> Why do rural people deserve subsidies by virtue of living in the sticks?
For the same reason urban people deserve subsidies, for things like public transportation, by virtue of living in the city. The first sentence of the US Constitution lists "promote the general Welfare" as one of the foundational reasons for the creation of the Constitution. There is no "one size fits all" for a nation this size - the welfare of people in the city and the welfare of people in the sticks requires differing allocations of tax money. Also, mail delivery to the sticks benefits people in cities given that mail in the sticks is often sent to or from cities.
I'm not who you're replying too, but I also live in a place with no delivery to the home, despite UPS and FedEx doing so. Everybody in our town gets a free PO Box - I suspect that's how the service obligation is covered. Once a year we have to prove that we still live here to keep the free box.
There is clearly a level of thinking that doesn't involve language. Participating in competitive sports has been a core part of my family, with some competing at elite levels. When you get to a certain level, if language is involved during the most intense moments, you fail because you act too slowly. Some say you are "reacting" or "using muscle memory" or "using intuition", but some very complex behavior comes from this.
To me, it feels a lot like meditation, or being in the flow when writing code, or being immersed in a video game. I don't have the skills to know, but I wouldn't be surprised if elite chess players are doing the same thing. It's also riding a bike or driving a car, and I think it's why we sometimes can't remember if we stopped at that stop sign behind us, but we almost always did. Language is almost always deeply involved in developing these skills, but gets in the way of top performance once the skills are there.
This feels like a higher level of consciousness to me than language-based thinking. And I think there's a good chance it's the same kind of thinking that we see in animals and primates. Our advantage is that we've figured out how to use language to inform that level of thinking / consciousness more deeply and in more domains.
This is not unique to SQLite... it should help with any database that uses B+Trees. Batch deletes are also typically faster when the IDs to be deleted are sorted in these same systems.
If this becomes law, it will give rise to a fun new form or protest art in this vain. What is the cutest thing you can design that nobody would consider to be related to guns, but which gets flagged? An obvious example... a llama sitting on the ground, legs hidden, and head held high in the air, chewing its cud. Llamas can be really cute! Sell them on Etsy/eBay/etc., printed by an out-of-state 3D printing service. I just used the EFF form to promise my state senator in Sacramento that I'd send her (and reporters that cover her) one of them if the bill passes.
Sometimes it's a "grass is greener on the other side of the fence" thing or a FOMO thing. But sometimes different roles fit different people better.
I started my career doing mostly full stack work. I couldn't get away from the front end part quickly enough. I had intuition for simplifying UI flows, but none at all for aesthetics. As requests for aesthetic changes came in from our excellent designers, they felt completely arbitrary to me, even though they probably weren't. Most of my career was as a data engineer, data engineering manager, or leading an ML-heavy org. That space fit me so much better.
I loved having a few self-starting front-end devs in my orgs - they could take various tools we were creating for ourselves and make them quite a bit more useful. But it was also always a stepping stone as they typically wanted to work on the public facing part of the product.
Around 2015, I found myself managing back end and machine learning engineers (not researchers) at the same time. Many of the back end engineers wanted to do more ML. Some of them did well when given a chance, but others wanted to revert to back end within a few months. At the same time, one of the ML leaders wanted to step away from ML and only do back end work to support ML.
As I studied these dynamics, something occurred to me... Different people need to see signs of success at different frequencies. Because of the nature of our product, measuring the performance of a new/updated model required the model to be live for at least a full calendar month. So, between initial work and final analysis, it was often a 2 month wait or more. For many back end tasks, you can build a quick prototype, run it to see if it works, and be on your way - the signals come all day long. The varying frequency needs of different people went a long way to determining which of them liked working on ML.
This is sort of a manager's version of feature engineering. ;-) The people on that team taught me a lot!
My understanding is that the core research team is between 100 and 200 people. I don't have a great source for that - a friend of a friend is on the team. By comparison, Open AI's Chief Research Officer said their core research team was about 500 at the end of 2025[1]. With so few people, DeepSeek would have be more selective.
I'm trying to keep this account anonymous so that I'll speak my mind more easily, and she's probably more private than I am, so I don't want to say her name.
Among other things, she worked on mission control software for tracking rover-carrying spacecraft en route to Mars and related ground data systems (I'm probably getting the wording wrong, but that's my outsider understanding of it). Later she worked on software used to analyze interferometer data for exoplanet research on the Caltech side (again, wording?). I'm not aware of her ever being near robotics work.
