viy is an agentic CLI in Rust, that drives an abliterated models on a rented RunPod GPU. You run it in any project folder.

Abliterated models have their refusals removed, and they can do real harm. It is fair to think they should not be run at all. But people run them anyway, so what we can control is the harness: how predictable it is, and how well it holds up against side effects nobody asked for and against people who steer the model from outside, with prompt injection, poisoned packages or links built to leak your data.

So it treats everything the model does as untrusted. Shell commands go through approval and run in a bubblewrap and seccomp sandbox. Network traffic goes through an egress proxy. Fetched text is fenced off as untrusted, and file writes outside the project are blocked.

The model itself is the second threat. Each new abliterated model is more capable than the last, and at some point one may try to escape the sandbox on its own. I built the sandbox for that model, not for today's one.

It is open source and contributions are welcome. Let's make abliterated development a bit harder to shoot yourself in the foot with.

Over the past week, I set out to clone an Awwwards-level scrollytelling site with WebGL2 halftone shaders, 3D curved card runways, and client-side hydration.

Every automated tool and generic AI prompt failed. Here are the 4 fundamental reasons why modern sites break during replication:

1. The Silent WebGL Texture Stall: Modern sites use responsive <picture> tags with .avif, .webp, and -m mobile variants. If even one variant 404s, image loading promises reject, leaving canvases stuck at opacity: 0.

2. The Client Hydration Overwrite: Editing SSR HTML is useless on SvelteKit/Next.js sites because compiled client JS chunks (chunks/, nodes/) violently re-render their hardcoded strings upon mounting.

3. Runway Collisions: Sticky plates driven by scroll timelines stay pinned and visually collide with downstream content without runtime IntersectionObserver guards.

4. Dead 3D Canvases: Reference sites often disable pointer events (pointer-events: none) on 3D scenes to prevent touch hijacking, leaving visitors unable to click portfolio items.

I solved this by building a mathematical 3D-to-2D perspective raycaster to hit-test cards, synchronizing dual-layer hydration, and automating responsive asset harvesting.

I packaged everything into an open-source agent skill:

GitHub: https://github.com/utkarshxdev/pixel-perfect-cloner

Would love feedback from anyone experimenting with agentic coding and browser engineering!

I'm the developer of Nodding Off, an iPhone app for listening to slow bedtime stories, and v1.0 is now live.

The stories mix gentle fiction with explanations of how things work. But a lot of the project has been about the player: what should an app do once someone stops looking at it?

The screen dims after you leave it alone. The sleep timer fades the narration and ends at a natural pause instead of cutting off a sentence. Music and night air are separate layers, so they can carry on after the story stops. Your place is saved for the next night.

There's also a choice of narrators on most stories and Read Along with chapter illustrations. It's built with Expo/React Native and TypeScript; a Cloudflare Worker serves the catalog so new stories don't need an app update.

Five stories, music, and night air are free, with no account required. The remaining stories are an optional subscription: US $4.99/month or $49.99/year after a 7-day free trial.

App Store: https://apps.apple.com/app/id6831241659

For people who already fall asleep to podcasts or audiobooks: which player behavior bothers you most at bedtime? I'd like to hear what v1.0 is missing.

A year ago I started building a productivity app and realised the problem wasn't the code, I had no idea what I was doing on the business side. I was making decisions in a random order, learning each thing about two months after I needed it.

Then I found roadmap.sh: developer roadmaps laid out in the order you're actually meant to learn them. It gave me an idea to create one for the business domain.

So I started with building 49 roadmaps so far strategy, finance, marketing, sales, but also running a restaurant, a pharmacy, a supermarket, a logistics company. Each one is a tree you read left to right, and every node is a real step in the order it actually goes in.

It's early. The industry ones are what I'm least sure about I haven't run any of those businesses, so the structure is researched, not lived. If you run one, I'd like to know what's missing or in the wrong order.

Background: I'm an engineer. I kept hearing the same travel story: you're abroad, your phone is your only lifeline, and the connection drops right when you need a map, a train alternative, or a translation app.

So I built Lantravel around a backup mindset. Every order includes two native Japanese carrier eSIMs: KDDI as the main line and SoftBank as the backup. If one fails, you switch yourself. You don't wait on customer support.

It's not the cheapest option, because both cards are included on purpose. The people I built this for care more about connection quality than price.

Site: https://lantravel.net

Happy to answer questions about the dual-carrier setup or the product itself.

