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Somewhere in the Laravel app you're running right now, there's a good chance an HTTP call goes out to OpenAI, Anthropic, or a local model. A chat feature, a summarizer, an agent that triages support tickets. Laravel 13 shipped in March 2026 with first-party AI primitives and the framework now literally brands itself as being for "Artisans and agents". The application layer has never been easier. - Source: dev.to / about 1 month ago
I have written about Bloom Filters before in Bloom Filters Applied In Real Life Application - Laravel Prototype and made a prototype for it using Laravel. - Source: dev.to / about 2 months ago
# Laravel's homepage through the years Wayback-video https://laravel.com --scroll --interval year --crossfade 0.4 # Wikipedia, month by month, over its first decade Wayback-video https://wikipedia.org --scroll --interval month --from 2001 --to 2010 # Fast mode: Wayback's pre-captured PNGs only, no browser needed Wayback-video https://mozilla.org --wayback-screenshot year --scroll. - Source: dev.to / 2 months ago
A common way to implement DDD in Laravel is to organize the app/ directory into three primary layers:. - Source: dev.to / 4 months ago
This post walks through how to build an Ansible repo to manage a production DigitalOcean setup: two Ubuntu droplets, six Laravel apps, a couple of PHP framework sites, and a Go microservices platform. The goal is to codify everything that was previously managed via manual SSH, without replacing the Deployer workflow that already handles app releases. - Source: dev.to / 5 months ago
Setup time matters too. The delta between Runpod and bare-metal providers like Lambda Labs is large. Reaching an equivalent setup on a bare VM requires provisioning the instance, configuring the OS and CUDA drivers, installing Docker, setting up your orchestration layer (Kubernetes or Slurm), deploying your inference container, configuring autoscaling rules, and wiring up your load balancer. Thatโs a realistic... - Source: dev.to / 5 months ago
Let's do the math for a representative setup: GPT-OSS-120B via Together.ai ($0.15/$0.60) vs self-hosting on H100s from Lambda Labs at $2.99/hr ($2,183/mo). A single H100 running a 70B model produces roughly 50 tokens/second on average, which works out to about 130M tokens per month. - Source: dev.to / 6 months ago
How does this compare to https://lambdalabs.com/. - Source: Hacker News / about 3 years ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: about 3 years ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: over 3 years ago
CodeIgniter - A Fully Baked PHP Framework
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Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...
ipinfo.io - Simple IP address information.