Hacker News thread on a year after a 100% AI mandate
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The feed snippet shows 49 points and 51 comments and does not include the post body.
The author distinguishes guided AI use from pasting agent output with no review, disclosure, or ownership of failures.
A solver-judge-repairer pipeline revises tests and descriptions; nine model setups were evaluated and artifacts released.

A no-human-code week still left a frequently red build and most human time at the whiteboard.
The pipeline produces 4,670 tasks across 2,865 repositories and a 100-task benchmark with 1,044 specifications.

Felix Rieseberg says prototypes and business sense now matter more than tenure or classic coding tests.

A SonarQube complexity flag on Codex-generated React code led the author to inspect, then have the agent split one file into ten.

By October 27 he logged 2,102 contributions in 2026, already above every full year from 2021 to 2025.
Authors report LAR-27B at 77.3% on TerminalBench-2.1 and a 9.89% average gain over the base model.
An engineer says frontier models like Fable helped leadership eliminate his role without matching his judgment.

A study finds AI coding agents speed coding but raise pull-request review time 49% and change requests.
AgentSWE shows coding agents rarely build agents that satisfy full held-out requirements.
ProgramGym supplies 1,648 verifiable environments and raises Qwen3.8-27B program-reproduction accuracy from 34.87% to 42.84%.
SWE-Duel has coding agents author and repair hidden bugs so difficulty scales with participants.
SWE-Game finds coding agents, led by Opus5, still score below 60 building games.
AgentMonBench and an evidence graph help monitors verify consequential decisions made by long-horizon agents.

An essay argues agentic coding creates unreadable slop, deskills engineers, and weakens team collaboration.
Retrospection-only fine-tuning lifts Qwen3.5-4B on SWE-bench without rewards or an external teacher.
PluginRSI evolves agent harnesses as reusable plugins that transfer across models and tasks.
CUA-SWE tests agents that repair software using code changes and visual GUI feedback.
A coding-agent benchmark finds compact documentation does not help agents fix real repository issues.
Willison argues coding agents raise the skill and discipline needed to engineer software well.
AEWM edits contaminated agent state and improves six benchmarks by 3.2 to 6.7 points over the strongest baseline.
Category-aware expert training and distillation lift a software-engineering agent to 58–59% resolution on SWE benchmarks.
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