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Kimi.ai@Kimi_Moonshotยท11h ago
๐Ÿ“‰ Price Cut

Kimi apologizes for K3.1 Pro plan rate limits and wasted quota, gives every subscriber a one-time 25% monthly quota reset and trims subagent calls and output length

At 20:14 UTC on 2026-10-25, Moonshot AI's official account @Kimi_Moonshot ([post](https://x.com/Kimi_Moonshot/status/2114450610724708492)) acknowledged rate limits and unnecessary quota consumption on the K3.1 Pro subscription plan, saying "these were our issues, and we should have fixed them sooner." Every subscriber on every plan gets a one-time usage reset that restores 25% of their monthly quota (usage below 25% resets to zero), equal to about one week of usage; usage bars update by end of day PST. Kimi also adjusted the plan's parameters and is cutting unnecessary subagent calls and output length so quota lasts longer.

โšก Key Takeaways
  • โ€ขCompensation: a one-time 25% monthly quota restore, about 1 week of usage (official post)
  • โ€ขUsers below 25% usage are reset to zero; applies to every subscription plan
  • โ€ขTiming: usage bars update by end of day PST on Oct 25 (about 15:00 SGT on Oct 26)
  • โ€ขFix direction: fewer unnecessary subagent calls, shorter outputs and adjusted plan parameters
  • โ€ขContext: K3.1 Pro launched Oct 23 with API pricing of $2/$10 per million tokens (vendor figures)
Read detailsโ†’
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Matt Pocock@mattpocockukยท14h ago
๐Ÿ› ๏ธ Tooling

X discussion: Matt Pocock sketches the ideal "language for agents" (Rust speed, Go-sized design space, strictest verification) and names Bend; Chris Lattner replies with Mojo

At 17:23 UTC on 2026-10-25, TypeScript educator Matt Pocock (@mattpocockuk, author of Total TypeScript and AI Hero) posted ([post](https://x.com/mattpocockuk/status/2114407583167705279)) that the next great "language for agents" should be as fast as Rust, have as little design space as Go, and have the strictest type/verification system ever written, calling Bend "an interesting first effort". When we pulled it, the post had about 3,130 likes, 84 reposts, 264 replies, 786 bookmarks and 220K views, and it reached X's trending stories. Mojo creator Chris Lattner replied ([reply](https://x.com/clattner_llvm/status/2114498806146531752)) recommending Mojo and suggesting "can target all hardware" be added to the list. This is an opinion debate, not a verified conclusion.

โšก Key Takeaways
  • โ€ขMain post: post, published 17:23 UTC on 10-25, with about 3,130 likes, 84 reposts, 264 replies, 786 bookmarks and 220K views when we pulled it
  • โ€ขThree criteria: Rust-level speed, a Go-sized design space (few ways to write the same thing), and the strictest type/verification system ever written, so the compiler rather than a human catches agent mistakes
  • โ€ขChris Lattner replied recommending Mojo, citing advanced type features such as linear types and symbolic constraints, and suggested adding "can target all hardware" (reply, about 40 likes when pulled)
  • โ€ขBend creator Victor Taelin (@VictorTaelin) also joined the thread, saying Bend blocking an agent from breaking an app is "working as intended"
  • โ€ขNo benchmarks or controlled comparisons were shared; whether Bend or Mojo suits agent coding remains personal opinion
Read detailsโ†’
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Thorsten Ball@thorstenballยท15h ago
๐Ÿ› ๏ธ Tooling

X discussion: Thorsten Ball's "How I work with Agents" lists 15 habits for coding with AI agents and draws nearly 4K bookmarks

At 15:58 UTC on 2026-10-25, developer Thorsten Ball (@thorstenball) published an X article, "How I work with Agents" ([post](https://x.com/thorstenball/status/2114386050638336082)), summarizing how he works with coding agents "as of October 2026": use only the latest and strongest models, prompt for what you want rather than how to do it, make the whole development lifecycle from deploys to production logs visible to the agent, ask agents for "irrefutable proof", and review only code with a high blast radius. When we pulled it, the post had about 1,935 likes, 143 reposts, 92 replies, 3,981 bookmarks and 137K views, and it reached X's trending stories. This is a personal account of practice presented as opinion, not a verified conclusion.

