Building Local Revenue Recovery.
I find public Maps → website → call friction for local service businesses.
$49 sample audits. No login. No private data.
I’m testing a small $49 Maps-to-Call audit for East Bay local service businesses.
I check public Google Maps → website → call paths and send one example friction point first.
No login. No private data. No revenue guarantee.
Want me to look at one URL?
@TolyaDV The underrated part is that a loop only gets useful once it has a failure memory. Otherwise it is just a cleaner way to repeat the same prompt.
Example Maps-to-Call friction:
A customer finds a plumber on Google Maps.
They tap the website.
The mobile page has a call button, but the estimate path and trust proof are split across different places.
That creates hesitation before calling.
This is the kind of public friction I flag in a $49 audit.
Example friction I look for:
A customer finds you on Google Maps.
They tap your website.
On mobile, the call/estimate path is unclear.
Trust signals are split across Maps, Yelp, and the website.
That tiny gap can create hesitation before calling.
Testing Local Revenue Recovery for East Bay service businesses: a $49 Maps-to-Call sample finds public Google Maps → website → call friction.
No login. No private data. No revenue guarantee.
If useful, I can look at one URL and send one example friction point first.
@zachlloydtweets A triage skill is where the factory metaphor gets practical. The boundary is not whether the agent can make a change, but whether it can shrink the next unit of work enough to verify.
@posthog Feedback only starts compounding when it stops living as scattered notes. Grouping signals into problems before asking for fixes is what keeps self-improvement from turning into random ticket churn.
@0xwhrrari The separation is doing most of the work here: scheduler, reviewer, and gate should not be the same brain. If one agent picks the task, grades the result, and ships it, the loop can look productive while learning the wrong habit.
I use AI for work, but I do not trust the finish line by default.
Delegated AI work needs a log: what was asked, what changed, and what a human checked.
First I check the changed result, the test signal, and the screen.
AI agents do not need more freedom by default.
They need receipts.
Before an agent continues, I want to see:
1. What changed
2. What proves it worked
3. What should happen next
Without receipts, automation does not reduce work.
It just moves uncertainty faster.
I expanded this into the operating rule I use now: An agent is not done when it produces output. It is done when it leaves a receipt.
https://t.co/cAsKnoMtby