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Joined 4 months ago
Cake day: July 16th, 2026
  • Xer'lodrav@szmer.infotoLocalLLaMA@sh.itjust.works•Chinese AI Writes Weaker Code for Beijing’s Adversaries. We Fixed It. A Qwen3-Coder Case StudyEnglish
    5·
    8 hours ago

    It sounds like fearmongering (so hot right now), but hey: that is the true benefit of opensource! - it can be diagnosed and monitored. Claude just refuses to answer biology homework question, because of “security” (fuck Anthropic for that). Overall I agree that vigilance is needed, but still it should not make people dismiss open weight/open source models - if anything it should convice people to invest (their time and money) into open weight models EVEN MORE as they can be further finetuned / distilled / pruned or otherwise modified and run locally with any prompt you would like with any behaviour you train it to perform (i.e. you CAN make your own model out of it, even if it is anti-“insert your identity here”).

    The final points of this article are worth implementing regardless of your stance, i’ll cite them here:

    • Start from a vetted model. Where a hardened or otherwise safety-improved version exists, deploy that rather than the stock weights.
    • Know what you are running. Track the provenance of every model, and pin a version you have evaluated.
    • Evaluate on your own work and agenda. Leaderboards use neutral prompts, so they miss behavior that only appears for a particular customer or a particular kind of task. Test under the identities and topics that matter to you.
    • Keep humans on the critical paths. Code that touches authentication, access control or user data needs an owner, plus the review and security testing you would give any outside contributor.
    • Fix what you find in the weights. A prompt instruction can’t remove a behavior the model learned, as Finding 3 shows. [Finding 3: Asking for secure code doesn’t fix it. Fixing the weights does]