X discussion: Thorsten Ball's "How I work with Agents" lists 15 habits for coding with AI agents and draws nearly 4K bookmarks
โšก Key Takeaways
  • โ€ขMain post: post, published 15:58 UTC on 10-25, with about 1,935 likes, 143 reposts, 92 replies, 3,981 bookmarks and 137K views when we pulled it; bookmarks are about twice the likes, suggesting many readers saved it as a checklist
  • โ€ขModels: the author says to use only the latest and strongest models, because when a model is wrong 10% of the time you do not know which 10%, so you end up retrying
  • โ€ขPrompting: describe the outcome, for example "Make the bytecode compiler and VM AS FAST AS YOU CAN. Do not stop until it's 2x faster."
  • โ€ขVerification and review: ask agents for end-to-end walkthroughs, demo videos and before/after numbers as proof; the author personally reviews only high-blast-radius changes such as fewer than 5 central files and database migrations
  • โ€ขWorkflow: do not wait for agents, let them wait on you; the author calls schedules, wake-ups and heartbeats a "killer feature" and says context compaction works well enough that manual token management is wasted effort
Read detailsโ†’
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Elon Muskelonmuskยท16h ago
๐Ÿš€ Release

Grok Bot can now phone people and businesses for you: Elon Musk says it makes the request, takes notes and sends a call summary (0.77 prerelease)

On Oct 25 SpaceXSI CEO Elon Musk (@elonmusk) posted on X that Grok Bot can now call someone or a business for you, make a request on your behalf, take notes on the response and send you a summary of the conversation. The post quoted a third-party rundown of the Grok Bot 0.77 prerelease: it dials, sits on hold, presses keys, gets transferred and shows a live transcript. As of Oct 25 SpaceXSI had not published release notes or docs for the feature, and supported regions, plans and AI disclosure to the callee are not announced.

โšก Key Takeaways
  • โ€ขSource: Elon Musk's own X post (post), Oct 25 14:35 UTC
  • โ€ขCapability: calls a person or business for you, makes the request, takes notes and sends a call summary
  • โ€ขPrerelease rundown (third-party @alexmichaelio): dialing, waiting on hold, key presses, transfers and a live transcript
  • โ€ขPer CellCog and The Tech Signal, the official changelog and docs did not cover the feature as of Oct 25
  • โ€ขAt capture the post had about 14.4K likes, 1,399 reposts, 3,332 bookmarks and 2.31M views
Read detailsโ†’
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Nanbeige@nanbeigeยท17h ago
๐Ÿš€ Release

BOSS Zhipin's Nanbeige lab open-sources Nanbeige4.3-33B, a 33B dense agent model with 512K context under Apache 2.0, reporting 80.9 on Terminal-Bench 2.1 vs 73.0 for Qwen3.8-27B

At 12:11 UTC on 2026-10-25, the Nanbeige LLM Lab of BOSS Zhipin (Kanzhun Limited) announced Nanbeige4.3-33B from its account @nanbeige ([post](https://x.com/nanbeige/status/2114328933181333798)), with the model and its Base version released as open weights on Hugging Face under Apache 2.0 ([model card](https://huggingface.co/Nanbeige/Nanbeige4.3-33B)). It is a 33B dense model on a Llama-style architecture with a 512K context window, positioned as the teacher model of the Nanbeige4.3 series that will be distilled into smaller models such as Nanbeige4.3-3B. The official model card compares it with Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B using the same harness at highest thinking effort: 80.9 on Terminal-Bench 2.1 (Qwen3.8-27B 73.0) and 65.5 on DeepSWE (42.5), but 60.3 on SWE-Bench Pro, slightly below Qwen's 61.7, and 84.8 on GDPval vs 86.4. All figures are self-reported and not yet independently reproduced.

โšก Key Takeaways
  • โ€ขOfficial sources: @nanbeige post on X and the Hugging Face model card; the post had about 616 likes, 79 reposts and 38K views when we pulled it
  • โ€ขSpecs: 33B dense, Llama-style architecture, 512K context, Apache 2.0, with a Base checkpoint released too
  • โ€ขSelf-reported coding scores: Terminal-Bench 2.1 80.9, Terminal-Bench 4.0 23.8, DeepSWE 65.5, SWE-Bench Pro 60.3 (Qwen3.8-27B: 73.0, 6.3, 42.5, 61.7)
  • โ€ขSelf-reported cowork scores: Automation Bench 54.2, JobBench 44.8, GDPval 84.8 (Qwen3.8-27B: 38.3, 34.5, 86.4)
  • โ€ขDeployment: Transformers, SGLang and vLLM, with official examples using 4-way tensor parallelism; Nanbeige4.3-3B distillation is next
Read detailsโ†’
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Arpit Bhayani@arpit_bhayaniยท17h ago
๐Ÿ› ๏ธ Tooling

