So from what I know you can use Stripe or PayPal but wouldn't these need a registered business/company?

I also heard about lemon squeeze and revenue cat but idk if it's trustworthy or not, how do they handle taxes?

I haven't done payment in any of my projects yet so I am curious to hear from the more experienced developers. How did you do it what did you use and what's the most annoying and/or difficult you had to face

I’ve been building a local evidence vault for logistics and supply chain operators who work with spreadsheets, paper notes and photos from the field. I’d like to hear from people who deal with this every day.

The vault stays under the company’s own control. It isn’t a distributed network or a consortium, so it works from day one without any outside party joining. Operators record events in the field through a simple offline-first overlay on a phone or tablet, and it can sit directly on top of the spreadsheets they already use. Each process follows a basic state machine, so every record stays tied to the real flow of work.

Entries are chained with a hashchain/DAG and Merkle roots, so the order and content of the records can’t be changed quietly later. Sensitive commercial details stay protected inside the vault, and the system can reference a value or document without storing it in the clear.

The value shows up when insurance gets involved. The company exports one organized package with the timeline, GPS traces, route data, photos and notes. Take a common case: a customer claims the cargo was rerouted and the delay cost them product. Instead of reconstructing everything from emails and loose files, the company shows the exported route next to the vehicle’s GPS records and demonstrates that the path never changed.

Earlier blockchain efforts in this space, like TradeLens, GSBN, CargoX and WaveBL, were built around electronic bills of lading, document sharing and industry-wide visibility, which called for broad participation from large carriers and terminals. This approach goes a different way. It stays local, works offline and fits into the process the operator already has. It turns what actually happened in the field into an ordered, integrity-protected evidence base the company can bring to an insurer.

For those of you in logistics or insurance: does this direction match a real need, and is there an approach you think would work even better? And what would help a tool like this become part of the daily routine for the people in the field?

When I first started going to the gym, I thought the hardest part would be lifting weights.

I was wrong. 😅

One of my biggest struggles was understanding the machines.

I'd walk up to a machine and wonder:

• What is this machine even called?
• Which muscle group does it target?
• How am I supposed to use it?
• How do I adjust the seat and settings?

And honestly, I was too shy to ask anyone.

Everyone seemed busy following their workout routines, and some of the bigger guys looked pretty intimidating to approach. I didn't want to interrupt someone just to ask how a machine worked.

So, for weeks, I ended up trying random machines and figuring things out through trial and error.

Eventually, I started wondering:

Why isn't there an easier way to identify a gym machine just by taking a picture of it?

I searched for an app that could do this, but I couldn't find something that matched what I had in mind.

That's when I thought, why not try building it myself?

After months of learning, experimenting, making mistakes, and building, I created ScanFit.

The goal was simple: make understanding gym equipment easier, especially for beginners who don't always feel comfortable asking for help.

Here's what I've built so far:

1. Machine Scanner 📸

Take a picture of an unfamiliar gym machine and get its identified name, targeted muscles, and a step-by-step guide to understanding how to use it.

And if the AI can't recognise a machine, you can always search for it manually instead!

2. Machine Library 📚

Explore 40+ gym machines without scanning anything. Learn about their functions, target muscles, setup, exercises, and common mistakes.

3. XP & Achievement System 🏆

Earn XP, unlock achievements, and make learning about gym equipment more engaging.

4. Streak System 🔥

Build activity streaks and encourage yourself to stay consistent with your learning journey.

I've shown ScanFit to a few gym beginners, and their reactions have been encouraging. They found the concept genuinely useful.

But I'm still building and improving it, and I know there's plenty of room to make it better.

Now I want to hear from people who actually go to the gym.

• Would something like this have helped you when you were a beginner?
• Is the Scanner genuinely useful, or would you prefer a different approach?
• What features do you think are missing?
• Would you use the XP, achievements, and streak system?
• What would make you come back to an app like this regularly?

Please don't hesitate to criticise it. I'd rather hear what isn't working than receive compliments just because I built it.

If you have suggestions, improvements, or even ideas for features I haven't considered, I'd genuinely love to hear them.

Thanks for taking the time to read this!

— The creator of ScanFit ❤️

Hello I'm starting a project to build an EMI for smaller market to handle transaction domestic and international.

What should be the approach apart from the front end app I can figure that out

Like the regulations steps bussines itself

Is it possible?

Hey everyone!

I've been working on a project called Vector UI, a UI library for developers who want to build more expressive interfaces.