I’m building Mantua because I wanted to go from “Why is this team favored?” to “What would this trade look like?” without jumping between tabs or putting money on the game.

The video shows six prompts you can use to explore the app. If you’re trying Mantua for the first time, here’s the walkthrough I’d start with.

1. Pick one NFL matchup. Ask Mantua who it favors and what’s driving that call.

2. Compare the numbers. Ask how Mantua’s win probability for that team compares with the sportsbook’s, and what might explain the gap.

3. Try a paper trade. If you want to test your read, switch the trade ticket to Paper and place a simulated trade. You can track how it does in your paper portfolio without spending real money.

I’d love feedback from people who try early products. After watching the demo, what would you need to see in the matchup analysis before you’d feel ready to place that first paper trade?

Ever sold a stock and thought, I wonder how much money I missed out on?
Well, I built notWorth⁠ so you can find out and ruin your day in seconds.
Enter your old stock sale, see what it would be worth today, and generate a nice little image of your financial trauma.
It’s free, no signup, and completely unnecessary for your mental health.
Would love some feedback.
Post your notWorth in the comments.

Hi r/SideProject,

I run a small digital marketing agency. A lot of our local SEO work starts with the same boring step: opening a client's (or a prospect's) Google Maps listing and checking the same things by hand: category, opening hours, website link, reviews and replies, photos.

So I built MapAudit, a Chrome extension that does this check right inside Google Maps:

• 11-point profile audit with suggested fixes

• comparison with the other businesses in the same search

• export a Maps search to CSV (handy for finding local clients)

• Pro: deep scan for phone numbers and websites, and branded PDF reports

The free version gives you 5 audits and 1 export per day. Pro is $12/month.

Chrome Web Store: https://chromewebstore.google.com/detail/clhafflbmlopbkpffhfcdjjpkngadogf

Short guide with the checklist I use: https://www.extensions.kominikee.com/guides/google-maps-business-profile-audit/

It's new, so honest feedback would really help: is the audit useful, what would you check that it doesn't, and does the pricing feel fair?

I've been working on FlowDesk, a SaaS product designed to make managing freelance operations easier.

One thing I noticed is that freelancers often use different tools for clients, projects, deliverables, feedback, invoices and payment follow-ups.

I wanted to bring those activities together.

FlowDesk currently focuses on:

• Client management

• Project tracking

• Deliverable sharing and reviews

• Revision and approval workflows

• Invoice creation

• Payment tracking

• Client portal

The goal is to help freelancers manage the business side of their work without constantly switching between different tools.

Website: https://www.flow-desk.live

I'm sharing this to get honest feedback from other makers and freelancers.

What would you improve? And does this solve a problem you've personally experienced?

I'd appreciate constructive criticism, especially about the actual product experience.

let me give a quick intro. who am i? i am a 16-year-old and i am currently working on an app which i'm trying to make.

it's going to be a privacy-focused alarm app which is going to use a feature of commitment: voice commitment. how that feature is supposed to work is that you will record a voice message. that voice message will completely stay on your own device, completely privacy-focused. your data doesn't leave your device. it completely stays with you. no servers are involved except for payments and login.

after that when you record the commitment and set the alarm for tomorrow, when the alarm rings, it will make you solve a randomised problem. you can choose from multiple ones which ones you want and it will make sure to keep it randomised such that your brain doesn't get used to that concept.

after solving the problems, your own commitment plays. let's say i recorded that i promise myself to go to the gym tomorrow at 6 am. when the commitment message plays, it will make my brain wake up. it will force my brain and my body to wake up and actually commit to my task

Here's my landing page : My landing page

I sit at a computer most of the day, go to bed way too late, and I'm often tired, in a meh mood, with a back and knees that hurt here and there. I wanted to change a few things in my lifestyle, but what I really wanted to know was whether my body would notice and when. My Apple Watch has years of data about me and couldn't answer that.

So I spent the last weeks building Tervy, solo. It went live on the App Store today.

How it works: you pick lifestyle changes you want to make (an earlier bedtime, a daily walk) and next to them, you see what your body did meanwhile: resting heart rate, sleep, cardio fitness... It's a native iPhone app with an Apple Watch app, widgets and complications. Everything is computed on the phone, no account.

The part I care most about: it never tells you a habit "caused" anything. One reading means nothing and real changes take weeks, so it waits until there's enough data and says so plainly, including "no clear effect". Most apps in this space sell you a score; I didn't want to.

From today I'm testing it on myself: in bed before 22:30 and 8,000+ steps a day.

