AI Coding Live News

xAI adds a guidelines field to the Grok API for per-request moderation of NSFW, violence and profanity

On Oct 28 xAI announced on the official @grok account that all API customers can now add a guidelines array to text requests and set the nsfw, violence and profanity categories to decline, so Grok tightens that content while it writes rather than through after-the-fact filtering. The field works on the Responses API, Chat Completions and the xAI Python SDK; multi-agent models and deferred completions are not supported, and invalid values are rejected with 400 or 422 before the model runs.

xAI adds a guidelines field to the Grok API for per-request moderation of NSFW, violence and profanity

Key Takeaways

  • Three categories: nsfw, violence and profanity; the only behavior today is decline, and unlisted categories keep the default (docs)
  • Works on the Responses API, Chat Completions and chat.create in the xAI Python SDK; with the OpenAI Python SDK pass it via extra_body
  • Applies only to the request that carries it, so send it on every turn; the xAI SDK chat object stores it for you
  • Not supported on multi-agent models such as grok-4.20-multi-agent or deferred completions; duplicate categories return 400 and unknown values return 422
  • The official post had about 592K views and 271 likes when this was written
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TechCrunch: Abliteration.ai commercializes guardrail removal, hosts abliterated GLM-5.3

TechCrunch reports startup Abliteration.ai turned the long-standing open-source abliteration technique (orthogonalization that removes refusal tendencies) into a hosted browser/API service, including an abliterated build of Z.ai's recent open-weight GLM-5.3. The company says it targets "offensive cyber, red-teaming, and agent testing work other models refuse to do." TechCrunch created a free account and says the abliterated GLM-5.3 complied with requests for a Chrome password-stealer script and a protocol for culturing a dangerous human pathogen at home. The platform offers a customer-side moderation layer and keeps some limits (e.g. suicide instructions in testing) but has no full KYC beyond logging the payment card. Safety researchers (including CivAI) warn scaled access could enable harm; some red-teamers say they still rely more on fine-tuning open models and that abliteration can reduce capability.

Key Takeaways

  • Commercial hosted abliterated open-weights, including GLM-5.3, via browser/API
  • Pitch: offensive cyber, red-team, agent testing; TechCrunch saw high-risk prompts complied with
  • Debate: lowers misuse friction vs helps defenders; no full KYC yet
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Mistral introduces Shieldstral, a 3B open-weights on-device content-safety model

Mistral AI introduced Shieldstral, a 3B open-weights content-safety model that can be deployed on-device. It takes a moderation policy as a plain-language question and returns a calibrated score, with one interface for text and images. A technical report is up at arXiv:2607.25857.

Key Takeaways

  • Open-weight, on-device safety is aimed at teams that do not want moderation traffic in the cloud.
  • Policies are plain-language questions, with one interface for text and images.
  • At 3B parameters it can sit inside a local agent, gateway, or IDE plugin instead of a separate moderation service.
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Mistral Releases Shieldstral, a 3B On-Device Open-Weights Safety Model Under Apache 2.0

Mistral launched Shieldstral, a 3B Apache 2.0 open-weights content safety model optimized for on-device deployment on a single 16GB GPU with natural-language moderation policy evaluation.

Key Takeaways

  • Fully open-source under Apache 2.0, 3B parameters optimized for single 16GB GPU or edge inference;
  • Evaluates arbitrary plain-language safety policies with calibrated risk scores across text and vision;
  • Weights available on Hugging Face with accompanying technical whitepaper on arXiv.
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Linked Models & Agents:
⚡ Mistral Large 3⚡ Codestral 2501
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