Part of her shift to Caltech was to try to get to an environment where there was more predictable focus on the science, but a lot of the grant money there was from the same or related budgets as JPL's money, with plenty of strings attached that weren't always pulling in directions she was excited about.
CodeWhale (formerly deepseek-tui) automates this over DeepSeek V4 Flash and Pro. My shallow understanding is that it prompts the model to evaluate the complexity of a given task, then decides on Flash vs. Pro at various reasoning levels for that task. This can help with both cost and speed. If other agent platforms don't already do this, I have to imagine they will at some point.
I'm retired and can't justify spending too much on these things. CodeWhale over DeepSeek is helping me understand this space much better (and have some fun!), and it's quite affordable. I've spent ~30 hours using it over the last couple of weeks, and I've spent $3.89 on DeepSeek in that time. If I don't feel like writing any code for a few weeks, I pay nothing. Looking at DeepSeek's dashboard, about 60% of my requests have gone to Pro and 40% to Flash. I've used 97M Pro tokens and 19M Flash tokens (well over 90% of each have been cache hits, so the price is much lower than it would otherwise be).
I want you to be wrong, but I think you're right. The cost discrepancy I pointed out is evidence of that. I've heard a lot of things that are consistent with what you're saying from one of my closest friends, who happened to work at JPL and Caltech from 1992 to 2011, much of that time on Mars rover related software.
The total cost of Curiosity to date is well under 5% of the cost of the recent trip humans took around the moon (something like $3B vs. $90B, or $20 vs. $600 per US taxpayer). Imagine the amount of science that could get done if we gave even half the budget of crewed spaceflight to rover / probe style exploration.
I think a lot of people don't understand how different it is to live in rural communities in general, and then to your point, how much more extreme rural Alaska is than "regular" rural areas.
I live in rural California. During the pandemic, a lot of people moved here and didn't last six months before leaving (I can relate, there's a lot I miss about living in SF!). There are so many services people expect, and they expect them to be prompt. We have the opposite of economies of scale in our small town. We have zero options for meal delivery. The nearest full size grocery is a 20-30 minute drive. Costco and Trader Joe's are a 90 minute drive. There are so few auto mechanics around that many of us drive 90 minutes for car service if we don't do it ourselves. Power outages often last 3-10 days. Large snow storms (4+ feet) typically make our roads impassable for 3-7 days. When the power goes out, internet follows 90 minutes later. There's no cell coverage inside or out, even when the power is on.
All of that is worse in almost all of Alaska. My brother worked on Alaska's North Slope for a few years - if you've ever seen the TV show Ice Road Truckers, that was his job. They'd fly him up north for a 1-2 week long shift, then fly him back to the little town he lived in for a week-long break. You have to worry about crazy things like hoping the summer doesn't get too warm, because roads will melt and collapse into what was previously permafrost. Uh oh, someone left a loading bay door in the warehouse open and now there's a polar bear in there breaking expensive stuff. Then you go home and can't go inside because a moose decided to go to sleep right in front of your door - better drive 50 miles and fuel up because you might be sleeping in your vehicle tonight and you don't want to freeze to death if your fuel runs out. These are all things my brother experienced. The pay was great, but he finally gave up and moved to Anchorage to drive a truck locally there.
My initial use was in a repo where I create models for 3d printing using a library called build123d. There are a handful of parametric models and then many instances of those models with parameters (one that's 24 mm in diameter with a cutout, another that's 42 mm in diameter but no cutout, etc.). I tend to be in a hurry when I want a new parametric model, so I've ended up just copying the one that's the most similar and changing what I want to be different.
The first big task was to find the common bits and abstract them out. It did a great job of creating a plan, summarized in a table, that gave a name to shared chunks, the line numbers in various files where they appeared, line counts of new functions vs. removed bits, and some pros/cons about splitting out each chunk. It was very well "thought out", so I told it to go ahead. It did a nice job other than straying from my coding conventions. That gave me a chance to build out my AGENTS.md file (it helped with that, too).
Once that was done, I had it create automated tests for the newly abstracted parts. I think this is probably a bad practice... I believe humans should at least define what the tests are testing so that there is a deeper understanding of what oversight is in place. But I was just trying things. It surprised me how well it did. The biggest surprise was that the tests seemed quite inspired by vision. It would try different parameters and then have comments about making sure the shape protruded in a certain way, then code that did that. I expected it to refactor a bunch of the code to make it more testable. It found a way to not touch the code while testing everything I asked it to with just two simple mocks - I hadn't foreseen that, but it felt quite practical. It was passing around several opaque tuples in the tests and accessing items in them by index. I prompted it to replace the first one with a frozen, kw-only dataclass. Then a second. On the second request, it saw the pattern and did the rest without me asking. It created 44 tests across a handful of files.
