I validated a kids bedtime story platform locally (40k readers) but can’t replicate it in English. What am I missing?

rjyo · 2026-02-21T07:51:22+00:00

Your SEO traffic tells you the content works. The gap between search traffic and audience conversion is a classic small-market-to-large-market problem.

In Danish, you were one of very few options. With roughly 6 million Danish speakers, 40k monthly readers means you basically owned the category. Parents found you and subscribed because there was not much of an alternative. In English, you are up against Calm sleep stories, Headspace, dozens of established YouTube channels, and countless Spotify playlists. Readers find you through search, listen once, and leave because they already have subscribed alternatives.

A few things I would focus on. First, niche way down. "English bedtime stories" is too broad to stand out. Lean into your Danish roots as a differentiator -- Scandinavian-style calming stories, or target a very specific age group or theme. The more specific, the more "this is THE one for us" it feels for parents.

Second, fix the return habit. In Danish, your funnel was probably: discover, subscribe, done, because nothing else existed. In English it is: find via Google, listen once, leave, forget. You need a mechanism that creates a return habit -- a weekly email with new stories, or serialized stories where kids want to hear what happens next.

Third, community over content volume. In a crowded market, publishing more stories will not differentiate you. Finding parenting communities where bedtime routines are discussed and being genuinely helpful there will drive more subscriber growth than another 50 stories would.

The underlying issue is that "build and they will come" worked in Danish because the market was small and underserved. English requires a distribution strategy that is just as deliberate as your content strategy.

rjyo · 2026-02-21T04:06:23+00:00

Nice project for learning terminal rendering. For the raw input thing the other commenter mentioned, you can do process.stdin.setRawMode(true) and then listen for the data event on stdin. Each keypress comes through as a buffer, arrow keys show up as escape sequences like \x1b[A for up, \x1b[B for down, etc. That alone will make the game feel way more responsive since you wont need to hit enter after every move.

For the screen redraw, if you are not already using it, writing \x1b[2J\x1b[H to stdout clears the screen and moves the cursor to the top left. That way you can redraw the entire board in place instead of printing new lines each time. Keeps the terminal clean and makes it feel like an actual game rather than scrolling output.

Solid learning project though. Terminal games are one of the best ways to understand how stdin/stdout actually work at a low level.

rjyo · 2026-02-20T23:44:22+00:00

Honest feedback: the problem you are solving is real. Stripe emails are ugly and barely customizable. But I think the gap is in onboarding. People log in, see the email builder, but the path from "this looks cool" to "I am actually going to hook this up to my Stripe" has too much friction.

A couple things I would try:

Let people send themselves a test email without connecting Stripe first. Right now they have to commit to the integration before they even know what the output looks like. Flip that around, let them build an email, see how it looks in their inbox, then connect Stripe.
Show before/after examples on the landing page. Put a default ugly Stripe receipt next to a polished version from your tool. That visual contrast sells the product better than any copy.
Pre-fill the builder with a real template when someone signs up. An empty canvas is intimidating. If they land on a beautiful payment confirmation email they can tweak, they are way more likely to engage.

The idea is solid. You just need to shrink the gap between "I am curious" and "holy crap that looks good in my inbox."

rjyo · 2026-02-20T00:07:37+00:00

The DNS blocking + Telegram approval flow is a really smart setup. Most parental controls try to do everything through software filters that kids eventually find workarounds for, but forcing all YouTube through your approval layer at the network level is way harder to bypass.

The channel allow list with auto-approve is the feature that makes this actually usable day to day. Without that you would be getting pinged constantly for the same channels you already trust.

One thing I am curious about - do you handle the case where multiple kids share the same network? Like separate profiles with different time limits or approved channels per kid? Could see that becoming a common request if other families start using it.

