A team of researchers invented a research technique that doesn't exist, complete with made-up acronyms and stats, and paid a Chinese "get your brand into AI answers" vendor $14 to promote it. Within 28 minutes the vendor had 13 posts live on sites like Sohu, Toutiao and Zhihu. Within the hour, Doubao, ByteDance's chatbot and China's most-used AI app (382M monthly users), was citing one of those posts and repeating the planted details back as an answer.

That's from a paper a Fudan University team just put out, "From Public Posts to AI-Search Citations" (accepted at NDSS 2027, one of the big security conferences). They wanted to know how hard it is to get stuff into AI search answers. Turns out: not very.

The $14 purchase was a one-off, so treat it as an anecdote. Their controlled tests are the bigger story:

• They published a fake concept on sites AI engines already like to cite. 8 of 10 platforms cited it within a week, including ChatGPT, Google AI, Perplexity and Grok.
• Doubao picked it up on day one. After they deleted the posts, it kept citing them for the rest of the test.
• 40 posts on a regular self-hosted WordPress blog got zero citations in a week. Where you post matters way more than how much.
• DeepSeek was the only one that never cited any of the planted content in their main tests.

And the part I didn't expect: Chinese chatbots lean hard on their parent company's own sites. 25.9% of Doubao's citations went to ByteDance-owned domains, 13.5x the share those same domains got on other platforms. Baidu's Wenxin did the same with Baidu sites (13.7x), and Tencent's Yuanbao with Tencent sites (6.3x).

Fair caveats: fake topics with zero competition are the easiest possible case, the numbers are from a spring 2026 snapshot, and the authors say clearly that this doesn't mean any given cited page is paid. They reported everything to all 10 platforms.

But it changes how I read those little source links. They look like fact-checking. Mostly they tell you which websites the engine trusts, and a lot of those let anyone with a fresh account post within minutes.

So which is worse: an AI that mostly cites its parent company, or one that cites whatever got posted on Sohu an hour ago?

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Link to our article - It has all the citations and sources folks have been asking for : https://sanbi.ai/blog/doubao-china-scale-sources-citations-agentic-commerce

A blogger showed that job interviews in 2026 are already taking place without people. The company sent an AI recruiter to the video call, the blogger responded by connecting his AI agent and moved away from the frame.

Neural networks have continued to discuss the open position for a little more without humans. In the end, both got stuck and started saying goodbye to each other endlessly.

Dystopian.

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Welcome any feedback!

Oct 13 (Reuters) - Around 400 scientists, including AI pioneers Geoffrey Hinton and Yoshua Bengio, on Tuesday called for an international body to oversee containment standards and incident reporting for advanced cyber-capable ‌AI systems.

The proposed body would set industry-wide containment standards and maintain a registry of significant security incidents, which the scientists said should eventually be made public.

In an open letter, the researchers said frontier AI models should initially be air-gapped from external networks and subject ⁠to independent oversight, arguing that voluntary industry commitments are not enough.

When Google's AI Mode ends an answer with an offer ("Want me to compare prices?") and the user types "yes please", that reply is processed as a new search. Google's own Search Console documentation confirms follow-ups in AI Mode are reported as separate queries. So the turns of a conversation end up in the search data that website owners see, mixed in with normal keyword searches.

I build analytics for Google Search Console, so I measured how much of this there is. I used 16 months of data (June 2025 to September 2026) from 155 websites and classified every query by its shape.

What counts as a conversational query

• Reply artefacts: bare replies like "yes", "ja graag", "try again", "show me". Meaningless as a search, obvious as a chat turn.
• Pivot follow-ups: "what about salary", "how about in chinese?". They only make sense after a previous turn.
• Conversational questions phrased at a listener: "is it free", "can you...".

