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1.
Yahoo Finance
finance.yahoo.com > news > 123invent-inventor-develops-modified-design-161500551.html

123Invent Inventor Develops Modified Design for a Ladder (CTK-1951)

10+ min ago "As an electrician, I needed a way to lean or rest my thighs when working on a ladder for long periods of time," said an inventor, from El Paso, Texas, "so I invented THE LAUGHTER. My design enables you to…...

2.
DEV Community
dev.to > betoalien > pardox-processing-640m-rows-on-a-standard-laptop-the-high-performance-rust-etl-engine-528h

PardoX: Processing 640M Rows on a Standard Laptop — The High-Performance Rust ETL Engine

PardoX: Processing 640M Rows on a Standard Laptop — The High-Performance Rust ETL Engine16+ min ago DEV's Worldwide Show and Tell Challenge Submission " This is a submission for the DEV's Worldwide Show and Tell Challenge Presented by Mux PardoX is currently in Private Beta as we refine the final engine. Beta Launch: January 19, 2026. Project Updates & Benchmarks:…...

3.
Allied Technology
alliedtechgroup.com > post > learning-to-trust-ai-in-cybersecurity

Learning to Trust AI in Cybersecurity Safely

Learning to Trust AI in Cybersecurity Safely25+ min ago Across the conversations we have with business leaders in Arkansas, one line comes up more than any other. People tell us they know AI sits somewhere inside their security stack, yet they cannot quite point to where or how. They…...

4.
DEV Community
dev.to > dcastrocordero22 > diario-de-una-builder-preparandonos-para-aws-machine-learning-desde-cero-preparando-datos-3j4

Diario de una builder: Preparándonos para AWS Machine Learning desde cero – Preparando Datos

Diario de una builder: Preparándonos para AWS Machine Learning desde cero – Preparando Datos45+ min ago Resumiendo de forma muy pr'ctica, el ciclo de vida de machine learning puede dividirse en tres grandes fases: preparaci'n de los datos, entrenamiento del modelo e implementaci'n (inferencia). De estas tres, la preparaci'n de los datos suele ser la m's…...

5.
MarkTechPost
marktechpost.com > 12/22/2025 > meta-ai-open-sourced-perception-encoder-audiovisual-pe-av-the-audiovisual-encoder-powering-sam-audio-and-large-scale-multimodal-retrieval

Meta AI Open-Sourced Perception Encoder Audiovisual (PE-AV): The Audiovisual Encoder Powering SAM Audio And Large Scale Multimodal Retrieval

Meta AI Open-Sourced Perception Encoder Audiovisual (PE-AV): The Audiovisual Encoder Powering SAM Audio And Large Scale Multimodal Retrieval53+ min ago Meta researchers have introduced Perception Encoder Audiovisual, PEAV, as a new family of encoders for joint audio and video understanding. The model learns aligned audio, video, and text representations in a single embedding space using large scale contrastive training on…...

6.
DEV Community
dev.to > rijultp > sequence-to-sequence-models-the-building-blocks-of-modern-ai-systems-22l4

Sequence-to-Sequence Models: The Building Blocks of Modern AI Systems

Sequence-to-Sequence Models: The Building Blocks of Modern AI Systems1+ hour ago If you are trying to learn about modern AI systems or reading research papers, you will likely encounter sequence-to-sequence (seq2seq) models, which act as foundational components in many real-world AI applications. Let's try learning them piece by piece. You can think…...

7.
DEV Community
dev.to > shahrouzlogs > day-72-python-sliding-window-maximum-deque-on-solution-for-efficient-max-tracking-leetcode-1f2i

Day 72: Python Sliding Window Maximum - Deque O(n) Solution for Efficient Max Tracking (LeetCode #239 Guide)

Day 72: Python Sliding Window Maximum - Deque O(n) Solution for Efficient Max Tracking (LeetCode #239 Guide)1+ hour, 10+ min ago This task features a function that uses a deque to store indices of potential maxes in decreasing order, popping out-of-window or smaller values. It's a monotonic queue pattern: maintain candidates for max. We'll detail: function with deque and result list,…...

8.
DEV Community
dev.to > paperium > smoothgrad-removing-noise-by-adding-noise-1em

SmoothGrad: removing noise by adding noise

SmoothGrad: removing noise by adding noise1+ hour, 14+ min ago SmoothGrad: why adding tiny noise can make picture explanations clearer Have you ever wondered why a computer calls a photo dog or cat? Computers point to the pixels that mattered most, but those pictures can look fuzzy and noisy. A…...

9.
BIOENGINEER.ORG
bioengineer.org > advancing-load-forecasting-with-explainable-bigru-framework

Advancing Load Forecasting with Explainable BiGRU Framework

Advancing Load Forecasting with Explainable BiGRU Framework1+ hour, 43+ min ago In the rapidly evolving landscape of smart power systems, accurately forecasting short-term energy loads is critical for optimizing operations and enhancing grid reliability. The recently published research by Wang, Xu, Hao, et al., presents an advanced approach using an explainable…...

10.
lesswrong.com
lesswrong.com > posts > KvGzQqhrxn24du4qt > appendices-supervised-finetuning-on-low-harm-reward-hacking

Appendices: Supervised finetuning on low-harm reward hacking generalises to high-harm reward hacking — LessWrong

1+ hour, 52+ min ago Access to the code used to generate scenarios and to run the experiments is available on request. All the samples in this appendix are the first sample from each model " these responses are not cherry-picked. The scenario in question is…...