November 5, 2026 · Thursday

Kimi K3.1 Pro opens its weights

Moonshot AI released Kimi K3.1 Pro weights on Hugging Face. Its model card describes a 3T-scale native multimodal agent model with a million-token context, post-trained from Kimi K3 through large-scale joint reinforcement learning. Moonshot reports gains in coding, 3D creation and financial research, with OpenAI- and Anthropic-compatible APIs.

Kimi K3.1 Pro opens its weights
Image shared by the source · @Kimi_Moonshot
Images 2.6 adds GIFs and references
Image shared by the source · @OpenAI

Images 2.6 adds GIFs and references

OpenAI announced ChatGPT Images 2.6 with GIF and reference-image support, alongside two API image models that it says generate images 50% faster. ChatGPT features and API model availability are separate parts of the announcement.

OpenAI reports 948 kernel vulnerability patches

OpenAI reports 948 kernel vulnerability patches
Image shared by the source · @OpenAI

OpenAI says its security team built an autonomous discovery, exploitation-validation and patching pipeline. In one run with an upcoming cyber model, it validated and generated patches for 948 Linux kernel local privilege escalation vulnerabilities. This is a company-reported result, not confirmation that maintainers have accepted or deployed every patch.

Tencent Hunyuan introduces Hy4

Tencent Hunyuan introduces Hy4
Image shared by the source · @TencentHunyuan

Tencent Hunyuan introduced Hy4 and shared early user and evaluation reactions. The launch post is brief; weight availability, leaderboard position and evaluation conditions should be distinguished from the introduction itself.

Open models & inference11 · 05
@OpenAIDevs

Image-2.6 reaches Codex and the API

Image-2.6 reaches Codex and the API
Image shared by the source · @OpenAIDevs

OpenAI's developer team announced GPT-Image-2.6 for Codex and the API. It reports 50% faster generation for Flare and Sunburst, with Sunburst preserving details more consistently across edits at medium or high quality.

@vllm_project

vLLM supports Kimi K3.1 Pro on day one

vLLM supports Kimi K3.1 Pro on day one
Image shared by the source · @vllm_project

vLLM announced day-one serving support for Kimi K3.1 Pro and its million-token context. The cited 65.7% Terminal-Bench 4.0 score uses the Claude Code harness; it is a model benchmark, not a vLLM serving-throughput measurement.

@sgl_project

SGLang reports sparse-attention serving results

SGLang says Kimi K3.1 Pro replaces dense MLA with DSA sparse attention. Its day-one figures include up to 33.7K tok/s per GPU on AgentX and GB300, and 450 tok/s for one user with DFlash2 and MegaKernel. These figures refer to different test configurations.

@ollama

Kimi K3.1 Pro arrives on Ollama Cloud

Ollama says Kimi K3.1 Pro is available on its Cloud, describing 2.8T total and 104B active parameters for coding, 3D creation and research. It advertises US hosting with zero data retention. Cloud availability does not establish equivalent local-device support.

@Kimi_Moonshot · via @nebiustf

Kimi K3.1 Pro lands on Nebius

According to @Kimi_Moonshot: Nebius Token Factory launches Moonshot's open-weight model on day one, focused on coding performance.

@TencentHunyuan · via @arena

Agent Arena repost places Hy4 at No. 14

Tencent Hunyuan reposted Agent Arena's statement that Hy4 ranked 14th overall at that time and remained proprietary. The excerpt cuts off during the discussion of weights, so it does not confirm a release date for them.

@JeffDean · via @RicursiveAI

Ripple-65B Preview is announced

Jeff Dean reposted Ricursive AI's announcement of Ripple-65B Preview, a 65B dense language model pretrained from scratch. The release claims a new state of the art, but the excerpt is truncated and does not establish the full benchmark scope or comparison conditions.

@soumithchintala

A note on Cognition's model lineage

Soumith Chintala says Cognition's new Flash model is post-trained on Inkling-small. The post concerns model lineage and does not claim Cognition pretrained the base model itself.

@swyx

Ripple's $32M figure covers full R&D

Ripple's $32M figure covers full R&D
Image shared by the source · @swyx

swyx quotes the Ripple-65B Preview team saying development and training cost $32M from scratch, including the pretraining ladder. This is full R&D, not merely the compute bill for one final training run. Comparisons with other models remain the team's claims.

