GPT-6 In 7 Minutes
7 articles
A new paper wraps code into an OWL2 ontology with SPARQL property paths to answer multi-hop structural queries for coding agents - 2.06x retrieval recall and 28.8% fewer tokens on SWE-bench Pro, versus treating code as plain text.
A new 188-task benchmark for non-functional improvements finds coding agents hit 70% on functional correctness but lag humans on refactors and structural changes - the quality gap that becomes tech debt.
Running Opus 5 through SlopCodeBench's multi-checkpoint gauntlet reveals that frontier models still degrade codebases over time. 24% strict pass rate, 5x more functions than Opus 4.8, and 93% of code lines trigger slop detectors.
A new essay argues that letting AI generate sloppy code creates a downward spiral where future AI absorbs those bad patterns. HN's 250+ comment thread is split between believers and pure vibe-coders.
A controlled study of 660 Claude Code trials shows clean codebases reduce token usage by 7-8% and file revisitations by 34%, while pass rates stay the same. Traditional maintainability principles still matter in the age of AI coding.
A new SonarSource study finds clean code doesn't boost agent pass rates - but it cuts token usage by 8% and file revisitations by 34%. Here's what that means for your codebase.
SWE-Bench has an 81% false-positive problem. FrontierCode replaces it with mergeability as the metric - and the scores are sobering for every AI coding tool on the market.
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