This is a significant proportion of the market. Having this amount of money not understood is harmful.

"An expert on a very niche subject" made me laugh out loud the hardest, because it's the one that's most "just a guy", and yet it's also exactly how it's described.

all of this stuff is very obviously sports betting. Kalshi can say the piss on our pantlegs is rain, but I don't feel very obligated to belive it. I recognize the legal argument, I personally think it's bunk, I think it's reasonable for a legislator to say it's bunk, and it is hardly settled to say that Utah can't do this.

In fact, we rewound to, say like 2010, this would be completely obvious that Utah is correct.

GLENDOWE: I can call spirits from the vasty deep.

HOTSPUR: Why, so can I, or so can any man, but will they come when you do call for them?

I can interpret weights too, but will that interpretation be correct?

> Keep activity

> Keeping your activity lets you pick up chats where you left off anytime and helps improve Google services, including AI models. When this setting is off, Google still saves chats for 72 hours to respond to you and help keep Gemini safe.

And like any responsible parents, they presumably also don't just defer to what they're elementary aged children "want to do with their life" nor feed into the exaggeration that "everybody else" does anything at all, let alone play tackle football.

If you disagree with the dangers of the sport or are afraid of shaping your children's life choices for their own benefit, that's fine for you, but the rhetorical and snide way you presume others should share that view seems strange.

I don't understand how Subaru stays in the business. Just how. It's a 4 digit time. Just broadcast it on CAN. Why not.

Also: a lot of these older, completely rectangular sat nav units actually follow what is colloqially known as "2DIN" standards. (cheap import Amazon)Aftermarket units are available and those can be swapped in with just a bit of work. That's what most people do when they have issues with the stock unit installed for their markets.

Most of the time it's quicker, sometimes it's just bad.

(The pun in the blog name might also have been a hint)

As usual, some good practical advice (try to understand the societal context your navigating in; don't attempt to fix a problem before you have understood it in depth) which leads to some questionable conclusions (don't try to fix bad situations at all and just learn to accept the injustice because alternatives could be worse?) and then shifts into weird otherworldly stuff where I'm not sure if I'm dealing with scifi literature or things people are actually believing. (darkworld, Agor).

The whole thing has an unpleasant vibe to it that reminds me of the way "pickup artists" used to talk a decade ago.

It's like watching that Sloth in Zootopia sometimes browsing the modern web.

First, the key result has nothing to do with making Qwen better at math, it just reduces reasoning loops.

Reasoning loops are a simple problem: the model generates the </think> token (which closes reasoning) with too low probability. You can fix this with simple steering approaches where you add a vector to different components of the LLM (https://arxiv.org/abs/2503.22048, https://arxiv.org/abs/2506.18167, etc). The method Astra used for identifying the vector is the standard procedure humans use (contrastive analysis). Such steering vector creation is basically the standard "intro-level interp project" for human researchers.

If you can code and understand LLM internals, you can do this yourself in 4-8 hours. The SWE equivalent would be doing binary patching with a debugger. Cool that frontier models can do this, but not unexpected since humans can do it without issue.

https://trixter.oldskool.org/2014/06/19/8088-domination-post...

As it turns out, the video was encoded in such a way that it could be played back using the display bandwidth of the original PC, BUT doing that encoding required a modern computer to perform in a reasonable amount of time.

It is also interesting to read about the Amsterdamse School, an early 1900's style with "mass appeal" of which you can see many beautiful examples throughout the city, which were also only built thanks to strong support from the political sphere. https://architecture-history.org/schools/AMSTERDAM%20SCHOOL....

Most of the non-technical people I see using LLMs to create software are not engaging in "cognitive surrender" as far as I can tell.

They didn't read the code before and they don't now. However, asking questions and improving understanding of the system and architecture is actually faster and easier for them now than it used to be.

They're evaluating what they can see, i.e, judging results. They build things for their own use. They test them, iterate on them to meet their needs and try to achieve their vision. This is roughly the same process they followed when they used to outsource to an agency except the loop is much tighter.

It seems to work well, mainly hurts when the problems are invisible, so security, backups, breaking migrations, perf, maintenance over time (language / runtime updates, dep updates, server updates etc). All the stuff that hurts if you have never been responsible for the uptime of something before. Not knowing that software is never "done". But junior devs trip on those exact same things.

I agree with the tail of the article; tooling and processes that make that stuff more visible would close a lot of the gaps.

At the same time, most of their junior devwork can be replaced completely by AI - so they really should start to learn how to work with it. I recommended everybody to get a private 20€/month claude subscription and use it for toy projects. I have younger devs (early 20s) in the team that told me they won't spend private money, or simply have no usecase in their private life for AI. We talk about software engineers here, and IMHO most of them are in denial of the fact that their skills have mostly become worthless if they don't start to pivot to something valueable.

The challenge took me a few weeks to make because "calibrating" a disk (determining the geometry of the pregroove) is incredibly sensitive.

