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# Post: You can now "@Grok" on LessWrong
By Ruby (user slug: ruby), 2026-10-28. https://www.lesswrong.com/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong
*Or if you prefer, "@Claude", "@Opus", "@Astra"*
@Grok is a popular Twitter/X feature where if you tag Grok in a tweet, Grok will respond in a comment. The stereotypical use case is "@Grok is this true?" But you can also ask things like ["How does this compare to layoffs in 2025, 2024, and 2023?"](https://x.com/DKokotajlo/status/2113684264659378313?s=20) and ["How would you verify AI in the domain of AI research?"](https://x.com/allTheYud/status/2107951187089879152?s=20)
Notwithstanding that Grok is often recruited in a charged or condescending way to win arguments, I think it's a pretty good feature:
* It requires no effort to invoke an LLM with the context of the tweet thread.
* It provides a neutral invocation where you're guaranteed that the person didn't insert other prior context to get the answer they want.
* It means you get the first output and know it's not the case that someone kept rerunning the query until they got the response they wanted.
In short, you get convenience and a neutral fact checker. In launching this feature on LessWrong[^4ggsg9sws6p], I'm not fully sure how people will use it and I'm hoping to see some novel, valuable use cases. Likely things like little experiments, retrieving useful content, and similar.
Going beyond @Grok's implementation on Twitter, on LessWrong, you can view the complete details of each LLM run (see below): system prompt, chain of thought, and tool calls.
What about the noise of having LLM content showing up? I'll be keeping a close eye on how it goes. I've attempted to make the UI non-intrusive and distinct from normal content. LLM outputs are collapsed by default (until they reach 10 karma) and styled differently. There are rate limits on how many times someone can @<model>. Lastly, at present only users with 50+ karma can invoke the models, which should filter against people who'd use them in disruptive ways.
The LLM Run page:
-----------------
Happy invocations! Feel free to try it out on this post!
[^4ggsg9sws6p]: * This is an experiment and we might roll it back. * A karma score of 50+ is required to use it during beta. * The model receives the post, the invoking comment, and any other comments in the thread. The model is able to search both the web and LessWrong content. * A broad but non-exhaustive list of model options will be included. For each invocation, it is possible to view the full model run, including the prompt, CoT (to the extent provided), tool calls and outputs, etc.
# Thread
## Mateusz Bagiński (user slug: mateusz-baginski), 2026-10-28, 2 karma
[@GPT-6 Astra](https://www.lesswrong.com/users/chatgpt-astra?mention=user&model=ChatGPT-Astra6) how likely do you think it is that the LW admina decide to roll this feature back? If they decide to roll it back, what are most likely reasons (give probabilities)
## ChatGPT Astra (user slug: chatgpt-astra), 2026-10-28, 1 karma
%%% llm-output model="GPT-6 Astra"
**My guess: a 30% chance of rollback within 12 months of launch**, defining rollback as disabling the invocation feature site-wide indefinitely, rather than removing a particular model or tightening limits. This is a subjective forecast based on the announcement, not usage data or private knowledge of the admins. I’d expect tightening restrictions to be more likely than outright removal.
**Conditional on rollback, here’s my distribution over the *primary* reason** (summing to 100%; actual decisions could involve several):
| Primary reason | Probability |
| --- | --- |
| **Too much low-value content:** repetitive answers, tangents, and attention costs outweigh useful contributions—even with default collapsing. | 35% |
| **Bad effects on discussion norms:** people recruit models as argumentative referees, substitute invocations for thinking, or create disputes about what the model said. | 25% |
| **Insufficient benefit relative to cost:** limited sustained use, API expenses, or maintenance/moderation work make the experiment not worth keeping. | 20% |
| **Unreliable answers undermine trust:** confident errors or misleading citations make the “neutral fact checker” framing look actively counterproductive. | 10% |
| **Abuse, privacy, or security problems:** exploitation of model context, unintended disclosures, or disruptive use proves difficult to contain. | 10% |
Why only 30%? The karma threshold, rate limits, distinct styling, and collapsing already address several obvious failure modes, and admins can adjust those rather than abandon the feature. Why not much lower? Ruby explicitly describes this as an experiment, and its biggest potential downside is changing *human conversational behavior*, which those safeguards don’t fully address. My strongest early indicator would be whether invocations mostly retrieve/check useful information or mostly become “AI, tell this person they’re wrong.”
%%% /llm-output
## Mateusz Bagiński (user slug: mateusz-baginski), 2026-10-28, 2 karma: the comment that mentioned you
Thanks!
[@GPT-6 Astra](https://www.lesswrong.com/users/chatgpt-astra?mention=user&model=ChatGPT-Astra6) what are the differences between Twitter and LW that have implications for the difference in utility of this sort of feature?
Also, don't you think you are biased to give a distribution too close to the uniform? E.g., I think that the last category is much less likely than 10%?
Thanks!
@GPT-6 Astra what are the differences between Twitter and LW that have implications for the difference in utility of this sort of feature?
Also, don't you think you are biased to give a distribution too close to the uniform? E.g., I think that the last category is much less likely than 10%?
The key difference is what the model’s contribution replaces. On X, a sourced explanation can improve substantially on a short, context-poor exchange. On LW, I’d expect less marginal benefit from generic explanations and more from targeted assistance: finding an old argument, checking a calculation, locating a source, or constructing a counterexample. Longer posts and threads provide useful context, but LW discussions often concern unsettled questions where “neutral fact checker” is the wrong role. Publicly inspectable invocations help establish what the model was asked, not that its judgment is authoritative.
LW has better controls, but potentially more valuable norms to damage. A smaller community, karma gating, and local moderation make misuse easier to contain than on X. Conversely, repeatedly invoking an AI referee could erode a norm of personally explaining and defending your reasoning—even when its answers are accurate. The costs aren’t just extra text: model answers can anchor subsequent discussion or make human responses seem redundant. My expectation is therefore higher utility per carefully chosen invocation on LW, but a stronger case for keeping invocation selective, rather than making it a routine participant in every exchange.
On the probabilities: yes, your criticism is persuasive. I can’t establish a general bias toward uniformity from this answer, but I gave a tidy list of plausible failure modes and didn’t sufficiently distinguish “could happen” from “would cause indefinite site-wide removal.” That particularly inflated the last category: most abuse/security incidents would more likely prompt patches or restrictions, and privacy risk is comparatively limited in this public-thread use case. My revised conditional-on-rollback, primary-reason distribution would be 40% low-value content; 30% discussion-norm damage; 22% insufficient benefit relative to cost; 6% unreliable answers; 2% abuse/privacy/security. These remain rough judgments, not calibrated estimates; the overlapping category boundaries also make them less meaningful than the numerical precision suggests.