Following the murder, 'Israel' shared a document it claimed was from Hamas' Al-Qassam Brigades, intended to prove Al-Sharif's membership. This document, whose authenticity is vehemently denied by his family, Al Jazeera and international organizations, was meant to be the final word, a definitive justification for targeting a member of the press.

Even if one were to entertain the authenticity of this document for the sake of argument, its contents incriminate the 'Israeli' military completely. The document alleges that in early 2023, months before the October 7 attacks and the subsequent 'Israeli' assault on Gaza, Anas Al-Sharif was wounded in a training explosion. It goes on to detail the consequences of this incident, stating he was left with severe, debilitating injuries: "Severe hearing loss in the left ear + vision impairment in the left eye + dizziness and headaches."

The document’s own conclusion is unambiguous: as a result of these injuries, Anas Al-Sharif was deemed incapacitated and unfit for military service. This is not a footnote; it is the central point. By the logic of the very document 'Israel' presented to the world as justification, Anas Al-Sharif held zero military capacity or role during the entire period of the war in which he was killed.

How 'Israel's' own "evidence" proved its lie

Slain journalist Anas Al-Sharif

^{Roya News}

Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. It's built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan.

The model in llama.cpp and vLLM and uses serper-mcp to access the web github.com/marcopesani/mcp-ser…

Model links:
* Jan-v1-4B: huggingface.co/janhq/Jan-v1-4B
* Jan-v1-4B-GGUF: huggingface.co/janhq/Jan-v1-4B…

Recommended parameters:

    temperature: 0.6
    top_p: 0.95
    top_k: 20
    min_p: 0.0
    max_tokens: 2048

Lucy: edgerunning agentic web search on mobile with machine generated task vectors

Small language models (SLMs) are inherently limited in knowledge-intensive tasks due to their constrained capacity. While test-time computation offers a path to enhanced performance, most approaches treat reasoning as a fixed or heuristic process.

^arXiv.org

Like many teachers at every level of education, I have spent the past two years trying to wrap my head around the question of generative AI in my English classroom. To my thinking, this is a question that ought to concern all people who like to read and write, not just teachers and their students. Today’s English students are tomorrow’s writers and readers of literature. If you enjoy thoughtful, consequential, human-generated writing—or hope for your own human writing to be read by a wide human audience—you should want young people to learn to read and write. College is not the only place where this can happen, of course, but large public universities like UVA, where I teach, are institutions that reliably turn tax dollars into new readers and writers, among other public services. I see it happen all the time.

There are valid reasons why college students in particular might prefer that AI do their writing for them: most students are overcommitted; college is expensive, so they need good grades for a good return on their investment; and AI is everywhere, including the post-college workforce. There are also reasons I consider less valid (detailed in a despairing essay that went viral recently), which amount to opportunistic laziness: if you can get away with using AI, why not?

It was this line of thinking that led me to conduct an experiment in my English classroom. I attempted the experiment in four sections of my class during the 2024-2025 academic year, with a total of 72 student writers. Rather than taking an “abstinence-only” approach to AI, I decided to put the central, existential question to them directly: was it still necessary or valuable to learn to write? The choice would be theirs. We would look at the evidence, and at the end of the semester, they would decide by vote whether A.I. could replace me.

What could go wrong?

In the weeks that followed, I had my students complete a series of writing assignments with and without AI, so that we could compare the results.

My students liked to hate on AI, and tended toward food-based metaphors in their critiques: AI prose was generally “flavorless” or “bland” compared to human writing. They began to notice its tendency to hallucinate quotes and sources, as well as its telltale signs, such as the weird prevalence of em-dashes, which my students never use, and sentences that always include exactly three examples. These tics quickly became running jokes, which made class fun: flexing their powers of discernment proved to be a form of entertainment. Without realizing it, my students had become close readers.

During these conversations, my students expressed views that reaffirmed their initial survey choices, finding that AI wasn’t great for first drafts, but potentially useful in the pre- or post-writing stages of brainstorming and editing. I don’t want to overplay the significance of an experiment with only 72 subjects, but my sense of the current AI discourse is that my students’ views reflect broader assumptions about when AI is and isn’t ethical or effective.

It’s increasingly uncontroversial to use AI to brainstorm, and to affirm that you are doing so: just last week, the hosts of the New York Times’s tech podcast spoke enthusiastically about using AI to brainstorm for the podcast itself, including coming up with interview questions and summarizing and analyzing long documents, though of course you have to double-check AI’s work. One host compares AI chatbots to “a very smart assistant who has a dozen Ph.D.s but is also high on ketamine like 30 percent of the time.”

