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Funes gives coding agents a local memory that can follow you across tools and machines

Funes treats agent memory as user-owned data rather than a hosted account feature: retrieval and reranking run locally, provenance stays attached to recalled passages, and cross-machine sharing is optional. The main risk is that publishing session-derived memory can still expose secrets if redaction misses them.

Amazon SES can now switch open and click tracking per email request

The new request-level controls make email measurement a per-send decision: an application can keep one SES configuration set while disabling open or click tracking for recipients who should not be measured. The override wins over the configuration-set default and adds no separate feature charge.

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.

Railway makes MySQL automatic failover generally available with Group Replication and HAProxy

Railway’s managed MySQL path can now gain automatic failover without rebuilding the database elsewhere. The trade-off is real operational complexity: conversion briefly drops connections, hard-coded URLs need manual repair, replicas are for failover rather than read scaling, and each extra database/proxy node consumes billable resources.

Nvidia reportedly pauses its revenue-sharing financing model for smaller AI clouds

The AI Compute Partnership tied Nvidia more directly to the capital structure and utilization risk of emerging cloud providers. Reuters says the initiative is now paused amid concerns about circular demand, control over partners and antitrust exposure, although Nvidia says the broader compute-access model continues to evolve.

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.