VibeSafe Builder
Build apps by chatting. Without the credit burn.
Quick update on VibeSafe Builder, my AI app builder.
The biggest drop-off was happening at step one: new users had to create an Anthropic account and paste in an API key before they could see anything work.
Most left.
So I changed it.
Now every new account gets one free build.
The decision I had to make was around cost. To keep free builds sustainable, the free build uses a lighter model, while paid builds use the more powerful model.
I decided to be completely transparent about that — before the build, in the results, and on the pricing page — instead of quietly switching models behind the scenes.
I’d rather give users an honest free experience than make the trial look better than what it actually is.
To protect the API budget, I’m also using:
1 free build per account
a small per-account usage cap
a monthly cap across all free builds
Would you have made the same call?
Use the lighter model for the free trial, or give everyone the full model for their first build?
That’s how much software development has changed.
You no longer necessarily need a large development team or months of coding to test a SaaS idea.
With vibe coding, people are turning simple prompts into real products:
💡 Describe an idea
🤖 Generate the code with AI
🎨 Build the interface
🔐 Add authentication
🗄️ Connect a database
💳 Add payments
🚀 Deploy
💰 Start finding customers
Some builders are even sharing stories of relatively simple AI-built SaaS products generating $1K, $5K, $10K+ in revenue.
But there’s another side of vibe coding that gets much less attention:
What happens when the AI-built app actually gets customers?
Your app can look great.
Payments can work.
Users can sign up.
The dashboard can work perfectly.
And underneath it, you could still have:
🔴 Exposed API keys or secrets
🔴 Missing authentication checks
🔴 Insecure database rules/RLS
🔴 Unprotected routes
🔴 SQL injection risks
🔴 XSS vulnerabilities
🔴 Vulnerable packages
🔴 Missing dependencies
🔴 Runtime errors
🔴 Insecure configurations
That’s why we're building VibeSafe Builder differently.
Typical vibe-coding workflow
Prompt → Generate → Fix until it works → Publish
VibeSafe Builder workflow
Prompt → Build → Scan → Fix → Rescan → Verify → Launch
With VibeSafe Builder, the goal is to bring more of the SaaS-building process into one workflow:
✨ AI-powered app generation
📝 Code editing
🔐 Separation of sensitive backend/environment information
🛡️ Built-in security scanning
🔑 Exposed-secret detection
🔒 Authentication/security checks
🗄️ Database/RLS checks
📦 Dependency & vulnerable-package checks
💉 SQL injection & XSS checks
⚙️ Configuration checks
💥 Runtime/error detection
🧰 Fix detected problems
🔄 Fix → Rescan → Verify loop
🚦 Launch Check before publishing
🚀 Vercel publishing
🛑 Critical security findings can stop an unsafe release
We're also exploring deeper checks such as authorization boundaries and cross-tenant security, because hiding another customer's page in the UI doesn't necessarily mean their data can't be accessed through the API.
The big difference?
Most vibe-coding tools focus on:
“Can AI build this?”
VibeSafe Builder is being built around a second question:
“Can I safely ship what AI built?”
Making $10K from a small SaaS is exciting.
But once real customers trust your product with their accounts, payments, or data, shipping fast isn't enough.
We want vibe coding to become:
Idea → Build → Secure → Verify → Launch → Grow. 🚀
If you're building a SaaS with AI right now, what worries you more:
getting the product built — or knowing it's actually ready for real users?
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1Signup and payments working is a necessary milestone, but it is not release readiness. I’d want evidence for secrets exposure, authorization boundaries, recovery, idempotent retries and one real browser workflow before calling an AI-built app ready for customers. Which Launch Check finding most often turns an apparently working product into a blocked release?
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1The biggest release blockers tend to be the ones the UI doesn’t reveal. For me, the most important findings are things like an exposed secret, missing authorization check, or a backend rule that lets one user reach another user’s data. The product can look completely finished and still fail there. I also think real-browser workflow failures are underrated — especially when signup, redirects, persistence, or mobile behavior breaks outside the happy path. That’s why I want Launch Check to move beyond “page loaded successfully” and give evidence of what was actually tested, what failed, and what still needs review.
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1The distinction between “it works” and “it's safe to ship” is important. I've noticed a related issue while building ThinkExec with AI coding agents. AI has dramatically reduced the implementation work I need to do myself. But it hasn't removed the engineering decisions around that implementation. I still need to decide how the system should be structured, decompose the work into coherent units, evaluate what the agents produce, test it, and make the trade-offs. My experience so far is that AI doesn't necessarily make technical expertise less valuable. It changes where that expertise creates leverage. Security is a good example of that: generating working code and judging whether you should trust it in production are very different problems.
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1Exactly. I think that’s the shift AI coding is creating: less time spent typing code, but more responsibility around architecture, verification, trade-offs, and production readiness. Security makes that especially obvious. An agent can generate something that looks complete and passes a happy-path test, but deciding whether the auth model, data boundaries, secrets handling, and failure cases are actually safe still requires judgment. That’s one of the problems I want VibeSafe Builder to help with — not replacing engineering judgment, but giving builders stronger checks and evidence before they trust what the AI produced.
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1The launch check becomes far more useful when it produces a release brief—not just a scan score: what changed, which critical findings were resolved, what risk remains, and which buyer journey was tested. That’s the artifact a nontechnical founder can share with a co-founder or client before going live. It also gives your security promise a visible outcome, not just a long checklist.
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1This is a really good point, and it's changed how I'm thinking about Launch Check. Right now the pieces exist separately: every version is saved with its prompt and a diff, the security scan runs on every build (critical findings block publishing), and Launch Check loads the app in a real browser on desktop and mobile to catch crashes, console errors and layout breaks. What's missing is exactly what you describe: one brief that pulls it together, something a founder can hand to a co-founder or client before going live. So I'm building it: a release brief showing what changed, which findings were fixed, what risk is still open, and what was actually tested. One honest gap: Launch Check doesn't click through a buyer journey yet, like sign-up to checkout. It loads and inspects the app. I'd rather the brief say "not tested" plainly than imply it was. Journey testing is the harder next step. Which journey would you want tested first on your app?
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1I’d start with the core first-success journey: sign up → verify email → create a workspace or first app → run the first scan/Launch Check → see a clear result. That path exercises auth, session handling, routing, persistence, and the real “aha” moment. I’d add checkout or upgrade next, once that path is stable. The brief should assert the outcome at each step—not only that screens loaded, but that the account, project, and result actually persisted.
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1That makes sense. The first-success journey is probably the right place to start because it tests the parts that matter before a user ever reaches payment. I especially like the point about persistence. A page loading successfully isn’t enough if the account, project, or scan result disappears after refresh or login. For VibeSafe Builder, I’d want that journey to eventually verify: sign up → verify → create first app → run security/Launch Check → confirm the result persists → reopen it later Then checkout/upgrade can be the next critical path. That turns Launch Check from “the app opens” into “the product actually works end to end.”
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0The “can I safely ship what AI built?” part is actually so true 😂 It’s pretty easy to get an AI-built app looking good and working, but the stuff underneath is where things can get messy. I like the Fix → Rescan → Verify idea. That’s probably a lot more useful than just getting a list of security issues and having no idea what to fix first.
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Ha, exactly — "it works" and "it's safe to ship" are two very different milestones 😅
The "what do I fix first" part was the main thing I wanted to solve. Anything critical, like a hardcoded API key or an exposed secret, actually blocks publishing, so criticals always come first and there's no override.
The loop is simple right now: you ask the AI to fix the finding in plain English, the new version is scanned again automatically, and Launch Check opens the app in a real browser before it goes live. If a fix breaks something, you roll back a version instead of burning more credits chasing it.
A one-click "fix this finding" button is next on my list, so it's really useful to hear the loop is the part that lands. What are you building with at the moment?
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1Haha yeah, that’s exactly the kind of thing that makes AI-built products risky — they may work, but that doesn’t mean they’re ready to ship 😅 I’m currently working on AI-built landing pages and product websites, mostly helping small startups make their product easier to understand and improve the user flow, onboarding and conversion. Your Launch Check idea is interesting because it solves a real problem for people shipping fast with AI. Are you mainly targeting solo builders, or small startup teams?
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1Thanks, and that's a great space to be in. Solo builders and non-technical founders are the main focus right now: people shipping a first product without an engineer to check what the AI wrote. Small teams can use it too, but there's no shared workspace yet, so it works best with one person driving the build. Your side of it is the other half of shipping safely, honestly. Launch Check can tell you the app loads and nothing is leaking, but not whether a visitor understands the product or gets through onboarding. When you review AI-built sites for startups, what's the most common thing that hurts conversion? And if you ever want to try it, your first build is free at vibesafebuilder.com. I'd genuinely value your eye on our own onboarding.
