Tootfinder

Chartbeat: over two years, search traffic fell by 60% for small publishers with 1K-10K daily page views, 47% for medium-sized publishers, and 22% for large ones (Sara Fischer/Axios)
https://www.axios.com/2026/03/17/chartbeat-search-traffic-ai-chatbots

Exclusive: Small publishers hit hardest by search traffic declines
Larger publishers with better brand recognition and stronger direct-to-consumer products are more insulated from declines in traditional search traffic.

Chartbeat: over two years, search traffic fell by 60% for small publishers with 1K-10K daily page views, 47% for medium-sized publishers, and 22% for large ones (Sara Fischer/Axios)
https://www.axios.com/2026/03/17/chartbeat-search-traffic-ai-chatbots

Exclusive: Small publishers hit hardest by search traffic declines
Larger publishers with better brand recognition and stronger direct-to-consumer products are more insulated from declines in traditional search traffic.

EDIT: solved, see answers
--
sorry to bother you, but I'm wondering:
Is there a better way to find past conversations with another user?
I mean written interactions in posts, or the so-called DMs.
Maybe a filter reply notifications by user or date? Or a search for username word?
I can't find some info a mutual gave me, while we were commenting a post I can't remember, and my only idea is going through all my reply notifications
Thanks!
#AskFedi

Investigation: Jeffrey Epstein spent hundreds of thousands on a campaign to manipulate Google search results and sanitize his Wikipedia, after his 2009 release (New York Times)
https://www.nytimes.com/2026/03/18/business/media/jeffrey-epstein-online.html

Inside Jeffrey Epstein’s Push to Cleanse His Past Online
After he left jail in 2009, Mr. Epstein hired a host of people to make him look better on Google, Wikipedia and many other places on the web.

from my link log —
Arcan-A12: Weaving a different web.
https://divergent-desktop.org/blog/2020/08/10/principles-overview/
saved 2026-02-07

As for other things, like looking up medical advice, "traditional" search engines on the Internet worked much better than any LLM to get you to trustworthy sources of information.

Still, per the OP’s point, we should learn from what it is about vibe coding that really appeals to people.
The OP makes the case that we should find better abstractions and better idioms to fight boilerplate. Yes. And that we should look to things like Hypercard that reward inexperienced experimentation and exploration. Very very yes.
The latter part of my thread argues that we should •also• search for better solutions to the “Don’t make me decide! Just do something typical!” problem. I don’t know what that looks like, but we should take that problem more seriously.

I am familiar with Komoot but never used it, and I guess I never will.
“Komoot has launched a ChatGPT integration…”
#noAI

Komoot Adds ChatGPT-Powered Route Discovery
Find better trails quicker & easier, with better search parameters, using this ChatGPT integration, then navigate in the komoot app.

Airbnb says its custom-built AI agent now handles ~33% of its customer support issues in North America, and it is preparing to roll out the feature globally (Sarah Perez/TechCrunch)
https://techcrunch.com/2026/02/13/airb

Airbnb says a third of its customer support is now handled by AI in the US and Canada | TechCrunch
Airbnb was poised to introduce an app that doesn't just search for you, but one that "knows you." CEO Brian Chesky said, "It will help guests plan their entire trip, help hosts better run their businesses, and help the company operate more efficiently at scale."

No Panic, No Misses: Raiders’ Coaching Search Stays on Course https://www.si.com/nfl/raiders/onsi/gm-report/no-panic-no-misses-coaching-search-stays-on-course

Been using #Kagi for almost three years now and very happy with it. Two spots in my Family Plan are available – anyone interested?
What you get:
✅ Ad-free search
✅ No tracking
✅ Better search results (you can even permanently hide Pinterest, listicles & co..)

$42.84/year (~$3.50 per month)
PM if interested.

With #openalex being mature enough, I have finally completed #degoogling my website 🥳
https://

Build software better, together
GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.

from my link log —
For better videoconferencing at home on lousy links.
https://mailarchive.ietf.org/arch/msg/ietf/_08ghaFmktiJ53GG2TJ8CuNjjhU/
saved 2020-04-27

Algorithms are NEVER neutral. https://zeroes.ca/@StaceyCornelius/115945749468362693

What really makes me irate about how LLMs are marketed and sold is that if these companies instead spend their time and money to make highly specialized versions for them we could have amazing and actually helpful tools without the ick.
This could both work much better for many use cases (for example for correlating documents and giving a list of results like a search engine instead of tedious palaver) and they wouldn't need to steal data (Professor Bender calls it succinctly "datasets too large to care")[1].
But they're pursuing "AGI" (which is provenly impossible to do with LLMs) and endless growth.
[1] https://dair-community.social/@emilymbender/116109627131276897

Nimble, whose AI agents structure real-time web data into tables that can be queried like a database, raised a $47M Series B, bringing its total funding to $75M (Ram Iyer/TechCrunch)
https://techcrunch.com/2026/02/24/nimble-way-raises-47m-to-giv…

Nimble raises $47M to give AI agents access to real-time web data | TechCrunch
Nimble uses AI agents to search the web, verify and validate the results, and then clean and structure the information into neat tables that can then be queried like a database.

Our Call for Papers for Berlin Buzzwords closes this Sunday, February 15!
We encourage everyone in modern data infrastructure, search and machine learning and focused on open source software projects to submit their talk proposals, especially first-timers and people from underrepresented groups! #bbuzz #OpenSource #Berlin #Conference #MachineLearning #Search #DataInfrastructure #DataScience