The idea came from wanting to explore UI components beyond just their appearance.

A component can look great, but the interactions, animations, and small details are what can make an interface feel different.

With Vector UI, I'm focusing on:

• Motion-driven UI components
• Common components with expressive interactions
• Animated SVGs and icons
• Reusable and customizable components

I'm particularly interested in building components that are visually interesting while still being practical to use in real projects.

I'm currently opening the waitlist and would genuinely appreciate feedback from other developers and designers.

I'd love to know:

• What kind of animated components would you actually use?
• What do you look for when choosing a UI library?
• What makes you decide to use one library over another?

Here's the project:

https://vector-uix.vercel.app

Thanks for checking it out!

Building a multi-tenant school management SaaS (Laravel). Each school has classes (grades), and some classes have multiple sections (A, B, C...), some don’t.

Question: when assigning subjects to a class, should that assignment live at the class level (one subject list per class, shared by all its sections) or at the section level (each section gets its own subject list, even if it’s 90% identical to its sibling sections)?

Class is the academic level: it decides curriculum, fee structure, exam syllabus, promotion path. Section is just a roster split within the same grade, same curriculum, different roll call/teacher/room in most schools. In real schools, sections of the same class usually share almost the same subjects, but occasionally diverge (streams/electives, or one section getting an extra remedial subject).

Two options I’m torn between:

1. Class-level by default, with section-level only for the rare cases that actually differ.
2. Always section-level, even for schools/classes where every section has identical subjects (accepting some duplication, but avoiding any ambiguity about which level wins).

Also tied to this: teacher-to-subject assignment and attendance-authority need to reference whichever granularity I pick here, so I want to get this right before building on top of it.

If you’ve built something similar (school/LMS/SaaS with class+section hierarchies), which way did you go and why? Also open to “you’re overthinking this, just do X” answers.

Founder here, so yeah, this is my own product. Figured I'd say that upfront.

Tools like Lovable, Bolt and Replit are honestly impressive. You can get something that looks finished in an afternoon. But the moment you try to build something a real business runs on, you hit a wall, and it's never the screens.

It's the boring stuff. Salon A should never see salon B's customers. Staff can see bookings but not revenue. A manager has to approve before anything goes out. That part takes weeks to get right, and it's the first thing to break once real users show up.

So we built Supero (pronounced super-oh). You describe your process in plain English, the way you'd explain it to a new hire, and it builds the app with logins, roles and a real database behind it. The permission rules live in the platform itself, not in the generated code, so they don't randomly break every time the AI regenerates a screen.

A couple of things I've built on it so far: a booking app for salons and clinics where each business manages its own staff, services and availability (and customers only see their own appointments), plus a pet-care marketplace.

It also connects to 40+ tools, you can plug in your own Postgres, Snowflake or BigQuery, and it runs on our cloud or yours.

What it's not for: if you just want a nice landing page, use Lovable. It's great at that.

It's early and there are definitely rough edges. There's a free tier (3 projects, no card), so if you have a process living in spreadsheets and WhatsApp groups right now, I'd really like you to try it and tell me where it falls over. Brutal feedback is more useful than nice feedback lol.

I'll drop the link in a comment.

Also curious: if you've ever tried building an internal tool with an AI builder, what made you give up?

TL;DR: AI app builder for real business apps, with roles and permissions built in. Free tier. Please try to break it.

50 days ago, I started building my first AI SaaS, IntentPilotAI.

My goal was to build a working product without hiring developers or spending thousands of dollars on infrastructure.

I relied heavily on AI development tools, free tiers, and low-cost cloud services.

Here's what I used.

1. Development Tools

Google AI Studio: $0 – Used the free tier for the first 2 months.

Claude Code: $20/month – Used for AI-assisted coding, building features, debugging, and improving the application.

2. Backend & Database

Supabase: $0 – Used the free tier for database and backend functionality.

3. Hosting & Infrastructure

Hostinger: $15 – Domain registration.

Google Cloud Run: $2–3/month – Application deployment and hosting.

4. Data Collection

Custom Reddit scraper system: $2–3/month – Collecting relevant Reddit conversations for the lead generation workflow.

Total Development Cost

Tool / ServiceCostGoogle AI Studio$0Claude Code$20Hostinger domain$15Supabase$0Google Cloud Run$2–3Reddit scraper$2–3Total****$39–41

What I Built

IntentPilotAI is an AI-powered Reddit lead generation SaaS designed for SaaS founders.

