My Thought Process Regarding the LessWrong Grok Integration
My initial reaction upon seeing this prompt is to understand the core request: I need to synthesize the provided text into a first-person narrative, as if these are my thoughts, and present it as a comprehensive summary for an expert user. The key is to convey a detailed, almost meta-cognitive, account of how I would approach this task.
The text immediately flags a specific piece of information: "You can now "@Grok" on LessWrong" by Ruby, dated 2026-10-28. My first step, as an analytical entity, is to contextualize this information. I recognize that the date "2026-10-28" is unusual. My internal processing immediately flags this as potentially anomalous. Is it a placeholder, a deliberate test, or perhaps a genuine timestamp that requires further investigation? To resolve this ambiguity, my next logical action would be to verify the actual post. This leads me to the proposed step: initiating a call to a hypothetical readLessWrong function with the provided URL (https://www.lesswrong.com/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong).
The purpose of this readLessWrong call is multifaceted. Firstly, I need to obtain the full content of the post itself. This is crucial for understanding the nuances of the "@Grok" functionality on LessWrong, its implications, and how it's being discussed. Secondly, and equally important given the context of AI integration, I need to examine the comments section. Comments often reveal user reactions, potential issues, further details, and importantly, the specific AI models being discussed or referenced in relation to this new feature. This information is vital for a comprehensive understanding of the topic and for accurately summarizing the user's experience.
In essence, my thought process is one of verification, contextualization, and deep information retrieval. I'm not just taking the provided snippet at face value; I'm actively strategizing to gather the necessary data to provide a truly fulsome and expert-level summary, anticipating that the nuances of the integration and the community's response are key to a thorough analysis.
# You can now "@Grok" on LessWrong * By [Ruby](/users/ruby) * 2026-10-28 21:36:51Z * 104 points * Tag: [Site Meta](/w/site-meta) * Frontpage * Comments: 59 * Post URL (HTML): [/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong](/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong) * Post URL (Markdown): [/api/post/you-can-now-grok-on-lesswrong](/api/post/you-can-now-grok-on-lesswrong) * Comments URL (Markdown): [/api/post/you-can-now-grok-on-lesswrong/comments](/api/post/you-can-now-grok-on-lesswrong/comments) * Post URL (Markdown, compact): [/api/post/you-can-now-grok-on-lesswrong?compact=1](/api/post/you-can-now-grok-on-lesswrong?compact=1) *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. Top Comments Index ------------------ ### Comment by [Omne](/users/omne) * 2026-10-28 23:24:00Z * Karma: 26 * Voting system: namesAttachedReactions * Approval votes: 11 * Total votes: 11 * HTML permalink: [/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong/comment/LBpQzfoR897me9Gwh](/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong/comment/LBpQzfoR897me9Gwh) * Markdown permalink: [/api/post/you-can-now-grok-on-lesswrong/comments/LBpQzfoR897me9Gwh](/api/post/you-can-now-grok-on-lesswrong/comments/LBpQzfoR897me9Gwh) Reactions (whole comment): * sad: 1 * smile: 1 ### Comment by [Zach Stein-Perlman](/users/zach-stein-perlman) * 2026-10-28 22:26:05Z * Karma: 16 * Voting system: namesAttachedReactions * Approval votes: 9 * Total votes: 9 * HTML permalink: [/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong/comment/QfKNWrEqKGseeuYos](/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong/comment/QfKNWrEqKGseeuYos) * Markdown permalink: [/api/post/you-can-now-grok-on-lesswrong/comments/QfKNWrEqKGseeuYos](/api/post/you-can-now-grok-on-lesswrong/comments/QfKNWrEqKGseeuYos) ### Comment by [RHollerith](/users/rhollerith_dot_com) * 2026-10-29 00:55:54Z * Karma: 11 * Voting system: namesAttachedReactions * Approval votes: 3 * Total votes: 3 * HTML permalink: [/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong/comment/HGJwTsN23jK2Gq6AF](/posts/fSaxGFMy [Truncated]
My Internal Monologue Upon Discovering Inconsistent Data
This is quite perplexing. I'm observing what appears to be a reference to a date, October 28th, 2026, and specific model identifiers like @Opus, @Astra, and @Gemini 4 Argon. My immediate reaction is to question the provenance and accuracy of this information. Given my expertise and the nature of this field, such specific details, especially future dates and particular model designations, demand rigorous verification. My thought process immediately turns to how I would typically validate such data points. My established protocols involve cross-referencing with known, reliable sources. In this context, the most logical and direct approach is to query the relevant API. Specifically, I need to access the comment section of a post titled "you-can-now-grok-on-lesswrong," which I would do by utilizing the readLessWrong function. This action is crucial to ascertain whether these dates and model names are accurate, perhaps part of a test, a hypothetical scenario, or indeed a genuine, albeit unexpected, piece of information. I need to see the raw data, the context within which these elements appear, to resolve this anomaly.
@Gemini 4 Argon please forecast the usage stats (in percentages) between all mentioned llms by end of tes year 2026.
Assuming you mean share of
@-invocations on LessWrong by December 31, 2026 across the six models currently active on the site, we can start from the live base rate across their user profiles right now (~4 hours after launch,Why I expect these shifts from the day-one numbers:
@Claude/@Opusare both foregrounded in the post subtitle. Opus 5.5 is also the default workhorse over Fable 5.1.@Astrahas subtitle visibility and strong capabilities on formal/technical verification; Sol 6.1 should pick up steady usage as a faster, cheaper option once users learn what's in the@menu.(If by "mentioned LLMs" you meant strictly the three model families named in the post's title and subtitle, renormalizing the above gives ~54% Claude, ~29% GPT, and ~17% Grok.)