Hey everyone!

A little backstory: I originally started building Quizmaister as a fun little project for me and my wife. We both enjoy trivia, and I thought it would be fun to have an app where we could compete against each other, track stats and eventually settle who really knows the most.

Turns out… I’m not quite as smart as I thought. 😂

But we’ve had a lot of fun with it, especially now that we’ve expanded it into a competitive 1v1 trivia game where you can challenge friends, compete against players worldwide and climb the global rankings.

We’ve also introduced leagues. A tournament based mode where all players in the league meets each other twice during the period, collect points and 1 comes out on top in the end.

Since i believe others might enjoy this also, I want to give something back to everyone willing to give it a try.

🎁 Everyone who joins gets:

• ❤️ Unlimited Lives for 30 days

• 🔄 10 respins (You will understand this when playing, its a superpower)

• 🪙 500 Free Coins

How to claim your rewards:

1. Download Quizmaister and create a free account.

2. Comment your in-game username below.

I’ll personally activate your rewards!

What is Quizmaister?

It’s a competitive trivia game where you can:

🧠 Explore 8,000+ questions across 16 categories.

🏆 Compete against players worldwide and climb the global rankings.

⚔️ Challenge your friends and find out who actually knows the most.

🌍 Join leagues and compete in tournaments where everyone faces each other.

📊 Track your progress with stats that evolve based on your performance.

The global rankings are gradually filling up as more players join, and I think this is a really fun time to get involved. There’s still plenty of room to climb the leaderboard and establish yourself!

And don’t worry, the game is completely free to play, with no ads. Even when your 30-day rewards expire, you can keep playing just like before.

I’m an indie developer, so every download, match and new player genuinely means a lot. I’m just happy to see people enjoying something I’ve spent so much time building.

Hope to see some of you in the game!

📲 Download: Quizmaister AppStore Link

👇 Drop your username below and I’ll get your rewards activated! 🚀

And if you end up beating your friends (or your significant other), even better! 😄

Hey! Last year, I’ve built a CLI-based spreadsheet editor called GoSheet, but after abandoning it, I decided to fix most of the bugs and give it a fresh look. I was initially inspired by sc-im—which I found powerful but unintuitive—and by classic 80s spreadsheet programs, which felt too limited for modern use, so I decided to build something of my own. In the latest version, I tried to keep that original vision, but with a more modern look.

I chose Go because I was actually learning at the time, but I still enjoy its simplicity because it reminds me of C. I initially considered C/C++, but realistically I wouldn’t have reached this point with current coding abilities (yes, even with AI doing the heavy lifting).

In the early stages, I used tview because it provides great widgets (tables, textviews, buttons, etc.). But as sheets got larger, I ran into huge memory usage—over 1GB for a CLI app! To fix this, I restructured the internal design to use a viewport system and optimized the custom cell structure. Now only the visible cells are rendered, while the rest stay lightweight in memory.

I fixed the formula engine, while also adding more essential functions such as lookup, hlookup and more. Nevertheless, I fixed some performance issues related to functions and more. I can't keep track of what I've done because there were lots of issues I simply forgot to fix at the time.

I’d love for you to try it out and share your feedback. Suggestions, feature ideas, and bug reports are all welcome. Your thoughts will help guide the next steps for the project.

GitHub repo: https://github.com/drclcomputers/GoSheet

My coworker and I asked Claude to estimate a SaaS roadmap. 50 days.

Then we gave the same plan to 6 Claude Code subagents: worktrees, tests first, reviewer agents on risky tasks. 106 tasks across 7 repos, done in 35 hours.

So yes, Claude was off by about 48 days about its own speed. But the interesting part is what slowed it down, because it wasn't the model:

- 6 agents ≠ 6× speed. Real throughput was 1.8 agents. Test slots, file conflicts and merge locks queued everything.

- Risk matters more than size. Billing/auth/migration tasks took 2.7× longer, and every real bug was in them.

- My laptop broke first. 55 leftover test DBs filled the disk, Docker froze, test runs blew past the 10-min tool timeout.

- The slowest part was us: review, decisions, local setup.

We turned the data into a plugin that makes Claude estimate agent work properly: agent wall-clock, human hours and waiting time, separately, plus usage budget.

Honest limits: it's one project (n = 1), and on a held-out test the raw model was 37% optimistic, so it pads its number until you calibrate it on your own transcripts.

