You would think you’re investing in a software technology company, but after reading a bit of news stories, you realize you’re quite literally funding war crimes. If I invested in an arms company, I’d have reasonable expectations about what I invest in. Investing in Anthropic at surface level looks like investing in software for hobbies and business.
It’s pretty depressing to be honest. I don’t know how I could work in any of these military industry companies.
Normally Danish pension companies and banks will refuse to invest your money in weapons manufactures (unless you have a lot of money, then they apparently don't care). But as long as your money is invested as a pool, they won't do weapons.
I think you're right that e.g. Anthropic wouldn't be on the block list, because: It's an IT company, and I suspect that even Palantir might make the cut. It is fairly annoying, because my pension fund won't invest in Rheinmetall, SAAB or Kongberg, which I think they should, but they will probably invest in Anthropic, OpenAI, and SpaceX, which I don't really like.
All major indices have always included defense contractors.
Also, when you buy into an index fund, you are not funding the companies that the index tracks. That’s a misunderstanding of how the markets and index funds work.
>I don’t know how I could work in any of these military industry companies.
You'd sing a different song quite quickly once the threat stops being abstract as you don't get to free-ride on the security a defense industry provides.
The defence industry that would be required to prevent an invasion of the US mainland is at least an order of magnitude smaller than what currently exists to sustain the US empire.
I wouldn’t, but thanks for the reply. I’ve gone through conscription and we are neighbours with Russia. I’ve not lived a day in my life without existential military threat.
Replace "war crimes" with "hardware" and it's an equally good reason not to invest.
They're valued like software companies, but they have terrible margins compared to software. Investors haven't figured out how to value these companies.
Arms sales where? The non-US arms companies are the biggest winners, namely South Korea and European companies. The US not only threatened its allies, it also pulled ammunition from bases and order pipelines so European nations are picking up even more speed with de-risking from the US arms corporations.
Technology has politics, and it often serves to reproduce terrible modes of operation instead of something that could be described as "good progress" for humanity. The renewable energy landscape is the best example of a space that has had to fight against the old world's financial interests, even in the face of obvious monetary and technological supremacy.
The software world unfortunately has followed adtech + social media companies' operational structures, and we lost decades of "good progress" to attention-funded software.
I have a feeling this is why very few novel companies are springing up from this LLM shift: the relationship of a) lines of code b) solving problems to achieve progress c) getting paid for it has been decoupled for so long, because attention has been the main currency online.
Unsurprisingly, the Chinese technology market leap is fueled by a focus towards the "physical" (raw materials, manufacturing) and it's no surprise that a highly educated population is beating many Western economies in the electronics market (from small gadgets all the way to cars and energy). It's not impossible to try catching up by educating our people to reorient money to industry that brings "good progress", instead of industry that brings virtual money in the form of stocks or tech that mainly serves vices and/or entertainment.
It’s my favorite analogy against the cliche LLM will revolutionize all white collar work. Excel is probably the most powerful business application most people have used, but as you said, people only scratch the surface. No LLM magic will make people more fluent with software. Sadly it’s a combination of not knowing what you don’t know, and time pressure from the employer. My analogy usually ends by saying that if the government mandated 200 hours of Excel courses, it would probably be a faster and cheaper productivity leap than adding an LLM into everything.
This is definitely most annoying when dealing with software or standards with slightly illogical or hard to grasp cases. Recently, I worked on one of the software community's favourite spaces, timezones, and kept getting myself and my LLM context polluted with the confusion that arises when using POSIX standard timezone notation and common human-readable formats.
This blog probably covers my exact headache [0]. In summary, "Etc/GMT+6" actually means UTC-6. I was developing a one-off helper script to massively create calendars to a web app via API, and when trying to validate my CSV+Python script's results, I kept getting confused as to when do the CSV rows have correct data and when does the web app UI have correct data. LLM probably developed the Python script in a manner that translated this on-the-fly, but my human-readable "Calendar name" column which had "Etc/GMT+6" would generate a -6 in the web app. This probably would not have been a problem with explicit locations specified, but my use case would not allow for that.
When trying to debug if something is wrong, the thinking trace was going into loops trying to figure out if the "problem" is coming from my directions, the code's bugs, or the CSV having incorrect data.
Learning: when facing problems like this, try using the well-known "notepad file" methods to track problems like this, so that if the over-eager LLM starts applying quick code fixes – although YOU were the "problem's" source – it will be easier to undo or clean up code that was added to the repository during a confusing debug session. For me, it has been difficult to separate "code generated due to more resilient improvements" vs. "code generated during debugging that sort of changed some specific step of the script".
(Do note that I am not an advanced software engineer, my practices are probably obvious to others. My repos are mainly comprised of sysadmin style shell/python helper code! :-) )
> when facing problems like this, try using the well-known "notepad file" methods to track problems like this, so that if the over-eager LLM starts applying quick code fixes – although YOU were the "problem's" source – it will be easier to undo or clean up code that was added to the repository during a confusing debug session. For me, it has been difficult to separate "code generated due to more resilient improvements" vs. "code generated during debugging that sort of changed some specific step of the script".
