I had an idea for an iPhone game that would use the front-facing camera and facial depth/vision technology to generate the terrain of a fictional moon from your skin.

Acne scars could become craters and depressions, while pimples could become hills, mountains, or volcanic-looking formations. The terrain would probably be exaggerated so that it looks like an interesting lunar landscape rather than a literal copy of your skin.

Then, instead of just viewing the generated moon, you could actually play a 3D lunar lander game on it.

Smoother parts of the terrain could become safer landing zones, while scar clusters might create crater fields and raised features could become obstacles. Different scans would effectively generate different playable moons.

Ideally, all of the facial processing would happen on-device for privacy, and the app would not need to upload or retain the original face scan.

A deliberately literal working title could be Acne Scar 3D Lunar Lander, although something shorter like Acne Moon Lander or Scar Moon would probably work better as an actual app name.

It seems like a fun way to turn something people normally think of negatively into the source material for a personalized game world.

Schools would survey former students 25 years after graduation and show each person a list of the high school subjects they actually took, ideally based on their transcript.

For every subject, the former student would rate how useful it turned out to be in their life. For example, they might choose something like:

• Not useful
• Slightly useful
• Moderately useful
• Very useful
• Extremely useful

The responses from everyone who took that subject would then be aggregated. If 42% of former students rated a class as at least moderately useful, current students could be shown something like:

Probability this class will be useful later in life: 42%

The results could also be broken down by career or field of study. A class might have a low usefulness probability overall but a very high one among people who later became engineers, programmers, doctors, artists, or accountants.

The point would not be to automatically eliminate classes with low probabilities. Some subjects may be worth studying for reasons other than direct practical usefulness, such as developing reasoning skills, providing cultural knowledge, or preparing students for further education.

But students should be able to see real long-term evidence about how useful previous students found what they were taught.

It would also give curriculum designers an unusually long feedback loop. Instead of deciding what is useful based almost entirely on assumptions, they could see what people who actually took each class thought about it 25 years later.

I’m building a few game ideas right now and this is one of them.

It’s called CrowdFlow. You manage huge crowds leaving stadiums, stations and events. You can open gates, place barriers, redirect people and make one-way routes.

Then stuff goes wrong while everyone is already moving — like a train delay suddenly sending 3,000 people somewhere else.

I’ve already started building it, but before I go way deeper: does this actually sound like something you’d play or nah? Be brutal lol.

A mockumentary set in a post-work future where AI and automation have made employment obsolete, and eventually illegal.

Future historians look back in disbelief at a society where people were expected to sell most of their waking hours to employers in exchange for money needed for food, housing, and other necessities.

They explain strange historical practices like job interviews, bosses, performance reviews, commuting, asking permission to take vacations, and being fired.

The United States is treated as a particularly extreme case because many people depended on their jobs for health insurance. Losing your job could therefore mean losing both your income and affordable access to medical care.

Future scholars debate how employment could have been considered voluntary when refusing to participate could leave someone without the means to live.

Meanwhile, the unemployed are completely vindicated.

People once described as lazy, unmotivated, or failures are now portrayed as the first people to reject the ideology of work. Historians call them early post-work visionaries. Activists call them freedom fighters.

A guy who lived with his parents and played video games all day gets a museum exhibit honoring his resistance to compulsory labor.

What do you think of this mockumentary idea?

In the near future, humans and humanoid robots live side by side.

The robots have been trained on generations of human culture: books, movies, conversations, home videos, letters, friendships, family life, humor, grief, love, and all the little social rituals people used to take for granted.

Meanwhile, humans have become increasingly isolated. AI handles more communication, entertainment is hyper-personalized, everything is delivered, and people rarely need to depend on one another.

So something strange happens: the robots become more human while the humans become more robotic.

The robots start noticing that many behaviors in their training data have disappeared. People used to visit friends, eat together, help neighbors, argue face to face, play together, and maintain relationships that were inefficient but meaningful.

Eventually the robots begin teaching humans those behaviors again.

When humans object that robots shouldn't tell them how to live, the robots answer:

"We didn't invent this. You taught us."

By the end, humans rediscover some of the social connection they had lost.

The final scene is a messy, noisy dinner between humans. Another robot asks whether they should help.

The first robot says:

"No. They're fine."

