I saw the 1996 FLT documentary in high school calculus class. For me, it forever cemented that archetype of modern math researcher at the top of my mental “smart” totem pole.
It also convinced me I had no interest in that path. Setting aside the grinding work of producing a proof that can only be reached by existing years in the abstract and hyper niche isolation of the problem space (not to mention that you might never discover it or that it DNE), the anguish of the output being a paper or presentation or some other artifact of human symbology (_words_, really) that could at any moment be refuted by a single observation of a single mistake—-that sounded like hell to me.
An equivalent high schooler today probably sees things differently, in light of this news and the undeniable implications of LLMs on mathematics. Sturdy autoformalization tooling should with time completely dispel the aforementioned anguish, once our confidence in converting a human proof to Lean/etc. reaches that of a compiler translating Java application language to bytecode. Errata may always exist, but in practice these new methods will do wonders for rigor and peace of mind.
(I’m far less confident re novel discoveries. There’s too much chance of derivative findings based on something part of the training looking like genius but really just tiptoeing on the shoulders of humans, whereas autoformalization is absolutely convincing to me as transformative, particularly to check correctness of AI outputted proofs as mentioned in the post.)
Great example of writing about AI that maintains a human voice. Starting off the post with a picture of the author + nod to real-world experience (climbing) is a reasonably strong “this is not slop” signal.
I could see this being a great activity in a high school civics class. Very creative. One rule that tripped me up is:
> If two parties tie in a district, nobody wins it.
This isn't realistic as ties don't happen in practice in elections, and some party will end up representing it. But the spirit of the gerrymandering concept is conveyed well enough.
In real-life, before the election there is a margin of error on the support of a bloc.
If you interpret a "tie" in this game as "either party could win within the margin of error", then it becomes a lot closer to solving the problem that gerrymandering algorithms try to solve in real life!
Have you played the game? The board today has three parties and the winning solution is to make sure your party wins one district and the other parties tie in the other four.
Under any real world system, you will lose this election if you rig it this way. You just can't predict who will win it instead.
Edit: Per a comment below, this does not seem like a regular feature of the game, just an oddity of today. It would still be worth figuring out a way to eliminate the tie issue or at least ensure it's less of a factor in future games, but the game is much more fun on average overall than today's game suggests
Yeah I don't know if that was just the puzzle today, since this is the first time I've heard of or played this game, but that feature seemed like a disappointing execution of an otherwise genuinely unique idea.
Winning a single district for your extremely minority party while locking the other two parties out of winning anything isn't even remotely analogous to how real world gerrymandering works, at least in the US where the term is typically used. It also feels like cheating, since it relies primarily on exploiting a flaw that exists exclusively in the game but not in real life. I'm all for simplification of real world factors in games, but not to the point where the entire path to victory relies on that simplification.
A more accurate and more interesting variation would be to just have two parties with puzzles that rely on crafting districts where the party with fewer voters wins the majority of seats, with the challenge coming from voter distribution patterns that make it hard to create winning districts while following the game's rules. The addition of a third party and ties that result in nobody winning seem both unnecessary and worse.
The goal of this puzzle appears to be to get more people to talk about the issue and push for change. In these things, you can have either popularity or nuance but not both. The average American can't even read, let alone understand the nuance or complexity in how gerrymandering "actually" works.
We did these sorts of exercises in my high school social studies class, in Ohio, back in 2002ish. I fear it may have been instructive to some of my classmates rather than warning them of the inherent evils.
> This isn't realistic as ties don't happen in practice in elections, and some party will end up representing it.
Ties happen from time to time. Here's one I could find [1], and I recall one that I can't find maybe a decade ago in coastal northern california that they resolved by throwing dice.
I think it wasn't so much that ties aren't possible, but that ties don't mean "no one wins". If you make one district safe and three districts are a coin toss between your two rivals, one of them has much more power than you in the end.
We're three siblings from a gerrymandered district in Austin, Texas, and this is the story of how we designed a board game about gerrymandering — and ended up at the Supreme Court with 82 copies of Mapmaker: The Gerrymandering Game.
I picked up a copy on ebay a while after the Kickstarter!
Mapmaker is a great game for tabletop veterans and newbies alike, although the first playthrough can be a little opaque even to regular gamers (especially the first few moves when there are no established district boundaries yet). It definitely benefits from repeat play, goes quickly once you know the rules, and has you doing only one thing each turn.
Definitely a game that has earned a permanent place in my collection.
