@kurtsh@mastodon.socialWelcome to the world of the field, engineering.
For a long time, we've hired very few into sales, mktg, support or consulting that don't already gobs of experience elsewhere.
✅ How #Microsoft’s developers are using #AI - The Verge
@philip@mastodon.mallegolhansen.com@… What worries me is that Software Engineering *is* my happy place.
After years of trying, I still can’t imagine what “something else” might look like for me.
@Techmeme@techhub.socialNeural Concept, whose 3D product design software uses deep learning to help cut development times, raised a $100M Series C, bringing its total funding to $130M (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-software-deals/20…
@deprogrammaticaipsum@mas.to"Before we go into what is missing, let us take a moment to understand why this partial story is so popular. Many software engineers do not engage with the broad “software engineering literature” very much: through the act of reading this magazine you are placing yourself at the pinnacle of software engineering curiosity!"
https…
@Techmeme@techhub.socialAdaptive Security, which uses AI to simulate social engineering attacks and help companies prevent them, raised an $81M Series B led by Bain Capital Ventures (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-software-d…
@tiotasram@kolektiva.socialWhen "self-driving" cars were first getting some hype back in ~2015 or so, I told people who asked me that I didn't think they'd be safe, and that I wished the same money were being invested in driver-assistance systems instead.
At the time, advocates were claiming that self-driving cars would be safer than human drivers.
We now have both self-driving cars and some nifty new driver assistance things, and it turns out that the self-driving cars are in fact being developed by corporations whose attention to the bottom line results in danger to others on the road pretty regularly. I don't actually have stats here for whether they're "safer than human drivers" or not, but the opportunity for one bad software update to make *all* self-driving cars dangerous at once kinda makes me doubt that.
Here's an example of Waymo cars getting "more aggressive" as they try to balance between being too timid and obstructing traffic (including emergency vehicles) and being too dangerous:
https://archive.ph/JJuGv
Here's another example of passing stopped schoolbusses leading to a software recall:
https://abcnews.go.com/GMA/News/waymo-issue-voluntary-software-recall-after-close-calls/story?id=128207776
In the first article, Waymo claims 91% fewer serious accidents per mile. Obviously an independent audit would be actually trustworthy, but even if we take that claim at face value, it's meaningless if an update tomorrow causes 100,000 accidents.
Note that they could be using better engineering practices, and the fact that they aren't shows that they don't care enough about the risks. They could be deploying new software versions incrementally and slowly, letting new versions rack up lots of miles only on a few vehicles before pushing them to a fleet. The should also have the equivalent of a simulation unit test for "schoolbus is stopped, what do?" and if a software version fails that test, it doesn't make it to the fleet. Clearly they don't have that.
I feel pretty vindicated in my earlier prediction that this tech is a bad idea in the hands of the current advocates.
@mro@digitalcourage.social⭐ The Simple Habit That Saves My Evenings | alikhil | software engineering, kubernetes & self-hosting
https://alikhil.dev/posts/the-simple-habit-that-saves-my-evenings/
Spoiler alert:
Here are the two main ideas of it:
* Don’t overwork
*…
@penguin42@mastodon.org.uk'graphviz' is a suite of programs for drawing graphs (In the nodes/edges senses, rather than upwards and to the right sense) - and it uses a file format called 'dot'. Lots of things generate dot output (such as systemd-analyze I mentioned) and it's really easy to generate from scripts and things. 'dotty' is probably the most common program in the suite.
There are some newer formats and programs - but this one is probably the most universal.
@thomasfuchs@hachyderm.ioMy most favorite subgenre of AI stan is the "AI software engineer" [programmers aren't engineers, engineering has formal requirements like e.g. engineering ethics].
Their logic goes like this:
"LLMs can generate a TODO-list application that pretty much works and only has a few hundred subtle bugs; therefore—as programming is the hardest and by far most intellectually taxing occupation that a human being such as myself can strive for to be an expert in—they obviously will excel and far outshine any human at any of those other, lesser, activities such as writing or art!"
@whitequark@mastodon.socialnew binary ninja release! it supports bitfield properly now!! and string deobfuscators! unbelievably good software https://binary.ninja/2025/11/13/binary-ninja-5.2-io.html
@cosmos4u@scicomm.xyzAn international group is organizing an observing campaign through the Citizen Science Working Group of the #LUMIO mission: LUMIO is an ESA space mission to observe lunar #impact flashes (LIFs) from space, on the lunar far side (#Geminid meteoroid stream, 13-15 Dec 2025. During the maximum of the stream, the number of visible impact flashes will be higher than during non-shower times, therefore there is a good chance of detecting at least some impact flashes.
Observations can be made using moderately-sized telescopes and a video camera. On the website https://lif.mi.imati.cnr.it/home_page.php there are now a recording of a thorough talk about the project and its slides at https://lif.mi.imati.cnr.it/open_item_page.php?item_idk=LDB-000000001 and slides about the preferred analysis software ALFI at https://lif.mi.imati.cnr.it/open_item_page.php?item_idk=LDB-000000002 -if you want to join in the LGC please sign on by 21 November.
