I suspect that it wildly varies based on user.

If you use video streaming from, say, YouTube and TikTok heavily, and you do so outside of WiFi networks, I bet that you can burn through a lot of data pretty quickly.

Another user could be sitting on Lemmy or whatever all day every day and just not use much data.

@dumbass@lemmy.world @dumbass@sh.itjust.works @dumbass@sopuli.xyz

Lot of that going around, apparently.

I think that one issue is that any time you are moving money with software, your computer becomes a more-tempting target to attack.

And, should the GenAI market deflate, it will be because all of the big players in the market – the hyperscalers, the cloud builders, the model builders, and other large service providers – believed their own market projections with enough fervor that TSMC will shell out an entire year’s worth of net profits to build out its chip etching and packaging plants.

The thing is that with some of these guys, the capacity isn’t general.

So, say you’re OpenAI and you buy a metric shit-ton of Nvidia hardware.

You are taking on some very real risks here. What you are buying is an extremely large amount of parallel compute hardware with specific performance characteristics. There are scenarios where the value of that hardware could radically change.

• Say generative AI — even a substantial part of generative AI — shifts hard to something like MoEs, and suddenly it’s desirable to have a higher ratio of memory to compute capacity. Suddenly, the hardware that OpenAI has purchased isn’t optimal for the task at hand.

• Say it turns out that some researchers discover that we can run expert neural nets that are only lightly connected at a higher level. Then maybe we’re just fine using a bank of consumer GPUs to do computation, rather than one beefy Nvidia chip that excels at dense models.

• Say models get really large and someone starts putting far-cheaper-than-DRAM NVMe on the parallel compute device to store offloaded expert network model weights. Again, maybe current Nvidia hardware becomes a lot less interesting.

• Say there’s demand, but not enough to make a return in a couple of years, and everyone else is buying the next generation of Nvidia hardware. That is, the head start that OpenAI bought just isn’t worth what they paid for it.

• Say it turns out that a researcher figures out a new, highly-effecitve technique for identifying the relevant information about the world, and suddenly, the amount of computation falls way, way off, and doing a lot of generative AI on CPUs becomes a lot more viable. I am very confident that we are nowhere near the ideal here today.

In all of those cases, OpenAI is left with a lot of expensive hardware that may be much less valuable than one might have expected.

But…if you’re TSMC, what you’re buying is generalized. You fabricate chips. Yeah, okay, very high-resolution, high-speed chips at a premium price over lower-resolution stuff. But while the current AI boom may generate a lot of demand, all of that capacity can also be used to generate other sorts of chip. You might not have made an optimal investment, but there are probably a lot of people outside the generative AI world who can do things with a high-resolution chip fab.

https://en.wikipedia.org/wiki/Multi-user_dungeon

For a list of still-active MUDs:

https://www.mudconnect.com/

In all fairness, while this is a particularly bad case, the fact that it’s often very difficult to safely fiddle with environment variables at runtime in a process, but very convenient as a way to cram extra parameters into a library have meant that a lot of human programmers who should know better have created problems like this too.

IIRC, setting the timezone for some of the Posix time APIs on Linux has the same problem, and that’s a system library. And IIRC SDL and some other graphics libraries, SDL and IIRC Linux 3D stuff, have used this as a way to pass parameters out-of-band to libraries, which becomes a problem when programs start dicking with it at runtime. I remember reading some article from someone who had been banging into this on Linux gaming about how various programs and libraries for games would setenv() to fiddle with them, and races associated with that were responsible for a substantial number of crashes that they’d seen.

setenv() is not thread-safe or signal-safe. In general, reading environment variables in a program is fine, but messing with them in very many situations is not.

searches

Yeah, the first thing I see is someone talking about how its lack of thread-safety is a problem for TZ, which is the time thing that’s been a pain for me a couple times in the past.

https://news.ycombinator.com/item?id=38342642

Back on your issue:

Claude, being very smart and very good at drawing a straight line between two points, wrote code that took the authentication token from the HTTP request header, modified the process’s environment variables, then called the library

for the uninitiated - a process’s environment variables are global. and HTTP servers are famously pretty good at dealing with multiple requests at once.

