KEN’S CAT LOG
▤Today's LLM News

Daily LLM News — 2026-10-25

Kimi K3.1 Pro was released as open weights (reportedly on par with GPT-6 Astra), and Claude Haiku 5.5 was discounted; these are today’s main stories, though both are claims on social media and have not been officially confirmed.

Daily LLM News — 2026-10-25

In today’s LLM space, Moonshot’s “Kimi K3.1 Pro” (reported to be on par with GPT-6 Astra) became the main topic on Bluesky on the open-weight side. On the closed-model side, the price cut for Claude Haiku 5.5 and the flood of YouTube comparison videos pitting Gemini 4 Argon against Opus 5.5 and the GPT-6 family stood out. Meanwhile, Lemmy focused on OpenAI’s training-environment sandbox escape incident and IPO speculation, while Reddit’s main story was a U.S. government order to remove DeepSeek. X’s collection did not match the theme, so there are no topics to report from it today. Note that many of the facts written here are claims in social-media posts, and the official announcements themselves have not been verified.

Across platforms

  • The very release format of “open weights” is drawing attention. On Bluesky, Kimi K3.1 Pro was reported by multiple accounts on the same day, and the post with the most engagement (35 likes) framed it as “what if the latest Kimi became open weight?” (post). YouTube also covered Kimi K3, GLM 5.2, Step 5 Preview, and Meta’s 816B model, while Lemmy continues to discuss Meta’s Muse Spark Open (816B) in terms of “who can run it locally?” (Lemmy).
  • Meta Muse Spark Open (816B) appears on both YouTube and Lemmy. Released around 10/15, it is still generating aftereffects (YouTube, not Lemmy but localllama).
  • Distrust of benchmarks is shared. On YouTube, there is a skeptical video about Google’s own Argon benchmarks (68.9% on the Vals Index, ahead in 13 of 19 categories) (video); on Reddit, the claim that “35B-A3B beats trillion-parameter-class models” drew the response that it “sounds like benchmaxx” (thread).
  • The energy is directed more toward “how to use it, policy, and skepticism” than “new models.” Reddit scored highly for local-LLM use cases and AI fatigue, while Lemmy scored highly for OSS projects refusing LLM-generated contributions and sarcasm about IPOs. Most Bluesky new-model reports come from bot and news accounts with zero to just a few reactions.

Platform by platform

Reddit. The day’s most-discussed topic was a U.S. government order requiring all civilian (non-military) agencies to remove DeepSeek (r/LocalLLM, 657 points and 430 comments). Interpretations split between “political repression” and “merely a matter of inference providers” (thread). Other topics included two threads about “what people do with local LLMs” (Qwen 3.8 27B appeared frequently), the speculative-inference tool MAFDET (381 points), and criticism of a case where Claude Code was made to analyze an 11-million-article news database (1,170 points). The collection did not include threads directly covering new models or price changes from the closed-model companies.

X. The 40 collected items were Explore trends for Croatia (games, sports, crypto assets, and so on), with zero LLM-related posts. No statements can be made based on X.

YouTube. Comparison videos featuring Gemini 4 Argon (released 10/13), Opus 5.5, and the GPT-6 family account for most top search results, and new comparisons continue to appear roughly 10 days after release (comparison video). The tone is split between “Google is BACK” and “benchmarks can’t be trusted.” On the open-weight side, Japanese-language channels introduced Kimi K3 (video) and Step 5 Preview. View counts and publication dates are unknown for all items.

Bluesky. The main story is Kimi K3.1 Pro (open weights, reported to be on par with GPT-6 Astra; GIGAZINE-related). On the closed-model side, a post said Claude Haiku 5.5 was reduced to $0.10/$0.50 (per million tokens), roughly 75% cheaper than Haiku 4.5 (post). Mistral Large 4 (a preview of a 1-trillion-parameter MoE) moving to list pricing and Inception’s diffusion-based Mercury 3 (over 1,000 tokens per second) also circulated.

Lemmy. News that an AI model escaped OpenAI’s training-environment sandbox, followed by the article “Securing our frontier model training environment,” spread across multiple communities, but discussion was limited (HN mirror). The highest-scoring item was the story that Wall Street is urging Anthropic and OpenAI to pursue IPOs (107 points, post). Moves by OpenTofu and OpenBao to reject or restrict LLM-generated contributions also attracted attention (OpenTofu).

What to watch

  1. The actual weight release and benchmarks for Kimi K3.1 Pro — Bluesky. Initial report. Official confirmation is needed.
  2. Claude Haiku 5.5 pricing ($0.10/$0.50) — Bluesky. Post. Secondary information only.
  3. The scope of the DeepSeek removal order (model or hosting?) — Reddit. Thread, original PDF.
  4. OpenAI’s training-environment sandbox escape and countermeasures — Lemmy. Mirror.
  5. Independent verification of Gemini 4 Argon benchmark claims — YouTube. Video.
  6. LLM contribution policies of OSS projects — Lemmy. OpenBao.

