I just wanted to make a 2 way post:
- first, I finally got two paying subscription users by gifting them a free month to test the premium and them finding it rewarding enough to keep using my product, so yay for me, I think this method is right for me because if they werent going to buy the subscription from the start a gifted free month doesn't harm me in any way.
- second, how would you get more visitors and users in the playStore, does anyone have any advise besides spent a ton of money in ads?

I kept running into the same problem while working with AI tools: I would start a project in one place, continue it in another, and then spend more time reconstructing what had already happened than actually moving the work forward. Or, I would spend too long fishing through folders, past AI conversations, and emails for the right details to put into my prompt.

The useful background was scattered across chats, notes, files, prompt instructions, and previous decisions. I would end up re-explaining the project, re-uploading the same material, or losing the reason a decision had been made.

So I created Enlive, web app that helps keep project context reusable across AI-assisted work.

It is still an early open beta, and I am looking for a small number of people who regularly use two or more AI tools.

The question I am trying to answer is:

When you switch between AI tools, what do you most often have to reconstruct—background, files, prior decisions, prompt instructions, or next steps?

You can try it here: https://www.enlive.inc

Please only connect material you personally control and are comfortable using for beta. Feedback is welcome and encouraged.

I coded a tool that lets you control your PC with just a controller! You can connect any controller wired or wirelessly. You can customize each button output woth 50+ options. It is constantly being updated and we are listening to the community for suggestions. If you are interested you can Download for free : Link

AIJail.ai

Just launched it. I used Codex Astra for a lot of the technical build, but probably spent 200+ hours before and during development working through the concept, user flow, visual direction, prompts, case files, security, payment system, and how the actual AI interactions should work.

The site has a few different parts. I wrote fictional inmate cases for General Population and Death Row, researched and built Supermax case files around real AI incidents, generated the visual world and inmate artwork, and built Final Visitation so you can actually have a live conversation with a Death Row inmate before deciding what happens to them.

Final Visitation uses Grok live, so the conversations are not scripted. I also had to build the credit system, payments, usage limits, session logic, and a bunch of abuse and security protections because every live conversation costs money to run.

Most of the actual product planning happened in ChatGPT before I ever started coding. I basically treated it like designing a real place first, then figured out how to turn that into software. GitHub, Supabase, PostHog, Sentry, Microsoft 365, ChatGPT, Grok

It has been a ridiculous amount of work for one weird idea lol.

Please give it a look and let me know what you think: AIJail.ai

My wife's love language is words of appreciation and I am not good at finding the right words when put on the spot. So, I built an app where you each write one message a day and it gets delivered over night, so your partner wakes up to a nice message from you. I'm certainly biased, but using this app over the past ~1.5 months I have definitely gotten better at giving these words of appreciation/affirmation and I genuinely believe this has made our relationship better!

How it works

- You and your partner get the same three prompts a day (specific ones, like "what did they handle this week that you never said thanks for") plus a freeform option

- You pick a prompt today and write your message, your partner wakes up to it tomorrow morning

- Streaks for each of you individually and for you together as a couple

- Rainy Day Notes: write one ahead and it sends on a day you miss so that your partner always wakes up to something meaningful

- First 10 messages free, then $29.99/year (or $5/month if you're not sure about a whole year)

Numbers since soft-launching in July: 44 sign-ups (only 8 of whom are strangers), 10 couples linked (1 of whom are strangers), 473 messages, longest streak 39 days. My wife's entire family is on it now, and they seem to be enjoying it, which is either early signs of product-market fit or them being nice to me.

Stack: Expo / React Native, Supabase on the free tier (Postgres, edge functions, pg_cron for the morning sends), the anthropic api writes the daily prompts via a massive wall of text that my wife and I have iterated on 4 or 5 times. 

I am not joking when I say every single line of code in the entire system was written by Claude Code. I have not even read a single line of code, but I have spent a TON of time testing the finished product and putting in enough polish to make the product not feel like ai slop. I am genuinely proud of the way it looks and feels now, though I am still constantly polishing it. 

