Tootfinder

RadixArk, led by former xAI employee Ying Sheng, raised a $100M seed at a $400M valuation to make AI inference more efficient via its open-source SGLang engine (Meghan Bobrowsky/Wall Street Journal)
https://www.wsj.com…

Even in #StarTrek there is a lot of conquest. And that means this subject makes for a good #TrekTriviaTuesday question.
As always no googling and no spoiling the answer for others. Please boost after voting! :BoostOK:
Vote will run for 24h, then I will reply with the correct answer…

Anyone good in statistics who can quickly answer a question? Assume I have an n-digit random binary number (for IT people: a bitstring). I calculate the number of 1s vs. 0s ("Hamming weight"). Expected to be usually ~0.5/50%. How does one calculate the probability for a given length n that it's above or below a certain value, i.e. <=40% or >=60%? And how many inputs would one on average need to get at least one such outlier?

Relight my fire <--> I'm rightly free
-- anagrama

Ranking the 10 worst QB rooms in NFL entering 2026; starting predictions for Week 1

https://www.cbssports.com/nfl/news/ranking-10-worst-qb-rooms-in-nfl-starting-week-1-p…

«KI-Modelle sind anfällig für wiederholte Angriffe:
Laut Forschern von Cisco versagen KI-Modelle bei realistischen Multi-Turn-Angriffen und lassen an Sicherheits-Benchmarks auf Basis weniger Prompts zweifeln.»
Der moderne Widerspruch ist die KI oder was ist es sonnst? So klug wie KI angeboten wird ist es einfach nicht.
🤖

KI-Modelle sind anfällig für wiederholte Angriffe
Laut Forschern von Cisco versagen KI-Modelle bei realistischen Multi-Turn-Angriffen und lassen an Sicherheits-Benchmarks auf Basis weniger Prompts zweifeln.

Here's an answer for a life-changing technology that truly stands out:
"The Bicycle
Selected by Reshma Saujani
In the 1890s, the bicycle, as we know it today, finally let women go where they wanted, on their own, without asking permission. It even played a central role in the fight for women’s suffrage—a simple machine with outsized impact. Today, it reminds us what technology should do: expand freedom and opportunity. Millions of American women are still fighting f…

The whole #LLM ROI thing reveals something interesting. It's basically impossible to figure out the ROI of an LLM. That makes it impossible for bean counters to make a comparison between human work and LLM work, or human work without an LLM and LLM-assisted work, to determine if the incredibly high price is worth it. But it's also impossible because you can't measure the ROI of a human, especially for skilled labor.
You can't measure the ROI of a human, because managers have no idea what people do. There's an eternally expanding amount of work designed to address this problem. But no matter how closely people are surveilled, interrogated, analyzed, there's never any real answer.
I've talked in the past about in relation to medical care. One of the dirty secrets of hospitals is that they have no way to figure out how much individual treatment costs. It's easy to understand at scale. You can know exactly how much something costs society. You can even identify patterns, using public health models, and decrease costs for society by trying to get people to avoid risky behavior (stop smoking, use protection during sex, etc). But it is absolutely not possible, at all, in any way, to figure out how much a single visit costs. This is similar to the problem of predicting climate change vs predicting the weather tomorrow in Amsterdam at 15:00. One is possible, the other is simply not.
But what is becoming painfully clear now is that this is true *everywhere*. It's trivial to know how much an industry costs. It's possible to figure out it's ROI for society. It is not possible to figure out how much value any individual worker provides. LLM ROI and cost comparison is an instance of this larger problem.
This is a problem for capitalism because it shows that the fundamental assumptions behind capitalism, that product value and labor value are quantifiable, that people can actually make comparisons between competing products, etc, are completely bullshit. The capitalist apologetics that makes up so much of economics, the lies that are told that hold this system together, are crumbling before our eyes.
If you make a lot of money, it's because you've been lucky. You have the right social networks, you have become good at convincing people to give you money. There is absolutely no way to connect that to actually providing value to society. If you make a lot of money, internalize that. Understand that you are not special, and things can change. If you don't make a lot of money, it's not because you don't provide value. Don't forget that. The system is a lie built to destroy you. Don't let it.
The ideology is sick, something something time of monsters and all that, we are together in this dying machine. We need to understand the lies. Your value can never be quantified. The way we have always figured out how to do the right thing for each other is through each other. Social connection has always guided us. But now the most socially disconnected people on the planet have hijacked the system. They direct the resources of the world, and game the system to avoid personal responsibility.
We have to build a system where everyone is accountable. We can't use abstract numbers and lies to figure things out for us. We have to build systems around people and accountability. There is no other solution.

Iran-Krieg treibt laut Bericht Platinenpreise hoch
Der wichtigste Lieferant von Polyphenylenether hat nach einem Angriff offenbar die Auslieferungen eingestellt. Das betrifft alle elektronischen Geräte.
https://www.

Iran-Krieg treibt laut Bericht Platinenpreise hoch
Der wichtigste Lieferant von Polyphenylenether hat nach einem Angriff offenbar die Auslieferungen eingestellt. Das betrifft alle elektronischen Geräte.

Anyone good in statistics who can quickly answer a question? Assume I have an n-digit random binary number (for IT people: a bitstring). I calculate the number of 1s vs. 0s ("Hamming weight"). Expected to be usually ~0.5/50%. How does one calculate the probability for a given length n that it's above or below a certain value, i.e. <=40% or >=60%? And how many inputs would one on average need to get at least one such outlier?