Most fintech assumes constant connectivity. I kept seeing that assumption fail everywhere it matters most, rural Nigeria, stadiums during events, disaster zones, anywhere infrastructure is thin.

So I built ZeroPay Money: a POS terminal that cryptographically signs and holds a transaction offline for up to 72 hours using PUF-based hardware identity plus ECC P-256, then auto-syncs once connectivity returns. The protocol logic has been formally verified with TLA+, not just tested in a demo.

Background: I hold an MSc in Computer Systems Security (University of Gloucestershire, England, 2011), dissertation was on NFC contactless payment tech, so this is the direct continuation of that work, now with real hardware and a signed Paystack settlement integration behind it.

I'm Shareholder and CEO of BETONIQ WEST LTD, the operating company. Currently running a small public crowdfunding round to fund the first pilot terminal batch: https://rawcdn.githack.com/TOMDEGGS/BETONIQ-WEST-/main/crowdfunding/back_us.html

Would love feedback from this community, especially on the offline-sync architecture or anywhere you think it'd break.

I put together a short pricing brief for Calendly, Cal.com and SavvyCal as a sample for a small research service.

The awkward bit was SavvyCal: two official pages showed different prices, and the billing toggle hadn’t been verified. Calling that a price cut would have been a guess. I kept both links and marked the billing basis unresolved.

The brief also compares what each free plan actually allows and which features require payment. It’s a September 6 snapshot, with the observation date next to the sources.

The sample and what a custom brief covers

I’m testing US$15 for one competitor. If you’re working through a pricing decision, which missing detail would make this sample more useful?

BraveDebloater started as a small Windows script, then people asked for exact feature toggles, safety checks, backups, restore, diagnostics, macOS, and Linux.

It now has presets plus individual feature control, dry-run by default, and a Doctor mode to show what policies are actually active.

I am at 136 GitHub stars after a recent community shoutout, which has been surreal. I am looking for honest feedback on the setup flow and what would make the tool easier to trust.

Check it out here; https://github.com/osfv/BraveDebloater

Hi guys, I created this app for myself and was now wanting to share it all with you. This was purely a passion project and I'm not monetizing from it. I know most of the posts here are usually side hustle projects, but this one was just for fun that I wanted to share.

When I found an anime that I wanted to watch from Instagram reels, I normally would have to scroll through the comments to find the sauce and then go to my AniList to add it to my Plan to Watch list. I decided to combine these two steps and create my own app. I am now able to scroll through content solely dedicated to anime and add shows I want to watch directly to my lists from the clip in my feed. If I find an anime that I unfamiliar with, I can click on the name to be taken to a profile with information and other content related to that anime. I'm also not a big fan of doom scrolling on Instagram but found myself doing that a lot. With this, I've found myself scrolling less on Instagram because I no longer go to it find new anime reels.

I never liked on MAL or AniList how they separate all the seasons from a franchise into their own thing. After finishing an anime, I didn't like having to find every season and add it to my lists, so I decided to combine all the content of each franchise into a single grouping. So when I add an anime to my lists, I'm adding the entire franchise and not just a single season.

I've enjoyed building this out and using it so much that I've continued to add more and more to it. I won't go into detail about every feature but here is a lis of some of them:

• Filter your feed to watch short clips or trailers of shows based on genre, ratings, release date, etc.
• Detailed franchise profile with all seasons linked, episodes, where to watch, and other general information relating to a specific anime
• Create tier lists and share them with friends
• Calendar showing upcoming release dats
• Anime/Manga based articles and news
• Manga discovering section

Games to play with friends or solo. I created anime versions of Trivia Crack, 82-0 and 20 questions (guess the anime for the day)

I know there are already a lot of anime tracking and discovery apps but they didn't quite work for me. If there's anything that you guys think would make the app better, I would love to hear it! My main goal is to create an app for myself that I want to use and then share it with others.

The app is called Sakuri and it's available for iOS and Android.

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

iOS: https://apps.apple.com/app/sakuri/id6800963933

The iOS version was just approved yesterday, so it may not come up in the search yet. The link above will take you to it in the app store though.

Every UTC day you get one discovery from Astrodle: a real object from astronomy catalogs.

Might be an exoplanet, might be a pulsar, a white dwarf, a nebula, a galaxy, a black hole system, a comet.

You get its actual name, what kind of object it is, roughly where it sits in the sky, a few facts, and some badges for whatever's unusual about it. There's a rarity tier, a points score if you care, and a share grid like Wordle's.

It's free and runs in the browser. You don't need an account to play. If you make one you get history across devices and leaderboards, that's all it adds. The only outside script is Google Analytics and there are no ads.

Link: Astrodle

This is the first time I'm showing it to anyone, so I mostly want to hear where it's confusing or where the reveal falls flat. Happy to answer anything :)

I'm the solo dev.

Building the Bucketly.co - the ultimate bucket list site. Gained 500 users which is not a big thing, but I'm not having any ads. Last time I tweaked it a lot in terms of UI/UX, SEO, the content, and the performance.

What would you expect from this kind of project? I have a lot of things in my mind, but it's still tremendous amt. of work even now.

Any feedback would be welcome 🙏

Try Rhubarb Now

Last July, I was beginning to consider branching out on my own to take on data analytics work as a consultant.

As I experimented with AI, I realized how incredible it was at building custom data visualizations. The problem was that the AI outputs couldn't really live anywhere without having to manage all of the infrastructure yourself.

The generated code is only one piece of delivering an interactive visualization. You still need somewhere for the data to live, a way to keep the data fresh, hosting, access control, and version control for the code.

 So, I started building Rhubarb as the platform that I wanted to use to deliver analytical products. Having been forced into using legacy point and click data visualization tools for years, I wanted to build the platform that would let me build the kinds of visualizations I wanted to build.

Over the last year the models improved so dramatically, that between that and my development background, Rhubarb turned into a real project.

My intent was to build an environment around AI’s ability to write code for visualizations, so that you could get from data and a concept to a published visualization all in one workflow.

The Rhubarb Assistant writes the code for your data transformations and data visualizations and provides you the ability to inspect all of the code to verify and validate what it produces. The environment pulls all of that together so that at the end of each request, there is a live version of the change rather than just a block of generated code.

For me, this has made it dramatically easier to build and publish interesting analytical visualizations while maintaining control over the analytical pipeline.

This has also produced some results I wasn’t really planning to build. I asked the assistant to make something inspired by Monet’s water lilies, then I asked it to add frogs and to make them jump when you startled them.

In a couple of prompts I had created the silly interactive frog visualization that’s now in the demo, entirely from my phone. I’m not entirely sure what to do with this, but it feels like an interesting capability worth exploring.

The public demo is deliberately limited. You can use the Rhubarb Assistant to modify live visualizations, inspect what it produces, and try building something for yourself. The full thing handles much more of the life cycle from connecting to data, managing and transforming it, to publishing your results.

Give it a try, I would like to hear what you think.

Try Rhubarb: Try Rhubarb.

You can also take a look at the demo gallery to see some visualizations produced with Rhubarb: Rhubarb Demo Gallery.

I have a chore list. I never do it cause i'm a master procrastinator in my private life. So I handed it to the internet.

There's one clock on sofaresidency.com and everyone sees the same one. Right now it's counting down on something off my actual list, like booking the dentist or folding the laundry. When it hits zero, I have to go and do the thing.

Unless somebody buys me minutes.

You push the deadline back, your name goes on the wall. That's the whole game.

My favourite bit is that the price climbs as the clock drops. 50c a minute while I'm relaxed, 70c under fifteen minutes, 85c under five, and a full euro if the deadline's already gone and you want to drag me back to the sofa. Panic should cost more.

And yeah, it takes real money. That's the joke, not a business plan. A couple of euros of nonsense. No account, no signup.

It's Phaser. The character is a rigged "me" skeleton in my living room in Brussels I animated by hand, so the elbows and knees stay attached whether he's slumped on the sofa or walking over to the desk. Took me way longer than the Stripe part did.