After the CTF I tried really hard to make a repeatable calibration process with the intent of building something releasable. I even ended up measuring the details on every CD-RW and CD-R I could buy on Amazon.

But despite trying very hard, I never did make calibration patterns that were reliable enough that I felt like other people would be willing to go through the process.

It seems someone else has succeeded where I failed. Great to see!

yet its title is:

> Small Business Cybersecurity: Non-Employer Firms (NIST Cybersecurity White Paper 50)

Small businesses don't identify as 'Non-Employer Firms', have no idea what 'NIST' is, most wouldn't know what a White Paper is, nor number 50.

'A Crash Course in Cybersecurity for Small Businesses' would be an improvement.

Good gosh. Goldman probably has 50-100 people, inc. maybe 20 Engineers just on that alone, full time.

And yes, Intel can fail 100%. They can absolutely go into receivership and/or be broken up and bought for pennies by other entities.

It would be a strategic dismantling, obviously the government won't allow certain things to happen, but they could 100% allow investors to be wiped out.

Boeing is a bit different as there are no substitutes, but it could go into receivership. Some parts of Boeing will 'always exist' but the corporate entity can fail.

Part of my research is in weight space learning. For starters, we have had the ability to edit weights, to generate new weights, to identify weights for a while. Here is an example for larger models as early as 2023: https://rome.baulab.info/

Weight space interpretability and learning is an active research community, has been for the past 3 years with many great papers. I suspect it is hitting non-interp folks harder because the authors hyped up their work, and people who are not in the field did not bother to read it before typing up X posts.

Some experiments here are done on very small models (20K params). We already know that small models are more interpretable. In fact, much of editing here you can do by hand. For Qwen, the model fixed a looping bug that may have been known apriori, and it is basically a steering vector, a very established technique. This is clear when they then attempt to "improve" math capability, and it doesn't work. For me, this paper rather shows that OpenAI has done training on mechinterp environment, in fact, maybe even on weight space learning datasets, which are open. But it does not show some insane "well past what we’d expect from humans or conventional algorithms" (from the blog post), more like past what I would expect from a large model with no specialized harness. I still think it is cool work, but do check out recent advances in autointerp, to ground your expectation.

On a personal note, I understand the need for promotion of your work, but I think healthy skepticism is fine too. Otherwise, your understanding of what problems need to be worked on and solved in science becomes warped.

sudo iptables -t raw -I PREROUTING -p tcp -m u32 ! --u32 "0>>24&0x0F=5" -j DROP

We had finally heard about the Microsoft store on campus, and had heard about the super discounted software. Turned out you had to be an employee. We were crushed because retail copies of Win2k were expensive for high schoolers. So we sat out in front of the store with signs "will work for cheap Microsoft software." Finally this girl came by and asked what we were doing, she was an intern or a new hire or something like that not much older than us, still in college. She went into to store and came out with boxed copies of Win2k Workstation for $20 a pop for us. We were stoked and tried to give he some cash, but she was just happy to help us out.

Those were some good times. I wonder if stuff like that still happens today.

I am not qualified to make a judgement on the research quality, but it strikes me as a little weird? And also weird to cite when trying to create an "AI is stalling" narrative, because the text of the paper doesn't really support that at all.

The claim seems to be based on a single figure that the authors present as additional context in an appendix, which shows the trends of four surveys, in which only one of the four shows a decline. The authors don't even seem to think the decline is significant enough to comment on except to say that "the plateau in late 2025 reflects declining interest and correlates with google search trends for ChatGPT and Gemini" (which they also show using a chart that does not look like a plateau or decline to me at all).

The methodology and survey questions are included, the numbers seem to be based on a straightforward "Do you use generative AI for your job"?

It's weirdly ChatGPT and Gemini centric, the word "Claude" only appears once, in a definition, and "Microsoft" and "Copilot" are nowhere to be seen. I don't know how much to read into that, it just strikes me as weird and noteworthy in a paper that by its own admission is detecting a trend that is the opposite of everyone else doing large scale surveys on the topic.

And what does “hardware and performance is there” mean? The latest Apple TV 4K has an A19 chip, same as the base iPhone 17. What console market are they eating alive with that exactly?

   1. What you like doing
   2. What you are good at doing
   3. What people will pay you to do

I think the "egoless" path is taking a 50,000-foot view of mathematics that ignores the above lens. Sure, mathematics will survive and even prosper with AI, but what about the mathematicians?

In time, the profession of mathematics will evolve so a new task is at the center of the diagram--maybe it will be explaining math results to humans or teaching math to future mathematicians. I don't know. But it's easy to see that not everyone is going to be happy with the transition, and many will be left behind.

For software engineering, it depends on what your center is. If you like "writing code", then #3 is under attack. But for those whose goal is, "make something work", all three are still viable and even better than before (or at least faster).

That's why we see so much disagreement on HN about whether AI is good or bad for software engineering.

I'm sure lots of other people have made this observation before.