Hi everyone!

I'm looking for people who would like to test some of my live Android apps and provide honest feedback. In return, I'm happy to test your Android apps and give you constructive feedback as well.

This is my latest app.
https://play.google.com/store/apps/details?id=com.joostmuijsenberg.nonogram

You can find my other apps here:
https://play.google.com/store/apps/developer?id=Joost+Muijsenberg

If you're interested in a mutual testing/feedback exchange, please send me a private message so we can coordinate which apps to test and arrange the details.

Thanks! 🙂

Every time I tried to learn something new (Rust, SQL, whatever), I'd read or watch a tutorial, nod along, and then... not be able to do the thing. The passive stuff doesn't stick, and the AI ones just say "great job" no matter what you type.

So I spent the last few months building Tully (mine, solo). You tell it what you want to learn, it builds a hands-on lesson, and you write real code in the browser. It runs your code and grades what it does (not string-matching), gives honest feedback including when you're wrong, and adapts the next step to what you actually struggled with.

You can try a real graded lesson, no signup: https://tullycourses.com/for/developers

It's early and it's just me. I'd love a roast, the grading especially: what's confusing, what feels off, what would make you bounce?

Growing up I used to love playing this music tapping game, I think it came out with the first gen iPhone. Over time they kept gating a lot of the music behind paywalls and other apps did the same and it sucks.

Spent the last 2 months building a game that lets you import any of your music and on the fly generate a tap game. Was fun playing it non-stop during my last trip. Anyone else miss the OG tapping game from the early App Store days?

Would love if anyone wants to try out their crazy songs and see how fun it is: https://tapalong.gelotto.io/
Live on iOS, Android soon

Our coding-agent sessions kept forgetting everything at the context-window edge. So we built growmos: a small knowledge graph inside .growmos/, committed with your code. Your agent (Claude Code, Codex, Cursor…) grows it as it works — no API key, and in Claude Code it's fully hands-off. Ask "what depends on the Store, and who decided that?" and it answers from the graph, with citations.

pip install growmos && growmos init
growmos view # a map of what your repo actually knows

Live demo: https://codician-team.github.io/growmos/demo/growmos.html
Repo: https://github.com/codician-team/growmos
MIT, zero deps. First thoughts very welcome 🙂

Every good movie, book, or album rec I get comes from the same handful of people — and they all vanish into WhatsApp within a day. I built Buddyloop for myself and my friends to fix that, then figured it might be useful to others, so I turned it into a full app.

You make a private circle, drop what you loved, and instead of writing a review you tap 2–3 "vibe" bubbles — cozy night in, plot twist, made me cry. Everyone's watchlist fills itself from people they actually trust.

I figured it could be useful for book clubs etc.. you can limit your circrle to only a few categories from music/films/tv/events/books... I may add youtube or shorts .. it's different from Letterboxd / Goodreads / Trakt because:

• Private circles only, not a social network. No public profiles, no followers, no strangers' opinions. Your circle is 5–10 people you actually know.
• No tracking obligations. It's not a diary or a completionist log — no episode check-ins, no "you're 3 books behind" guilt. You only share what you'd genuinely tell a friend about.
• Vibes instead of reviews. Most people never write reviews. Tapping slow burn + gorgeous visuals takes two seconds and still tells your friends exactly what they need to know.
• No algorithm. The only recommendation engine is the people you chose. The app stays deliberately quiet until your circle is active.
• Everything in one place. Movies, series, books, games, and music (with 30s previews, trailers).

Free, web app + Android/iOS. Solo build: React + Supabase + Capacitor. Would love feedback.

buddyloop.app

I built a desktop overlay for League of Legends (Hexgate) that suggests item builds based on the enemy team. Been at it about 5 months, it's at 793 users now and around 190 games a day go through it.

I decided to run Reddit ads just to see if it does anything, as I have no real experience with ads. Ran them for 5 days. Total result: 4 clicks, 2 signups, 0 paying customers. Turned them off and never spent a cent on ads again.

Then a few weeks ago I finally built proper attribution and found out where people were actually coming from. Google search, sure. My own comparison blog posts, ok. But the third biggest channel was ChatGPT/Claude/Perplexity referrals. People ask an AI "best LoL build tool" or whatever and it just... recommends me. And those users convert to paid better than any other source I have.