X discussion: engineer Arpit Bhayani says "no human will ever write code better than LLMs", drawing 7K+ likes, 240+ replies and a debate over AI coding ability

At 13:37 UTC on 2026-10-25, engineer Arpit Bhayani (@arpit_bhayani) posted: "No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on." ([post](https://x.com/arpit_bhayani/status/2114350692551082390)). When we pulled it, the post had about 7,193 likes, 496 reposts, 104 quotes, 241 replies, 529 bookmarks and 174K views, and it reached X's trending stories. Replies are split: some agree LLMs already write code faster and better, while others push back that LLMs often produce more code than a task needs and that production edge cases, architecture and final verification still depend on people. This is an X discussion presented as opinions, not a proven conclusion.

โšก Key Takeaways
  • โ€ขMain post: Arpit Bhayani's post, published 13:37 UTC on 10-25, with about 7,193 likes, 496 reposts, 104 quotes, 241 replies, 529 bookmarks and 174K views when we pulled it
  • โ€ขThe wording is "No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on"; it is a personal opinion with no data or experiment attached
  • โ€ขFor: one reply says the author coded for years and LLMs now code better and faster than they do
  • โ€ขAgainst: other replies say LLMs tend to generate far more code than a simple use case needs, and that browser quirks, accessibility and production edge cases still need human judgment
  • โ€ขCommon ground: many replies place human value in design, supervision and verification; layoff and hiring-ratio figures circulating online were not verified here, so we do not use them
Read detailsโ†’
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Tibo@thsottiauxยท1d ago
๐Ÿ› ๏ธ Tooling

X discussion: OpenAI Codex/ChatGPT lead Tibo posts a hiring call to "scale dots to 1B users", and Meta chief AI officer Alexandr Wang replies with a Muse jab

At 19:21 UTC on 2026-10-24, Tibo (@thsottiaux), whose X bio reads "Codex & ChatGPT @OpenAI", posted: "DM if you want to help scale dots to 1B users and have done similar things in the past" ([post](https://x.com/thsottiaux/status/2114074885622501706)); it had about 5,524 likes, 1,506 replies and 710K views when we pulled it. Alexandr Wang (@alexandr_wang), whose X bio says he is chief AI officer at Meta, replied "oops you misspelled muse" (about 1,935 likes), and Tibo shot back with "Celebrate your 12 users" (about 1,845 likes). The public back-and-forth between the two companies' executives became one of the most talked-about AI threads of the past half day. It is a hiring post and banter between executives, not a user count or product plan published by OpenAI.

โšก Key Takeaways
  • โ€ขMain post: Tibo's post, about 5,524 likes, 126 reposts, 131 quotes, 1,506 replies, 515 bookmarks and 710K views when we pulled it
  • โ€ขAlexandr Wang replied "oops you misspelled muse" (post), about 1,935 likes and 240K views; Muse is the Meta model and product name he has been promoting lately
  • โ€ขTibo fired back: "Step 1: Quit posting butts and go buy containers / Step 2: ??? / Step 3: Celebrate your 12 users" (post), about 1,845 likes and 185K views
  • โ€ขWang answered "is the implication you have under 12 users?" (post), about 222 likes
  • โ€ข"1B users" is a goal stated in a hiring post; OpenAI has not published Dots' actual user count, so this entry is treated as an X discussion
Read detailsโ†’
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CapCut@capcutappยท1d ago
๐Ÿ› ๏ธ Tooling

CapCut announces a ChatGPT plugin is coming soon, bringing its video generation and editing tools inside ChatGPT with drafts that move to the CapCut web editor

At 18:34 UTC on 2026-10-24, during its CapCut World event in Los Angeles, ByteDance's CapCut announced from its official account (@capcutapp) that a new CapCut plugin is coming soon to ChatGPT ([post](https://x.com/capcutapp/status/2114062937094823955)). According to CapCut, the plugin brings its video editing and generation tools inside ChatGPT: users can start with an idea in ChatGPT, create it with the plugin right in the chat, then move the draft to the CapCut web editor to refine and publish. When we pulled it, the post had about 3,646 likes, 319 reposts, 504 replies and 450K views. The event site lists OpenAI's Head of Partnerships Mahak Sharma and Head of Creator Product Sam Yam as speakers. Many creator posts on X say the plugin is already in early access on ChatGPT desktop with mobile and web around mid-November, but CapCut's own post still says coming soon, so timing and pricing should be taken from CapCut's later announcements.

