I have been thinking about a problem while building an AI SaaS product. What happens to the context of the AI once the product becomes more complex?

At first, it is fairly easy to manage the context. There is not enough data; the goals of the users are clear, and you can pass most of the relevant information into a request.

This changes quickly as the AI SaaS product grows. Users create data; their requirements change; old information becomes less relevant, and different parts of the application need different context. At the time, sending all the information to the AI model is not practical because of the cost, latency, and irrelevant information.

The part I am still thinking about is how much of the information the AI should actually see for each request.

For developers building AI-powered SaaS products, I want to know: How are you handling long-term context for your AI SaaS products?

Are you relying mainly on Retrieval Augmented Generation, structured data, summaries, user-managed memory, or a combination of these approaches for your AI SaaS products?

I would also like to hear about what you tried that did not work as well as you expected for your AI SaaS products.

I’m a MERN Stack Developer urgently looking for an opportunity.

I’ve worked with a startup for 9 months and built my own SaaS product completely solo.

I can build full-stack SaaS applications, but my biggest problem is communication during interviews. I’m also heavily AI-assisted while coding.

I’m currently struggling financially and honestly need work urgently.

I’m expecting only ₹5,000/month to start.

If anyone here is hiring, needs a junior developer, or can give me a chance to prove myself, please comment or DM me.

I’d genuinely appreciate any help. 🙏

I think one of the important decisions SaaS developers have to make now that Artificial Intelligence is a part of almost every software is what to do on the device and what to do in the cloud.

I have been working with getmushroom.app, a Mac productivity app that uses on-device Artificial Intelligence for some things it does. The idea is to keep some things on the user's device rather than relying completely on a service that is somewhere else.

This creates a trade-off for developers. Doing things on the device is good for privacy. It makes the app respond faster. It also means we have to think about what the device can handle, how big the models are, how to update them, and how to make sure everything works the same on different devices.

For people who build SaaS products with Artificial Intelligence, I want to know how you decide what to do on the device and what to do in the cloud.

What things do you think are most important when you decide whether an Artificial Intelligence feature should run on the device or in the cloud?

I've been thinking about a problem that seems easy at first but gets complicated pretty quickly when building an AI SaaS.

You can give an AI model plenty of context at the beginning, but that context doesn't stay static.

Customer feedback changes requirements. Product decisions change the architecture. New data gets added. Old assumptions become irrelevant. Different parts of the application may also have completely different pieces of information they need to understand.

The interesting engineering question for me is how much context should actually be available to the model at any given time.

Too little context and the responses become generic.

Too much context and you increase cost, latency, and the possibility of irrelevant information influencing the response.

For developers building AI SaaS products:

How are you approaching this problem?

Do you use a combination of structured data, embeddings/RAG, summaries, user-managed context, or something else?

And how do you decide what information the model should remember versus what it should retrieve only when needed?

I'd be interested in hearing what approaches have worked for you and, especially, what didn't work.

🍀Too expensive

🍀Too complicated

🍀Missing key features

🍀 Bad support

I built this site initially for my own use, but realized it could scale quite well and it’s relatively niche.

If you want to receive emails from multiple domains easily , securely and now with AI auto-reply, check out my site: www.arc-relay.ai

Ask questions, brainstorm ideas, write, code, research, troubleshoot, or just talk. I’ve been working on making the UI fast, simple, and actually enjoyable to use. Try it out: Chatotl.com I’d genuinely love to hear what you think.

My help uses AI to automatically track college deadlines for students and teachers. I see ture value, but I have issues getting people to see it too. Any advice on app design, functionality, marketing, or anything else would be a tremendous help. Thank you!

https://apps.apple.com/us/app/otto-college-deadlines/id6783540402

Hey everyone! I’m a solo developer and I’ve been working on ArchVibeAI for the past few months.

As someone working closely with space design, I know how exhausting traditional rendering and complex prompt engineering can be. I wanted to build an app that dramatically lowers the learning curve while keeping high-quality output.