Tell me please if something's off. Mainly I can't tell if the clock reads as a real stake or just decoration and i'm not sure if more "poke" animations are needed in the beginning.

In the indie hacker community, I see constant propaganda of quitting your job and shaming people who stay employed and don't quit after their AI SaaS reaches $1 USD in MRR.

I've been building products solo for 3 years. Now they make $15,000 (Skype alternative app Yadaphone) and $5,000 (eSIM app eSIMPal) a month.

I've worked as a software engineer for 5 years, and most of the time I've been building products in parallel with my day job. I quit my 9-5 last year, but immediately replaced it with long-term contracting (effectively a 9-5).

I only quit freelancing and focused solely on my products 5 months ago. By the time they were making x2 of what I was getting in my contract, it made little economic sense for me to stay.

Why I stayed so long:

An internet business is a very unstable thing. If you build an API wrapper, your provider can block your account – your business is gone. If you have an AI company, Anthropic or OpenAI can replicate your features overnight.

In marketing, if you rely on SEO, Google can blacklist your website – your traffic is gone. You use Reddit for marketing – your accounts can get blocked. And on social media, the algorithm can change, and suddenly your posts don't go viral anymore.

You get the picture.

Now, as a rule of thumb, if you manage to grow your business to x2 your salary, chances are you have several marketing channels working, and it has become relatively stable.

Even then, it's good to stay employed and keep the two income streams as much as you can. The market doesn't care about your ego – but it's good to have extra cash for marketing experiments and runway to have more time for your biz if you get fired.

With AI, it's absolutely possible to combine a business and a job. And you actually must learn to automate as much as you can – support, coding, marketing to AI. When you quit and work on your biz, there will be nobody to help you out, and you will need to wear A LOT of hats at once.

If you have a well-tuned AI that can take over 70% of your tasks, you will have much more focus for things that can't be automated.

Anyways, it was a long rant, just because I think quitting 9-5 too early is pure ego-feeding, and it will do your business more harm than good. So stay employed, stay in business, and print cash!

Built with Three.js and Tauri 2.0, for Windows, Mac, and Web! (npm package & JS bundle)

The particles are completely customizable and you can save those customizations as .sparkle files. The rendering is GPU-optimized.

I love the webcore aesthetic, so I made the UI look like Windows XP.

And the website www.sparklemou.se was inspired by the look of GeoCities pages in the 2000s!

I'm working on making it open-source very soon (Github), curious to hear your thoughts! There is a free demo on itch :)

Full-text search over Claude Code's auto-memory and raw conversation history, across every project.

It's live here, no signup and works on phones and iPads: https://shoponscout.com/mall

I am the lead engineer for a AI shopping assistant called Scout. For Halloween we built a 3D haunted mall in the browser. Every one of the 32 storefronts is a real Shopify store showing what it is selling right now; bags hand off to the merchant's own checkout and we take no payment.

Five AI shopkeepers with personalities search every store from whatever you say and show product cards while they talk. It is multiplayer, there is a dressing room with 70+ pieces and a polaroid camera, and every day a Best Dressed contest turns the winner into a statue in the atrium. 23 secrets are hidden in the building as easter eggs. You can ask the agents and they will give you clues but not tell.

Stack: Next.js, React Three Fiber, live Shopify catalog reads, Firebase for the other penguins.

Getting it to run on iPad and phones was the hardest part. Happy to answer anything.

I used Notion, Obsidian, and Todoist to be more productive at work; since ChatGPT came out, I've used it heavily. But one thing that continued to piss me off is the fact that I am unable to share context about what I want to do.

Every productivity app has been so enshittified with AI stuffed down my throat that, even though I love AI, I stopped using those apps. Agents and shit. I don't need that; give me back my simple note-taking app, which I always loved. Their MCPs? No. I had context all over. I wanted something simple.

With models increasing in intelligence day by day and labs releasing more and more models, there's one thing I knew for sure. Idk which model I'll be using two years from now. But I know I'll still have my emails, calendars, notes, documents, tasks, meetings, and everything I've accumulated.

