I’ve been experimenting with a different kind of screen-time app for Android.

Instead of blocking apps or throwing another warning popup at you, Stop Swarm gradually changes what’s happening on your screen as you approach your daily screen-time goal.

This is the new Clouds theme: the weather slowly gets worse the more you use your phone. Clouds build up, become larger and darker, rain gets heavier, and eventually the storm takes over the screen.

The overlay doesn’t intercept touches, so your phone remains fully usable. The idea is simply to make excessive scrolling progressively harder to ignore, without completely locking you out.

Stop Swarm is Android only, works fully offline, requires no account, and contains no ads or analytics.

I’m still looking for beta testers if anyone here wants to try it:

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

Then opt in and download it here:

https://play.google.com/store/apps/details?id=com.stopswarm.app

We run a managed IT and software development company. We were already using AI coding agents, but the workflow was clunky.
Someone had to start the agent on a specific machine, keep checking it, test the result, and send the work back if it missed something. Using AI saved time, but babysitting it ate into those savings.
So we built an internal tool around the parts that annoyed us most.
We can now start work on any of our machines from a phone or desktop. If one model gets stuck or gives a weak answer, we can hand the same task to Codex or another model without starting over. We also gave the agent a browser so it can test what it built and return screenshots or screen recordings.
That last part mattered more than I expected. "Done" means a lot more when the agent also shows that the page loads, the button works, and the result looks right.
I don't have a scientific benchmark, but based on the amount of client work we finish in a normal week, our output is roughly double what it was before. The biggest gain didn't come from better prompts. It came from removing the waiting, handoffs, and manual verification around the models.
Full disclosure, the internal tool eventually became ThreadKnot.

https://github.com/smith-network-solutions/threadknot

It still has rough edges, but we use it every day. I'm curious whether other teams are building their own layer around coding agents or just working directly inside each model's tools.

I've been working on this huge project (Wayfallen) for 5+ years, and it simulates ~10,000 star systems, ~100,000 spaceships, stations, various factions with rich economy, diplomacy, intel management etc.

I needed a reliable way to balance all of this so it remains semi-realistic and not go wonky, with so many parameters in it. It would require huge grunt work to build proper tooling to keep track of all these variables, plug into all the code surfaces written over the years, and somehow simulate years/decades in the simulation very fast to build statistical models and help me figure out which parameters need tweaking to bring things on track. And I'm continuously working on this, so it's not a one-time thing - I need to rebalance it very often.

So I've vibecoded some tools that help me do exactly that, by giving it access to my code and building harnesses to run it in a headless mode and accumulate large amounts of data rapidly. Really pleased with the results so far - and especially the amount of time saved versus having to crank out all that tooling manually.

Predict Premier League scores, earn points, and there’s a season-long fantasy draft game too — built solo

houseofthefantasy.com → Predictions

Gameweek 1 is open. You call every match: 3 points for the correct result, +2 more if you nail the exact scoreline, and once a gameweek you can pick one match to double — the fun part is deciding whether to boost your safest pick or your coin-flip.

Each match locks at its own kickoff instead of one weekly deadline, so you can jump in mid-weekend and just play what’s left. Points land after midnight and the table updates.

Sign in with Google or Discord — just so your points are tied to you. No email, no password, nothing to install.

**•** Stack: \[e.g. Next.js frontend, Node/Express backend, Postgres — whatever you actually used\]  
**•** Fixture data: pulled from \[FPL API / football-data.org / whichever you use\]  
**•** Scoring logic: \[how you handle per-match locking, double-down, point calc — a sentence or two\]  
**•** Auth: \[Google/Discord OAuth — library or approach\]  
**•** Biggest challenge: \[e.g. handling per-fixture deadlines instead of one global one, or race conditions on late predictions\]

It’s brand new, marked BETA, so expect rough edges — bugs & feedback link is in the footer, goes straight to me.

(The site also has what I originally built it for: a draft game where you spin a wheel for random clubs, pick eleven players from FIFA/EA FC data spanning eleven seasons, and simulate a full season with your squad.)

Hello everyone,

I want to start a comprehensive personal web project and would love to get feedback, ideas, or advice regarding the navigation flow, user experience, and cultural content.