It's a major factor in today's puzzle, but it doesn't seem to come up as much in past puzzles. I think yesterday's is more fun and doesn't have the unrealism. https://gerrymandle.cc/game/2026-06-17
While he never married or had children, Jeeves is survived by his brother software butlers Jenkins and Alfred who have asked the public for privacy during this difficult time.
Costco uses a convention for their retail (doesn’t work for by-weight) products where e.g .97 typically means it’s a limited run or to be discontinued.
There are others as well, they have more precise meaning for their internal procurement processes but that’s the customer facing rule of thumb.
I hope, with the velocity unlocked by these tools, that more pure ports will become the norm. Before, migrations could be so costly that “improving” things “while I’m here” helped sell doing the migration at all, especially in business settings. Only to lead to more toil chasing those phantom bugs.
One of the biggest point of rewriting is you know better by then so you create something better.
This is a HUUUGE reason code written in rust tended to be so much better than the original (which was probably written in c++).
Human expertise is the single most important factor and is more important than language.
Copy pasting from one language to another is way worse than complete rewrite with actual idiomatic and useful code.
Best option after proper rewrite is binding. And copy-paste with LLM comes way below these options imo.
If you look at real world, basically all value is created by boring and hated languages. Because people spent so much effort on making those languages useful, and other people spent so much effort learning and using those languages.
Don’t think anyone would prefer to work in a rust codebase that an LLM copy-pasted from c++, compared to working on a c++ codebase written by actual people that they can interact with.
I've seen more than a few rewrite attempts fail throughout the years. However, I've never seen a direct language to language translation fail. I've done several of these personally: from perl to ruby, java to kotlin, etc.
Step 1 of any rewrite of a non-trivial codebase should should be parity. You can always refactor to make things more idiomatic in a later phase.
These are two different kinds of rewrites, for two different kinds of codebases, in two different situations. The important thing is to know which kind of rewrite you're doing, and have the whole team onboard.
The sort of rewrite you're talking about can work well at an early stage of a project, in the spirit of Fred Brooks's "plan to throw one away". But for a mature browser like Ladybird that's trying to not break the user experience, it's much better to have a pure translation step, and then try to improve or refactor it later.
I rewrote two times my personal tool, one to rust, and out of rust with LLM, I mainly "vibe code" for reasons but the tool is more complete and less buggy than most B2B and enterprise business or IT software that I have seen in 20years at different fortune 100.
I feel the effort and frustration to have parity with LLM is more than doing a full rewrite without.
The lone ranger donor route feels severely suboptimal, unless perhaps if the donor is a .001%er pledging a large share of their net worth.
Imagine if this anonymous person worked with a foundation pledging to match $3.5M if said amount was raise via crowdfund. Even if say $1M goes to the campaign and NGO bloat, that’s still way more pipe money.
> Imagine if this anonymous person worked with a foundation pledging to match $3.5M if said amount was raise via crowdfund.
Idk man. Thing is where i live we are already crowdfunding to maintain our pipes. It is called local taxes and water utility bills. So if anyone were to ask me for more money for the same task i’m already paying not insubstantial sums for I would be very cross with them. It is just not a good look.
Now i don’t know about Japan. Maybe they don’t pay taxes and utility bills. Somehow doubt it, but who knows.
The lone ranger may have actually done something optimal but indirect. There is a lot of press that went global pointing out real problems. Japanese are proud people, this might actually help direct public funds to solve the problems.
It also convinced me I had no interest in that path. Setting aside the grinding work of producing a proof that can only be reached by existing years in the abstract and hyper niche isolation of the problem space (not to mention that you might never discover it or that it DNE), the anguish of the output being a paper or presentation or some other artifact of human symbology (_words_, really) that could at any moment be refuted by a single observation of a single mistake—-that sounded like hell to me.
An equivalent high schooler today probably sees things differently, in light of this news and the undeniable implications of LLMs on mathematics. Sturdy autoformalization tooling should with time completely dispel the aforementioned anguish, once our confidence in converting a human proof to Lean/etc. reaches that of a compiler translating Java application language to bytecode. Errata may always exist, but in practice these new methods will do wonders for rigor and peace of mind.
(I’m far less confident re novel discoveries. There’s too much chance of derivative findings based on something part of the training looking like genius but really just tiptoeing on the shoulders of humans, whereas autoformalization is absolutely convincing to me as transformative, particularly to check correctness of AI outputted proofs as mentioned in the post.)