โšก Key Takeaways
  • โ€ขOfficial source: CapCut's post on X, published 18:34 UTC on 10-24, with about 3,646 likes, 319 reposts, 216 quotes, 504 replies and 450K views when we pulled it
  • โ€ขWhat it does: CapCut's video editing and generation tools inside ChatGPT, so a chat idea can become a video draft directly
  • โ€ขWorkflow: drafts made with the plugin can move to the CapCut web editor for further editing, sharing and publishing
  • โ€ขOpenAI involvement: the CapCut World Los Angeles site lists OpenAI's Mahak Sharma (Head of Partnerships) and Sam Yam (Head of Creator Product) as speakers
  • โ€ขStatus: CapCut says coming soon; creator claims of desktop early access now and mobile and web in mid-November are not confirmed by CapCut, and pricing is not announced
Read detailsโ†’
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Zhihao Zhan (Microsoft Research Asia)@zhan8855ยท1d ago
๐Ÿ› ๏ธ Tooling

Microsoft Research open-sources Agensh, a self-organizing harness that scales to 1,024 coding agents

Zhihao Zhan, a Microsoft Research Asia researcher and co-first author, announced on X on Oct 24 that Agensh is now open source under MIT. It is a multi-agent coding harness with no central orchestrator: workers claim sub-tasks, verify and merge asynchronously. Per the authors, scaling from 1 to 1,024 agents lifts the final pandoc test-pass rate from 33.89% to 55.06%.

Microsoft Research open-sources Agensh, a self-organizing harness that scales to 1,024 coding agents
โšก Key Takeaways
  • โ€ขOpen-sourced on Oct 24 under MIT: harness, experiment launch scripts and reproduction guide at microsoft/Agensh.
  • โ€ขAuthors report: on the 5 hardest ProgramBench tasks (GPT-5.6-sol high, 6h budget, no Internet), mean final test-pass rate rises from 19.31% with 1 agent to 28.78% with 128 agents, about 49% relative.
  • โ€ขAuthors report: on pandoc, 1 to 128 to 1,024 agents moves the pass rate from 33.89% to 50.94% to 55.06%.
  • โ€ขCoordination runs through Gitea (shared workspace), Mattermost (messaging) and a shared-context board adapted from DeLM; each worker uses the Copilot harness agent loop.
  • โ€ขThe release ships a single-node Docker Compose team setup plus a single-agent baseline, both on native Copilot ACP sessions.
Read detailsโ†’
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Thariq@trq212ยท2d ago
๐Ÿ› ๏ธ Tooling

X discussion: Thariq of the Claude Code team says he has left Anthropic; his farewell post drew 24K likes, and a joking "is Opus 5.5 dumber" reply also went viral

On 2026-10-24 (04:04 UTC), Thariq (@trq212), whose X bio reads "Claude Code @anthropicai", posted that he has left Anthropic after more than a year working on Claude Code and plans to take a break before starting something new. When we pulled it, the post had about 24K likes, 2.82M views and 1,800+ replies, with Claude Code colleagues such as Lydia Hallie saying goodbye. About 20 minutes later he replied to Lydia, "wait is it just me or is Opus 5.5 dumber", and that joke drew 8,600+ likes. This is an X discussion and a personal post, not an official Anthropic announcement.

โšก Key Takeaways
  • โ€ขThe farewell post went up at 04:04 UTC on 2026-10-24 and had about 24,160 likes, 2.82M views, 1,813 replies and 828 reposts when we pulled it (post)
  • โ€ขIn his words, he worked on Claude Code for more than a year and it is "time for a bit of a break before diving into something new"; he did not say where he is going
  • โ€ขHe pointed readers to @addyosmani, @lydiahallie, @_catwu, @ClaudeDevs and Claude Code creator @bcherny
  • โ€ขThe "is Opus 5.5 dumber" reply had about 8,659 likes and 584K views; it riffs on the long-running community joke that models get "dumber" and is not an evaluation result (reply)
Read detailsโ†’
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Md Ismail ล ojal@0x0SojalSecยท2d ago
๐Ÿ› ๏ธ Tooling

X discussion: viral posts claim Anthropic "quietly killed" Claude's reverse engineering with the REA toolkit after a Samsung TV firmware teardown went viral; no official statement yet