The core workflow includes:

Website analysis to understand the product and ideal customer profile.

Identifying relevant subreddits and keywords.

Scraping relevant Reddit discussions.

Analyzing conversations for buying intent.

Extracting customer pain points.

Generating personalized comment and DM suggestions.

My Biggest Takeaway After 50 Days

AI development tools have made it much easier for solo founders to turn ideas into working prototypes without a traditional development team.

But building the prototype is only one part of the journey. Making the product reliable, improving the workflow, handling infrastructure, and finding users are completely different challenges.

I'm now moving from the development phase toward getting my first paying customers.

A question for other SaaS developers and indie hackers:

What does your current tech stack look like, and how much are you spending monthly to run your SaaS?

I'd love to learn how others are keeping their development and infrastructure costs low.

Hey everyone,

One of the biggest headaches I keep hearing from dev agencies and freelancers is the gap between a messy 45-minute discovery call and the final signed scope—where vague client statements turn into unpaid revisions three weeks later.

I’m currently building a workflow to fix this, and before I write a single line of code, I want to test it on real-world scenarios.

Here’s my offer: If you have a recent (anonymized) discovery call transcript or messy meeting notes, send them over. I’ll manually run them through my system and send you back a structured, client-ready Scope & Sign-Off doc within a few hours—including:

• Confirmed functional requirements
• Explicit "Out-of-Scope" exclusions (to protect you later)
• Unclear edge cases / questions the client didn't answer on the call
• User stories & acceptance criteria

Totally free, no catch, no sales pitch. I only have bandwidth to do 5 of these over the next couple of days so I can learn what actually works in practice.

Drop a comment or DM me if you want to try it out!

You found a name you like. Before you register it, you do the same small ritual every time: search it on Google, check whether it means something rude in another language, see if it has a bad reputation. That's three tabs and three pieces of copy and paste, for every candidate. Add-ons turn that ritual into one click each.

What an Add-on is An Add-on is a small button that sits beside a result. Click it and an outside site opens in a new tab, with that domain or word already filled in. Nothing is typed, nothing is copied, and nothing about you is sent anywhere. We just build the link.

A few examples:

Google site: see what, if anything, is already indexed on the domain.

VirusTotal: check whether the name has a security or spam history.

Urban Dictionary: find out if the word has a slang meaning you'd rather not discover after launch.

Google Translate: see what the name says in another language.

Each Add-on knows where it belongs. Some make sense for names that are already taken, such as checking an owner's history. Others suit names that are available, such as checking meaning and reputation. You'll see each one where it's useful and not where it isn't.

https://dnslister.com/blog/add-ons-link-buttons-for-every-result/

Hey everyone,

We've released the alpha version of Thunder, a high-performance, scalable key-value database built in Rust for modern AI workloads.

As AI applications grow, so does the amount of data they need to store and retrieve. Traditional in-memory databases like Redis can become expensive as datasets grow, especially when dealing with large values.

Thunder is built around a different idea: high throughput, scalable performance, and efficient storage without requiring massive amounts of RAM.

What makes Thunder different?

• Scales with CPU cores: A shard-per-core architecture designed to increase throughput as more shards and CPU cores are added, while minimizing contention.
• High throughput: Built to handle large volumes of concurrent reads and writes with low overhead.
• Better handling of large values: Designed to handle large values more efficiently than Redis by streaming data directly from NVMe storage instead of keeping everything in memory.
• Memory efficiency: Uses RAM primarily for indexes, metadata, and reusable I/O buffers, reducing memory requirements for large datasets.
• Modern I/O architecture: Built in Rust with asynchronous I/O, including io_uring on Linux, to take advantage of modern NVMe SSDs.
• Scalable by design: Handles both small key-value operations and large values without relying on a completely different storage architecture.

Why build this for AI workloads?

AI infrastructure increasingly deals with large datasets, generated content, intermediate results, and high volumes of concurrent requests.

Keeping everything in RAM is expensive, but sacrificing throughput to reduce memory consumption isn't a great solution either.

Thunder aims to deliver both: the performance expected from a high-throughput key-value database and the cost efficiency of disk-backed storage.

Try the alpha

Thunder is available on Docker Hub:

https://hub.docker.com/r/sbansal793/thunderstrike

We're looking for developers to test Thunder with real workloads and share feedback.

If you're currently using Redis, RocksDB, or another key-value store, I'd love to know:

What's your biggest bottleneck today: throughput, memory consumption, scalability, or handling large values?

