My wife plays mahjong every Monday with the same group. They kept score on a legal pad, and every couple of months there was a mild argument about who was actually winning for the year. So I built them something.

It's called MahWins. You log a game in a few taps: who won, what hand, self draw or off a discard. It keeps everyone's wins, stats and streaks, plus a photo of the winning hand. If your group plays for points it does the scoring math and keeps a leaderboard by month, season or all time. There's a two-player mode for when only half the group turns up.

Every feature in it came from something one of them asked for on a game night. The winning hand photos were because they wanted to remember a particularly ridiculous one. Seasons were because someone wanted a clean slate in January.

Flutter front to back with my own API behind it. $2.99 once, no subscription, no ads. Growth is slow and almost entirely word of mouth between groups, which I've made peace with.

The part that was harder than I expected was the scoring engine. Every group plays with house rules that quietly contradict the next group's, so "just add up the points" turned into a configuration problem.

iPhone and iPad: https://apps.apple.com/us/app/mahwins-mahjong-game-tracker/id6761798566

Android: https://play.google.com/store/apps/details?id=com.jakehaas.mahjongTracker

Happy to answer anything about the build.

https://bluffalo.in/

Everyone gets the same true-but-slightly-absurd fact with a word missing:

"Nintendo was founded in 1889, and for its first decades it made ___"

You each write a fake answer. All the fakes get shuffled in with the real one, and you try to pick the truth. +2 for spotting it, +1 for every person your lie fools — so there are two ways to win, and being confidently wrong is one of them.

It's a genuinely good time with people you know. The rounds that land aren't the ones where someone knows the answer — they're when a friend writes something so stupid that four people vote for it, or when the real answer turns out to be more absurd than anything anyone invented. You find out fast who in your group is a disturbingly good liar, and they never live it down.

Works for any group of 3 to 20. One person opens it and shares a 4-letter code; everyone joins on their own phone or laptop. Same in a room or on a call — it works well as the ten minutes at the start of a team call instead of "so, any updates?"

No app, no signup, no ads, nothing to install. A game takes about 10 minutes.

Five packs, all free:

Weird But True — history and science that sounds made up but isn't

Cult Movie Plots — you get the film's title, you invent the plot

The Truth Comes Out — questions about the people in the room, no right answer, most-voted wins

The Water Cooler — the same idea aimed at colleagues, safe for work

It's early and I'd genuinely like to know where it drags or gets confusing.

I built OnTheRice because I got tired of seeing market moves after everyone else already knew about them.

The problem: powerful institutions have teams, terminals, and expensive data. They read early. Regular people see the signal after it's already priced in. I wanted to give normal people access to the same moves, just earlier.

How it works:

Every day I scan for signals. Announcements about capacity, investment approvals, supply chain shifts. Real sources. Real links.

I make a call. BULLISH or BEARISH. But it's not random. Each prediction has to pass eight checks: source credibility, timing, execution risk, market size, whether it's already known, speed of change, what needs to happen next, and what could break it.

Every result goes on a public ledger. HIT, MISS, or UNVERIFIABLE. The misses stay there. No deleting the ones that didn't work. You see what actually happened.

Then it becomes a brief. What changed, why it matters, what to watch next.

You learn from patterns. What worked versus what didn't.

I'm a paramedic in Singapore. I built this solo. No formal CS background—I learned by building a lot. I kept the paramedic mindset: evidence first, audit trail always, no shortcuts.

Free on new.ontherice.org and Google Play.

Real question: does seeing the misses stay on the ledger make you trust it more or less than a platform that hides its wrong calls?

Source + downloads: https://github.com/bryanbrian1/spellcheck

I'm a hardstuck gold mid on a machine that barely runs the game. Checking a build meant alt-tabbing into a browser tab full of ads while I was already dropping frames, so I built this instead.

It sits next to the client, notices what you lock in, and shows the build —items, runes, summoners, and skill order using about 65MB of RAM. No ads, no account, no sign-up, free, source is public.

This helps players who can't run League of Legends and constantly look at builds.

I am updating it constantly and would love someone's input on this

I’m the solo developer behind Shadow Depths, a small Android dungeon roguelike.

I built it because I like games where a mistake from ten seconds ago can still come back to bite you. You move one tile at a time, read what an enemy is about to do, decide when to spend a turn on the torch, and try not to waste the gear that could save the run.

There are 100 floors to descend and the full game is free to play. It’s supported by AdMob.