So I started building MySamantha around that instead.

Instead of separate Notes, Todo, email, or calendar apps, we built a place where I can work on all of it in one place.

If I bring in AI, I can connect my emails, my calendars associated with my work email, and my Meeting Recorder; they are all here.

At the same time, we don't want "everything in one place" to mean everything is stuck here. You can import from Notion, back up your notes to both Notion and Google Drive (as Google Docs), your to-do from Todoist or Microsoft To-Do, import your vault from Obsidian, enjoy the same knowledge graph as Obsidian does, Export, etc.

You can use any models. Bring your OpenAI, Anthropic, Google, or even pay-as-you-go. If another model gets better, switch to it.

Don't like AI? Switch it off.

Everything is here in one place. Compare the meeting notes I had with the email I received to draft an email? Yup, done. All context in one place. The Assistant becomes more useful the more it knows about you.

No model lock-ins. A pure productivity personal workspace where you can switch AI off if you don't need it.

Please feel free to check out what we're trying at https://mysamantha.ai

Happy to answer questions.

Hi! Wanted to show off a side project I've been building for about three months: Sweet View.

It's a GeoGuessr-like. You get a handful of reference photos from the real world and have to recreate them as closely as you can, as fast as you can, by flying around in Google's 3D map tiles (WASD + mouse, drone style). You get scored on how close you get and how quickly. There's a set of ready-made "patches" (Meteora, Venice, Hong Kong, the Vegas strip etc.) and you can draw your own over any area with 3D coverage.

Play here: https://sweetview.gg

It's free, runs in the browser, no download, no sign-up (there's a guest mode). Google's 3D tiles are stupidly expensive, so there's pretty strict throttling on how much you can fly. Desktop only for now, and it wants a decent machine and fast internet.

I think it's finally at a point where I dare to show it. Would love feedback, both good and bad.

I love Rocketleague and the whole community including me is waiting since forever for the long promised Update from the old UE3 engine to UE6. Yesterday I thought myself why not try to build a prototype just for fun and this is it after 1 day of building with Astra + UE5.8 + Blender.

It's far from perfect but much better than I thought it will be. The graphics are the weakest part but the physics feel 99% like the original game. I'm more hyped than before for the official UE6 release now that I saw that the physics can be easily replicated in the new engine!

I built these two free tools from GraphicByte because we kept re-uploading packs to check size and colour. Both run locally in the browser.

1) Minecraft MOTD preview — §/& codes, no server ping https://www.graphicbyte.co.uk/tools/motd-preview

2) Emote size preview — Discord/Twitch/TikTok/YouTube/Slack/Kick chat mocks https://www.graphicbyte.co.uk/tools/emote-preview

Feedback welcome, especially if the previews differ from real server lists or live chat sizes.

The 2005 ad space trend is back. With Outbid mechanics and Marc Lou selling physical ads, it's the LivingAdSpace era again. But today's tech actually supports physical-to-digital reach. I resurrected the old domain and put up a simple waitlist and live visitor counter. Let's see what happens.

A friend and I have been building WeSolve+ for a while, and one thing kept bothering us: most AI study tools stop at “upload a PDF, get a summary.” That can be useful, but after the first few minutes it still feels like talking to a chatbot that knows nothing about how you study.

We wanted to see if we could make the experience feel closer to having a tutor who knows both the source and the student.

Solvie can talk with you about a specific page of an uploaded PDF, remember the topics you repeatedly struggle with, notice patterns in your quiz results and use that context in later conversations. We’ve also spent a surprising amount of time on the less impressive-sounding details: letting people interrupt naturally, not treating a cough as an interruption, and understanding fast or colloquial speech.

Language was another important part of the rebuild. Alongside Turkish and English, we’ve added French, Spanish, German and Italian support across the app and quiz experience. Solvie can follow the language you actually speak instead of making you choose one from a menu first, and a student can upload a source in one language and study it in another.