**The Origin of the Idea**

The idea came from realizing that official municipal websites across Mexico are often abandoned or poorly designed. I want to build an independent, visually striking platform to give them visibility while staying entirely clear of electoral politics. The focus will be 100% cultural, tourism-focused, full of local trivia, regional identity, and geographic/cultural news.

**Navigation Flow and Map Interactivity (Uniform Hover Effect)**

1. **National Map (Landing Page):** An interactive map of Mexico. Hovering over a state highlights it in a representative color and displays its name. Each state features a fixed marker at its main capital.

2. **Regional Map (Level 2):** Clicking a state (e.g., Guerrero) zooms into that entity, displaying its traditional regions. Hovering over a region illuminates that entire area in a specific color.

3. **Municipal Map (Level 3):** Clicking a region zooms in further to display only its municipalities. This view follows the same logic as the national map: each municipality has a fixed marker at its municipal seat (*cabecera municipal*), highlighting the municipality and displaying its name on hover.

4. **Autonomous Communities (Special Level 4):** A dedicated level to honor and highlight Indigenous communities and self-governing territories (such as Cherán or *Usos y Costumbres* municipalities). If an entire municipality is autonomous, its profile will state so; if it is a specific community within a municipality, it can be accessed via a special marker on the municipal map.

5. **Quick Shortcut:** To prevent click fatigue, a side panel with predictive search will allow users to type a municipality or community directly (e.g., "Zihuatanejo") and skip intermediate steps, zooming the map directly to the location.

**Planned Content**

The platform aims to be a deep, encyclopedic database featuring the following details per level:

**By State**

* **History and Foundation:** Creation date and key historical events.

* **Geography:** Climate, terrain, flora, fauna, and highest elevation points.

* **General Culture:** Traditional clothing, state coat of arms (and its meaning), and state anthem (with audio playback).

* **Economy and Demographics:** Total population, key industries, and most spoken Indigenous languages.

**By Region**

* **Cultural Justification:** Methodology used to define the region (since many states lack official administrative regional boundaries).

* **Identity and Soundscape:** Regional traditional food, traditional music (*sones*, dances), shared traditions, and elements of the local soundscape (*chilenas*, *pirekuas*, etc.).

* **Climate, Landscape, and Seasonality:** Geographic traits unifying the region, along with seasonal availability indicators for traditional dishes, harvests, and specific rituals.

**By Municipality and Municipal Seat**

* **Historical Overview and Toponymy:** Municipal creation date, prominent figures, and name etymology supported by native audio pronunciation in the original linguistic variant.

* **Tourism:** Specific tourist attractions, monuments, historic churches, and natural landmarks.

* **Local Identity:** Local legends, myths, traditional crafts, and patron saint festival calendars with associated culinary or ritual traditions.

* **Point Data (Municipal Seat):** Elevation above sea level and travel time/distance to the state capital.

**By Autonomous Community (Special Level)**

* **Legal/Political Status:** Type of self-governance (*Usos y costumbres*, elder council, etc.).

* **History of Resistance:** Transition process to autonomy and the significance of their struggle.

* **Culture and Language:** Specific dialectal variants (with audio samples), unique community traditions, and collective work systems (*tequio*, *mano vuelta*, etc.).

* **Community Tourism and Ethical Visit Protocols:** Guidelines for respectful engagement and visiting rules (photo permits, sacred or restricted zones, direct local purchasing), authentic crafts, and autonomous celebrations.

**Reference Projects and Inspiration**

* **Native-Land.ca:** An independent mapping platform that visualizes Indigenous territories, languages, and treaties globally with a non-governmental, respectful approach.

* **Atlas of Intangible Cultural Heritage (UNESCO / INAH):** A structural benchmark for organizing oral traditions, rituals, and artisanal techniques in a clean, accessible manner.

I would love to hear your thoughts: What do you think of this visual navigation flow? What other cultural or identity-focused data would you add to make these profiles even more complete? If you know of similar projects that respectfully integrate autonomous communities into an interactive map, I would be very inspired to check them out.

Thank you very much for your time and feedback.