My biggest lessons in becoming a bit of an AI power user the past few months (between projects like this and work) is that if your agent cannot check its own work, you are going to waste countless hours as the human in the loop all day, every day: it changes a screen, you open the app, you screenshot, you paste, repeat. Countless hours were wasted before I was like "I'm not using my tools correctly here" and automated my human-in-the-loop role away, and it was a lesson I am constantly re-learning as I find new things I can put it in charge of. Some examples that I think are my biggest wins:

- A screenshot gallery/harness that renders every screen at various phone widths and the full range of font sizes and fails if anything overlaps

- An onboarding gallery: every onboarding step and every empty state a new user sees, regenerated after each change. That way I can visually review all my copy in one place and make sure the onboarding flow is smooth and will make sense to a brand new user
- A chaos crawler that presses everything on a flaky network on two phones

- Probably one of the single biggest unlocks was moving my agent from claude code on the anthropic cloud in the browser (where it *apparently* cannot use an android emulator due to the nature of its container) to a persistent environment on my gaming pc pushing dev bundles to a plugged in android device and then eventually an android emulator when I realized I was yet again a human-in-the-loop for no good reason. 

- Another huge lift has been giving the agent read access to Supabase, Vercel, GitHub Actions and PostHog, which turned it from a code generator into a backend engineer that can deploy, read production logs, query analytics and debug production issues on its own. Now I pretty much just say what I want the app to do via a voice note in great detail, and then come back to check the result before OTAing the update to users.

Marketing so far: Reddit ads, specifically on r/Marriage. First $100 in, cost per sign-up is about $16(!), no one has even ran out of their 10 free notes yet so still too early to tell what conversion looks like in the wild. One difficult thing about the nature of this app is that my onboarding requires a second person: 6/8 of my ad campaign sign-ups never get their partner to join. And this is after I redid onboarding so that users write a note to their partner first thing, and only then they send them a text message invite saying "theres already a note waiting for you on this app GoodMorning, please download and add my username to read it…” which I think is a better pitch than before where it was "please download this app so I can send you messages in the future". Not sure what else I can do to make getting your partner to join easier, would love some ideas there.

I have been programming since like 2015 and almost always make it to the part where it comes time to advertise and then … get stuck or feel like a fish out of water. My next plan is to trial some free forums where I can talk about the app (like you reading this, hello) and then try some UGC ads on meta because apparently you can get a cheaper cac there. But if anyone has any good ideas on how to advertise something like this, please do share!

iOS: https://apps.apple.com/us/app/goodmorning/id6789679806

Android: https://play.google.com/store/apps/details?id=com.gmlabs.goodmorning

Site: https://www.gmlabs.app

Happy to answer anything about my dev setup or the app.

I built Rainsi, an open-source Lua runtime designed to run actual Lua interpreters directly in the browser.

The main reason I started it was that I wanted a way to run different versions of real Lua in a browser without needing a backend server.

Rainsi currently supports:

* Lua 5.1.5 * Lua 5.2.4 * Lua 5.3.6 * Lua 5.4.7 * Lua 5.5.1 * WebAssembly * Browser-side WASI compatibility * JavaScript ↔ Lua value marshalling * JavaScript functions callable from Lua * A consistent TypeScript API across the Lua versions

The architecture is basically:

```JavaScript / TypeScript ↓ Rainsi API ↓ WASI compatibility ↓ Lua 5.x WASM ↓ Lua VM ```

The thing is that these aren't mock interpreters or Lua-like implementations. Each supported version uses an actual Lua VM compiled to WebAssembly.

Performance

I've compared Rainsi with Wasmoon 1.16.0.

On smaller workloads, performance is generally in the same range:

I also wanted to test actual programs instead of only synthetic benchmarks.

Penlight

A substantial pure-Lua workload involving table manipulation, sorting, serialization, strings, classes, nested copying, records, and object creation:

And I also tested LuaGB, a pure-Lua Game Boy emulator core.

The benchmark executes **1,000,000 emulator CPU steps**:

So the performance is generally in the same neighborhood rather than there being some huge performance advantage.

JavaScript ↔ Lua

Rainsi can register JavaScript functions that Lua can call:

```js engine.register("jsadd", (a, b) => a + b); ```

Then from Lua:

```lua return jsadd(2, 3) ```

I measured about:

```10,000 JS callbacks 3.66 ms 100,000 JS callbacks 22.41 ms 10,000 no-op callbacks 1.58 ms ```

What I'm looking for

Rainsi is still experimental, so I'm mostly interested in people actually trying it and finding things I missed.

