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Supabase fixes broken client trace propagation and now preserves trace IDs even for unsampled requests

supabase-js 2.112.3 materially improves the tracing rollout BTN covered earlier: unsampled requests now still carry traceparent for backend log correlation, tracing misconfiguration produces warnings, and browser Edge Function calls need current CORS headers to admit W3C trace context.

Homebrew 7.0 turns package vulnerability checks into a built-in workflow — after closing a sudo-capable cask flaw

The practical change is bigger than another package-manager version. Homebrew can now tell operators whether vulnerabilities are actually outstanding in the formula revisions they installed, while its own recent advisories show why package-manager metadata, uninstall paths and build isolation deserve the same scrutiny as package contents.

GitSpawn shows how a repository’s own Git config can escape AI coding-agent safety boundaries

The useful lesson is architectural rather than vendor-specific: coding agents inherit execution paths from ordinary developer tooling. If an agent shells out to Git without sanitising repository-local configuration, a hidden `.git/config` can become a host-level command channel that bypasses the controls users think govern the model.

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.

TRACE gives AI agents a portable, hardware-attested runtime evidence format

TRACE targets a gap between audit promises and what an AI agent actually did at runtime. Its v0.2 developer preview can bind model, policy, data and tool-use claims to confidential-computing attestation, but it is still pre-ratification and explicitly not ready to treat as a production compliance guarantee.

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.