That is ok. As far as I understand, this is how lemmy<->mastodon activity-pub integration is supposed to work.
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For the first time ever in space, scientists discovered a novel carbon molecule known as methyl cation (CH3+). This molecule is significant because it promotes the synthesis of more complex carbon-based compounds.
Thanks for following us! That's surely a cool feature on the fediverse. Feel welcome and free to post and comment anything related to software engineering.
There is also !seresearch@lemmy.ml which @rahulgopinath@lemmy.ml moderates.
3
The Future of Software Engineering: A Guiding Vision and a Look at Possible Scenarios by Software Engineering Institute
(insights.sei.cmu.edu)
NOTE: This is primarily an invitation for the "SEI and White House OSTP to Explore the Future of Software and AI Engineering", but there is this big section on the "future of software engineering" that is very interesting.
As discussed in Architecting the Future of Software Engineering: A Research and Development Roadmap, the SEI developed six research focus areas in close collaboration with our advisory board and other leaders in the software engineering research community ... AI-Augmented Software Development: By shifting the attention of humans to the conceptual tasks that computers are not good at and eliminating human error from tasks where computers can help, AI will play an essential role in a new, multi-modal human-computer partnership... taking advantage of the data generated throughout the lifecycle
Assuring Continuously Evolving Software Systems: ...generating error-free code, especially for trivial implementation tasks... generating surprising recommendations that may themselves create additional assurance concerns... develop a theory and practice of rapid and assured software evolution that enables efficient and bounded re-assurance of continuously evolving systems
Software Construction through Compositional Correctness: ...unrealistic for any one person or group to understand the entire system... need to integrate (and continually re-integrate) software-reliant systems.. create methods and tools that enable the intelligent specification and enforcement of composition rules that allow (1) the creation of required behaviors (both functionality and quality attributes) and (2) the assurance of these behaviors at scale
Engineering AI-enabled Software Systems: ...AI-enabled systems share many parallels with developing and sustaining conventional software-reliant systems. Many future systems will likely either contain AI-related components, including but not limited to LLMs, or will interface with other systems that execute capabilities using AI... focus on exploring which existing software engineering practices can reliably support the development of AI systems and the ability to assess their output, as well as identifying and augmenting software engineering techniques for specifying, architecting, designing, analyzing, deploying, and sustaining AI-enabled software systems
Engineering Socio-Technical Systems: ... As generative AI makes rapid progress, these societal-scale software systems are also prone to abuse and misuse by AI-enabled bad actors via techniques such as chatbots imitating humans, deep fakes, and vhishing... leverage insights from such as the social sciences, as well as regulators and legal professionals to build and evolve societal-scale software systems that consider these challenges and attributes.
Engineering Quantum Computing Software Systems: ...enable the programming of current quantum computers more easily and reliably and then enable increasing abstraction as larger, fully fault-tolerant quantum computing systems become available... create approaches that integrate different types of computational devices into predictable systems and a unified software development lifecycle.
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Penetration Testing Findings Repository by Cybersecurity and Infrastructure Security Agency
(github.com)
The repository consists of three layers:
- Finding Category layer lists the overarching categories
- General Finding layer lists high-level findings
- Specific Finding layer lists low-level findings
Just an overview of the general findings
More: https://cmu-sei-podcasts.libsyn.com/a-penetration-testing-findings-repository
It is almost like the things like PMBOK (which now changed to a principles-based body of knowledge)... these things have no base in scientific method (empirically-based), having origins back to all the DOD needs
Also reminds me of this important research article "The two paradigms of software development research" posted here before https://group.lt/post/46119
The two categories of models use substantially different terminology. The Rational models tend to organize development activities into minor variations of requirements, analysis, design, coding and testing – here called Royce's Taxonomy because of their similarity to the Waterfall Model. All of the Empirical models deviate substantially from Royce's Taxonomy. Royce's Taxonomy – not any particular sequence – has been implicitly co-opted as the dominant software development process theory [5]. That is, many research articles, textbooks and standards assume:
- Virtually all software development activities can be divided into a small number of coherent, loosely-coupled categories.
- The categories are typically the same, regardless of the system under construction, project environment or who is doing the development.
- The categories approximate Royce's Taxonomy. ... Royce's Taxonomy is so ingrained as the dominant paradigm that it may be difficult to imagine a fundamentally different classification. However, good classification systems organize similar instances and help us make useful inferences [98]. Like a good system decomposition, a process model or theory should organize software development activities into categories that have high cohesion (activities within a category are highly related) and loose coupling (activities in different categories are loosely related) [99].
Royce's Taxonomy is a problematic classification because it does not organize like with like. Consider, for example, the design phase. Some design decisions are made by “analysts” during what appears to be “requirements elicitation”, while others are made by “designers” during a “design meeting”, while others are made by “programmers” while “coding” or even “testing.” This means the “design” category exhibits neither high cohesion nor loose coupling. Similarly, consider the “testing” phase. Some kinds of testing are often done by “programmers” during the ostensible “coding” phase (e.g. static code analysis, fixing compilation errors) while others often done by “analysts” during what appears to be “requirements elicitation” (e.g. acceptance testing). Unit testing, meanwhile, includes designing and coding the unit tests.
it’s simply too difficult for average person to login and apply to every single instance that they’re interested in
Maybe there are some misunderstanding, but why would one want to apply to multiple instances at the same time? Just applying and being active on 1 good instance isn't enough?
Totally supportive. Great to have a wayland Rust implementation (and Rust increasing adoption by FOSS community); more specifically, smithay, which further than System76 is building upon, like projects by the community this WM for example https://github.com/MagmaWM/MagmaWM
Wow, this is truly good, as long ago I did read many delays on public healthcare services are due to no-shows. I liked the fact that with the information of who were more likely to no-show, UHP then contacted these people.
UHP was able to cut no-shows for patients who were highly likely to not to show up, by more than half. That patient population went from a dismal 15.63% show rate to a 39.77%. A dramatic increase. At the same time, patients in the moderate category improved from a 42.14% show rate to 50.22%.
Of course, this article sounds like an ad for eClinicalWorks, but interesting and very good application of AI regardless.
For now I mostly see work like these towards the construction phase. Little by little we automate the whole thing.
Just me doing a literature note:
Authors try to find faster algorithms for sorting sequences of 3-5 elements (as programs call them the most for larger sorts) with the computer's assembly instead of higher-level C, with possible instructions' combinations similar to a game of Go 10^700. After the instruction selection for adding to the algorithm, if the test output, given the current selected instructions, is different form the expected output, the model invalidates "the entire algorithm"? This way, ended up with algorithms for the "LLVM libc++ sorting library that were up to 70% faster for shorter sequences and about 1.7% faster for sequences exceeding 250,000 elements." Then they translated the assembly to C++ for LLVM libc++.
A cosmic hydrogen fog??? That sounds like an atmosphere, something that a space-dwelling creature from a sci-fi novel could develop in
not gonna lie, i lol
Well; darwin users, just as linux users, should also work on making packages available to their platforms as Nix is still in its adoption phase. There are many already. IIRC I, who never use MacOS, made some effort into making 1 or 2 packages (likely more) to build on darwin.
as Reddit now going to IPO. That happened to Twitter->Mastodon, can happen to Reddit->Lemmy as well.
We had seen it coming haha
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oh this is one of my wallpapers
i did a 1920x1080 version out of it by horizontally tiling 3 duplicates of it like this (i got the freely licensed version from wikimedia commons under https://creativecommons.org/licenses/by-sa/4.0/deed.en)