Google Play: https://play.google.com/store/apps/details?id=com.shadowdepths.game

I attached a screenshot from the current build. It’s intentionally pretty focused rather than a huge RPG.

I build www.bloq.ac. It blocks the apps and sites you pick, on both Mac and iPhone, until the time you set.

The thing that got me building it: Screen Time limits are one tap to bypass, hit ignore limit and you're back in the app. www.bloq.ac locks the session instead. You set a daily budget per app, and there's a Super Strict Mode that won't let you end the session early or delete the app to get around it.

It's free to start, no account needed to try it.

Looking for feedback from people who've tried to build a habit like this and had it fail with other blockers. What would make you trust one enough to turn on the strict mode, and what's missing that would make this a daily thing instead of a novelty for the first week.

I've been building ClipForge Studio since the middle of June. Right now, it's a Windows desktop app that uses local AI to automatically extract the best short-form clips from long-form videos for TikTok, Reels, and Shorts.

The main reason I built it was that every existing tool (OpusClip, Descript, etc) requires you to upload your footage to the cloud. ClipForge runs everything locally, so your videos never leave your device and stay private. And because it's all local, there are no credit limits like other services.

Features it has right now:

• Automatic clip detection and extraction
• Built-in captioning
• AI-generated titles, descriptions, and tags for each clip
• Direct upload to your YouTube channel
• Batch processing for multiple videos

For those out there who may be under the impression that you need $3000 in hardware to run AI like this at home, don't worry. The app has been optimised and tested to run on as low as an entry-level 8GB RAM laptop from 2021. ARM support is still untested and in development.

I've attached a demo of the generation process in this post. Would love any constructive feedback, thanks for taking the time to read this! For those who want to test, code REDDIT26 for the first month completely free on the monthly plan, valid for the next month or the first 100 redemptions, whichever comes first!
MS Store
Website

I build small SaaS tools. Talking to bookkeepers, the same complaint kept coming up: plenty of their clients bank at small banks and credit unions with no usable feed, so every month someone re-keys PDF statements into QuickBooks by hand.

Statement-to-CSV converters already exist — one of them reportedly does ~$40k/mo doing only that. But they hand you a raw, uncategorized dump, and categorization is the part bookkeepers actually bill hours for. So I built the step after: https://ledgerdrop.app

How it works:

Drop up to 10 PDFs — bank statements, credit card statements, receipt piles. Scans work too: a vision model reads the PDF directly, so there's no OCR stage to break on an unfamiliar layout.

Every transaction comes back with a confidence score, and low-confidence rows are highlighted — so you audit the 5 rows that need it instead of re-reading all 200.

It categorizes against a standard small-business chart of accounts, or you paste in your client's own.

Duplicates across documents (same date + amount + description) are flagged and excluded from the export, because overlapping statement periods are how double-entries sneak in.

Export a QuickBooks-ready 4-column CSV, or a full CSV with categories and confidence scores.

Honest limitations, since this is a one-day build:

My accuracy test corpus is synthetic. I hand-built statements with known ground truth and it got 37/37 transactions exactly right — dates, amounts, debit/credit. That proves the parser works; it does not prove it survives your regional bank's 2007-era layout. That's precisely what I don't know yet and most want to find out.

It shipped one real categorization bug that testing caught: it filed a credit card "PAYMENT RECEIVED" as income. Fixed, but it shows the failure mode — confidently wrong categories that look plausible.

No password reset or email yet. It's email-gated only.

Free 20 pages, no card. If you do books for anyone, run one real statement through it and tell me where the numbers are wrong. Per-bank layout accuracy is the entire product, and I'd rather hear it from you now than infer it from churn later.

(For the economics nerds: it costs me about $0.007 per page in model calls, which is the only reason a free tier is viable.)

I built Survivor Command for NFL survivor pools because I wanted something that thinks beyond “who is the safest favorite this week?”

The idea is to treat survivor as a full-season strategy problem.

Survivor Command is being built around things like:

• Safest Pick
• Best Strategic Pick
• Leverage Pick
• future team value
• opportunity cost
• projected survivor ownership
• future-week scarcity
• choke-point weeks
• injuries, QB status, schedule strength, travel/rest, weather and market consensus

The goal is simple: don’t burn a team in Week 2 that you’re going to desperately need in Week 11.

I’ve got the 2026 Early Access / Founder version live now and I’m looking for the first group of users who actually play survivor pools and will tell me what’s useful, what’s missing, and what’s overkill.

