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Paddle brings recurring Pix payments to SaaS subscriptions in Brazil

Brazilian customers can authorize Pix Automático mandates for Paddle subscriptions without a separate early-access application. The path broadens local-payment access for SaaS, while delayed renewals, fixed mandate amounts and re-authorisation requirements still create implementation caveats.

Appeals court revives the Pentagon’s Anthropic supply-chain blacklist, restoring a Claude procurement barrier

The Anthropic procurement fight changed materially on September 25: a 2–1 federal appeals-court ruling backed the Pentagon’s supply-chain-risk designation. Builders serving defense customers should no longer rely on the August district-court ruling as evidence that the Claude procurement barrier is gone.

Cloudflare Browser Run can now hard-limit agent sessions to approved hostnames

The useful change is containment rather than another browser-agent feature. Teams can let an agent operate a real browser while constraining its HTTP and HTTPS reach to the site and dependencies the task actually needs, reducing the blast radius of prompt injection, bad tool decisions or untrusted page content.

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.

Vercel Sandbox expands from four regions to all 20 — with ordered failover

For agent and untrusted-code workloads, the useful change is not simply lower latency. Sandbox location becomes an explicit execution policy, so teams can align code execution with nearby data and avoid a resilience fallback quietly moving work outside an allowed region.

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.