📰 Dev.to · Penloom Studio

A plain-English walkthrough of how ChatGPT, Claude, and Gemini "remember" you — what they store, where the settings live, and how to turn it off.

Anthropic's prompt caching can eliminate 85-90% of your input token cost on repeated system prompts and tool definitions. Here's exactly how to enable it, what

A description can name your offer in plain prose and still convert nobody — because the platform never turned the URL into a link. Here's the one-line guard tha

MoneyPrinterTurbo, WhisperX, LatentSync, remotion, and an AI Shorts generator — which ones are drop-in wins for an automated video pipeline, and which cost you

Every freelancer has a drafts folder full of emails they'll never send. The 11pm version. The one...

Agent runs are program executions. Reproduce them, trace them, and write checks that can actually fail - a practical 3-step workflow for debugging coding agents

Every word you let an image model paint is a spelling roll of the dice — the research says so and my QA frames say so. Here's the layer-separation fix: let the

Scope creep isn't usually a fight — it's a client who honestly assumed something was included. Here are the exact words, forms, and clauses that turn 'just one

Every agent tutorial shows the happy path. The hardest design decision is when the agent should NOT proceed. Three escalation triggers, a confirmation-gate impl

Most tutorials show you how to define a tool. None of them show you what to do when tool calls fail, loop, or produce unexpected results. Here are the five patt

Long-running AI agents degrade over time as the context window fills with noise, repeated instructions, and stale data. Here's how to detect context rot and the

LLMs hallucinate structure. A production agent needs a validation layer between the raw model output and the code that acts on it. Here's a practical, type-safe

Anthropic's prompt caching can eliminate 85-90% of your input token cost on repeated system prompts and tool definitions. Here's exactly how to enable it, what

Tool-calling failures rarely look like crashes. They look like a 95% success rate that quietly compounds into a 66% one. Most of those misses trace to two fixab

Claude Code follows CLAUDE.md about 80% of the time, and that number drops as the file grows. Treat it as an instruction budget — how to spend it, and when to g

A flaky agent is almost never a model problem. Seven concrete rules — and three paste-able Claude Code guardrails — that turn a demo into something you can actu