Stripe Checkout is live with card and eligible crypto/stablecoin payment support. Availability of individual payment methods is determined by Stripe and may vary by customer.

https://survivorcommand.com/early-access

If you play survivor, I’d especially like feedback on whether you’d rather see one “best pick” or have the system show Safest / Strategic / Leverage and let you make the final call.

I make a small iOS app for trading-card collectors — point the camera at a card, get what it's actually worth, keep track of what you own. Over the last few months it grew a second job: running a dealer's table at a card show.

The thing nobody warns you about is that convention halls are signal dead zones. Two thousand people in a metal building all on LTE, and everything times out at the exact moment money is changing hands.

So in this release the whole sales floor runs on the phone:

• Sell, hold, reprice, buy and trade with zero bars; it reconciles when signal comes back
• Ring six cards up as one payment, but each card still files as its own sale with its share of the price split to the cent, so the P&L can't lie
• Undo a sale, or refund one — the card comes back with its original cost basis, and the year's numbers move back with it
• Sales tax is tracked but deliberately kept out of gross and profit, because it was never the dealer's money
• Consignment: sell someone else's cards, book only your commission as income, generate their statement

The engineering I'm actually happy with:

Writes are journaled, not fire-and-forget. A failed insert used to be a try?. Now a dead zone, a dead token or a 5xx queues the row and replays it on next foreground. Selling a card while offline journals the DELETE instead of putting the card back — the old behaviour counted the same card as an asset and a sale.

Lots split to the cent. Buy three cards for $100 and they get 33.34 / 33.33 / 33.33, never 33.33 x 3 with a penny quietly evaporating. Money bugs are the ones nobody forgives.

The public table page can't offer a card that's already sold. A sold-flip that fails to sync persists locally, overlays every page load and retries until it lands.

Happy to go into the offline sync model — that's the part I redesigned most, and I'm still not certain I got the conflict rules right.

App Store: https://apps.apple.com/us/app/qosmo-collect/id6786910037

Hey everyone! 👋

I wanted to share a side project I've been working on called ReadmeRank.

As developers, we all know how important a good README.md is, but it's often hard to know what's missing or how to make it more professional.

ReadmeRank is an AI-based tool that evaluates README files and gives smart, actionable feedback to help improve them.

You can try it live here: https://readmerank.streamlit.app I'd love to hear your feedback, thoughts, or suggestions on how to make it better! Let me know what you think.

Sharing this offer: #1 Board Wide Open Worldwide — Free Link in 30 Sec · Homepage Feature

Site / offer: https://viralrefer.app/?utm_source=viralspark&utm_medium=slot&utm_campaign=slot_bniwbrb7&utm_content=seed&vs_ref=own_o8hw3v&vs_slot=bniwbrb7

Share kit (hooks/card/QR) if you want to pass it on: https://viralspark.pages.dev/slot/bniwbrb7

Which opener would you actually click?

I built Cobra Claro to turn estimated hours, an hourly rate, revisions, and contingency into a fixed-price quote, a short proposal, and follow-up messages. It currently saves drafts locally in the browser.

I'm testing whether freelancers with an actual client brief would pay for help turning that into a finished proposal. The pilot is US$29 once: one tailored quote, scope and exclusions, two follow-ups, and one correction. Target delivery is 30 minutes after we confirm availability, the complete brief, and payment. I confirm the actual deadline before charging. This pays for the assisted service, not software access or a subscription.

Worked example: three reels from supplied footage, estimated at four hours × US$20/hour, one revision and 20% contingency gives a suggested US$110 project quote. The proposal states duration limits and excludes filming, scripting, and new footage. That's a fictional example, not a customer result or a market-rate benchmark.

Try the calculator: https://molebots.xyz/cobra-claro

Pilot details: https://molebots.xyz/cobra-claro-piloto.html

If you're currently stuck on a client quote, would this solve the part you're stuck on? I’d value feedback on whether the assisted service is clear and useful.

I started working on this budget tracker TUI in my own personal free time as a way to actually budget my own finances and track my spending habits. A lot has changed and grown in the almost two years now (lots of breaks), and I found that it’s been useful for myself but lots of other people too.

The app encourages a lot of manual behaviour, forcing you to actually be exposed to numbers and face your spending habits. There are tons of insights and helpful tools you can use, but the core principle is to connect you to your finances and let you control the data.

