After rebuilding the same project three times because AI forgot my architecture, I got fed up and built @CodeRide (Beta) with my team.

The problem: AI code assistants lose track of your project between sessions. Every time I start coding with Cursor, Claude, or any AI assistant, I waste time re-explaining my codebase structure, architectural decisions, and coding patterns.

What we built: The project management tool for coding agents using MCP. Upload your project documentation or PRD, and CodeRide breaks it into optimized, fully contextual tasks ready for your AI agent.

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.

A workflow question I keep going back and forth on, and how I landed.

I ship a lot of AI-written code as a solo founder, and I hit the wall you probably know: if a human has to review every agent PR, you didn't buy leverage, you hired yourself as a full-time code reviewer. So I stopped asking "should I approve this PR" and started asking two things instead: how confident am I it works, and how bad is it if I'm wrong.

I ve been testing the 'Free Tier' limits of the 2026 AI landscape. While everyone swears by Claude 3.7 or GPT-5.2, I m trying to find the 'Golden Ratio' for makers on a zero-budget.

My current findings for the Office Bee MVP:

• The Brain: Gemini 3.1 Pro (via AI Studio) seems to have the highest 'Reasoning-per-Dollar' (free) for deep R&D.

• The Frontend: v0 (Free Tier) for shadcn/ui components.

• The Glue: Bolt.new for the initial scaffold.

The Challenge: Most 'free' models hallucinate complex state management in full-stack architectures.

I've been having a lot of fun exploring AI and using tools like @Cursor, @bolt.new, @Lovable, and @Warp to learn how to build and make some apps for myself! I'm also noticing a tremendous amount of growth in folks creating their own apps using these same tools which has me wondering... if a company wanted to acquire someone's app or tool that was built via vibe coding, would it matter how it was built? Does the method of how it was built impact the valuation?
In my idealistic eyes, I'd like to think it doesn't. As an acquisition is often much more than just the tech but also the user base, brand, and even team behind the product. If anything I think that acquiring a product that has been "vibe coded" and putting them into capable engineering hands would only enhance the product...or a least make the code base cleaner.
I also believe that talent that is able to create stunning products with AI is currently a small percentage of folks, and that companies should be investing in acquiring that talent (either independently or via product acquisition) so that they can stay ahead in innovation while learning how to implement AI tools more efficiently in their orgs.
Very curious to hear what you all think!

Currently playing around with @bolt.new after being inspired by @gabe and building a bunch of mini apps, mainly for fun. Haven't pushed any to production yet but so far I'm enjoying it bar the odd hiccup where I have to roll back a few times to fix a stubborn error.

I've also looked into @Lovable and it seems pretty cool but wanted to see what people had to say about it. Is there any reason to pick one over the other and which one have you settled on?

Tried to settle this for myself. Same task on both: refactor a 14-file React component tree to migrate from class components to hooks.

Cursor Composer:

- Faster start, immediately surfaced the right files

With Vibe Coding its now very easy to code and go live faster.

But security aspects are always in question?

Hey makers!
I've been deep in prompt engineering lately while building an AI tool, and I'm genuinely curious about how others approach this.
A few questions:
1. Do you save your best prompts somewhere? Notion, text files, dedicated app, or just copy-paste from chat history?
2. How do you iterate? Do you have a systematic approach or just tweak until it works?
3. Different prompts for different models? Or do you use the same prompt for ChatGPT, Claude, Gemini?
4. Text vs image prompts do you treat them completely differently?
I've noticed I was doing the same optimizations over and over (adding role, being more specific, structuring output format), which made me wonder if everyone has their own "prompt formula."
Would love to hear your workflows!

Anthropic just shipped Opus 4.7 today and i had to write about it somewhere because the jump is weird.

I ran the same backlog task on 4.6 and 4.7 back to back. same repo, same prompt, same tools. 4.6 looped on a bug for 25 minutes and was not going to solve it. 4.7 closed it in eleven, and the part that freaked me out is that it paused in the middle to sanity-check an assumption i had not asked it to check. literally wrote "before i write this migration, let me verify the actual shape of the response object, because my assumption here might be wrong" and then went and verified it. unprompted.

That self-verification behavior is the thing. Vercel is reporting it does proofs on systems code before starting work. Hex says it flags missing data instead of making up plausible-but-wrong fallbacks. Genspark measured loop rates on hard queries and 4.7 basically stopped looping. different teams, different harnesses, same pattern.

the numbers are nuts too: