My solo iOS transcription app made $7,591 in the last 28 days. An update on LoroNote.

A while ago, I shared an earlier update about LoroNote when its last-28-day revenue was $5,373. Here’s the latest RevenueCat snapshot: * **Revenue over the last 28 days: $7,591** * **Current MRR: $1,295** * **Active subscriptions: 258** The revenue figure is before store fees and taxes, not take-home profit. LoroNote offers both subscriptions and lifetime purchases. Lifetime sales contribute to revenue, but not MRR or the active subscription count. These are rolling 28-day figures, not revenue from the first 28 days after launch. I’m still building the app alone. **What the app actually does** LoroNote turns recordings into text on iPhone and iPad, with speaker separation also handled on-device. Once the model is downloaded, recordings can be transcribed without an internet connection or uploading the audio to a transcription service. It uses OpenAI’s Whisper Large V3 Turbo. My work is the custom Core ML inference implementation and the app around it, not a speech model I trained from scratch. I chose that model rather than switching to a smaller Whisper model. Making it practical across different iPhones has meant a lot of work on memory use, performance, and recording handling. Optional Apple Intelligence summaries and iCloud sync are separate from the local transcription pipeline. **What I’ve been working on** In the earlier thread, I mentioned trying Apple Search Ads and stopping because the campaigns weren’t profitable for me. So this isn’t a “never spent anything on ads” story. Since then, I’ve continued focusing on App Store keywords, localization, screenshot messaging, and screenshot order. The question I keep coming back to is whether someone looking for a transcription app can quickly understand what this one offers. The model name matters to developers. Being able to transcribe a meeting without uploading the recording is easier to explain to everyone else. The other half has been product work. Recent published updates include importing recordings through other apps’ share menus, exporting multiple transcripts together, and fixes to speaker labels in exported text. They’re small changes compared with building the engine, but they matter when someone is trying to use a transcript outside the app. I’m also still improving speaker separation and recording reliability. There are real limitations, especially with overlapping speech, so I don’t want to present it as a solved problem. **What I can and can’t conclude** Revenue is higher than in the previous snapshot. I’ve also kept working on ASO and the product. I can’t tell you that one screenshot, keyword, or update caused the increase. I haven’t isolated those effects, so calling this proof of a single growth trick would be misleading. One practical benefit of the on-device approach is that another hour of transcription doesn’t create another cloud transcription API charge. That gives me more room to offer lifetime access, although development, testing, and support still cost time and money. For me, the encouraging part is seeing a product I’m steadily improving turn into a small business. It’s still a lot of code, support emails, and App Store work, not passive income. LoroNote on the App Store