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@ErikUden@mastodon.de
2025-06-23 19:05:59

Can you describe specific ways you have integrated Al tools into your development workflow? Please include any custom setups, automations, or use cases beyond simple prompt usage.
there is a monster in the forest and it speaks with a thousand voices. it will answer any question you pose it, it will offer insight to any idea. it will help you, it will thank you, it will never bid you leave. it will even tell you of the darkest arts, if you know precisely how to ask.…

@Techmeme@techhub.social
2025-06-23 20:45:44

Salesforce launches Agentforce 3 with an observability tool called Command Center and MCP support, and says 8,000 customers have signed up to deploy Agentforce (Larry Dignan/Constellation Research)
constellationr.com/blog-news/i

@tiotasram@kolektiva.social
2025-06-21 02:34:13

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

@tante@tldr.nettime.org
2025-07-21 08:03:34

It does have use cases (we use it for prototyping spatial experiences at work) but for mainstream use that is spot on. The tech doesn't work good enough to combat all the negative aspects that come with its usage.
mastodon.social/@anon_opin/114

@arXiv_csSE_bot@mastoxiv.page
2025-06-24 11:55:40

Use Property-Based Testing to Bridge LLM Code Generation and Validation
Lehan He, Zeren Chen, Zhe Zhang, Jing Shao, Xiang Gao, Lu Sheng
arxiv.org/abs/2506.18315

@arXiv_csCR_bot@mastoxiv.page
2025-07-22 07:53:50

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design
Richard M. Charles, James H. Curry, Richard B. Charles
arxiv.org/abs/2507.14207

@arXiv_quantph_bot@mastoxiv.page
2025-07-24 10:12:29

Development of a Standardized Testing Environment for QRNGs based on Semiconductor Laser Phase Noise
Matthias Ostner, Innocenzo De Marco, Christian Roubal
arxiv.org/abs/2507.17471

@arXiv_csSD_bot@mastoxiv.page
2025-07-23 09:08:52

SDBench: A Comprehensive Benchmark Suite for Speaker Diarization
Eduardo Pacheco, Atila Orhon, Berkin Durmus, Blaise Munyampirwa, Andrey Leonov
arxiv.org/abs/2507.16136

@jtk@infosec.exchange
2025-07-22 20:00:47

I'm at best a combat C programmer so this latest zmap fix may be perfectly reasonable.
github.com/zmap/zmap/pull/944
Is it common, and is it a best practice to not free memory allocated in some use cases? That may be two separation questions.
I have always freed memory allo…

@arXiv_csCY_bot@mastoxiv.page
2025-08-21 07:56:20

Documenting Deployment with Fabric: A Repository of Real-World AI Governance
Mackenzie Jorgensen, Kendall Brogle, Katherine M. Collins, Lujain Ibrahim, Arina Shah, Petra Ivanovic, Noah Broestl, Gabriel Piles, Paul Dongha, Hatim Abdulhussein, Adrian Weller, Jillian Powers, Umang Bhatt
arxiv.org/abs/2508.14119

@arXiv_astrophHE_bot@mastoxiv.page
2025-06-24 09:45:00

Dim cores of radio-bright AGN jets: VLBI and Gaia astrometry pinpoint different parsec-scale features
A. V. Popkov (MIPT, LPI), Y. Y. Kovalev (MPIfR), A. V. Plavin (BHI Harvard), L. Y. Petrov (NASA GSFC), I. N. Pashchenko (LPI)
arxiv.org/abs/2506.17769

@arXiv_mathNA_bot@mastoxiv.page
2025-06-24 11:39:40

Optimal adaptive implicit time stepping
Michael Feischl, David Niederkofler
arxiv.org/abs/2506.18809 arxiv.org/pdf/25…

@arXiv_csIR_bot@mastoxiv.page
2025-08-21 09:03:20

Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
Lorenz Brehme, Benedikt Dornauer, Thomas Str\"ohle, Maximilian Ehrhart, Ruth Breu
arxiv.org/abs/2508.14066

@seeingwithsound@mas.to
2025-08-19 07:49:42

(LinkedIn) Revision Implant is despite its name already quickly looking for markets beyond visual prostheses linkedin.com/posts/revision-im

@aardrian@toot.cafe
2025-08-11 20:49:45

I think we should use CSS logical properties wherever we can. Chris Coyier has outlined some cases where we cannot:
frontendmasters.com/blog/shoul
I made a traditional to logical mapping in [checks wat…

@arXiv_csPL_bot@mastoxiv.page
2025-07-22 08:19:50

A Few Fit Most: Improving Performance Portability of SGEMM on GPUs using Multi-Versioning
Robert Hochgraf (Rochester Institute of Technology), Sreepathi Pai (University of Rochester)
arxiv.org/abs/2507.15277

