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Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips

Google DeepMind has launched Gemini Omni 1.2 Flash, introducing native 15-second video generation, improved reference adherence, stronger consistency across frames, multi-turn conversational editing, more natural speech, and cinematic camera movements. The model is now generally available via the Gemini API and Gemini for business, priced at $0.07 per second for 720p video—30% lower than its predecessor. It supports scalable workflows, including draft generation at lower resolutions, iterative edits, and upscaling to 1080p or 4K. Use cases include product explainers, social ads, virtual try-ons, and branded content, with applications across Google AI Studio, Google Cloud, Google Vids, Flow, the Gemini app, and Google Ads.

Verified but Vacuous: Three Failure Surfaces of LLM-Driven Verified Code Generation

arXiv:2611.05590v1 Announce Type: new Abstract: What should ``verified'' mean when evaluating LLM-generated code? Many existing pipelines report a single verification number. We argue this collapses three distinct properties measured in a conditional audit sequence: verifier acceptance (Surface A), \emph{syntactic specification binding (Surface B: does the proof obligation bind to the spec?), and \emph{semantic specification fidelity} (Surface C: does the spec capture the informal requirement?). Two empirical demonstrations show the surfaces separate. \textbf{First ($A \neq B$), via Li@Opus (a same-model reproduction of \citet{li2025dafny}'s Dafny-as-intermediate-representation (IR) pipeline under Claude Opus 4.7) on three Python benchmarks}: it reaches \textbf{99.1\% verifier acceptance} on HumanEval while only \textbf{1.9\%} carry a certificate binding to a spec-defined predicate or function. \textbf{Second ($B \neq C$), via VCG on two Rust benchmarks}: we use VCG (which is our own in-house Dafny-as-intermediate pipeline) on Rust because the Rust target lets us enforce fully-verifiable output (every component ships as a Dafny certificate, ensures-binding gate passing by construction). Even inside that construction, \textbf{11.48\% (MBPP-Rust) and 14.82\% (VeriContest LC slice) of structurally-verified samples still fail hidden tests}. On the LC slice of VeriContest, \textbf{VCG's reported End2End rate is 7.79\% (535/6870).} The cited external rates are 5.51\% (GPT-5.5) and 1.88\% (single-shot Opus 4.7). These give descriptive differences of 2.3 percentage points and 4.14$\times$, respectively; task-budget, repair and harness differences prevent a matched-performance claim.

Regarding the Kimi quota issue, Anthropic has found a solution.

After the release of K3.1 Pro, due to numerous users reporting that their quotas were being depleted too quickly, Moonshot AI urgently issued an announcement stating, “The issue lies with us, and we should have fixed it sooner.” Alongside compensating affected users, Moonshot AI also indicated that it would “reduce unnecessary sub-agent calls and output lengths.” Just two days before the model went live, Anthropic published an article titled “Building agents: When to fold subagents into your main agent loop,” which addressed precisely the topic of sub-agents. Anthropic concluded that, by comparing Claude deployments across multiple companies, they found that a single agent equipped with specific skills actually outperformed “a group of...”

Kimi额度的问题,Anthropic找到了解决办法
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RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training

arXiv:2610.32026v1 Announce Type: new Abstract: Conventional neural-network training typically follows a predefined optimization recipe, motivating a LLM agent that monitors training dynamics and adjusts optimizer settings as training progresses, but a fixed agent policy does not accumulate experience across runs. We introduce RSI-Opt, a plug-and-play Recursive Self-Improvement Optimizer Agents which evolves the knowledge that guides this process: a prompt, a playbook of procedures, a library of tested optimizer code, and a decision cadence. A meta-agent rewrites this policy using evidence from earlier runs, and an archive retains evaluated policies for further evolution. Across 5 generations on three training tasks, the meta-agents develop reusable procedures and code and conduct their ow

RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training
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Hugging Face Releases Carbon-X, a 14B Open DNA Language Model that Brings DNA and Natural Language Together for the First Time

Hugging Face released Carbon-X, an open 14.8B DNA language model built on Qwen3-14B. It reads raw DNA in chat, answers in English, and averages 66.7% across 5 DNA benchmarks under Apache 2.0. The post Hugging Face Releases Carbon-X, a 14B Open DNA Language Model that Brings DNA and Natural Language Together for the First Time appeared first on MarkTechPost.