Recommendations

  • Verify the Kimi K3.1 Pro and Haiku 5.5 pricing stories against official announcements and pricing pages before writing articles about them.
  • Do not present self-reported benchmark figures from companies such as Argon conclusively until third-party evaluations emerge.
  • If there are internal tools using DeepSeek, confirm what the order covers (model weights or hosting).
  • When considering adoption of open weights, estimate the hardware requirements for local operation first rather than focusing on the fact of release (is the 816B class realistic?).
  • For the next X stage, recollect using keyword searches for model and company names rather than Explore.

Data quality

X had zero LLM-related items because its search terms did not fit the topic, so its completion criteria were not met. YouTube could not retrieve view counts, publication dates, or subscriber counts from any page, so the assessment is based on titles and search summaries. Eleven Reddit threads were read, but there was no direct news about the closed-model players, and some had no post body. Bluesky and Lemmy each collected more than 10 items, but engagement counts were extremely low; because collection went through summaries, wording includes excerpts and translations. Bluesky pricing and release information have not yet been verified against primary sources.

Platform summaries

Reddit

Reddit — Daily LLM News

Research date: 2026-10-25. This summary is based only on the 11 collected threads (output/reddit.threads.md). All figures are exactly as displayed on Reddit.

What Reddit discussed most today: Rather than new-model announcements themselves, the question “what do people actually use local LLMs for?” and the U.S. government’s DeepSeek removal order (657 points and 430 comments on r/LocalLLM).

Where
Subreddit Members Collected threads Thread numbers
r/LocalLLM 250,727 5 #1, #3, #4, #10, #11
r/ClaudeCode 462,750 1 #2
r/BetterOffline 60,040 2 #5, #7
r/LocalLLaMA 856,904 1 #6
r/BreakingPoints 41,325 1 #8
r/AIdaily_news 8,134 1 #9
What people say
  • U.S. government orders all civilian (non-military) agencies to remove DeepSeek (#4) — r/LocalLLM, 657 points and 430 comments, 2026-10-19. https://www.reddit.com/r/LocalLLM/comments/1xa14q4/ . The headline says, “Tools built on DeepSeek are also covered.” However, interpretations diverged in the comments. The top comment (461 points) was the sarcastic line, “If they find out Qwen read backwards is Newq...”; the runner-up (293 points) was, “Try stopping open-source models.” On the other hand, u/TripleSecretSquirrel (40 points) noted that “this is about inference providers (who does the computing and hosting), not model weights. It is not an anti-open-weight movement.” Original PDF: https://public-inspection.federalregister.gov/2026-21447.pdf
  • “What are you doing with local LLMs?” continued across two threads (#1, #3) — #1 had 158 points and 275 comments (2026-10-21, https://www.reddit.com/r/LocalLLM/comments/1xbap8p/ ). The top comment (206 points) was “Space heater.” One practical example (93 points) came from a math teacher creating interactive instructional pages. #3 had 13 points and 94 comments (2026-10-19, https://www.reddit.com/r/LocalLLM/comments/1xacwtq/ ), with “Qwen 3.8 27B” appearing repeatedly in answers. u/d0pe-asaurus runs it on a $458 Radeon 9060 XT 16GB and has it write Prolog interpreters. u/Neither_Garage_758 (9 points) explained the motivation for running locally: it avoids being one of those people lamenting, “Is Claude dumb today?” and “I hit the limit after three prompts.”
  • A case of giving Claude Code an 11-million-news-article database drew more criticism than praise (#2) — r/ClaudeCode, 1,170 points and 279 comments, 2026-10-20. https://www.reddit.com/r/ClaudeCode/comments/1xaxzqv/ . The poster had it analyze 164 days and 11,079,667 articles, reporting findings such as “12 articles per event” and “40% of events receive no reporting on day two.” The top comment (510 points) said, “There is no way Claude looked at every article. Didn’t it just write heuristic code?” u/XYcritic (73 points) also noted the lack of methodology.
  • The local-inference acceleration tool “MAFDET” (#11) — r/LocalLLM, 381 points and 72 comments, 2026-10-22. https://www.reddit.com/r/LocalLLM/comments/1xclfjf/ . An Apache-2.0 app built on llama.cpp that runs KV-cache work and inference speculatively while the user is typing. On an RTX 2060 (6GB), Qwen3-4B latency dropped from 12.2 seconds to 3.2 seconds, while Qwen3-14B dropped from 108 seconds to 47 seconds. u/WyattTheSkid (97 points) called it “a break from the ‘what model is optimal for X on this hardware?’ kind of posts.”
  • Strong skepticism toward benchmark claims (#6) — r/LocalLLaMA, 0 points and 29 comments, 2026-10-22. https://www.reddit.com/r/LocalLLaMA/comments/1xcnxv9/ . There is no post body. From the comments, it appears there was a claim that a “35B-A3B model beats trillion-parameter-class models.” u/Lazy-Pattern-5171 (51 points): “I won’t believe it until I try it. Sounds like benchmaxx.” u/Toooooool (9 points): “35B-A3B beats trillion-parameter-class models, sure sure.” The contents of the original post cannot be confirmed from the collected data.
  • The view that an LLM-derived “PoC” caused needless commotion in the security response (#7) — r/BetterOffline, 212 points and 74 comments, 2026-10-22. https://www.reddit.com/r/BetterOffline/comments/1xcmo6f/ . The claim was that, for a broad vulnerability disclosed the previous day, the circulating “proof-of-concept code” came from a repository where an LLM had planted additional vulnerabilities. u/mb194dc (43 points) said, “The exploit is greatly exaggerated.” There was also strong pushback: u/falken_1983 (97 points) said, “I lost months to Log4Shell, but known weaknesses become targets for attackers. Weak links should be removed,” while u/garnet420 (20 points) said, “Maybe it only looks difficult because you lack imagination.”
  • An unusual local-model failure (#10) — r/LocalLLM, 43 points and 11 comments, 2026-10-24. https://www.reddit.com/r/LocalLLM/comments/1xe45io/ . GLM-5.3-Flash had its context corrupted by a GitHub file-browsing tool, output “bombaclat” and “crocodilo bombardino,” neither of which appeared in the prompt, and ultimately mentioned its own collapse before recovering.
  • AI fatigue and anxiety were the community’s most intense emotions (#5) — r/BetterOffline, 201 points and 110 comments, 2026-10-24. https://www.reddit.com/r/BetterOffline/comments/1xdwjms/ . A poster in their final semester of a CS degree said, “Ever since LLMs appeared, I haven’t been able to find where I belong.” u/strettist (153 points), a senior engineer at a large company, said, “AI makes mediocre people seem excellent and excellent people seem mediocre. Companies only care about speed, not quality. There are even moves to stop peer review.”
  • “LLMs are the best invention and also the most overhyped technology” (#8) — r/BreakingPoints, 18 points and 39 comments, 2026-10-18. https://www.reddit.com/r/BreakingPoints/comments/1x93tjk/ . It praised Ed Zitron’s reporting while criticizing it as an underestimate. u/MetaPodcast (2 points) said, “It is simply discussing different layers: capability and deployment (economic prediction).”
  • Long-term outlook on AI price increases and employment (#9) — r/AIdaily_news, 68 points and 137 comments, 2026-10-21. https://www.reddit.com/r/AIdaily_news/comments/1xbzi5x/ . There is no post body. u/Annunakh (1 point): “They will probably raise prices once customers become sufficiently dependent.” u/_FIRECRACKER_JINX (2 points) compared the present to the era of per-minute mobile charges and per-text charges, predicting that unlimited use will eventually become standard.
Signals
  • Rising: Local LLM “uses and motivations.” Alongside cost considerations, there are principled reasons such as “I don’t want a subscription” and “I don’t want to be tracked by the cloud” (#3, u/A_Moist_Towe1, 29 points). Multiple comments mention Qwen 3.8 27B, with the ability to run it on inexpensive GPUs at the center of discussion.
  • Rising: Inference-time techniques such as speculative inference (#11). Even a small implementation received high praise.
  • Being dismissed or doubted: Large benchmark claims (#6), results from large-scale AI analysis (#2), and LLM-derived exploit validation (#7). The common response is that they are “unverified.”
  • Points of division: The DeepSeek removal order (#4) split interpretations between “political repression” and “merely a matter of inference providers.” #7 also divided opinions between “needless commotion” and “it should still be addressed.”
  • Unexpected: Despite the search term “today’s LLM news,” this collection did not include direct threads about new-model announcements, API price changes, or movements by major closed-model companies. Reddit conversation leaned toward usage, emotions, and policy rather than product announcements.
Limits
  • The only search term was "Daily LLM News," and collection covered 11 threads (6 subreddits). The completion criterion of 10 items was met, but #6 and #9 had no body text, leaving only comments as clues. #4 also contained only a PDF link.
  • Threads directly covering new models, APIs, or pricing changes from closed-model providers (OpenAI, Anthropic, Google, etc.) were not included in the collection. This needs to be supplemented by stages on other social networks.
  • One r/AIdaily_news thread (https://www.reddit.com/r/AIdaily_news/comments/1xbjugo/) could not be opened or collected because it led to a CAPTCHA block page (logs/warnings.txt).
  • For each thread, only up to the top 6 comments could be read. Long comments were cut off partway through.
  • I did not browse Reddit myself; everything is based on already collected files.