@tiotasram@kolektiva.social
2025-07-17 13:31:49

To add a single example here (feel free to chime in with your own):
Problem: editing code is sometimes tedious because external APIs require boilerplate.
Solutions:
- Use LLM-generated code. Downsides: energy use, code theft, potential for legal liability, makes mistakes, etc. Upsides: popular among some peers, seems easy to use.
- Pick a better library (not always possible).
- Build internal functions to centralize boilerplate code, then use those (benefits: you get a better understanding of the external API, and a more-unit-testable internal code surface; probably less amortized effort).
- Develop a non-LLM system that actually reasons about code at something like the formal semantics level and suggests boilerplate fill-ins based on rules, while foregrounding which rules it's applying so you can see the logic behind the suggestions (needs research).
Obviously LLM use in coding goes beyond this single issue, but there are similar analyses for each potential use of LLMs in coding. I'm all cases there are:
1. Existing practical solutions that require more effort (or in many cases just seem to but are less-effort when amortized).
2. Near-term researchable solutions that directly address the problem and which would be much more desirable in the long term.
Thus in addition to disastrous LLM effects on the climate, on data laborers, and on the digital commons, they tend to suck us into cheap-seeming but ultimately costly design practices while also crowding out better long-term solutions. Next time someone suggests how useful LLMs are for some task, try asking yourself (or them) what an ideal solution for that task would look like, and whether LLM use moves us closer to or father from a world in which that solution exists.

@geant@mstdn.social
2025-08-20 12:33:09

🌟 New SIGs Spotlight: SIG-AI 🌟
A new space for collaboration on Artificial Intelligence within the NREN community is here.
SIG-AI brings the Research & Education community together to share expertise, best practices, and explore practical use cases of AI in NREN context—from cybersecurity and High-Performance Computing (HPC) to network automation and next-generation networks.
📖 For more insights, read the full interview with Leonie Schäfer (@…

Picture from the SIG-AI meeting in Prague on April 7, 2025. Credits to Leonie Schäfer, DFN.
Picture from the SIG-AI meeting in Prague in December 2024.
@arXiv_csSE_bot@mastoxiv.page
2025-08-20 07:39:09

A Comparative Study of Delta Parquet, Iceberg, and Hudi for Automotive Data Engineering Use Cases
Dinesh Eswararaj, Ajay Babu Nellipudi, Vandana Kollati
arxiv.org/abs/2508.13396

@arXiv_csCL_bot@mastoxiv.page
2025-08-21 09:41:20

ISCA: A Framework for Interview-Style Conversational Agents
Charles Welch, Allison Lahnala, Vasudha Varadarajan, Lucie Flek, Rada Mihalcea, J. Lomax Boyd, Jo\~ao Sedoc
arxiv.org/abs/2508.14344

@rperezrosario@mastodon.social
2025-07-19 01:09:31

Software Engineer Will Larson unpacks a lot in this July 2025 post. Key takeaway use cases of agentic AI include:
1. Using an LLM to evaluate a context window and get a result.
2. Using an LLM to suggest tools relevant to the context window, then enrich it with the tool’s response.
3. Managing flow control for tool usage.
4. Doing anything software can do to build better context windows to pass on to LLMs.
"What can agents actually do?"

@Techmeme@techhub.social
2025-07-17 01:50:38

Bengaluru-based QpiAI, which is integrating AI and quantum computing for enterprise use cases, raised a $32M Series A at a $162M post-money valuation (Jagmeet Singh/TechCrunch)
techcrunch.com/2025/07/16/indi

@arXiv_condmatsoft_bot@mastoxiv.page
2025-08-22 09:30:41

Reinforcement learning of a biflagellate model microswimmer
Sridhar Bulusu, Andreas Z\"ottl
arxiv.org/abs/2508.15561 arxiv.org/pdf/250…

@arXiv_csAI_bot@mastoxiv.page
2025-08-20 09:43:50

Discrete Optimization of Min-Max Violation and its Applications Across Computational Sciences
Cheikh Ahmed, Mahdi Mostajabdaveh, Samin Aref, Zirui Zhou
arxiv.org/abs/2508.13437

@arXiv_csCV_bot@mastoxiv.page
2025-08-20 10:16:10

Shape-from-Template with Generalised Camera
Agniva Sengupta, Stefan Zachow
arxiv.org/abs/2508.13791 arxiv.org/pdf/2508.13791

@arXiv_quantph_bot@mastoxiv.page
2025-06-19 10:07:18

What is a good use case for quantum computers?
Michael Marthaler, Peter Pinski, Pascal Stadler, Vladimir Rybkin, Marina Walt
arxiv.org/abs/2506.15426

@migueldeicaza@mastodon.social
2025-06-06 16:51:12

Cute skeleton app that embeds Godot into a native iOS app:
github.com/xander-carruth/swif