Note also that a number of webservers used to fork to handle requests — and I’m sure that there are still some now that do so, though it’s certainly not the highest-performance way to do things — and in that situation, this code could avoid problems.

searchs

It sounds like Apache used to and apparently still can do this:

https://old.reddit.com/r/PHP/comments/102vqa2/why_does_apache_spew_a_new_process_for_each/

But it does highlight one of the “LLMs don’t have a broad, deep understanding of the world, and that creates problems for coding” issues that people have talked about. Like, part of what someone is doing when writing software is identifying situations where behavior isn’t defined and clarifying that, either via asking for requirements to be updated or via looking out-of-band to understand what’s appropriate. An LLM that’s working by looking at what’s what commonly done in its training set just isn’t in a good place to do that, and that’s kinda a fundamental limitation.

I’m pretty sure that the general case of writing software is AI-hard, where the “AI” referred to by the term is an artificial general intelligence that incorporates a lot of knowledge about the world. That is, you can probably make an AI to program write software, but it won’t be just an LLM, of the “generative AI” sort of thing that we have now.

There might be ways that you could incorporate an LLM into software that can write software themselves. But I don’t think that it’s just going to be a raw “rely on an LLM taking in a human-language set of requirements and spitting out code”. There are just things that that can’t handle reasonably.

Viess said field reports coming into her organization suggest the growth of death caps may be slowing in the Bay Area, while another kind of poisonous mushroom known as the destroying angel, or Amanita ocreata, is starting to pop up.

Oh, great.

https://en.wikipedia.org/wiki/Amanita_ocreata

A. ocreata is highly toxic, and has been responsible for mushroom poisonings in western North America, particularly in the spring. It contains highly toxic amatoxins, as well as phallotoxins, a feature shared with the closely related death cap (A. phalloides), half a cap of which can be enough to kill a human, and other species known as destroying angels.[3][14] There is some evidence it may be the most toxic of all the North American phalloideae, as a higher proportion of people consuming it had organ damage and 40% perished.[15]

However, investing in U.S. farmland has become popular among the ultra wealthy as a hedge against inflation and stock market volatility.

There are lots of inflation hedges, and stock market volatility doesn’t matter much to someone who can ride it out.

There are tax strategies around real estate, and I’d guess that one goal may be leveraging that. Like, IIRC historically farms got certain tax-advantaged status as regards estate taxes, because there were lots of small farmers and farmers thus had a lot of votes. Some of that law is, no doubt, still on the books, and the wealthy will have tax and estate planners who can exploit that. I can’t name specifics off-the-cuff, but I bet you that they’re there.

searches

https://www.mossadams.com/articles/2024/12/estate-tax-exemption-for-farmers-and-ranchers

The primary reason heirs don’t need to sell the farm is that the federal government allows heirs to pay farm-related estate tax over time at a very low interest rate. Given the first $7 or $14 million of net value escapes all federal estate taxes, it’s only the excess that’s subject to the 40% estate tax.

The executor of a qualifying estate elects under Section 6166 to defer paying a portion of the estate taxes. Normally, the estate tax is due in full nine months after the date of death. However, the portion of the estate tax related to active assets, such as a farm, can be deferred. The active farm assets must be at least 35% of the adjusted taxable estate.

Yeah.

So, let’s say you have, oh, $500 million in assets sitting around. You’re getting old and gonna die. Theoretically, when you die, estate tax kicks in. You can hand down maybe $14 million without smacking into estate tax, the lifetime gift tax exemption. But then you’re gonna pay 40% tax on the rest. That’s not the end of the world — if you put the stuff in an S&P 500 index fund, that’d be made up in about 8 years of market-rate returns, on average. But it’ll definitely slow things down. You gotta pay that each generation, and a generation averages a bit over 30 years.

So what I bet the estate planners these guys have are doing is making them legally farmers, and defer their estate taxes. They gotta pay the IRS something, according to this page, but it’s effectively getting to borrow from the IRS at a really low rate. Then their heirs can use those assets that would have gone to the government in tax to make money in the meantime.

Plus, there are other tax games that you can play with real estate. Instead of selling it, borrow against it, and no capital gains tax.

Have income from some source and don’t want to pay income tax? Use depreciation on the farm as a deduction to offset that income, make it non-taxable.

How much is that real estate worth? Well, now, that’s a complicated question. If you can get the tax authorities to buy an argument that it’s worth a lot less than fair market value, then you’re only paying estate tax on whatever the government will agree the value of the real estate is.

According to the link I posted above, in Washington state, farms are completely exempt from state-level estate tax.

