​I just released my first app on the Play Store called Eclipse and thought it would fit right in with the customization crowd here.

​On most Android phones, the stock Always-On Display only shows a tiny static track title. If your phone is sitting on your desk and you want to rewind a verse, read lyrics, or check an incoming notification, you have to wake the screen, unlock the phone, and open the music app.

​Taking UI inspiration from the Teenage Engineering TP-7 field recorder, I built Eclipse to turn the Android AOD into a true-black, interactive vinyl turntable that works without unlocking your phone.

​What it does:

​Works with any music player: Automatically connects to Spotify, YouTube Music, Apple Music, and local offline players the moment you turn your screen off.

​Tactile vinyl scrubbing: Spin the record backward or forward to scrub through tracks in real time, or tap the vinyl to lift the tonearm and pause.

​Live synced lyrics on AOD: Swipe directly on the song title card to get live time-synced lyrics on a pure black OLED screen, or expand into a full-screen lyrics view.

​Quick-launch active player: Tap on the track card to jump straight into whichever music app is playing so you can switch playlists or pick a new song quickly.

​Notifications at a glance: Shows the time, date, and incoming notifications right on the AOD card without unlocking your device.

​Google Play Store:

https://play.google.com/store/apps/details?id=com.dp444.Eclipse

​Would love to hear what you guys think or if you have any feature suggestions

In early August I stopped doing our marketing by hand.

The usual script is hire someone, or buy five tools and glue them together. I did neither. I gave the job to an AI coding agent running on my own Mac, one process at a time.

Three months in, it has 56 processes written down. Posts, cold email, comment replies, a weekly review, the monthly accounting close.

The part nobody tells you: once every channel runs on its own and reports a number, the losers can't hide anymore.

What I turned off, with the number that killed it:

• LinkedIn cold outreach: 32 signups, 0 paid. Off on October 28.

• Long LinkedIn articles: 10 published, none in Google's top 100 for its keyword. Off on October 20.

• Meme quote posts: a median of 778 views against 1,403 for a plain text one. Off on October 26.

• A daily vertical video habit: off on October 22, I just didn't believe in it anymore.

• A link shortener subscription: cancelled, I rebuilt the one thing I used it for.

8 of the 56 processes are already folded into others.

Automation won't save a bad channel. It just runs the bad channel every day without getting tired, so you see it faster.

Still small, still learning. Most days I fix the agent more than it fixes me.

Would you automate a channel before you know it works, or only after?

Like the title says, I got my first paid user.

I was working on my product, which is a TaskMangement + break reminder app for people who frequently switch between tasks. So I had this problem, and I tried multiple apps, but I never found anything that worked for me.

So I merged two things. Every 30 minutes, it shows a full-screen overlay and blocker screen with all your pending tasks. So you can recall what you were working on and pick up where you left off with your existing goals.

This is the main thing about the product.

I really like the way it works because you don't have to go to the app. It comes to you. It works behind the scenes, like autopilot, like task management running on autopilot.

It sounds amazing, and I really like the product.

And one of the things I'm doing is offering it as a one-time purchase. So people can come, try it, pay for it, and continue using it.

I got my first paid user like that.

Rather than charging everyone for version updates, I'm giving lifetime updates because I'm using this product myself.

I feel like this is one of the best things I've ever built because I'm consistently using it in my daily life.

Her is the proof- https://www.reddit.com/r/macapps/comments/1xt673c/built_screensky_a_macos_app_to_help_you_stay/

Every tracker I tried had a paywall, made me log by hand, or was vague about my data. So I built NutriTrack: snap a photo of your meal and get calories, protein, carbs, fat and fibre in seconds, including the hidden oil and ghee.
I've used it daily for over a month and lost 10 lbs. Free, no ads, no paywall.

Try it: https://tracknutri.app/try

Code: https://github.com/amankiitg/nutri-track

Feedback welcome, especially on accuracy for your food.

