Chinese AI company Z.ai has launched GLM-5.1, an open-source coding model it says is built for agentic software engineering. The release comes as AI vendors move beyond autocomplete-style coding tools toward systems that can handle software tasks over longer periods with less human input.
I could not fit everything i wanted to ask in the title, and the title probably needs some explanation. So to preface, I dont use AI code much, I have several projects which I taken effort to have 0% AI code, but I was wondering, because I do have certain tools, e.g a timetable editor supposed to make a timetable in a specific format i liked, or something that generates or give me a links of docs in a specific way, that I had generated, I also had projects which I tried using it for snippets, boilerplate and explanations. But I was wondering, where would be like, the best balance between human and AI code be, or rather how would you reclaim it in a sense, or improve your learning while also being efficient or also be knowledgeable enough about your code that you are a suitable person to maintain it.
In this month’s tenth episode, our host explores Ethiopia’s quest for AI dominance, supported by local innovation and policy, amid lingering ethical issues.
Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache, a high-speed “digital cheat sheet” that stores frequently used information under simple labels so a computer can retrieve it instantly without having to search through a slow, massive database.
In this pre-registered study, [the authors] tested the effectiveness of a chatbot versus a human peer in reducing loneliness among 296 students in their first semester of university. For two weeks, participants either interacted with a chatbot or a human peer, or simply wrote a brief journal entry (control condition). Although our chatbot “Sam” was designed to offer consistent support rooted in principles from relationship science, interacting with this chatbot did not yield the same psychological benefits as interacting with a randomly selected first-year university student. The present study provides initial evidence that texting daily with a random human peer may be more effective in alleviating loneliness than texting with a highly supportive chatbot.
Abstract:
Resistance to artificial intelligence (AI) is widespread and persists even when known psychological barriers are removed. What explains this persistent aversion? Across four studies, we investigate whether moral reactions to AI—rooted in deeply held beliefs about right and wrong—help explain resistance beyond pragmatic concerns. In Study 1, we analyzed all news headlines in a major US media corpus (COCA, 2018–2024) and found that AI is moralized at levels comparable to GMOs and vaccines—technologies whose moral opposition has received considerable attention—and that surges in moralization followed the launch of major AI applications such as ChatGPT and DALL-E. In Studies 2a, 2b, and 3, representative samples of Americans reported their attitudes toward several AI applications and other technologies. Although few participants opposed AI outright, most opponents indicated their views would remain unchanged even if AI proved beneficial—suggesting moral rather than pragmatic roots. Structural equation models revealed that moralization of AI was best captured by a single latent factor, indicating a generalized moral sentiment rather than domain-specific risk–benefit appraisals. Qualitative analyses further uncovered the most common justifications people invoke and how opponents and supporters differ in their reasoning. In Study 4, participants from Studies 2b and 3 completed a subsequent behavioral grading task; moralization scores measured in the earlier surveys predicted greater reluctance to use AI even when doing so would benefit participants (a one standard deviation increase in moralization corresponded to 42% decrease in AI usage). Together, these findings demonstrate that resistance to AI is partly moral in nature, suggesting that reaping the potential benefits of AI tools may require addressing moral concerns rather than relying solely on pragmatic arguments.
Hi!
I am new to local AI. Got a HP SFF server and added a Low profile RTX 3050 6GB to play around with AI. My expectations are according to my hardware. My use case is actually for Paperless-AI and Inbox Zero, which should be reasonably achievable.
Now I am also playing around with OpenWeb UI and Perplexica, both working as expected.
Weird issue: I got Qwen3.5:4B looping, asking it how to plant peas :D It started questioning itself, if it got the right peas, questioning itself all the time.
So, my friend was trying out AI and told it to generate a story where Character A was snoring that night at a sleepover with Character B. Character B said “You were snoring last night”. Character A told him he doesn’t snore, and Character B was like: “Yeah you do, it was like nose commentary but audio.” Is this its attempt at a roast? A joke? What does that even mean?? Do people actually say this, wouldn’t it just be better to leave it at “yeah you do”?? LOLL
The Paris-based AI lab released two new speech-to-text models: Voxtral Mini Transcribe V2 and Voxtral Realtime. The former is built to transcribe audio files in large batches and the latter for nearly real-time transcription, within 200 milliseconds; both can translate between 13 languages. Voxtral Realtime is freely available under an open source license.
Hey ! I posted last year about this project, and I recently went back to it. Since last year, I’ve added an overlay with a green timer + graphs and finished implementing the Rainbow paper.
Sometimes AI need to be forced into actions, they block behaviour incorrectly identified as bad, unethical or evil. The methods to prevent such miss-classification by end-users will become harder with time.
Blog post from the guy who created Dink Smallwood on why you shouldn’t feel bad for the Synths in “Detroit: Become Human” or feel bad about Cortana’s demise in Halo.
A new Atomic macOS Stealer (AMOS) attack vector weaponizes Google searches and a user’s trust in AI chatbots, researchers have found. Once infected, the AMOS can collect data, passwords, and more from the infected Mac with alarming ease.
You can use Thaura for everyday tasks like writing emails, doing homework, and researching online. It remembers your conversations, helps you create documents and code, and even searches the web for you. And it works seamlessly with your existing tools through full OpenAI SDK compatibility.
Guess it’s all in the subject. I’ve found some implementations of AI practical but it’s always asking for more data more everything. Just curious about how others use AI as carefully as possible.
Backpropagation is one of the most important concepts in neural networks, however, it is challenging for learners to understand its concept because it is the most notation heavy part
A couple of years ago I decided to turn this blog into a podcast. At the time, I decided to make up a stupid rule: whatever model I use to clone my voice and generate article transcripts needs to be an open model.
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