AI is a great, cheap foundation we can all agree on that (depending on token usage, of course).

When you re building your first product, and you re not sure if it will work and earn money, the obvious goal is to keep costs as low as possible and avoid burning money early on.

I have been thinking about developing a tool that can show statistics like below to show for all my cloud or any infra that are hosted for a company. I know each cloud have their own way to show this but do we already have a common tool for this, that can show the details for all servers(multi-cloud)? Will this be a worthy product? Can someone give me some suggestions please.

"Here's what changed this month."

+2 services ADDED : analytics-01 & app-02 came online.

Lately I've noticed most discussion around AI coding tools focuses on which assistant or terminal agent people use, but not much on the actual architecture decisions behind that, things like framework, hosting, database, and auth choices that affect cost and security down the line.

For context, my current default stack is Next.js on the frontend, Supabase for database and auth, Vercel for hosting, and Stripe for payments. I picked it mainly for speed of shipping and because it keeps infra costs predictable early on, but I'm not sure it's the best call for security or scaling past MVP stage.

A few things I'm curious about:

• Do you settle on a default stack before prompting your AI agent, or does the agent end up influencing your stack choice?

• What's been your biggest regret, cost, security, or scaling wise, with a stack you picked early on?

• Any stack you'd actively avoid for a new SaaS in 2026?

Not another chatbot builder. Not another connect your API to an LLM tool.
I mean the boring, painful infrastructure that you don't want to build every time you ship an AI agent.

Things like:

• Agent orchestration

• RAG

• 900+ integrations

• Evaluations

• Observability & analytics

• LLMOps

• Payments & usage tracking

Basically:

Cloud World Model now has a streaming MCP server. Paste the URL into Cursor, Windsurf, or Claude Desktop and simulate AWS/GCP/Azure/OCI/DigitalOcean architectures, inject chaos, run RL training - no install, no API key required to start.

Most devs I talk to are quietly overpaying AWS or GCP. Not by a little but by a lot.

We've been building Huddle01 Cloud for a while now and honestly, the pricing difference is wild. Same bare-metal performance, global edge infrastructure with sub-100ms latency, no egress fees, no hidden markups.

What's everyone paying for cloud compute right now? Curious if others have found good alternatives.

Hey everyone!

I love the concept of "vibecoding", getting into the flow, using AI to build fast, and focusing purely on the product. But for a lot of developers (especially in emerging markets like Indonesia), that vibe instantly dies when it's time to deploy.

Suddenly, you are dealing with complex server setups, fighting DevOps configurations, or getting blocked because you don't have an international credit card to spin up a basic server.

Deployment shouldn't ruin the vibe. It should be just as seamless as writing the code.

Every developer I talk to has the same story. The idea is clear, the stack is decided, but before anything real gets built there's 3 hours of AWS configs, Dockerfiles, environment variables, and CI pipelines that have nothing to do with the actual product.

AI coding tools made writing code faster. Nobody has solved what comes after.

I just started solo dev last year and have been trying to keep all early prototype projects low budget. I personally build in a serverless way on @AWS and pay only for what I use (<$1 per month). I'm curious what do you all use for low-budget development? Maybe I can find even cheaper build stack from you guys lol :)

I'm building Clarity Cloud AI, an AI Native analytics platform for technical and non-technical folks alike. As a first time builder, the journey has been challenging yet fun. I'm planning to launch soon!

I have made a list of platforms where you can create your community and share your knowledge in a closed circle (and in some cases sell memberships).

The list is according to my preferences, so the platforms I use the most are at the top.

I ve been working on some AI projects recently things like scheduled agents, API responders, and multi-agent systems that need to run continuously. One of the biggest headaches I ve run into is deployment.

Most cloud platforms (AWS, GCP, etc.) are built for stateless apps or short-lived functions. But for long-running, stateful agents, the kind that need to persist data, auto-recover from crashes, and expose custom endpoints it gets surprisingly messy. I ve spent so much time setting up VMs, Docker configs, and recovery logic than actually writing agent behavior logic.

Thanks to supabase I was able to get my project up and running in record time. I use oauth, edge functions, storage, database ofcourse, queues:pgmq and pgvector.

Please check out the project at https://github.com/vpuna/vpuna-a...

It's a semantic search platform for structured and unstructured data , with MCP support and more

Curious what you re actually shipping with right now. Which stack are you using day-to-day, and why did you choose it over the alternatives? A bit of context (product type + team size) helps a ton.

If you ve switched stacks recently, what did you move from/to and what pushed the change? Cost, speed, hiring, DX, vendor limits, something else?