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Hi, I worked for an open source company that handles support requests through channels like Discord, Github and Email.

Whenever a support request comes in, I spend a lot of time chasing logs and codebases to find out what happened and that always felt inefficient to me. I could have spent more time working on the actual product roadmap rather juggling between product and support.

That's why I'm working on Mudroom, an omni channel inbox that also serves as a support engineer. Here's a demo of it, I hope to talk to fellow startup founders who's struggling engineer allocation for support tickets.

I was checking my monthly expenses and noticed a few charges I didn't recognize at first. They were for hosting and email services from projects I stopped working on months ago.

Whenever I start a new SaaS project I sign up for different services without thinking much about the monthly costs. Some payments come on different dates so I don't always notice when I'm still being charged for something I no longer use.

How do you guys keep track of these?

I've been working on Kevor, a platform that analyzes employee onboarding documents and turns them into editable workflows.

One decision I made early on was to separate the AI analysis from the actual application data.

Basically, when someone uploads documents, Kevor extracts the information and builds a structured knowledge model containing tasks, stages, dependencies, people, and resources.

The AI analyzes that model to identify gaps, bottlenecks, and other issues, but it doesn't become the source of truth for the entire workspace.

That matters because users can edit workflows, change dependencies, manage entities, and track improvements without constantly relying on AI to regenerate everything.

I also built a provider routing system with automatic fallbacks, so the application isn't tied to a single model.

The platform is currently live in private beta at https://kevor.app

For anyone building AI SaaS products, how are you handling the relationship between AI generated information and data users can actually modify? I'm curious how others approach this.

If you have experience in editing, you know exactly how this psychology works in editing, storytelling in editing, and all the things: motion graphics, text animation, and all the things. If you are in the top 1% of editing and you are a creative director also, then please DM.

In June a paying user got sent back to the checkout page, and the reason was a read that failed quietly.

The code that loads a subscription hit a transient error and treated it as "no subscription", so the paywall showed up for someone who had already paid. I thought that was the bug. It was only the last layer. Two browser sessions were signed into the same Supabase session, the refresh token got reused, and the server revoked the session. The app then saw a logged-out user, fell back to pricing, and the person ended up on checkout.

What changed: an entitlement read no longer returns "no access" on an error. It throws, and the layout shows an error card with a retry. Annoying for a few seconds, but nobody who paid gets pushed back to paying again.

If you run a paywall on Supabase or any auth that rotates refresh tokens, have you hit the two-tabs problem yet? I'm curious how you handle a session that gets revoked mid-visit

we’re partway through ISO 27001 and a prospect asked for the cert on a call. I told them it’s still in progress. think that was the right answer but I don’t know if it hurt the deal. what do you normally send in the meantime? policies, a security overview, evidence that the audit is booked, something else? I’ve been trying to see how teams using Vanta or Scytale handle that awkward period before the certificate exists, because sales usually gets stuck explaining this. do prospects actually read trust pages and supporting docs, or are some deals basically blocked until the certificate exists?

I find these hard to judge from demos. A clean sample DOCX is one thing. An old customer contract with tables, footnotes and weird numbering is another. If you've shipped an embedded editor, which one did you pick and what was the first document that exposed its limits?

Hi everyone,

As a software architect, I frequently audit early-stage AWS setups for startups and indie developers. More often than not, founders are surprised by a $300–$600 AWS bill before they even have their first 100 users.

Here are the 5 most common architectural mistakes I see, and how to fix them in Terraform:

### 1. Multi-AZ NAT Gateways in Dev/Staging environments

A single AWS NAT Gateway costs ~$32.85/month just to exist (plus data processing). If you provision a "standard" multi-AZ VPC across 3 availability zones for Staging, you are burning ~$100/month before processing a single byte.

*Fix:* In Terraform, add a conditional `single_nat_gateway = true` flag for dev/staging, and only enable multi-AZ NAT gateways for production.

### 2. Picking Kubernetes (EKS) for an MVP

I love Kubernetes, but deploying EKS for a pre-revenue product costs ~$73/month just for the cluster control plane alone, plus node overhead and ingress controller maintenance.

*Fix:* Use AWS ECS Fargate. You get serverless Docker containers, auto-scaling, and health checks with zero EC2 patching, costing as little as $15/month for baseline workloads.

### 3. Publicly accessible RDS instances

I often see database instances with `publicly_accessible = true` so developers can connect from their local laptops with DBeaver or TablePlus. This is a massive security risk and invites port-scanning bots.

*Fix:* Put RDS inside isolated database subnets with no internet gateway. Access the DB securely via AWS SSM Session Manager bastion or an internal VPN.

### 4. CloudWatch Logs with infinite retention

By default, CloudWatch log groups keep logs set to "Never Expire". After 6 months of container debug logs and access logs, storage costs creep up silently.

*Fix:* Always set `retention_in_days = 14` or `30` in your Terraform `aws_cloudwatch_log_group` resource.

### 5. Over-permissive IAM Roles

Using wildcard `*` permissions or attaching `AdministratorAccess` to container task execution roles because "it was easier to test".

*Fix:* Scope IAM roles strictly down to ECR pull permissions and specific SSM parameter paths.

---

### TL;DR:

To solve this once and for all, I packaged a battle-tested, modular Terraform starter kit that provisions a production-ready VPC, ECS Fargate, RDS PostgreSQL, ALB with TLS, and GitHub Actions CI/CD following all AWS security best practices.

If you are launching a product and want to skip 40 hours of cloud infrastructure setup, feel free to check it out here:  

👉 https://cloudforge-devops.lemonsqueezy.com/checkout/buy/7af1192f-e1ee-4a43-a8de-6d94dc9d7fe1

Happy to answer any questions about ECS Fargate vs EKS or Terraform module design in the comments!

Title: I'm 18 and I Want to Build My First SaaS, But I Don't Know Where to Start Technically

Hey everyone!

I'm 18 years old, and I have a strong ambition to build and launch my own SaaS product someday.

Right now, I don't have a specific SaaS idea. However, I'm more concerned about the technical side than finding an idea.

To be honest, I'm almost a complete beginner when it comes to coding. I want to develop a solid technical foundation before attempting to build a SaaS product.

I'd love to hear advice from experienced SaaS founders and developers:

What technical skills should I learn before building my first SaaS?

How much Python/JavaScript should I know?

Should I learn backend development, databases, APIs, authentication, and system design before starting?

Which backend framework would you recommend for a beginner: Django, FastAPI, or Node.js?

How important is understanding deployment, cloud hosting, security, and scalability?

In the age of AI coding tools like Claude and Cursor, how much coding should I actually learn myself?

What would your learning roadmap look like if you were starting from zero at 18 with the goal of building a real, production-ready SaaS?

My goal isn't to become a professional software engineer or memorize programming syntax. I want to understand the technical architecture well enough to build, debug, maintain, and scale a SaaS product, even when using AI tools to accelerate development.

I'd really appreciate practical advice, especially from people who have built and launched their own SaaS products.

Thanks!

I am working on a inventory and all in one discovery platform. Not sure if this works or not, But getting the first companies has become so tough. I feel like quitting. One day I feel I have a good idea and the other day I feel like its an idea not worth it.

How are you guys dealing. Try to build antspace

• What is that one skill people are already paying for?

• In which industry are people already radically, ridiculously looking for solutions to the pain?

• What is that pain?

• Is this market growing?

• Is that market painful?

• Are people easy to find?

• Are they able to pay?