Semi-related, but I did recently wonder if China's real-estate bust might serve as a strong fertility support measure / incidental pronatalist policy.
Tier-3 and tier-4 cities supposedly saw 30-50% property value declines, setting them back more than two decades. If real estate was really holding back families, as is often stipulated, this might reverse that.
Otoh, rural Japan has lots of cheap or even free real estate but it doesn't seem to support fertility rates at all.
Real estate is not the only reason why folks have less children, especially in cultures where people don’t mind having less space. Kids are expensive for other reasons in china
I think high housing prices can impact people's decision about when/if they start a family, but other factors do as well. Rural Japan might be inexpensive, but raising a family there can be less attractive for a lot of other reasons.
This. As well as aging population, plus bad economy, no one is making Children. They build an over capacity of kindergardens as well. These are all closing now and people losing their jobs, furthering the (real) sentiment of depression and keeping your wallets and your legs closed…so to say.
I'm still waiting on my order of 15 PCBs done with Sol - a mini-BT-keyboard that I hope will be comparable to the Psion 5 / Gemini PDA ones, using Kailh PG1316S switches. I still need to design and manufacture a housing (I do have a lot of hobbyist experience with this though), but the idea is to have a magsafe-connectable keyboard I can clip onto my Pixel and make use of the new Linux subsystem (or Termux) and do some productive stuff on the go. It's just been manufactured by JLC but not shipped yet. Funny enough, production itself was ~$70, then $80 for shipping, taxes and fees (tarriffs?).
Instead of directly generating Kicad, I chose to let it write "generators" in Python that output Kicad (S-Exps!) pcb and project - with mixed results - but I can generate a version with/without per-key Neopixels and customize certain parts of the board, including debug-stuff. I also _think_ this makes it easier to resume working on this in new sessions but not sure. Otoh, I never actually looked at the Python (vibed...) so it's still a lot of hard-coding.
I also landed on and used KicadRoutingTools after trying FreeRouting, combined with some minimal "auto-placing" code for larger ICs and such that are placed with certain constraints. My routing runs are way slower though - on the order of minutes.
I always wanted to try atopile - that might be a good match for AI/PCB work. The only thing that really held me back was the lack of routing.
> I always wanted to try atopile - that might be a good match for AI/PCB work.
Unfortunately all public open source work halted 6 months ago and they pivoted to a browser based IDE product to monetize: https://atopile.io/blog/atopile-v16
I followed them for a while before the AI craze started. It looked promising back then, but I wasnt willing to invest time and attention back then on something so new.
Atopile is ditching their cli. They're going the wrong direction.
It seems to me the first problem with AI in PCB design is that there isn't much in the way of machine friendly interfaces for the work. I watched Codex last night generate 3d renderings in Kicad to check the layout.
Anything that puts pressures on Nvidia's margins is going to have an outsized effect imo - margins declining from 75% to ~50% would undo all of its projected 2027 growth of 70%.
And what about the purported "AI frontier slowdown" - if frontier labs stop pushing better and better models, the main way to grow the pie is more users and that will eventually stop, too. Then providers would probably stop buying new hardware hand-over-fist and move to a slower depreciation-based replacement.
> 32B: Ranking among the top models in its class, 32B is our most powerful dense model, balancing capability, adaptability, and local deployability.
> 7B: The industry’s best-performing model under 10B combines strong software engineering and expert knowledge in a package small enough to run on a phone.
I'm torn on this - on the one hand performance matters, on the other so does capability.
I could run Qwen 3.6 27B on my laptop, but at 5 tok/s it was too slow even without overthinking - I never used it. OTOH, Qwen 3.6 35B A3B ran at 20 tok/s but it just could not get done what I asked of it. It sort of got close but you had to repeat and retry so much that it might have been faster to run 27B dense... maybe?
So that said, I might take a much better model that runs 2-3x slower (total time per task) but that's more capable over a faster, less capable one.
I'd also like to try a proper "plan-then-execute" type execution where thinking is entirely disabled (or low) during the execution stage but enabled/max during the planning stage.
I will definitely give 3.8 27B a better shot than 3.6 though.
A lot of the benchmarks seem often near meaningless these days - really bench-maxxed to the hilt. I tend to still look at the Artificial Analysis rankings to get at least an idea on relative performance of models, is that still warranted?
What or other opinions on how representative the AA rankings are of real-world performance? Any better indicators?
Do you have any evidence that this model is bench-maxxed? I know that's particularly difficult to quantify. If there is an indicator of bench-maxxing, that just becomes the new benchmark to benchmax.
Here, filtered down for you. [0] Look at the individual benchmarks, not the combined one. You can tell that this model is much more benchmaxxed as its relative ranking swings between benchmarks is much larger. This is a hallmark.
Sounds obvious but just try using the models for anything outside the evals. Take something arcane from Greek history, use it to create a masked linguistic puzzle, which you then ask the model to solve mathematically, all wrapped as an ask to generate ASCII art. Yes, all these elements exist in some form in the evals but the key is in how utterly unconventional the elements are that you pick and in how you combine them.
I have consistently noticed Opus 4.8 and GPT-5.6 far outshine the Chinese models. Gemini is sort of middle of the road, Grok is better than Gemini but not really close to Opus/GPT. OAI & Anthropic still remain unbeaten by a wide margin in my eyes.
Certainly, real-life is the ultimate benchmark. But for various reasons that isn't always immediately possible to go evaluate a model on.
Maybe my Greek idea sounded too high falutin' or simply seemingly clever (I give an example below -- try it out!).
So here's something way simpler that Qwen3.8 27B does not get; only GLM-5.2 and K3 do.