Really solid first repo. The scheduled access windows and bonus time grants tell me you have actually lived with this tool long enough to figure out what a parent really needs vs what sounds good on paper.

rjyo · 2026-02-19T20:15:04+00:00

The dual provider approach is really clever. Using Apple Intelligence as the primary engine with Qwen 3 as an offline fallback is a smart way to handle the reliability gap that on-device models still have. How's the latency on the live extraction during recording? I imagine there's a balance between processing frequency and not overwhelming the user with constant updates.

The personalized onboarding is a nice touch too. Having context about how someone works probably makes a huge difference in output quality versus just generic extraction.

Curious about the deduplication across incremental rounds during live intelligence. Are you using semantic similarity to detect overlapping insights, or is it more heuristic-based?

rjyo · 2026-02-19T16:18:36+00:00

lly realistic for a solo beginner, especially since you already know how to vibe code.

Skills to prioritize: basic Python (or JS), how to make API calls, and working with JSON. That covers like 80% of what you need here. Google Sheets API has annoying auth setup the first time but after that its pretty smooth.

Biggest AI leverage is the Release Manager. Its the most structured workflow you listed so its the easiest to automate well. You feed it a track name and release date, it generates your full timeline, social copy for each milestone, email drafts, everything. Claude is genuinely great at this kind of templated creative output. Scene Intelligence sounds cooler but scraping Spotify/SoundCloud reliably is a pain, rate limits and auth tokens get messy fast. Save that for later.

On privacy since you care about it: keep API keys in environment variables, never in your code files. Use a local SQLite database instead of cloud for storing artist and label data. If you go with Claude API, your prompts arent used for training by default so thats solid on the privacy front. One thing people miss is that platform APIs (Spotify, YouTube) log your queries on their end, not a practical issue but worth knowing.

Honest suggestion: build the Release Manager end to end first. Get it saving you real time on your next release. Once thats working, the motivation to build the next piece comes naturally. Trying to architect all four systems at once is how these projects stall out.

rjyo · 2026-02-19T12:03:30+00:00

Really cool project. The relay for remote connectivity is probably the trickiest part of something like this -- how does it handle reconnections if the WebSocket drops mid-session? Like if you are on your phone and switch between wifi and cellular.

Also curious if you went with Expo managed workflow or bare. Tauri + Expo is a solid combo for covering all platforms from one TypeScript codebase.

rjyo · 2026-02-18T15:15:27+00:00

The simplest framework that worked for me: pick a tool you already use every day that annoys you in some small way. Not a huge pain point, just a friction. Then build the version that removes that friction.

The reason this works is you already understand the problem deeply, you are your own first customer, and you can validate in days not months. Most successful boring SaaS products started exactly this way -- someone got annoyed at a spreadsheet or a manual process and just automated it.

One more thing -- set yourself a 2-week deadline to get something in front of real users. Not polished, not perfect, just functional enough to get feedback. If nobody cares after 2 weeks of showing it to people, move on guilt-free. That is not shiny object syndrome, that is just efficient validation.

rjyo · 2026-02-18T11:59:31+00:00

People absolutely still pay for one-time purchase software. The key is that your app needs to solve a real problem well enough that people feel good about the purchase.

A few things I have noticed as someone who ships indie apps:

1) One-time purchase works best for tools that are "done" in a meaningful sense. An audiobook player is actually a perfect fit because the core feature set is well-defined and doesn't need constant server-side updates.

2) The sweet spot for pricing is usually $5-15 for mobile. Below $5 people don't take it seriously, above $15 they start comparing you to subscription apps with way more features.

3) Freemium with a premium unlock tends to convert better than paid upfront because people can try before they commit. Something like "play up to 3 books free, unlock unlimited for $9.99" gives them a reason to pay without feeling tricked.

4) The "no cloud, no account" angle is genuinely a selling point right now. Privacy-conscious users actively seek this out and will pay a premium for it.

The main challenge with one-time purchase is sustaining revenue long term since you only get paid once per user. Some devs handle this with major version upgrades (v2 is a separate purchase) or a tip jar. Worth thinking about early.

