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A verification tool that just says "passed" or "failed" is almost useless. If it fails, you still have to figure out what's wrong and where.
So the check doesn't return a red light. It returns a plain-language report: which field is off, what the record says, and what the registry says instead. "Signatory name doesn't match the registry — record says one thing, the official source says another." The manager reads one line and knows exactly what to fix.
Then they fix that one field and re-run. No guessing, no reopening five government sites to hunt for the mismatch.
Automation that only gives you a verdict just moves the hard part somewhere else. The useful version tells you what to do next.
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Orvex is a Sales CRM SaaS dashboard designed to make sales data easier to understand and manage. With real-time insights, clear visualizations, and a streamlined interface, it helps teams track performance, spot trends, and make faster, more confident decisions.
Want to create something like this?
Let's Talk!
What makes an A/B test readout useful to a product team?
My preferred first page answers four questions:
What changed, and by how much?
How uncertain is the estimate?
Did an important guardrail get worse?
What decision does the evidence support, and what remains unresolved?
A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.
I built an independent A/B analyzer to make these checks easier to inspect. The project includes a live demo, downloadable reports and documented limitations:
https://contra.com/p/mgoXb3qY-ab-test-analyzer-correct-p-values-power-and-fdr
If you work on product or CRO, what do you wish analysts put on the first page more often?
This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.
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.