One of the hottest AI developer topics on X over the past day. At 21:55 UTC on 2026-10-23, security account @0x0SojalSec (Md Ismail ล ojal) posted that "Claude can no longer Reverse Engineer Anything" and that "Anthropic just quietly killed REA", tying the timing to viral cases of people using Claude to reverse engineer Samsung TV firmware and find data-collection services; the post had about 8,345 likes, 1,055 reposts and 514K views. An earlier post making the same claim by @AbhinavXJ had about 10,477 likes and 640K views. As of this run we found no statement from Anthropic or its staff, and some users replied that it still works for them. These are user reports and opinions on X, not a confirmed policy change.

X discussion: viral posts claim Anthropic "quietly killed" Claude's reverse engineering with the REA toolkit after a Samsung TV firmware teardown went viral; no official statement yet
โšก Key Takeaways
  • โ€ขMain post: @0x0SojalSec's post, about 8,345 likes, 1,055 reposts, 2,545 bookmarks and 514K views when we pulled it
  • โ€ขAn earlier post making the same claim: @AbhinavXJ's post (11:16 UTC on 10-23) said "anthropic just killed the REA" and argued this is why local models matter; about 10,477 likes and 640K views
  • โ€ขOne trigger was a Samsung TV firmware teardown: @log4j posted that the TV classifies the audio it hears before reporting back, about 6,720 likes; in another post he said his OpenAI account was banned after local reverse engineering work tripped its checks, about 6,454 likes and 729K views
  • โ€ขREA is an open-source reverse engineering toolkit (GitHub morluto/rea), showing about 377K stars in the GitHub API this run; we covered its viral launch on 10-08
  • โ€ขWe found no response from Anthropic's official accounts or staff this run; some users replied that REA still works for them and is refused only in specific cases, so the real scope awaits an official word
Read detailsโ†’
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Grok Imagineimagineยท2d ago
๐Ÿš€ Release

X discussion: Grok Imagine's official account posts Episode 1 of an AI-made Odyssey series (about 7 minutes, 4K), drawing 2.4M views and a debate over whether AI video can carry a long story

At 19:23 UTC on 2026-10-23, the official Grok Imagine account (@imagine) posted Episode 1 of its Odyssey series ([post](https://x.com/imagine/status/2113712964419063911)), saying it was made with Grok Imagine in a creative collaboration with Wonder Studios (@WonderStudiosX) and director Dev Walker (@glumlot_). The video runs about 7 minutes 12 seconds and is served in a 3840x2160 version. When we pulled it, the post had about 12,055 likes, 1,275 reposts, 433 quotes, 974 replies and 2.4M views, and the account asked whether people want a full breakdown of how it was made. Discussion on X kept growing over the past half day: supporters see it as AI video moving from short clips to whole episodes, while critics point to uneven acting and visible AI artifacts. This is an X discussion presented as opinions, not a verdict on the film's quality.

โšก Key Takeaways
  • โ€ขMain post: official Grok Imagine post, published 19:23 UTC on 10-23, with about 12,055 likes, 1,275 reposts, 433 quotes, 974 replies, 6,361 bookmarks and 2.4M views when we pulled it
  • โ€ขWhat the account says: made with Grok Imagine, in collaboration with Wonder Studios and director Dev Walker; the video is about 7 minutes 12 seconds and comes in a 4K (3840x2160) version
  • โ€ขThe account asked whether people want a full breakdown of how it was made; no workflow, clip count or cost has been published yet
  • โ€ขOpinions are split: some say AI video can now ship by the episode and that directing and storyboarding matter more than the model; others criticize uneven acting and visual artifacts
  • โ€ขTreated as an X discussion; production-day and asset counts circulating online are not confirmed by the official account, so we do not use them
Read detailsโ†’
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Kimi.ai@Kimi_Moonshotยท2d ago
๐Ÿš€ Release

Moonshot launches Kimi K3.1 Pro: 2.8T-total / 104B-active open-weight MoE with 1M context, 65.7% on Terminal-Bench 4.0, API at $2/$10, weights by Nov 4

On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.

