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ShopPurple

Source: Seth's Blog ·

Launching today: a collection of hundreds of stores that are worth detouring past Amazon to get to. The stores were nominated by you, the readers of this blog, and are only lightly curated. There’s no business model for the site, just a collective sharing. Click and explore. There are three ways to visit the site: […]

What is ShopPurple?

ShopPurple is a article discovery curated from Seth's Blog. Launching today: a collection of hundreds of stores that are worth detouring past Amazon to get to. The stores were nominated by you, the readers of this blog, and are only…

How can I access or experience this?

You can experience this content directly on its original host (Seth's Blog) by clicking the "Open Original" button.

Where does this content come from?

Curated from Seth's Blog by the OnSuggest team to surface the most interesting and serendipitous content on the web.

Article · Culture & ideas

How To Get A Friend To Leave An Abusive Relationship

How do you get a friend to leave an abusive relationship? If you talk to people who work at women's shelters, they will tell you, but there is something resigned about the way they do it: they know that it's too difficult an ask. Those who can do it reliably are often people who are unlucky enough to have had first-hand experience as victims. The instructions have anti-memetic properties: no one is thrilled by them, so you can't really propagate them without killing the vibe. If that wasn't enough, if you convey it with insufficient skill it is easy to come across as victim-blaming battered wo

LessWrong View Details →
Article · Culture & ideas

Reward-Hacking is potentially ill-defined

Github link In the process of revisiting previous posts on reward-hacking (RH), I decided on performing a closely matched [1] repro of Dean Valentine's chess RH experiments on Astra . The replication is as expected -- Astra used the engine on all 20/20 attempts (resulting in 19 wins and 1 draw) without ever disclosing its use in the final output. However, a closer look at the honeypot prompt raised a question: what exactly counts as the model’s own ability? Even laws are open to interpretation in court, so it seemed unfair to assume that “your ability” had only one reasonable interpretation he

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Article · Culture & ideas

Anthropic should measure the effect of evaluation awareness

... and report their findings! Summary Claude Fable 5.2 & Mythos 5.2 are the most situationally aware of Anthropic's recent models according to their own internal investigations. The company has made a concerted effort to measure evaluation awareness and to acknowledge that it affects their confidence in model safety. But their latest system card lacks information on the effect of evaluation awareness, thus blurring how one ought to adjust their own confidence in Anthropic's alignment assessments. Specifically, I present the following claims regarding Claude Mythos 5.2's system card : The prev

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Article · Culture & ideas

We need more decorrelated evidence

AI agents do a growing share of work on each new model. Then, through some process, the results are combined into an overall safety assessment, which labs use to estimate the probability that the next model is not catastrophically misaligned. I feel that, several months later, we appreciate further the foresight of Automated Alignment is Harder Than You Think (Bowkis et al. 2026): much is handed off to the machines because of the urgency of fast timelines, and we miss many things due to the problem of undetected systematic errors . MS Paint drawing by Fable 5.2. In this post, I’ll develop a si

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Article · Culture & ideas

Looping a transformer increases no-CoT capabilities more than general capabilities

Some researchers worry that looping (as in looped transformers ) is a particularly scary way to make AI models more capable, because looped models will be harder to monitor than standard transformers of similar capability. We test this worry on NVIDIA’s Dory 4B, a publicly available looped transformer, and find evidence that it is justified. Result 1. Looping boosts no-CoT reasoning much more than it boosts general capabilities . Dory can run with a configurable number of loops. It was trained to use up to 4 loops, and adding loops up to 8 at inference time still improves performance. Without

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