What Happened When I Tried to Replace Myself with ChatGPT in My English Classroom

My students call it “Chat,” a cute nickname they all seem to have agreed on at some point. They use it to make study guides, interpret essay prompts, and register for classes, turning it loose on t…

^{Literary Hub}

WPlace is a desktop app that takes its cue from Reddit’s r/place, a sporadic experiment where users placed pixels on a small blank canvas every few minutes. On Wplace, anyone can sign up to add coloured pixels to a world map – each user able to place one every 30 seconds. By internet standards one pixel every 30 seconds is glacial, and that is part of what makes it so powerful. In just a few weeks since its launch tens, if not, hundreds of thousands of drawings have appeared.

Scrolling to my corner of Scotland, I found portraits of beloved pets, anime favourites, pride flags, football crests. In Kyiv, a giant Hatsune Miku dominates the sprawl alongside a remembrance garden where a user asked others to leave hand drawn flowers. Some pixels started movements. At one point there was just a single wooden ship flying a Brazilian flag off Portugal. Soon, a fleet appeared, a tongue-in-cheek invasion.

Across the diversity and chaos of the Wplace world map, nothing else feels like Gaza. In most cities, the art is made by those who live there. Palestinians do not have this opportunity: physical infrastructure is destroyed while people are murdered. Their voices, culture, and experiences are erased in real time. So, others show up for them, transforming the space on the map into a living mosaic of grief and care.

No algorithm, no leaders, but on Wplace, collective actions emerge organically. A movement stays visible only because people choose to maintain it, adding pixels, repairing any damage caused by others drawing over it. In that sense it works like any protest camp or memorial in the physical world: it survives only if people tend it. And here, those people are scattered across continents, bound not by geography but by a shared refusal to let what they care about disappear from view.

Wplace Is Exploding Online Amid a New Era of Youth Protest

Wplace is exploding onling amid a new ear of youth protest. From political pixel art to vigils over Gaza, this beautifully chaotic internet project is showing how young people are reinventing protest.

^{Kristie De Garis}

The unsolicited offer is higher than Perplexity’s valuation.

Perplexity offers to buy Google Chrome for $34.5 billion

The AI search startup Perplexity has offered to buy Google Chrome for $34.5 billion, according to a report from The Wall Street Journal. 

^{Emma Roth (The Verge)}

https://www.boomlive.in/news/grok-pulled-offline-then-reappears-did-israel-gaza-comments-spark-it-29247

Since both lemmy and Mastodon use the fediverse, is it possible to view comment threads under posts from lemmy in Mastodon? How to find a link that works in both/ is it related to the posts id?

Would these work with #hashtags ?

Edit: It seems that it may be 40MB and that the other 629 MB is from the Texturino plugin that generally gets bundled with it. I believe it is just two separated direct downloads. Not sure why there would be inconsistencies in the file size though (669MB vs 678MB)

Note: I am not requesting for a link nor a source, but rather I just want to know if I am direct downloading the correct file. Specifically, is the bundle supposed to be 40MB or 678MB?

I found torrented versions are 678MB, but direct downloaded versions are only 40MB. motka (dot) net (from the megathread) had one for 678MB, but the download is a 404 sadly.

Also, is the latest version 3.9.1? I see direct download ones showing up as 4.1.0, and 4.2.0 (which doesn't seem right to me)

Thank you.

The job market is queasy and since you're reading this, you need to upgrade your CV. It's going to require some work to game the poorly trained AIs now doing so much of the heavy lifting. I know you don't want to, but it's best to think of this as dealing with a buggy lump of undocumented code, because frankly that's what is between you and your next job.

A big reason for that bias in so many AIs is they are trained on the way things are, not as diverse as we'd like them to be. So being just expensively trained statistics, your new CV needs to give them the words most commonly associated with the job you want, not merely the correct ones.

That's going to take some research and a rewrite to get it looking like those it was trained to match. You need to be adding synonyms and dependencies because the AIs lack any model of how we actually do IT, they only see correlations between words. One would hope a network engineer knows how to configure routers, but if you just say Cisco, the AI won't give it as much weight as when you say both, nor can you assume it will work out that you actually did anything to the router, database or code, so you need to explicitly say what you did.

Fortunately your CV does not have to be easy to read out loud, so there is mileage in including the longer versions of the names of the more relevant tools you've mastered, so awful phrases like "configured Fortinet FortiGate firewall" are helpful if you say it once, as does using all three F words elsewhere. This works well for the old fashioned simple buzzword matching still widely used.

This is all so fucked.

Your CV is not fit for the 21st century – time to get it up to scratch

: And yes, that means (retch) catering to AI searchers

^{Dominic Connor (The Register)}