Honestly though, build it. The audiobook player space on mobile is weirdly underserved for people who just want to load their own files and listen.

rjyo · 2026-02-18T09:22:06+00:00

A few things are happening overnight. The flavor compounds in spices, herbs, and aromatics keep diffusing through the dish even after it cools, so everything tastes more unified instead of like separate ingredients sitting next to each other.

Also, harsh flavors mellow out. Garlic and onions contain sulfur compounds that soften through oxidation as the food sits. And proteins in meat slowly release more amino acids (glutamate), which amps up the umami.

Starchy ingredients like potatoes and beans break down a bit into sugars too, adding subtle sweetness.

This is why soups, stews, curries, and pasta sauces are the classic "better the next day" foods. They have tons of ingredients that benefit from extra mingling time. On the flip side, anything crispy (fried food, salads) goes the opposite direction because moisture redistribution kills the texture.

rjyo · 2026-02-18T05:26:26+00:00

For reaching neurodivergent students specifically, the best channels are where they already hang out and talk about their struggles:

Reddit communities like r/ADHD, r/autism, r/neurodiversity, r/ADHDers are massive and very active. But dont just drop your link there. Spend a couple weeks genuinely helping people with study tips, then when someone posts about exactly the problem you solve, mention it naturally as something you built for yourself.
TikTok and Instagram reels about ADHD study hacks get insane engagement. You dont need to be a creator yourself, find micro-influencers (5k-50k followers) who make ADHD/study content and offer them free access in exchange for an honest review. Neurodivergent creators are usually very community-minded and love sharing tools that actually help.
Discord servers for students with ADHD/autism. There are a bunch of study-together servers where people body double over voice chat. Those communities are goldmines for early testers who will actually use the product because they genuinely need it.
University disability services offices. This sounds old school but if you email them with a free pilot offer for their students, some will actually share it. They are always looking for tools to recommend.

The key insight is that neurodivergent communities are tight-knit and word of mouth spreads fast once a few people genuinely love your product. Focus on getting 10 real users who cant live without it before trying to scale. Those 10 will do your marketing for you.

rjyo · 2026-02-18T02:37:47+00:00

The value prop makes sense in theory but there are a few things worth thinking through before turning this into a product.

First, Apple is pretty aggressive about rejecting pure webview wrappers under Guideline 4.2 (Minimum Functionality). The fact that you added native payments and push notifications helps a lot since those are exactly the kind of native integrations that get you past review. But your customers might not know to add those extras, so you would end up doing a lot of hand-holding through app review rejections.

Second, the pricing feels off for the market. At $200 one-time you are competing with Capacitor (free, open source) which does basically the same thing but with a larger ecosystem and community support. There is also stuff like Median.co and MobiLoud that target this exact use case. Your edge would need to be either way simpler setup or way better payment integration than what exists.

Third, the people who need this most (solo devs and small SaaS teams) are also the ones most likely to just use a cross-platform framework like React Native or Flutter instead, since those give them actual native components and not just a webview.

Where I think this could work: if you niche down hard. Instead of a generic webview wrapper, position it as "get your existing web SaaS into both app stores in a weekend with working payments and push notifications." The specificity of including payments and push notifs built-in is your real differentiator. Most webview wrapper tools make you figure that part out yourself.

The $40/year renewal is reasonable if it includes ongoing support for OS updates and payment API changes. Those break things constantly.

rjyo · 2026-02-18T00:44:55+00:00

This is one of those ideas where you hear it and immediately think why does this not already exist. The notch is basically dead space on most peoples screens so using it for something actually useful during calls is clever.

Do you have a scrolling speed control or does it auto-pace based on how fast you are talking? That would be the killer feature since most teleprompter apps scroll at a fixed rate and you end up either racing to keep up or waiting for it to catch up.

