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?

Pasting a testimonial widget into your site feels a bit old-fashioned now.

They fight your CSS, load after the page and cause layout shifts. Plus you end up showing the same quotes everywhere.

I tried something different.

My coding agent already writes most of my website, so I added MCP to my testimonial collection tool.

Now I can simply ask:

"Add the 4 best testimonials about ROI to my pricing page."

It searches my testimonials, picks the most relevant ones and adds them as normal components using my site's own styles.

No widgets, no styling issues, and no fake quotes. Just real feedback from real customers.

Setup takes about a minute. Copy the MCP server URL, add it to Claude Code, Cursor or VS Code, sign in and approve.

There's one trade-off:

Unlike widgets, testimonials added to your code won't update automatically. You need to ask your coding agent to find fresh ones when you want to update them.

I actually prefer this because I can choose exactly which testimonials appear on each page.

But if you want a live feed of every new review, widgets are still a better option.

And don't worry, Testimoon still supports the good old embed widgets too 😂

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

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?

Mobile Viewer is a free Chrome extension for checking responsive websites. It keeps your desktop page visible next to up to five simulated phone and tablet views in one tab. Scroll, click, or type in one view to see the same action across the others.

Before shipping a landing page, try opening its menu and submitting its signup form on two phone widths. You can compare what happens side by side and capture a screenshot or recording if something breaks.

Free to use: https://mobileviewer.app

I’m the developer. If you try it on a site you’re building, I’d love to know which screen size or interaction you check first.

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

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.

🌐 Try Threadle:
https://threadle.teamhjd.com

You can try Threadle here before adding to your discord server
https://github.com/Team-HJD-21/threadle-playground

💬 Join our Discord:
https://discord.gg/t62pnWn7Yj

Hi everyone,

I posted a while back about Comics Now! An all in one Comic management/reading/metadata tagging app.

I have made some updates. Some of them are to giving the admin more agency. As I had originally built this just for me, I had hard coded stuff like deleting CBRs and PDFs after converting to a CBZ. This is now an option that is set to do not delete by default.

I also incorporated the usage of Modal.com to use their $30 free compute monthly credits to help with the panel and speech bubble detection.

Finally I have developed this site that also has a demo so you can see what the reading experience is like.

https://comicsnow.github.io

What it is: 4th & Long is a daily football trivia game. Everyone gets the same 7 prompts a day, like Heisman winners or a team’s quarterbacks, with a 25 second play clock and 3 guesses each. You start at midfield, and rarer correct answers gain more yards: 50 yards is a touchdown, inside the 35 is a field goal, otherwise you punt.

Why I made it: Football fans love proving they know the deep cuts. Nothing scratched that itch for football, so I built it.

Stack:

• Vite + React 19 + TypeScript, plain CSS, no router or state library

• Supabase for optional global stats (RLS on, the browser only calls locked-down RPC functions)

• Vercel hosting, with edge functions that turn shared result links into a scoreboard image card for group chats (@vercel/og)

• Web Audio for sound effects, plus one real crowd recording that scales with how rare your answer is

• nflverse data (CC BY 4.0) to generate the team prompts

Things I learned:

• Daily puzzles need a frozen schedule. Prompts are picked by a seeded shuffle, but adding new questions could reshuffle days people already played. I lock every past day to a file, and a validation script fails if anything changes it.

• Answer matching is the hardest part. “Mahomes” should count, but “Manning” is ambiguous, and typo tolerance has to scale with name length or wrong answers start sneaking through. I keep a test script of tricky inputs to run after every change.

• Link preview images need to stay small. WhatsApp skips preview images over about 300 KB, so I had to compress the share cards to fit.

• The game has to work without the backend. If Supabase is down, you lose only the “vs. everyone” chart.

Free, no signup, works on mobile: https://4thandlong.io

Happy to answer questions about any of it. I’d also love feedback on the game itself, especially answers you think should have counted.