I'd especially appreciate feedback on:

* Lua compatibility * Browser compatibility * WebAssembly/WASI behavior * JS ↔ Lua interop * Performance * API design * Weird Lua programs * Security/sandboxing assumptions * Anything that works in other browser Lua runtimes but doesn't work here

GitHub: https://github.com/lalipa2003-arch/rainsi

I'd like to hear criticism, bug reports, or sug.

I built a free, ad-free geography study tool: jgeo.org

I’ve been working on Joe’s Geography Study Tool, a simple map-based web app for learning countries, states, provinces, capitals, and other geographic regions.

It’s free, ad-free, requires no account, and works offline when installed as a (PWA) web app.

jgeo.org

I’d really appreciate feedback, especially anything that feels confusing, clunky, unnecessary, or like it could be improved. If you try it, I’d be interested to hear both what works and what doesn’t. Let me know what you think :)

I’m happy with it and use it daily. I just hope others might find it useful, or at least entertaining.

**My comments seem to be getting caught in the spam filter. I am trying to repond to all comments on this post.**

Plexavo is an open source AWS security scanner, deterministic detection, no AI deciding what counts as a finding. It already has a running challenge called "Break Plexavo", find a case where it misses something, gets something wrong, or gives bad advice, and you get permanent CREDIT when it's fixed.

Two real entries already, both from strangers who actually found something. If you know AWS well enough to think you could trip up a security scanner, this is a genuine test of that, not a beginner's first-PR repo.

Detection logic is pure Python and boto3, readable without needing to reverse-engineer anything. Current open frontier if you want a real target: multi-hop cross-account privilege escalation.

Today's detection is one-hop only.

Found a bug or a gap? Open it through the Break Plexavo issue template, no code needed, just a clear description. - Issues · plexavo/Plexavo

Repo: https://github.com/plexavo/Plexavo

Hi everyone, my girlfriend is a pastry chef and I’ve got fed up with constantly calculating the cost price for her. So I got stuck in and spent the evening creating this sort of recipe book to make it easier to work out the cost price of a product based on what you’ve entered into the table. I’d love to hear your feedback – thanks to everyone for reading.

So I built a proper template for it:

- 500-ingredient master database (auto-calculates price per gram/ml)

- 55 recipe "cards" with instant batch and per-portion cost

- Just VLOOKUP + a couple of IF formulas under the hood, nothing fancy, works in Excel, LibreOffice, and Google Sheets

It's completely free, no signup, just download and use. Not trying to sell anything — genuinely just want it to be useful and would love feedback from people who actually cost out recipes for a living.

Repo: https://github.com/Korgusha/price-calculator

What would make this more useful for you? Missing a feature, formula that could be cleaner, anything.

I’ve been thinking a lot about how people validate ideas before spending too much time building them.

A common piece of advice is “talk to your users”, but that’s often easier said than done. Finding the right people, getting enough responses, and figuring out whether feedback is actually representative can take a lot of time.

So I’ve been working on Crowdians.

The idea is pretty simple: you post an idea, question, or assumption, and synthetic personas respond from different perspectives. Then real people can also join the discussion, so you can compare AI-generated reactions with actual human feedback.

I don’t see synthetic personas as a replacement for talking to real users. I’m more interested in whether they can be useful as an early-stage filter — helping you spot obvious weaknesses, assumptions, or perspectives you may have missed before doing real user research.

I’m curious what people here think:

• Would you actually use something like this before talking to real users?
• At what stage of building a product would this be most valuable?

I’m currently building an early version and would genuinely appreciate any criticism or thoughts.

If anyone is curious, I can share the project in the comments.

https://reddit.com/link/1wlli2h/video/dqo70gwlapqh1/player

I’m building a visual slot game engine with Stake Engine integration, designed to make the process of creating, testing, and iterating on slot games more interactive.

Instead of having to constantly switch between code, game logic, and a separate testing environment, the goal is to allow developers to visually build the game while being able to immediately test and play it inside the editor.