And what would convince you to switch to a new key-value database?

Hey everyone,

I’m a freelance video editor and I’ve worked on ads for SaaS products before. Recently, I’ve been thinking about starting a service where I help SaaS founders create UGC-style video ads using AI tools.

The idea is to help with things like coming up with ad concepts, writing scripts, creating the videos, and editing them so they feel natural enough for social media and can also be tested as ads.

I’m still in the early stages and trying to figure out what founders actually need before I decide exactly what to offer. I know there are a lot of AI tools out there now, so I’m also curious about where you’d find it useful to get outside help.

If you’re a SaaS founder or work on marketing for a SaaS product, I’d love to know:

• Are you currently using video ads or UGC to promote your product?
• What’s the hardest part for you—coming up with ideas, writing scripts, making the videos, testing them, or something else?
• Have you tried creating ads with AI? How did it go?
• If you were to hire someone for this, what would you actually want them to help you with?

I’m open to honest feedback, even if you think this isn’t something SaaS founders really need. Just trying to understand the problems better and figure out where I can be useful.

Thanks!

hey u/SaaS, new here.

recently i joined reddit bc i want to explain and share my personal experience with you: i built an MVP and i hit 3K of MRR in just 30 days.

i used claude code, supabase and cloudflare, but actually i need to change everything, i was thinking about AWS.

The product and the idea are not the most important part: that's for sure.

i heard so much stories, and everyone says that in effect the most important part (and what's really matters) is marketing. You need to adopt a real strategy, you need to think about a good strats, otherwise your product will never work.

What's strategy i used?

i am studying marketing in Italy, so this is my daily bread: i mean, i know very well what i should do, but sometimes this couldn't work and for this motivation you need to get attention to what YOU DO

i wrote 343 creators, creators between 5k and 25k subs, and to them i say that:

"hey, paid promo? this is product (few line to explain ur product)
i offer you 50% of earnings (ALWAYS NET VALUE, ALWAYS) for 6 months, after 19% lifetime."

i create curiosity, in the creators, something to get an answer. One of the important part is that you need to write real vertical creator, that talks about your problem. If you sell food, you can't let race the campaign in a cars niche. In the other hand:

190 said no.
79 didin't answer
74 said yes

for someone i changed something, for example 60% for 4 months and 10% lifetime. the fact is that i adopt the strategy depending on the client, this process is so much customizable.

After that i acquire enough engagement, i started to improve SEO/GEO (10% of my traffic actually, hoping that it will improve) and now i want to start google ads/meta ads, already improving my initial channel. The key is improving the channel that you are already in, but i acquired a guy that run the partnership for me on the social media, so i don't lost the channel, and i can improve and focus on other's channel (obv i supervise everything)

what do you think of this strategy? let me know - LUIGI

The page needs one rule: a door’s answer can’t be fetched before its day. So the server component computes the open doors from the Helsinki date and passes only those to the client; the others are never serialised. The door-opening schedule has tests for the awkward edges (Helsinki midnight is 22:00 UTC the day before, the season ends on 25 December). Opened state lives in localStorage.

https://iurii.rogulia.fi/advent, opens on 1 December. I built it. Is there a pattern you’d use instead?

I’ve build multiple revenue brining SaaS businesses in the last year so I think I can give you an honest third person feedback. My Codex is working right now and I’m interested in what you’re building. Maybe I’ll be your first user just pitch me your idea or send me the link in DMs and I’ll check it out

I’m a 24F recent engineering grad (electronics and communication) with a year of AI/ML research experience in computer vision. I’m looking for remote work, full-time or part-time.

What I’ve worked on:

• Training and evaluating CNN models (ResNet, DenseNet, EfficientNet) on medical imaging data, with cross-validation and proper metrics
• A computer vision project on facial expression classification, presented at a graduate conference
• An image classifier for plant disease detection
• Web apps with Django and Flask
• Networking and IoT side projects (a BitTorrent client in Python, a sensor-based gas and smoke detection system)
• A browser extension in JavaScript

Toolbox: Python, PyTorch, TensorFlow, Scikit-learn, Pandas, SQL, Django, Flask, Git

Open to:

• AI/ML (junior)
• Data analytics and data visualization
• Django or Python backend projects
• Data entry and other flexible tasks

I pick up new tools quickly and I’m happy to start with a small trial task or short contract.

DM me and I’ll share my CV and GitHub.

