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AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

Hey all. I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text. The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output. The tricky part is I have no clean data pair for supervised approaches. I've been looking at a few directions: Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register. Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it. A few things I'm stuck on: Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence? How do people handle domain-specific terms like terminology and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone? Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks. submitted by /u/Divine_Invictus [link] [留言]

Meta has quietly launched Pocket, an experimental AI app that lets users generate and share interactive mini games using text prompts.

TL;DR: Most extensions block Google's AI Overviews by hiding the panel with a content script after it renders — fragile, flickery, and always a step behind Google's markup changes. A better approach: force udm=14 at the network layer with declarativeNetRequest , so the AI Overview never loads. The content script becomes a backstop, not the main mechanism. One Chrome API mystery — AI Mode being invisible to four different extension APIs — shows why the DOM was never the right layer. Google puts an AI Overview at the top of most search results now, and a lot of people would rather it didn't. So there's a whole shelf of Chrome extensions that remove it. Almost all of them work the same way, and I think that way is a mistake. The obvious approach, and why it's a trap The default move is DOM-hiding: inject a content script, wait for the AI Overview panel to render, find it by class name or attribute, and set display: none . It's the first thing anyone reaches for, and it works — until it doesn't. The problems are all baked into the approach. You're reacting after the render, so there's a flash of AI content before your script catches it. You're matching against Google's markup, which is obfuscated and reshuffled constantly, so every layout change is a silent breakage. And you're paying for DOM churn on a page you don't control. You end up in a permanent game of catch-up against a page that changes whenever Google feels like it. The deeper issue is that you're operating one layer too high. The panel is a symptom . By the time it's in the DOM, the work is already done — the server decided to send it, the page rendered it, and now you're scrambling to un-render it. If you can move the decision earlier, none of that scramble has to happen. The thesis: prevent it at the network layer Google Search takes a parameter, udm , that selects which result vertical you get. udm=14 is the plain "Web" results view — the classic list of links, no AI Overview, no AI Mode. It's Google's ow

Hopper will pay $35 million to settle FTC allegations that it used deceptive “dark patterns” to hide fees and mislead travelers about the cost and benefits of services.

Everyone reads the Hugging Face trending page in a browser. Almost nobody knows the whole Hub sits behind a plain JSON API with no key, no login, and cursor pagination. If you want a weekly report of what the AI community is actually adopting, you can build it with fetch . The endpoints GET https://huggingface.co/api/models GET https://huggingface.co/api/datasets GET https://huggingface.co/api/spaces Useful parameters, same across all three: sort ranks results: trendingScore , downloads , likes , createdAt , lastModified direction=-1 for descending search matches names, author restricts to one org like meta-llama filter matches Hub tags: text-generation , license:mit , even arxiv:2606.23050 limit up to 100 per page So the top trending models right now: https://huggingface.co/api/models?sort=trendingScore&direction=-1&limit=100 trendingScore is the interesting one. Downloads and likes rank all time popularity, which is dominated by the same old models. Trending score is Hugging Face's own measure of current momentum, and it moves daily. Today it puts a four day old OCR model from Baidu at the top, which no downloads sort would surface for weeks. Slim payloads with expand By default the models endpoint returns a siblings array listing every file in the repo, which bloats a 100 item page. Ask for exactly the fields you want instead: const fields = [ ' downloads ' , ' likes ' , ' trendingScore ' , ' pipeline_tag ' , ' tags ' , ' createdAt ' ]; const params = new URLSearchParams ({ sort : ' trendingScore ' , direction : ' -1 ' , limit : ' 100 ' }); for ( const f of fields ) params . append ( ' expand[] ' , f ); const res = await fetch ( `https://huggingface.co/api/models? ${ params } ` ); const models = await res . json (); Pagination is a Link header There is no page parameter. Each response carries a Link header with a cursor for the next page, GitHub style: function nextUrl ( res ) { const m = ( res . headers . get ( ' link ' ) || '' ). match ( /< ([^ > ] + ) >; \s *r

The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.

One of Kalshi’s most prominent traders tells WIRED he’s swearing off Spotify-related markets until the issue is resolved.