#ai

2026-06-24 · 7 min read

Build a Laravel Package for Reusable AI Features Across Projects

Three apps is when the copy-paste strategy breaks down. Not because the Anthropic client setup is complicated — it isn't — but because the three copies drift. App A's client retries on 529 errors; app B's doesn't. App C still has a model name from three months ago when you were testing. By the time you need to upgrade the SDK, you're hunting through three codebases and hoping you got them all.

2026-06-17 · 7 min read

Implement Sentiment Analysis on User Reviews in Laravel

A 4.1-star average tells you almost nothing. Two hundred four-star reviews can mean "genuinely excellent product" or "persistent problems that one good feature nearly canceled out." The number collapses the signal you actually need — whether customers are frustrated about shipping speed, satisfied with build quality, or writing reviews that sound positive while meaning something else entirely.

2026-06-16 · 8 min read

Laravel Job Queue for Async AI Processing at Scale

The first time I shipped a feature that called an AI API synchronously from a web request, I wasn't thinking about load. I was thinking about whether the output was good enough. Three days later, a batch of users hit the feature simultaneously, and the dashboard went from responsive to a spinning wheel. Every user sat there waiting 8–12 seconds for a response the server was holding a thread for. The queue existed. I just hadn't used it.

2026-06-15 · 8 min read

Build a Smart FAQ System in Laravel Using Vector Search

A user types "how do I get my money back" into your FAQ search box. Zero results. Your FAQ has a perfectly clear "Refund Policy" entry — the concepts are identical, the words aren't. That's a keyword search problem, and it's embarrassing in a way that's hard to explain to a product manager. The fix is semantic search: match by meaning instead of exact terms. Laravel's official AI package gives you an embeddings API and a `whereVectorSimilarTo` query builder method that handles this in about 40 lines of real code. This article shows you how to wire it together, from migration to search endpoint.

2026-06-14 · 8 min read

Automate Bug Triage with AI: Classify GitHub Issues Using Python

Come back to a side project after three months away and you'll find it waiting for you: a backlog of 40, 80, sometimes 200+ open issues, none of them labeled, none of them prioritized. Someone reported a crash. Someone else asked how to configure the timeout. A third person opened a feature request that's really a duplicate of an issue from last year. You can't tell which one to touch first without reading every single one. That's the problem. Not the bugs themselves — the triage debt. No labels means no filters, no filters means everything looks equally urgent, and everything equally urgent means nothing gets fixed.

2026-06-13 · 7 min read

AI-Powered Product Descriptions Generator in Laravel E-Commerce

800 products. Three days until the store launch. One copywriter who just handed in her notice. That was the situation a client described to me before I built this. The answer wasn't a contractor. It was four hours of work in Laravel and a GPT-4o API key. This article shows you how to build a product description generator that integrates cleanly with a Laravel e-commerce backend, handles different product categories and tone requirements, and scales to bulk generation via queues. The code uses the `openai-php/laravel` SDK — if you've been working with the Anthropic client, the structure's the same but the method signatures differ.

2026-06-12 · 8 min read

Build a Laravel AI Writing Assistant with Streaming Responses

You're demoing the writing tool you just built. You paste in a paragraph, click "Adjust Tone," and wait. The output field stays blank. Six seconds pass. Eight. The client glances at you. "Did it freeze?" Then 380 words appear at once, already formatted, already correct — but the damage is done. The wait felt like an error. Streaming fixes this. Instead of waiting for the model to finish before sending anything, you send tokens as they're generated. The user sees text appearing word by word within a fraction of a second of hitting submit. Same total latency, completely different feel.

2026-06-11 · 8 min read

Integrating OpenAI API into Your Laravel Application

ou already know the situation. You need AI in your Laravel app, so you reach for `openai-php/client`, wire up the HTTP calls, write your own retry logic, figure out how to serialize conversation history, and by the time you've got something working you've built a custom integration layer that nobody else on the team knows how to touch. Laravel's official AI package takes a different approach. `laravel/ai` is first-party — built by the Laravel team, not a third-party wrapper — and it brings a full agent architecture to your application: class-based agents, database-backed conversation memory, structured output, tools, streaming, and queued execution. OpenAI is one supported provider. Anthropic, Gemini, Groq, Mistral, and a dozen others are in the same list.

2026-06-10 · 9 min read

Creating an AI Code Reviewer That Comments on GitHub Pull Requests

Your team ships fast. PRs stack up on Friday and get reviewed by Tuesday, if you're lucky. By then the author has context-switched twice and the reviewer is catching up from memory. An AI reviewer won't replace a senior engineer. It won't catch the bug that requires knowing how a database migration two years ago left a nullable column in the wrong state. What it catches: raw SQL built with string formatting, bare `except` blocks that swallow errors silently, missing auth decorators, a `list` variable shadowing the built-in. That's the class of issue that slips through when someone's reading twenty files at once and getting tired.

2026-06-09 · 8 min read

Automate LinkedIn Outreach Messages Using AI and Python

Cold messages fail for a simple reason. They're not actually cold — they're warm in theory and cold in execution. You've researched the person, found something interesting, written what you think is a relevant opener. Then you send the same structure to 150 people and wonder why three reply. The bottleneck is drafting. Personalization takes time: reading a profile, finding a hook, writing an opener that references it, keeping the message short enough that someone actually reads it. Ten minutes per person, 200 people, do the math.