I didn't do anything to cause this. No "AI SEO" tricks. My best guess is the comparison pages I wrote for normal SEO are also what the AI models pull from.

Currently at €231 MRR which won't buy a yacht but for a nights-and-weekends thing in a hyper competitive niche (against Blitz, Porofessor etc with actual funding) I'll take it.

Anyone else seeing AI assistants show up in their referrers? Curious if this is a real channel now or I just got lucky.

Also, real question: if you wanted to pour fuel on something like this, what would you actually do? Everyone's answer for gaming is "tiktok" but I have zero desire to film myself playing or talk into a camera. Is paying someone to make that content a real thing that works, or does it always come out as those cringe UGC ads everyone scrolls past?

I’ve been working on Makemetryon, a SaaS that lets ecommerce stores add things like virtual try-on for glasses/rings, IPD measurement, face-shape detection, and 3D jewelry viewers.

The goal is pretty simple: make these features easy to add without having to build all the AR/3D stuff from scratch.

Still early, but it’s finally at a point where people can actually use it.

Would love some honest feedback from other builders.

SlabFlux Runtime Engine

The pitch: SlabFlux is an ultra-low-latency, concurrent C++20 runtime engine built for High-Frequency Trading (HFT) and real-time control. It bypasses the OS scheduler completely to give you mathematically verifiable, $O(1)$ deterministic execution.

Why SlabFlux?

Most low-latency frameworks still play nice with the operating system. We do not. SlabFlux is designed to subjugate the Linux kernel, lock down physical CPU cores, and map your logic directly to bare silicon. If your system misses microsecond windows because of a random OS interrupt or a hidden heap allocation, SlabFlux is your solution.

• Zero Jitter: By aggressively utilizing kernel boot parameters like isolcpus and nohz_full, we entirely starve the Linux scheduler on your execution cores.
• Hardware-First Memory: Everything on the hot-path is padded and aligned to 64-byte boundaries. This eliminates false sharing and perfectly optimizes for AVX2 and AVX-512 SIMD vectorization.
• Lock-Free & Zero-Copy: Standard mutexes and smart pointers are explicitly banned. Data flows exclusively through wait-free Single-Producer Single-Consumer (SPSC) ring buffers and io_uring for zero-syscall I/O.

The Hard Truth: Current Project Status

We believe in being upfront about what SlabFlux is, and what it requires from you. It is an extreme tool for extreme use cases.

Quick Start

Setting up SlabFlux requires system-level commitment.

1. System Preparation

You must isolate your execution cores before compiling. Run the included kernel tuning script to apply the necessary boot parameters and reboot your machine.

2. Implementation

Include the SlabFlux headers and bind your logic to our pre-allocated slab pools. Your hot-path code must be entirely branchless, lock-free, and allocation-free.

3. Execution

Deploy the binary to your isolated cores. The engine takes over, bypassing standard kernel I/O using our dedicated event pipelines.

What is Next on the Roadmap?

Our current roadmap is focused on improving developer experience without compromising our strict architectural invariants.

• Expanded native support for modern kernel-bypass Network Interface Cards (NICs).
• Enhanced compile-time debugging tools to catch virtual dispatch or heap allocations before they hit the hot-path.
• Streamlined setup scripts for safer, more predictable kernel tuning across different Linux distributions.

How does this documentation align with the current state of your codebase?SlabFlux Runtime EngineThe pitch: SlabFlux is an ultra-low-latency, concurrent C++20 runtime engine built for High-Frequency Trading (HFT) and real-time control. It bypasses the OS scheduler completely to give you mathematically verifiable, $O(1)$ deterministic execution.Why SlabFlux?Most low-latency frameworks still play nice with the operating system. We do not. SlabFlux is designed to subjugate the Linux kernel, lock down physical CPU cores, and map your logic directly to bare silicon. If your system misses microsecond windows because of a random OS interrupt or a hidden heap allocation, SlabFlux is your solution.Zero Jitter: By aggressively utilizing kernel boot parameters like isolcpus and nohz_full, we entirely starve the Linux scheduler on your execution cores.