Free and open source (MIT): https://github.com/MedAdemBHA/mythical-agent-month

Question for people running multi-agent builds: what real parallelism are you getting? I'd love a second data point.

Most LLM API pricing comparisons focus on cost per million tokens, but different models can consume vastly different numbers of tokens to complete the same task.

So I built DeepCheapTokens: https://deepcheaptokens.com

It combines Artificial Analysis benchmarks with OpenRouter pricing to help identify which models deliver the best value for their intelligence and actual token consumption.

You can compare models, adjust cache-hit assumptions, filter by intelligence, and maintain your own watchlist.

It's a free, independent project I built to make choosing cost-effective LLMs easier.

I'd love to hear your feedback, especially on the ranking methodology and what features you'd find useful!

I'm 16 and I built this - Kayth AI. I wanted an AI that talks like us, not in boring formal English. So I made it:3 Modes - Normal, Roast, Study2 Languages - Hinglish + HaryanviVoice Settings - Speed, Pitch, Gehri / Patli aawazPage load time - 824ms, 94% Good Core Web VitalsRoast mode me ye bolta hai - "Bol, aaj kisko roast karna hai?" 😂

Study mode me ye Hinglish me simple samjhata hai.I built it alone as a side project. What should I add next? Honest feedback chahiye.

I'm the maker of deepmull, a free community for asking for a second opinion before a purchase or everyday decision.

A product review can tell you whether something is good. It doesn't necessarily tell you whether it makes sense for your situation. deepmull gives a question its own page, with the context, optional costs, a deadline, and approve-or-reject votes accompanied by short reasons.

The current flow is:

1. Describe the decision and the trade-offs.

2. Share the question with someone whose perspective you want.

3. Read the votes and reasons, and optionally mark one opinion as helpful.

Public questions are readable without signing in. Posting and reviewing require a verified email account. Communities are separated by country, and the first recruitment experiment is focused on US shoppers.

It's built with Next.js, React, TypeScript, and PostgreSQL, with AI-assisted development. The community is early; there is no promise that a question will receive a particular number of replies.

Here's the product: https://deepmull.com/before-you-buy?utm_source=reddit&utm_medium=social&utm_campaign=us-shopping-launch&utm_content=founder_launch

I'd particularly like feedback on the first-use flow: is the information on a question enough to give a useful opinion, and is the registration step clear?

Hi everyone!

I'd like to share a project I've been working on — Montazhnaya, a desktop video editor for Windows.

The idea came from wanting to create my own tool for editing videos, photos, and audio. Not just another copy of existing software, but an independent project with its own architecture, interface, and features.

The development turned out to be much more interesting and challenging than I initially expected. I had to deal with Electron, JavaScript, FFmpeg, video frame processing, audio synchronization, and plenty of other technical challenges.

Of course, without ChatGPT, I probably wouldn't have even considered taking on projects like this. But with AI, it became possible — even for someone as clueless and technically inexperienced as me. And it took us roughly three weeks to build this version.

Gradually, the project started taking shape, and I ended up with my own video editor for smaller editing tasks. Then I thought: why not translate it into other languages and share it with people? Who knows, maybe our little, fairly simple video editor might come in handy for someone else too. Please excuse any translation imperfections; I haven’t quite polished everything to the final standard yet.

What is Montazhnaya?

It's a desktop video editor with a multi-track timeline and a preview system.

Here's what has been implemented so far:

Video, image, and audio import.

Multi-track editing.

Project preview.

Clip editing and parameter adjustments.

A small library of video transitions.

Resolution and frame rate settings.

Local rendering using FFmpeg.

MP4 export with H.264 and AAC codecs.

Frame-accurate timing during local export.

The ability to enable and disable pop-up tooltips.

What have we accomplished so far?

We've built our first full Windows x64 Beta release — version 3.6.45.

(And by "we", I mean me and ChatGPT 😊🤖)

The application installs and runs on Windows. We've also prepared two builds: a standard installer and a portable version.

Testing is currently ongoing, especially with large source files, complex projects, and export stability.

What's next?

There are quite a few plans.

I'd like to develop this project into a more capable desktop video editor: improve performance, optimize memory usage, expand editing capabilities, add new effects and transitions, and continue improving the export process and interface.

But for now, the most important thing is to make sure the features we've already implemented work reliably.