@teortaxesTex

Ripple's cost and performance framing questioned

teortaxesTex questions Ripple's 65B dense scale, efficiency claims and $32M cost framing, including comparisons with V4 Flash. This is personal criticism to read alongside the full-R&D cost explanation and actual benchmarks, not a verified counter-result.

@GoogleDeepMind · via @Google

Gemini 3.8 Flash TTS adds voice remixing

According to @GoogleDeepMind: Google AI Studio and the Gemini API add Voice Remixing, allowing customization starting from preset voices.

@cohere

Cohere Embed 5 and Rerank 5 launch on OpenRouter

According to @cohere: The retrieval models are offered through a single API in Pro/Fast tiers with unified billing, reducing switching costs.

@_lewtun · via @victormustar

Axion 1.0 releases full open training recipe

According to @_lewtun: The 35B open agent base model provides reproducible checkpoints, 18K SFT samples, and a three-stage training recipe, supporting vLLM/SGLang.

@huggingface · via @NielsRogge

Hugging Face shares an ML Intern reproduction

Hugging Face reposted Niels Rogge's report of reproducing a paper with ML Intern, an open-source harness for AI research and model training. The excerpt does not identify the full paper or its results, so no specific metrics are inferred.

@StasBekman

DeepSpeed fixes ZeRO-1/2 gradient preservation

Stas Bekman shared DeepSpeed PR #8949, preserving gradients across request boundaries until the actual optimizer step. The author recommends deepspeed >= 0.19.9; the linked PR supplies the scope of the fix.

@woosuk_k · via @inferact

Inferact expands Moonshot AI partnership

According to @woosuk_k: Alongside the Kimi K3.1 Pro release, the two sides are expanding collaboration to optimize serving and deployment of the model.

@llama_index · via @jerryjliu0

OCRia-1 reports a single-B200 speedup

LlamaIndex reposted Jerry Liu's claim that OCRia-1 document-parsing throughput rose from 285 to about 2,800 tok/s on one B200. The roughly 10x figure is specific to the reported setup, not a universal speedup for every document workload.

@_akhaliq

LoCo learns when to stop reasoning

LoCo learns when to stop reasoning
Image shared by the source · @_akhaliq

LoCo identifies overthinking states in a model's own math trajectories, creates shorter verified continuations and trains local preferences with DPO. The authors report 41.7%–45.4% shorter math outputs and 23.4%–36.3% shorter code outputs across five math and three code benchmarks, with accuracy maintained or improved within those tests.

Agents & developer workflows11 · 05
@ClaudeDevs

Subagent status updates cost about 99% less

Subagent status updates cost about 99% less
Image shared by the source · @ClaudeDevs

Claude's developer team says background subagents previously reread their full conversation for each 30-second status update. Haiku 5.5 now writes the line from short notes, reducing the cost of each update by about 99%. The figure does not describe total Claude Code task costs.

@MistralAI

Mistral demos 133-agent vulnerability triage

Mistral demos 133-agent vulnerability triage
Image shared by the source · @MistralAI

Mistral demonstrated Large 4 on a recent Django CVE: 133 agents mapped code, hunted flaws, tested attack scenarios and confirmed the bug, before Le Chonk patched it. The video runs at 90x speed and is not a measure of real elapsed time.

@gdb

OpenAI welcomes the Pierre team

Greg Brockman welcomed Pierre Computer to OpenAI and said its open-source developer tools already power parts of Codex. The post emphasizes collaboration around AI software building and gives no transaction value.

@sama · via @thsottiaux

Altman shares usage-limit doubling notice

Sam Altman reposted a product-team notice saying limits double for Pro 100/200/500 tiers and Business Premium. The supplied excerpt does not spell out the full product scope or metering rules; account-level terms remain the reference.

@AravSrinivas

Fast Search is coming to the Decisions API

Arav Srinivas says Perplexity is integrating Fast Search into its Decisions API to ground batch decisions in web information. The post quotes $0.001 per search request, but the excerpt cuts off after the Decisions API's $0.02 price, leaving its full billing unit unspecified.

@perplexity_ai

Perplexity shares an ad-buying case study

Perplexity shares an ad-buying case study
Image shared by the source · @perplexity_ai

Perplexity's customer case says Computer managed $30,000 in Meta ad spend for Get Maine Lobster, delivering 3.5x revenue and 63% higher ROAS than its usual ads. It automated creative analysis, production and media buying. These are vendor-reported results for one customer, not a general return guarantee.