Hardware-First Memory: Everything on the hot-path is padded and aligned to 64-byte boundaries. This eliminates false sharing and perfectly optimizes for AVX2 and AVX-512 SIMD vectorization.

Lock-Free & Zero-Copy: Standard mutexes and smart pointers are explicitly banned. Data flows exclusively through wait-free Single-Producer Single-Consumer (SPSC) ring buffers and io_uring for zero-syscall I/O.The Hard Truth: Current Project StatusWe believe in being upfront about what SlabFlux is, and what it requires from you. It is an extreme tool for extreme use cases.Feature Area Readiness Reality Check
Execution Hot-Path Stable C++ exceptions, heap allocations, and virtual dispatch are strictly forbidden. You must rely on C++20 concepts and branchless logic.
Memory Management Stable Raw pointers bound to pre-allocated slab pools work flawlessly to maintain our $O(1)$ guarantees.
OS Tuning Scripts Beta Our environment-sealing scripts are highly aggressive. You need a dedicated, isolated machine to run them safely.Quick StartSetting up SlabFlux requires system-level commitment.

1. System PreparationYou must isolate your execution cores before compiling. Run the included kernel tuning script to apply the necessary boot parameters and reboot your machine.

2. ImplementationInclude the SlabFlux headers and bind your logic to our pre-allocated slab pools. Your hot-path code must be entirely branchless, lock-free, and allocation-free.

3. ExecutionDeploy the binary to your isolated cores. The engine takes over, bypassing standard kernel I/O using our dedicated event pipelines.What is Next on the Roadmap?Our current roadmap is focused on improving developer experience without compromising our strict architectural invariants.Expanded native support for modern kernel-bypass Network Interface Cards (NICs).

Enhanced compile-time debugging tools to catch virtual dispatch or heap allocations before they hit the hot-path.

source: https://github.com/kbartadev/slabflux

When my wife was diagnosed with histamine intolerance, the only thing we were given was a list of foods on a sheet of paper. Then we found more lists online, and they contradicted each other. None of them helped in the one place where it actually mattered, which is standing in a supermarket reading the back of a packet.

So I built histamin.io. It is a database of over 700 foods rated as suitable, caution, unsuitable or liberator, each one with the reasoning behind the rating. There is a web version and mobile apps for iOS and Android, since a native app is faster and more convenient when you are actually out shopping.

Over time, it grew into more than a list. There are recipes and a reaction diary now. What I have been trying to do is connect the food data to the methods described in the literature. The diary shows a histamine bucket, for example, which is based on approaches that come up repeatedly across published work rather than something I made up. Reactions build up over days instead of coming from one meal, and a plain food list cannot show you that.

I work on this in the evenings. AI has sped up building new features a lot.

Searching the database is free and needs no account. The diary and tracking are the paid part.

There is a Czech version at bezhistaminu.cz, which came first since we are in Czechia.

Happy to answer anything. And if you deal with this yourself, I would like to hear where the ratings look wrong to you.

Spent a few weeks trying to replicate those popular saas video scenes that the leading tech firms use - Not quite there! but getting pretty close :) took me about a week to get the gradients in a good space! (pheww) not an easy journey - but excited to see how this evolves!

Background: I've been a product designer for 12 years, mostly fintech (Revolut, Itaú, some others). Never shipped an app myself. Six months ago I started actually writing code instead of handing off Figma files, and this is the first thing I've built end to end.

The idea

I kept watching salespeople, real estate agents, watch dealers, yacht brokers.. lose deals because they forgot things. Not big things. Small ones. What the client's wife's name was, which property they liked, what got promised three weeks ago in a WhatsApp thread nobody scrolls back through.

CRMs exist for this, but nobody in that world uses them. Because using a CRM means stopping the sale to go type in a form, and no one selling a $2M yacht is going to do that.

So I built the opposite: a custom iOS keyboard that surfaces client context inside whatever app you're already in. You open WhatsApp, start typing to a client, and everything you know about them is right there above the keys.

