Tootfinder

Künstliche Intelligenz: Amazon will künftig mit weniger Mitarbeitern auskommen
Ansy Jassy, Chef von Amazon, glaubt, dass der Online-Händler in Zukunft mit weniger Mitarbeitern in den Büros auskommt. KI soll sie ersetzen können.

Did some high res imaging of the standard cell area of the TPS54527 (and the TI logo in the corner).
Two scans, with focal planes roughly at M1 and M2.
https://siliconpr0n.org/map/ti/tps54527/azonenberg_roi1-fpm1_mit100x/

A progressive (#Mamdani) has a chance of winning the #NYCMayoralRace D primary, so of course billionaires are going to throw money at the problem. But rather than propping up someone more reasonable, they're picking the scummiest, most corrupt piece of shit in the race (

A perfect matching reciprocity method for embedding multiple hypercubes in an augmented cube: Applications to Hamiltonian decomposition and fault-tolerant Hamiltonicity
Da-Wei Yang, Hongyang Zhang, Rong-Xia Hao, Sun-Yuan Hsieh
https://arxiv.org/abs/2507.12834

A perfect matching reciprocity method for embedding multiple hypercubes in an augmented cube: Applications to Hamiltonian decomposition and fault-tolerant Hamiltonicity
This paper focuses on the embeddability of hypercubes in an important class of Cayley graphs, known as augmented cubes. An $n$-dimensional augmented cube $AQ_n$ is constructed by augmenting the $n$-dimensional hypercube $Q_n$ with additional edges, thus making $Q_n$ a spanning subgraph of $AQ_n$. Dong and Wang (2019) first posed the problem of determining the number of $Q_n$-isomorphic subgraphs in $AQ_n$, which still remains open. By exploiting the Cayley properties of $AQ_n$, we establish a l…

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
Payal Bhattad, Sai Manoj Pudukotai Dinakarrao, Anju Gupta
https://arxiv.org/abs/2507.12126

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
Text data augmentation is a widely used strategy for mitigating data sparsity in natural language processing (NLP), particularly in low-resource settings where limited samples hinder effective semantic modeling. While augmentation can improve input diversity and downstream interpretability, existing techniques often lack mechanisms to ensure semantic preservation during large-scale or iterative generation, leading to redundancy and instability. This work introduces a principled evaluation frame…

There has long been a quiet debate, currently drowned out but still very much happening, about human replacement vs human augmentation. Think of Gary Kasparov remarking years ago that he thought chess played by humans with computer assistance could be a far more interesting game than either human-only or computer-only chess.
Here’s an argument for augmentation over automation, and note how far it diverges from the current hype despite it being written from a very AI-friendly point of view:
https://digitaleconomy.stanford.edu/news/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence/

Magic Leap: Wie der Traum von der Wunder-AR-Brille platzte
Zwischen Vision, Hype und Restrukturierung: Wie ein Augmented-Reality-Start-up mit Milliardenkapital beinahe kollabierte und heute neue Wege geht.
…

ADI / Linear Tech LTC4367 power protection controller, from one of my failed 48V IBC designs that blew up from inductive spikes when I applied power.
I didn't do extensive circuit analysis but it looks like one particular net is blown out in several places. Guessing this is the same pin that cratered on the package.
Besides the damage, some other features of interest:
* Colored fringes on large top metal features. Maybe spin-on glass top metal or something.
* The LT…

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Chandana Cheerla
https://arxiv.org/abs/2507.12425 https://

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.…

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Jiazheng Li, Hong Lu, Kaiyue Wen, Zaiwen Yang, Jiaxuan Gao, Hongzhou Lin, Yi Wu, Jingzhao Zhang
https://arxiv.org/abs/2507.13266

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Reinforcement learning (RL) has become a key component in training large language reasoning models (LLMs). However, recent studies questions its effectiveness in improving multi-step reasoning-particularly on hard problems. To address this challenge, we propose a simple yet effective strategy via Question Augmentation: introduce partial solutions during training to reduce problem difficulty and provide more informative learning signals. Our method, QuestA, when applied during RL training on mat…