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Code review last week. A "fullstack" dev shows me his Laravel API. Clean on the surface — well-organized controllers, Eloquent migrations, Form Request validation. I ask him why he's using $request->all() instead of $request->validated() in his controller. Blank stare. He didn't know that all() returns everything in the payload, including unvalidated fields. Claude generated the controller, he copied it, it worked. Six months of Laravel and a dormant mass assignment vulnerability in every endpoint.

If you've never written a compiler, the word "MLIR" probably looks like alphabet soup. This article is for you. By the end you'll understand, in plain language, what problem MLIR solves and why it had to exist at all. Let's start with the origin story — because where something comes from tells you almost everything about what it's for. The origin story: from TensorFlow to a universal framework The story of MLIR starts in 2018 at Google.

Search is shifting from ranked links to AI-generated answers. If you're building or marketing a product, there's a new metric worth tracking: does ChatGPT/Gemini/Perplexity cite your brand when someone asks a relevant question?

12+ years in PHP. I still ship production code in both Laravel and CodeIgniter 4 today — Laravel runs a surveillance analytics dashboard I maintain, CodeIgniter sits under a CRM I've customized for years. This isn't a "which is better" post. It's "which one for what," with actual code so you can see the difference instead of just reading opinions about it. The Core Difference in Code Same task — fetch active users with their related orders — in both frameworks.

There is a seductive moment when AI coding assistants start pulling real weight: a meaningful share of your diffs are machine-drafted, velocity spikes, and everyone feels ten feet tall. Then the first subtle bug from unreviewed generated code reaches production, and you realize the tool changed how fast you write code without changing how much it costs to own it. Reviewing, testing, securing, and maintaining that code costs exactly what it always did.

Not so long ago the Fable model was ripped from our adoring arms. In little over two days I had developed four fun applications essentially from a single prompt. Unlike other models which I had closely overseen, I had given Fable the freedom to simply deliver. And boy did it. The sirens of myth are attractive mermaids singing beautiful songs, enticing sailors onto the rocks. Willing victims they are enthralled by the music and leave better judgement behind only to be dashed on the rocks.

Hello, DEV! Hi everyone, I'm Sravan Sai Vuppula, a final-year Computer Science student from India and the founder of LYFSpot, a long-term initiative where I'm building software products that aim to solve practical, real-world problems. Over the past few years, I've been exploring modern web technologies, experimenting with ideas, and turning them into working products.

As organizations increasingly integrate large language models (LLMs) into applications, API tokens have become one of the most valuable credentials within modern software environments. Whether connecting to OpenAI, Anthropic, Google, GitHub, or cloud infrastructure, these tokens provide authenticated access to services that often incur financial costs and may expose sensitive organizational data.

Sanity Mux video playback in Next.js is where most tutorials stop short. The upload side — pushing a file to Mux via Sanity Studio — is well documented. What I rarely see covered is the read path: pulling a stored Mux asset reference out of Sanity, generating a signed playback URL, initialising hls.js in a React component, and making sure the poster frame does not wreck your LCP score. This post covers exactly that.

I've been building browser-based image tools for a while now, and color replacement is one of those features that seems simple on the surface but hides a lot of interesting problems. I want to write about the main one: why the obvious approach (RGB distance matching) fails on real photos, and how HSV fixes it. Plus some notes on shading preservation, edge detection, and where intelligent scissors fit in.