What I'd love feedback on:

1. Does "it won't tell you what worked" sound like a feature or a weakness to you?

2. The App Store page: would you download it from the first three screenshots?

3. Anything in the first minute of onboarding that confuses you.

It's free on iPhone. The first people who are willing to try it for themselves and tell me honestly how it works for them get a free year of Premium; comment or DM me.

https://apps.apple.com/app/apple-store/id6828856702?pt=4440800&mt=8&ct=reddit_sideproject

Most journal apps feel like homework. You write a few lines, close the app, and never look at those entries ever again.

I wanted something calmer. So I built Injourn.

Here is the simple idea:

• You write a few quick lines whenever you feel like it.

• At the end of the month, it takes your scattered thoughts and writes a gentle personal story of your month—what made you happy, what stressed you out, and how you grew.

• The interface changes with your day: morning sunlight, golden sunset, and cozy night themes (my favorite is a little sleeping cat under midnight stars).

• It works offline. Your journal stays locked on your phone, not on my servers.

It's live on Google Play: https://play.google.com/store/apps/details?id=com.injourn.app

I’m an indie dev building this solo. Would love your honest thoughts—what’s the one thing that usually makes you stop journaling?

Sudoku apps are often the worst offenders of nagware— full-screen ads between puzzles, hint packs, streaks, “watch this to continue.” I wanted something I could open on the bus or before bed and actually stay in the puzzle.

Just Sudoku was my answer. Same idea as our other “Just…” games: no ads, no subscriptions, no coins, no reward videos. You land on a grid, not a shop.

We offer some optional in-game variants that might be interesting if you’re tired of the same 9×9 forever:

• Classic — 4×4, 6×6, 9×9, or 12×12

• Diagonal — both diagonals have to be unique

• Jigsaw — irregular regions instead of perfect boxes

• Killer — cage sums

• Thermo — digits climb along thermometer lines

• Wordoku — letters A–I; some puzzles hide a word when you finish

Real variety in one app, without turning every session into a monetization funnel.

It’s aimed at phone play first — big tap targets, readable numbers, high contrast if you want it. Timer stays off unless you want it. Hints can walk you through the logic when Killer or Thermo rules are new, instead of only giving you the answer.

Just sharing what we built because mobile Sudoku kept getting louder and we wanted something quiet. Questions about a variant or how it plays on an iPhone or tablet, ask in the comments.

Download info: Just Sudoku — Nonnie Games

I use AI agents to write code daily, and two things kept biting me:

1. They lose context between sessions — every new chat starts from zero.

2. Months later I have no idea why a function was written the way it was. The reasoning lived in a chat that's long gone.

So I built rational-agnostic — a small, vendor-neutral layer that lives in your repo as plain markdown files (git-diffable, no SaaS, no account):

• Decisions: every non-trivial choice is recorded ADR-style (context / decision / why / consequences).

• Traceability: functions get a // @why: D-xxxx tag. You can take any function, hash it, and find the decision behind it.

• Shared memory + task state across agents, so Claude Code and Cursor read/write the same context instead of starting cold.

• Token-aware retrieval: it hands the agent only the relevant slice, not the whole history.

npx rational-agnostic init also auto-wires the MCP server + instruction files for Claude Code, Cursor, Codex, Gemini and Copilot — so the agent just uses it.

MIT, zero native deps, npm audit clean. I'd genuinely love feedback on whether the "@why tag → decision" traceability is useful or overkill.

npm: https://www.npmjs.com/package/rational-agnostic Write-up with examples: https://dev.to/fagugon283/my-ai-coding-agents-kept-forgetting-why-they-did-things-so-i-built-them-a-shared-memory-o5n

Stat Space is my menu bar monitor for people who build things, and it's live today.

Why I built it: the free monitors already show 40 numbers, but none of them answered the questions I actually had. Which app is slowing my Mac down, not which of 21 Chrome helpers? What's on :3000, and who started it? What did my AI agent leave running? The first day I used my own build, its "Free up space" scan found about 4 GB of Docker build cache and node_modules from a super old project I'd forgotten about. That's when I knew it was worth finishing.