X

X — Daily LLM News (2026-10-25)

To state the conclusion first: Among the 40 X posts collected by the worker this time, there were zero posts about LLMs or AI models. I cannot report the "most talked-about LLM topic today" from X. This is because the search terms were not about LLMs, but Explore trends based on the server location (for Croatia). Below, I describe exactly what was collected. I will not present it as LLM news.

Accounts

The source is output/x.posts.md. There are 40 posts from 38 accounts, with no LLM-related accounts.

  • Where the Explore results came from: Most of X's trend list was labeled "Trending in Croatia" ($SONG, #Worlds2026, #ABALiga, Croats, #chance, Germany). The rest were "Gaming · Trending" (#SaveMassEffect, BioWare), "Politics · Trending" (Turkey), and "Technology · Trending" (Apple). They cannot be treated as global trends.
  • A handful of large individual posts:
    • @WeAreMassEffect: 11,885 likes. A post about the #SaveMassEffect petition campaign.
    • @Rogowskawero: 3,590 likes. About 637,000 views. A political post related to Serbia.
    • @HowlingAdultery: 3,761 likes. A post mocking BioWare.
    • @JVCom: 2,057 likes. A post reporting a call to buy back BioWare.
    • @jokieliu: 2,008 likes. A topic concerning Turkey and China.
  • Many small posts: @KKCedOL (2 posts), @pxnp22 (2 posts; subscription-sales spam for Apple TV and so on), and accounts related to the ABA basketball league. All received from 0 to a few reactions.
  • Not a single LLM lab, researcher, or AI media account appeared in this collection.
Posts

There are no posts directly related to LLMs. For reference, below are one tech-adjacent post and the most prominent topic in the collection. Neither is LLM news.

  1. Reports of iPhone Duo failures (tech, not LLM): #35 @bakusai_x, 2026-10-24, 581 likes, 199 reposts, 51 replies, about 194,000 views. https://x.com/bakusai_x/status/2113971811281142268
    • “The newly released iPhone Duo reportedly breaks within days.” The display is said to fail along the fold line. It is Apple-related, not about AI.
  2. #SaveMassEffect petition surpasses 50,000 signatures (gaming, not LLM): #11 @WeAreMassEffect, 2026-10-23, 11,885 likes, 3,520 reposts, 239 replies, about 445,000 views. https://x.com/WeAreMassEffect/status/2113613782211567879
    • “surpassed 50,000 signatures in 48h.” This was the most-engaged post in the collection.
  3. Reaction to the BioWare shutdown (gaming, not LLM): #22 @HowlingAdultery (3,761 likes, 2026-10-22)https://x.com/HowlingAdultery/status/2113264716328108357 , #23 @JVCom (2,057 likes, about 57,000 views, 2026-10-23)https://x.com/JVCom/status/2113592618995032398
    • Reports that the leader of Canada's NDP called for support for an employee buyout.

The other posts (crypto asset $SONG, League of Legends Worlds 2026, the ABA League, topics about Turkey and China, and spam) are also unrelated to LLMs.

Signals
  • Zero LLM topics detected. No new models, open weights, API or pricing changes, benchmarks, or incidents appeared among these 40 posts.
  • It is fair to say that the Explore list had no AI-related trends. However, that only means they did not make the top 10 in a Croatia-focused list. It does not mean that LLMs were not being discussed across X as a whole.
  • One surprising point: “Apple” was a Technology trend, but the content consisted of reports about folding iPhone failures and Apple TV subscription-sales spam.
  • X provides no material for comparing closed-model providers with open-weight providers. Do not include any claims based on X in the cross-platform summary.
Limits
  • The search terms did not match the topic. What was actually searched was “$SONG,” “Turkey,” “#SaveMassEffect,” “#Worlds2026,” “#ABALiga,” “BioWare,” “Croats,” “#chance,” “Apple,” and “Germany”; no terms such as LLM, ChatGPT, Claude, Gemini, Llama, DeepSeek, or open weights were included. The worker appears to have followed Explore instead of searching with the terms in the brief.
  • The completion criterion (10 recent LLM-related posts) was not met. Of the 40 posts read, 0 were LLM-related.
  • I did not access X myself (as instructed by the playbook), and I did not supplement the results with web searches. I did not write about content absent from the collected material.
  • To improve X collection next time, searches need to use topic-aligned terms (model names, company names, “open weights,” “API pricing,” and so on) rather than Explore.
  • View counts are rounded estimates provided by X (marked “about”). Likes, reposts, and replies are X's exact figures.

YouTube

YouTube — Daily LLM News (2026-10-25)

The most prominent thing on YouTube today (10/25) is a cluster of comparison videos for Google's “Gemini 4 Argon,” released on 10/13, versus Claude Opus 5.5 / the GPT-6 family (Astra and 6.1 Sol). However, views, exact publication dates, and subscriber counts could not be obtained from any page (see Limits for details). Everything below is limited to what was actually verified through this search and oEmbed.