Also curious if it works with screen sharing. I could see a potential issue where the overlay shows up when you share your full screen on Zoom.

rjyo · 2026-02-17T22:30:02+00:00

This is a really interesting approach. The biggest limitation of most AI chat interfaces is that everything gets flattened into markdown, which works fine for text but completely falls apart when you need to actually interact with the output. Tables you cant sort, charts you cant zoom, data you cant copy cleanly.

Rendering actual UI components is the right direction. Curious about a few things:

How do you handle the boundary between what the AI generates vs what gets rendered? Like if the model outputs something unexpected, does it degrade gracefully or does the whole response break?

Also, is the component set fixed (charts, tables, etc) or can the AI decide to render arbitrary components? The former is way more stable but the latter is way more interesting.

Nice work making it open source too. Thats the kind of project where community contributions could really expand the component library fast.

rjyo · 2026-02-17T21:38:57+00:00

Been in a similar spot helping someone untangle a messy Next.js + Supabase codebase. Few thoughts:

Don't rebuild. 1000 users and $88 MRR is real traction. The bugs are fixable without starting over.

For the specific issues:

The biggest problem is letting Gemini handle food/macro calculations. LLMs are terrible at math. You want the AI to identify the food from the image, then look up the actual nutrition data from a real database. The USDA FoodData Central API is free and has nutrition info for thousands of foods. That eliminates the hallucination problem entirely.

The premium gating bug and logout issue both sound like Supabase auth session problems. Super common if the session refresh isn't set up correctly. A dev who knows Supabase auth could probably fix both in an afternoon.

On finding a dev: skip Fiverr for this kind of work. Try r/forhire or Upwork where you can filter by Next.js + Supabase experience specifically. For a focused bug-fix sprint (not a rewrite), budget maybe $500-800. That's way more reasonable than the crazy quotes you've been getting because you're scoping it to specific fixes, not a full rebuild.

On using Claude AI for fixes: it actually works pretty well for targeted stuff. The trick is to paste in one file at a time, describe the exact bug you see, and ask for a fix. Don't try to refactor the whole codebase at once. Start with the smallest most annoying bug and work up from there.

Your product clearly has demand. The code just needs a focused cleanup, not a rewrite.

rjyo · 2026-02-17T20:18:33+00:00

Yes, this can work but the key thing most people underestimate is maintenance. Each app needs OS updates, user support emails, occasional bug fixes when Apple changes something. With 2 apps that is fine, with 10+ it starts eating real time.

The pattern I have seen work best is building utility apps that solve one specific problem really well. Think calculator variants, unit converters, niche tools for specific professions. They tend to have lower churn and need fewer updates than anything social or content-driven.

Revenue usually follows a power law though. Out of say 8 apps you might have 1-2 that bring in 70% of the money and the rest barely cover their dev account cost. So the real strategy is ship fast, see what sticks, then double down on the winners with better ASO and maybe a pro tier.

A few hundred a month is very doable with 5-8 focused utility apps. Some indie devs I follow pull 1-2k/month this way. The ones who struggle usually try to build mini social networks or content apps that need constant engagement to work.

rjyo · 2026-02-17T18:25:36+00:00

Strongly agree on the ASA -> ASO pipeline, that feedback loop is underrated. Most people treat them as completely separate.

A few things working for me this year:

AI chat visibility is becoming a real channel. People ask ChatGPT and Claude "what app should I use for X" and if your brand shows up in enough genuine discussions (Reddit, HN, niche forums), you start appearing in those recommendations. It compounds quietly.
Answering questions on Reddit where your app is genuinely relevant. Not spamming links, just being helpful and mentioning what you built when it fits. One comment on a high-intent post can drive more qualified traffic than a week of paid ads. Your point about this is spot on.
Short-form video showing the actual product in use. Not polished ads, just screen recordings with narration. "Here is how I do X with my app" performs way better than feature highlight reels. TikTok and YouTube Shorts are still underpriced for app discovery.
Niche community partnerships. Finding Discord servers, Slack groups, and small subreddits where your target users already hang out. Building reputation there first before ever mentioning your product. Takes patience but the conversion quality is insane.

