Tootfinder

"#GitHub Actions Is Slowly Killing Your Engineering Team"
#Azure over GitHub. Yes, they managed to make something worse. Much worse.

Caitlin Kalinowski, OpenAI's head of hardware and robotic engineering, resigns citing concerns over domestic surveillance and lethal autonomous weapons systems (Sharon Goldman/Fortune)
https://fortune.com/2026/03/07/openai-…

The hidden #technicaldebt of #agenticengineering
https://newsletter.port.io/p/the-hidde

One of the weirder cold contacts I've gotten:
> I cannot write this email opener.
>
> The research signals explicitly disqualify this contact and company. City of Cape Town operates municipal infrastructure (water, sanitation, electricity, waste) with no database technology stack, distributed systems, or data engineering workloads that align with <cold email org>'s offerings.
Misfiring LLM or some dark pattern, Edgelord nonsense?!?

Anthropic details how it used 16 parallel Claude Opus 4.6 agents to build a Rust-based 100,000-line C compiler, incurring ~$20K in API costs over 2,000 sessions (Anthropic)
https://www.anthropic.com/engineering/building-c-compiler

Building a C compiler with a team of parallel Claudes
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Steve Bourne is internationally known for his work on the UNIX operating system.
During his career he spent 20 years in senior engineering management positions at computer systems and networking companies.
These included Cisco Systems, Sun Microsystems, Digital Equipment and Silicon Graphics.
Since 2000 he has been Chief Technology Officer at El Dorado Ventures (now Rally Ventures) in Menlo Park, California
Upcoming
The Design of Unix Shell, Stephen R. Bourne
…

"Building a C compiler with a team of parallel Claudes"
#AI #LLM #slop #NoAI

Jimi Hendrix Was a Systems Engineer - IEEE Spectrum
https://spectrum.ieee.org/jimi-hendrix-systems-engineer

from my link log —
Building resilient services at Amazon Prime Video with chaos engineering.
https://aws.amazon.com/blogs/opensource/building-resilient-services-at-prime-video-with-chaos-engineering/
saved 2020…

Building resilient services at Prime Video with chaos engineering | Amazon Web Services
Large-scale distributed software systems are composed of several individual sub-systems—such as CDNs, load balancers, and databases—and their interactions. These interactions sometimes have unpredictable outcomes caused by unforeseen turbulent events (for example, a network failure). These events can lead to system-wide failures. Chaos engineering is the discipline of experimenting on a distributed system to build confidence […]

Fabrizio Sgura will speak on 'Platform Engineering Starts at the Node: The Power of Immutable Operating Systems' as part of our Cloud Native Days track at SCaLE 23x. Full details: https://www.socallinuxexpo.org/scale/23x

On Tuesday famed climate scientist Kate Marvel joined the more than 10,000 people with PhDs in science, engineering and mathematics who, according to Science, are reported to have left the U.S. federal workforce since
Donald Trump took office in January.
Marvel resigned from NASA’s Goddard Institute for Space Sciences (GISS), where she studied climate change and its effects on Earth’s systems. In her resignation letter, she wrote that “the decision to leave was not an easy one.”…

Top climate scientist Kate Marvel just resigned from NASA. Here's why
Climate scientist Kate Marvel talked to Scientific American about her decision to leave NASA amid federal government turmoil and funding challenges

