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Showing 241–260 of 415 dossiers

Google DeepMind is piloting double-blind frontier-model evaluations with confidential computing

The pilot attacks a persistent evaluation trade-off: labs do not want to reveal frontier-model internals, while evaluators do not want benchmark prompts leaking back to the model provider. DeepMind says a Singapore AI Safety Institute pilot kept both sides’ sensitive assets hidden during execution.

Anthropic’s Model Hardware Standard gives AI agents a shared interface for physical devices

MHS is an attempt to make microscopes, liquid handlers, robotic arms and other programmable hardware look like a consistent tool surface to AI agents. It is still a research preview, but the interoperability layer is already being tested with research institutions and hardware vendors.

OpenAI’s Codex client exposes a GenUI layer for refreshable interfaces inside conversations

RuntimeWire found a generic `genui` message path, a server-directed widget refresh endpoint and 467 versioned Learning Block manifests inside OpenAI’s Codex desktop client. The material development is not another visualization feature: it is evidence of a reusable interface layer beneath conversational answers, with important limits around what is actually public or enabled.

beehiiv has turned newsletter recommendations into a unified paid-and-organic growth marketplace

beehiiv has documented the economics and guardrails behind its rebuilt Recommendation Network, including the 20% fee on paid recommendations, verified-subscriber charging, quality-based auto-pause rules and more granular control over recommendation slots and partner selection.

NVIDIA Groq 3 LPX enters full production with 3,431-token/s long-context inference

Groq 3 LPX is moving from architecture announcement to manufactured infrastructure. Artificial Analysis measured about 3,400 output tokens/s at both 10K and 100K context on an NVIDIA-hosted private endpoint, but the single-concurrency benchmark does not yet establish public-cloud price, multi-tenant throughput or end-to-end agent speed.

Google is adding hard spend caps and commitment pricing for AI agent workloads

Google’s new agent FinOps model combines hard monthly spend caps that pause agent API calls, Flexible Savings Plans with one- or three-year commitments, pay-as-you-go Gemini Enterprise usage and planned deferred execution at up to half normal inference cost. The controls are useful, but commitment economics and task eligibility need to be modeled carefully.

Amazon joins YouTube Shopping so creators can tag products instead of relying on description links

The integration brings Amazon’s catalog into YouTube’s native shopping layer. It reduces the gap between product recommendation and purchase compared with description links, but access currently requires both YouTube and Amazon affiliate eligibility and is still limited to a select group of creators.

GitHub Actions is starting runtime brownouts for outdated self-hosted runners

Teams with pinned, custom-image or auto-update-disabled GitHub Actions runners can now see registration or job execution fail before the September 25 cutoff. The migration is not just a one-time jump to v2.329.0: already-registered runners must also stay within 30 days of the latest runner release.

Gemini 3.5 Transcribe gives developers separate live and file speech-to-text APIs at about $0.009 and $0.005 per minute

Gemini 3.5 Transcribe turns Google’s audio understanding into a purpose-built developer surface: low-latency live transcription costs roughly $0.009/minute at Google’s published assumptions, while file transcription is roughly $0.005/minute and supports richer metadata.

Stripe's 200-platform survey finds AI pushing SaaS beyond seats — but vertical businesses favour embedded finance

The interesting signal is segmentation, not 'seat pricing is dead': vertical SaaS operators expect payments and finance to become a larger part of revenue, while horizontal platforms expect usage and hybrid software charges to lead. The survey is vendor-produced and forward-looking.