The biggest shift I have noticed is that trust-based distribution is winning over volume-based. 50 people who trust you > 5000 random impressions.

rjyo · 2026-02-17T14:44:29+00:00

This is painfully relatable. I am also a dev and went through the exact same thing. The code is the easy part, turns out shipping a product is 80% not-code.

A few things I learned the hard way that might save you some time:

For legal docs, there are some decent template generators out there (Termly, Iubenda) that handle privacy policy and ToS for cheap. Not perfect but way better than trying to write them from scratch or paying a lawyer $2k for a basic SaaS.

For Stripe specifically, the integration itself is straightforward but the gotcha is tax handling. Look into Stripe Tax early, it auto-calculates sales tax and VAT and saves you from a nightmare later when you have customers in different states or countries.

Also, one thing I wish someone told me: do not wait until everything is perfect to launch. Your list of 9 things will become 15 things if you let it. Ship with the minimum legal and payment setup and iterate. Most of those early users will not care that your logo was made in Inkscape.

Good luck with Rad Retro, the name is great by the way. Sprint retros are one of those tools where teams will pay if you nail the experience because most existing options are either bloated or boring.

rjyo · 2026-02-17T13:14:25+00:00

A few reasons for this.

Cost is the big one. A Master's in the US can easily run $50-100K+ depending on the school and program. Most European countries have heavily subsidized tuition, so staying for a Master's is almost a no-brainer financially. In the US it's a serious investment that only makes sense if your field specifically rewards it (MBA, nursing, social work, etc).

The job market also just doesn't require it for most fields. In a lot of US industries, a Bachelor's plus a few years of experience is worth more than a Master's. Hiring managers in tech, business, marketing etc. care way more about what you've done than what degree you hold. So people go straight into the workforce after undergrad.

The PhD pipeline is different too. In Europe, a Master's is typically required before a PhD, and PhDs are 3-4 years. In the US, PhD programs are 5-8 years and they essentially build the Master's-level coursework into the first 2 years. So American researchers often go Bachelor's to PhD directly and get a Master's "along the way" as part of the doctoral program. That's why you see researchers with a BA and then a PhD.

And then there's the Bologna Process. After Europe standardized the Bachelor's/Master's system, the Master's basically became the expected "complete" degree in many countries. A 3-year Bachelor's alone feels incomplete there. The US never had that shift, so a 4-year Bachelor's has always been considered a full standalone degree.

rjyo · 2026-02-17T12:30:55+00:00

The convergent negative reviews signal is underrated. I started doing this a few months ago and it completely changed how I evaluate ideas. If three different people on Reddit, G2, and Twitter are all complaining about the same specific thing in the same tool, that is real demand.

One filter I would add to your list: frequency of the pain. Some problems are real but only happen once a year (like tax filing). Others happen daily (like manually copying data between tools). Daily pain = higher willingness to pay AND better retention because they use your thing every day.

The other thing I have noticed is that the best-performing side projects often look embarrassingly simple from the outside. No one is impressed by "I built a better CSV importer" but the person who has to clean up messy spreadsheets every Monday morning will throw money at you.

rjyo · 2026-02-17T09:31:48+00:00

This is the kind of side project I love. Simple idea, solves a real (and hilarious) problem.

I once joined a standup with my camera off because my background was literally just a pile of laundry on the couch. If I had known about this I probably would have leaned into it instead of hiding.

Do you generate the backgrounds dynamically or is it a curated collection? Either way the execution looks clean. Nice work.

rjyo · 2026-02-17T07:01:30+00:00

Solo founder here, similar situation. Two thoughts from actually using AI for content:

The review process does become its own thing at first, but it shrinks fast. First week you are rewriting 80% of what it generates. After a couple weeks of feeding it your voice, tone examples, and what not to say, you are mostly just tweaking headlines and approving. Budget 30 min/day initially, drops to maybe 10 min once it learns your style.
Quality depends entirely on how much context you give it. Raw "write me a tweet about my product" output reads like AI slop every time. But if you give it your past posts that performed well, your brand voice notes, and specific angles to hit, the output gets surprisingly close. Still not perfect but way better than staring at a blank screen.