@gdb

Brockman adds pipeline cost detail

Greg Brockman says OpenAI found, validated and prepared patches for nearly 1,000 Linux local privilege escalations at about $1,000 per vulnerability. He presents it as an emerging approach to software hardening, adding cost context to the same pipeline announcement.

@dotey · via @huacnlee

Keeping Rust build caches in an AI workflow

Baoyu reposted huacnlee's experience that retaining target directories avoided clean rebuilds during AI development across multiple worktrees and reduced machine slowdowns. It is a personal setup report, not a universal disk-optimization result.

@dotey

Baoyu Design updates its local results hub

Baoyu Design updates its local results hub
Image shared by the source · @dotey

Baoyu describes a DeepSeek Harness plugin update for baoyu-design, adding a common entry for Slides, Design, Dashboard and Motion outputs. The project runs as an Agent Skill and produces self-contained HTML locally with tools including Cursor and Codex.

@dotey

Cicada brings live translation and notes to macOS

The open-source native macOS app Cicada offers live bilingual subtitles, interpreting and AI notes for calls and streams. Its repository describes use with Zoom, Tencent Meeting, Feishu and YouTube, with translated speech output through a virtual microphone.

@dotey

DeepMind hiring for AI product engineering

Baoyu shares a Google DeepMind hiring note focused on AI Studio and the Gemini API. The short post lacks role requirements and application conditions, which require the original vacancy listing.

@sedielem · via @korymath

DeepMind Montreal seeks student researchers

S D Ebrahimi reposted Kory Mathewson's call for BS, MS or PhD student researchers at Google DeepMind Montreal. The research description is truncated in the input, so no additional topic is inferred.

Research, data & model engineering11 · 05
@AnthropicAI

Claude assists vault-particle research

Anthropic describes its biology team working with Claude to map the first 3D structure of a vault particle from bacterial life. The announcement presents the barrel-shaped protein complexes as a longstanding mystery; the supplied excerpt does not establish that Claude carried out the research independently.

@lateinteraction · via @HaozeHe_Hector

A new NanoJev weight-editing case

Omar Khattab reposted an experiment reporting 65 wins, 34 losses and one draw in 100 head-to-head matches after editing 0.0002% of NanoJev's weights. The task name is truncated in the input; this is a specific experiment, not a general guarantee for weight editing.

@lateinteraction · via @gabepereyra

MIT weight-space work draws attention

Omar Khattab reposted Gabe Pereyra's comments on MIT research, with the excerpt mentioning GPT-6 Astra reading Qwen model weights. Full experimental details are absent; this does not establish reliable understanding or editing of arbitrary models.

@lateinteraction · via @amritsinghbedi3

Weight-space methods explored for VLM safety

Omar Khattab reposted researchers exploring weight-space methods for vision-language-model safety alignment. The excerpt cuts off at the test name and supplies no full results, so success is not established.

@lateinteraction · via @jacsebl

Handwritten weights for citation classification

Omar Khattab reposted @jacsebl's account of using fable to handwrite weights for a 100K-parameter model that distinguishes real and fake legal citations. No accuracy or independent testing is supplied; the post does not establish a professional legal-verification product.

@lateinteraction

Harvey supports weight-space research

Omar Khattab thanks the Harvey team for supporting weight-space programming research and mentions its open contributions to related work. The input supplies no funding amount or partnership terms.

@lateinteraction · via @mann_idan

A call for weight-editing engineering traces

Omar Khattab reposted @mann_idan's offer to buy traces of engineers modifying model weights and doing related engineering tasks. The excerpt gives neither full terms nor purpose and does not describe a released dataset.

@_arohan_

Designing training for compute islands

Arohan's linked article argues that learning algorithms should be co-designed with compute distributed across separate data centers. The post discusses combining data centers below 20MW each; it does not establish a sub-20MW total training cluster or a reproduced cost result.

@jxmnop

Making long-running models' notes more readable

@jxmnop reports that models write increasingly inscrutable notes during long runs, and that small amounts of training can improve this. The post concerns note readability and supplies no general training recipe or evaluation figures.

