u/SaaSy_lad

A lot of people are blaming the models, but I don’t think that’s the real problem. Most SaaS slop apps are failing before a single line of code gets written because the builder has no real product context. They ask AI to build “a dashboard for X” or “an app like Y” and expect it to magically fill in the market, the user, the edge case, the workflow, the wedge, the pricing, the onboarding, and the actual reason anyone would care.

Bad prompts are usually a symptom of bad thinking. If you don’t know who the product is for, what painful workflow it replaces, why your version should exist, and what needs to be in scope for v1, the AI is just going to average together a bunch of generic SaaS patterns. That’s why so many of these apps look fine in a demo but fall apart once you click around. They’re thin, fragile, buggy, and missing the complexity that makes real software useful.

The uncomfortable truth is that AI did not remove the need for product taste. It just made it easier to expose when someone doesn’t have any. You still need to understand the user, choose a sharp wedge, define the scope, and give the model enough context to build toward a real outcome. Otherwise you’re not building a SaaS company. You’re generating another login page, sidebar, empty dashboard, and Stripe button for a product nobody asked for.

I’ve been building apps for almost 20 years the traditional way, and over the last year I’ve been deep in the whole agentic AI workflow.

And I kept running into the same problem: You can technically build fast now… but the thinking gets messy.

Prompts get longer and lose context even with an MD, Context gets scattered or lost within a chat, You start shipping features without a clear product direction, features feel fragile and architecture isn’t durable.

You end up with something that works, but doesn’t really make sense as a product and feels generally fragile.

So I built something for myself to fix that.

It’s basically a workspace that acts like a product manager and prompt engineer sitting next to you the whole time. The goal isn’t “generate ideas” it’s to take something from idea to an actual launched product that people can use reliably with feature complexity and scalable architecture.

Here’s how it works in practice:

• Market + validation

You’re not just brainstorming. It pushes you to pick a wedge, look at competitors, and actually decide what you’re building and why.

• MVP blueprint

It starts with an auto-generated blueprint based on your idea, then you can edit, expand, and shape it into something real.

You define screens, flows, and core features in a structured way so there’s actual clarity before building.

• Dynamic prompts (this is the core thing)

Instead of writing giant prompts yourself, it generates feature-by-feature build prompts based on your spec.

Each one includes scope, constraints, acceptance criteria, and context so the output doesn’t drift.

• Build phase

You use whatever LLM you already like. The prompts evolve as your spec evolves, so you’re not constantly rewriting context or fixing broken outputs.

• Distribution workspace

Once something is working, there’s a place to actually launch it. Landing page, SEO structure, and ways to start getting users instead of stopping at “MVP done”.

It’s not trying to replace coding or pretend AI magically builds great products. It’s more about putting structure around the chaos so the thing you build actually holds together.

We just put it live this week and a couple people are already building pretty serious stuff on it (one is doing an OCR-heavy app, another is building a Slack bot with NLP), which has been a good signal.

Not posting this as “look at my startup” just genuinely curious if this approach resonates with anyone else building with AI right now.

If you’ve been vibe coding, what’s been the most painful part for you?