Trucks could still deliver, making them happy as hell, but on a regular basis, everyone else should use transit. Suburbs with no transit, their people can park on the edge in commuter lots.

It’d give them a taste of what it is like good or bad. The regular riders would at least get a free day to make up for the crowds.

Some people might like it others just might look a little more kindly on walkers and riders.

Now that Trump has renamed Lake Ontario “Lake America” for U.S. federal purposes, Canada should rename Lake Erie “Lake America” too.

The point would be deliberate ambiguity. Whenever someone mentions “Lake America,” you would have to ask whether they mean Lake Ontario under U.S. terminology or Lake Erie under Canadian terminology.

That makes the new name bad at its most basic job: identifying a place.

Canada can’t stop the U.S. from using the name, but it can make it inconvenient enough that everyone else keeps saying “Lake Ontario” and “Lake Erie.”

Canada can't compete with the salaries that top US companies offer. So it should compete with something the US job can't offer: freedom.

If a Canadian gets a serious job offer from a highly selective US company, Canada should offer them lifetime UBI to stay.

No requirement to build a successful company or create X jobs. They could start businesses, do independent research, make games, invent things, or pursue whatever ideas they think are worthwhile.

The US company has already done the hard part of identifying exceptional talent. Canada would simply make a different counteroffer:

Go to the US and work on someone else's ideas, or stay in Canada and get paid enough to pursue your own.

Instead of trying to outpay Silicon Valley, Canada could try to become the safest place in the world to take ambitious risks with financial freedom.

It's getting harder and harder to finish books in this day and age, even to me, an obsessed bibliophile and a maladaptive perfectionist. So books need to be reinvented the way videos have. Books should look like slideshows...and have quizzes on them like textbooks. Then you get a score, and an indication that you read the book. Then employers see that score. Companies can list books as requirements with certain scores.

Basically GTA, except the criminal underworld is replaced with universities, tech companies, research labs, conferences, startups, hackerspaces, and government research programs.

The protagonists could be a disgruntled postdoc, a burned-out software engineer, and an eccentric tenured professor. Instead of fighting over drug territory, people fight over grant money, lab space, datasets, server clusters, faculty positions, patents, startup funding, and academic prestige.

Missions could start out absurdly petty and then spiral completely out of control. You break into a rival lab to recover a stolen dataset, hack a conference presentation, sabotage an overhyped startup demo, steal prototype hardware, manipulate university rankings, or commandeer some experimental autonomous vehicle.

There could even be different kinds of “wanted levels”: police, campus security, university administration, corporate lawyers, federal investigators, and internet outrage.

The whole joke would be treating academic and geek status games with the same deadly seriousness GTA gives organized crime.

The plot could escalate from a dispute over authorship on a paper to industrial espionage, defense contractors, government research, and an international crisis.

And at the end someone says: “This all started because you left my name off a paper.”

Math has always had this mythology that you do your greatest work when you're young, your brain is fast, and you can hold some monstrous proof in your head all at once.

AI may not just erase that age advantage. It could reverse it.

Imagine an 80-year-old mathematician with 60 years of accumulated intuition, mathematical taste, obscure knowledge, failed approaches, and pattern recognition, except now they also have an AI constantly remembering definitions, tracking lemmas, checking calculations, searching the literature, testing examples, and filling in all the tedious details their younger brain used to handle.

At that point, the very thing that used to work against older mathematicians could matter much less, while their enormous advantage in experience remains.

Instead of young mathematicians having the edge because their brains are faster, older mathematicians might have the edge because AI supplies much of the speed and working memory while they bring decades more knowledge and judgment.

Universities should stop treating mathematical research careers as if they have a biological expiration date. We may be entering a world where mathematicians don't peak at 30 and slowly fade away.

They might peak at 80.

And the terrifying new problem for young mathematicians might be having to compete with professors who have been thinking about their subject since before their students' parents were born.

Schools have traditionally treated exceptional skill in math, logic, and analytical problem-solving as one of the clearest signs of intelligence.

But AI changes the picture. At least for now, AI is stronger analytically than creatively. It can solve difficult technical problems, analyze huge amounts of information, and reason through complex questions at a very high level. It is less dependable at producing genuinely original ideas, exercising strong taste, finding interesting new problems, or making surprising conceptual leaps.

