Four AI search tools, nine questions, and one Mac app that was not named

TL;DR

• On 4 November 2026 the audit asked Perplexity, Google AI Mode, ChatGPT search and Claude who makes the best Mac notch app. NotchStar was named in 0 of 9 non brand answers. On its own brand query, the answer was correct.
• Domain Rating did not explain who got named. A site with DR 6 and a site with DR 8 were named. Two sites with only a handful of pages were named in 6 of 9 answers each. Real usage signals such as Homebrew installs and GitHub stars looked like a stronger pattern, though five apps is a small sample.
• Cost: one afternoon of queries plus free tools. What changed afterwards is listed below, along with what is still unknown.

The problem

NotchStar is a native Mac app that puts ten small tools in the notch. At the time of the audit it was about four weeks old. Its website had Ahrefs Domain Rating 2.1, 26 backlinks and 8 linking sites.

People now ask AI search tools what to buy. “What is the best Mac notch app” is a typical question, and the answer is a short list of names with a few sources underneath. If your app is not in that list, the person never sees it, however good the product is.

So the audit asked one question: when someone asks AI search tools about Mac notch apps, is a new app named, and if not, what separates the apps that are?

This post is for indie developers who want to run the same check on their own product. It uses one product’s real numbers and says where they are weak.

The method

Everything here comes from one working day, 4 November 2026, and from the notes recorded that day.

The queries were worded the way a buyer would type them. The audit recorded 0 of 9 non brand answers, and some wordings were run in more than one tool.

best mac notch app 2026
best mac app that keeps screenshots in the notch to drag into chats
notchnook alternative mac
best mac teleprompter app that lives in the notch
best one time purchase mac notch app no subscription
mac notch app with clipboard history, file shelf, timers, one app
What is the best Mac notch app in 2026?
notchstar mac app

The last one is the brand query. It tests something different: whether the tool knows the product exists and describes it correctly.

ChatGPT search knew the account holder builds NotchStar and mentioned it only as “your” project. That reading is polluted, so ChatGPT counted only for the apps it listed as the market.

What the four tools said

The headline result is short. NotchStar was named in 0 of 9 non brand answers across the four tools. On the brand query, Google AI Mode named it and described it using the TinyLaunch listing and notchstar.com facts.

Query type	Tool	NotchStar named	Named instead
Best Mac notch app 2026	Perplexity	no	Boring Notch, NotchNook, Alcove, Droppy, Brow
Best Mac notch app 2026	ChatGPT search	no	Alcove, NotchNook, Boring Notch, Atoll, DynamicLake, Droppy, MediaMate
Best Mac notch app 2026	Google AI Mode	no	NotchNook, Alcove, Boring Notch, NotchNest, Notch Buddy
Best Mac notch app 2026	Claude (logged in)	no	NotchNook, Alcove, Boring Notch, TopNotch, NotchPop, DynamicLake, NotchNest
Screenshots in the notch	Google AI Mode	no	QuackScreen, NotchNook, Notchet, CleanShot X
NotchNook alternative	Google AI Mode	no	Boring Notch, Alcove, Notchy, Dynamic Notch, MediaMate
Teleprompter in the notch	Google AI Mode	no	CueNotch, Notchie, Notchy, NotchPrompter, Notchprompt, Avocado
One time purchase, no subscription	Google AI Mode	no	NotchNook, Notchy, Alcove, Droppy, Boring Notch
Clipboard, shelf and timers in one app	Google AI Mode	no	Notchy, Korinotch, Droppy, NotchBox, NotchPop
Brand query (notchstar mac app)	Google AI Mode	yes	not applicable

Two rows describe exactly what NotchStar does. The query for clipboard history, a file shelf and timers in one app is the product in one sentence, and other apps won it. NotchStar has a Teleprompter too, and six other names were listed for that query.

On the brand row, Google’s answer said a $4 licence covers up to three Macs, and that the Disk tool handles duplicate files and leftover application data. The audit marked both statements to be checked against the site and the feature page. Perplexity asked for a sign in on the teleprompter query, so that one was not run there.

Where the audit also looked: classic Google

The same day, the audit searched Google the old way. For “best mac notch app”, the top results were a getdroppy.app blog post, a Reddit thread on r/macapps, a Medium roundup by Mohit Vaswani, Apple’s Notchmeister, raphaeljourney.com, notchy.dev, theboring.name and topnotch.app. NotchStar was not in the top 20 shown. For the query “notchstar”, notchstar.com was first with sitelinks and TinyLaunch was second.

