The boilerplate built for vibe coding. Auth, payments, emails and more already done. Ship your next project this weekend.
Joined February 2026
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The hobby project I’ve built with @AI_Boilerplate is about to hit 1.1k users!! 🥳🤌 I’ve missed the 1k mark…
Btw, I have screen cleaner phobia
USA: 🚫
Japan:
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.
Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.
Try it: sakana.ai/fugu 🐡
AI Boilerplate retweeted
looks like we’ll also be getting gemini 3.5 this week so it’s
fable 5 (the return of the king)
5.6 and a new voice mode
gemini 3.5 pro
sonnet 5
i’m most excited by sonnet 5 as it could compete with 5.6 to be my daily warrior. let’s see how the price wars unfold.
None of it is hard. It's just slow, repetitive, and easy to get subtly wrong.
That's the gap we built AI Boilerplate to close: all 160 hours, production-ready, in a 15 minute setup.
One time €199. aiboilerplate.dev
Add it up and it's 160+ hours.
At €75/hour that's around €12,000 in dev time. Before you've built one thing that makes your product different from anyone else's.
Then the rest:
- Admin dashboard (~10h)
- User dashboard (~12h)
- Transactional emails (~6h)
- Docs site (~16h)
- Marketing page (~24h)
- Legal pages, design system, linting, dark mode
Payments: Stripe subscriptions, trials, plan changes, the customer portal, webhooks, maybe a credit system.
About 28 hours. This is where most timelines quietly die.
Auth: email/password, magic links, Google and GitHub login, sessions, device tracking.
Sounds simple. It's about 10 hours done right, and a security minefield done wrong.
Nobody starts a SaaS excited to build a password reset email.
But every SaaS needs one. Plus about 14 other boring things.
Here's the pre-launch checklist most people underestimate, with hours:
7. Don't rebuild the boring stuff by hand.
Auth, payments, emails, dashboards: 160+ hours every single time.
Start from a structured foundation instead. Ours is at aiboilerplate.dev, but these rules work anywhere.
6. Make the codebase easy to navigate.
Predictable folders. Clear naming. Comments that explain why, not what.
The easier your codebase is for a human to read, the better the AI works inside it.
5. Keep secrets off the client.
The most common AI security slip: API keys in client-side code.
Keep them server-side, always. Assume the AI won't do it for you.
4. Validate every input.
AI loves to trust data. Wrap inputs in schema validation so garbage gets rejected at the edge, not deep inside your database.
3. Make types strict.
End to end type safety means broken code fails to compile instead of failing in production.
If it compiles, it probably works. That's a free QA layer against AI mistakes.
2. Isolate features into modules.
Auth in one package. Payments in another. Email in another.
If the AI physically can't reach payments while editing auth, it can't break payments while editing auth.
1. Give the AI a rules file.
A rules file with your conventions, folder structure, and the why behind decisions.
The AI reads it before every change. No more re-explaining your architecture every prompt.
After 15+ years building software and a year of heavy vibe coding, here are the 7 rules that keep AI-generated code clean.
Steal them for your next project:
Do that and the AI feels like a superpower again, at feature 50, not just feature 5.
That's the whole reason we built AI Boilerplate: the structure is already there, so you keep the speed and skip the spaghetti.
aiboilerplate.dev