Moonshot launches Kimi K3.1 Pro: 2.8T-total / 104B-active open-weight MoE with 1M context, 65.7% on Terminal-Bench 4.0, API at $2/$10, weights by Nov 4
โšก Key Takeaways
  • โ€ข2.8T-total / 104B-active MoE, 1M-token context, multimodal; full weights promised by 2026-11-04 (official blog)
  • โ€ขVendor-reported: 65.7% on Terminal-Bench 4.0, ahead of GPT-6 Astra (58.2%) and just behind Claude Opus 5.5 (66.4%); 61.2% on the internal Kimi Code Bench 3.0 Long-horizon (K3: 55.95%)
  • โ€ขAPI pricing: kimi-k3.1-pro at $2 input / $10 output / $0.20 cached input per million tokens; Highspeed at $4 / $20 (pricing page)
  • โ€ขOn the internal Kimi Hallu Bench Hard stress test, claim-level hallucination fell from 23.4% (K3) to 8.3%; Moonshot notes this is not a daily-use error rate
  • โ€ขCyber-related data was removed from mid-training and RL for the open-weight release: xdev-bench-hard pass@3 drops from 90.5% (unmitigated checkpoint) to 81.0%
Read detailsโ†’
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NVIDIA AI@NVIDIAAIยท2d ago
๐Ÿš€ Release

NVIDIA open-sources Nemotron 3.5 Bolt E4B 4GB, a Gemma 4 E4B-based small model for local assistants, characters and robots with tool use

On 2026-10-23 (15:41 UTC) the official @NVIDIAAI account announced Nemotron 3.5 Bolt E4B 4GB, a small open model for local assistants, interactive characters and robots that can hold a conversation and use tools. The official Hugging Face model card says it is post-trained from Gemma-4-E4B-it, has 8B total parameters (4B effective), ships in Q4_K_M quantization, supports up to 64K input and 64K output tokens, and has a switchable thinking mode, plus a DFlash speculative-decoding drafter and a BF16 version. Weights, data and recipes are open under the OpenMDW-1.1 license, with commercial use allowed. On the model card the 4GB build scores 55.60 on MultiChallenge (Gemma4 E4B-it at the same quantization 39.36, Qwen3.5 4B 52.23), but trails Qwen3.5 4B on Tau2-Bench (52.32) and BFCL-v3 (67.31).

NVIDIA open-sources Nemotron 3.5 Bolt E4B 4GB, a Gemma 4 E4B-based small model for local assistants, characters and robots with tool use
โšก Key Takeaways
  • โ€ขNVIDIA officially released Nemotron 3.5 Bolt E4B 4GB for local assistants, interactive characters and robots
  • โ€ขPost-trained from Gemma-4-E4B-it: 8B total parameters (4B effective), Q4_K_M quantization, 64K context, switchable thinking mode
  • โ€ขOpen weights, data and recipes under the OpenMDW-1.1 license with commercial use allowed, plus a DFlash speculative-decoding drafter
  • โ€ขModel card results: 55.60 on MultiChallenge leads its size class, but it trails Qwen3.5 4B on Tau2-Bench and BFCL-v3
Read detailsโ†’
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Block@blocksยท2d ago
๐Ÿš€ Release

Block unveils Dessa: a full-duplex speech model trained from scratch only on human conversation audio, running locally on a MacBook Pro M4 Max

On 2026-10-23 (16:53 UTC) the official @blocks account unveiled Dessa, and Jack Dorsey called it the most authentic full-duplex speech model ever built. The official page block.xyz/dessa says Dessa is a true speech-to-speech model trained only on recordings of human conversation, with no text and no pretrained models. It is full-duplex, so it listens and talks at the same time, cuts in and backchannels naturally, runs faster than real time, and runs locally on a MacBook Pro M4 Max or better. Block says it was trained on a single NVIDIA GB200 NVL72 and learned language from hundreds of times fewer words than LLMs. There is no public API or weights yet; a small group of AI safety researchers is getting early access.

Block unveils Dessa: a full-duplex speech model trained from scratch only on human conversation audio, running locally on a MacBook Pro M4 Max
โšก Key Takeaways
  • โ€ขTrained only on human conversation audio, from scratch, with no text and no pretrained models (official page)
  • โ€ขFull-duplex speech-to-speech: listens while talking, cuts in and backchannels, and natively hears and expresses emotion, sarcasm and laughter
  • โ€ขInference runs faster than real time and runs locally on a MacBook Pro M4 Max or better
  • โ€ขBlock says training used a single NVIDIA GB200 NVL72 and needed hundreds of times fewer words than LLMs
  • โ€ขNo public API, weights or third-party benchmarks yet; early access is limited to a small group of AI safety researchers
Read detailsโ†’
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Cursor@cursor_aiยท2d ago
๐Ÿ› ๏ธ Tooling

