Tootfinder

[BLOG] BitBastelei #662 – DMX-Lampen und WLED https://www.adlerweb.info/blog/2025/09/21/bitbastelei-662-dmx-lampen-und-wled/

Sometimes innovation is quite simple 🙂
""Our measurements show that wheat and grass-clover mixtures grow just as well between vertical solar panels as in open fields. At the same time, the panels produce electricity in a daily pattern that better matches energy demand. It's a win-win,"
#solarpanels

Double harvest: Vertical solar panels and crops thrive side by side
Imagine a field where solar panels and crops coexist—with no trade-off. It sounds like science fiction, but that's precisely what researchers from Aarhus University have now documented in a full-scale agrivoltaic pilot project in the Danish countryside.

"Our government is being run by aggrieved elites who were thrown onto social platforms with everyone else in the 2010s, where they could finally read what we all think about them. And it caused them so much psychic damage that they have decided to destroy both the internet and democracy to make sure nothing like it will ever happen again."
(Original title: Petty, powerful, and pathologically online)

Petty, powerful, and pathologically online
Read to the end for an academic paper on puppygirls

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Muhammad Ayub Sabir, Junbiao Pang, Jiaqi Wu, Fatima Ashraf
https://arxiv.org/abs/2510.12686

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aw…

Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
Humza Ashraf, Syed Muhammad Danish, Zeeshan Sattar
https://arxiv.org/abs/2509.09947 https…

Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
There is a growing concern about the environmental impact of large language models (LLMs) in software development, particularly due to their high energy use and carbon footprint. Small Language Models (SLMs) offer a more sustainable alternative, requiring fewer computational resources while remaining effective for fundamental programming tasks. In this study, we investigate whether prompt engineering can improve the energy efficiency of SLMs in code generation. We evaluate four open-source SLMs…

I suspect we’re doing something similar to the tank classifier when we ascribe intelligence to AIs: there are patterns such as (for example) grammatical correctness which we •associate• with this abstract thing called “intelligence,” and we thus mis-infer the existence of everything else we associate with the notion of “intelligence” when we see (for example) correct grammar.
Or should we just call the machine intelligent because we classify it as intelligence because our brains, which we assume are intelligent, think it fits the pattern of intelligence? And now you see what the OP means about “begging the question of intelligence.”

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Sara Makenali, Babak Rokh, Ali Azarpeyvand
https://arxiv.org/abs/2509.04244 https://

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to the large number of layers and parameters, which result in considerable computational and memory demands. To address this issue, pruning and quantization are two widely used compression techniques, commonly applied individually in most studies to reduce model size and enhance processing speed. Nevertheless, combining…

DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Esakkivel Esakkiraja, Denis Akhiyarov, Aditya Shanmugham, Chitra Ganapathy
https://arxiv.org/abs/2509.25716

DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Current search techniques are limited to standard RAG query-document applications. In this paper, we propose a novel technique to expand the code and index for predicting the required APIs, directly enabling high-quality, end-to-end code generation for auto-completion and agentic AI applications. We address the problem of API leaks in current code-to-code benchmark datasets by introducing a new dataset built from real-world ServiceNow Script Includes that capture the challenge of unclear API us…

🇺🇦 #NowPlaying on #BBC6Music's #FreakZonePlaylist
Maxime Denuc:
🎵 Agoraphobia
#MaximeDenuc
https://maximedenuc.bandcamp.com/track/agoraphobia
https://open.spotify.com/track/2tosp3JAI9RxoiJmk6lDQB

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
George Frideric Handel, Johanna Koslowsky & Musica Alta Ripa:
🎵 Agrippina condotta a morire: Dunque sara pur vero, HWV.110
#GeorgeFridericHandel #JohannaKoslowsky
https://open.spotify.com/track/0ejCgHcefjtvnBog4Ve845