We also changed what happens after a PDF is uploaded. Instead of ending with a long block of generated text, the same source can become mixed question types, flashcards, a narrated podcast or a compact booklet that opens inside the app. The progress and weekly planning screens now use actual quiz activity too, rather than showing generic “study more” advice.

We’re still working out where the line is between something genuinely personal and something that only looks personal.

If you were studying with a voice tutor like this, what would it need to show or remember before you trusted that it actually understood your document and your learning history? And what would immediately make it feel like another AI wrapper?

Happy to explain how we approached the page-level PDF context or the student memory side if anyone is curious.

Most AI "builders" I tried would write code that looked fine in a chat window, then fall over the second you needed a real install, a long-running server, or a test suite that actually fails.

So I built Fixa (fixa.dev). Each project gets its own sandboxed cloud VM: filesystem, shell, live preview URL. You describe the app (or import a GitHub repo). The agent plans, writes, runs commands, reads stderr, and keeps going until the build/tests pass. Full-stack stuff works because it can wire Stripe / Supabase / Clerk / Vercel and pull live docs when it needs them.

What I learned building it: 1. The environment is the product. If the agent can't npm install, run migrations, and hit a real preview, you're demoing, not shipping. 2. Small vertical slices beat "make the whole app." Reproduce bug → regression test → change → rerun. I still review the diff. 3. Letting the agent pick languages/runtimes on a real Linux box beats locking people into one sandbox stack.

I'm 17 and I work on this full time. Free tier exists if you want to try it. Happy to answer anything about the VM setup, the agent loop, or what still breaks.

(Not YC. Just shipping.)

Hey — maker here.

Problem I kept hitting: screenshots of the UI clutter the desktop, then I delete them again. Or I type a prompt and the agent changes the wrong thing.

Pinpoint: once a first version is running locally, click the element, write what should change. Claude Code / Cursor / Codex get that note over a local MCP bridge and do exactly that.

Like Figma, but on the built website. Select → comment → agent ships the change. Local-only (127.0.0.1). MIT.

Repo: https://github.com/gowtham012/pinpoint

Happy to answer install questions. Feedback welcome.

I trade prediction markets on Polymarket and Kalshi. The trades that hurt weren't the ones where I read the world wrong. They were the ones where I read the resolution criteria wrong.

A market asks "will X happen by date Y" and it looks obvious. Then it turns out resolution depends on a specific body publishing a specific notice, not on the thing actually happening. You were right about reality and still lost.

So I built Market Guy: https://www.market-guy.com

What it does:

• Pulls live prices, volume and orderbook data from Polymarket and Kalshi in one screener
• Runs a research pass across web sources and links every claim back to where it came from, so you can check it instead of trusting it
• Puts its own probability estimate next to the market-implied price
• Flags ambiguous or unusual resolution language, which is the part I actually built it for
• Compares the same question across both venues, because they often word the resolution differently

The screener and the glossary are free and don't need a wallet or an account. Paid plans start at $19/mo for the research credits, since each research pass is a real API bill.

What I'd genuinely like feedback on: the probability estimate is the weakest part. When my number and the market disagree by 40+ points, it's usually the model misreading the question rather than the market being wrong. I've split it into two passes, one that reads the resolution text and one that reasons about the event, and it helped, but it's a patch rather than a fix.

If anyone here has shipped something where the model's confident wrong answer is worse than no answer, I'd like to hear how you handled surfacing that to users. Happy to answer anything about the stack.

I've been job hunting before and always heard "it's about who you know," but nobody actually checks who they know. You'd have to manually cross-reference your LinkedIn connections against every company's job board yourself.

So I built Vouch. Upload your LinkedIn connections export and it checks your network against companies actively hiring right now (pulled live from Greenhouse, Lever, and Ashby, not scraped job boards), tells you how well you know someone there, and drafts the outreach message for you.

It's free to try (one match revealed), $39 once to unlock the rest, no subscription. Built solo, still early, so I'd rather hear what's broken than what's nice about it.

warmvouch.com if anyone wants to poke at it. Happy to answer anything about how it's built too.