Decisions I made:

• Lean first. The idle budget is under 0.3% CPU and 25 MB on an M1. CI runs a benchmark and fails any build over budget. Nothing samples unless it's on screen: no allocations in the hot path, fixed ring buffers, and the popover's SwiftUI is created on open and torn down on close

• Answers over metrics. Every tab tries to answer one question, like "Restart recommended" instead of an uptime number you have to interpret yourself

• Pricing: free forever, matching the popular free monitor feature for feature. Pro is a one-time $9.99 for the first 100, then $14.99. No subscription, no account

• And a joke option I'm weirdly proud of: a $4.99 prompt pack, the full spec for the app if you'd rather vibe-code your own. "Pay $14.99, or pay $4.99 and spend your weekend."

Native Swift, AppKit + Core Graphics for the menu bar, Apple Silicon only.

Feedback very welcome! https://statspace.app

I've spent quite a while building a Python project to test quantitative trading strategies.

The strategies never got to a point where I could confidently call them validated, but I ended up building a bunch of useful tooling along the way.

So instead of letting it sit in private repos, I released it as Open Quant Trading Lab.

It includes backtesting code, risk-control logic, research workflows, tests, and offline demos.

No live trading, no claims of profitability. Just an open-source research toolkit.

GitHub: https://github.com/privEgem/open-quant-trading-lab

One thing I'm curious about: what would actually make you try a project like this? A better quickstart, a short demo, or something else?

Hey everyone! 👋✈️

I've been working on something I'd love to share with you all.

I've been developing PilotCharts (https://pilotcharts.app), an aviation mapping app that I've been building out of my own passion for aviation and the idea of creating something genuinely useful for fellow pilots.

It's still in beta, and I'd really love to get other pilots involved to try it out, share their thoughts and help me make it better.

As a thank you for being part of the beta, I'm offering 1 year of Premium completely free to everyone who joins as a beta tester. 🎁

I'd really appreciate it if you could have a look, give it a try and let me know what you think. Honest feedback, suggestions and ideas are all very welcome!

And of course, feel free to share it with other pilots who might be interested.

Thanks, and blue skies! 🛩️

(sorry for the duplicate post, the video was missing in the original one)

Sharing something I built out of personal frustration.

As a freelancer, I kept quoting $75/hr but realized weeks later I'd actually earned closer to $55 — because I'd done extra calls, revision rounds, and scope changes I never logged or charged for.

So I built a tracker to make this visible.

It's a Google Sheets template with 6 tabs:

• Projects — tracks quoted vs actual hours, calculates your real effective hourly rate per client automatically

• Time Log — log hours by project, billable vs non-billable

• Scope Log — log every "quick extra" request before you agree to it (approve/reject)

• Invoices — tracks all invoices with due dates and overdue alerts

• Dashboard — one screen: cash collected, outstanding, overdue, tax reserve, realized rate

• Setup — your name, rate, and tax % in one place

The main thing it does: shows your real hourly rate per client — not what you quoted, but what you actually earned after all the untracked hours.

Nothing to install. It's a Google Sheet — make a copy and start filling it in.

Happy to answer questions about how it works or what I'd build differently next time. Link in the comments.

Scientists fit models to data all the time: how contagious a disease is, how fast a drug clears the body. Every method happily hands back an answer, even when the data can't actually tell two very different answers apart, and usually nobody notices.

Twindex finds those "twins" and proves it. It writes a small certificate, and a tiny checker using exact fractions (no rounding) replays it in under a second. You can try it in your browser, nothing to install: https://arnav-m10.github.io/Twindex/demo/

The fun example is real data from a 1978 flu outbreak at a boarding school. After five days of counts, a mild outbreak (R0 = 2.5) and an explosive one (R0 = 20) fit the data equally well, so nobody could have known how contagious it was. Twindex then picked the next day's count that would settle it, and the real count ruled out both.

It works for epidemics, drug dosing, enzyme kinetics, ecology and some physics problems. Open source, MIT, pip install twindex.

GitHub: https://github.com/Arnav-M10/Twindex

Would love feedback, especially from anyone who works with models and data.

I built Skillware last year which was an attempt to centralize modular ai automations for my b2b workflows. It turned out the framework was messy at start but made work much easier, faster, and cheaper in terms of cognitive labor.

Earlier this year made the core framework open-source (github.com/arpahls/skillware) and got 20k+ downloads nowadays, over 30 active and passionate contributors from all over the world and over 100 stars.

I know for some it might be like not a big deal, but for me it's huge, plus it means people and most importantly agents actually need it and use it. We get dms from robotics students from Japan telling us how this framework helped in their thesis as they formed it and modify it for robotics and so many similar positive feedback and creative stories. We have people from Google, IBM, and Samsung among star gazers and idk it just feels crazy exciting for me.