The brand search was already fine. The category searches, where a stranger decides what to try, were the problem.

Where the answers came from

The engines did not mostly cite the apps’ own sites. They cited other people.

Source type	Examples recorded on 4 November 2026
Medium roundups	“I Found the Best MacBook Notch Apps in 2026” by Mohit Vaswani (240+ likes)
YouTube reviews	Yabluko “I Tried Every Mac Notch App” (21K views, a 4 minute video), Jono Finds, Wesley Power, Fensy
Product Hunt pages	QuackScreen, Korinotch
Reddit threads	A post by aqib_98 about Notchet, and several live r/macapps threads
App Store pages	CueNotch, and the Apple App Store in general
Listing sites	TinyLaunch, Scroll Launch, chatgate.ai
Competitor blogs and roundups	theodorehq, 1dot, getdroppy, brow-app.com, favtray
Apps’ own feature pages	Notchy’s “NotchNook Alternative” and “Best Teleprompter App for Mac 2026” pages

Two patterns stand out.

First, the category answers are built from roundups. Perplexity’s answer to the best app query cited 11 sources, and most were competitor blogs and roundups. Being in those lists is how an app enters the answer, which is why outreach to roundup authors became part of the work.

Second, topic pages do get cited. Google AI Mode cited Notchy’s own pages on 4 of 5 feature queries. A page that answers a specific question in the words a buyer uses can be a source, and the audit found no NotchStar page in any citation list.

Fresh facts got quoted too. Perplexity repeated a dated caveat from favtray: NotchNook was offline in September and relaunched in October.

Domain Rating did not explain it

The tempting explanation for 0 of 9 is authority: DR 2.1 and 8 linking sites. The data does not support that cleanly.

Site	DR	Backlinks	Linking sites
lo.cafe (NotchNook)	45	288K	1.2K
tryalcove.com (Alcove)	47	not recorded	1.6K
getdroppy.app	31	4.5K	691
theboring.name (Boring Notch)	28	1.5K	931
notchy.dev	25	2.0K	1.3K
favtray.com	16	1.6K	1.0K
crestnotch.app	8	1.1K	838
brow-app.com	6	1.4K	1.0K
notchstar.com	2.1	26	8

Source: Ahrefs free Website Authority Checker, 4 November 2026.

Read the table against who was named. Brow, at DR 6, and Crest, at DR 8, were both named by AI engines. Alcove has a five page site with DR 47, and Boring Notch has a two page site with DR 28. Both were named in 6 of 9 answers. Notchy, with DR 25 and the largest site, was named in 4 of 9, and Droppy, with DR 31, was also named in 4 of 9. So a higher DR did not mean more mentions among these apps.

There is a caveat on the other side. Brow and Crest have 838 to 1,000 linking sites each, against 8 for NotchStar. They publish roundups, and they are on Product Hunt, Reddit and listing sites. So DR did not gate who was named, yet NotchStar’s 8 linking sites do show how few places mention it. The audit’s reading was that presence on the surfaces the engines cite matters more than the DR number.

Real usage looked like a better signal

If authority does not explain it, what does? The audit recorded one more column, and it is the most interesting one.

App	Named in (of 9)	Real usage signal recorded
NotchNook	6	Homebrew 1,924 installs a year, Product Hunt 384 followers, 4.6 of 5 from 5 reviews
Alcove	6	Homebrew 3,076 installs a year, Product Hunt 268 followers
Boring Notch	6	GitHub 11.3K stars, 1.2K forks
Droppy	4	Homebrew 675 installs a year
Notchy	4	Product Hunt 96 upvotes
NotchStar	0	none

The pattern is that the apps named most often are the ones with measurable adoption somewhere public. Homebrew install counts and GitHub stars are third party facts that an engine can read and cross check. A DR number is a statement about links, and a link is a weak statement about whether anyone uses the product.

This is a hypothesis, not a result. It rests on five apps, one day and four tools. Droppy has the lowest Homebrew figure of the three that report one and was named in 4 of 9, and Notchy has no install count at all and was named in 4 of 9. The honest reading is “this looks more promising than DR”, and nothing stronger.