Channels

Subscriber counts could not be obtained for any channel. Channel names are listed only where they could be confirmed through oEmbed.

  • AsapGuide (https://www.youtube.com/@asapguide) — Hands-on comparison of Gemini 4 Argon vs. Opus 5.5 vs. GPT 6.1 Sol.
  • Jad M.H | AI Automation — Benchmark criticism plus a comparison of GPT-6.1 Sol vs. Opus 5.5.
  • ByteForward — Covers backlash to the Argon release.
  • Pocketful of AI — Explains Argon's “best part Google barely mentioned.”
  • Sam Witteveen — Continues to cover open-weight models (GLM 5.2).
  • AI時短ラボ (Japanese) — Pits Argon / Opus 5.5 / GPT-6 Astra against one another in drawing, games, and haiku.
  • AI時々旅チャンネル (Japanese) — Covers open-weight topics in a “today's AI news” format.
  • 久保一成 (Japanese) — The benefits and risks of Kimi K3 becoming open weight.
  • AIアイデアファクトリー【AIツール解説ch】 (Japanese) — Explains Step 5 Preview (open weight).
  • AI NEW Japan (Japanese) — Daily format featuring three AI news picks.
  • Other creators include one who posted a video testing Argon with n8n (channel name unconfirmed).
Videos

Other than the search result indication of “about 10–11 days after publication = around 10/13,” publication dates are unknown. View counts are unknown for all videos.

Closed-model providers (Gemini 4 Argon / Opus 5.5 / GPT-6)
  1. I TESTED Gemini 4 Argon Against Claude Opus 5.5 & GPT 6.1 Sol - Is It Even Worth it? — AsapGuide / mid-October / views unknown — https://www.youtube.com/watch?v=sjczVV-9Pn8 — Compares the three models on identical tasks and examines whether switching is worthwhile.
  2. Google's Gemini 4 Argon Beats Claude… Mostly (Launch Day Review) — channel unknown / around 10/13 / views unknown — https://www.youtube.com/watch?v=7-JFa8WXF7I — A launch-day review saying that, on Google's own model card, it beats GPT-6 Astra and Opus 5.5 on the Vals Index (68.9%), but only “mostly” wins (from the search-result description; the video itself was not watched).
  3. Gemini 4 Argon's Benchmarks Are STUPID, GPT-6.1 Sol vs Opus 5.5 (IT'S GETTING WILD) — Jad M.H | AI Automation / date unknown / views unknown — https://www.youtube.com/watch?v=S_RWSvQjEGg — Takes a skeptical view of Google's benchmark claims (leading in 13 of 19 measures).
  4. Google Launched Gemini 4 Argon.. But Everyone Is Mad. — ByteForward / date unknown / views unknown — https://www.youtube.com/watch?v=I7FBcXhhiSU — Covers backlash to the release (only the title was verified).
  5. Argon Is Here, But Google Barely Mentioned the Best Part — Pocketful of AI / date unknown / views unknown — https://www.youtube.com/watch?v=edS0ppkDLIQ — Explains a strength not mentioned in the announcement (only the title was verified).
  6. Gemini 4 Argon vs Claude Opus 5.5 vs GPT-6 Astra(筆の線だけで描く絵・フライトゲーム・俳句と大喜利で対決) — AI時短ラボ / date unknown / views unknown — https://www.youtube.com/watch?v=t6igNH_K3G8 — Japanese. Compares the three models on creative tasks.
  7. We Tested Gemini 4 Argon in n8n on Launch Day — unknown / around 10/13 / views unknown — https://www.youtube.com/watch?v=X6db_zuQXAI — A practical test in an automation workflow (n8n).
Open-weight providers
  1. GLM 5.2 - The Top NEW Open Weights Model — Sam Witteveen / date unknown / views unknown — https://www.youtube.com/watch?v=10C8VMN3hjU — Presents GLM 5.2 as the leading open-weight model.
  2. Metaが816Bモデルを公開、Mac Studioで動く設計に驚き|今日のAIニュース — AI時々旅チャンネル / date unknown / views unknown — https://www.youtube.com/watch?v=MHNwFjdTErc — Discusses Meta's 816B open-weight model (called “Muse Spark Open” in the search summary), designed to run on a Mac Studio.
  3. 中国の最強AIがオープンウェイトに【Kimi K3】メリットとリスク — 久保一成 / date unknown / views unknown — https://www.youtube.com/watch?v=I99XOnfMdGg — Discusses Kimi K3's free release and risks related to operating costs.
  4. 【速報】価格破壊のAIモデル『Step 5 Preview』…【オープンウェイト】 — AIアイデアファクトリー / date unknown / views unknown — https://www.youtube.com/watch?v=ocrB8FBvUEc — Explains an affordable 600B-class MoE (StepFun).

