Coding agents copy your worst test files, so cleaning them up matters

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A developer argues that the existing tests in a codebase act as a prompt, since coding agents imitate their patterns closely. Simon Willison has observed that agent-written tests tend to match the quality of a project's clean baseline tests. A common flaw he points to is heavily duplicated test setup, which he addresses by asking the agent to refactor with parametrize or shared fixtures. He notes that even a few well-written examples can steer an agent toward a preferred testing style. The author also notes that Anthropic and OpenAI docs recommend keeping instruction files short and letting conventions live in the code itself.

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Four Laravel Query Fixes That Make Slow Database Code Much Faster

A Laravel developer explains that slow pages and jobs usually stem from everyday query habits rather than complex issues. Common culprits include wrapping columns in functions that block index use, computing totals in PHP instead of the database, and loading large row sets into memory just to aggregate them. Benchmarks on a one-million-row MariaDB table show dramatic gains, such as sums dropping from 5.8 seconds to 0.15 seconds and grouping falling from 25.8 seconds to 1.1 seconds. The author also recommends wrapping bulk inserts in a single transaction to avoid paying the commit cost on every row. The fixes center on letting the database do the heavy lifting and returning only the results that are actually needed.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

A Ten-Second Formatting Habit That Speeds Up Code Reviews

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ProgrammingDEV Community ·

Judged passage labels, not clicks, should gate RAG retrieval releases

The author argues that a RAG retrieval evaluator for a documentation-monitoring alert system should rely on judged relevance labels rather than click proxies as its release gate. Recall@K is computed against a versioned set of question-to-passage labels, and builds fail when agreed slices regress, while clicks serve as a separate online signal. Averaging across all queries can hide complete misses on high-risk cases such as removed configuration constraints, so per-slice reporting by change type, template, language, and freshness is recommended. Labels should be defined at the same granularity the retriever returns, and the corpus should be frozen during evaluation. The approach does not by itself measure ranking quality, citation correctness, or answer faithfulness.

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