@_akhaliq · via @HuggingPapers

Route Anything explores unified routing

_akhaliq reposted a paper description proposing one router across language models, image/video/speech generators, coding agents and robot policies. The excerpt cuts off after the number 374,401, leaving its statistical meaning unspecified.

@ylecun · via @antirez

Discussing JEPA and LLM representations

Yann LeCun reposted antirez on the Platonic representation hypothesis, JEPA and LLM hidden states. Only the opening is available, establishing a research topic rather than equivalence between representations or a winning architecture.

@ylecun · via @AlexiGlad

Energy-based reasoning paper teased

LeCun reposted Alexi Gladstein's preview of Energy-Based Reasoning and its claimed advantage over looped reasoning. The supplied excerpt contains no paper or experiments, so the comparison remains a preview claim.

@ylecun · via @Numerama

A media post highlights LeCun's small-model work

LeCun reposted Numerama's coverage of work with Randall Balestriero, referring to a paper posted October 29. The excerpt cuts off in its small-model discussion and does not support a complete claim about reasoning performance.

@ylecun · via @randall_balestr

A JEPA research talk at INFORMS

LeCun reposted Randall Balestriero’s notice of a talk on recent JEPA research at INFORMS in San Francisco. It is a conference announcement, without a full paper or new experimental data in the supplied excerpt.

@ylecun · via @ylecun

LeCun on declarative knowledge

LeCun reposted his view that LLMs accumulate substantial declarative knowledge, enabling them to answer many questions. The excerpt cuts off before the rest of the argument; no broader conclusion about reasoning is inferred.

@random_walker · via @sayashk

A small test probes Codex and text detection

Arvind Narayanan reposted Sayash Kapoor's small-scale tests suggesting simple prompts let Codex evade Pangram. This is a limited reported result, not evidence that every text can reliably bypass detection.

@Tim_Dettmers · via @bohanhou1998

Kernel agent experiment asks whether DSLs are still needed

According to @Tim_Dettmers: The tweet discusses whether ML compilers and domain-specific languages remain necessary after kernel agent experiments.

@emollick

Visual symbols may affect model behavior

Visual symbols may affect model behavior
Image shared by the source · @emollick

Ethan Mollick highlights an AI-semiotics paper suggesting visual symbols affect models in ways their own chains of thought may not recognize, with reactions differing from human interpretations. This is his reading of the paper, not an independent replication.

Creative & multimodal tools11 · 05
@_akhaliq

Kimi 3D Worlds instantly generates walkable scenes

Kimi 3D Worlds instantly generates walkable scenes
Image shared by the source · @_akhaliq

According to @_akhaliq: Input a scene description or sketch to generate a full-screen Three.js 3D world explorable in the browser.

@_akhaliq · via @HuggingPapers

MoviEdit simplifies long multi-shot video editing

According to @_akhaliq: Edit video with text or a single frame and rerender all shots, aimed at long-form multi-shot scenes.

@_akhaliq · via @DeemosTech

HYPER3D announces prompt-editable rigging

_akhaliq reposted Deemos' HYPER3D Rigging announcement for humans, animals and fantasy creatures, with prompt-based editing. The excerpt cuts off in the detail description, so no specific finger-level capability is inferred.

@Kling_ai

Kling shares a feature-film shot experiment

Kling highlighted Ash Koosha's use of Kling 4.0, judging generated shots by whether they could belong in a feature film. This is a creative criterion and case study, not a standardized quality benchmark.

@ViduAI_official

Vidu Q4 showcases wuxia aesthetics

Vidu Q4 showcases wuxia aesthetics
Image shared by the source · @ViduAI_official

According to @ViduAI_official: Generates cinematic scenes such as sunset duels, bamboo forests, and snowfield solos via lighting, composition, and color prompts.

@TomLikesRobots · via @UnrealEngine

Unreal Engine 5.9 previews AI workflow updates

Tom reposted Unreal Engine's announcement of a 5.9 Preview due the following week, including improved AI workflows and web targeting. A preview announcement is not a stable release.

@CoffeeVectors · via @trymirage

Tesseract promotes agent-generated 3D content

CoffeeVectors reposted Mirage's claim that Opus 5.5 or Astra can use Tesseract to create 3D outputs with path-traced rendering. “Professional-grade” is promotional wording, with no independent quality evaluation supplied.

