GenMB Vibe Coding Tool
Describe Your Idea. Get a Real App.
24 followers
Describe Your Idea. Get a Real App.
24 followers
Every AI app builder has the same problem: they stop at the frontend. You still need to wire up a database, build an API, set up auth, and figure out deployment yourself. GenMB does all of it from a single prompt. You get a complete app — not a mockup. Real backend logic, a real database, authentication, 75+ integrations (Stripe, Supabase, OpenAI), and one-click deploy to a live URL. Edit visually, refine through chat, or let the AI agent build entire features on its own.
This is the 2nd launch from GenMB Vibe Coding Tool. View more
GenMB
Launching today
GenMB turns a plain-English prompt into a complete, deployed web app: frontend, backend functions, database, and auth included. Then it goes further than generation - automate your app with a visual workflow builder (20+ node types), scheduled agents that run Python on cron, chat agents on Telegram/Slack/Email, and 80 integrations across 15 categories. Free tier includes 8 credits a day and a real Postgres database, no card required.
Free
Launch Team
Congratulations on the second launch! Good to see code export and ownership also stated up front - that's the first thing I'd worry about. You list a wide range, from landing pages up to CRM and booking systems, so I'm curious where the practical ceiling sits. At what point does app complexity outrun the AI, so you're editing the exported code by hand rather than describing changes in plain English?
@alieksia Thanks! Honest answer: the ceiling is less about app category and more about two specific things.
First, architectural scope. Everything GenMB generates is a React SPA plus serverless functions plus Postgres, with integrations, workflows and scheduled agents layered on top. A CRM or booking system fits that shape fine because it's mostly CRUD, auth, forms and notifications, complexity that's wide rather than deep. What outruns the platform is anything that needs a genuinely different architecture: real-time multiplayer, native mobile, heavy background compute, or a long-lived custom server process. Those aren't "describe harder" problems; the platform just doesn't build them, and we say so rather than generate something half-working.
Second, accumulated size. Plain-English editing stays reliable surprisingly deep into an app's life - the failure mode isn't one big feature the AI can't do, it's an app that's been refined 100+ times where a vague instruction like "fix the filtering" touches state spread across many files. We've put most of our recent work exactly there: an agent mode that reads the codebase, plans, edits and then verifies the change against a live preview before showing it to you, rather than pattern-matching a diff. That pushed the practical ceiling up a lot, but I won't claim it's infinite.
The code export exists precisely for the residual case. In practice the people who export aren't hitting an AI wall on features but they're hitting a preference wall: they want their own CI, their own review process, or a hand-tuned change they'd rather own in git. That's a legitimate way to graduate off the platform, and we'd rather make that clean than trap anyone.
@ambuj_ambujone Thank you! That's an unusually straight answer, and the architecture-vs-size distinction is the useful part. Knowing its actual capabilities and limitations tells me exactly which of my ideas fit and which don't. Appreciate the detail.
@alieksia Thank you. Let me know if I can help with anything else as well. We are giving away 1 month free team plan (Code: O7TONVR6) so I would definitely recommend trying us out. You can also reach out to me directly at ambuj@genmb.com if you need any support or have any feedbacks for us.
@ambuj_ambujone Thank you, that's generous! I'll try it on one of the ideas that fits and reach out if I hit anything. Really appreciate the support :)
How does the AI agent handle complex business logic, like multi-step workflows or conditional branching, when you're not actively guiding it? Trying to understand how much hand-holding it really needs before you get something production-ready.
@cihanatagan Great question. There are two layers, because "business logic" shows up in two places:
1. Inside the generated app (code-level logic). When you generate or refine an app, Agent Mode runs an autonomous verify loop: it writes the code, loads the app in a live preview, checks that it actually works, searches its own codebase when something's off, and iterates (up to 60 rounds) before handing it back to you. So multi-step logic like "cart → stock check → payment → confirmation email" doesn't need you to babysit each step; the agent catches its own runtime errors rather than just producing code that looks right. Backend logic lands as serverless Functions (TypeScript or Python) with a real Postgres or KV store behind them, not mocked data.
2. Orchestration-level logic (the stuff that shouldn't live in app code). For multi-step workflows and conditional branching specifically, we don't make the AI improvise it in code. There's a visual workflow builder with typed nodes: condition (branch on an expression), switch (route to one of several branches by matching a value), loop (iterate over arrays), plus AI generation, database queries, HTTP, delays, Slack/Gmail/Sheets/Notion, and calling other workflows. Every workflow is DAG-validated before it runs, so a broken branch fails at save time, not at 3am. You can describe the workflow in plain English and the guided creator builds it, then drop to the canvas to tweak.
Honest answer on hand-holding: for a first pass, one prompt gets you a working app with real backend logic, and the agent self-verifies before you see it. Where you still steer is the same place you'd steer a junior engineer: telling it your actual business rules ("refunds only within 30 days, unless the plan is annual"), because no AI can infer policy it was never told. The difference is that iteration is a chat message plus an automatic re-verify, not a debugging session. Production-ready pieces like auth, data isolation, deploy, and cron-scheduled agents are platform features rather than generated code, so they don't depend on the AI getting them right each time.
the fact that it actually ships a working backend with auth and a real database from one prompt is wild, most "vibe coding" tools still leave you wiring up Supabase at 2am
@gamzewxea Thanks! That 2am Supabase wiring session is exactly the pain we built against. The take we landed on: the backend shouldn't be generated code you have to trust, it should be platform infrastructure the generated code plugs into.
So when you prompt an app, auth, the database, and the API layer aren't hallucinated boilerplate. Auth is built into every app out of the box. Data goes to either a key-value store (zero setup, the default) or a real dedicated Postgres database provisioned per app, your app gets its own isolated instance, not a shared table with a prefix. Server logic ships as sandboxed serverless functions the app calls directly. None of that requires an API key, a connection string or a dashboard in another tab.
The part I'm most opinionated about: because the AI writes code against that infrastructure rather than generating the infrastructure itself, there's a whole class of "it looks done but the backend is duct tape" failures that just can't happen. The agent still verifies the app actually runs before handing it to you, but the load-bearing pieces (auth, isolation, provisioning, deploy) are the same tested platform code for every app.
And yes, the Postgres database is included on the free tier, so you can see the full loop - prompt to deployed app with a real DB without a card.