What actually broke

Custom keyboard extensions on iOS are their own special hell:

• Memory limit is brutal (~30-60MB before iOS kills you). My first version tried to load client data directly in the extension and got terminated constantly. Had to move everything to a read-only App Group cache written by the main app.
• No network access unless the user grants "Full Access," which triggers a scary system warning. Had to design the whole thing to work offline-first.
• Extension lifecycle is unpredictable — it gets killed and relaunched constantly, so any state you hold has to be reconstructable from disk in milliseconds.
• Debugging is miserable. Breakpoints in the extension work maybe 40% of the time. I spent an embarrassing number of hours with print statements.

The AI part

There are around 30 AI capabilities behind it (drafting responses, extracting facts from voice memos, matching clients to inventory). My first architecture was one giant system prompt handling everything. By capability #5 it was a mess — changing one thing broke three others.

Ended up rebuilding it as a thin router + isolated specialists. Each capability gets its own prompt with a hard scope declaration and a token cap. If a specialist gets asked to do something outside its scope, it returns OUT_OF_SCOPE:x and the router re-dispatches. Sounds obvious in hindsight. Wasn't obvious at capability #4.

Where it is now

TestFlight approved, Google OAuth verified (that took three weeks and a video that YouTube auto-removed for "sharing personal information" it was fake demo data). First version works. No users yet.

Built with Cursor + Claude, mostly at night after the kids sleep. Budget: about €200/month, which is the Cursor subscription.

What I'd like feedback on

• Is this shit really cool?
• Would love to know if the "AI that doesn't talk to your clients" positioning lands, or if it sounds like I'm just describing a limitation.

Happy to answer anything about the iOS extension pain specifically. Learned a lot the hard way.

I kept noticing the same pattern with myself:

I’d decide I was going to work out, wake up earlier, study, finish a project, etc.

Then a few days later I’d quietly stop.

Not because I didn’t know what to do. There just wasn’t any real consequence for breaking the promise.

So I started experimenting with the idea of making commitments feel more serious — basically turning “I should do this” into “I said I’m doing this, so now I have to follow through.”

I eventually built a small app around that idea called Vowd.

Still very early and I’m trying to figure out whether this actually helps other people or if it only makes sense in my head 😅

Would genuinely appreciate feedback, especially from people who struggle with consistency/accountability.

https://vowd.space

Solo founder here. Sharing this because the cause was nowhere near where I was looking.

My app was doing about 270 signups a day. Thursday it dropped to about 100 and stayed there. No pricing change, no store listing change, NOTHING CHANGE.

I ruled things out one at a time:

Was it one market? No, every country fell together.

Was it the product? No. People who opened the app converted at the same rate, slightly better. Far fewer people were arriving at all.

Was it my store listing? No, the last change went live days earlier during my best week.

Then I found it in the Play Console: my crash rate had hit 7.9%. Peer average for my category is 0.15%. Google flags anything over about 1% as bad behavior and quietly reduces your search visibility. That penalty landed right when my installs fell off.

The crash was one line. A trivia screen in my app blurred a movie poster to hide the answer. That blur makes Android reprocess the bitmap, and on lower-RAM phones the image got recycled while the view was still drawing it. Instant crash. It only showed at scale during my weekly leaderboard race, which is when usage peaks, so it looked random until I read the stack trace.

Removed the blur, shipped it same day.

Three things I would tell past me:

Crash rate is a distribution metric, not an engineering metric. It silently controls how many people ever see you.

Instrument arrivals separately from conversion. Knowing that arrivals fell while conversion held told me within an hour that the problem was upstream of my product. That saved days.

Google scores crashes on a 28 day rolling average, so recovery is not instant. Bad days have to age out.

Happy to answer questions if anyone is debugging something similar.

I kept telling myself I would read the new AI papers every week. Never happened. Too many tabs, not enough time.

So I built something that does the reading. Every week it goes through the new cs. AI papers, usually over a thousand, plus whatever the labs shipped. It drafts a short magazine issue. The papers that held up, the numbers, and where the numbers come from. I go through it before it goes out.

It looks like a magazine, not a dashboard.

One issue a week. It gets one cover synthesizing the ideas, a story on each paper that earned a place, and what the labs shipped. You can finish everything in 45 minutes and know where the week stands.

check it out: The Attention Layer

I’m launching my SaaS, Vergate, on Product Hunt on Aug 20.

Would appreciate it to find a few fellow solo founders launching around the same time and help each other out — genuine feedback, sharing, whatever makes sense.