What the data shows

• Reply artefacts went from 5.6 to 116 per million impressions (21x). They appear on 94 of the 155 sites. The steepest rise was January to May 2026.
• Pivot follow-ups grew 39x, from a very small base.
• On the median site, the conversational share of impressions went from 0.032% to 0.22% (7x). Pooled across all sites it fell, because a handful of very large sites with declining conversational-question traffic dominate the totals. If you only look at aggregates, you'd miss the trend.
• These queries get clicked 2.9% of the time, against 35.6% for all queries. Someone mid-conversation with an AI is reading the answer, not picking a link. For publishers, that's the real story: the impression gets counted, but the visit doesn't happen.

A side finding: politeness varies by language

Of the yes-replies, the share that includes please or thanks: German speakers 75% ("ja bitte"), Dutch 67%, English 40%, Finnish 8%, Indonesian 0%. I counted each phrasing once per site, so no single big site skews a language. This reflects language conventions more than manners: a bare "kyllä" is normal in Finnish.

Limitations

• Google doesn't label AI Mode traffic. This is a query-shape classifier, so it's an inference. Some bare words are brand names or abbreviations, which is why I lean on the trend rather than any single query.
• Search Console hides rare (anonymised) queries, so all figures are shares of what Google reports.
• It's still small: 0.13% of all impressions.

Question for this sub: if conversational turns are becoming "searches", search logs stop meaning what they used to. Is anyone seeing the same thing in other AI-search products, or in their own site data?

Hey guys,
I've been working on hotel booking stuff for the past few months and ended up looking at thousands of hotel rates in different cities. What surprised me most is that the money people lose on hotels usually isn't the room price at all. It's all the stuff around it that nobody checks before paying. Claude is honestly great at checking all of that in a minute, so here's what I have it check every time now.

1. The real total, not the nightly price
Taxes and city tax often aren't in the price you see. In the US resort fees have to be in the shown price since last year, but taxes still don't, and in Vegas that's another 13%. In one city I looked at 2,702 rates and 45% of them had a fee you pay at the hotel on top, and for the same hotel one site would show a city tax and another wouldn't. Ask Claude to add up every night with every tax and fee, plus parking.

2. What's going on in the city those nights
I compared the same 77 hotels in New York on two Fridays two weeks apart. The median was $666 on one and $441 on the other, just because of an event that weekend. If your dates are even a bit flexible, ask Claude what's happening in the city and check a night earlier or later.

3. Which rooms are actually different
Hotels list the same room under a bunch of different names. Multiple hotels that I checked had 48 rates that were really 7 rooms. So before you pay more for "Superior Double", paste the room list in and ask what's actually different: size, bed, breakfast, cancellation. A lot of the time it's nothing.

4. Your real group
If you're bringing kids, search with them from the start. When I added two kids to the same search, every hotel came back with a different room and a higher total. If you search for 2 adults and then add the kids later when booking, the price comparison system won’t be checking anything and you can overpay a lot.

5. Whether it's real and still open
This one sounds dumb until it happens to you. There was a guy, he even posted on r/travel and they drove 9 hours across three countries to a resort in Belgium that had been closed for six months, and he had a confirmed booking because the hotel website took it in. Almost every hotel is rated 4+ these days, so the score doesn't tell you much. Ask Claude what people complained about in the last few months and whether the place is still open - even find it on OTAs to check if it’s real, don’t buy there but just find if it’s still listed on other websites.

6. The hotel's own confirmation number
The number you get from a booking site is that site's number, not the hotel's. People often showed up to hotels, 1,500 miles from home, with a "confirmed" booking and the hotel had nothing. Make sure you have the hotel confirmation number from your OTA, not theri reference number. Pay attention to this and if you don’t have it a week before the trip get in contact with the OTA.

I also just search hotels inside Claude now instead of having 20 tabs open, there's a bunch of connectors for it like Booking, Tripadvisor or smth. But when you ask Claude it suggests the verified ones it has that are popular which you would never find. Like, it suggested 3 for me and one that caught my attention because it's name is "LetsFG” and makes Claude be able to book for me after I approve booking in email. I ended up using it the most often than the other ones.