@arXiv_csOH_bot@mastoxiv.page
2025-07-18 07:45:32

Digital Twins in Industrial Applications: Concepts, Mathematical Modeling, and Use Cases
Ali Mohammad-Djafari
arxiv.org/abs/2507.12468 arxi…

@nfdi4culture@nfdi.social
2025-06-17 12:29:37

👉 Ihr könnt euch noch für den zweiteiligen Online-Workshop „Iconclass - Content Classification and Technical Features“ am Montag, 30.06. und Dienstag, 1.07. (jeweils 10 - 13 Uhr) anmelden!
💡 In Kooperation mit dem @… werden ausgewählte Use Cases präsentiert und die Möglichkeit zum Austausch angeboten, unter anderem in Breakout-Sessio…

@cdp1337@social.veraciousnetwork.com
2025-08-18 06:58:34

Continuing on my Meshtastic kick, (probably because I keep buying radios to play with...) This time I have a configuration guide for common settings I've found which are useful. Still a work in progress but I think most of the common options are there.
If I've forgotten something, gotten something wrong, or you have a trick I should add, let me know!

@arXiv_astrophSR_bot@mastoxiv.page
2025-06-12 09:46:51

Noise in Maps of the Sun at Radio Wavelengths II: Solar Use Cases
Timothy Bastian, Bin Chen, Surajit Mondal, Pascal Saint-Hilaire
arxiv.org/abs/2506.09843

@samir@functional.computer
2025-06-16 10:35:59

@… I have made the decision to simply not bother with floats in the language I’m making.
I think I will eventually add both units and ratios, which should cover most use cases, I think.

@arXiv_mathDG_bot@mastoxiv.page
2025-07-18 09:08:12

Search for Z/2 eigenfunctions on the sphere using machine learning
Andriy Haydys, Willem Adriaan Salm
arxiv.org/abs/2507.13122

@arXiv_csCL_bot@mastoxiv.page
2025-06-18 09:08:18

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Daniel D'souza, Julia Kreutzer, Adrien Morisot, Ahmet \"Ust\"un, Sara Hooker
arxiv.org/abs/2506.14702

@johnleonard@mastodon.social
2025-06-26 15:13:31

A US judge has said that Meta’s use of copyrighted books to train its AI models constitutes "fair use" under US copyright law. It follows a similar judgement about Anthropic earlier this week, and will come as disappointment to authors and other creators looking for compensation in what they see as use of their work without permission.

@grumpybozo@toad.social
2025-06-07 23:19:36

I maintain a heterogeneous MSP environment for backup which consists of a collection of sh scripts (they mostly run on FreeBSD) with (c) notes dating back to 2004, with 5 authors, 4 of whom are no longer my cow-orkers. As the unfortunate 5th, I am still doing tweaks to catch edge & corner cases >20y after the 1st author had the idea that rsync, shell, mt, & standard POSIX tools could be assembled into a decent free backup world.
Use Python. Or Go. Or even Perl.

@mia@hcommons.social
2025-08-13 16:11:14

I read 'The Public Interest Corpus Update – NYC Edition'. More work on the project's principles and goals, research and library service use cases, and thinking ahead to prospective year 1-3 and year 4-6 activities publicinterestcorpus.org/the-p

@arXiv_csNI_bot@mastoxiv.page
2025-08-20 08:46:50

Architecture Considerations for ISAC in 6G
Sebastian Robitzsch, Laksh Bhatia, Konstantinos G. Filis, Neda Petreska, Michael Bahr, Pablo Picazo Martinez, Xi Li
arxiv.org/abs/2508.13736

@arXiv_eessAS_bot@mastoxiv.page
2025-08-11 09:11:30

Use Cases for Voice Anonymization
Sarina Meyer, Ngoc Thang Vu
arxiv.org/abs/2508.06356 arxiv.org/pdf/2508.06356

@hacksilon@infosec.exchange
2025-06-11 07:11:06

Seeing some people claiming that Apple killed @… with the announcements at #WWDC25. I don't know how these people use Raycast, but for me, the updated spotlight does not actually cover my use cases. Especially the ability to install extensions and add your own sc…

@digitalnaiv@mastodon.social
2025-07-14 06:23:06

SAP-Chef Klein erklärt die #DigitaleSouveränität Europas für gelöst, Die Kontrolle über Daten sei längst Realität, entscheidend sei nicht mehr die Hardware, sondern wer den Schlüssel besitzt. Die eigentliche Herausforderung sieht er im Mangel an klugen Köpfen, nicht an Serverfarmen. Sein Appell: Weniger Infrastrukturträume, mehr Fokus auf Bildung und Innovation. Europas Zu…