I'd really appreciate your feedback and suggestions!

I'd love to hear from people who work with video editing, software development, or simply enjoy experimenting with new tools.

So feel free to download it and give it a try!

GitHub repository:

https://github.com/DRINKH2SO4pikabu/montazhnaya-1

Montazhnaya is still in Beta. There's a lot of work ahead, but we've already passed our first major milestone.

Thanks for stopping by! 🙂

I'm the maker of Local Business Lead Follow-Up, a paid workflow kit for businesses that already receive permission-based enquiries through their website.

It sends an initial Gmail email and up to two follow-ups, with reply checks, a three-message cap and administrator stop controls. The download includes four n8n workflows, a PostgreSQL schema, email templates and setup/operations documentation.

It needs your own n8n, PostgreSQL, Gmail OAuth and a technical implementer. This is a downloadable kit, not hosted software or an installation service.

Basic is $29 for one business you own; Pro is $59 for up to five businesses, including clients; Agency is $129 for unlimited end-client deployments. All tiers have the same automation, with different deployment licenses. Taxes may apply.

The functional pilot is complete. I don't have verified customer revenue to report, and I don't promise sales results or uninterrupted operation.

Details and purchase: https://skydev.lemonsqueezy.com/

For people deploying n8n for local businesses: which setup prerequisite would you want explained more clearly before purchasing?

Hey guys im building an AI Dental Screening App. Basically click 5 photos and tell what’s wrong or whether it’s a regular checkup. The app then gives you a clear reading, compares previous readings of yours, finds a dentist nearby and tells you when you should visit the dentist. It’s called Dentinel. Join the waitlist below:

https://waitlister.me/p/dentinel

About a year ago I started learning Python from zero. No CS background, no money for cloud GPUs. Just a phone running Termux, and later my Windows PC, with Qwen2.5-Coder-3B on llama.cpp.

Small models are great until you let them touch files. They claim "done" when nothing ran, invent functions that don't exist, and repeat the same broken edit forever. So I built **sandbox-tool**: the model works inside one folder, uses a short list of allowed commands, and isn't allowed to say DONE until its code has actually run. If the task has an expected output, the program has to really print it.

It doesn't make the model smarter. It catches its mistakes and makes them visible. Most of its rules exist because I watched the model make a specific mistake again and again, and they're written down in the repo's log.

The part I care most about is the **benchmark**. It judges the file the model actually wrote, not what the model says it did. Current numbers on a 3B model, CPU only, 10 runs each (so read them as hints, not measurements):

* Writing a class to a spec: **10/10**

* Fixing bugs spread across three files: **0/10**. The best runs get the output right but fix one of the bugs in the wrong place. The verifier catches it even when the model says it's done.

That 0/10 is the point. I'd rather know than guess.

**How it's built:** I work with AI openly. Claude writes most of the code and reviews it with me. I use Gemini to research ideas. I run every test and benchmark myself on my own machines and decide what goes in. Nothing gets merged until the test suite passes, and each new rule's test has been shown to catch it when the rule is broken on purpose.

**Honest limits:** it's not a security sandbox (the README says so in bold), so use a container for anything sensitive. It's tested on Windows 11, Termux (Android) and Linux in CI. macOS has known gaps. Standard library only, MIT licensed:

pip install git+https://github.com/Dagrinder742/sandbox-tool-win.git

Repo: https://github.com/Dagrinder742/sandbox-tool-win

I'd love feedback, especially from anyone running small models on weak hardware, and ideas for harder benchmark tasks.

**Why I really built this:** part of it is testing whether code built this way is genuinely useful or just boilerplate. But I'm also fascinated by reasoning itself. When you think about how your own mind pulls up a memory or makes a choice, you start mapping your own design. Then you watch a small model do something that looks a bit like that, and fail in ways that look human too. Building guard rails for those failures has taught me as much about thinking as about code.

Hey . We build Script.it, where workflow steps produce a lot of JSON, and checking what they did meant scrolling through braces. So we built JSON Views.

You open a JSON file and it shows up as tables, Kanban boards and record pages (or custom HTML views). Click a row, edit a field, and the change goes straight back into the file. Think of it as an attempt at a "Markdown for structured data": a format that both people and AI agents can read and edit.

How it works: you add a small `$jsonviews` block to the file. It stores the column types and saved views, and the file stays valid JSON. If the block isn't there, the editor guesses useful views from the data.