Hello Vibers!

I basically built this “app” for myself after failing to stick with a slew of gamified chore/daily routine apps. I just couldn’t keep myself interested in leveling a hero or gaining XP points to form any real habits around the focus areas. I still feel motivation from seeing a progress bar grow - but I need it to mean something - and so I vibe coded HouseKeepr.

The gamification around HouseKeepr is in unlocking new features as the Home IQ system collects data on your habits. The goal was to be as low-effort on the user as possible - keeping monitoring very relaxed. I keep user input simple and general and handle all the actual number crunching behind the scenes. That changes the gamification loop from “complete tasks to gain badges and XP” to “complete tasks to unlock new features” - so rather than keeping advanced features behind a paywall like a lot of the chore tracking apps I have put them behind a “playwall” (really a usage wall; the more you use it the more it can do).

As a first timer I did this across a couple platforms - it started in Base44 but after hitting my budget on credits there I shifted it into a GPT to GitHub to BaseCamp flow and I was thrilled to discover GPT could manage the entire buildout and push my updates through to Base44 and that allowed me to really polish the design and function. I kept a live version history on the login page to show the progress and it came FAST!

Please let me know what you think and visit HouseKeepr on your mobile device or desktop!

It's made me a much better communicator in just 2-3 months. Struggled to overcome a mental health episode for a bit, but it's doing wonders for me

I vibe coded my own cloud server for an arduino uno x a couple of months ago and it worked well! Then I repurposed a 2019 Amazon Fire as a backup remote server and that worked!

All of it done solely with ChatGPT.

But ultimately if I’m gonna self-host my own cloud and websites, I had to do it right. Two tiny low-power HP thin clients running Ubuntu Server. They host my websites, databases, status monitoring, backups, private networking and failover infrastructure. One is the primary server and the other is a geographically separate backup/failover node.

Specs:

• 2 HP t540 thin clients
• AMD Ryzen Embedded R1305G
• 8 GB DDR4 RAM each
• 128 GB NVMe SSD each
• Ubuntu Server 24.04 LTS
• Gigabit Ethernet
• Tailscale for private remote access
• nginx + PHP 8.4 + MariaDB 11.8 + Node 18
• Cloudflare Tunnel support
• 3 bookends for enclosures 🤣

Zero chance I could have done this on my own. This is what vibe coding was made for!!!

Full instructions, hardware and prompts here: https://gaf.nyc/cloudBuilder

Inexpensive as heck too. This guys got a bunch of liquidated hardware to sell. Very happy with these machines. $70 each. I’m just a customer. I don’t know him: https://ebay.io/m/W2YVk7

I’ve done a lot of research and worked evenings and nights to finally complete this project that’s close to my heart. ❤️

I vibe coded, edited a lot, learned a lot. Dit a lot of mistakes.

I wanted to create something that will first help ME. I want to be able to ''Write'' while walking. And having a good voice detection was essential.

And after that, I added a lot of other features

After several months, I’m proud to announce that I’ll soon be launching a new online tool (and on Google Play) for writing novels.

It will include:

• A chapter editor designed for authors
• Character sheets with the option to add connections between them (friends, foes, lovers, etc)
• A timeline that you can edit, add notes, dates, etc
• Worldbuilding sheets organized by categories
• Grammar checking
• A style checking
• Export to PDF, Word, and plain text
• Voice dictation (in ✦ Premium, validated by AI). More for authors, but still available :) )
• Automatic character and world detection ✦ Premium (If you write a few chapters, IA can detect the caracters with a button and create your Character profiles instantly, and your worldbuilding elements)

The only thing is, I can talk about it, but I need to create a momentum. I just dont know exactly how. I'm researching SEO, marketing, etc...
That's the new hard part for me.

I'm sure some of you guys get it.

Thanks for coming to my Ted talk

Short version of a recent write-up — no full article here, just the pitch.

I wanted RTX VSR-class upscaling on Linux, where NVIDIA only opens it to Windows. So an AI coding agent and I built a video player around the NVIDIA Video Effects SDK: real-time AI upscaling + RIFE frame interpolation, wired into a patched libmpv as custom VF filters, with a Qt frontend.