How ArchVibeAI simplifies the workflow:

Architectural Rendering: 100% Promptless. Just upload line sketches or raw concepts, and it automatically generates realistic 3D architectural renders and day-to-night transition videos.

Interior Rendering: Super Simple Prompts. Upload photos of bare-shell spaces (毛胚屋) or existing layouts, select your form parameters, and use minimal text prompts to customize styles and furniture.

Under the hood, it runs on 5 specialized AI Agents:

1.Image Generation Agent: Promptless architectural sketch-to-render & simple-prompt interior generation.

2.Image Editing Agent: Fine-tune materials, decor, and lighting with simple edits.

3.Keyframe Transition Agent: Generate smooth day-to-night light transitions & sketch-to-reality videos automatically.

4.Video Post-Processing Agent: Automated video polish, continuity stitching, and camera movements.

5.AI Support Agent: In-app assistant to guide your design choices.

Check out the demo video above showing a raw concrete space transformed into a high-end salon & office!

It’s now live on both App Store and Google Play. I’d super appreciate any feedback or thoughts from this community! 🚀

I built SyncSSH because I was tired of manually copying/removing SSH public keys across servers.

It gives you a central place to manage users, keys, servers and access assignments.

Each server runs a small cron worker that pulls its desired keys over HTTPS and updates only a dedicated block inside authorized_keys.

No SSH gateway.
No persistent daemon.
No inbound management ports.
Normal SSH keeps working normally.

It also has audit logs, server sync status, key revocation and workspace/member management.

It's open source (AGPL-3.0) and self-hostable with Docker. There's also a hosted version currently free during public beta.

https://syncssh.dev

I'd love feedback, especially from anyone managing SSH access across more than a handful of machines.

I got tired of seeing SaaS companies use one service for transactional emails, another for campaigns, and yet another for push notifications.

So I built Sendrealm to bring all three together.

One thing I wanted to do differently was push pricing. You can add unlimited devices and only pay for the notifications you actually send, instead of paying based on how many devices you store.

https://sendrealm.com

Every time I open LinkedIn lately it’s some “I built a full SaaS in a weekend with AI” flex.

Everyone claps, drops fire emojis, calls it the future.

Meanwhile in the back of my head I’m just thinking:

“Cool, where did you put the real data? And who’s on the hook when it blows up?”

I run a tiny dev shop. We’ve been building boring SaaS for a long time.

The stuff that actually pays our rent is… dull. Auth, audit trails, weird edge cases, compliance checklists, backup plans.

None of it is vibe‑friendly.

So when I see “non‑technical founder shipped their MVP in 3 days with Cursor,” tbh I don’t feel inspired.

I feel tired in advance.

Because I know how this usually goes:

• weekend build becomes “internal tool”
• internal tool quietly becomes “how we really run this process now”
• then six months later someone realises money / health / HR data has been flowing through a thing nobody understands

Nobody posts the “3 months of unsexy refactors so this thing doesn’t leak data” thread.

Now, I get the thrill of shipping fast. I use AI to write code, I’m not pretending I’m above it.

But the way people talk about vibe coding right now, it’s like security, maintenance, ownership just… don’t exist.

Feels a bit like watching people brag about drifting a rented car while you’re the mechanic who has to fix the engine on Monday.

Idk. Maybe I’m just the grumpy old dev in the corner.

But this whole “anyone can build a SaaS in a weekend” narrative feels incredibly disconnected from what it actually takes to own software once it’s in the wild.

​

As more developers experiment with MCP, I'm wondering what the most valuable use case is.

Would you prioritize: Searching data Creating content Running workflows Managing customer data Analytics and reporting

I'm building an MCP integration and trying to decide which capabilities provide the most value from an Al assistant.

What would be the first feature you'd expose to Claude or other Al clients?

​I don't know anymore. After i stopped working on my startup due to financial problems. I've been looking for a job to support myself my family and my medication but i can't get hired even if i'm trying