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That’s how much software development has changed.
You no longer necessarily need a large development team or months of coding to test a SaaS idea.
With vibe coding, people are turning simple prompts into real products:
💡 Describe an idea
🤖 Generate the code with AI
🎨 Build the interface
🔐 Add authentication
🗄️ Connect a database
💳 Add payments
🚀 Deploy
💰 Start finding customers
Some builders are even sharing stories of relatively simple AI-built SaaS products generating $1K, $5K, $10K+ in revenue.
But there’s another side of vibe coding that gets much less attention:
What happens when the AI-built app actually gets customers?
Your app can look great.
Payments can work.
Users can sign up.
The dashboard can work perfectly.
And underneath it, you could still have:
🔴 Exposed API keys or secrets
🔴 Missing authentication checks
🔴 Insecure database rules/RLS
🔴 Unprotected routes
🔴 SQL injection risks
🔴 XSS vulnerabilities
🔴 Vulnerable packages
🔴 Missing dependencies
🔴 Runtime errors
🔴 Insecure configurations
That’s why we're building VibeSafe Builder differently.
Typical vibe-coding workflow
Prompt → Generate → Fix until it works → Publish
VibeSafe Builder workflow
Prompt → Build → Scan → Fix → Rescan → Verify → Launch
With VibeSafe Builder, the goal is to bring more of the SaaS-building process into one workflow:
✨ AI-powered app generation
📝 Code editing
🔐 Separation of sensitive backend/environment information
🛡️ Built-in security scanning
🔑 Exposed-secret detection
🔒 Authentication/security checks
🗄️ Database/RLS checks
📦 Dependency & vulnerable-package checks
💉 SQL injection & XSS checks
⚙️ Configuration checks
💥 Runtime/error detection
🧰 Fix detected problems
🔄 Fix → Rescan → Verify loop
🚦 Launch Check before publishing
🚀 Vercel publishing
🛑 Critical security findings can stop an unsafe release
We're also exploring deeper checks such as authorization boundaries and cross-tenant security, because hiding another customer's page in the UI doesn't necessarily mean their data can't be accessed through the API.
The big difference?
Most vibe-coding tools focus on:
“Can AI build this?”
VibeSafe Builder is being built around a second question:
“Can I safely ship what AI built?”
Making $10K from a small SaaS is exciting.
But once real customers trust your product with their accounts, payments, or data, shipping fast isn't enough.
We want vibe coding to become:
Idea → Build → Secure → Verify → Launch → Grow. 🚀
If you're building a SaaS with AI right now, what worries you more:
getting the product built — or knowing it's actually ready for real users?
AI makes building apps faster, but it can also waste credits and introduce security risks.
I’m building VibeSafe Builder to help people create, secure, and launch AI-built apps in one place.
What’s your biggest challenge when building with AI: cost, reliability, or security?
7 Comments
If generation breaks, does retry cost me again?
Do you show credit cost before big generation?
Can I cap spend per day?
Good questions. My goal is that users shouldn’t pay twice when a generation fails because of a system or model error. I’m working on fair retry handling, upfront credit estimates for larger generations, and spending limits users can set themselves.
These safeguards are still being developed, so I don’t want to promise they’re all available yet. Cost transparency is a core priority for VibeSafe Builder.
The API-key drop-off gives you a concrete reason for the change. The useful comparison now seems to be whether the free first build improves activation enough to offset the lighter-model experience—are new users actually reaching a second build or paid usage more often now?
That’s exactly the metric I’m watching now. The API-key step was clearly killing activation, so the free first build is meant to get users to value faster. I’m tracking how many complete the first build, come back for a second, and convert to paid. If the lighter model hurts that too much, I may need to rethink the tradeoff.
The first-build to second-build step is the key signal now, especially with the model tradeoff in play. If you’re open to it, what’s the best email to reach you on?
That first-step drop-off is painfully familiar. I keep a short list of the one action a new user must complete before seeing value, which helps me spot friction early.
That’s a great way to look at onboarding. In my case, the “one action before value” was actually too much — create an Anthropic account, get a key, then connect it before seeing anything. Removing that friction made the first experience much simpler. I’m trying to keep the path to first value as short as possible now.