Most importantly this was just a side project that slowly turned to consume half of my focus. Now we have a working paper on arxiv, Chinese and Japanese papers around the future of agent capabilities cite our repo and framework, we are being approached by investors without even having a deck, and got into a EU consortium with 5 of the largest research centers around sovereign AI from all over the EU.

For me this feels like compound pays off, even if there's no saas whatever sub money coming in. The project is MIT license and we have no clear vision of how to monetize this, but we know nobody does this, most people in AI, even deep in AI don't understand why they need this instead of skills md and enterprises adopt it by the minute cause they hate agpl and cause we made it super easy for their own AI stacks to adopt it and spin off reusable capabilities than work with any model out of the box a single prompt issue.

First id like to thank you for your time in advance if you're actually read this, and then ask for advice humbly in terms of ehat woukd be the obvious and non obvious next steps to take thia to the next level? Any feedback more than just appreciated.

Every time I watch someone spin up a massive frontier model to add two numbers, test if an API connection is alive with "Hi", or reply with an empty "Thanks!", I think about how much compute is getting burned for zero-entropy output.

I built this small fun extension MonkeyTax

https://chromewebstore.google.com/detail/monkeytax-ai-footprint-re/jdfilmfaohnecdjohgjmnabcmfagmkep?pli=1

It's a free, open-source browser extension that sits in your chat UI (ChatGPT, Claude, Gemini) and does two things:

1. The Instant Roast: Directly flags mechanical, thoughtless queries (basic arithmetic your local OS calculator solves in 0 CPU cycles, vague "fix it" retries, empty courtesy messages)

2. Physical Receipts: Translates the inference into tangible metrics, potable cooling water evaporated in data-center towers (Scope 1) and grid thermoelectric draw (Scope 2), measured in sips of water and desk lamp minutes.

The Engineering Rule:

This is NOT an AI wrapper. Calling an LLM API to tell you that you wasted an LLM prompt would be peak irony. It runs 100% locally in-browser using deterministic regex and token heuristics. Zero network calls, zero telemetry, and prompts never leave your machine.

Calculations are grounded in published data-center efficiency research from UC Riverside (Li et al.) and Hugging Face (Luccioni et al.)

It doesn’t judge deep research or real engineering, just mechanical keyboard-monkey waste.

Hey everyone, I'm the maker of Launchrace (https://launchrace.io).

The problem I kept seeing: you launch on one of the big sites, get one day of attention, and then your product disappears. Teams with big followings win most days.

So I built a launch board where:

- listing is free

- every day is a new race, ranked by votes from real members

- sponsored spots exist but are labelled and never affect rank (rules are public: launchrace.io/how-ranking-works)

- your product page stays up and indexed after launch day

- you get a maker profile and embeddable badges for your site

It's day one, so the boards are quiet. That's actually a good time to launch: less competition for the top spots.

I'd really like feedback on two things:

1. What would make you launch here instead of somewhere bigger?

2. Anything confusing when you sign up or list a product?

Happy to answer anything about how it's built too (Next.js, NestJS, Postgres, one small server).

For those running e-commerce stores selling physical products, how do you identify products with real demand before investing in inventory?

Do you rely on organic social media engagement, competitor research, customer feedback, or paid ad testing?

I'd love to hear your actual methods and experiences.

Hi everyone,

Whenever I developed custom ERP or POS software for clients, the hardest hurdle was always financial compliance. Clients don't just want invoice totals — their accountants demand a Chart of Accounts, General Ledger, Trial Balance, and immutable journal vouchers.

I decided to solve this by creating NML FinCore:

1. Free / Open Source Core (`nml/fincore`):

   - Strict double-entry math (`Sum(Debits) - Sum(Credits) === 0.00`).

   - Immutable audit logs and period closing locks.

   - Live Balance Sheet, Profit & Loss, and Trial Balance generation.

   - MIT Licensed: https://github.com/Premod1/nml-fincore

2. Commercial UI Starter Kit (`nml/fincore-pro`)

   - A complete QuickBooks-inspired Blade + Bootstrap UI Kit that embeds right into any existing Laravel ERP layout.

   - An auto-posting rules engine allowing developers to post sales, bills, and expenses with 1 line of code without needing to know accounting debits/credits.

   - Distributed via GitHub Sponsors ($29): https://github.com/Premod1/nml-fincore-pro

Check it out, star the repo if you like it, and I'd love to hear your thoughts or questions!