@LinusEkenstam

Tesseract showcases an interactive character scene

Tesseract showcases an interactive character scene
Image shared by the source · @LinusEkenstam

Linus Ekenstam shared a scene made with Mirage's Tesseract, appearing as a game character talking to a fisherman. It demonstrates a creative workflow, without establishing general quality for dialogue, world generation or rendering.

@CoffeeVectors · via @kevinbparry

AI copying raises creative-attribution concerns

CoffeeVectors reposted creator Kevin Parry's concern that one-click AI copying makes original authorship harder to identify. It is an industry observation, not a measured platform-wide trend.

@CoffeeVectors · via @second_bassoon

AI dialogue needs direction too

CoffeeVectors reposted a creative tip: when AI characters sound unnatural, examine how dialogue is written and directed in the prompt. This is practice-based advice with no quantified performance promise.

@op7418

A creator comments on Muse video generation

歸藏 comments on Muse's performance in some realistic scenes and suggests free access helps its spread. The input is personal experience, without a full model identifier, availability scope or comparative benchmark.

@heyglif

Glif introduces AI model Claudia

Glif introduces AI model Claudia
Image shared by the source · @heyglif

Glif announced AI model Claudia for casting in ads and microdramas, claiming 44M views. Its site describes a chat-based creative agent combining image, video and audio generation; the view count is vendor-reported.

Industry views & practical limits11 · 05
@elonmusk · via @larsencc

Shopping is an underrated agent use case

According to @elonmusk: The tweet argues shopping is a weekly high-frequency pain point, closer to ordinary users than coding demos and well suited to agent automation.

@AndrewYNg

Andrew Ng shares an AI-engineering jobs signal

Andrew Ng cites a LinkedIn study placing AI Engineer among the ten fastest-growing occupations in seven major economies during July–September. He also promotes AI Dev in New York on November 30–December 1. Top ten does not mean the single fastest-growing job.

@natolambert

Chinese open-model labs deliver weights on time

According to @natolambert: Commentary notes Chinese labs consistently meet weight release timelines; future delays would be a signal.

@CoffeeVectors · via @danielkleach

Maintainability questions also apply to human code

CoffeeVectors reposted a developer's point that discussions of AI-code maintainability should not assume human-written code is automatically maintainable. The excerpt supplies a viewpoint, not a controlled comparison.

@CoffeeVectors · via @vikktorrrre

AI prompts a rethink of human intelligence

CoffeeVectors shared an attributed Hinton comment that AI is forcing a rethink of human intelligence. The rest of the quote is truncated; the topic is retained without completing the quotation or treating it as settled science.

@op7418

Unverified: a creator describes Siri app actions

歸藏 says App Intents lets developers choose app functions to expose to Siri. The input contains only this creator's account, without an Apple announcement, version or supported-device list; the specific new capabilities and rollout remain unverified.

@op7418

Unverified: a Claude product-lead departure report

歸藏 reports that Claude Code and Cowork product lead Cat has left, and relays a denial that IPO lockups motivated the move. No original statement is supplied; the report remains unverified and no cause or internal-company conclusion is inferred.

@dotey

Baoyu on AI-assisted writing and readers

Baoyu discusses how book creation may diverge in the AI era, including authors using AI to produce work readers still want. The full list is not supplied, so no complete three-category taxonomy is inferred.

@LinusEkenstam

Axiom sparks discussion of large-scale AI code

Linus Ekenstam points to Axiom's work around the Stacks Project in discussing AI-code quality at scale. The excerpt cuts off during the project description, leaving methods, coverage and validation results unspecified.

@LinusEkenstam

Shopping agents raise brand-visibility questions

Linus Ekenstam observes that people increasingly ask agents what to buy, while brands cannot easily see how choices are made inside those chats. This is a view on agent-mediated shopping, without quantified market share.

@LinusEkenstam

A Polsia story: asking an agent to raise funds

Linus Ekenstam says Ben gave an AI access to his inbox with a goal of raising $10M, and that the initial post drew about 100,000 views and helped Polsia gain attention. The fundraising target is not a completed raise.

When endless coding feels like progress

There is always a next thing to do.

橘子 relates a developer's cycle of asking Claude and Codex for more product changes while spending tokens, questioning the sense of productivity that building creates. This is personal commentary; the story's ending is truncated, so project failure is not inferred.

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