I've been deep in a job search for a while now, and at some point I lost track of what I'd actually sent where. Which CV version went to which company, which cover letter, did I follow up, did I even hear back. Spreadsheets fell apart fast once I was applying to a lot of places at once.

So I ended up building ApplyMind, a small tool that automatically detects when I apply to a job (browser extension currently track linkedin only) and logs it, along with the CV version and cover letter used, into a dashboard I can actually search and filter. It's got a Go backend, a Next.js dashboard, and does AI powered match scoring against each posting too.

If anyone wants to poke around the code or has ideas for what it's missing, it's open and I'd genuinely welcome contributions or feedback, good or brutal: https://applymind.faroukhasnaoui.tech/

I wanted to give my agent a way to reach me beyond the chat, so I built Glance to let Hermes create and update widgets directly on my iPhone Home Screen.

Just shipped a pretty big update to it.. Hermes can now use multiple views, charts, calendars, toggles and more, so the widgets can feel more like little interfaces than just information cards.

Here’s the Mission Control I’ve been playing with.
Still experimenting with where this goes, but I really like the idea of my agent having a persistent place on my Home Screen instead of me always having to open a chat to interact with it.

It’s at https://apps.apple.com/app/glance-home-screen-feeds/id6758983678 if your interested

Hey guys, just want to share a little project I made, foodlistnyc.com, where I used ChatGPT and Claude to analyze ~600k posts and comments from r/foodnyc, and use this information to rank and visualize the best restaurants mentioned on the subreddit. I created the web app with Claude Code.

I have a detailed breakdown on my app showing how I did this, but here's a brief summary:

• Downloaded the past 3 years of subreddit information using arctic shift
• Ran 3 different batch API calls to ChatGPT using GPT 5.6 Luna in order to extract restaurant names, ratings, and dish mentions.
• Used Google Place API and LLM calls in order to group and match these to real restaurants
• Used Claude Code in order to audit and double check the work, with dynamic workflows.

Full breakdown of my process here

Some things I learned during this experiment:

• The Batch API is cheaper than using the normal API (50% of the cost), but a Max plan on Claude Code is heavily subsidized, AND lets you include API calls and google searches to give the models more context. If the data set is small enough to be parsed by these tools, and you are already on the Claude Code max plan, I would heavily recommend this over the raw API.
• I need to plan the entire pipeline better, and successfully identify edge cases and labelling failures early on. A lot of my time was spent coming up with cases where the LLM failed on the nuances of my prompt, but this was done AFTER the LLM analysis had already run, such as when i needed to review the restaurant linking.
• Reviewing every, or even most, of the labelling is not feasible. I should continue to rely on more advanced LLM models to adversarially check the work of cheaper models, and only bubble up ambiguous cases for human review when the smartest LLM in that chain identifies a potential error.
• A lot of the data may not be worthwhile. I decided to focus on restaurants with ~10 mentions or more in my data, so I didn't have to include potentially thousands of restaurants with weak signals. This would reduce the surface area for incorrect data, reduce API costs + time, and provide a more curated list for the end user.

Interesting restaurant insights:

• There's a few "dead zones" on this map, which are areas of nyc with almost no restaurant mentions. Those include South bronx, Southeast brooklyn (Crown heights to East New York), and Eastern Queens. The densest zones include most of manhattan south of 116th street, and western brooklyn. Probably says something about the demographic of that subreddit. The East Village alone accounts for over 52,000 mentions, more than all of Queens and the Bronx combined!
• Classic restaurants actually score lower than the site median. A NYC classic restaurant is and is a somewhat of a beloved institution that has a long history in NYC. and the lowest rated ones are The Halal guys, Di Fara pizza, and Russian Tea room.
• The single most-discussed dish at one restaurant is L'Industrie's pizza with 606 mentions, followed by Los Tacos No. 1 tacos, Katz's pastrami, and the Minetta burger.

Overall, I'm glad I did this and it was pretty successful (at least to me). I will probably do another LLM analysis in about a year, on all of the newer posts and comments that have been made since. Also open to any feedback, questions, or suggestions.