Cursor launches Computer Use: agents can control your computer and drive desktop apps like the iOS Simulator to test what they build

On 2026-10-23 (15:59 UTC) the official Cursor account @cursor_ai announced "Cursor can now control your computer": agents can drive desktop apps such as the iOS Simulator to test the features they build. A follow-up post says Computer Use lets agents click and type across any app on your computer, not just the browser. Cursor turns it on by itself when a task needs it, and you can also request it by @-mentioning Computer Use in your prompt; updating Cursor is enough to try it. A community changelog in September mentioned Computer Use on Mac in version 3.20.21, and this is the official launch. Supported platforms, the permission model and benchmarks were not published.

Cursor launches Computer Use: agents can control your computer and drive desktop apps like the iOS Simulator to test what they build
โšก Key Takeaways
  • โ€ขCursor officially says its agents can now control your computer and drive desktop apps like the iOS Simulator to test the features they build
  • โ€ขComputer Use lets agents click and type across any app, not just the browser
  • โ€ขCursor uses it automatically when a task needs it, and you can request it by @-mentioning Computer Use in a prompt
  • โ€ขUpdate Cursor to try it; supported platforms, the permission model and benchmarks were not published
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BytePlus@BytePlusGlobalยท2d ago
๐Ÿš€ Release

ByteDance's BytePlus launches Seedance 2.5 4K: a native 4K, 10-bit video generation model with a low-cost 480p Draft preview mode

On 2026-10-23 (15:31 UTC), ByteDance's enterprise cloud BytePlus (@BytePlusGlobal) launched Seedance 2.5 4K, calling it the most capable model in the Seedance 2.5 generation. It brings more nuanced facial expressions and movement, fuller textures, contours and fine detail, and 10-bit colour depth for smoother gradations that suit the big screen. A new Draft mode lets creators test storyboards and refine shots with lower-cost 480p previews, then render the final version in 4K. It is currently available only to selected enterprise customers, and BytePlus says API availability updates are coming. It debuted in the short film "Otter is Nobody 2".

ByteDance's BytePlus launches Seedance 2.5 4K: a native 4K, 10-bit video generation model with a low-cost 480p Draft preview mode
โšก Key Takeaways
  • โ€ขThe most capable model in the Seedance 2.5 generation, with native 4K output (official announcement)
  • โ€ขMore nuanced facial expressions and movement, with fuller textures, contours and fine detail
  • โ€ข10-bit colour depth for richer gradations and smoother transitions
  • โ€ขDraft mode: refine storyboards with low-cost 480p previews, then render the final cut in 4K
  • โ€ขSelected enterprise customers only for now; no public API, pricing or benchmarks yet
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Ethan Mollickemollickยท3d ago
๐Ÿ“Š Benchmark

X discussion: a new paper from Harvard's Gary King says AI article reviews beat top human peer reviews "by every measure we could construct", and Ethan Mollick urges academia to adapt fast

At 00:27 UTC on 2026-10-23, Harvard social scientist Gary King (@kinggary) posted a new paper with Steven Worthington, "AI Article Reviews Dominate Human Peer Reviews at Their Best: Evidence and Policy Choices". Its abstract says AI-generated reviews are now better, by every measure the authors could construct, than reviews by well-compensated human experts that editors and authors judged to be unusually good. Wharton professor Ethan Mollick (@emollick) then wrote that the study covers 124 Harvard social science papers and used Claude Opus 4.8, and that academics need to figure out how to adapt science to AI fast; his post had about 1,675 likes and 97K views. The topic was still on X's trending news list over the past 12 hours. This is an X discussion of a single paper, not a conclusion from any journal or from Anthropic.

โšก Key Takeaways
  • โ€ขThe paper is by Harvard's Gary King and Steven Worthington; its page says it was last updated on 2026-10-22 (paper page; King's post, about 472 likes, 121K views and 371 bookmarks when we pulled it)
  • โ€ขIn the abstract's words, AI reviews are better "by every measure we could construct" than reviews by "well-compensated" human experts judged to be of "unusually high quality"; the paper also discusses journal policy choices and their "concerning downsides"
  • โ€ขEthan Mollick's post (post) says the study used Claude Opus 4.8 on 124 papers; it had about 1,675 likes, 97K views and 249 replies when we pulled it
  • โ€ขMany reposts on X also cite figures such as "former journal editors picked the AI review 87% of the time" and "5.5% vs 15.9% wrong claims"; we have not checked each of these against the paper itself, so treat the paper as the source of record
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K
Kimi.ai@Kimi_Moonshotยท3d ago
๐Ÿš€ Release

