I Am Not a Reverse Centaur

Impact of LLMs on Open Source and PR Quality

• Many maintainers report a flood of low‑effort, AI‑generated pull requests that waste review time.
• AI makes it easy to generate “plausible” but shallow code and write verbose, generic PR descriptions, breaking the old norm that authors expend more effort than reviewers.
• Some see LLM‑generated libraries and patches as adding noise, making it harder to find and trust high‑quality projects.
• Others note AI can still produce useful fixes, especially when paired with good tests, but stress that unreviewed AI code is not a drop‑in replacement for well‑maintained libraries.

Maintainer Responses and Process Changes

• Strong support for adding friction: require issues (or even discussions) before PRs, auto‑reject PRs without linked issues, and prioritize clear human explanations of problems.
• Some argue this friction filters out “drive‑by” contributions and bikeshedding; others find it demotivating or bureaucratic.
• There is tension between curating a small, respectful contributor set and maintaining the “open” feel of open source.

Empowering Non‑Programmers vs “Reverse Centaur” Concerns

• Several commenters celebrate that non‑coders can now create custom tools and “home‑cooked apps,” especially for niche or accessibility needs.
• Critics compare mass LLM use to unskilled people building houses with power tools: empowering but risky for shared, long‑lived software.
• “Reverse centaur” is framed as humans mechanically executing AI plans; some see ticket‑driven corporate work as a precursor to this.

Pride, Craft, and Use of Tools

• One camp insists pride should correlate with effort, skill, and understanding; mere prompting or commissioning work is not a meaningful “accomplishment.”
• Another camp argues it’s valid to feel proud of causing a useful thing to exist, even if tools (compilers, libraries, LLMs) do most of the mechanical work.

Does Open Source Still Matter?

• Some fear OSS is being strip‑mined to train models and that open licensing mainly benefits AI labs.
• Others counter that modern software (including AI stacks) still critically depends on open source, so it remains essential, though under strain.
• A few suggest “free software” ideals (copyleft, anti‑relicensing, user freedoms) may now matter more than open source as a branding or business model.

Detection and Use of AI in the Workflow

• Detecting AI‑generated code is mostly “by vibe” (style, boilerplate, tone) and considered unreliable to automate.
• Some propose using LLMs themselves to pre‑triage or summarize PRs, but there’s discomfort with leaning further on the same technology causing the problem.