The next part is where I was the least happy. I use ruff and ty to check my code with almost all checks enabled. It was mostly good about the ruff issues. But for the type checking, it just wanted to disable 6-8 rules for the entire repo in pyproject.toml, or at least for all the tests. I had to repeatedly tell it not to and it kept telling me it wasn't recommended. When it finally gave in, it fixed most of the type issues (build123d has lots of types specified, but many operations result in type conflicts because things are so deeply overloaded). The things it didn't fix, it just left a comment to ignore type checking altogether on that line. After I did a little more brow beating, it finally changed the comments to only disable specific rules. To be fair, and unlike most of my other repos, I've had to spend way too much time getting types right in this repo myself.
My last task involved a small library management system for our little town library (tracking library cards, books, DVDs, check-outs/check-ins, etc.). I inherited it from someone who had built the entire web app out of bash/awk/troff scripts with the data in text files burdened by a lot of schema changes that he didn't really know how to deal with. I'm halfway through moving it to Python/FastAPI/SQLite. I asked it to do a security audit of the entire code base, both the newer parts and the old parts that are still in bash/awk/troff. It found everything I knew about and a few things I didn't know about. It made a decent assessment of the risks/impact of each issue. It also called out design decisions that were good security practices. One of the next big tasks will be to see how it does at continuing the migration - it has enough examples of how I've done it that I suspect it can do something fairly consistent with my thinking. I'll probably have it do one or two web pages. When I feel like it understands what I'm after, I'll tell it to use sub-agents to do the rest. I'll be very happy if I don't have to tease apart any more troff scripts that are generating PDF files!
I retired a few years ago, but I still write a fair bit of code. I was using Copilot's code completion before I retired, but coding agents hadn't come around yet. I've been wanting to try them, but I kept putting it off, and now the price increases make it hard to justify.
So I just started trying CodeWhale (https://github.com/Hmbown/CodeWhale) with DeepSeek V4. I expected to be impressed by the abilities (which still require plenty of oversight). I didn't expect to be completely shocked by how cheep it is. After most of a week of using it 4-8 hours a day, which would amount to a full week of coding in many jobs after you account for non-coding activities, I'm about to hit $3 in total usage. So we're talking $10-20 per month for single-agent use by a full time software developer? And I'm sure some of my usage is waste as I'm still getting my head around things like compaction. If I take a break for a few weeks, I pay nothing because there is no subscription.
If DeepSeek and Xiaomi MiMo stay within a few months of the US-based models in terms of capabilities and US companies don't figure out how to drastically cut prices, I can't see how China hasn't already won. Protectionism would be one reason, but that might be ceding 50-90% of the total addressable market, and bring us closer to moving knowledge work out of the US the same way we did with manufacturing because it's too expensive in the US.
I worked on a predecessor to that at Xerox at the start of the 1990s. The first was the Xerox 4850 which was the size of 3-4 clothes washers, cost several hundred thousand dollars, and printed at 50 pages per minute in black plus one color (Xerox called it Highlight Color), but no CMY. It was exactly for the logo use case you mention. A big customer was AT&T for printing phone bills with their blue logo on it. It let them replace many millions of pages of letterhead not sold by Xerox with blank paper sold by Xerox. Xerox loved paper phone bills.
> You still need a prime tower when switching tool heads, but that is significantly less than a full purge. But I believe the INDX has the same restriction.
INDX no longer needs a tower. They say there is 13 milligrams of waste (which they call less than a grain of rice) on each filament change. So a print with 1,000 changes wastes 13 grams of filament. Details:
I can imagine filament vendors making bundles of filaments with interesting mixes of colors. CMYKW is an obvious one, but there must be other color combinations that will mix in interesting ways.
For the same reason urban people deserve subsidies, for things like public transportation, by virtue of living in the city. The first sentence of the US Constitution lists "promote the general Welfare" as one of the foundational reasons for the creation of the Constitution. There is no "one size fits all" for a nation this size - the welfare of people in the city and the welfare of people in the sticks requires differing allocations of tax money. Also, mail delivery to the sticks benefits people in cities given that mail in the sticks is often sent to or from cities.