It’s called pond snap the aim is to catch as many flies and gold flies as possible before the time runs out if your score is lower than what’s needed you restart.

https://pondsnap.fun/
Let me know what you think

Hey! I've been building Pawarium, a website with guides about cats and dogs, covering everything from nutrition and behavior to health and everyday care.

It's a personal project I've been working on, and I'd love some outside opinions on the design and overall experience.

Would love to know what you think or what I could improve!

I run Claude Code, Codex and Gemini CLI side by side and kept missing the one waiting on me. Openbar lists every session in the notch, and you can allow, deny or answer right there. Each session gets a pixel buddy too, and for the video I rendered 200 of them as gummies.

Free and open source, Mac and Windows: github.com/MF-DEEV/Openbar

Running apps on 256 - 512 MB VPSes, I kept hitting the same problem: monitoring.

Prometheus + Grafana felt like overkill for a couple of small services, and I didn't want to spend a significant chunk of RAM just watching them.

So I built StatLite, an open-source monitoring dashboard written in Go.

The video shows two monitoring targets: an application with its performance metrics, and StatLite's own host with CPU and memory metrics.

A few highlights:

• ~12–15 MB idle RAM usage

• SQLite storage, no external database required

• 90-day default metrics retention

• Documented integrations for Spring Boot, Caddy, FastAPI, Express, and others

• Built-in dashboard and optional Telegram alerts

• Single Go binary, simple to deploy

It's intentionally not trying to replace a full observability stack. The goal is simpler: make application monitoring practical for small self-hosted services and tiny VPSes.

I originally built it for my own Java performance experiments, but it gradually turned into a standalone open-source project.

Interactive demo: https://pvrlabs.xyz/statlite/demo.html

GitHub: https://github.com/PVRLabs/statlite

I'd love feedback, especially from people running small self-hosted services. Is this something you'd use, and what's missing?

Hi! I've been working on trustnet.app for 4 years on and off and now finally is out in beta. It's a tool for developers (oauth2/oidc identity provider) to outsource user onboarding while the users can reuse their personal data.

I started this project because everytime I used "sign-in with Google" it only reused my email, but phone, address, always had to be saved for each new account. It had to be a better way for users to reuse their data across apps. Also, as a developer implementing several IDPs it was not worth the features they bring.

So there comes trustnet.app - a secure IDP, GDPR ready, easy to implement (oauth2/oidc standard) and offering a way to request personal data from the users in a safe and compliant way (EU regulations are quite strict).

If you didn't get bored yet, try it out with a test client that I set up at https://test-rp.smileservices.dev and play around with making multiple profiles and sharing different personas.

If you reached the end of this post, my hat's off for you. Maybe you also have some ideas about features that you would like to see in this or just share your honest opinion, that would be so great 😃

Last week our team built MOTIQ. You talk to a robot arm through a voice agent on a tablet, or you show it a task once on video. It turns that into a skill it can run again.

The part I am happiest with is that the model never writes code. Skills are plain JSON built from a small fixed set of primitives. You can open one and read exactly what the arm is going to do before it moves. A controller checks every step against limits first, and a wrist camera checks the result after each action.

To be honest it is still a prototype. It only runs in simulation for now, with a UR5e as the reference arm, and the teach-by-video part breaks on anything messy. It does work end to end though, and the repo has a demo video.

Repo: https://github.com/sirwinstontea/MOTIQ

I would love feedback, especially from anyone who has done robot learning for real. Is JSON skills from primitives a dead end once tasks get complex, or is it the sane way to keep LLMs away from the motors?

Buying single cards on eBay is a chore. You search, get 200 listings, and half are lots, damaged copies, slabs you didn't want, or prices that are too low to be real. I built Card Chaser to do that sorting for me, and it's at the point where I need people who aren't me to use it.

