In final weeks of CT session, AI policy bills come into focus - News From The States
In final weeks of CT session, AI policy bills come into focus News From The States
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How to Handle Sensitive Data Securely in Terraform
Day 13 of my Terraform journey focused on one of the most important topics in real infrastructure work: secrets. Every serious deployment eventually needs sensitive values: database passwords API keys tokens TLS material provider credentials The challenge is not just using those secrets. The challenge is making sure they do not leak into places they should never be. Terraform makes infrastructure easy to define, but if you are careless with secrets, they can leak through your code, your terminal output, your Git history, and even your state file. This post is the guide I wish I had before learning this lesson. Why Secrets Leak in Terraform There are three major ways secrets leak in Terraform. If you understand these clearly, you will avoid most beginner and intermediate Terraform security
Anthropic essentially bans OpenClaw from Claude by making subscribers pay extra
Using OpenClaw with Claude AI is about to get a lot more expensive, thanks to Anthropic's new policy changes. Beginning April 4th at 3PM ET, users will "no longer be able to use your Claude subscription limits for third-party harnesses including OpenClaw," according to an email sent to users on Friday evening. Instead, if users [ ]
ARCUS-H: Full Evaluation Results — 979,200 Episodes, 51 RL Policies
We completed a large behavioral stability evaluation of trained RL policies of : 979,200 evaluation episodes across 51 policy configurations , 12 environments, 8 algorithms, and 8 structured stress schedules. Here are three findings that matter for deployment. Finding 1: Reward explains 3.7% of behavioral stability variance. The primary correlation between ARCUS-H stability scores and normalized reward is r = +0.240 [0.111, 0.354], p = 1.1×10⁻⁴ (n = 255 policy-level observations, 2,550 seed-level). R² = 0.057. 94.3% of the variance in how a policy behaves under sensor noise, actuator failure, or reward corruption is not captured by its return in clean conditions. 87% of policies rank differently under ARCUS-H vs reward rankings, with a mean rank shift of 74.4 positions. Finding 2 : SAC’s e
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