Press lined up as well. NotchNook has Macworld, Digital Trends, t3n, BGR, MacMagazine and MacUpdate. Alcove has ifun.de, mac4ever, appgefahren and SlashGear. Boring Notch has Digital Trends and Yahoo. Notchy has Stuff.tv. NotchStar has none.

Site size and the biggest sitemaps

The audit also fetched each competitor’s sitemap, robots.txt, llms.txt and schema, to see whether the biggest sites were simply the most named.

	NotchNook	Alcove	Boring Notch	Droppy	Notchy	NotchStar
Named (of 9)	6	6	6	4	4	0
Sitemap URLs	163	5	2	523	773	181
llms-full.txt	80 KB	page copy	page copy	668 KB	197 KB	none at the time
AI crawlers named in robots.txt	0	8	0	9	10	2
Schema	basic	none	basic	Review, Product, FAQ, ItemList	AggregateRating, FAQ, HowTo	FAQ, Offer, SoftwareApplication

The two largest sitemaps, Notchy at 773 URLs and Droppy at 523, belong to the two apps named in 4 of 9. The three apps with tiny or mostly single topic sites, Alcove with 5 URLs, Boring Notch with 2 and NotchNook with 163 URLs (mostly a journal), were each named in 6 of 9. NotchStar’s own site, at 181 URLs, is larger than three of those five.

So volume of pages did not win. The audit’s reading was that the small sites win on product reach: Boring Notch is free and open source, NotchNook and Alcove have press and a long running brand, and all three have real installs. Droppy and Notchy win on content. Droppy has 303 documentation pages in its sitemap, and Notchy has 135 help pages. Both publish very large llms-full.txt files, at 668 KB and 197 KB.

Both content sites have real wins. Notchy’s own pages were the ones cited most in feature answers, and Droppy’s blog ranked first on Google for the category search. On depth, ten sampled Notchy alternatives pages had a median of 1,285 words, against 2,795 and 3,058 words on NotchStar’s longest comparison pages. Volume is Notchy’s strength.

What changed afterwards

The audit produced a gap list of 40 items. The plan that followed ranked them. Here is what was written in response, in plain terms.

What	Why it came from the audit
Comparison and alternatives pages worded as the query is worded, with prices, macOS requirements and checked dates from each app’s official page	Engines cited topic pages, and NotchStar had none for “NotchNook alternative”
A page for each feature query: teleprompter, one time purchase, clipboard plus shelf plus timers, screenshot trays, and Macs without a notch	The audit showed four apps winning queries that describe NotchStar exactly
A safety page covering signing, notarisation, permissions and what the app sends	Competitors own the “is it safe” searches, and engines quote caveats
A llms-full.txt file and named AI crawlers in robots.txt	The competitors that publish them are the ones with the largest files
A public feedback repository with releases and a plain README	Notchy has one, and GitHub is a place the engines read
A fixed query set, rerun every Friday	The audit had no stored baseline before this run

The comparison and alternatives pages were written and published first. On 4 November 2026 the query worded comparison pages (all in one tools, notch teleprompters, screenshot trays and one time prices) and a safety page were added, an llms-full.txt route was added with explicit rules for AI crawlers in robots.txt, and a public feedback repository with the product facts was created at github.com/ArinPrajapati/notchstar-feedback.

Every comparison page follows the same rule. It has to say who should stay with the other app, and it uses only facts from the app’s official page with the date they were checked. A page with no honest difference to state is not published. One earlier correction shows why. The first notes said NotchNook costs $25 once, and its official page now shows Classic $15 and Pro $19 once, or $2 and $3 a month, for two Macs. That was fixed on the day. Prices as of 4 November 2026.

The plan also names what it will not do: copy 277 alternatives pages with the names swapped, since thin templated pages risk a quality penalty across a whole site; invent a rating or a review count; buy links or fake votes. The AI agent notch category is the hottest one, with projects like Vibe Notch at 2.5K stars, but NotchStar does not do Claude Code status, so it was left alone. The narrower opening was the Screenshot Tray, since “Mac screenshot manager” is a smaller search.