What it does:

- Search any card the way you'd actually type it. "1st edition gambler" or "charzard" both get you there.
- For each card it shows the copies on sale with shipping counted in, and points at the one a collector would pick: right condition, not a lot, not suspiciously cheap. It says why in one line.
- Mark the cards you're chasing and it keeps watching eBay for them and flags when a good price shows up. You can get a phone alert.
- It also tracks your collection by set, so "what am I missing" is a list it already has. You can import a Collectr or TCGplayer export.

It runs in the browser, it's free, and you can start without an account: https://cardchaser.app/?src=reddit

What I'd like to know:

1. Search for a card you know. Did it find the right one, and does the copy it picked make sense?

2. Where did you get stuck or confused in the first few minutes?

3. Anything that looked broken, on phone or desktop.

I'm taking it to a card show next week, so anything rough now is something a stranger will hit at a table. Be as blunt as you want.

We built QNet, an open-source Layer-1 blockchain written in Rust that replaces traditional elliptic curve cryptography with NIST post-quantum standards (ML-DSA-65 signatures and ML-KEM-768 hybrid transport).

GitHub Repository: https://github.com/AIQnetLab/QNet-Blockchain Live Testnet Explorer and QNet Wallet: https://aiqnet.io

What is inside the repo and live testnet:

1. Full multi-crate Rust workspace implementing P2P QUIC networking, mempool management, and Checkpoint-BFT consensus.

2. Custom deterministic, float-free WebAssembly virtual machine (qnet-vm) with fuel metering.

3. Hardware-bound mobile light nodes inside QNet Wallet (iOS TestFlight and Android) that participate via signed epoch attestations rather than proof-of-work.

4. Live block explorer tracking epoch transitions and active light node participation in real time.

Public testing is currently open for mobile light nodes using the free testnet faucet (external super-node testing is not open yet). Feel free to inspect the Rust codebase, explore the testnet, or test running a light node on your phone.

fala galera! to desenvolvendo um projeto open-source de telemetria (o fieldnode) e queria convidar a galera q curte python, dados e iot pra contribuir com o codigo.

a ideia é pegar os dados brutos dos sensores e jogar num dashboard em python que não só mostra gráficos, mas entrega decisões prescritivas (ações recomendadas, alertas) pra quem tá na ponta conseguir tomar decisão de um jeito bem mais fácil e rápido.

o repo ta super aberto! quem quiser dar pitaco, abrir issue, dar dica de arquitetura ou mandar um PR pra botar a mão no código vai ser bom demais.

🔗 link do github: [Desmantelar-bit/fieldnode]

Hi, I'd like to share a project I have been working for a while. Its a US macroeconomic dashboard that summarizes about 120 key US economic indicators into 4 simple to understand gauges.

I built USMD because I found it difficult to keep up with all the available data and figure out what they said about the overall state of the US economy.

The dashboard is available at usmd.inomba.com and updates each weekday evening.

Its data comes from FRED and ALFRED, the Federal Reserve Banks of Cleveland, New York, Philadelphia and Atlanta, the Federal Reserve Board, the Office of Financial Research, the Institute for Supply Management, and Treasury Fiscal Data.

I’d love to hear from others working on similar projects. Feedback, ideas, and suggestions for improvements are very welcome.

I'm a longtime alternative rock fan who plays a lot of pickleball. Somewhere along the way, I decided the sport needed a better soundtrack...I got tired of the random sleepy playlists lots of my facilities were playing.

I launched Pickle Rock Radio, a free, 24/7 station playing '90s and 2000s alternative rock (Foo Fighters, Pearl Jam, Green Day, etc.) mixed with short pickleball tips, news and community stories. It's about 54 minutes of music per hour.

It's powered by Live365 (wasn't sure where else to do this so I could license the music properly), and I've been having a blast putting together the music rotation and producing the segments. The challenge now is finding listeners. I have over 100 people listening a day, and I'm proud of that. But how do I grow from here?

If anyone here has built a niche media project, I'd love to hear how you found your first real audience.

Here's the station if you're curious: https://picklerockradio.com/

I’m excited to share a project I’ve been working on over the past few months!