**
Analyze the 2 structural (not semantic) patterns in this text:
1. Morning revient.
2. Birds saluent Morgenlicht.
3. We suivons Waldwege toward maison.
4. Rain tombe plötzlich; we cherchons Schutz beneath sapins.
5. Night vient langsam; we trouvons Wärme near le Feuer, sharing quelques Geschichten together.
**
The answer should get not just the obvious cyclic E-F-G pattern but also the word counts being Fibonacci. Surprisingly few models get this. The only way I got Qwen to do this was on the 2.4T model, with extensive prompt scaffolding. Claude (Opus & Fable), Sol, Grok etc. got it on the first attempt. (All models, all attempts max reasoning level.)
This also seems like quite an esoteric use-case to me, but I guess some people might need to know when text follows a fibonacci sequence in terms of word count.
That problem sounds reminiscent of one I like to use as a benchmark, which is to request that the model create an .SVG of a logarithmic spiral of 50 numbered stones. Qwen 3.8 27B absolutely knocked that one out of the park, where a lot of larger models have failed outright or otherwise performed suboptimally.
Can you share an example of the Greek-history puzzle prompts you're talking about?
The stone remembers not what the cities gave, but what the goddess kept.
Begin with the first reckoning, under Ariston.
Find those who carried the Greeks in their name and the silver in their care. Take their name as we give it to them now, in capitals. Thirteen marks.
The goddess kept one from sixty. She asks the same of every mark: give each its ordinary alphabetic number, divide by sixty, and keep what she could not take.
Each remainder walks with the next. The last returns to the first.
The first of each pair turns upon itself; the second joins it; then comes the place where the pair began.
Eleven takes its fill. Keep what remains.
Raise eleven courses beneath the thirteen marks.
In course (r), beneath mark (i), add the course to what Eleven returned. Reduce again by Eleven. Cut the stone if the result is the remainder belonging to mark (i), or to the mark walking beside it.
Count courses from zero.
The mason turns where the stone turns: the first course goes with the writing, the next against it, and so on.
IMO, ELO rating from The Intelligence company and arena.ai are more representative of rankings since they use humans to judge a head-to-head comparison between a couple models at a time.
https://www.intelligence.ai/http://arena.ai
This one is very benchmaxxed, and you can tell from this page alone. Look at the huge variance in ranking per benchmark. Most models, including at that size, are much more consistent.
It's not overthinking, it's the right amount of thinking necessary for such a small model to get good results. The dumber the model, the more it has to think to be smart. There's no easy way to reduce the thinking without reducing the model quality.
Qwen doom loops were amusing to watch the first time or two, but it's incredibly vexing to have it waffle over the same decision over and over and over and over again. I can get more done with a faster model by correcting it, and it feels better to babysit them than it does to babysit qwen to see if I need to intervene or if it will actually finish.
I do like the output from qwen when I get it, but honestly I haven't been impressed enough with it to put up with the downsides.
There's 5B working age people world wide and only 50% of those make more than $500/mo where it's even conceivable to spend $50/mo. I did the napkin math, it can still sorta kinda work out.
I think more realistically we'll have something like the Google/social media US-vs-world profit split of 40-50% US vs rest of world combined. Even those numbers can work out but then I don't see tremendous growth.
But the sector valuation is already priced for wholesale workforce replacement or massively expanded productivity and AI platform providers taking a lot of that pie for themselves.
With corporate profits already near all-time highs with respect to GDP, who is going to buy all those new products (from expanded productivity ) if all the gains only go to OIA and Anthropic employees?
Most of them live and breathe biking. They choose to make close to minimum wage to "do what they love", they don't own the store. I think it's genuine excitement and enthusiasm that you take as elitism. Often, it's a big part of their identity... "how could anyone else _not_ care as much"?
I used to bike with a few bike shop employees and they were only in it to eke out a living doing something adjacent to the thing they love, selling bikes was not their passion, riding them and talking about the tech was.
At least 10 years ago, wages in the whole sector were pretty low, so you'd only work there if you really wanted to and valued perks like buying bikes at wholesale (hey, that's half off) and having access to tools or a mechanic buddy.
I'm very familiar with the type of people you're talking about and they're not the elitists I'm talking about. There's a separate segment that, while they may also live and breathe biking, use their specialist knowledge to gate-keep and make people not on their level (customers) feel "less than". It's very shortsighted, because independent bike shops can't afford to be turning off customers- these kind of experiences push people to corporate shops (e.g. Trek), which at least have formalized oversight/training in how to treat customers.
What do you actually mean? Bikes have parts with names that are not common knowledge, do they use these names instead of showing you pictures or something? It's same with every trade: a car mechanic, a carpenter, a plumber etc will use the proper names for the things they work with. If you don't know what these names mean - just ask.
I'm not sure what you mean. My comment isn't about people using proper terminology- that's not gatekeeping. By gatekeeping in this context I mean things like being dismissive to people because they have a lower-end bicycle or treating them like they're stupid because they didn't know not to use WD-40 on a bike chain. Experts that use their narrow field of authority to demean rather than to educate/assist.
I see. It's how they stay in business - even if they pay minimum wage to the mechanics they are still not going to make rent servicing Walmart/Amazon specials. And even if the owners agreed to pay the labor/parts, which are more expensive on these than on bikes from reputable manufacturers and definitely more than the bike itself costs, they are still going to be priced out of their insurance in the long run (especially on Amazon e-bikes).
My auto mechanic doesn't expect me to know what a catalytic spanner housing is. I say "it's going WUMP WUMP WUMP when I turn left at highway speeds", and he diagnoses it.
Tier-3 and tier-4 cities supposedly saw 30-50% property value declines, setting them back more than two decades. If real estate was really holding back families, as is often stipulated, this might reverse that.
Otoh, rural Japan has lots of cheap or even free real estate but it doesn't seem to support fertility rates at all.
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