Claude
Practical writing about Claude from a developer's point of view: choosing between model tiers, calling the API, testing a new release on your own work, and wiring Claude into real applications.
8 articles tagged Claude.
Claude Sonnet 5.5: what's new, and how to test it on your own work
Anthropic released Claude Sonnet 5.5 with faster responses and lower typical task costs at the same per-token price. Here's what was announced, which numbers are vendor-reported, and a simple way to test whether it's worth switching for your workload.
Add Claude to a Laravel app with the HTTP client
You don't need a package to call Claude from Laravel. This tutorial builds a small service class on the HTTP client, adds config, error handling, queued jobs, a tool-use example, and tests with Http::fake, so the integration stays simple and easy to change.
Computer use and browser agents: what they can and can't do yet
Agents that look at a screen and click, type, and scroll can operate software that has no API. Here's how they work, where they help, why they're slower and less reliable than API-based agents, and how to run them safely.
Choosing the right Claude model for the job
Bigger models cost more and run slower, and most tasks don't need the biggest. This guide shows how to think about model tiers, test the choice on your own data, route between models, and avoid paying for capability you never use.
Prompt caching: cheaper and faster LLM calls
Agents resend the same long prefix on every step. Prompt caching lets the provider reuse that work at a fraction of the price and with lower latency. Here's how it works, how to set it up, and what quietly breaks it.
Build your first agent with the Claude API in Python
A complete, runnable agent in about fifty lines of Python: two read-only tools, a loop, a step limit, and logging. No framework required, and every line explained.
Tool use explained: how an LLM calls your code
A language model can't run code or hit an API. It can only ask you to. This post explains how tool use works, what the model actually sees, and where things quietly go wrong.
How the agent loop works, step by step
Every agent runs the same loop: send the conversation, get back text or a tool request, run the tool, append the result, repeat. This post walks through one full run and the bugs that trip people up.