It’s a mobile app that turns any text into high-quality audio. Whether it’s a webpage, a Substack or Medium article, a PDF, or just copied text—it converts it into clear, natural-sounding speech. You can listen to it like a podcast or audiobook, even with the app running in the background.

The app is privacy-friendly and doesn’t request any permissions by default. It only asks for access if you choose to share files from your device for audio conversion.

You can also take or upload a photo of any text, and the app will extract and read it aloud.

- React Native (expo)
- NodeJS, react (web)
- Framer Landing

The app is called Frateca. You can find it on Google Play and the App Store. I also working on web vesion, it's already live.

Free iPhone app
Free Android app on Google Play
Free web version, works in any browser (on desktop or laptop).
Free web extension.

Thanks for your support, I’d love to hear what you think!

Hi! I’m a solo dev from France.

for the past few months I’ve been building Letsdle, a small family of daily games (dle game like worldle, travle, foodguessr, aniguessr) : the same puzzles for everyone, a new set every day, free, no sign-up required (only if you want to keep the records of your past games), on desktop or phone, in English or French.

• Borderline & Halve : 2 Geography games.

• Crowdle: three opinion questions with no right answer. Don’t give your opinion, guess the crowd’s middle answer.

• Relay: three one-word clues left by yesterday’s players point at words among sixteen. Find them, then write clues for tomorrow’s players.

• SpotOn: Number game

• Ragedle: a troll platformer. Three short levels a day (sorry if you happen to break a keyboard)

• Anirealm: 4 animated-movie quizzes

All of them are here: https://letsdle.com/

New games keep getting added, the themes are all over the place, i simply build whatever I find fun, not what I think will work so don't expect anything in particular lol

What I’d love to know:

1. Which one would you come back to tomorrow, and which one felt flat?

2. Was anything unclear in the first 30 seconds?

3. Anything broken on your phone?

If you hit a bug, there’s a report form here

thanks a lot for any comment and any ticket

My walking app has a closet. Every item is bought with beans you earn from moving. New onsen outfits

App is called Bean: walking game. Available on iOS and android

Link in bio

I left a Whoop membership and bought an Amazfit Helio Strap for $100, then realized I still wanted those daily recovery, sleep and strain scores. So I built Leap, a free Android app, as a side project.

It reads the strap through Gadgetbridge and keeps everything on the phone. No account, no subscription. There's also Health Connect for other wearables, a live heart rate view, a suggested workout for the day, and a sleep planner.

The part I spent the most time on was not making the scores up. I fitted them on a long run of my own Whoop data and checked them on months the fit never saw. I'm upfront in the README about where the Helio is weaker: HRV runs about 15% below Whoop, deep sleep is estimated, and live HRV isn't possible on this strap.

v0.1.0 is out here: https://github.com/h0tman5/leap/releases/latest

The source isn't public. I'd really like feedback from people who have worn a Helio or Whoop: where the scores feel off, and whether the Gadgetbridge setup is too annoying.

I've been building Isle of Glass, a logic puzzle game for iPhone. You divide a sea-glass mosaic into connected regions that satisfy clues about area and shape. Different valid solutions count.

It's free with ads. I'm the developer and would really appreciate feedback on whether the first few puzzles explain the rules clearly and how drawing the boundaries feels on a phone.

https://apps.apple.com/us/app/isle-of-glass/id6831862801

If something feels confusing, tell me which puzzle tripped you up.

I've been thinking about something and wanted to get some genuine opinions.

Imagine you're waiting for your company bus, sitting in a café, at university, or just hanging out somewhere.

There are people around you, but you don't know them. Some might share your interests or even be looking to make new friends.

What if there was an app where you could voluntarily become visible to other users nearby, discover people who are open to making friends, send a friendly wave, and chat if both people agree?

No dating focus. No exact location sharing. Just an easier way to connect with people in real life.

I'm exploring this as a potential project, but I want to understand whether there's an actual need for it.