Washington

Farmers in Washington state are eligible for an unlimited farm deduction equal to 100% of the farm value under the state’s estate tax law, which has the highest rate in the country at 20% on taxable estates over $9 million.

If certain conditions are met, the estate may deduct the total value of the farm from the estate tax return, potentially saving significant amounts in estate tax. Unlike federal rules, Washington state doesn’t require heirs to own the property for at least 10 years after death. After the decedent’s death, the heirs are free to do as they wish with the assets.

So, say someone buys a lot of farmland before they die on the guidance of their estate planner. Now Washington state gets no estate tax from them. They can pass hundreds of millions or whatever in assets to their heirs, whereas someone else would have Washington state take a 20% cut.

I’ve got no experience with buying farmland. But I strongly suspect that the reason that specifically very wealthy, elderly people would buy farmland is for tax purposes. Aiming to avoid taxes, especially estate tax. It’s not because farms are some sort of remarkable, amazing investment absent that.

Citing various GeForce partners, including ASUS, we learn that the GeForce RTX 5070 Ti has essentially reached end-of-life status - less than a year after its debut.

The reason for the RTX 5070 Ti being shelved is fairly straightforward: it’s a GPU with 16GB of VRAM, and with current price increases and GDDR7 shortages, NVIDIA is cutting back on GeForce RTX 50 Series cards with 16GB of memory. In fact, the report indicates that the GeForce RTX 5060 Ti 16GB is also on the chopping block.

I think that the problem will be if software comes out that’s doesn’t target home PCs. That’s not impossible. I mean, that happens today with Web services. Closed-weight AI models aren’t going to be released to run on your home computer. I don’t use Office 365, but I understand that at least some of that is a cloud service.

Like, say the developer of Video Game X says “I don’t want to target a ton of different pieces of hardware. I want to tune for a single one. I don’t want to target multiple OSes. I’m tired of people pirating my software. I can reduce cheating. I’m just going to release for a single cloud platform.”

Nobody is going to take your hardware away. And you can probably keep running Linux or whatever. But…not all the new software you want to use may be something that you can run locally, if it isn’t released for your platform. Maybe you’ll use some kind of thin-client software — think telnet, ssh, RDP, VNC, etc for past iterations of this — to use that software remotely on your Thinkpad. But…can’t run it yourself.

If it happens, I think that that’s what you’d see. More and more software would just be available only to run remotely. Phones and PCs would still exist, but they’d increasingly run a thin client, not run software locally. Same way a lot of software migrated to web services that we use with a Web browser, but with a protocol and software more aimed at low-latency, high-bandwidth use. Nobody would ban existing local software, but a lot of it would stagnate. A lot of new and exciting stuff would only be available as an online service. More and more people would buy computers that are only really suitable for use as a thin client — fewer resources, closer to a smartphone than what we conventionally think of as a computer.

EDIT: I’d add that this is basically the scenario that the AGPL is aimed at dealing with. The concern was that people would just run open-source software as a service. They could build on that base, make their own improvements. They’d never release binaries to end users, so they wouldn’t hit the traditional GPL’s obligation to release source to anyone who gets the binary. The AGPL requires source distribution to people who even just use the software.

I will say that, realistically, in terms purely of physical distance, a lot of the world’s population is in a city and probably isn’t too far from a datacenter.

https://calculatorshub.net/computing/fiber-latency-calculator/

It’s about five microseconds of latency per kilometer down fiber optics. Ten microseconds for a round-trip.

I think a larger issue might be bandwidth for some applications. Like, if you want to unicast uncompressed video to every computer user, say, you’re going to need an ungodly amount of bandwidth.

DisplayPort looks like it’s currently up to 80Gb/sec. Okay, not everyone is currently saturating that, but if you want comparable capability, that’s what you’re going to have to be moving from a datacenter to every user. For video alone. And that’s assuming that they don’t have multiple monitors or something.

I can believe that it is cheaper to have many computers in a datacenter. I am not sold that any gains will more than offset the cost of the staggering fiber rollout that this would require.

EDIT: There are situations where it is completely reasonable to use (relatively) thin clients. That’s, well, what a lot of the Web is — browser thin clients accessing software running on remote computers. I’m typing this comment into Eternity before it gets sent to a Lemmy instance on a server in Oregon, much further away than the closest datacenter to me. That works fine.