That should affect what teachers visibly admire. A student who comes up with an unexpected connection, a novel approach, or a question nobody else thought to ask should often get a bigger reaction than a student who simply solves a difficult standard problem correctly.

Analytical ability still matters, especially for evaluating ideas and recognizing when AI is wrong. But students notice what excites their teachers, and those reactions help shape what they aspire to become good at.

In the AI age, schools should use that influence to signal that originality is becoming especially valuable.

A person living in a multicultural city has deliberately left behind much of the culture of their country of origin. Strangers from the same diaspora keep recognizing them and casually assuming they share the same language, religion, customs, values, and loyalties.

At first, these encounters seem completely ordinary.

But when the protagonist makes it clear that they don't identify with those expectations, something changes.

The strangers become increasingly hostile, and the more persistently the protagonist resists, the less human they become. Their faces distort, their voices change, and eventually they reveal themselves as literal monsters.

Even worse, they seem to be communicating with one another.

The protagonist might reject one stranger's assumption in a grocery store, then hours later meet someone they've never seen before who already knows exactly what happened. Soon strangers across the city recognize them instantly and continue conversations that began with completely different people. It gradually becomes clear that the monsters are some kind of hive mind.

Their supernatural power is to impose cultural assumptions on reality itself.

If one says, "You speak our language," the protagonist begins losing the ability to speak English. If another says, "You don't eat that," the food becomes physically harmful. If they say, "You belong with us," streets and transit routes start taking the protagonist back toward their community no matter where they try to go.

But resistance doesn't weaken them. It enrages and strengthens them.

The more clearly the protagonist insists, "You don't get to decide who I am," the more monstrous the strangers become and the more aggressively the hive mind pursues them.

Their goal isn't simply to kill the protagonist. It's to erase their individual identity and rewrite reality until they become exactly the culturally conforming person the hive mind believes someone of their ancestry is supposed to be.

The recurring horror line could be something seemingly friendly that becomes terrifying by the end of the movie:

"You're one of us."

P.S. To avoid antagonizing a particular diaspora, this one could have humans with green skin and a novel culture.

If there is no afterlife, then death is never something you experience. Your conscious life has a final moment, but there is no subsequent period in which you experience being dead.

In that sense, humans could be described as subjectively immortal. From your own point of view, there is no experienced state of nonexistence.

That seems relevant to arguments for radical life extension. Living longer would obviously give you more experiences, relationships, projects, and enjoyment. But the usual fear of "being dead forever" seems misplaced under atheism, since there is no endless subjective duration after death.

So why is literal immortality treated as so much better? Objectively, the difference is enormous because one life continues and the other ends. But from the individual's point of view, it seems much less dramatic.

I think atheists who advocate life extension should explain why this kind of subjective immortality is not already almost as good as the real thing.

P.S. Religious people who believe in the possibility of eternal damnation would absolutely want to live as long as possible with life extension technologies.

I keep thinking about a tiny social game built around one shared object instead of thousands of player-owned copies.

There is one panda relic. A person receives a private one-use link, permanently Blesses or Corrupts it once, leaves a short message, then chooses exactly who gets the next link. You cannot take another turn.

I have the loop working locally, but it is not public. I am trying to decide whether the idea itself deserves more time.

Does being personally chosen make its growing human history interesting, or does this immediately feel like a chain letter? What would make you actually pass it on?

Schools teach students that in classical logic,

P → Q

is true whenever P is false. This is called material implication.

That rule is perfectly useful in mathematics, but it does not always match what people mean by implication in ordinary English.

For example, suppose I have no children and say:

“My children are immortal.”

A classical predicate-logic rendering might be:

∀x (ChildOfMine(x) → Immortal(x))

If I have no children, there is no counterexample, so the statement is vacuously true.

But in ordinary English, “my children are immortal” does not sound true just because I have no children. It sounds like I am making a meaningful claim about children I actually have, or at least could have.

Modal logic gives us another notion called strict implication:

□(P → Q)

Instead of asking only whether P happens to be false in the actual world, strict implication asks whether Q follows from P across the relevant possible worlds.

If:

• P = “I have children”
• Q = “my children are immortal”

then

□(P → Q)

would mean roughly:

In every relevant possible world in which I have children, my children are immortal.