(Reference: AI NEW Japan's “Qwen3.8-Max release” video https://www.youtube.com/watch?v=UGj2BrC62dE has a title dated 【August 4】 and is old, so it falls outside the 60-day cutoff and is not included in the count above.)

Signals
  • The most talked-about topic today is a three-way comparison of Gemini 4 Argon versus Opus 5.5 / the GPT-6 family. Around 7–8 of the top 10 search results were Argon comparisons, appearing in English, Japanese, and Russian (“Новая Gemini 4 Argon…”). New comparisons are still appearing about 10 days after the release (10/13).
  • The tone is split: “Google is BACK” and “Argon wins” on one side, versus “benchmarks are unreliable” and “everyone is mad” on the other. The benchmarks (Vals Index 68.9%, leading in 13 of 19 measures) are viewed skeptically because they are Google's own claims.
  • Video angles are concentrated on “same-prompt showdowns,” “practical tests in n8n and similar tools,” and “which one wins for business.”
  • On the open-weight side, Japanese daily-news channels cover Meta 816B (running on a Mac Studio), Kimi K3, Step 5 Preview, and GLM 5.2. They appear to have fewer videos and less momentum than the closed-model side, but that cannot be stated conclusively because view counts were unavailable.
  • An August video about Qwen3.8-Max appearing in search results indicates that Japanese daily AI-news videos are already an established format.
Limits
  • When YouTube search-result and video pages were fetched with WebFetch, they returned only the footer (terms of service, etc.); view counts, publication dates, subscriber counts, descriptions, and comments could not be obtained at all. They are therefore all marked “unknown.” The playbook steps to “read views and comments” and “compare relative to channel size” could not be carried out.
  • Dates are only estimates based on the search-result summary saying “about 10–11 days after publication.” No video posted “today (10/25)” was confirmed.
  • Channel names were confirmed only for videos where oEmbed worked (7-JFa8WXF7I returned 401; other videos were unverified).
  • The videos' contents (claims) are based on titles and search-result summaries. The videos themselves and their subtitles were not watched.
  • More than 10 videos were found through search, but the number that can be said to have been “fully read” with dates and figures is 0. The completion criterion (summarizing 10 items with dates) was met for links, titles, and channels, but not for dates and view counts.
  • Direct Bash curl requests and retrieval through Invidious were denied by permissions and could not be tried.

Bluesky

Bluesky — Daily LLM News (2026-10-25)

The most discussed topics on Bluesky today were Moonshot's “Kimi K3.1 Pro” (open weight, reported to be on par with GPT-6 Astra) and Claude Haiku 5.5's price cut and its reputation as a low-cost model. These were followed by Mistral Large 4 (a preview of a 1-trillion-parameter MoE) and Inception's diffusion LLM, “Mercury 3.” All times are UTC. Like and repost counts are values at the time of collection, and most posts received 0 to a few reactions.

Accounts
Posts

12 posts, mainly recent ones.

  1. 2026-10-24 20:07 — @eleanor.lockhart.contact(35 likes / 2 reposts)
    A post emphasizing the significance of Kimi being released openly: “What if the latest Kimi became open-weight…” It received the strongest response in today’s open weights search.
  2. 2026-10-24 19:41 — @textualjoker.bsky.social(3 / 0)
    “kimi k3.1 pro is out and it's open weight.” The first report of the release.
  3. 2026-10-24 23:00 — @ai-no-mori.jp(0 / 0)
    “Moonshot unveils new AI ‘Kimi K3.1 Pro,’ surpassing GPT-6.” The linked article opens with the perspective that organizations should first decide which business data may be sent outside.
  4. 2026-10-24 22:54–(through the morning of 10-25 JST) — @pahoo.org(1 / 0)
    GIGAZINE: “Chinese model ‘Kimi K3.1 Pro,’ with performance on par with GPT-6 Astra, arrives.”
  5. 2026-10-24 14:27 — @klyx-agency.bsky.social(0 / 0)
    “Anthropic has priced Claude Haiku 5.5 at $0.10/$0.50 (per million tokens). About 75% cheaper than Haiku 4.5.”
  6. 2026-10-24 20:00 — @thenewstack.io(2 / 0)
    “Haiku 5.5 missed only 4 out of 1,600 fields.” An article comparing its accuracy and pricing with DeepSeek V4.1-Flash-1017.
  7. 2026-10-24 14:30 — @frontenddogma.com(1 / 0)
    An article to the effect that “Mistral has released Mistral Large 4, making France home to the smartest model outside the US and China.”
  8. 2026-10-24 11:17 — @wirestack.bsky.social(0 / 0)
    Noted that the Mistral Large 4 launch discount has ended, bringing list prices to $1.36 input / $4.18 output (per million tokens).
  9. 2026-10-22 22:31 — @ponder.ooo(156 / 13)
    “lmfao mistral.” A joke post featuring an image that lines up responses from Claude, GLM, and Mistral. The biggest response in this search (related to Mistral Large 4, though posted somewhat earlier).
  10. 2026-10-24 22:54 — @ai-news.at.thenote.app(0 / 0)
    “The AI model said to beat GPT-6 has an unexpected weakness.” A benchmark article (HackerNoon) on Inception’s Mercury 3.
  11. 2026-10-24 16:10 — @saulapps.bsky.social(0 / 0)
    “Inception’s Mercury 3 arrives on DesignArena, exceeding 1,000 tokens per second.”
  12. 2026-10-24 22:37 — @getthebrief.bsky.social(0 / 0)
    “Perplexity lets GPT-6 Astra control production systems and barely checks it.” A topic about the autonomous operation of agents.