But “do a lot of stuff in a browser” isn’t the same thing as “eliminate the PC entirely”.

Historically, Usenet clients tended to respond to both groups in response to articles posted to multiple newsgroups.

This tended to result in trolls doing things like posting “I’m in the market for a computer. Which is better, PC or Mac?” to comp.os.ms-windows.advocacy and comp.os.mac.advocacy with the intention of starting flamewars.

“Write code without learning it!” I get it. I’ve struggled learning to program for 10 years. But every time I hear a programmer talk about AIGen code, it’s never good, and my job’s software has gotten less stable as AIGen code as been added in.

I’m similarly dubious about using LLMs to do code. I’m certainly not opposed to automation — software development has seen massive amounts of automation over the decades. But software is not very tolerant of errors.

If you’re using an LLM to generate text for human consumption, then an error here or there often isn’t a huge deal. We get cued by text; “approximately right” is often pretty good for the way we process language. Same thing with images. It’s why, say, an oil painting works; it’s not a perfect depiction of the world, but it’s enough to cue our brain.

There are situations where “approximately right” might be more-reasonable in software development. There are some where it might even be pretty good — instead of manually-writing commit messages, which are for human consumption, maybe we could have LLMs describe what code changes do, and as LLMs get better, the descriptions improve too.

This doesn’t mean that I think that AI and writing code can’t work. I’m sure that it’s possible to build an AGI that does fantastic things. I’m just not very impressed by using a straight LLM, and I think that the limitations are pretty fundamental.

I’m not completely willing to say that it’s impossible. Maybe we could develop, oh, some kind of very-strongly-typed programming language aimed specifically at this job, where LLMs are a good heuristic to come up with solutions, and the typing system is aimed at checking that work. That might not be possible, but right now, we’re trying to work with programming languages designed for humans.

Maybe LLMs will pave the way to getting systems in place that have computers do software engineering, and then later we can just slip in more-sophisticated AI.

But I don’t think that the current approach will wind up being the solution.

“Summarize a book!” I am doing this for fun, why would I want to?

Summarizing text — probably not primarily books — is one area that I think might be more useful. It is a task that many people do spend time doing. Maybe it’s combining multiple reports from subordinates, say, and then pushing a summary upwards.

“Generate any image!” I get the desire, but I can’t ignore the broader context of how we treat artists. Also the images don’t look that great anyway.

I think that in general, quality issues are not fundamental.

There are some things that we want to do that I don’t think that the the current approaches will do well, like producing consistent representations of characters. There are people working on it. Will they work? Maybe. I think that for, say, editorial illustration for a magazine, it can be a pretty decent tool today.

I’ve also been fairly impressed with voice synth done via genAI, though it’s one area that I haven’t dug into deeply.

I think that there’s a solid use case for voice query and response on smartphones. On a desktop, I can generally sit down and browse webpages, even if an LLM might combine information more quickly than I can manually. Someone, say, driving a car or walking somewhere can ask a question and have an LLM spit out an answer.

I think that image tagging can be a pretty useful case. It doesn’t have to be perfect — just be a lot cheaper and more universal than it would to have humans doing it.

Some of what we’re doing now, both on the part of implementers and on the R&D people working on the core technologies, is understanding what the fundamental roadblocks are, and quantifying strengths and weaknesses. That’s part of the process for anything you do. I can see an argument that more-limited resources should be put on implementation, but a company is going to have to go out and try something and then say “okay, this is what does and doesn’t work for us” in order to know what to require in the next iteration. And that’s not new. Take, oh, the Macintosh. Apple tried to put out the Lisa. It wasn’t a market success. But taking what did work and correcting what didn’t was a lot of what led to the Macintosh, which was a much larger success and closer to what the market wanted. It’s going to be an iterative process.

I also think that some of that is laying the groundwork for more-sophisticated AI systems to be dropped in. Like, if you think of, say, an LLM now as a placeholder for a more-sophisticated system down the line, the interfaces are being built into other software to make use of more-sophisticated systems. You just change out the backend. So some of that is going to be positioning not just for the current crop, but tomorrow’s crop of systems.

If you remember the Web around the late 1990s, the companies that did have websites were often pretty amateurish-looking. They were often not very useful. The teams that made them didn’t have a lot of resources. The tools to work with websites were still limited, and best practices not developed.

https://www.webdesignmuseum.org/gallery/year-1997

But what they did was get a website up, start people using them, and start building the infrastructure for what, some years later, was a much-more-important part of the company’s interface and operations.