@saraislet@infosec.exchangeIf I had a penny for every time I heard something like
"We're going to track increases in productivity that we gain by adopting GenAI"
1. So you're assuming it's an increase
2. Against what control group
3. With no acknowledgement of confounding variables or experiment design
4. Around the...famously open problem of measuring software engineering productivity?
@arXiv_csLG_bot@mastoxiv.pageCrosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot
@theodric@social.linux.pizzaFinally, we've automated copy-pasting from Stack Overflow
@frankel@mastodon.top
@mooncorebunny@social.linux.pizzaSIE PlayStation UK is hiring a test engineer. You have to be a UK resident, and not terribly far from Bristol; the position should be majority remote AFAIK. I think the pay should be at least 50k GBP annually.
The job entails being a flexible special agent who gets assigned to different PlayStation software engineering teams as needed to help bring projects to life. That includes possibly helping my team for a while. Management might ask you to travel to a different team's office t…
@arXiv_csLG_bot@mastoxiv.pageCrosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach
Xinyu Guan, Shaohua Zhang
https://arxiv.org/abs/2512.16927 https://mastoxiv.page/@arXiv_csDS_bot/115762062326187898
- SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
https://arxiv.org/abs/2512.16956 https://mastoxiv.page/@arXiv_csSE_bot/115762248476963893
- MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Saksham Sahai Srivastava, Haoyu He
https://arxiv.org/abs/2512.16962 https://mastoxiv.page/@arXiv_csCR_bot/115762140339109012
- Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Clas...
Faisal Ahmed
https://arxiv.org/abs/2512.16964 https://mastoxiv.page/@arXiv_eessIV_bot/115762196702065869
- Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, et al.
https://arxiv.org/abs/2512.16969 https://mastoxiv.page/@arXiv_csAI_bot/115762050529328276
- PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Kamer Ali Yuksel
https://arxiv.org/abs/2512.16970 https://mastoxiv.page/@arXiv_csAI_bot/115762054461584205
- A Women's Health Benchmark for Large Language Models
Elisabeth Gruber, et al.
https://arxiv.org/abs/2512.17028 https://mastoxiv.page/@arXiv_csCL_bot/115762049873946945
- Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
https://arxiv.org/abs/2512.17075 https://mastoxiv.page/@arXiv_csCL_bot/115762077400293945
- Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
https://arxiv.org/abs/2512.17127 https://mastoxiv.page/@arXiv_statML_bot/115762251753966702
- Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Huixin Zhan
https://arxiv.org/abs/2512.17146 https://mastoxiv.page/@arXiv_csCR_bot/115762318582013305
- Application of machine learning to predict food processing level using Open Food Facts
Arora, Chauhan, Rana, Aditya, Bhagat, Kumar, Kumar, Semar, Singh, Bagler
https://arxiv.org/abs/2512.17169 https://mastoxiv.page/@arXiv_qbioBM_bot/115762302873829397
- Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
Sandeep Neela
https://arxiv.org/abs/2512.17185 https://mastoxiv.page/@arXiv_qfinRM_bot/115762275982224870
- Do Foundational Audio Encoders Understand Music Structure?
Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
https://arxiv.org/abs/2512.17209 https://mastoxiv.page/@arXiv_csSD_bot/115762341541572505
- CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu, Zhicheng Jiao, Bin Jing, Quan Wang
https://arxiv.org/abs/2512.17213 https://mastoxiv.page/@arXiv_csCV_bot/115762574180736975
- Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Fun...
Deriyan Senjaya, Stephen Ekaputra Limantoro
https://arxiv.org/abs/2512.17245 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115762447037143855
- AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
https://arxiv.org/abs/2512.17251 https://mastoxiv.page/@arXiv_csCR_bot/115762396593872943
- Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, Zheli Liu
https://arxiv.org/abs/2512.17254 https://mastoxiv.page/@arXiv_csCR_bot/115762402470985707
- Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling A...
Abhivansh Gupta
https://arxiv.org/abs/2512.17259 https://mastoxiv.page/@arXiv_csMA_bot/115762225538364939
- Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan
https://arxiv.org/abs/2512.17277 https://mastoxiv.page/@arXiv_csIR_bot/115762214396869930
- LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass, Zheng-Hua Tan
https://arxiv.org/abs/2512.17281 https://mastoxiv.page/@arXiv_csSD_bot/115762361858560703
- Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose
https://arxiv.org/abs/2512.17340 https://mastoxiv.page/@arXiv_statME_bot/115762446402738033
toXiv_bot_toot
@Techmeme@techhub.socialArchy, which offers AI-powered dental practice management software, raised a $20M Series B led by TCV, bringing its total funding to $47M (Mary Ann Azevedo/Crunchbase News)
https://news.crunchbase.com/venture/dental-saas-provider-archy-raises-seriesb/
@mro@digitalcourage.social @Techmeme@techhub.socialResearch: AI's ability to complete long and complex software engineering tasks doubles every 6-7 months, but there is a "messiness tax" for real-world tasks (Boaz Barak/Windows On Theory)
https://windowsontheory.org/2025/11/04/thoughts-by…
@smurthys@hachyderm.ioI'm both sad and mad writing this commit comment, but it is true. 🥺 😤
#webdev #Jekyll #Liquid #GitHub #software_engineering
@Techmeme@techhub.socialCognition releases SWE-1.5, a new coding model in Windsurf, saying it partnered with Cerebras to serve SWE-1.5 at speeds up to 13x faster than Claude Sonnet 4.5 (Cognition)
https://cognition.ai/blog/swe-1-5
