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Android Bench 2.0 shows frontier coding agents still fail most multi-day Android tasks

Android Bench 2.0 moves coding-agent evaluation away from small repository fixes toward dependency upgrades, app builds, migrations and other jobs that can take a human engineer days. The results expose a much larger reliability gap than short-task benchmarks—and show that the agent harness can materially change cost and outcome.

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.

Cloud Run adds singleton instances for long-lived, individually addressable workloads

Cloud Run instances sit between autoscaling serverless services and a small VM. They run one individually addressable container continuously, can be stopped and restarted, and use shared CPU economics; Google’s launch example prices 1 vCPU plus 1 GiB running for 30 days at $5.70.

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.

Codex 0.149.0 ships asynchronous user messaging so agents can keep working after questions

Codex 0.149.0 includes the async-message tool, delivery metadata and removal of the client-side feature gate that BTN previously tracked only on main. Parallel human-agent work is now in a stable client, but late replies can still race with decisions and model capability metadata remains the final exposure gate.