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Showing 161–180 of 211 dossiers

WordPress Studio is making an agentic build loop the default desktop experience

Studio Code was already available in WordPress Studio, but the August 24 redesign changes the default workflow: the coding agent now sits at the center of the desktop app beside a live local WordPress preview, with point-and-annotate feedback and one-click hosting sync. The beta also ends the earlier unlimited-free framing by introducing a credit limit and paid top-ups.

Safari 27 gives coding agents a local MCP path into live browser debugging

WebKit’s Safari MCP server turns browser debugging into an agent-callable interface. It runs locally and makes no network calls itself, but captured page data is sent directly to the connected agent, so browser-session trust and model data handling become part of the development security model.

Zigpoll’s founder says agency-focused packaging lifted revenue per account 24% without a price increase

Zigpoll is a useful tiny-team pricing case because the claimed gain came from segment fit rather than simply charging everyone more. The founder says moving integrations down to the standard plan removed friction for agencies managing many client stores; current product pricing remains tiered primarily by survey-response volume.

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.

GitHub Spark is shutting down August 31 — export code now and replace broken `llm()` calls

GitHub Spark stops being available to existing users on August 31, 2026. Deployed apps are meant to keep running, but owners should export code to a repository now; Spark apps using `llm()` need a separate inference provider because the underlying GitHub Models service retired July 30.

Abacus.AI’s Smaug Agentic fine-tune targets the failure tail in long-running coding agents

The useful part of Smaug Agentic is not another frontier-style benchmark claim. Abacus.AI is publishing a drop-in Kimi K3 derivative that targets a specific production failure mode in coding agents: long runs that burn the reasoning budget without converging. The weights and model card are public, but the training data is not disclosed and the benchmark gains remain vendor-produced.

Google Ads starts showing how many conversions your first-party data recovered

The shift is broader than another Ads dashboard metric. Google is connecting first-party data pipelines, conversion-recovery estimates, open-source marketing-mix modeling and causal geo experiments into one measurement stack — useful, but still heavily dependent on Google’s own modeling and internal benchmark claims.