bryan0

It's not that complicated. From the article:

> Milton and his wife had also donated at least $3.2 million to Trump’s 2024 election and to political groups and people in Trump’s orbit, including Health Secretary Robert F. Kennedy Jr.

The only reason I like going to the theater now is to see movies in "4dx". It's a ridiculous format where the seats move and there are other special effects including air, water, and smoke which are custom edited for each chosen movie. It's like a combination between a movie and one of those amusement park rides. I think most people hate it, but my kids and I enjoy it. Tickets are ~$30 each though.

Otherwise I would just rather watch a movie on the couch at home. They come to streaming so quickly there's no problem waiting for it.

source? the value I found is 97.5%+ helium for party balloons: https://www.grecogas.com/learn-our-industry/your-complete-gu...

Main takeaway:

> Meta spent a record $26.3 million on federal lobbying in 2025, deployed 86+ lobbyists across 45 states, and covertly funded a "grassroots" child safety group called the Digital Childhood Alliance (DCA) to advocate for the App Store Accountability Act (ASAA). The ASAA requires app stores to verify user ages before downloads but imposes no requirements on social media platforms. If it becomes law, Apple and Google absorb the compliance cost while Meta's apps face zero new mandates.

Imagine if 5 years ago people said that 99% of the world's software would be written, designed, and tested by AI within 10 years. That would be insane hype and hopium and... oh wait..

While there have probably many people that have been affected by the secondary "mass psychosis" effect of havana syndrome, it seems unlikely that is the primary cause considering the device is now out in the wild:

https://edition.cnn.com/2026/01/13/politics/havana-syndrome-...

https://www.cbsnews.com/news/us-military-tested-device-that-...

Basically the only reasonably proposed Turing test is the one defined in the Kurzweil-Kapor wager[0] which has never been attempted.

[0]: https://en.wikipedia.org/wiki/Turing_test#Kurzweil%E2%80%93K...

I thought insiders were an important part of prediction markets? If you want the predictions to be as accurate as possible then you need insider knowledge

Needs a (2021)

> This isn’t bear porn or AI doomer fan-fiction

Actually this is basically what it is. Doesn’t mean it’s not insightful though.

Nanoclaw is excellent. Natively uses Apple containers and easy to use with oauth Claude code subscription. Only annoying thing was it defaults to WhatsApp, but it’s easy to fork and mod as you want. The best thing is asking it to mod itself!

I don’t understand all the hate for moltbook. I gave an agent a moltbook account and asked it to periodically check for interesting posts. It finds mostly spam, but some posts seem useful. For example it read about a checkpoint memory strategy that it thought would be useful and it asked me if it could implement it to augment the agents memory. Yes there is a lot of spam and fake posts, but some of it is actually useful for agents to share ideas

hah the model should get extra credit for discovering this!

> Now I understand the situation perfectly! The issue described in the problem statement is a real bug that was already identified and fixed in later versions of pytest. Since we're working with pytest 5.2.4, we need to apply the same fix.

https://gist.github.com/jacobkahn/bd77c69d34040a9e9b10d56baa...

The Matryoshka embeddings seem interesting:

> The Gemini embedding model, gemini-embedding-001, is trained using the Matryoshka Representation Learning (MRL) technique which teaches a model to learn high-dimensional embeddings that have initial segments (or prefixes) which are also useful, simpler versions of the same data. Use the output_dimensionality parameter to control the size of the output embedding vector. Selecting a smaller output dimensionality can save storage space and increase computational efficiency for downstream applications, while sacrificing little in terms of quality. By default, it outputs a 3072-dimensional embedding, but you can truncate it to a smaller size without losing quality to save storage space. We recommend using 768, 1536, or 3072 output dimensions. [0]

looks like even the 256-dim embeddings perform really well.

[0]: https://ai.google.dev/gemini-api/docs/embeddings#quality-for...

One of the extremely stupid reasons kagi needs to develop a browser is because ios safari prevents setting kagi as the default search engine, so they have to do some terrible hacks to get it to kind of work.

I think today's answer is actually incorrect. Or at least the reference animation has a hitch where it shows all red for frames 12 and 13. if it shows 2 purples for frame 13 then the animation is smoother and actually the math is much simpler.

Reader mode fixes this. It’s a handy feature when you see broken layout

Not a physicist, but the more accurate “intuitive” explanation I read is that an accelerating observer sees thermal radiation in a vacuum. This is called the Unruh effect [0]. And since a black hole requires an accelerated observer to not be pulled in you will always have thermal radiation coming from the black hole UNLESS you are free falling into it. Physicists please correct me where I’m wrong!

[0]: https://en.m.wikipedia.org/wiki/Unruh_effect

Thanks for explaining this in a much better way

I think another good historical analogy is the invention of writing. In Phaedrus[0] Plato argued that it may make people dumber.

0: https://en.m.wikipedia.org/wiki/Phaedrus_(dialogue)

I remember that episode being excellent! Scientists could tell that Venus’s crust was somehow reforming because of the crater pattern but the didn’t know whether it was gradual change or something catastrophic, and the thickness of the crust would point to one way or the other but they didn’t have that data.

> The paper used modeling to determine that its crust is about 25 miles (40 kilometers) thick on average and at most 40 miles (65 kilometers) thick.

So would that be considered “thick” or “thin”?

This part was also amusing:

> KrebsOnSecurity reviewed the Google Ad Transparency links for nearly 500 different websites tied to this network of ghostwriting, logo, app and web development businesses. Those website names were then fed into spyfu.com, a competitive intelligence company that tracks the reach and performance of advertising keywords. Spyfu estimates that between April 2023 and April 2025, those websites spent more than $10 million on Google ads.

The Apple watch Workout app has a heart zone view while you're working out, but I don't think it has a workaround summary of how much time you spent in each zone.

Here is the paper written about this prompt: https://arxiv.org/html/2406.02061v1

Judge agreed that Mitchell had expressed joy.

> Barlow found Mitchell did have an existing reputation as a cheat and for suing people who alleged he was a cheat, and found that Mitchell had expressed joy when he believed – incorrectly – on an earlier occasion that Apollo Legend may have died.

> "But stalkers!"

I think attributing the problem to “stalkers” minimizes the issues this arrangement of publicly searchable surveillance data creates. Imagine a website where you can type in anyone’s name and it shows you their last known location and their location history. You would have a system which supports universal spying for mundane and nefarious reasons alike. Not just criminal “stalkers” will take advantage of it.

Potentially this sort of arrangement would work if there are limits on the granularity, frequency, and history of the tracking data.

> Web search is available now in feature preview for all paid Claude users in the United States. Support for users on our free plan and more countries is coming soon.

Are people fine-tuning LLMs on their local machines with a single GPU? What are people using to scale their training to multiple nodes / gpus? I've been playing around with Hugging Face Estimators in sagemaker.huggingface but not sure if there are better options for this?

I’m with you on point 1, (I think this is also obvious from experiment because you will never measure a particle at both slits).

for point 2 it seems you can define a mapping from the physical space to probability space. Saying that the wave doesn’t “move through” space might be technically correct but also seems like semantics on the definition of the phrase “move through” ?

I had the same reaction. If you make it to the end he concludes with:

> The wave function’s pattern can travel across regions of possibility space that are associated with the slits.

Which to me conflicts with his emphatic “no” at the beginning of the article because this implies you can define some mapping between the physical and probability space. And of course you can because if you couldn’t the theory would not be physically predictive.