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US Weighs Antitrust Guidance on AI Safety, Top DOJ Official Says

The Trump administration is considering whether to update interagency antitrust guidance on cybersecurity to address a new wave of threats posed by artificial intelligence, according to Associate US Attorney General Stanley Woodward. At an event in New York on Sept. …

We haven't written up this one. Insurance Journal has the full story — the link below goes straight to it.

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The Setup Screen Is Not Evidence

The first fifteen minutes of an AI coding setup usually fail for a boring reason, not a model reason. The wizard says you are ready while your project folder still looks untouched and slightly…

  • Setup screens lack evidence of code readability.
  • Canary file proves test success after AI coding.
  • Focus on code changes and test results, not screens.

Where Trust in Automated Review Actually Comes From

There's a tempting fix for the moment your team stops trusting its AI code review...add a second AI to check the first one. I get why.

  • AI models trained on similar data have similar blind spots, leading to inconsistent judgments.
  • Human reviewers build instincts in diverse environments, spotting issues models might overlook.

I Counted Drops as Wrongs. The Chart Was Theater.

The first number on an eval dashboard is usually a lie. Not a scam. A folding error. You asked a model for an answer, the path blinked, and your scorer filed the blink under incorrect.

  • Evaluation dashboards often contain inaccurate first numbers due to calculation errors.
  • Free endpoints generate unnecessary noise and can lead to incorrect grading of model paths.
  • A robust evaluation environment is crucial to ensure models can handle failures gracefully.

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