Kimi teaser video shows the model name Kimi K3.1: Kimi Code terminal reads "K3.1 thinking: high" while generating a Three.js 3D scene

On 2026-10-23 (01:41 UTC) the official Kimi account @Kimi_Moonshot posted a 24-second, retro-CRT-style video with no caption text. The Kimi Code terminal at the start of the clip reads "Model: Kimi K3.1" and "Version: 2.2.0", the status bar shows "Kimi K3.1 thinking: high", and a notice says "Kimi Code Desktop is here". The user then types "Build a complex procedural 3D scene in Three.js." and the model calls the Write tool to create ./scene.js. This is the first time the Kimi K3.1 name has appeared in official material. The post has no caption and no release date, benchmark, price or parameter details, so this is a teaser and not a confirmed release.

Kimi teaser video shows the model name Kimi K3.1: Kimi Code terminal reads "K3.1 thinking: high" while generating a Three.js 3D scene
โšก Key Takeaways
  • โ€ขIn the official teaser the Kimi Code terminal shows "Model: Kimi K3.1" and the status bar reads "Kimi K3.1 thinking: high", the first official appearance of the K3.1 name
  • โ€ขThe clip also shows Kimi Code version 2.2.0 and the notice "Kimi Code Desktop is here", with install via the /desktop command or kimi.com/code
  • โ€ขThe demo task is "Build a complex procedural 3D scene in Three.js." and the model uses the Write tool to create ./scene.js, in a clip of about 24 seconds
  • โ€ขThe post has no caption; release date, benchmarks, price and parameters are not officially announced, so treat this as a teaser and not a release. As of the evening of 10-23 SGT the post had about 1.63M views and 17.6K likes (public X counts)
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A
Artificial Analysis@ArtificialAnlysยท3d ago
๐Ÿ“Š Benchmark

Artificial Analysis launches Coding Agent Index v2.0: FrontierCode from Cognition replaces DeepSWE, and Claude Code with Opus 5.5 leads at 61.7

On 2026-10-23 Artificial Analysis launched Coding Agent Index v2.0, which scores model-plus-harness combinations across 290 tasks with three equally weighted benchmarks: FrontierCode v1.1 from Cognition (100 tasks on real open-source repos, graded on whether a maintainer would merge the change) replaces DeepSWE v1.1, SWE-Atlas-QnA becomes the stricter SWE-Atlas-QnA-AA, and Terminal-Bench 4.0 is unchanged. Claude Code with Claude Opus 5.5 (max) leads at 61.7, ahead of Sonnet 5.5 at 59.3 and Muse Wonder Full at 58.9. Scores fall across the board: the 21 configurations measured on both versions drop from a mean of 54.8 to 47.4.

Artificial Analysis launches Coding Agent Index v2.0: FrontierCode from Cognition replaces DeepSWE, and Claude Code with Opus 5.5 leads at 61.7
โšก Key Takeaways
  • โ€ขTop of the index: Claude Code with Claude Opus 5.5 (max) 61.7, Claude Sonnet 5.5 (max) 59.3, Muse Code with Muse Wonder Full (max) 58.9, Claude Fable 5.1 (max with fallback) 57.6 (Artificial Analysis figures)
  • โ€ขOn FrontierCode v1.1, Opus 5.5 scores 56.0%, ahead of Codex with DeepSeek V4.1 Flash at 52.6%; 15 of 24 configurations sit between 43% and 49%
  • โ€ขVersion shift: 21 configurations average 54.8 on v1.5 and 47.4 on v2.0, with 20 of 21 moving down; the same set averages 45.1% on FrontierCode versus 65.6% on DeepSWE
  • โ€ขFour points on the cost frontier: DeepSeek V4.1 Flash 47.3 at $0.39 per task, GPT-6.1 Sol 54.1 at $1.50, GPT-6 Astra 54.7 at $7.04, Opus 5.5 61.7 at $11.42 (pay-per-token API costs, not subscriptions)
  • โ€ขSWE-Atlas-QnA-AA rewords 127 of 1,306 rubric criteria and uses GPT-6 Luna (xhigh) as judge with five votes per criterion; dataset and judge config are on Hugging Face
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