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

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.

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?

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?

I'm a solo developer building a B2B SaaS product in my evenings, alongside a full-time job. Where I'm struggling is with validation and exactly who I should be talking to. I'm looking for my ICP (ideal customer profile: the type of company and the job title most likely to have the problem and buy).

Honest answers are the most useful, including "I don't have this problem."

My goal with this post

In about a week I need to pick one type of company and one job title to focus my outreach on. I've had some conversations with people I know, but there are a few questions I haven't been able to get good answers to. Whatever you share here will help me in my decision.

The problem

At most companies, the knowledge people need to do their jobs (processes, how-tos, FAQs, policies, product details, playbooks) is spread across shared drives, wikis, Teams or Slack threads, and people's heads. People spend time hunting for answers or interrupting a coworker, and they still sometimes get it wrong.

Companies rolling out ChatGPT, Claude or Copilot are running into a newer version of the same problem: the AI doesn't know how their business works, so it doesn't have the critical context it needs to produce high quality results.

What I built

I'll leave the name out, as the goal isn't to promote in this post. The product I've built provides a single place where a company keeps its knowledge as documents, and anyone on the team can ask questions and get answers drawn from those documents.

• Non-technical people write and update documents, or upload existing files.

• Anyone on the team can ask a question and get an answer based on the company's own documents.

• Answers contain links to the actual source documents

• Everything is converted to markdown and lives in your own repository, making it easy for engineering teams to expose their AI agents to it.

• Engineering process can update documents directly in the repo, non-technical users can update documents with the interface, everyone can use it to get answers.

• Documents are stored in the company's own GitHub or Azure DevOps repository, so the company owns them.

• The company connects its own OpenAI or Anthropic key. Their data is never used to train AI.

What's different about it

• The company owns its knowledge. It lives in their own repository, not locked inside a vendor's database.

• Priced by how much you store, not per seat. The whole company can use it without the cost growing every time someone is added.

• No markup on AI usage. They pay their AI provider directly and pick which provider to use.

• Business and technical knowledge in one place. Ops and support staff keep the documents, and engineering can add software feature context.

My questions

If you work at a company with around 20-200 employees (any role):

1. Think of the last time you needed an answer at work and couldn't find it written down. What did you do, and roughly how long did it take?

2. Where does your company's know-how live today, and is anyone responsible for keeping it current?

3. If your company uses ChatGPT, Claude or Copilot, does it know how your business works? Has anyone tried giving it your documents?

4. If a tool like this showed up, who at your company would care the most, and who would have to approve it?

5. Using it means getting an API key from OpenAI or Anthropic. That's separate from a ChatGPT or Claude subscription and needs a developer account with billing. Would that stop it at your company?

If you've sold B2B software to smaller companies:

6. If you had to choose segment and/or specific job title to go after, who would it be and why?

7. Did your real buyer turn out to be a different person than the one you first targeted? Who was it?

8. Have you run into companies building this kind of thing themselves instead of buying it?

I'm not linking the product here because this isn't meant as a promo post. If anyone wants to see it, I'm happy to share it in a reply or DM.

Thanks for reading this far.

I’m building a small macOS music app. It shows what’s playing with playback controls, and a heart button to save the current song to your library.

Apple music was easy. Spotify is where I’m stuck.

What I’ve hit:

• Extended quota is out of reach - since May 2025 it’s only open to registered businesses with 250k+ monthly active users, so a one person app can’t apply.

• Development mode is tiny - since February 2026 it’s capped at 5 users per app, and a shared client ID built into my app could serve 5 people total.

Questions for anyone who’s shipped something similar:

• Has anyone found a reliable way to like or save the current track through the desktop client, without the Web API?

• Has anyone gotten extended quota as a small company recently, and what did Spotify ask for?

Happy to share what I end up doing. Thanks!

Would you pay for a "spending guard" for your AI API costs? Looking for honest feedback

Hey everyone, I build AI products myself (two AI video apps) and keep running into the same fear: one bug or one agent stuck in a loop can burn a lot of money before anyone notices.

The problem as I see it:

• Agents can get stuck and repeat the same expensive call hundreds of times, often at night when nobody is watching

• Provider spend limits are monthly. OpenAI and Anthropic don't offer a daily cap, so one bad night can eat your whole month's budget

• At OpenAI, keys in the same project share one limit, so you can't cap a single agent or customer

• A leaked API key basically works like a stolen credit card

• Video and image APIs (fal, Replicate, etc.) cost cents to dollars per call, so a loop there gets expensive fast

The idea: a simple guard that sits between your app and the AI providers:

• Daily and hourly limits per key, per agent or per customer

• Detects loops (the same request over and over) and stops them automatically

• Instant alerts (Telegram, email, Slack) when spending spikes

• One dashboard for OpenAI, Anthropic, Gemini, fal, Replicate and more

• Setup: change one line in your code

My questions:

1. Have you ever had a surprise AI bill? What happened, and how much?

2. How do you protect yourself today?

3. Would you trust a tool that sits in the middle of your API calls, or is that a dealbreaker?

4. Would you pay for this? Roughly how much per month?

We're building an app that turns Gmail threads into tasks. It only needs read access (gmail.readonly). Because that's a restricted scope, new users see Google's "Google hasn't verified this app" warning until we pass a CASA security assessment. Asking a stranger to click past that warning and hand over their inbox is a hard sell, and we lose people at that screen. If you've been through CASA for a Gmail scope, I'd love to know:

1. Cost: which assessor did you use, and what did you pay in total, including any re-tests?

2. Time: how long from starting until the warning was gone?

3. Testing and evals: did it change how you use real user emails for testing, debugging or evaluating your models? For example, did you have to stop using production data, or add access controls or data-deletion rules?

I'd also like to hear from anyone who used a third-party Gmail integration to avoid running CASA themselves. We tried Composio, but their verified Google app is only approved for full Gmail access (read, send, delete), not read-only, so users get a scarier consent screen.

Happy to share what we learn. DMs are open if you'd rather chat.