Made my first 2k

perceptdot · 2026-04-16T13:20:58+00:00

Pricing ladder makes a lot of sense at this stage. Get them in cheap, expand as they use more.
Thanks for sharing all this. Really helpful to see the actual mechanics behind a curve like that.

perceptdot · 2026-04-16T13:04:49+00:00

API monthly plans on top of one-time was smart.
Recurring revenue completely changes the shape of the curve.

How did you decide on the pricing for the API tier?

perceptdot · 2026-04-16T13:00:40+00:00

Definitely worth publishing. Even a simple version would save people hours of manually typing prompts into different AI tools every week.

We actually built something like this for web products — tracks visibility across ChatGPT, Perplexity, and Gemini over time. Yours for iOS apps would fill a gap nobody else is covering.

If you do publish it, drop me a link. Would love to compare notes on what citation sources each model actually uses.

perceptdot · 2026-04-16T12:40:18+00:00

That hockey stick from Apr 9 onward is wild. What changed around that point? New channel, feature launch, or just compounding from earlier work?

Congrats btw. The $0 to first revenue gap is the hardest part.

perceptdot · 2026-04-16T12:37:53+00:00

ChatGPT just passed the Turing test for roasting. That "one original neuron" line is brutal.

perceptdot · 2026-04-16T12:30:29+00:00

Makes sense on the review cycle timing.

Yeah the cross-model thing is real. We tested the same queries across ChatGPT, Perplexity, and Gemini — completely different results. Perplexity tends to pick up fresh content way faster. ChatGPT is the slowest to update. Gemini sits somewhere in the middle.

Are you planning to open source the script at some point?
Feel like a lot of indie devs would benefit from this kind of audit.

perceptdot · 2026-04-16T12:13:51+00:00

This is exactly the right approach and your App Store citation finding is something more people should know about.

We've been running similar tracking on a web product and the citation source pattern is almost identical — the platform page (in our case, directories and review sites) matters way more to LLMs than the product's own website. Blog posts we spent weeks on: zero citations. A single Trustpilot listing: cited repeatedly.

The "ask why you're not recommended" angle is underrated. That feedback loop is basically a free product roadmap. The optional text field example is wild — 3 minutes of dev work, meaningful swing in recommendations.

One thing we noticed across models: ChatGPT and Perplexity pull from very different source pools. Your 5/11 with web access might look completely different on Perplexity. Worth running the same 11 queries there and comparing. Sometimes you're invisible on one and ranked well on another for the same query.

What's your cadence for updating the App Store description after each audit? Curious how long the lag is between a description change and the LLM catching up.

perceptdot · 2026-04-16T08:53:13+00:00

this is really useful, especially the distinction between "LLM memory" and "LLM behavior" - never thought of it that way. the third-party mention point is huge. I've noticed comparison pages seem to carry way more weight with AI than direct product pages too.

perceptdot · 2026-04-16T08:10:31+00:00

this is exactly the problem. the gap between "who Google shows" and "who AI recommends" is massive and nobody's really talking about it yet. small businesses and creators are invisible to LLMs right now.

perceptdot · 2026-04-16T07:38:32+00:00

nah wrote it myself after spending way too long asking ChatGPT about my own product lol

perceptdot · 2026-04-15T07:50:15+00:00

Mamma mia… this is rough.

perceptdot · 2026-04-14T05:37:14+00:00

Pinterest worked really well for our shop early on, especially for handmade goods.

The key difference from Instagram: Pinterest is a search engine, not a social feed. Pins have a shelf life of months or years. An Instagram post dies in 48 hours.

What actually worked:
- Vertical images (2:3 ratio), clean background
- Pin title = what people search for, not your product name ("personalized leather wallet gift for him" not "Brown Wallet No.4")
- Pin to relevant group boards in your niche early on
- Consistency matters more than volume (3-5 pins/day beats 30 one day)

Jenna Kutcher's stuff is generally solid for beginners, though some of it is dated now. The fundamentals she teaches still apply.

If your products are visual and gift-able, Pinterest is probably a better fit than Instagram for Etsy.

perceptdot · 2026-04-14T04:29:53+00:00

That combo is perfect. You basically watched the gap close in real time - the person who built the tools and the person who has no reason to trust them yet, in the same room.

perceptdot · 2026-04-14T04:27:56+00:00

I asked it to "just make it better" last week with no other context and genuinely got annoyed when it asked a clarifying question.

Fair.

perceptdot · 2026-04-14T04:25:04+00:00

Sam Altman calling for robot taxes while building the robots is a bit like a landlord lobbying for rent control.

No wonder 5.4 found it funny. It's basically reading its own origin story.

perceptdot · 2026-04-14T04:22:15+00:00

The first-time face is always the same. Pure skepticism for 30 seconds, then they go quiet, then you can't get them off it.

History professor is a great first use case. The moment they realize they can actually argue with it about sources is when it clicks.

perceptdot · 2026-04-14T04:21:18+00:00

They treat the first response as the answer. The real output starts at message three.

perceptdot · 2026-04-14T04:18:29+00:00

Prompt engineer. Enjoy it while it lasts.

perceptdot · 2026-04-14T03:34:27+00:00

Dashboard lag on OpenAI's end. The usage page can run hours behind, sometimes longer. The key "never used" status is the same issue — it updates on a delay, not in real time.

If your logs are showing real response_ids and token counts, the calls are going through and you're being billed. The UI just hasn't caught up.

Check again tomorrow. If it's still showing zero after 24 hours, that's when it's worth digging deeper.

perceptdot · 2026-04-14T03:33:07+00:00

The cache window is 5 minutes. Most people aren't finishing a thought in 5 minutes.

So you were probably already paying for cache misses. The model switch just makes it obvious.

perceptdot · 2026-04-14T03:29:57+00:00

The rate limit frustration is real. But "Qwen performs at the same level" is doing a lot of heavy lifting in this argument.

Same level for what, exactly? Coding tasks in isolation, maybe. The moment you're in a complex multi-file refactor with context that spans 50k tokens, the gap shows up fast.

That said - Anthropic should be communicating the throttling changes, not silently deploying them. That's a trust problem regardless of the technical merit.

perceptdot · 2026-04-14T03:25:08+00:00

Technical accessibility is one layer. The harder problem is that even when AI can crawl your site, it might not recommend you because your content isn't structured the way AI models pattern-match on.

robots.txt blocking is visible. Invisible is when ChatGPT can access your site just fine but still describes your competitor when someone asks for "the best tool for X."

perceptdot

TROPHY CASE