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
- Retrieving Climate Change Disinformation by Narrative
Upravitelev, Solopova, Jakob, Sahitaj, M\"oller, Schmitt
https://arxiv.org/abs/2603.22015 https://mastoxiv.page/@arXiv_csCL_bot/116283633674519408
- PaperVoyager : Building Interactive Web with Visual Language Models
Dasen Dai, Biao Wu, Meng Fang, Wenhao Wang
https://arxiv.org/abs/2603.22999 https://mastoxiv.page/@arXiv_csCL_bot/116289015432093128
- Continual Robot Skill and Task Learning via Dialogue
Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan
https://arxiv.org/abs/2409.03166 https://mastoxiv.page/@arXiv_csRO_bot/113089412115632702
- Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Zara Siddique, Irtaza Khalid, Liam D. Turner, Luis Espinosa-Anke
https://arxiv.org/abs/2503.05371 https://mastoxiv.page/@arXiv_csLG_bot/114136994263573386
- SkillFlow: Scalable and Efficient Agent Skill Retrieval System
Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos
https://arxiv.org/abs/2504.06188 https://mastoxiv.page/@arXiv_csAI_bot/114306773220502860
- Large Language Models for Computer-Aided Design: A Survey
Licheng Zhang, Bach Le, Naveed Akhtar, Siew-Kei Lam, Tuan Ngo
https://arxiv.org/abs/2505.08137 https://mastoxiv.page/@arXiv_csLG_bot/114504972217393639
- Structured Agent Distillation for Large Language Model
Liu, Kong, Dong, Yang, Li, Tang, Yuan, Niu, Zhang, Zhao, Lin, Huang, Wang
https://arxiv.org/abs/2505.13820 https://mastoxiv.page/@arXiv_csLG_bot/114544636506163783
- VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction
Fan, Zhang, Li, Zhang, Chen, Hu, Wang, Qu, Zhou, Wang, Yan, Xu, Theiss, Chen, Li, Tu, Wang, Ranjan
https://arxiv.org/abs/2505.20279 https://mastoxiv.page/@arXiv_csCV_bot/114578817567171199
- Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Bhattacharjee, Tian, Rubin, Lo, Merchant, Hanson, Gounley, Tandon
https://arxiv.org/abs/2506.04450 https://mastoxiv.page/@arXiv_csCR_bot/114635189706505648
- L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search
Ziqi Wang, Boqin Yuan
https://arxiv.org/abs/2509.00761 https://mastoxiv.page/@arXiv_csAI_bot/115140304787881576
- Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Han, Huang, Liao, Jiang, Lu, Zhao, Wang, Zhou, Jiang, Liang, Zhou, Sun, Yu, Xiao
https://arxiv.org/abs/2509.23392 https://mastoxiv.page/@arXiv_csAI_bot/115293169353788311
- Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith, Trevor Darrell, Zeynep Akata
https://arxiv.org/abs/2510.03721 https://mastoxiv.page/@arXiv_csCV_bot/115332690912652473
- Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Zhang, Hu, Upasani, Ma, Hong, Kamanuru, Rainton, Wu, Ji, Li, Thakker, Zou, Olukotun
https://arxiv.org/abs/2510.04618 https://mastoxiv.page/@arXiv_csLG_bot/115332999596603375
- Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Giannone, Xu, Nayak, Awhad, Sudalairaj, Xu, Srivastava
https://arxiv.org/abs/2510.05825 https://mastoxiv.page/@arXiv_csLG_bot/115338159696513898
- Complete asymptotic type-token relationship for growing complex systems with inverse power-law co...
Pablo Rosillo-Rodes, Laurent H\'ebert-Dufresne, Peter Sheridan Dodds
https://arxiv.org/abs/2511.02069 https://mastoxiv.page/@arXiv_physicssocph_bot/115496283627867809
- ViPRA: Video Prediction for Robot Actions
Sandeep Routray, Hengkai Pan, Unnat Jain, Shikhar Bahl, Deepak Pathak
https://arxiv.org/abs/2511.07732 https://mastoxiv.page/@arXiv_csRO_bot/115535941444003568
- AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance
Chandrachur Bhattacharya, Sibendu Som
https://arxiv.org/abs/2511.14043
- VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Yufei Yin, Qianke Meng, Minghao Chen, Jiajun Ding, Zhenwei Shao, Zhou Yu
https://arxiv.org/abs/2512.12360 https://mastoxiv.page/@arXiv_csCV_bot/115729238732682644
- RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
L\'eo Butsanets, Charles Corbi\`ere, Julien Khlaut, Pierre Manceron, Corentin Dancette
https://arxiv.org/abs/2512.17396 https://mastoxiv.page/@arXiv_csCV_bot/115762705911757243
- Measuring all the noises of LLM Evals
Sida Wang
https://arxiv.org/abs/2512.21326 https://mastoxiv.page/@arXiv_csLG_bot/115779597137785637
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Cryogenics and the use of superfluid helium in high-energy particle accelerators (1980-2000)
Philippe Lebrun
https://arxiv.org/abs/2602.14298 https://arxiv.org/pdf/2602.14298 https://arxiv.org/html/2602.14298
arXiv:2602.14298v1 Announce Type: new
Abstract: The period 1980-2000 saw the impressive development of applied superconductivity in high-energy particle accelerators, from single components to long strings of superconducting magnets and high-frequency acceleration cavities. Large and powerful cryogenic systems were designed ancillary to superconducting devices operating generally close to the normal boiling point of helium, but also above 4.2 K in supercritical and below 2 K in superfluid. Low-temperature operation in accelerators also involves considerations of ultra-high vacuum, limited stored energy and beam stability. We recall the rationale for cryogenics in high-energy particle accelerators and review its development over the period of interest, with reference to the main engineering domains of cryostat design and heat loads, cooling schemes, efficient power refrigeration and cryogenic fluid management. In view of its importance and novelty, a specific section is devoted to the developments that led to the LHC at CERN.
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