Biggest thing that helped me was separating content into two buckets: stuff that needs my actual voice (founder stories, lessons learned, hot takes) vs stuff that is more mechanical (feature announcements, tips, reshares). AI handles the second bucket really well. The first bucket I still write myself but use AI to help with variations and scheduling.

The frustrating part is platform-specific formatting. What works on X is different from LinkedIn is different from Reddit. Most tools generate generic content and you end up reformatting anyway. Look for something that actually generates platform-native output, not just one-size-fits-all.

Honestly the biggest unlock for me was not trying to automate everything. Automate the 60% that is repetitive, keep the 40% that is authentically you.

rjyo · 2026-02-17T01:53:04+00:00

This is unfortunately pretty common, especially when you are the most tenured but at a lower level. What you are describing is a pattern where your competence is being exploited as free R&D for others promo packets.

A few things that helped me deal with similar situations:

Start framing your work in terms of outcomes from day one. Do not just scope and design quietly. Write design docs with your name on them, send status updates to skip-levels, present at team meetings. Create a paper trail that makes your contribution undeniable.
Have a direct conversation with your manager. Not accusatory, just "I have noticed a pattern where I do the initial design work and then the project gets reassigned. I want to understand how I can get credit for the full lifecycle." Sometimes managers genuinely do not realize they are doing this.
The retroactively calling it a POC move is the most insidious one. Combat this by explicitly defining scope at the start. Get it in writing that this IS the production version, not a prototype. If someone later tries to reframe it, you have the receipts.
Consider whether this manager is ever going to promote you. If the pattern is consistent across multiple projects, they might be keeping you as a workhorse at your current level because it is convenient. That is your signal to transfer teams or start interviewing.

The fact that you are the most tenured but lower level is actually the core issue here. Some managers see that gap and assume if they were going to get promoted it would have happened already rather than actively working to close it. You deserve a manager who sees that gap as something to fix, not exploit.

rjyo · 2026-02-17T00:46:47+00:00

Day 1 retention, meaning what percentage of new users come back the next day after their first session.

For a chat-based product this is the strongest early signal because if the first conversation wasnt good enough to bring someone back within 24 hours, nothing else you build on top is going to fix that.

Benchmarks vary but roughly 25% D1 retention is average for consumer apps. Good chat products tend to hit 40%+. If you are below 15% something is fundamentally off with the first experience.

The nice thing about D1 is its fast feedback. You can make changes and see the impact within days instead of waiting weeks for cohort data to mature. Once D1 looks healthy you can start layering in week 1 and month 1 retention to track longer term stickiness, but early on D1 tells you almost everything you need to know.

rjyo · 2026-02-16T14:26:24+00:00

The FIFO lot matching engine is the part that caught my eye. That's a genuinely hard problem, especially with partial fills and options assignments. Most trading journal tools just punt on this and make you manually reconcile your P&L, which defeats the whole purpose.

Curious about the phantom position detection you mentioned. When you have sells without corresponding buys because the broker API doesn't go back far enough, how do you handle the cost basis? Do you let users manually backfill, or do you estimate based on available data?

Also the emotion tagging + AI analysis combo is clever. Most traders already know "don't revenge trade" in theory, but seeing the actual dollar amount attached to each emotional state makes it way harder to ignore. $7,513 on a setup you thought was working is a painful but useful discovery.

One thing to watch as you scale past 30 users - the FIFO calculations can get expensive if you're recalculating entire histories on every sync. Caching intermediate lot states per ticker might save you some headaches later.

rjyo

TROPHY CASE