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AI agents are moving into production faster than security teams can track them. They hold credentials, carry entitlements, and act on systems of record, yet most enterprises cannot say which agents are running, who owns them, or whether anyone can stop them. Gartner expects a typical Global Fortune 500 enterprise to run roughly 150,000 AI
OpenAI just introduced dots at their DevDay today. Dots are persistent AI agents powered by GPT-6 Astra. Each dot gets its own cloud computer and browser. It works across 4,000+ apps through ChatGPT plugins and keeps going after you log off. Is it deployable today? Yes, as a managed product. Dots are rolling out in
Once a physical AI product is in the field, the compute problem changes shape. Fleet data starts arriving faster than a team can label it, every retraining cycle has to be validated before it ships to hardware, and the sim-to-real loop stops being a research exercise and becomes a release process. That is the stage
Nebius Opens 2026 Physical AI Awards: Five $150K Compute Credit Prizes Read More »
Once a physical AI product is in the field, the compute problem changes shape. Fleet data starts arriving faster than a team can label it, every retraining cycle has to be validated before it ships to hardware, and the sim-to-real loop stops being a research exercise and becomes a release process. That is the stage
Alibaba’s Qwen team has released Qwen-Audio-3.1, a 5-model audio stack spanning ASR, TTS and realtime interaction. The main model is Qwen-Audio-3.1-Realtime, a full-duplex speech model built for voice agents that call tools. Qwen also cut prices: about 85% on Realtime, about 70% on TTS and up to 95% on ASR. Is it deployable? Yes, as
NVIDIA has launched the NVIDIA Open Agent Safety Platform, an open software platform and reference system design for AI agent security. It pairs the OpenShell secure runtime with NVIDIA Sentry, an out-of-band watchdog on BlueField-4 DPUs. The core idea is simple. Safety controls should not live inside the agent they are meant to control. Today,
Fireworks AI has released Ember-1, a specialized model from Fireworks Research built by post-training Moonshot AI’s open-weight Kimi K3. Ember-1 learns to produce shorter reasoning traces while keeping task accuracy. This is different from lowering the reasoning effort setting at inference time. According to the Fireworks release post, Ember-1 delivers Kimi K3’s quality with about
Fireworks AI Releases Ember-1: A Post-Trained Kimi K3 That Uses About 40% Fewer Tokens Read More »
Last week, TypeSafe AI released Jev, its first System One model. Founder Diogo Almeida previously worked at OpenAI on the instruction-following research behind ChatGPT. Jev does not chat, write code or summarize. It takes unstructured state and returns typed decisions with calibrated probabilities. That makes it a natural fit for the thousands of small judgments
Our ‘Top AI Coding Agents and Development Platforms‘ guide covered what each AI coding agent does and where it fits. This piece is for a different reader. It is written for the procurement lead, the general counsel and the security reviewer. Those readers ask 4 questions before any rollout. Who pays if generated code triggers
AI Coding Agents for Enterprise: IP Indemnity, Data Residency and 500-Seat Cost Compared Read More »
Sarvam AI has released Saaras V4, the newest generation of its speech recognition model. It covers all 22 scheduled Indian languages plus English, now including global English accents. Sarvam reports state-of-the-art accuracy across all 22 languages. Is it deployable? Yes, through Sarvam’s API today, using model=”saaras:v4″. Weights are not public, and Sarvam’s SageMaker self-hosting docs
