Give Django your time and money, not your tokens

Money vs tokens & alternative support models

• Many agree that donating money or funding specific features is more helpful than “spending tokens” on AI-generated PRs.
• Some note big projects may already get free credits from AI providers, so extra “AI budget” donations may be limited.
• A few argue contributors should be free to offer AI-generated PRs instead of cash; maintainers can simply reject them.

Impact of LLMs on OSS contributions

• Strong consensus that LLMs have greatly increased volume of PRs, bug reports, and “security” reports, with only modest or negative impact on quality.
• Maintainers report many obviously LLM-generated issues that seem plausible enough to demand investigation, draining limited time.
• Several commenters distinguish between using LLMs as learning/assist tools vs. outsourcing understanding and communication entirely.

Maintainer experience, quality, and trust

• Django and similar mature codebases are described as dense, high-quality, and hard to enter; LLMs don’t naturally produce code in that style.
• Reviewers find it demoralizing to interact with PR authors who appear to be “facades,” with AI replying to review comments and faking understanding.
• Broad worry that trust is eroding: previously PRs were assumed to be in good faith by people who understood their changes; that assumption no longer holds.

Policies and proposed mitigations

• Examples cited: projects requiring AI-use disclosure, banning external PRs, disallowing users from opening issues directly, or enforcing strict no-LLM policies.
• Others adopt “allowed with conditions” rules (e.g., DCO signoff, no LLM-based security reports).
• Suggestions include repo-level AI policy files (like robots.txt) or explicit “LLM honeypot” projects to isolate low-effort contributions.
• Some advocate private or more tightly curated contributor communities and mentoring programs.

Culture, incentives, and broader concerns

• Hiring practices that reward GitHub activity are seen as driving people to game contributions with LLMs.
• Debate exists over whether OSS should “embrace” AI (e.g., AI code review) or largely exclude it to preserve quality and sanity.
• Multiple comments stress that the real problem is contributors who lack diligence or understanding; LLMs amplify their impact, rather than creating the issue.