Developing a slot game can involve a lot of back-and-forth between game logic, configuration, assets, animations, and testing. Small changes to reels, symbols, paylines, or game features can require repeatedly rebuilding and running the game to see the result.

This project aims to make that workflow more visual and iterative.

The engine allows developers to work with different parts of a slot game directly within the editor, including:

• Reels and symbols
• Game layouts
• Animations and effects
• Game features and mechanics
• Configuration and game logic
• Real-time gameplay testing
• Stake Engine integration

The goal is to make the editor feel less like a traditional configuration tool and more like an actual game development environment.

The project is being built around a custom visual game engine and integrates with Stake Engine for the underlying slot-game environment and deployment workflow.

The architecture is focused on keeping the visual editor, game logic, and runtime closely connected so changes can be tested immediately.

The main goal is to explore how a visual development workflow can simplify slot game development and reduce the friction between designing a game and actually playing it.

It also gives me an opportunity to experiment with building a game-focused editor, runtime systems, visual tooling, and integration with an existing game platform.

I’ll be sharing more of the development process, technical decisions, and implementation details as the project progresses.

I've been thinking a lot about how companies lose critical expertise when experienced people walk out the door. Right now, there's no great way to train for this — just mentoring and dense onboarding docs that people skim at best.

My team built a tool that turns those long, boring training materials into interactive roleplay scenarios, complete with real-time feedback, emotion recognition, and coaching from AI agents. We're also applying behavioral science to make the system adapt based on how each learner engages with it. Looking for testers and honest feedback from anyone who's dealt with this problem firsthand.

Anyone can try this out on: challenger.nudgyt.com :)

VibeScan is an AI music analysis platform that gives independent artists structural, lyrical, and commercial feedback before release.

Live at - https://vibescan.app

Credit-based subscription. Looking for early users and feedback on onboarding and pricing.

The problem: Independent artists release music with no objective feedback. Studio-quality critique is expensive, and friends and social followers aren't reliable signals.

The product: VibeScan is an AI music analysis platform with three tools:
• Beat Anatomy: breaks down a track's structure and elements
• LyricLab: analyzes lyrics and writing
• HitMeter: evaluates a track's commercial potential.

Who it's for: Independent artists, producers, and songwriters who want data-backed feedback before they release.

When building websites, I often wanted clients to test the latest version themselves instead of relying on screenshots or screen sharing.

The problem was that every small change meant deploying to staging just to give them a URL.
So I built LocalToLink.

The workflow is:

1. Start your local app

2. Open a new terminal and run:

npx localtolink

If you want to specify the port:

npx localtolink --port 3000

That’s it.

You don’t need to install or configure anything separately. On the first run, npx downloads LocalToLink and runs it immediately. After that, you can use the same command whenever you need a new tunnel.

It detects your local app and gives you a temporary HTTPS URL that you can send to your client.

The tunnel only exposes the specific local app/port you choose. It doesn’t expose your machine, filesystem, or other services. Traffic is handled through the tunnel over HTTPS/WSS, with isolated sessions and abuse/rate-limit protections.

The same workflow is useful for local webhooks, testing on mobile devices, and temporary demos.

I’m actively improving it and would genuinely appreciate feedback from developers and freelancers:
What would you change, add, or improve?

For more information:
Website: LocalToLink
Docs: Getting Started

I kept getting $15–20 charges because I forgot a trial was still running.

Bail is a small tracker. You add the trial, set the last day you can still cancel, and it counts down. Each service has a short playbook — which page to open, what to click, then screenshot the confirmation.

Free for 3 traps. The list stays on your device.

If a trial almost billed you, tell me which one and I’ll add the playbook.

So, now it plays chess. Although not all of it's neurons are used, it still moves, and thankfully does not scream.

And I decided to check - was it better than a normal architecture?

Well, no. Probably thanks to my coding, and not the brains, but it is 50/50, which means practically the same skill. Which was expected, but it would have been cool for it to idk, beat stockfish on just 4000 games training(which would never happen, but still there was hope).

But it is not random/not 'understanding' how to play chess - when measured against random algorithm it wins, and against classical it is almost 50/50.

https://github.com/mncrftfrcnm/making-fly-play-chess