiling techniques using flame graphs, call tracing, and tools like eFlambè and tprof.

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