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Overturning Obergefell at the Supreme Court is unlikely; only Justice Clarence Thomas has suggested he would go that far. Mary Bonauto, the lawyer who successfully argued the landmark case, says the decision is protected by precedent “lifting up liberty, equality and association” rights. Yet growing opposition to gay marriage worries Leah Litman, a law professor at the University of Michigan. She is concerned that recent Supreme Court decisions allowing business owners to turn away LGBT customers celebrating same-sex weddings on religious and moral grounds could further corrode public support for marriage equality.
Life can be made very hard for queer people like myself without technically overturning Obergefell (though I’m not nearly as confident as The Economist that other Justices won’t join Thomas, given an opportune case).
Bottom line: if I’m not allowed to be protected from your religious beliefs that would like for me not to exist, there’s only one direction that slides.
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I also wanted to pursue this [project] as younger generations of LGBTQ+ folks have been increasingly visible across the media landscape, while older generations of lesbian women exist in all corners of this country, and the world knows too little about their incredible lives. What has been eye-opening and inspiring in the process is discovering all the ways these women were visible and made themselves visible, despite a society that, for so long, often told them there wasn’t a place for their dreams, hopes or connections with one another.
A beautiful project. Spend some time with these lovely photos and their stories.
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Let’s first talk about the world knowledge model that your robot is using to help you. It’s useful to think of it as a brain because it’s this ginormous file packed full of crazy math that gives you the impression it’s thinking, but it’s not. It gives the appearance of thinking because human language itself contains reasoning patterns, and the models learn to mimic those patterns. More importantly, at the time of this writing, the model remains unchanged; it does not learn. In fact, the robot can only retain a certain amount of context, which is unique to each conversational session.
I learned this while working on a project with Claude Code, and after 45 minutes, Claude forgot everything. I’d reached the window of how much context it could keep for this conversation, and it forgot everything about the project.
It’s good to know how things work and this piece by Michael Lopp is a great primer on a couple complexities of LLMs that I’ve seen trip up even technologically-savvy people (or worse, leave them mistaking anthropomorphism for intelligence).
When you know how they work, you can set your expectations accordingly and rely less on magical thinking.