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IRONSCALES Report: AI Agents Are Becoming the New Target of Phishing

The "From Deepfakes to Double Agents" report, published by IRONSCALES after commissioning Censuswide to survey 501 U.S. IT and security practitioners, shows that 77% of respondents said their organization's AI agents or tools had processed malicious, misleading, or unauthorized content in the past year, and 57% reported at least one confirmed incident. 96% of surveyed organizations already use AI tools that can perform actions on their behalf, of which 67% allow them to access ticketing or help desk systems and 49% allow access to email and messaging systems, while 78% of such tools are approved under the leadership of IT departments, with only 6% led by security departments.

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Salesforce Announces $5.66 Billion Acquisition of Marketing Automation Company Braze

Salesforce announced it will acquire marketing automation company Braze for $5.66 billion in cash, or $47.5 per share, a premium of about 58% over Braze's previous closing price. Braze's revenue grew 26% year over year last quarter to $227.2 million; its platform aggregates customer data through more than 190 prebuilt connectors and offers BrazeAI to automatically generate marketing materials and Agentic Standards to set constraints for AI-generated campaigns. Salesforce plans to use its technology to enhance Marketing Cloud's cross-channel personalization capabilities, and the deal is expected to close in its fiscal year 2028.

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ElevenLabs Releases Two Real-Time Speech-to-Speech Models, Eleven Realtime

ElevenLabs has released two speech-to-speech models, Eleven Realtime, among which v1 Max scores 98.8% on Big Bench Audio with a response time of 0.57 seconds. The smaller v1 Mini targets low-cost natural conversation and can tell whether the other party has finished speaking or only said "mm-hmm," while v1 Max targets more complex tasks such as customer service. Both models are available through the ElevenAgents platform and the OpenAI-compatible Realtime API, billed per minute plus tokens, with v1 Max priced higher than v1 Mini.

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How AWS Uses X.509 PKI to Secure AI Agent Identity

AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK, AWS Secrets Manager, and Amazon RDS, and covers three trust boundaries—user authentication, agent-to-agent communication, and MCP tool invocation—through four layers of mechanisms: signed request envelopes, user-signed delegation assertions, RFC 8693 audience-restricted tokens, and real-time OCSP revocation checks.

Meta's Open-Weight Model Muse Spark Open Launches on Fireworks Serverless API

Fireworks launched Meta's open-weight model Muse Spark Open on the day of release. It is an 816B-parameter MoE model that activates 42B parameters per token, with a 1M-token context window, targeting long-horizon coding and agentic tasks. Fireworks says it ranks first among 75 models on Vals AI's Harvey Legal Agent Benchmark leaderboard with 25.4%, nearly 4 times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench it surpasses Claude Opus 5.5 at every difficulty level. On Fireworks, the model has zero data retention enabled by default, uses only US-based compute at no extra charge, and supports fine-tuning with serverless LoRA.

Daytona Raises $60M Series B

Daytona has raised a $60M Series B at a $1B valuation, led by FirstMark Capital, with the goal of building the "Agent Cloud." Matt Turck said FirstMark led the Series A earlier this year and has now led the Series B as well, adding that the Daytona team is building one of the defining companies of the AI era.

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Stanford CME 295 Lecture 4 released: Teaching reinforcement learning with LLMs

The Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, Reinforcement learning with LLMs, and both the video and lecture notes are now public. This lecture goes from RL and Reward Design to PPO, GRPO, RLVR, and DAPO, explaining the notation in each one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention inference optimization, MCP and AI Agents, and diffusion language models.

Development Methods and Tech Stack for an AI English Writing Agent

An AI English writing agent is implemented through the collaboration of a multi-agent pipeline, and can be split into agents for outline and idea generation, grammar and spelling, discourse logic, style polishing, and evaluation and scoring, built on LangGraph or PydanticAI. The recommended tech stack is Python (FastAPI) paired with PydanticAI, LangGraph, and LlamaIndex; the main reasoning model can be DeepSeek-V3, Claude 3.5 Sonnet, or GPT-4o, with GPT-4o-mini and Qwen-Turbo for lightweight validation. The system also uses RAG to connect to authoritative corpora such as the Oxford and Longman dictionaries and the Academic Phrasebank, and sets up anti-hallucination and academic integrity guardrails.

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Asymptote Labs Raises $5.5M Seed Round to Monitor and Govern Enterprise AI Agents

Asymptote Labs has closed a $5.5 million seed round led by Tower Research Ventures, with participation from Lytical Ventures, 1984 Ventures, and South Park Commons; the funds will be used for engineering, product development, hiring, and market expansion. Its solution consists of Beacon, an MIT-licensed open-source telemetry layer, and an enterprise-grade detection, response, and governance platform. Beacon can record activity from more than 20 agent harnesses, including Claude Code, Codex, and Cursor, and normalize it into a runtime data model that extends OpenTelemetry. The company says Beacon's GitHub star count grew more than 460% in September and that it reached No. 3 on GitHub's global trending list.

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Alibaba Tongyi MAI-UI team open-sources general computer-use agent tech stack OpenGCUA

Alibaba Tongyi MAI-UI team open-sources the complete general computer-use agent tech stack OpenGCUA, including harness, cross-device SDK, native macOS app, evaluation trajectories, and reference agent implementation. Its core design is that code execution and decision-making shift from the harness to the model; the model takes screenshots on demand, writes Python programs with loops and conditional judgments to orchestrate GUI operations, API calls, and shell commands, and maintains its own todo list. In evaluations of 8 frontier models on OSWorld-v2, the average partial score rose from 45.3 to 64.9, and the average number of steps per task decreased by 53%; in real-machine macOS tests, under the same harness, Qwen3.8-Max's inference token consumption was 19.4 times that of GPT-6 Astra, completing 3 of 4 tasks.