Over the last few weeks, I reviewed more than 15 vibe-coded fintech and startup-style web apps.

The surprising part was this:

Even when teams were using AI coding tools, security agents, framework defaults, and modern deployment platforms, I still kept finding serious external security gaps.

Not always one-click hack type issues, but the kind of problems that slowly stack up into real risk:

I used a few different tools to vibe code my saas. I used lovable, chatgpt, gemini to code my saas. The thing is, there is a huge problem of these AI's of forgetting when you chat for a longer time.

This has happened to me a lot of times. One solution I found is that you can start a new chat to overcome this problem. But, here the problem arises to explain the AI all the context which you had in the previous conversation.

Curious, has anyone else ever ran into this problem? How did you manage to overcome this?

I currently only use the Paper MCP. And I'm really looking forward to try the Cloudflare MCP. An AWS MCP would be amazing too!
I don't really need to connect my email or calendar, are you using that?
But I think MCP connectors will become more popular, i.e. apps will become like a "canvas" the ai agents uses to display things that don't fit nicely in the chat window. Almost like a window manager.
What are your favorite MCPs?

I've been vibe coding for a few months as a non-coder and I'm still annoyed with the fact that I can't understand what's happening under the hood.

I've got a decent understanding of code but I can't actually write it and I don't know the best practices for stuff like architecture and security which apparently aren't baked into most vibe coding tools. So my question is...

i have vibe coded many tools and processes. but when sometime something breaks, i go bonkers to figure out what why and when it happened?
how do you decide test cases and run against them? and i recently realised i can have evals agents but again how ?
need some help

I was inspired by @chrismessina to build my own Raycast extensions after he released his own! I have been using Raycast a lot more thanks to a gentle nudge from @jakecrump (and @Raycast's latest AI releases), so I knew I wanted to build my own Raycast extension at some point.

There was a lot of news about Cyberpunk for Mac released and I wanted to search if there was a deal for it on PC. I instinctively went to search Raycast .but it wasn t there. So my extension idea was born!

My goal was simple: make it easier to hunt for video game deals right from the Raycast command bar. What followed was a rapid journey of prototyping, debugging, and learning. I m excited to share what I learned and hope it will encourage more makers to ship their ideas.
You can view my IsThereAnyDeal (ITAD) Extension on GitHub here! I will launch it when it's approved on the Raycast store :) I'll publish a more in-depth, nerdy guide later. But for now...here's my experience building a Raycast Extension!

The story started in 2019. I wanted to build my own company. Back then, I was running a service-based business as a developer, but deep down, I never felt connected to that model. I wanted to create a product that could grow while I slept. Something truly mine.

So I started building. From 2019 to 2024, I built the product almost entirely on my own. Every system, every architecture decision, every scalable practice, every multi-tenant layer. It was hard, but I always felt in control of the machine I was creating.

Recently I've worked with a group of non-corders trying to "vibe code" their apps with AI.
While knowing code is clearly not a must these days, it helps to get technical.
People who were familiar with basic software engineering concepts were 10x more likely to success and get better results.
So, with the hope of providing value to the non-coders people, I've created a quick roadmap for the basic terms and concepts you should be familiar with.

• Requirements: Building apps with AI is all about being able to clearly guide AI and express your app features and requirements.
  You need to be able to express those ideas and explain them as you d explain to a human developer. Think like a Technical Product Manager.

• Frontend: The face of your app.
  It's what your users see and interact with. It could be a website, a mobile app, or a desktop app. Most popular frontend libraries and frameworks are React, Next.js.

• UIs: They are the buttons, the forms, the modals, the tooltips, etc. In React, the UI is built with components. For design & styling, Tailwind CSS is the most popular library.
  For animations, Framer Motion is the most popular library.

• Packages & npm: Apps are not built from scratch.
  They are built on top of existing libraries and frameworks, like lego blocks.

  The most popular package manager is npm. For example, "react-hook-form" is a famous package that helps you build forms.

• Backend: The backend is the part of your app that runs on the server.

  It's where you store your data, your business logic.

  e.g: If you want to send an email, or process payments - this is where you'll do it.

  Vibe tip: Use minimal backends with serverless functions.

• Database: The database is where you store your data.

  It's where you store your users, your projects, your tasks, etc. Think of it as a big spreadsheet.

  I recommend using a database that is integrated with your frontend.

  For example: Fine, or Supabase.

• API: Real-life apps almost always need to integrate with other apps.

  For example: if you want to send email, or get weather data, or integrate with AI - it's all done through APIs.

• Hosting & Deployment: For your app to be accessible to the public, you need to host it.

  The code is usually hosted on GitHub, and deployed to platforms like Fine, Vercel, Netlify.

Finally, being comfortable with code is helpful - even if not a must.

AI often makes minor mistakes (like importing a wrong package), and if you re not afraid of reviewing code - you will get better results faster.

Building something used to take enough time that you had to think twice before starting.

Now you can get a working version together in a weekend.

But I wonder if that creates a different problem.

When you ve spent months building something, it s pretty obvious why you feel attached to it. What s strange is that even a project built in a few days can be surprisingly hard to let go of.

I keep seeing advice like use this model for the easy stuff and that one for complex problems. But it makes me wonder what really counts as a complex problem for an LLM?

For us, complex usually means lots of steps, deep reasoning, or tricky knowledge. But for AI, the definition might be different. Some things that feel easy for us can be surprisingly hard for models, while things that seem tough for us (like scanning huge datasets quickly) might be trivial for them.