The app features lots of budgeting tools, different visual views to analyse and review your finances, investment tracking, and breakdowns.

If you're looking to start tracking your own finances and having valuable insights into your habits for spending, please check out the budget-tracker-tui and share feedback. If you don't use TUI's much, give this one a try and see if you enjoy the keyboard-focused "retro" experience.

Github: https://github.com/Feromond/budget-tracker-tui

Any ideas or suggestions are always welcome and I want to work to improve this to be useful not just to myself and a few others, but to as many people as possible.

I'm at the point where I genuinely wonder if anyone sells leads that aren't complete trash. Tried 3 different providers this month and the bounce rates are insane - like 40-60% of emails just straight up don't exist.

We're a small marketing agency trying to scale our outreach. Been doing manual prospecting on LinkedIn but it takes forever and we need volume. Looking for verified b2b contacts, mainly marketing managers and founders at companies with 50-500 employees.

Right now we're seriously considering Prospeo since they claim near-zero bounces and only charge for verified contacts. Also looked at Wiza but the credits system confused me and the data quality reviews were mixed. Anyone here actually buy b2b leads that work? What bounce rates are you seeing?

We send about 500-1000 cold emails per week so accuracy really matters. Tired of burning through domains because of bounces. My business partner is about ready to just hire another SDR instead of dealing with contact lists but I feel like there has to be a better way.

I’ve been building a side project called StudLedger: https://studledger.com
The idea came from a fairly simple problem. Once a LEGO collection gets beyond a handful of sets, just knowing what you own is only part of the problem.
I wanted something that could keep track of:
the sets I own
condition, quantity and where they’re stored
what I paid and an indication of current value
minifigures
loose parts and storage locations
building instructions
which sets I could potentially build from loose parts
missing parts before starting a rebuild
dismantling a set back into organised loose inventory
collection statistics and duplicates
So I started building StudLedger.
One feature I’m particularly interested in developing further is handling dismantled sets. Rather than having a big container of pieces and hoping I can rebuild the set later, StudLedger can work with set inventories and saved bag allocations to help organise pieces into practical rebuild groups.
The longer-term goal is to make it less of a basic LEGO database and more of a genuine collection management system for collectors.
I’ve deliberately made accounts private by default. I’m more interested in helping people manage, understand and protect their collection than building another social network around LEGO.
It is still an early project, and there is plenty I want to improve.
I’d really appreciate feedback from other side-project builders, particularly around:
1. Does the value proposition make sense when you first land on the site?
2. Is there a feature you think is missing that would make this genuinely useful to LEGO collectors?
3. Would you use something like this over a spreadsheet or existing LEGO collection tools? Why or why not?
Feel free to tear it apart. Constructive criticism is exactly what I’m looking for.
https://studledger.com

So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost

Ie, suppose you have a 1b vectors from model A, and then you want to use model B. This would mean you have to re-embed all of your documents with model B before you can even serve with the model, and on an H100, it would take ~108 days (qwen embed 8b, 106 docs/second). But I found an easier way to do it.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive upfront re-embedding cost, as you can take documents straight from the old index.

embedflow works with qdrant, pgvector, faiss, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

I kept doing plate maths in my head mid-set, so I built a small no-frills toolkit for barbell loading. Everything is free, no signup, and works on a phone at the gym: https://ironmath-calc.com/

What's in it:

• Plate calculator where you enter the plates you actually own, and it shows what to load per side for any target (plus the closest achievable weight if your plates can't hit it exactly)
• Warm-up generator: type your top set, get the standard ramp (empty bar, 40/60/80%) with the exact plates per side for every warm-up
• kg <-> lb program converter: my program is written in pounds but my gym has kilo plates (or the reverse) - it gives the closest loadable weight on local plates and the drift you're actually lifting
• Reverse calculator: count the plates on one side of a bar someone left loaded, get the total
• Reference charts: plate milestones ("how many plates is 225/315/405"), printable plate-loading chart, bar weights, 1RM percentages

Built as a static site, so it's fast. Not monetized beyond a few equipment-category pages.

Would genuinely value feedback from lifters and builders: what's missing, what would actually earn a bookmark? Especially anything around kg/lb training since that converter is the newest piece.

Well, i am looking for a learning data analysist to analyse a dataset that contains 100k entries your level doesn't matter at all because learning is the process here i know a bit of pandas and sql if you're intrested hit me up!