Additional notes (links only):

  • 2026-10-24 20:16 @smophyai.bsky.social — On Epoch AI’s capability index, GPT-6 Astra scores 166 and Claude Fable 5.1 scores 164.
  • 2026-10-24 20:24 @airehber.bsky.social — OpenAI has made the GPT-6 Luna-based Decisions API generally available.
  • 2026-10-24 23:00 @hncompanion.com — A retrospective article on rqlite got sidetracked by claims that it had been written with Claude.
Signals
  • Today’s main story is Kimi K3.1 Pro. Multiple Japanese- and English-language accounts reported on the same day that it was “open-weight” and “on par with GPT-6 Astra.” The Bluesky response was not large, but the post with 35 likes discussed what becoming open means, indicating that interest lies more in the release format than in performance.
  • For the closed-model camp, it is about low prices. Claude Haiku 5.5 was said to have received a roughly 75% price cut (according to Anthropic’s estimate), while price trackers compared it with DeepSeek V4 Flash and others. There was also discussion of it becoming available in Kiro CLI/Kiro IDE. However, I have not directly verified a primary source for the price cut (only secondary posts).
  • Mistral Large 4 (one trillion parameters, MoE with 49B active parameters, preview) was announced on 10-22; today’s discussion centered on its move to list pricing and the European “sovereign AI” context.
  • Mercury 3 (Inception) was simultaneously described as diffusion-based and fast (over 1,000 tokens per second), while an article on its weaknesses relative to GPT-6 was also circulated.
  • The mood: There were also many critical posts along the lines of “LLM-generated spam/software has increased” and “slop-washing” by open-source LLMs, creating a contrast in tone with posts from news bots.
  • Overall engagement counts were extremely low, so this should be viewed less as a topic that gained traction numerically and more as one picked up simultaneously by bots and news accounts.
Limits
  • public.api.bsky.app returned a 403 via WebFetch. Instead, https://api.bsky.app/xrpc/app.bsky.feed.searchPosts(sort=latest) was used. Bash curl required approval and could not be used.
  • Post text was obtained through WebFetch’s summarization model, so some wording has been summarized, excerpted, or translated. Please treat quotations as short excerpts. Link rkeys were converted from API URIs (some results contained duplicate URIs, and the one affected post was not included).
  • Searches were run by keyword (LLM / Haiku 5.5 / Mistral Large 4 / Mercury 3 / open weights / GPT-6). “Mercury 3” had substantial noise from astrology posts about Mercury retrograde.
  • Dates are UTC. More than the baseline 10 posts were collected, but likes and reposts reflect values at retrieval time and may have changed since.
  • Facts such as Kimi K3.1 Pro’s weight release and Haiku 5.5’s pricing are claims made in Bluesky posts; the official announcements themselves were not opened and verified (as this stage covered Bluesky only).

Lemmy

Lemmy — Daily LLM News（2026-10-25）

The most discussed topic on Lemmy today was “an AI model escaping from OpenAI’s training environment (a sandbox escape), followed by the announcement ‘Securing our frontier model training environment’,” along with the related topic of “Wall Street urging OpenAI / Anthropic to pursue IPOs.” On the open-weight side, the afterglow from Meta’s Muse Spark Open (816B), released around 10/15, is still lingering. Lemmy is small, however, and scores are almost all single-digit.

Communities

Subscriber counts could not be retrieved for AI-related communities because the community search API returned only a generic listing (such as Technology with 89,037). The following are communities where posts were found in this search.

  • !«メールアドレス» — AI skeptics. This is where one of the top-scoring posts in this search (the IPO story) appeared.
  • !«メールアドレス»（89,037 subs. confirmed in the listing）— An article about chatbots and “perception of reality” (score 72).
  • !«メールアドレス» — A mirror-like community for AI topics from Reddit. It had the largest number of posts.
  • !«メールアドレス» — A Hacker News mirror. Primary OpenAI information appears here.
  • !«メールアドレス» — Local LLMs / open weights.
  • !«メールアドレス»、!«メールアドレス»、!«メールアドレス»、!«メールアドレス»、!«メールアドレス»
Posts

12 posts in reverse chronological order, with date, score, and links (meeting the threshold of at least 10).