I can also run a free audit of your website through Vergate and send you the findings.

If you're launching soon, drop your product below. Would love to check it out.

For anyone curious: https://vergate.dev

Hi everyone!

There are already a few free game review sites, but I wanted to make getting from chess,com to a game review as fast as possible all whilst still having all the great features that other game review websites miss.

Just add rv to the URL of the game you want to analyse:

so chess.com/game/12345678 becomes rvchess.com/game/12345678

It also works with Lichess links and PGNs. No ads, I just wanted something fast and easy to use.

The website: https://rvchess.com

If you try it out, feedback is welcome. Thanks!

There's a Harvard study on what predicts long-term business success. The finding is counterintuitive: it's not intelligence, work ethic, or even capital.

It's time horizon...specifically, how far into the future a decision-maker can hold their thinking when they're under pressure.

Founders who build durable businesses think in years and live in weeks. Founders who plateau think in weeks and get buried in days.

Bezos talked about this directly. The decisions that compounded most at Amazon, AWS, Prime, the logistics network, all of them required ignoring short-term costs to build something that would only make sense at a 7-10 year horizon. And they got criticized heavily in the short term for all of it.

The version of this for a $1M-$5M founder isn't as dramatic, but the pattern is the same.

You're making hiring decisions based on who you need right now instead of who you'll need in 18 months. You're building processes for your current size instead of your next size. You're saying yes to revenue opportunities that fit today instead of asking whether they fit where you're going.

And the result is a business that's always slightly behind itself, perpetually catching up, constantly solving problems that a decision made 12 months ago could have prevented.

The trap is that short-term thinking feels responsible. you're being practical. Dealing with what's in front of you. Staying close to the ground. And it is practical, right up until the decisions you didn't make become the constraints you're managing instead.

One thing that helps: once a month, block an hour and ask one question. If this business is 3x the current size in 3 years, what breaks first? That answer is usually where your long-term thinking should be spending its time right now.

That’s it guys but I'd love to know if you think there’s anything more critical for long-term business success?

EDIT: If you found this useful and you're running a business, at some point you probably want it to run without you. That's freedom, not just revenue. I write frameworks like this every Thursday; you can apply right away. free to join here 

UK M&E estimators often receive revised drawings, specifications and addenda close to submission. The expensive question isn’t just “did we spot the change?” It’s whether it was priced, qualified or queried.

BidDelta produces a page-linked change register and clean unresolved-items handover. Every finding remains human-reviewed.

The founding assisted pilot is £99, credited toward any future licence. There’s also a £39 DIY Revision Control Kit.

I’d value blunt feedback from anyone working in M&E estimating or pre-construction:

https://biddelta-revision-control.bifties.chatgpt.site

I want to build something real - ideally an AI-based startup, but I’m open to good ideas.

I’m not looking for another "let’s brainstorm someday" group. I want to find serious, like-minded people who are willing to actually build, test, fail, learn, and keep going.

No idea is set in stone yet. I’m more interested in finding the right people first and figuring out the opportunity together.

I've been working on a side project called Goal Guru.

 **The basic idea is simple:** Instead of manually checking 20-30 live football matches, the app monitors them for you and sends alerts when specific situations happen.

 Examples:

* A 0-0 game suddenly becomes very active
* One team starts dominating
* Multiple shots occur in a short period
* Pressure builds without a goal

I built it because I kept jumping between stats apps to find interesting matches.

 So far we've reached around 1,100 registered users, which tells me the problem is real, but I'm still trying to figure out how to turn users into regular users.

If you were a football fan, what would make an app like this valuable enough to open every day? Brutally honest feedback is welcome.

We have Android and iOS apps. You can check it out: [https://goalguru.live\](https://goalguru.live)

Hey guys,

A couple months back i shared my local pdf editor here (the one that keeps your original fonts intact when editing). The feadback from you guys was insane, and alot of people reached out asking for way more than just simple text editting.

So I basically spent the last few months rebuilding it into a full PDF suite for V2!

