📰 Dev.to · Alex Chen

I was up at 2 AM, staring at seventeen PDFs that refused to tell me anything. My midterm was in six...

I was one merge away from shipping. The AI reviewer had already spoken: "No issues found. Looks good...

I have a small Python library that parses CSV files and extracts course schedules. It has no tests. I...

Last Tuesday a model told me it had created a file. The response looked perfect: done, exit_code: 0,...

Everyone is arguing about whether model output can be detected and whether agents should be allowed...

AI text detectors are probabilistic classifiers, not authorship proof; a tiny auditor shows how...

Core point: a free model tier is not a yes/no answer; it is a budget. Before I send a batch job to an...

Most fallback code looks complete until you test the path where the primary endpoint fails. This is...

Here is the output this project produces before you read another word: === tool-boundary harness...

There's a debate running on DEV this week about whether AI tools are erasing the junior developer's...

"A tiny standard-library lab shows why reconnects and duplicate requests must not depend on transport memory."

"Use Python’s standard library to generate many strings, state invariants, shrink failures, and cleanly discard a learning workspace."

Build a beginner-friendly pure-function filter and unit tests that keep only fields required for one declared purpose.

A beginner-friendly capability exercise showing why a sandbox needs narrow, revocable authority.

Build a small Python dependency scheduler that orders valid graphs and returns a concrete cycle when ordering is impossible.

A beginner workflow for converting the trending open-source AI Agent book into one reproducible experiment per chapter.

Build a tiny linter for long AI instructions instead of assuming more characters mean clearer behavior.

Learn Bloom filter tradeoffs with a standard-library Python experiment and reproducible inputs.

Build a small TypeScript example that gives each AI tool only the configuration it needs and fails closed on missing context.

Build a dependency-free Node.js validator for structured issue data before exposing writes through an MCP tool.

Learn to separate JSON syntax errors, shape errors, and semantic errors while limiting model repair attempts.

OpenAI's new SWE-Bench Pro analysis is a reason to learn how benchmark overlap and noisy tasks can distort coding-model conclusions.

Learn Server-Sent Events by building a zero-dependency task stream that resumes with Last-Event-ID and deduplicates replayed events.

Use the Tiny Emulators trend as a starting point for a small fetch-decode-execute exercise you can test by hand.