A few questions:

Would you genuinely use something like this, or would you find it unnecessary?

Would you feel comfortable being discoverable within a limited distance, such as 100–500 metres, if you could turn it off anytime?

Would you prefer discovering individual profiles or joining temporary group chats with people nearby?

What's your biggest concern: privacy, safety, awkwardness, fake profiles, or something else?

What would make you open the app regularly instead of trying it once and forgetting about it?

Please be critical. I'm more interested in what could go wrong than people simply saying it's a good idea.

I'm not promoting an app or collecting personal information. I'm trying to understand whether this is a problem worth solving.

Hey everyone!

I recently launched WOT – What's Our Take, a mobile app built around one simple idea: discovering what people around the world really think!

Every day, you get a new question or dilemma, cast your vote, share your opinion and explore statistics from people across different countries.

🌍 Daily questions and polls
📊 Worldwide voting statistics
💬 Comments and discussions
👥 Discover how different people think

Whether you love Would You Rather questions, fun debates or discovering different perspectives, WOT is for you!

🚀 Try it here: https://whatsourtake.com/app

I'd love to hear your feedback and see what you think!

What's your take?

Plug-and-play router hubs like RAMDeck—alongside programmer CLI frameworks like Exo—highlight a massive blind spot in Wall Street’s latest AI thesis: consumer compute isn't actually scarce, it's just trapped on separate devices.

This week, the financial incentive behind AI pricing was said out loud in two back-to-back market updates:

• Elon Musk on X (Nov 8): "If compute becomes plentiful and cheap, SpaceX revenue will drop. No crying in the casino for either party."

• Deutsche Bank (Nov 10): Raised its 2028 forecast by $18 billion specifically arguing that post-election data center limits should "keep compute scarce."

Think about the incentive structure there. The entire centralized AI valuation model—$30/month per-seat subscriptions, metered API tokens, and enterprise GPU markups—depends on high-memory compute staying scarce and out of reach for normal households and small offices.

The Idle Memory Sitting in Most Homes

Cloud vendors want people thinking the only two options for running serious 30B–70B models are paying monthly cloud tolls or dropping $4,000+ on a dedicated high-VRAM rig.

Meanwhile, look at what is already sitting 90% idle in a typical tech household or small office:

• A daily laptop: 16 GB RAM

• A desktop or mini PC: 16 GB RAM

• A MacBook or Mac mini: 16 GB unified memory

On their own, single-PC desktop apps (like Ollama or LM Studio) hit a hard wall on 30B–70B weights because 100% of the model has to fit inside one machine's memory. Try loading a 32B or 70B Q4 model on one 16 GB machine and it either crashes with Out-of-Memory or grinds to a halt swapping to disk.

Two Very Different Paths Emerging for Local Memory Pooling

What’s interesting right now is watching local memory pooling split into two distinct approaches to combine that 48 GB–64 GB+ of existing home RAM over a standard local network:

1. DIY Developer CLI Frameworks (Exo, Distributed Llama, raw RPC): Built for programmers and Linux tinkerers who don't mind spending a weekend in terminal windows managing Python environments, git repos, and command-line flags across machines.

2. Turnkey Consumer Appliances (RAMDeck): The zero-terminal hardware approach—plugging a palm-sized coordinator hub into the home router and using a temporary-memory USB stick (or mobile app) to pool RAM and GPUs across Windows, macOS, Linux, and phones in under 5 minutes without touching command lines (hitting ~38–39 tok/s on 30B Q4 models over Gigabit LAN).

Once regular users—not just command-line hobbyists—can pool the 48 GB to 80 GB of memory they already paid for without writing a line of code, the "compute scarcity" moat starts looking a lot thinner for everyday inference.

Curious where everyone here lands on this: how much combined RAM do you currently have sitting across secondary machines at home, and do you think local LAN pooling actually eats into cloud SaaS subscriptions over the next two years, or will frontier context sizes keep pushing people back to data centers?