I think that that’s where we are now regarding use of AI. Some people are doing things that won’t wind up ultimately working (e.g. the way Web portals never really took over, for the Web). Some important things, like widespread encryption, weren’t yet deployed. The languages and toolkits for doing development didn’t really yet exist. Stuff like Web search, which today is a lot more approachable and something that we simply consider pretty fundamental to use of the Web, wasn’t all that great. If you looked at the Web in 1997, it had a lot of deficiencies compared to brick-and-mortar companies. But…that also wasn’t where things stayed.

Today, we’re making dramatic changes to how models work, like the rise of MoEs. I don’t think that there’s much of a consensus on what hardware we’ll wind up using. Training is computationally expensive. Just using models on a computer yourself still involves a fair amount of technical knowledge, the sort of way the MS-DOS era on personal computers prevented a lot of people from being able to do a lot with computers. There are efficiency issues, and basic techniques for doing things like condensing knowledge are still being developed. LLMs people are building today have very little “mutable” memory — you’re taking a snapshot of information at training time and making something that can do very little learning at runtime. But if I had to make a guess, a lot of those things will be worked out.

I am pretty bullish on AI in the long term. I think that we’re going to figure out general intelligence, and make things that can increasingly do human-level things. I don’t think that that’s going to be a hundred years in the future. I think that it’ll be sooner.

But I don’t know whether any one company doing something today is going to be a massive success, especially in the next, say, five years. I don’t know whether we will fundamentally change some of the approaches we used. We worked on self-driving cars for a long time. I remember watching video of early self-driving cars in the mid-1980s. It’s 2026 now. That was a long time. I can get in a robotaxi and be taken down the freeway and around my metro area. It’s still not a complete drop-in replacement for human drivers. But we’re getting pretty close to being able to use the things in most of the same ways that we do human drivers. If you’d have asked me in 2000 whether we would make self-driving cars, I would say basically what I do about advanced AI today — I’m quite bullish on the long-term outcome, but I couldn’t tell you exactly when it’ll happen. And I think that that advanced AI will be extremely impactful.

https://en.wikipedia.org/wiki/Parasite_aircraft

You could also just only use Macs.

I actually don’t know what the current requirement is. Back in the day, Apple used to build some of the OS — like QuickDraw — into the ROMs, so unless you had a physical Mac, not just a purchased copy of MacOS, you couldn’t legally run MacOS, since the ROM contents were copyrighted, and doing so would require infringing on the ROM copyright. Apple obviously doesn’t care about this most of the time, but I imagine that if it becomes institutionalized at places that make real money, they might.

But I don’t know if that’s still the case today. I’m vaguely recalling that there was some period where part of Apple’s EULA for MacOS prohibited running MacOS on non-Apple hardware, which would have been a different method of trying to tie it to the hardware.

searches

This is from 2019, and it sounds like at that point, Apple was leveraging the EULAs.

https://discussions.apple.com/thread/250646417?sortBy=rank

Posted on Sep 20, 2019 5:05 AM

The widely held consensus is that it is only legal to run virtual copies of macOS on a genuine Apple made Apple Mac computer.

There are numerous packages to do this but as above they all have to be done on a genuine Apple Mac.

• VMware Fusion - this allows creating VMs that run as windows within a normal Mac environment. You can therefore have a virtual Mac running inside a Mac. This is useful to either run simultaneously different versions of macOS or to run a test environment inside your production environment. A lot of people are going to use this approach to run an older version of macOS which supports 32bit apps as macOS Catalina will not support old 32bit apps.
• VMware ESXi aka vSphere - this is a different approach known as a ‘bare metal’ approach. With this you use a special VMware environment and then inside that create and run virtual machines. So on a Mac you could create one or more virtual Mac but these would run inside ESXi and not inside a Mac environment. It is more commonly used in enterprise situations and hence less applicable to Mac users.
• Parallels Desktop - this works in the same way as VMware Fusion but is written by Parallels instead.
• VirtualBox - this works in the same way as VMware Fusion and Parallels Desktop. Unlike those it is free of charge. Ostensible it is ‘owned’ by Oracle. It works but at least with regards to running virtual copies of macOS is still vastly inferior to VMware Fusion and Parallels Desktop. (You get what you pay for.)