@alsutton@snapp.social
2025-06-14 09:54:14

Some days it just blows my mind how some pieces of software have gained so much traction, with so little in the way of support for some common use cases.
Today; #Docker running on an IPv6 only host.
Who thought assigning IPv4 addresses to containers running on an IPv6 only host was a sensible default that is so difficult to override?
🤯

@alexanderadam@ruby.social
2025-06-13 08:43:50

The #oslo #airport refuel application is using an #Android application written in @… says

Charles Nutter presenting a slide with different use cases of JRuby
@mgorny@social.treehouse.systems
2025-06-29 16:44:37

So #Gentoo #Python eclasses are pretty modern, in the sense that they tend to follow the best practices and standards, and eventually deal with deprecations. Nevertheless, they have a long history and carry quite some historical burden, particularly regarding to naming.
The key point is that the eclasses were conceived as a replacement for the old eclasses: "distutils" and "python". Hence, much like we revision ebuilds, I've named the matching eclasses "distutils-r1" and "python-r1". For consistency, I've also used the "-r1" suffix for the remaining eclasses introduced at the time: "python-any-r1", "python-single-r1" and "python-utils-r1" — even though there were never "r0"s.
It didn't take long to realize my first mistake. I've made the multi-impl eclass effectively the "main" eclass, probably largely inspired by the previous Gentoo recommendations. However, in the end I've found out that for the most use cases (i.e. where "distutils-r1" is not involved), there is no real need for multi-impl, and it makes things much harder. So if I were naming them today, I would have named it "python-multi", to indicate the specific use case — and either avoid designating a default at all, or made "python-single" the default.
What aged even worse is the "distutils-r1" eclass. Admittedly, back when it was conceived, distutils was still largely a thing — and there were people (like me) who avoided unnecessary dependency on setuptools. Of course, nowadays it has been entirely devoured by setuptools, and with #PEP517 even "setuptools" wouldn't be a good name anymore. Nowadays, people are getting confused why they are supposed to use "distutils-r1" for, say, Hatchling.
Admittedly, this is something I could have done differently — PEP517 support was a major migration, and involved an explicit switch. Instead of adding DISTUTILS_USE_PEP517 (what a self-contradictory name) variable, I could have forked the eclass. Why didn't I do that? Because there used to be a lot of code shared between the two paths. Of course, over time they diverged more, and eventually I've dropped the legacy support — but the opportunity to rename was lost.
In fact, as a semi-related fact, I've recognized another design problem with the eclass earlier — I should have gone for two eclasses rather than one: a "python-phase" eclass with generic sub-phase support, and a "distutils" (or later "python-pep517") implementing default sub-phases for the common backends. And again, this is precisely how I could have solved the code reuse problem when I introduced PEP517 support.
But then, I didn't anticipate how the eclasses would end up looking like in the end — and I can't really predict what new challenges the Python ecosystem is going to bring us. And I think it's too late to rename or split stuff — too much busywork on everyone.

@mxp@mastodon.acm.org
2025-08-11 17:41:57

AGI is “not a super useful term”? But IIRC, as defined by OpenAI, they’ll hit AGI when they generate $100 billion in profit. So, how’s that coming along? Not so great, huh?
cnbc.com/2025/08/11/sam-altman

@arXiv_csDB_bot@mastoxiv.page
2025-07-18 07:59:12

Transforming Football Data into Object-centric Event Logs with Spatial Context Information
Vito Chan, Lennart Ebert, Paul-Julius Hillmann, Christoffer Rubensson, Stephan A. Fahrenkrog-Petersen, Jan Mendling
arxiv.org/abs/2507.12504

@arXiv_csCY_bot@mastoxiv.page
2025-06-18 08:10:40

Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
Bonam Mingole, Aditya Majumdar, Firdaus Ahmed Choudhury, Jennifer L. Kraschnewski, Shyam S. Sundar, Amulya Yadav
arxiv.org/abs/2506.13805

@arXiv_csDC_bot@mastoxiv.page
2025-07-16 09:12:11

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Arnab Mukherjee, Raju Halder, Joydeep Chandra
arxiv.org/abs/2507.11430

@pbloem@sigmoid.social
2025-07-11 17:26:10

It's worth bearing in mind that all AI companies are in that phase where they burn money to attract the most customers and hope that the competition blinks first. That means all AI is pretty badly underpriced.
For coding, that's a problem. It's just on the edge of being arguably positive for some. If the price goes up by an order of ten, the bubble is going to burst. And it may take the other AI use cases with it. After all, coding was kind of a killer app.

@ripienaar@devco.social
2025-08-08 11:13:17

Not much to say about GPT-5 yet for my use cases but OMG FINALLY NO FUCKING EMOJIS EVERYWHERE.