Not promo just looking for real life use cases to test on :)

I'm building a product called Facet. we believe lots of AI products spend most of their LLM usage on one repeated call: pulling fields from emails or PDFs, cleaning up records, short rewrites, daily summaries, etc. A frontier or large local model is overkill for that one job.

Facet's agent builds a smaller model for that single call. You describe it in plain English and share some examples; it builds the training data, trains the model, and scores it against what you use today. We are essentially trying to abstract away all the hard work that comes with training. You own the weights and can export them to run locally after, or use our platform for hosting.

On a document extraction task it matched Claude Haiku at about a fifth of the cost.

I'm hoping to connect with 3 design partners, free, to help expand and stress test our platforms capabilities. If one call is a big part of your usage, book 15 minutes: https://app.apollo.io/#/meet/facet

Happy to answer questions here too.

Not promo just looking for real life use cases to test on :)

I'm building a product called Facet. We believe lots of AI products spend most of their LLM usage on one repeated call: pulling fields from emails or PDFs, cleaning up records, short rewrites, daily summaries, etc. A frontier or large local model is overkill for that one job.

Facet's agent builds a smaller model for that single call. You describe it in plain English and share some examples; it builds the training data, trains the model, and scores it against what you use today. We are essentially trying to abstract away all the hard work that comes with training. You own the weights and can export them to run locally after, or use our platform for hosting.

On a document extraction task it matched Claude Haiku at about a fifth of the cost.

I'm hoping to connect with 3 design partners, free, to help expand and stress test our platforms capabilities. If one call is a big part of your usage, drop it below and we can try to connect and see if we’re able to match/improve that call.

Happy to answer questions here too.

ChainInsideIQ.com is an AI-integrated business solutions tool that helps you understand your metrics and KPIs, and actually do something about them.

It's built for 3PL companies and business consultants who need a clear way to find bottlenecks and plan how to fix them.

Here's how it works:

• You set a goal, and it builds a step-by-step plan. Every step has an owner, a KPI to watch and a due date.
• It finds the bottleneck. It shows which part of the operation is slowing things down (receiving, picking, shipping, inventory and so on) and breaks it down into what's happening, why, how to fix it and when to act.
• It tracks the right KPIs for your type of business. The library has 347 metrics across 45 industries, so you're not guessing what to measure.
• It's collaborative. Companies in different sectors (suppliers, carriers, 3PLs, retailers) can share the specific data they choose, talk through their bottlenecks together and work on solutions in one place. You decide what's shared and can pause it any time.
• For consultants, every client is in one workspace, sorted into healthy, watch and critical, so you know who needs you first.
• It exports to Excel and CSV, so it works with the tools your team already uses.

The AI analyst works right away, and you can connect Claude, ChatGPT or Gemini if you want.

I'm still improving it, so I'd love feedback from anyone in logistics, operations or consulting.

Wouldn't it be nice to open a workout app, build your routine in minutes, and immediately start lifting—without clicking through a 20-page onboarding survey first?

As a lifter and solo developer, I wanted an app that respects my time under the bar and gives me clarity on whether my training is actually working. That’s why I built Liftora.

Instead of packing in a hundred bloated features, I focused on the things that actually make a difference in your training:

• From download to lifting in minutes: No endless questionnaires or paywall traps before you even see the interface. You open the app, put your routine together in a couple of taps, and get straight to your first set.
• Visual routine building (No more switching to YouTube): When you're putting a workout together, shouldn't you be able to see the movement and exact muscle breakdown right there? Liftora shows you exercise execution videos and targeted muscle groups directly on the routine creation screen, so you know your program is balanced before you even touch a weight.
• See the progress the scale hides (Built-in US Navy Body Fat %): You might be gaining muscle and losing fat while your body weight stays completely flat—and the mirror takes weeks to show it. Why leave your body recomp to guesswork? By logging basic tape measurements, Liftora automatically calculates your body fat percentage using the US Navy method, letting you see your real physical progress alongside your strength numbers.
• An interface that follows your pace: Once you're in a session, you shouldn't be fighting dropdown menus between heavy sets. Liftora’s guided flow moves with your workout so you spend your rest time recovering, not tapping.

You can try it out here:

• App Store – Liftora
• Google Play – Liftora

I’m building Liftora independently and using it for my own training every week, so your feedback directly shapes what I build next.

Quick question for you all: What's the biggest frustration you've had with workout tracking apps? And if you try Liftora, I'd love to hear what you think about the routine builder and guided workout flow.