What is still bad

Limit	What it means
No traffic data for competitors	The audit had no paid Ahrefs access, so “who wins” is measured only by who the engines named
One search engine per query	Most queries were run in a single tool. A fair test runs each query in all four, several times
Results change daily	A name that appears today can vanish tomorrow, so 0 of 9 is a snapshot
Reddit could not be read	The browser tool blocked it and web search only returned AlternativeTo. Reddit is a heavily cited source, so part of the picture is missing
ChatGPT was personalised	Its brand answer was discounted. A clean reading needs an unpersonalised session
The usage signal idea is untested	Five apps do not make a pattern
Little third party presence	NotchStar had 1 upvote on Product Hunt, no Reddit activity, no review video and no roundup mention, and four outreach pitches had no replies. Pages on its own site do not fix that

• NotchStar has little third party presence. It had 1 upvote on Product Hunt, no Reddit activity, no review video and no roundup mention. Four outreach pitches had no replies when the audit was written. None of that is fixed by writing pages on the site.

Try it yourself

You can run this audit on your own product in one afternoon. The steps work for any product category.

1. Write the query set. Write 10 to 30 queries a buyer would type, in four groups: category (“best X app”), feature (“X app that does Y”), alternative (“Z alternative”) and brand (your name). Save them in a file so you can repeat them.
2. Use clean sessions. Run each query in at least two AI search tools, and more if you can. Log out, or use a session without memory, so the tool is not answering about you. Note in the log if you could not.
3. Record four things per answer. Were you named, who was named, which sources were cited, and what the tool said about you.
4. Group the sources into types: roundups, video reviews, Product Hunt, Reddit, store pages, listing sites, competitors’ feature pages. The biggest group tells you where to show up.
5. Check the authority numbers. Use the Ahrefs free Website Authority Checker on every named site and on yours. Write down DR, backlinks and linking sites.
6. Look for a real usage signal. For each named product, record what is public: Homebrew install counts, GitHub stars, Product Hunt followers, store ratings, press. Compare it with how often the product was named.
7. Audit two or three competitors. Fetch the sitemap, robots.txt, llms.txt and schema. This takes a few minutes with curl.
8. Check your brand answer against your own site, claim by claim.
9. Pick changes, and write down the shortcuts you will refuse, such as fake reviews or thin pages.
10. Repeat weekly. A single run tells you little. Several runs tell you whether anything moved.

A starting point for the competitor fetch:

SITE=https://example.com
curl -s "$SITE/sitemap.xml" | grep -c "<loc>"        # URLs in the sitemap
curl -s "$SITE/robots.txt" | grep -i -E "gptbot|claudebot|perplexity|oai-searchbot|google-extended|ccbot"
curl -sI "$SITE/llms.txt" | head -1                  # 200 means it exists
curl -sI "$SITE/llms-full.txt" | head -1
curl -s "$SITE" | grep -o '"@type":"[A-Za-z]*"' | sort | uniq -c   # schema types on the home page

If a site uses a sitemap index, the first command counts child sitemaps, so follow each one. The schema line is a rough guide only.

Cheat sheet

Save this part.

• A saved query set in four groups: category, feature, alternative, brand
• Each query run in at least two AI search tools, with the date logged
• A session without memory, or a note that the answer was personalised
• Named or not, who was named, and the sources cited, for every answer
• Cited sources sorted into types
• DR, backlinks and linking sites for you and every named competitor
• A public usage signal for each named competitor
• Sitemap, robots.txt, llms.txt and schema checked for two or three competitors
• Your brand answer checked against your own site
• A list of shortcuts you will not take
• The same run repeated every week

Signal	Where to read it	What it told this audit
Named or not	The AI answer itself	NotchStar 0 of 9, brand query correct
Cited sources	Links under the answer	Medium, YouTube, Product Hunt, Reddit, App Store, listing sites
Domain Rating	Ahrefs free Website Authority Checker	Did not explain who was named
Homebrew installs	The cask’s analytics page	Looked like a better signal, on five apps
GitHub stars	The repository page	Same, for the open source app
Sitemap size	sitemap.xml	773 and 523 URLs did not win more answers than 5 and 2
llms-full.txt size	/llms-full.txt	The two content sites had the largest files
Google organic rank	An incognito search	Brand page first, category page not in the top 20

The audit does not show that a new app cannot be found by AI search. It shows that on one day an app four weeks old, with almost no third party mentions, was missing from every category answer. The next step is the same audit, rerun. Until then nothing here is a trend.

If you want to try NotchStar, it is at notchstar.com. It costs $4 once for up to 3 Macs, with a 7 day free trial. Prices as of 4 November 2026. If something in this post is wrong, write to hello@notchstar.com and the post will be corrected.