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RT @VittoStack: Indians are some of the kindest, most generous, and genuinely welcoming people I’ve ever met 🇮🇳
Product & Devs Growth @Cyfrin | Ex @Alchemy | Created @cyfrinupdraft and @AlchemyLearn | Robotics | Making web3 mainstream
"How suitable is Laravel for a SaaS with millions of users?" Here's my answer to a YouTube comment, see below. --- Question: Thank you so much for your content. I've been watching your videos for a long time and they’ve helped me improve my Laravel skills a lot. I also wanted to ask something that might even be an interesting idea for a future video. How suitable is Laravel for building a SaaS platform that could potentially serve millions of users? What kind of architecture would make the most sense in that scenario? For example, would a well-optimized monolith be enough, or would microservices, horizontal scaling, queues, caching, etc. be more appropriate? I’d love to hear your thoughts on this. Thanks again for all the valuable videos! --- My answer: This is such a broad topic and broad question that I don't have a definite answer. It depends on what those millions of users actually do. The structure may be depending on team preferences almost. And yes, you would probably use down the road a lot of things like horizontal scaling, queues, caching and others. But there is no specific recipe and I cannot shoot a video with one definite solution for any possible SaaS with millions of users. In my personal experience, usually even bigger SaaS projects start as small projects and then they scale later with refactoring kind of spiral version by version. Probably the biggest question is the database structure, which becomes usually the biggest bottleneck of queries and database operations if the application grows. So database structure and also caching is probably more important topic than the Laravel itself. So how suitable Laravel is? That is kind of the wrong question to ask. It's more about how well you write your own code and database structure around Laravel and on top of Laravel. There is a great article called "Does Laravel Scale": https://t.co/MNP9zzmpFt --- Their reply: @LaravelDaily Thanks a lot for taking the time to reply. What you said actually helps put things into perspective. I realize now that my question was a bit too broad, and “millions of users” can mean very different things depending on what the app is doing. Your point about database structure and caching being the real challenges makes total sense. I’ll definitely look more into that, and I appreciate the link to the “Does Laravel Scale” article I’m going to read it. And yeah, you're right: it’s less about Laravel itself and more about how well the whole system around it is designed. Thanks again for the thoughtful answer!
~20 yrs in web-dev, now mostly Laravel. My Laravel courses: https://t.co/HRUAJdMRZL My Youtube channel: https://t.co/qPQAkaov2F
Hacker News can always be counted on for some solid virtue theater. It produces more of Aesop's sour grapes than any other wantrepreneur space. So many aggrieved techies with a need to rationalize why they haven't made it to the moon and why other, lesser, fools have. SAD!
Father of three, Creator of Ruby on Rails + Omarchy, Co-owner & CTO of 37signals, Shopify director, NYT best-selling author, and Le Mans 24h class-winner.
Flux4D: Flow-based Unsupervised 4D Reconstruction Abstract (excerpt): Flux4D is a simple and scalable framework for 4D reconstruction of large-scale dynamic scenes. It directly predicts 3D Gaussians and their motion dynamics to reconstruct sensor observations in a fully unsupervised manner. By adopting only photometric losses and enforcing an "as static as possible" regularization, Flux4D learns to decompose dynamic elements directly from raw data without requiring pre-trained supervised models or foundational priors. This is achieved simply by training across many scenes. Our approach enables efficient reconstruction of dynamic scenes within seconds, scales effectively to large datasets, and generalizes well to unseen environments, including rare and unknown objects. Experiments on outdoor driving datasets show that Flux4D significantly outperforms existing methods in scalability, generalization, and reconstruction quality.
Paper: https://t.co/xfwoiS8Ac5 Project: https://t.co/4BN4pxzIWa
Not sure what Stubb even proposes here. More power to the East and South? They'll take it, thanks, but why would they then buy into liberal democracy that can't build their roads? Honestly reads like desperation of a man watching his world dying and trying to come up with a plan.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1
Jason is so committed to bring color back to modern web design that our homepage literally pops a new vivid one every time you reload 😄 https://t.co/YtIREI07ya
Father of three, Creator of Ruby on Rails + Omarchy, Co-owner & CTO of 37signals, Shopify director, NYT best-selling author, and Le Mans 24h class-winner.