@benrosstransit@mastodon.social
2025-07-09 19:43:33

@… I don't buy this argument. It assumes the means-tested service is used involuntarily. You can't equivalently tax eg a library user fee or a bus fare because use is unpredictable & even depends on the fee.
The political argument (which goes back way before 1998) is the real one. It also guides you in cases like bus fares where it's not …

@arXiv_mathOC_bot@mastoxiv.page
2025-06-16 09:51:10

Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
Mathukumalli Vidyasagar
arxiv.org/abs/2506.11904

@arXiv_mathAP_bot@mastoxiv.page
2025-06-18 08:48:00

Exact Poincar\'e Constants in three-dimensional Annuli
Bernd Rummler, Michael Ruzicka, Gudrun Th\"ater
arxiv.org/abs/2506.13891

@arXiv_csHC_bot@mastoxiv.page
2025-08-04 09:32:50

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Rebecca Yu, Valerie Chen, Ameet Talwalkar, Hoda Heidari
arxiv.org/abs/2508.00723

@arXiv_mathNT_bot@mastoxiv.page
2025-08-15 08:56:02

Local-global compatibility and the exceptional zero conjecture for GL(3)
Daniel Barrera Salazar, Andrew Graham, Chris Williams
arxiv.org/abs/2508.10225

@arXiv_statML_bot@mastoxiv.page
2025-08-06 08:51:00

Hedging with memory: shallow and deep learning with signatures
Eduardo Abi Jaber, Louis-Amand G\'erard
arxiv.org/abs/2508.02759 arxiv.o…

@arXiv_grqc_bot@mastoxiv.page
2025-08-15 09:24:22

Compact Binary Coalescence Sensitivity Estimates with Injection Campaigns during the LIGO-Virgo-KAGRA Collaborations' Fourth Observing Run
Reed Essick, Michael W. Coughlin, Michael Zevin, Deep Chatterjee, Teagan A. Clarke, Utkarsh Mali, Simona Miller, Nathan Steinle, Pratyusava Baral, Amanda C. Baylor, Gareth Cabourn Davies, Thomas Dent, Prathamesh Joshi, Praveen Kumar, Cody Messick, Tanmaya Mishra, Amazigh Ouzriat, Khun Sang Phukon, Lorenzo Piccari, Marion Pillas, Max Trevor, Thom…

@arXiv_mathCO_bot@mastoxiv.page
2025-08-12 08:42:33

Tropical fans supporting a reduced 0-dimensional complete intersection
Linxuan Li
arxiv.org/abs/2508.06694 arxiv.org/pdf/2508.06694

@arXiv_eessIV_bot@mastoxiv.page
2025-06-13 08:03:10

The Iris File Extension
Ryan Erik Landvater, Michael David Olp, Mustafa Yousif, Ulysses Balis
arxiv.org/abs/2506.10009

@arXiv_eessSP_bot@mastoxiv.page
2025-07-01 11:35:03

Limited Feedback in RIS-Assisted Wireless Communications: Use Cases, Challenges, and Future Directions
Weicong Chen, Jiajia Guo, Yiming Cui, Xiao Li, Shi Jin
arxiv.org/abs/2506.22903

@arXiv_astrophIM_bot@mastoxiv.page
2025-07-16 08:45:41

Mapping Diffuse Radio Sources Using TUNA: A Transformer-Based Deep Learning Approach
Nicoletta Sanvitale, Claudio Gheller, Franco Vazza, Annalisa Bonafede, Virginia Cuciti, Emanuele De Rubeis, Federica Govoni, Matteo Murgia, Valentina Vacca
arxiv.org/abs/2507.11320

@arXiv_statME_bot@mastoxiv.page
2025-06-13 09:42:50

Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'
Luis R. Pericchi, Maria-Eglee Perez
arxiv.org/abs/2506.10114

@jae@mastodon.me.uk
2025-07-29 18:56:34

Literally one of the best use cases for the Switch 2 mouse. nintendolife.com/news/2025/07/

@servelan@newsie.social
2025-07-01 03:55:04

"[T]he Trump administration would not only charge individual journalists under the Espionage Act, but would also indict their employers as "co-conspirators" and bring cases against publications. A separate source described as a "senior Trump administration official" affirmed that the question of whether to invoke the Espionage Act wasn't merely theoretical."
**'Why not the press?' Trump may use this century-old law to prosecute and jail journalists** - Alternet.org
alternet.org/trump-prosecute-j

@arXiv_mathAG_bot@mastoxiv.page
2025-06-10 17:41:20

This arxiv.org/abs/2505.12156 has been replaced.
initial toot: mastoxiv.page/@arXiv_mat…

@azonenberg@ioc.exchange
2025-07-26 04:38:42

Quick test board for STM32H750 UFBGA240 25 paired with the Trion T20 BGA256 FPGA devkit.
Goals:
* Validate my KiCAD symbol for STM32H750 in TFBGA240 25
* Bring up a basic BSP and stm32-cpp support for the STM32H750 (should be straightforward with most stuff very similar to the H735)
* Play with the H7 QUADSPI and see how it works for both XIP and copy-code-to-SRAM use cases
* Add QUADSPI flash driver for microkvs (which currently only supports internal flash)
*…

KiCAD 3D render of a board with a single big BGA MCU surrounded by connectors and not much else
Underside of the board showing decoupling caps
KiCAD layout view of the board showing dense routing on both outer signal layers
@arXiv_csNI_bot@mastoxiv.page
2025-07-18 08:47:52

Predictability-Aware Motion Prediction for Edge XR via High-Order Error-State Kalman Filtering
Ziyu Zhong, Hector A Caltenco, Bj\"orn Landfeldt, G\"unter Alce
arxiv.org/abs/2507.13179

@samueljohn@mastodon.world
2025-08-02 15:38:16

This resonates 50% with me. But the other 50%, I am like you and your manager have to become more the architects and less the lines-of-code-checker. Also thinking about tests and edge cases is even more important now. exquisite.social/@thomholwerda

@arXiv_csSE_bot@mastoxiv.page
2025-06-05 07:22:50

From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation
Peter Pfeiffer, Alexander Rombach, Maxim Majlatow, Nijat Mehdiyev
arxiv.org/abs/2506.03801

@rperezrosario@mastodon.social
2025-06-09 03:56:37

Deepintodev.com reviews how modern database storage engines store, retrieve, and update database table rows on memory and disk. This serves as a foundation to understanding how indexes and clustered indexes work, the concerned data structures, and their use cases; these are reviewed as well.
"How Databases Store Your Tables on Disk"

@tiotasram@kolektiva.social
2025-08-02 13:28:40

How to tell a vibe coder of lying when they say they check their code.
People who will admit to using LLMs to write code will usually claim that they "carefully check" the output since we all know that LLM code has a lot of errors in it. This is insufficient to address several problems that LLMs cause, including labor issues, digital commons stress/pollution, license violation, and environmental issues, but at least it's they are checking their code carefully we shouldn't assume that it's any worse quality-wise than human-authored code, right?
Well, from principles alone we can expect it to be worse, since checking code the AI wrote is a much more boring task than writing code yourself, so anyone who has ever studied human-computer interaction even a little bit can predict people will quickly slack off, stating to trust the AI way too much, because it's less work. I'm a different domain, the journalist who published an entire "summer reading list" full of nonexistent titles is a great example of this. I'm sure he also intended to carefully check the AI output, but then got lazy. Clearly he did not have a good grasp of the likely failure modes of the tool he was using.
But for vibe coders, there's one easy tell we can look for, at least in some cases: coding in Python without type hints. To be clear, this doesn't apply to novice coders, who might not be aware that type hints are an option. But any serious Python software engineer, whether they used type hints before or not, would know that they're an option. And if you know they're an option, you also know they're an excellent tool for catching code defects, with a very low effort:reward ratio, especially if we assume an LLM generates them. Of the cases where adding types requires any thought at all, 95% of them offer chances to improve your code design and make it more robust. Knowing about but not using type hints in Python is a great sign that you don't care very much about code quality. That's totally fine in many cases: I've got a few demos or jam games in Python with no type hints, and it's okay that they're buggy. I was never going to debug them to a polished level anyways. But if we're talking about a vibe coder who claims that they're taking extra care to check for the (frequent) LLM-induced errors, that's not the situation.
Note that this shouldn't be read as an endorsement of vibe coding for demos or other rough-is-acceptable code: the other ethical issues I skipped past at the start still make it unethical to use in all but a few cases (for example, I have my students use it for a single assignment so they can see for themselves how it's not all it's cracked up to be, and even then they have an option to observe a pre-recorded prompt session instead).

@knurd42@social.linux.pizza
2025-06-25 17:10:47

'"Time handling is everywhere in software, but many programmers talk about the topic with dread and fear. Some warn about how difficult the topic is to understand, listing bizarre timezone edge cases as evidence of complexity. Others repeat advice like "just use UTC bro" as if it were an unconditional rule - if your program needs precise timekeeping or has user-facing datetime interactions, this advice will almost certainly cause bugs or confusing behavior. Here's a co…

@arXiv_csHC_bot@mastoxiv.page
2025-06-03 16:54:22

This arxiv.org/abs/2503.18792 has been replaced.
initial toot: mastoxiv.page/@arXiv_csHC_…

@arXiv_csDB_bot@mastoxiv.page
2025-06-16 07:24:49

Jelly: a fast and convenient RDF serialization format
Piotr Sowinski, Karolina Bogacka, Anastasiya Danilenka, Nikita Kozlov
arxiv.org/abs/2506.11298

@aardrian@toot.cafe
2025-06-04 18:14:03

Apropos of yet another conversation today, I’m a big fan of using automation in WCAG testing.
But I also know WCAG well enough to understand the limitations (and lies) the tools.
adrianroselli.com/2025/04/auto