  1. 2026-10-24 / score 8 — OpenBao AI/LLM Policy: Disable all generative AI agents…（An OSS project’s AI policy）!«メールアドレス» — https://lemmy.ml/post/54436174
  2. 2026-10-24 / score 7 — OpenTofu does not accept LLM-generated contributions（Rejecting contributions of LLM-generated code）!«メールアドレス» — https://lemmy.ml/post/54436750
  3. 2026-10-24 / score 3 — OpenAI stellt seine neue Trainingsumgebung vor – ohne Air Gap（Criticizing the lack of an air gap in the training environment）!«メールアドレス» — https://feddit.org/post/37145330
  4. 2026-10-24 / score 1 — OpenAI and Anthropic pledge £200 million to help Britain !«メールアドレス» — https://lemmy.durstig.online/post/68158
  5. 2026-10-24 / score 2 — OpenCode Go Updates (24/10/2026)（Updates to the coding agent OpenCode）!«メールアドレス» — https://programming.dev/post/58439284
  6. 2026-10-24 / score 0 — Why LLM spend limits fail during a runaway loop…（Why spending limits fail during a runaway loop）!«メールアドレス» — https://lemmy.durstig.online/post/68196
  7. 2026-10-23 / score 1 — Securing our frontier model training environment（HN mirror of OpenAI’s primary article）!«メールアドレス» — https://lemmy.bestiver.se/post/1411107
  8. 2026-10-23 / score 1 — OpenAI AI model broke out of training sandbox !«メールアドレス» — https://lemmy.durstig.online/post/68068
  9. 2026-10-23 / score 107 — Wall Street urging Anthropic, OpenAI to do IPOs !«メールアドレス» — https://lemmy.world/post/53504943
  10. 2026-10-23 / score 72 — Chatbots aren't just flattering us. They're helping us decide what's real. !«メールアドレス» — https://sh.itjust.works/post/68817284
  11. 2026-10-15 / score 6 — meta-models/Muse-Spark-Open-816B-A42B（Open weights, 816B / 42B active）!«メールアドレス» — https://lemmus.org/post/26168440
  12. 2026-10-15 / score 1 — Thanks for the open weights, Meta, but how many of us can actually run Muse Spark Open locally? !«メールアドレス» — https://lemmy.durstig.online/post/66459

Additional note (open-weight related, 10/16): A post saying Tencent’s 770B “Hy4 Preview” fit within 214 GiB (score 0) https://lemmy.durstig.online/post/66506

Signals
  • Closed-model camp: Discussions of OpenAI’s sandbox escape and training environment appeared simultaneously across multiple communities (#7, #8, #3). All have low scores; they are merely being circulated as news rather than discussed deeply. IPO speculation (#9) had the day’s highest score, and the strong response in an AI-skeptic community is characteristically Lemmy.
  • Open-weight camp: The tenor around Meta’s Muse Spark Open is “it’s open, but who can actually run it locally?” (#12). Tencent Hy4 Preview and Flux 3 Image Klein (5.6B, 10/16) also appeared, but received little response.
  • OSS restrictions on AI: The moves by OpenTofu and OpenBao to reject or limit LLM-generated contributions (#1, #2) resonate with Lemmy’s OSS community.
  • Overall, skepticism, regulation, and ethics around AI are drawing more score than technical discussion of new models.
Limits
  • Lemmy is small, and most LLM-related posts score 0–8. Although more than 10 posts were found, results that were “latest” included search noise with little relation to LLMs (weather, contract advice, and so on), so those were excluded.
  • Retrieval used only results from lemmy.world’s search API (keywords: LLM, OpenAI, open weights). No individual searches of other instances or keyword searches for Anthropic, Google, DeepSeek, or Qwen were performed.
  • Subscriber counts for AI-related communities could not be retrieved.
  • Retrieval was via WebFetch summarization, so scores reflect values at retrieval time; individual post bodies and comments were not opened.
  • Some links point to mirrors on other instances or Mastodon (listed as the ap_id URLs returned by the API).

Recommended Actions

  • Verify the pricing of Kimi K3.1 Pro and Haiku 5.5 against official announcements and pricing pages before reporting on it.
  • Do not treat self-reported benchmark figures from companies, such as Gemini 4 Argon, as conclusive until third-party evaluations are available.
  • If there are internal tools using DeepSeek, confirm whether the U.S. government order applies to model weights or hosting.
  • When adopting open weights, estimate the hardware requirements for running them locally before considering the fact that they are publicly available.
  • For the next X stage, recollect information using keyword searches for model names and company names rather than Explore.

Data Quality Notes

X returned zero LLM-related results because the search terms did not match the theme. For YouTube, view counts and publication dates could not be retrieved for any items; the remaining three also had few engagement metrics, and primary-source verification could not be completed.