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Death of the Browser
Rebirth of the Web
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
AgentQL.com
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
My
site
for
teen
girl
cartoonists
~2007
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Today’s Internet
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Cory Doctorow
TikTok’s
Enshitti
fi
cation
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Enshitti
fi
cation
Whereby a pla
tf
orm traps marketshare
in three stages.
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
collect
suppliers
from free and open → gated and monetized
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Demonstrate a typical
workflow to check up on
incoming information
from multiple platforms
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
BUFFER
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
An Agentic Web
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
The Web is the API
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
The agent is the client.
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
This is possible today.
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Machines that act like humans
Tools for Agents
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
From scrapers to data extractors
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
AgentQL
From automation to interaction
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Evading anti-bot measures
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
blogs.opera.com/news/2025/03/opera-browser-
operator-ai-agen
ti
cs
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Platforms can’t hold us
hostage if we send our agents to
fetch our data…
Or directly share it with each other.
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Evolving the Web and Client
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Solved
Connecting to people, data, and
services
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
SEO for LLMs
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Authentic(ated) Agents
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Subscribe to your friends
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Solved
Information Overload
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
LLMs are good at transforming text into
less text.
Laurie Voss
What I've learned
about writing AI
apps so far
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
developer.chrome.com/docs/ai/built-in
On-device LLMs
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
Solved
Bespoke interfaces
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
blog.langchain.dev/introducing-ambient-agents
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
mewho.com/system47
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
My prediction
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
aios.founda
ti
on
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
The Agentic Web
The Authentic Web
Rachel-Lee
Nabors
(they/them)
nearestnabors.com
[I]nforma
ti
on… wants to be expensive
because it is so valuable—the right
informa
ti
on in the right place just
changes your life. On the other hand,
informa
ti
on almost wants
to be free because the costs of
ge
tti
ng it out is ge
tti
ng lower and lower
all of the
ti
me. Stewart Brand
Author of the
Whole Earth
Catalog
Rachel-Lee
Nabors
(they/them)
nearestnabors.com

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