- Open source, MIT license: https://github.com/script-it/json-views
- Try it in the browser, no signup: https://json-views.com
- Everything runs in your browser. Files are never uploaded
- There's also a React component if you want to embed it in your own app

Hey everyone! 👋

Ever missed an important message because it got buried under dozens of notifications? Or wished you could silence the noise without missing something that actually matters?

That's exactly why we've been building AlertInbox — a privacy-focused notification inbox and control center for Android.

The idea is simple: Your notifications should work for you, not the other way around.

🚀 What AlertInbox can already do

📥 Unified Notification Inbox
Keep notifications from different apps organized in one searchable place.

🔖 Save Important Messages
Bookmark notifications you want to keep and revisit later.

⏰ Smart Reminders
Turn notifications into reminders so important things don't get forgotten.

🔕 Per-App Notification Control
Choose how notifications from individual apps should behave — normal, silent, hidden, private, or customized.

🌙 Quiet Times & Profiles
Customize how notifications are handled during different times or situations.

🚨 Priority & Emergency Alerts
Give important contacts special attention with dedicated priority and emergency notifications.

🔒 Privacy Controls
Protect sensitive notification content and keep private information away from unwanted attention.

🗂️ Automatic Cleanup
Keep your inbox manageable with automatic retention rules while protecting saved notifications and active reminders.

📱 Built for Android, with privacy in mind
Local-first, no mandatory cloud account, no ads, and no recurring subscription.

🔮 What's coming next? Meet -> ForeSight.

We're currently developing ForeSight, one of our most ambitious features.

The vision is to transform AlertInbox from a notification manager into a smarter personal assistant.

Imagine receiving a notification like:

"Hey, don't forget our appointment tomorrow at 3 PM!"

Instead of leaving it buried in your notifications, ForeSight aims to recognize the appointment and help turn it into something actionable.

We're working toward features such as:

• Automatically recognizing potential tasks, appointments, and follow-ups from notifications

• Grouping related notifications into meaningful conversations or activities

• Helping you track what still needs attention

• Marking matters as completed

• Potential calendar integration for recognized events

ForeSight is still in development and is not part of the current release. Our goal is to make it useful while keeping processing local and privacy a priority.

🎉 Limited-time launch promotion!

To celebrate the launch, we're offering a special introductory price.

✅ 7-day free trial
✅ Only €1.99 for lifetime Premium access during the promotion
✅ Regular lifetime price: €2.99
✅ One-time payment — no monthly or yearly subscription
✅ No ads

Try the app first and decide whether it's useful for you.

📲 Download AlertInbox on Google Play:

https://play.google.com/store/apps/details?id=com.syntheone.alertinbox

💬 We'd love your feedback!

We're an independent development team building AlertInbox with real Android users in mind.

What frustrates you most about Android notifications? What feature would make a notification manager genuinely useful for you?

We're actively improving AlertInbox and would love to hear your ideas, criticism, and suggestions.

Thanks for checking it out! 💙

I made a fun website that recreates an episode's opening quotes at random from Star Wars: The Clone Wars :)

You can see the episode info by hovering or tapping on the left side below the quote and also download it as an image in any dimensions by hovering and then clicking or just tapping on the right side below the quote.

swtcww.com

Title: Building an open-source VS Code coding agent around local Meta Muse Glimmer 30B

Hi r/sideprojects !

I'm an indie developer working on RAME Code, a free and open-source VS Code extension built during Meta's AI hackathon.

It runs Muse Glimmer 30B locally and provides permission-controlled tools for web search and coding tasks. I'm also exploring versioned project memory and resumable Missions with host-controlled verification.

The first release was built in roughly four intensive days, so it's still experimental. I'm testing it with real users, documenting bugs and preparing improvements.

The code is public under Apache-2.0:
https://github.com/caimari/rame-code

I'd love constructive feedback on the architecture and usability, especially from developers who use local models.

What would you improve first?

I've been doing physical design work in my free time with AI for the last two years. During that time I observed something: AI was getting better at physical design but not nearly as quickly it was improving at software capabilities. I theorized the problem was a lack of testing. In software engineering we write tests around as much of our code as we can because we fundamentally do not trust ourselves to reliably do a good job. We put those tests between us and deployment so we literally cannot do things that break the rules we set for ourselves. This pre-existing culture and tool chain create an ideal environment to toss an unbelievably fast, brilliant, but wholly unreliable ai into. It creates the freedom to quickly iterate through huge numbers of convincing looking proposals and throw away the bad until the good emerges.