Yeah, I have definitely hit this as well. Sometimes I've named a function or variable in a way that misuses a term or concept, or I've changed what something does without fully thinking it through. The LLM sees that code, notices an inconsistency, and makes a guess about what I meant. But because I screwed up, only I know what I really meant (or what I "should have meant"). So the LLM ends up writing a fix that breaks assumptions made in other parts of the code— assumptions that fit with my overall original mental picture, but not the misnomer the LLM got snagged on. Or it writes a small-scoped fix but the mistake of mine it stumbled upon actually merits rethinking and redesigning how some parts interact, so even if its fix is better than what I had before, I want to unwind that change so I can redefine my interfaces or whatever.
That's definitely worth calling out: it's not only the LLM's mistakes that make it more likely to commit future mistakes. Any mistakes in the codebase can compound like that. If you want an LLM to do useful work for you, it's more relevant than ever to "tidy first".
I'm gonna speak on behalf of language models' capability of making online communities better. In recent times, the frustrating forum phenomenon of "learned helplessness" is making me too annoyed to participate. Even in a fantastic subreddit as /r/LocalLLaMA, there are people posting replies in the vein of
> user1: please help me understand this acronym the post title speaks of
> user2: (explains in detail what it means)
In the "good old days", a low effort, surface level question would result in someone either muting or banning the person to keep the discussion high quality.
There I am, browsing a forum dedicated to LLM enthusiasts, and an unbeliavable number of people are asking LMGTFY/RTFM-level questions they could even find an answer to from a free Google Search AI summary, and people are rewarding them by actually responding to them with effort.
Thanks to models being quite intelligent at answering basics, the ban-hammer should be used more swiftly if people keep polluting forums with low-quality posts. There's no need to feel bad for them not having the time or capabilities to read through years of forum posts to feel qualified to answer.
Maybe even these sloppy posts authors can be outright muted or banned with a heavier hand for the sake of quality.
Is that even possible with medial grade wrist devices? Apple Watches can perform it only during sleep which makes sense. It seems like a difficult problem to solve without a chest strap, or just measuring during sleep.
The only other alternative I can think of is a screen strap (some companies make those screenless ones, Polar, Whoop) around the bicep, as it’s relatively close to the shoulder and chest areas which gently move with our breath.
Garmin measures "photoplethysmography-derived respiration" (using the optical HR sensor). Error rates are under 1 breath per minute during sleep or at rest but rises during exercise, up to 4 bpm above the lactate threshold.
Impedance pneumography is more consistently accurate, but requires a chest (not bicep) strap.
I fear this is only the start of it. A minimum of 3-4 constellations more will probably be launched in the near future (Russia, China, EU).
Their obvious dual-use nature makes them tempting, and a military target if a large conflict will take place in the near future. I hope their lower orbit will help any space junk burn up fast.
Add a black umbrella to each satellite: when they pass through the critical region where they are visible in the night sky while still being sunlit, pop the brollies up. We will fly them in the shade!
You could paint them black but they’d probably get quite hot.
Won't the shade then reflect the light instead? It's nighttime, so sunlight will be aimed up, from the Earth-based observer's point of view, so the shade will need to be pointed down in order to shade the satellite.
If you blow up a satellite, half of it will end up going slower and half will go faster. The slower bits will probably burn up nicely, but the faster bits will just elevate their orbit.
I doubt they will elevate their orbit by enough to be a problem. Some bits will come down in hours, some will come down in a year - even in the worst case where it takes out everything in low earth orbit in 5 years everything will be clear and we can start over. Higher orbits are the real worry, even the things slowed down mostly stay in orbit for centuries - but higher orbits are mostly a lot higher.
"LEO" is a big place, those satellites collided ~1.5x higher than e.g. the maximum Starlink altitude and the debris lifetime relationship is not a linear one.
Yup! Smaller quants will fit within 24GB but they might sacrifice context length.
I’m excited to try out the MLX version to see if 32GB of memory from a Pro M-series Mac can get some acceptable tok/s with longer context. HuggingFace has uploaded some MLX versions already.
I have an Mini M4 Pro with 64GB of 273GB/s memory bandwidth and it's borderline with 3.5-27B. I assume this one is the same. I don't know a ton, but I think it's the memory bandwidth that limits it. It's similar on a DGX Spark I have access to (almost the same memory bandwidth).
It's been a while since I tried it, but I think I was getting around 12-15 tokens per second an that feels slow when you're used to the big commercial models. Whenever I actually want to do stuff with the open source models, I always find myself falling back to OpenRouter.
I tried Intel/Qwen3.6-35B-A3B-int4-AutoRound on a DGX Spark a couple days ago and that felt usable speed wise. I don't know about quality, but that's like running a 3B parameter model. 27B is a lot slower.
I'm not sure if I "get" the local AI stuff everyone is selling. I love the idea of it, but what's the point of 128GB of shared memory on a DGX Spark if I can only run a 20-30GB model before the slow speed makes it unusable?
Tbf the Sparks usefulness isn’t for inference IMO. Its memory bandwidth is too low for that.
But on the other hand, running Qwen 3.5 122B A10B locally on it using ~110GB of memory and getting 50tk/s generation and quite excellent prefill… I couldn’t do that on many other machines at this price point
For me this has been awesome to learn CUDA on, fine tuning models (until I get it close to what I want then it’s off to H100 or something clusters) and a bit of inference on the side
It’s pretty depressing to be honest. I don’t know how I could work in any of these military industry companies.
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