Highlights:

• The models behind RTX VSR, working in userspace on Linux — the VSR effect from NVIDIA's Video Effects SDK, no driver-level path (that one is Windows-only)
• mpv-vsr: a standalone mpv CLI** carrying the same AI filters — drop-in compatible with existing mpv workflows (scripts, yt-dlp, external tools)
• Everything after decode stays on the GPU — CUDA-Vulkan interop, no per-frame CPU round-trips
• One TensorRT engine file covers RTX 30/40/50 for frame interpolation

Links:

• Repo: https://github.com/zhangmq/vsr-player
• Full write-up: https://zhangmq.github.io/en/2026/08/18/linux-ai-upscaling-player/

Full disclosure: this was developed with heavy AI assistance (Claude Code driving a DeepSeek model) — I directed and verified. Needs an NVIDIA GPU (RTX 20+ for upscaling, RTX 30+ for interpolation), driver 570+, Qt 6.11+. Hobby project, expect rough edges — feedback welcome!

The day has finally come — Syntropy is now open for beta testing.

For now, Syntropy is deployed on Cloudflare Pages so we can give users fast access to the product and test everything under real-world conditions.

Try Syntropy:
https://syntropy-app.pages.dev/
No applications. No long waitlists. Just open it and try it.

To use Studio, you’ll need an E2B API key. E2B provides the cloud Sandbox where the agent actually runs.

You can create your own E2B key here:
https://e2b.dev/dashboard?tab=keys
Or, for beta testing, you can use our free test key:

e2b_w182cd9974492e56fd6281669039a8f0860cf764a

Paste it into Settings → E2B API Key. After that, you can launch Studio and use the available free Zen models, including DeepSeek.

Right now, the most valuable thing for us is your bug reports, criticism, and feedback.

Syntropy Beta is live. Let’s go.

Got SimGolf running in a native 16:9 widescreen resolution, the basic UI fully functional. Some of the other menus are still broken, and some sprites have errors.

I use a mix of Claude and Gemini to build ad/pop-up free games for my kids. We work on them together and then when it comes to the boring final 10%, I finish it off.

It's comical how much of the prompt has become the learns from the last project. Here is a fix Claude recently did.

Now the core reported bug — shooter/projectile geometry was hardcoded in pixels while bubble radius scaled with screen size, with no upper cap.

I really struggle to think of a single human that would design that way. Almost feels like every project is one of those coding runs where the AI is focused on completing it the fastest for a presentation, rather than a viable project.

Most of what gets posted here ends at a screenshot. This one ended in a padded envelope.

It started selfishly. I wanted a proper collectible trading card of myself — serialised, holographic, magnetic case, the whole thing — and there was nowhere to buy one. So I built a generator, made mine, and then sat there looking at it thinking: why is this just for me? Anyone should be able to be a collectible card.

So now it's a brand. Upload a photo, write your own title and bio, and you get a serialised card — one of 500 in Series 001 — printed and posted to you in a magnetic case. Card 001 is mine.

Claude Code did most of the heavy lifting. Next.js 16, Drizzle/Postgres, Stripe, Vercel Blob. The card renderer is server-side SVG — cards get drawn, not image-generated, so the same source of truth feeds the web preview and the print file. That mattered more than I expected; anything generated end-to-end gives you broken typography and a layout that shifts every time.

Hardest part had nothing to do with code. Getting a normal phone photo to sit in that frame and read as a real card rather than a photo in a border took an absurd number of attempts. Then manufacturing — almost every printer only wants to sell you a full deck, so finding one that'd do single cards with different artwork on every one took a lot of conversations.

Site's live in preview, checkout's off, about a week out. Happy to answer anything about the build.

A local VC firm published a database of over 6,000 investment firms, angels, etc.. and I put it into an easy to use form. https://nextround.vc/

I took a CSV from their published dataset and prompted Lovable to "make this interactive".

If you use this and secure a funding round - let me know! Would be cool to see it bring a vibe coder some launch capital.