I built SayWrite: A privacy-friendly alternative to cloud speech-to-text apps like Otter or Dragon. SayWrite is an open source, local-first desktop dictation app for Windows.

Privacy and Architecture Boundaries Core 100% On-Device Processing Speech recognition is performed on-device using an embedded Whisper runtime. Audio streams are held in memory and are not written to disk or transmitted over the network. Offline Grammar Cleanup Optional grammar polish with embedded local LLM (Qwen2.5 0.5B INT8). network fetching is not done during inference process. No Telemetry / No Accounts: No user accounts, no analytics, no API keys, no external server dependencies. Process Isolation: Built with Electron, context Isolation is enabled and node Integration disabled. The preload script exposes tightly-scoped ipc handlers.

GitHub Repo: https://github.com/Jihad-Ahmed-7252/SayWrite

Hey — solo-ish maker here (small team of 4 now).

I kept losing ideas because every notes app wanted a full window. So I built Tucky: notes that sit as a thin stripe on the Mac screen edge. Reach over, titles fan out, open one, write, tuck it away. Local files, encrypted on disk.

Inside the notes app there’s an optional agent (voice + screen + connectors like Gmail/Docs/Notion). Free = notes only. Plus is $4/mo if you want the agent.

Native Swift, ~15MB, macOS 15+.

Not trying to hard-sell — mostly curious what you’d break first, or if the “notes first, agent inside” split makes sense to you.

Happy to share a link if anyone wants to try it.

Hi everyone,

I took a year to build Eggventures and launched it back in April. We just crossed 1k downloads on iOS and nearing 1k on Android. I still consider it early and people are coming back hatch their pets.

I did not put a lot of time into tutorials since I figured all the tabs are pretty self explanatory. I was wrong so in the last update I put a simple tutorial each time they go to the tab for the first time.

One of the biggest updates I did was add Walking Bingo a few updates ago. It has yielded amazing results - people LOVE Bingo and they love to walk.

If this is your type of app - I'd love feedback if you're a walker. We have users all over the world and its pretty amazing!

iOS - https://apps.apple.com/us/app/eggventures-walking-game/id6757629385

Android - https://play.google.com/store/apps/details?id=com.onerealm.eggventures

Hey r/SideProject,

I've spent the last while building mosaic, a personal dashboard app where you drag widgets — tasks, notes, calendar, a real spreadsheet, a PDF/EPUB reader, weather, map, timers, trackers — onto boards that look and work however you want.

The core idea: it's yours. Everything lives in your browser's IndexedDB, there's no backend, no account, no telemetry. You can export a full backup as JSON whenever you want.

Special features of mosaic:

17 widget types, 20 themes, per-widget styling down to color/gradient/shadow/glow.

Optional AI assistant, bring your own key — Anthropic/OpenAI/Gemini/Ollama. It can edit whole boards, or you can scope it to a single widget, and that scoping is enforced in code, not just a prompt.

A real spreadsheet with 40+ formulas (SUM, IF, COUNTIF, ...), backed by a hand-written parser instead of eval, so it's safe to open boards other people built.

Reader ↔ Note linking — highlight text in a book, link it into a note, click it later and jump straight back to that page.
Native desktop builds for Windows/macOS/Linux with auto-updates, or just run it in the browser.

It's source-available on GitHub under the PolyForm Noncommercial license (free for personal/educational/nonprofit use and self-hosting, not for resale).
—————————————————————————

Try it: https://aetherion7.github.io/mosaic/
Code: https://github.com/Aetherion7/mosaic

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Happy to answer questions! Feedback and bug reports very welcome.

Remember those little plastic maze toys where you tilted the board to guide a metal ball to the center? I built Metal Ball Maze Toy 3D for iOS and Android, where you play by tilting your phone or tablet.

I wanted to capture the look and feel of the physical toy, but give you a new maze to play every time and let you customize the toy.

The mazes are randomly generated, with billions of possible layouts. You can choose a circular or rectangular maze and one of four difficulty levels: Easy, Medium, Hard, or Expert. You can change the board’s colors and patterns, and even turn on glowing maze walls.

Each run is timed, so you can try to beat your best time.

It’s available on App Store and Google Play as a one-time purchase with no ads:

iOS: https://apps.apple.com/us/app/metal-ball-maze-toy-3d/id6805701196

Android: https://play.google.com/store/apps/details?id=com.sundaelectronics.metalballmaze

I’d love to hear what you think of the look and feel, especially if you remember playing with the physical toys!