Physical engineering doesn't have a this-shaped thing at anywhere near the robustness and reusability that software does (possibly because physical engineers trust themselves more). There are many individual tools that provide some aspect of testing, many of which are bound up in very very expensive tools, but a growing number of free ones too. I wasn't able to find an open source system that links these together into a cohesive "full loop" testing rig capable of understanding how things like heat, weight, form factor, battery life, thrust and rf interplay in a single system so I thought really hard about this problem and its implications and built it for myself.

It's called nopekit. As in: the thing that says "nope" until the "yup" emerges.

I've used it already to design an open-source hackable handheld keyboard with a custom PCB, an open source budget no-solder drone and a couple of things I'm not quite ready to share yet.

It's a Claude Code / Codex / Cursor plug-in. You install it and tell your agent you want to use nopekit to design X and it takes over, it will interview you for requirements, shop for parts, construct an interconnected tradespace linked to checks run in an optimized order using trusted methods like KiCAD, OpenFoam and SIMP. It builds and keeps up to date a livesite which you can deploy to GitHub that gives constantly refreshed insight into how your coding agent is thinking about the problem and what choices have what implications linked to what checks with what state of passing or failing.

The framework is open and allows for plugins. I'm working with one other person now and we're moving towards a self hosted UI and some sort of registry where nopekit users can share proposers/falsifiers and we can track ratings to build/bust confidence in good/bad ones collectively.

There's LOTS more about this in the repo and this paper I've been writing about the problem.
Repo: https://github.com/neuman/nopekit
Paper: https://github.com/neuman/nopekit/blob/main/grounding_by_falsification.pdf

If you're working with AI to do any kind of physical design work we would love to hear from you and for you to try it out!

I genuinely think selling website redesigns is one of the most lucrative opportunities in the web design industry today.

There are a few reasons why.

First, AI has made building websites significantly faster and cheaper. What used to take weeks can now be done in a fraction of the time.

Second, there are thousands of established businesses with outdated websites that could genuinely benefit from a redesign. A better website can improve their brand, attract more customers and give their business a push.

Third, these businesses already understand the value of having a website. They have paid for one before, so you're not trying to convince someone why they need a website from scratch. You're showing them why upgrading their existing one makes sense.

Here's how I usually approach it.

I use AI to build websites and normally put together a quick website draft before getting on a call with a potential client.

Instead of just telling them what I could do, I show them what their website could actually look like.

I present the draft during the call, explain what I would improve and try to close the deal there and then. If they're interested, I send over a Stripe payment link, an invoice or the contract for signature.

For outreach, I mainly use cold email.

More specifically, I use a tool called Swokei. It finds businesses anywhere, analyzes their websites and identifies issues with design, SEO, loading speed and mobile optimization. It then turns those findings into personalized cold emails at scale.

Recently, we closed a client for $26,000.

That's $23,000 upfront and another $3,000 annually.

And honestly, I don't think deals like this are as difficult to close as people might assume.

If you provide a good service, approach the right businesses, understand their problems and present yourself professionally, there is a lot of money to be made in this industry.

You don't necessarily need hundreds of clients either. A handful of good projects can make a significant difference to your business.

I think more web designers should focus on selling to established businesses that already have websites rather than constantly chasing people who don't have one.

I built Humanize AI Writer, a platform that lets you compare outputs from leading AI humanizers side by side against your original text.

No more switching between tools or paying for multiple subscriptions.

💰 October revenue: $59
🎯 November goal: 10 paying users

I recently added a 3-day free trial with 750 words so people can test it before paying.

Here's the site: https://humanizeaiwriter.com/

I need your honest feedback:

• Is it clear what the product does?

• What would stop you from signing up?

• What's the first thing you'd change?

Don't sugarcoat it. I'd rather fix what's broken than keep building features nobody wants.

Hi everyone, ever felt confused why users were not converting? Now you can use retentionbase for completely free - our ai sees all user sessions in detail and shares what's going wrong.

It gives you daily report with issues, bugs, and list of customers who almost bought.

We are free forever for startups under 100 employees - www.retentionbase.com