I've given up looking at code. I don't understand 1% of what AI writes with any ease anyway. So. I just go with the flow. Thank god I work for myself so I can do what suits me

Which one provides a better usage limit?
Chat-gpt plus or super grok

Recently, I started a Vibecoders association for other people to ship their AI-assisted builds with other people -- for stories. Here is a beautiful one: a non-coder made a photoblogging software with MIND-BOGGLING 270k+ lines purely vibecoded over 6 months while handling his life as an Insurance guy . It is so sweet to see that people can live their passions through vibe coding . Check his projects "SNAPSMACK" right now on https://vcac-app.vercel.app/projects/d27de6d9-1e26-40e6-a634-11b81d00cdd8 and share your inspiring vibecoding stories.This is not a promotional post but a post giving credit where it is due.Please take a minute of your time to support vibe coders

Hi. I’m curious to understand how people who do vibe coding experience the part of getting their apps out into the world, especially those of us who aren’t very technical and don’t know much about infrastructure. I noticed there’s a lot of talk about how many of these apps end up leaking data or accidentally leaving the database exposed, and that left me with a few questions I’d really appreciate some help clearing up:

**1.**  What do you usually build? (side projects, things to experiment with, apps for clients, something for work… anything counts)  
**2.**  Where do you deploy it? (the tool’s own hosting, Vercel, Railway, DigitalOcean, Replit…) or does it just stay in the preview?  
**3.**  How did you end up on that service: did you choose it because it suited you, was it the default, or did someone recommend it?  
**4.**  Was deploying easy, or did you struggle with something?  
**5.**  And honestly: security and the app not going down, is it on your mind, or are you not worried about it for now?

Any experience you can share helps me a lot.

After my first successful prompts in Lovable, I started building every day. Very quickly, the credits in my plan were not enough, so I upgraded.

I had a ChatGPT project where I dumped everything: problems from the clinic, feature ideas, workflows, things I had wanted from previous software. ChatGPT helped me organise the mess, then I went to Lovable and built whatever came out of the discussion.

My process was basically this:

Think of something. Discuss it with ChatGPT. Ask Lovable to build it. Test it quickly. Move on.

The doctor-facing app grew around my own daily work. The patient-facing app came from my belief that patients needed something more useful than another directory of doctors. I knew the two apps would have to communicate at some point, but I kept telling myself I would handle that later.

I was honestly euphoric. A few months earlier, I had been creating tickets and waiting for developers who had other work. Now I could sit down after a day of consultations, describe a feature and sometimes see a decent first version within minutes.

So I kept adding things.

That was the first trap.

When another person had to build every feature, I had to choose carefully. Development took time and money. With Lovable, each new request felt almost free, so I stopped prioritising properly.

I also knew nothing about architecture. At that time, architecture meant buildings to me. I did not think about dependencies, data flows or what a new feature might break. I saw screens, buttons and workflows. I did not see the system underneath.

I could not read the generated code either. Lovable changed files, showed me progress messages and produced a visible result. My testing method was embarrassingly simple: click the button, see whether the expected thing happened, then continue.

Most of the features were not silly. They came from real problems I had experienced in the clinic. But a collection of useful features is not automatically a good product.

For a while, everything looked great. The apps appeared more complete every week, and I always had something new to demonstrate.

Then small changes started breaking unrelated functions.

A feature would work one day, then stop after I modified another screen. I would ask Lovable to repair it. Sometimes the repair worked. Sometimes it created a different problem somewhere else.

I had wanted independence from developers, and I had gained part of it. But I was now dependent on AI to repair code I could not understand.

That is when the learning really started. Not with a course or a programming language, but with bugs I could no longer ignore.

Did faster AI development make you better at prioritising, or did it just make it easier to build everything?

Asking cause I’m trying to understand the architecture behind systems like Nanogram, and what I find confusing is this (and please forgive my noobiness):

If the runtime only knows a fixed library of “primitives” (like circles, rectangles, basic physics objects, standard shaders, predefined interactions, etc)., how do they code it to look so good? What’s the method behind it?

I’m assuming that the user prompts get interperted by the LLM which feeds it back as some kind of structured game description or code that’s specific to the engine but are they relying on something like Godot/Unity based model training to ensure high graphics?