Last time I checked Apple’s terms you could do the following.

• Run a virtualised copy of macOS on a genuine Apple made Mac for the purposes of doing software development
• Run a virtualised copy of macOS on a genuine Apple made Mac for the purposes of testing
• Run a virtualised copy of macOS on a genuine Apple made Mac for the purposes of being a server
• Run a virtualised copy of macOS on a genuine Apple made Mac for personal non-commercial use

No. Apple spells this out very clearly in the License Agreement for macOS. Must be installed on Apple branded hardware.

They switched to ARM in 2020, so unless their legal position changed around ARM, I’d guess that they’re probably still relying on the EULA restrictions. That being said, EULAs have also been thrown out for various reasons, so…shrugs

goes looking for the actual license text.

Yeah, this is Tahoe’s EULA, the most-recent release:

https://www.apple.com/legal/sla/docs/macOSTahoe.pdf

Page 2 (of 895 pages):

They allow only on Apple-branded hardware for individual purchases unless you buy from the Mac Store. For Mac Store purchases, they allow up to two virtual instances of MacOS to be executed on Apple-branded hardware that is also running the OS, and only under certain conditions (like for software development). And for volume purchase contracts, they say that the terms are whatever the purchaser negotiated. I’m assuming that there’s no chance that Apple is going to grant some “go use it as much as you want whenever you want to do CI tests or builds for open-source projects targeting MacOS” license.

So for the general case, the EULA prohibits you from running MacOS wherever on non-Apple hardware.

I didn’t realise Pearlman voiced the intro.

According to the video, neither did Perlman:

A year and a half later, I get a call, ‘Hey, you remember Fallout?’ No.

I’m pretty sure that he did more than just the into, though. There’s a bunch of narration in, for example, the Fallout: New Vegas ending covering what happens to all the characters and factions depending upon the decisions you made, and I’m pretty sure that that’s the same narrator.

goes looking

https://fallout.fandom.com/wiki/Ron_Perlman

Perlman narrated the following cutscenes in the Fallout games listed below. The intro narration in each of these games starts with the iconic line, “War. War never changes.” He did not narrate Fallout: Brotherhood of Steel or Fallout 4, though he did voice a prominent character (the television newscaster) in Fallout 4’s prologue and appeared in its first trailer.

• Fallout intro
• Death messages
• Fallout endings
• Fallout 2 intro
• Fallout 2 endings
• Fallout Tactics intro
• Fallout Tactics chapter endings
• Fallout Tactics endings
• Fallout 3 intro
• Fallout 3 endings
• Fallout: New Vegas intro
• Fallout: New Vegas endings
• Fallout 76 intro

Now my question is, who’s making that one query that leaks my domain name? Is it Apache on startup

If you’re wanting a list of DNS queries from your system, assuming that it’s DNS and not DoH, maybe:

# tcpdump port domain

Then go start Apache or whatever.

It’s “smugly”.

Okay, this is unfortunately DIY, but if you’re willing to spend time:

Get a plywood box and put it in there.

If you hear vibrations, put sorbothane between the NAS and the box.

If you need more sound absorption, put acoustic foam on the inside.

If you need more cooling, drill two holes, mount a case fan, and run the air through some kind of baffle. Like, maybe attach insulated flex-duct, like this:

https://www.amazon.com/Hon-Guan-Silencer-Reducer-Ventilation/dp/B07HC8CXQG

Milsim games involve heavy ray tracing

I guess it depends on what genre subset you’re thinking of.

I play a lot of milsims — looks like I have over 100 games tagged “War” in my Steam library. Virtually none of those are graphically intensive. I assume that you’re thinking of recent infantry-oriented first-person-shooter stuff.

I can only think of three that would remotely be graphically intensive in my library: ArmA III, DCS, and maybe IL-2 Sturmovik: Battle for Stalingrad.

Rule the Waves 3 is a 2D Windows application.

Fleet Command and the early Close Combat titles date to the '90s. Even the newer Close Combat titles are graphically-minimal.

688(i) Hunter/Killer is from 1997.

A number of of them are 2D hex-based wargames. I haven’t played any of Gary Grigsby’s stuff, but that guy is an icon, and all his stuff is 2D.

If you go to Matrix Games, which sells a lot of more hardcore wargames, a substantial chunk of their inventory is pretty old, and a lot is 2D.