Law firm subreddit debates whether AI delivers real leverage

Wired's Will Knight had Codex build a multi-agent harness that reads news, analyzes results, and critiques picks; the first roster benched Erling Haaland. Splitting agents into specialized roles improved picks, but scaling past 100 agents only raised the API bill.

The AWS post argues the model and tool use aren't the production blocker — governance around them is: how many teams can build in a secure environment, which team owns each model and tool, and keeping AgentOps and error handling inside a pre-defined boundary.

Former Fed Vice Chairman Richard Clarida says the central bank is weighing whether AI-driven growth is a demand shock that could call for higher rates, or whether productivity gains will eventually boost supply and ease inflation.

AWS's walkthrough targets the container, not the model code, as the usual blocker to scaling PyTorch across a cluster — each container needs a matching CUDA runtime, a PyTorch build compiled against it, NCCL, and EFA userspace libraries.

A hobbyist reverse engineer says mainstream AI assistants block their personal RE work, ranking Gemini as the strictest, then ChatGPT, then Claude. They ask r/ChatGPT for ways to work around the refusals.

Prompted to build a realistic horse model and walking animation in three.js, the model took ~15 minutes and consumed 30% of a 5-hour Plus limit. The user had to stop it from fetching existing horse models online and require it to generate the model itself.