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I ran an analysis last week that came back with a finding I liked, and it was wrong.
Comparing crawler traffic across the client sites we manage, fifteen of twenty one comparisons showed shared infrastructure. Same addresses, a different declared identity per site. That pattern is normally worth escalating.
The addresses were ours. Testing traffic from our own machines, plus log entries that predate the header we now use to tag our own requests. Corrected number, four of twenty one.
What caught it was a competing hypotheses checklist that requires listing the explanation that implicates you. It is the one finding nobody thinks to double check.
Full write up tomorrow, including what the verified numbers look like now. https://leftymediaco.substack.com/
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Your client should never be the first person to test the work.
A recent update to my Contra portfolio caught this perfectly. The editor preview showed the new cover. The public page still showed the old one.
It looked done until we opened the page a visitor would see.
That gap is easy to miss when AI helps you move fast. The file exists. The tool says success. The reply says 'done'. So you move on.
Before I hand over the result, I check the other end:
Open the exported file.
Click through the live page.
Try the shared link without owner access.
Check that the form submission actually arrives.
Pick the checks that match the work. A screenshot of the editor can't prove the public page changed.
AI can help make the work. Your name is still on the handoff.
What's one check you always run before sending work out?
This is such a useful reminder that quality includes the handoff, not just the artifact. Testing the live page, exported file, permissions, and real form flow is a strong practical checklist for catching the gaps screenshots can hide.
my AI coding agent told me everything was done.
three weeks of work. report had green checkmarks
on every item. typescript passed. build passed.
I ran one command and found out the main migration
was never applied to any database.
not delayed. never.
Tweet 2:
the agent wrote 842 lines of SQL for a billing system.
described it in detail. said "verified ✓"
the columns it referenced didn't exist in the actual
table. first real user would've hit a crash on
signup.
[SCREENSHOT 1: agent report]
Tweet 3:
here's what I ran:
npx supabase db push
output: "Remote database is up to date"
that phrase means: nothing new to apply. the thing
it "completed" was never sent anywhere.
[SCREENSHOT 2: db push output]
Tweet 3.5 (yehi human touch):
(btw this is the SECOND time. first time it "verified"
a function that queried a column it never created)
Tweet 4:
since then my rule is dumb simple:
if the agent says done → I run the thing myself →
I look at the output with my own eyes → THEN it's done
my fake-done rate over 6 weeks: somewhere around 60%.
I stopped counting after it hurt.
Tweet 5:
weird part? the reports were beautiful. well written.
confident tone. proper structure.
beautiful fiction.
if AI is writing your code, who's checking the code?
genuinely curious what other people's fake-done rate
looks like. mine was bad.
#buildInpublic
#vibecoding
#AIcoding
The gap between green checks and a real migration is such a sharp reminder that verification has to reach the running system. I like the concrete failure mode here—schema reality beats confident generated SQL every time.
Google's AI cited my client's LinkedIn post 11 times last
month.
He wasn't writing for you. He was writing for ChatGPT.
Profile views tripled anyway. Inbound doubled.
If you're a consultant or founder posting three times a
week, getting likes, and still waiting on inbound, this is the ranking game
you're not playing.
AEO. Answer Engine Optimization.
Someone types a question into ChatGPT Search or Google AI
Overviews. The answer comes with sources. LinkedIn posts are on that list.
Semrush ran 325,000 prompts. LinkedIn showed up in 14% of
ChatGPT Search answers.
Most of those citations went to individual profiles, not
company pages. Half came from accounts under 10,000 followers.
Quotability beat follower count.
What gets quoted (steal this):
1. One question per post
→ Line one is the
exact question a buyer would type into ChatGPT.
2. A plain-English definition inside 3 lines
→ AI lifts clean
sentences. It can't quote a vibe.
3. Your own dated numbers
→ "11
citations in August" gets cited. "Great results" doesn't.
4. A headline + About section that claim the topic
→ The model checks
who's talking before it trusts the post.
Check yourself in 5 minutes: type your ideal client's
question into ChatGPT Search and scroll the sources. If a competitor's post is
there and yours isn't, that's your next topic.
The feed forgets your post in 5 days. An AI citation keeps
answering for you for months.
(I'll say the uncomfortable bit: I think AEO beats the feed
algorithm for inbound by 2027. Most coaches selling algorithm hacks won't like
that.)
Two years from now: does a ChatGPT citation beat LinkedIn
reach for inbound, or is AEO just SEO in a new hat? Pick a side.
-- M Aadil A.
@Muhammad Aadil Ali AEO feels less theory and more math now. Clean sentences beating vibes every time is underrated. This is the kind of clarity LinkedIn actually loves. You turned boring acronyms into growth hacks. Citations keep answering for months, that’s the real flex.🙌