63 articles tagged #AI Tools
ChatGPT can generate mutex and locking code that looks correct at a glance but silently deadlocks under load. Learn the prompting techniques that force it to produce safe, production-ready concurrency code.
Google I/O 2025 dropped a dense payload of developer tools, APIs, and model updates. Here's a clear-eyed breakdown of what actually matters for your workflow right now — and what you can safely ignore until later.
ChatGPT can scaffold DI containers fast, but it often gets the wiring wrong — circular deps, missing scopes, or services registered in the wrong lifetime. Here's how to prompt your way to configs that actually work.
GPT-4.1 and GPT-4o look similar on paper, but they behave very differently on coding tasks, long-context retrieval, and API costs. Here's exactly how to pick the right one for your production workload.
MCP is rapidly becoming the standard wiring layer between AI models and real-world tools. If you haven't integrated it into your stack yet, here's exactly what you need to build, connect, and ship before it becomes table stakes.
ChatGPT can draft Elasticsearch queries fast, but without your actual index mapping it guesses field types and analyzers — and those guesses break at runtime. Here's how to get accurate queries every time.
ChatGPT can generate structured logging configs fast, but without the right prompts, you end up with fields that shift between services, missing required keys, and parsers that break in production. Here's how to fix that.
ChatGPT can generate connection pool configs that look right but silently bleed connections under load. Learn exactly how to prompt it for production-safe settings that won't exhaust your database.
ChatGPT can draft Dockerfile multi-stage builds quickly, but it often ships dev dependencies, root users, and massive base images into production. Here's how to prompt it accurately and audit what it gives you.
ChatGPT can draft Playwright tests fast, but the selectors it picks often break the moment your UI shifts. Here's how to prompt it for stable, maintainable test scripts that actually pass in CI.
ChatGPT can scaffold Celery configs fast, but it often misses retry logic, acks_late settings, and visibility timeout traps that let tasks disappear without a trace. Here's how to prompt your way to configs that actually hold up in production.
ChatGPT can draft gRPC proto files fast, but it routinely gets field types, service method signatures, and streaming patterns wrong. Here's how to prompt it correctly and catch the mistakes before they hit your build.