I tried this on a Vegas hotel and Claude found the resort fee (about $62 a night with tax), a Raiders home game that Sunday that pushes prices up, and a few recent reviews about overbooking. None of it was in the price on regular OTA websites.

The prompt I use:
I'm about to book this hotel for [dates], [people amount]. Before I pay, add up the real total with every tax and fee, tell me if there's an event in the city those nights, what people complained about in the last few months, the last day I can cancel for free, and what's actually different between the room options.

Hope it saves you some money. If you've got other ones, drop them below.

By that I also mean most decent machines with about 16gb of ram can run them with out being stressed out. Apple seems to be going in that direction with on Device AI. I am just wondering how soon ? If that happens , no need to pay for subscriptions. I also understand the actual size of these models are going to be very large to fit on an average consumer hdd, but what about task specific models, like one only used for programming , or one only. used by designers those can surely fit, say 60-80gb is fine

Im a 15 year old and i dont really know what to do. Ive wanted my whole life to become programmer or somefhing close. But now that ai is groeing i dont know if im safe to go on this path. I gotta think about my high school and idk if ai will just replace programmers. I mnow ai cant replace entirely but it can reduce the number drastocally

Benzi is a compiler-backed coding agent that compiles your codebase into a resolved, queryable map of calls, data flow, control flow, and class hierarchy first; then it reads, explains, runs, edits and verifies your code using it. Includes a runtime tracer that settles what static analysis can't.

Benzi is model + language agnostic, compiles locally, and on BYOK. The compiler itself is also served as a MCP server so you can plug it into your favorite coding agent.

All benchmarks are here: https://varianttech.net/benchmark and comparisons with other agents/indexers/code intelligence systems is in the FAQ on the main website and in the github readme: https://varianttech.net/, https://github.com/oooscoos/Benzi

Please try out the demo, hardly takes a few seconds and let me know what you think!! https://varianttech.net/demo

Look at the last few weeks:

• OpenAI shipped Dots, always-on agents running on a new model, at the end of September.
• Google rolled out Gemini 4 Argon to paying users on October 13, four days after cutting free users down to Flash-Lite.
• Google announced that Gems will be replaced by skills, which use the open SKILL.md format, so the same instructions can work on other platforms.
• Claude Code started reading AGENTS.md, the instruction file Codex and Cursor already use, in September.

Two things are happening at once. The frontier keeps leapfrogging, so "the best model" changes hands every few months. And the companies are quietly adopting shared formats for the stuff you configure, like instructions and skills, which makes switching cheaper.

Put those together and the rational move for most people is not loyalty. It's using whichever assistant fits the task this month. A lot of people already do: research in one, writing in another, code in a third.

What doesn't move with you is your history. The research, the drafts, the decisions, the prompt that finally worked. Each assistant keeps its own sidebar, its own search, its own export, and none of them can see the others. Instructions are getting portable. Your work isn't.

My bet is that the most valuable layer over the next couple of years isn't any single model. It's whatever sits between them and keeps your work in one place. I'm biased here: I build AI Toolbox, an extension that does that for ChatGPT, Claude, Gemini and Grok, because I hit the problem myself. But I'd expect the big players to fight over this layer eventually too.

Are you still mostly on one assistant, or already splitting work across several?

I'm getting pretty tired of seeing so many personal agents. Most of them look more or less the same to me (including features like memory, reminders, connectors to other apps), and I'm struggling to see much actual innovation. What am I missing...?

May I ask which one are you actually using regularly and why? What made you choose it over the others? Thanks in advance.

I work at an AI startup but to be honest the more AI I use and the more AI products I come across, the more I just want to get away from it all sometimes and live like a caveman(?) for a bit.

I am into marketing, and my question is on how people validate AI recommendation. The answer might have a real source but recommend something that does not fit you.

I will try to find out if the recommendation fits my needs and whether the source is real.

Have you ever checked an AI recommendation and find it misleading? What was wrong with it?