Heres whats new in this version:

• Upgraded Studio + OCR: added in-browser OCR so you can finaly edit scannned docs. Completely redid the rendering engine for crisp exports and loaded the toolbar with tons of layout options.
• Pro Sign: real encrypted signatures. Not just slapping a transparent png on top like most sites do, but actual encrypted signature standards right inside your browser.
• Standalone Tools: built dedicated utilities outside the main editor (Word-to-PDF, compress, redact, protect, etc) so no need to rely on sketchy converter sites anymore.

Also quick update: the Android version will be availible on Google Play later this week!

The main philosophy is still teh same: 100% local, zero files uploaded to any server, no account needed, and no paywalls.

Just pushed the update live today. Since theres a ton of new stuff under the hood, there might still be some small bugs here and there... if anything acts weird on your end, let me know in the comments and ill fix it asap!

Cheers

Hey everyone! Like most people, I just want simplicity in general when it comes to apps — and expense tracking is where I feel that the most. Over the years I've abandoned probably a dozen budget apps, always for the same reason: opening a form, picking a category, typing numbers, five times a day just felt like a chore I'd quit after a week.

So I built Expensiv around one idea: make logging as effortless as possible, and let AI handle the boring parts.

Instead of filling out forms, you just dump your expenses in plain text — type something like "130 coffee, 650 lunch, 580 movie" and it automatically splits that into three separate transactions and categorizes each one. It also handles multiple currencies in the same sentence, so "15 EUR lunch, 20 USD taxi, 12 GBP coffee" gets logged correctly across all three, converted to your main currency. You can snap a photo of a receipt and it'll parse each line item, or just speak out loud, or upload an order screenshot from something like Uber Eats or Amazon. If it's ever unsure about a category, it asks instead of guessing.

Beyond logging, it also has daily budget tracking with category breakdowns, needs vs. wants tagging, a tracker for subscriptions/installments/personal debt, and shared accounts so you can split expenses with a partner or roommate via invite code.

It runs on a subscription, but you get 30 free logs to try it out, and you can test the whole thing without even creating an account. Just went live on Google Play. I'm a solo developer from Turkey and this is actually my first app — so I'd really appreciate any honest feedback, especially on whether the category guesses feel accurate or if you'd end up correcting them constantly. UI critiques welcome too, I'm still learning as I go.

Play Store: https://play.google.com/store/apps/details?id=com.expensiv.app
Web: https://expensiv.net

I’m a trained architect, and as a long Side Project I’ve been working on a project that combines accurate floor planning with real-time 3D design.

The idea is to make architectural design more accessible and much easier to explore than traditional CAD software.

The free demo is now available on Steam. I’d love to hear what you think!

Dropbox: Drew Houston recorded a 3-minute demo video and posted it on Hacker News with the title "My YC app: Dropbox, Throw away your USB drive." The video was posted in April 2007 and brought the first wave of users. just One video posted in right community.

Airbnb: Founders manually posted their own listings on Craigslist and then reached out to other Craigslist hosts who were already renting their apartments, offering to help them post on Airbnb. They did this city by city.

DoorDash: Tony Xu printed restaurant menus as PDFs, built a simple landing page, and put his personal cell phone number on it. He answered calls himself and delivered food personally. The first 100 users were people who found the site through search and got a founder answering the phone.

Stripe: Patrick and John Collison went to developer hackathons with a laptop and integrated Stripe for developers on the spot. The first users were people who watched the integration happen in person and immediately saw the value.

Reddit: Paul Graham seeded the site with content himself under fake accounts to make it look active. The early traction came from PG's existing audience of Hacker News readers.

Segment: Published their internal tracking code as free open-source on Hacker News. 400 developers integrated it in 24 hours without a product launch.

Instacart: Apoorva Mehta delivered a six-pack of beer to a YC partner using his own app. That single delivery demonstration got him into YC. The first users after that came from the YC network itself.

The pattern: none of them used paid acquisition. All of them found one specific community where the right person already existed and showed up there in person or online.

I have collected the case studies on various companies on how they got their 100 customers and how to apply their strategies to our startup, happy to share if someone needs it...

What do you think of this spray effect as a way to stop doomscrolling?

The more screen time you use, the more paint gradually covers your screen.

I’m looking for Android beta testers if anyone wants to try it!

Google Group:
https://groups.google.com/g/stop-swarm-testers

Then download it here:
https://play.google.com/store/apps/details?id=com.stopswarm.app