@samir@functional.computer
2025-06-05 06:41:40

@… @… Agreed, I think GHC *used* to be primarily a place for experimentation, but now there's a lot more focus on industrial use cases.
IMO the real problem is that there are 2 people who actually work on it profess…

@tante@tldr.nettime.org
2025-06-27 15:20:42

This comes down to the cyberlibertarian roots of most digital movements (thing Archive.org, EFF, EDRI etc.): To them "open" is a value in itself and any political values are read as "restrictions" or "regulation" or "lack of freedom".
indieweb.social/@jaredw…

@arXiv_csCV_bot@mastoxiv.page
2025-07-10 10:19:31

4KAgent: Agentic Any Image to 4K Super-Resolution
Yushen Zuo, Qi Zheng, Mingyang Wu, Xinrui Jiang, Renjie Li, Jian Wang, Yide Zhang, Gengchen Mai, Lihong V. Wang, James Zou, Xiaoyu Wang, Ming-Hsuan Yang, Zhengzhong Tu
arxiv.org/abs/2507.07105

@mgorny@social.treehouse.systems
2025-08-14 20:04:24

New on #Quansight PBC blog: Python Wheels: from Tags to Variants
#Python distributions are uniform across different Python versions and platforms. For these distributions, it is sufficient to publish a single wheel that can be installed everywhere. However, some packages are more complex than that; they include compiled Python extensions or binaries. In order to robustly deploy these software on different platforms, you need to publish multiple binary packages, and the installers need to select the one that fits the platform used best.
For a long time, Python wheels made do with a relatively simple mechanism to describe the needed variance: Platform compatibility tags. These tags identified different Python implementations and versions, operating systems, and CPU architectures. Over time, they were extended to facilitate new use cases. To list a couple: PEP 513 added manylinux tags to standardize the core library dependencies on GNU/Linux systems, and PEP 656 added musllinux tags to facilitate Linux systems with musl libc.
However, not all new use cases can be handled effectively within the framework of tags. To list a few:
• The advent of GPU-backed computing made distinguishing different acceleration frameworks such as NVIDIA CUDA or AMD ROCm important.
• As the compatibility with older CPUs became less desirable, many distributions have set baselines for their binary packages to x86-64-v2 microarchitecture level, and Python packages need to be able to express the same requirement.
• Numerical libraries support different BLAS/LAPACK, MPI, OpenMP providers, and wish to enable the users to choose the build matching their desired provider.
While tags could technically be bent to facilitate all these use cases, they would grow quite baroque, and, critically, every change to tags needs to be implemented in all installers and package-related tooling separately, making the adoption difficult.
Facing these limitations, software vendors have employed different solutions to work around the lack of an appropriate mechanism. Eventually, the #WheelNext initiative took up the challenge to design a more robust solution.
"""
#packaging

@arXiv_csPL_bot@mastoxiv.page
2025-06-10 07:56:52

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
Hiromi Ishii (Jij, Inc), Taro Shimizu (Jij, Inc), Toshiki Teramura (Jij, Inc)
arxiv.org/abs/2506.06495

@arXiv_csCR_bot@mastoxiv.page
2025-08-11 09:45:29

On Digital Twins in Defence: Overview and Applications
Marco Giberna, Holger Voos, Paulo Tavares, Jo\~ao Nunes, Tobias Sorg, Andrea Masini, Jose Luis Sanchez-Lopez
arxiv.org/abs/2508.05717

@arXiv_csDC_bot@mastoxiv.page
2025-08-12 09:41:43

The Fused Kernel Library: A C API to Develop Highly-Efficient GPU Libraries
Oscar Amoros (Universitat Politecnica de Catalunya), Albert Andaluz (Independent researcher), Johnny Nunez (NVIDIA), Antonio J. Pena (Barcelona Supercomputing Center)
arxiv.org/abs/2508.07071

@arXiv_eessIV_bot@mastoxiv.page
2025-07-11 09:03:21

Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
Heet Nitinkumar Dalsania
arxiv.org/abs/2507.07254 a…

@arXiv_astrophHE_bot@mastoxiv.page
2025-08-13 09:14:22

CORSIKA 8: A modern and universal framework for particle cascade simulations
Marvin Gottowik (for the CORSIKA 8 collaboration)
arxiv.org/abs/2508.08755

@arXiv_csSE_bot@mastoxiv.page
2025-07-08 10:19:20

Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy
Francisca Lemos (ALGORITMI Research Centre/LASI, University of Minho), Victor Alves (ALGORITMI Research Centre/LASI, University of Minho), Filipa Ferraz (ALGORITMI Research Centre/LASI, University of Minho)
arxiv.org/abs/2507.0…

