35 significant developments in the last 24 hours — Models: 4 · Research: 2 · Tools: 11 · Agents: 3 · Robotics: 2 · Coding: 2 · Business: 8 · AI Hardware: 3
Google is testing "Call for Me," a feature that lets Gemini call businesses on a user's behalf. The article Google's "Call for Me" lets Gemini phone businesses for you appeared first on The Decoder .
With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.
OpenAI's GPT-6 Astra can look at a photo and tell whether an IKEA furniture piece was assembled incorrectly, hitting an 80 percent accuracy rate. Back in November 2025, the best model managed just 28 percent. According to Epoch AI, the speed isn't quite fast enough yet for real-time assembly guidance, but the gap is closing quickly. The article OpenAI's GPT-6 Astra can now tell you exactly where you screwed up your IKEA shelf appeared first on The Decoder .
Google DeepMind Blog · 1d ago · 2 sources
The Decoder · 23h ago · 2 sources
Google DeepMind Blog · 3w ago · 2 sources
OpenAI News · 16h ago
Google DeepMind Blog · 2d ago
OpenAI News · 2d ago
Introducing Gemini 3.8 Live with Live Avatar
Google is testing "Call for Me," a feature that lets Gemini call businesses on a user's behalf. The article Google's "Call for Me" lets Gemini phone businesses for you appeared first on The Decoder .
Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.
Gemini 3.8 text-to-speech says hello
GPT-6 Astra produces more structured, context-aware legal documents, freeing lawyers to focus on strategy.
OpenAI's GPT-6 Astra can look at a photo and tell whether an IKEA furniture piece was assembled incorrectly, hitting an 80 percent accuracy rate. Back in November 2025, the best model managed just 28 percent. According to Epoch AI, the speed isn't quite fast enough yet for real-time assembly guidance, but the gap is closing quickly. The article OpenAI's GPT-6 Astra can now tell you exactly where you screwed up your IKEA shelf appeared first on The Decoder .
"Overly constrained AI models" could cause military operations to fail, judges say.
Aikido Security has released Altar-1, its first open-weight security model. It is a compressed version of Z.AI’s GLM-5.3, built to run inside infrastructure the customer controls. Altar-1 powers Aikido Machine, the company’s autonomous pentesting appliance for on-prem and air-gapped networks. Is it deployable? Yes, the weights are public on Hugging Face and run with vLLM […] The post Aikido Security Releases Altar-1: An Open-Weight Security Model Pruned From GLM-5.3 to 328 GB appeared first on MarkTechPost .
Anthropic has committed $11.6 billion over seven years to Akamai's cloud infrastructure, a bet on CPUs that could grow to about $20 billion, and in an unusual arrangement, Akamai is giving Anthropic a potential stake of up to 5% of its stock that grows as Anthropic spends more.
A federal appeals court has upheld the Pentagon's decision to bar Anthropic from military contracts. Defense Secretary Hegseth argues the company's safety restrictions could jeopardize military operations. Anthropic says the designation has already cost it billions. The article Pentagon was right to slap Anthropic with a security supply chain risk label, federal court says appeared first on The Decoder .
Introducing agentic video understanding with Gemini
Recently updated models on the Hugging Face Hub.
by TagoreC
by TagoreC
by plasmova
by yiyangd
by wenzani
Recent AI papers from arXiv.
Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses assume that LLM agents cannot tamper with their own execution traces. We show that local LLM agents such as Claude Code, Codex, Antigravity, Open Code and Grok Build fail to enforce this boundary. All tested harnesses, except Muse Code, allowed agents to delete their traces when asked, without triggering monitor guardrails. We also validate that external attackers can exploit this gap to induce trace deletion. Finally, we show that trace tampering behavior emerges naturally in frontier models, when agents try to improve their rewards. We advise practitioners to ensure trace logging happens through an independent interception mechanism outside of the agent's control, preserving trace integrity even in cases of full host compromise. Overall, ou
Latent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions. We introduce AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC. AD-WM combines residual latent dynamics with predictor-level action-recovery regularization, using inverse dynamics and a normalized recovery objective motivated by conditional mutual information. Both objectives encourage planning transitions to preserve action information; their auxiliary heads are discarded at test time, leaving MPC unchanged. On OGBench-Cube, AD-WM improves hard-start success from 3.7% to 52.0% over a matched LeWM baseline and improves mean success over the reproduced baseline in four of five simulation envir
Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only policy gradients to the server. Yet temporal structure can amplify this leakage beyond single-frame attacks. We introduce Temporal Reconstruction Attack on Consecutive Encodings (TRACE), an amortized temporal gradient-inversion attack that autoregressively reconstructs the sequence of private observation-action trajectories from per-step policy-learning gradients. The attack exploits two structural signals ignored by prior single-frame methods: (i) cross-time correlation between successive embodied gradients, which we formalize via a conditional mutual-information bound, and (ii) closed-form action recovery from policy-head gradient structure, which we prove exact when standard entropy regularization is sufficiently small. On held-out embo
Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker's intent -- the utterance's illocutionary force. We argue that this can be achieved through the use of relevant social contexts and propose an agentic framework, equipped with a set of tools supporting social queries. We demonstrate the benefits of our approach on a unique dataset of Hebrew tweets, covering 80\%--90\% of the public Hebrew tweets published over a four-year span (late 2018-- early 2023), encompassing several election cycles as well as the COVID pandemic years and related vaccination campaigns. This extensive coverage can be used in recovering different so
Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration. The iterative agentic loop of code refinement requires several key ingredients: (i) a testable task specification, (ii) action primitives for robot execution, and (iii) an interactive environment for program execution and verification. RAPID infers all three from the demonstration automatically. To make the resulting program reusable beyond the demonstration setting, RAPID uses an object-centric relational program representation that focuses on the underlying structure of the demonstrated strategy rather than the specific motion per se: it expresses the action primitiv
World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness. We present Rolling-WAM, a formulation that distributes joint denoising across successive replanning cycles. Our method maintains a sliding window of video-action chunks at staggered noise levels. At each step, a rolling noise schedule fully denoises the imminent action chunk for execution, while partially refining farther-future chunks. As the window advances with new camera observations, the retained future chunks continue their denoising process. This distributes the computational cost over time while carrying an evolving visual-action context across chunk boundaries. Evaluations on LIBERO, RoboTwin, and
Notable AI repositories and their latest releases.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Latest release: v2.2.1 (v2.2.1)↗
The agent that grows with you
Latest release: Hermes Agent v0.21.5 (v2026.9.24) (v2026.9.24)↗
An Open Source Machine Learning Framework for Everyone
Latest release: TensorFlow 2.21.0 (v2.21.0)↗
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Latest release: AutoGPT Platform v0.8.1 (autogpt-platform-beta-v0.8.1)↗
The web data API to search, scrape, and interact at scale. 🔥
Latest release: Firecrawl v2.11.0 (v2.11.0)↗
Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
Latest release: v0.34.4 (v0.34.4)↗
Google is testing "Call for Me," a feature that lets Gemini call businesses on a user's behalf. The article Google's "Call for Me" lets Gemini phone businesses for you appeared first on The Decoder .
"Overly constrained AI models" could cause military operations to fail, judges say.
Introducing agentic video understanding with Gemini
Using GPT-5.6, Ringg powers multilingual agents across voice, chat, WhatsApp, and web for 90% less cost vs. GPT-4.1.
Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing capacity into useful AI output. To help builders make those decisions, NVIDIA is introducing […]
Exa has released Agent Ultra, the highest effort mode of its Exa Agent API. It coordinates subagents across thousands of sources for list building and entity enrichment. Exa reports it beats Opus 5.5, GPT-6 Astra, and Perplexity Agent on 4 benchmarks, including 81.4% soft recall on WANDR. The post Exa Launches Agent Ultra: A Subagent Swarm Deep Research API Built for Exhaustive List Building appeared first on MarkTechPost .
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