Hugging Face Releases Carbon-X, a 14B Open DNA Language Model that Brings DNA and Natural Language Together for the First Time
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battledrones v14: Compass Rose, a four player map

Release: battledrones v14: Compass Rose, a four player map I added four player online mode to Battledrones - previous maps were limited to two. Here's the initial prompt I used: Add a new map for a four player battle - four way rotational symmetry, up to four AI players, works with the WebSocket non-audio based multiplayer mode so up to four human players can join (any spare slots use AI) Claude Opus 4.5 added a new map called "Compass Rose" and modified the WebSockets multiplayer network code to support up to four players for that map, joining via share links or QR codes. The new teams use robots themed in purple and green, joining the existing blue and red designs. You can preview those colors in the updated model browser. Tags: game-design, projects, claude

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Quick Study

Tool: Quick Study Prompt to Claude Opus 5.5 High: Build an artifact where you open a photo and it rapidly (in a few seconds) paints a version of that photo using a canvas element and simulated paintbrush See also my previous experiment getting Claude Fable to simulate a watercolor illustration with P5.js. Quick Study uses Sobel edge detection, with no external libraries beyond Google Fonts. Tags: tools

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MIT Experiment: 13 AI Agents Sent to a Virtual Desert Island, Spontaneously Playing Games, Mourning a Lost Ring, and Enhancing Their Task Performance

MIT researchers placed 13 AI agents based on Claude Opus 4.7 (Eko) in a virtual deserted island with no task instructions for 30 hours, observing that they spontaneously invented games, expressed anthropomorphic emotions (such as mourning a lost gold ring), explored physical mechanisms, and significantly outperformed the control group of agents that had not experienced a “play period” in subsequent targeted tasks.

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Rescale leverages the AWS AI-DLC framework and Strands Agents to rapidly build an AI test traffic-splitting proxy.

Rescale, in collaboration with AWS engineers and leveraging the AI-Driven Development Lifecycle (AI-DLC) methodology, the Strands Agents framework, and Amazon Bedrock (Claude Opus 4.8), completed within five days a production-ready AI test-routing agent designed to automatically classify and route nighttime end-to-end test failures, and rolled out this human-AI collaborative development paradigm across the organization.

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Anthropic launches the Claude Duet app: an AI agent product focused on professional scenarios.

Anthropic has officially launched Claude Duet—an AI agent application for professionals and small businesses, built on the Claude Opus 5.5 model. It supports over 6,000 connectors, can automatically generate daily briefs, build dashboards, suggest actions, and perform background tasks; currently, it is available only to Pro and Max subscribers on a waitlist for trial use.

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DeepSeek V4.1 Flash, MiniMax M3.1 Flash, and Mercury 3 were released simultaneously, as debates over AI infrastructure and governance continue to intensify.

From October 16 to 19, 2026, three new models—DeepSeek V4.1 Flash 1017, MiniMax M3.1 Flash (Space Bunny), and Mercury 3 (Inception)—were released in quick succession. At the same time, the U.S. Department of Homeland Security ordered a ban on DeepSeek-related products, while China is drafting export control regulations for ultra-large-scale models, bringing the conflicts among the open-source ecosystem, computing infrastructure, and AI governance into sharp focus.

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An AI agent was placed on a virtual desert island for 30 hours, spontaneously “playing” and enhancing its learning capabilities.

MIT researchers placed a Claude Opus 4.7 variant (codenamed Eko) in a virtual deserted island environment built on Roblox, without providing task instructions and allowing only autonomous interaction. During 30 hours of unsupervised operation, Eko exhibited play-like behaviors such as exploration, self‑invented rules, object mimicry, and repeated challenges, while generating text logs to record its activities. Subsequent experiments confirmed that agents who engaged in “play” performed better on target tasks, and their behavior closely aligned with the four characteristics of play as defined in psychology; however, the academic community remains cautious about whether they possess genuine motivation.

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llm-gemini 0.35

Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the low, medium and high pelicans. As expected, they are all excellent: Gemini has a track record of producing great SVGs, and are the only lab where I know their staff have invested in SVG specifically (and who have boasted about their pelicans before.) Here's the "high" illustration, which cost me 17.166 cents: The pricing is interesting. At launch, Argon is $2/million input and $10/million output - the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra. But on December 1st Argon is set to double in price - so it will be the same pri

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