@arXiv_csIR_bot@mastoxiv.page
2025-08-11 09:29:49

Fine-Tuning Vision-Language Models for Markdown Conversion of Financial Tables in Malaysian Audited Financial Reports
Jin Khye Tan (Faculty of Computer Science,Information Technology, Universiti Malaya), En Jun Choong, Ethan Jeremiah Chitty, Yan Pheng Choo, John Hsin Yang Wong, Chern Eu Cheah
arxiv.org/abs/2508.05669

@arXiv_eessSP_bot@mastoxiv.page
2025-06-11 08:03:45

Timing advance and Doppler shift estimation in LEO satellite networks: A recursive Bayesian study
Ashutosh Balakrishnan, Pierre Popineau, Philippe Martins
arxiv.org/abs/2506.08739

@tiotasram@kolektiva.social
2025-08-04 15:49:00

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

@arXiv_csCY_bot@mastoxiv.page
2025-08-14 07:41:12

From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Training
Yuan Yuan, Tina Sriskandarajah, Anna-Luisa Brakman, Alec Helyar, Alex Beutel, Andrea Vallone, Saachi Jain
arxiv.org/abs/2508.09224

@arXiv_statME_bot@mastoxiv.page
2025-06-10 10:00:12

Efficient and Robust Block Designs for Order-of-Addition Experiments
Chang-Yun Lin
arxiv.org/abs/2506.07096 arxiv.org…

@mgorny@social.treehouse.systems
2025-06-03 09:47:08

Fun fact: #Windows 95 (and in some cases the later versions) had a very suboptimal implementation of idle state — they would run a busy loop, heating the CPU unnecessarily. Of course, you could work around this by installing a third-party program that would detect this state and use a more optimal HLT instruction instead.
So you could say that the purpose of such a program was to pretend that it's doing something, so that the system wouldn't fall into its own idle loop.
benchtest.com/cooler.html

@arXiv_csNI_bot@mastoxiv.page
2025-07-14 08:58:12

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design
Akshay Jain, Sylvaine Kerboeuf, Sokratis Barmpounakis, Crist\'obal Vinagre Z., Stefan Wendt, Dinh Thai Bui, Pol Alemany, Riccardo Nicolicchia, Jos\'e Mar\'ia Jorquera Valero, Dani Korpi, Mohammad Hossein Moghaddam, Mikko A. Uusitalo, Patrik Rugeland, Abdelkader Outtagarts, Karthik Upadhya, Panagiotis Demestichas, Raul Mu\~noz, Manuel Gil P\'erez, Daniel Adanza, Ricard Vilalta

@arXiv_csIR_bot@mastoxiv.page
2025-07-03 08:44:00

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
Puspendu Banerjee, Aritra Mazumdar, Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
arxiv.org/abs/2507.01058

@arXiv_csCY_bot@mastoxiv.page
2025-06-02 07:16:52

Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Ankit Anand, Avishkar Bhoopchand, Brett Wiltshire, Daniel Gillick, Daniel Kasenberg, Eleni Sgouritsa, Gal Elidan, Hengrui Liu, Holger Winnemoeller, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Komal Singh, Lisa Wang, Markus Kunesch, Miruna P\^islar, Niv Efron, Parsa Mahmoudieh, Pierre-Alexandre Kamienny, Sara Wiltb…

@arXiv_csDC_bot@mastoxiv.page
2025-06-10 16:25:19

This arxiv.org/abs/2506.02709 has been replaced.
initial toot: mastoxiv.page/@arXiv_csDC_…

@arXiv_csCR_bot@mastoxiv.page
2025-08-05 11:49:51

A Comprehensive Analysis of Evolving Permission Usage in Android Apps: Trends, Threats, and Ecosystem Insights
Ali Alkinoon, Trung Cuong Dang, Ahod Alghuried, Abdulaziz Alghamdi, Soohyeon Choi, Manar Mohaisen, An Wang, Saeed Salem, David Mohaisen
arxiv.org/abs/2508.02008

@arXiv_csNI_bot@mastoxiv.page
2025-06-06 09:37:19

This arxiv.org/abs/2506.00834 has been replaced.
initial toot: mastoxiv.page/@arXiv_csNI_…

@arXiv_csCY_bot@mastoxiv.page
2025-06-02 09:55:50

This arxiv.org/abs/2502.07287 has been replaced.
initial toot: mastoxiv.page/@arXiv_csCY_…

@arXiv_csSE_bot@mastoxiv.page
2025-08-01 08:25:52

On LLM-Assisted Generation of Smart Contracts from Business Processes
Fabian Stiehle, Hans Weytjens, Ingo Weber
arxiv.org/abs/2507.23087 ar…

@arXiv_csCY_bot@mastoxiv.page
2025-06-03 16:04:44

This arxiv.org/abs/2409.03219 has been replaced.
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