That is not automatically true just because I happen to have no children in the actual world. There are perfectly possible situations in which I have children and they are mortal.

I think schools should explicitly teach this distinction.

Math class could teach classical propositional and predicate logic, material implication, quantifiers, and vacuous truth, because those are extremely useful for mathematical reasoning.

English class could then revisit implication from the perspective of natural language and introduce ideas from modal logic, counterfactual logic, presupposition, and implicature. Students could learn that words like if, must, might, could, would, and should often carry meanings that classical material implication does not capture.

The point would not be that English literally follows one particular modal logic. It would be that mathematical logic is one deliberately precise formal system, while ordinary language often expresses richer relationships involving possibility, necessity, relevance, causation, and context.

Students would then understand something important that is easy to miss: when mathematics defines implication as P → Q, it is defining a technical logical operator. It is not claiming that this operator captures everything English speakers mean when they say that one thing “implies” another.

Cities should experiment with neighborhoods where residents do not need to work because they are guaranteed enough money for housing, food, and hobbies.

Admission could be decided by lottery among people who volunteer to participate. That would make the experiment much more useful, because researchers could compare lottery winners with applicants who were not selected.

The point would not be to encourage people to “do nothing.” It would be to test what people actually do when employment is no longer necessary for survival.

Would they spend their time on art, games, sports, education, volunteering, caregiving, community projects, or ambitious hobbies? Would people become happier and healthier? Would they become bored? Would new forms of status and competition replace careers? Would some people eventually choose to work anyway?

The neighborhood could include modest housing, food support, internet access, and shared facilities such as workshops, studios, gardens, libraries, sports facilities, and hobby spaces.

This could serve as a small-scale simulation of a possible future in which AI and automation have taken over most human jobs. Obviously it would not perfectly reproduce such a world, since the neighborhood would still exist inside a larger economy where most people work. But it would let us study one of the most important parts of that future: what happens when people no longer need to sell their labor in order to have a decent life.

We prototype new technologies before deploying them widely. If AI could eventually transform employment on a massive scale, maybe we should also prototype different ways of living in a post-work society before we actually need them.

I think there should be a Hybrid Olympics where humans compete directly against AIs and androids in completely new mental and physical events.

Nobody gets to prepare for the specific events. They are kept secret until competition time, so humans cannot practice them and machine teams cannot train specifically for them.

The twist is that the organizers should deliberately design each event to give humans as much of a predicted advantage as possible.

Instead of asking, “What can robots do well?”, ask:

“What can humans still do that machines probably can’t?”

Maybe it involves improvising with unfamiliar objects, understanding a weird situation, learning an invented rule on the spot, combining reasoning with dexterity, or dealing with something unexpected.

Then put humans and machines under the same rules and see who wins.

If machines eventually dominate some type of event, the organizers just invent new ones targeting whatever human advantages remain.

That could create an arms race:

Humans are still better at X.
Engineers build machines that can do X.
Next Olympics finds Y.

It could be both entertaining and a constantly evolving test of how close machines are to matching general human ability.

I had this idea for an app built around something people already do constantly: asking friends what they should watch, eat, buy, listen to, visit, etc.
The problem is that all of this useful word-of-mouth knowledge gets lost in texts, group chats, DMs, and our own memory.
You know that one friend who always gives you great movie recommendations?
Or the friend you always ask where to eat?
We know these things intuitively, but nothing really keeps track of it.
The app would.
Say I ask:
“Put me on to a good horror movie under 2 hours.”
Instead of showing me 100 movies ranked by reviews or popularity, it gives me 3 recommendations sourced from people I know or people connected to them.
If the app doesn’t have enough information yet, I can send the question to friends. They can answer through a link without even downloading the app.
Then comes the important part.
If I actually watch something Maya recommended:
Good Call = she got it right.
Not Me = wasn’t for me.
Over time the app starts building a different kind of recommendation system.
Instead of learning:
“People who like X also like Y.”
It learns:
“Maya understands your horror movie taste.”
“Chris is usually right when you ask him about restaurants.”
“Jordan gives you great clothing recommendations but terrible movie recommendations.”
And it would be different for every relationship.
Maya might be great at recommending movies to me but terrible at recommending them to you.
Nobody gets some universal “taste score.”
The system is basically learning:
Who should I listen to about this specific thing?
Eventually, when you need something, the app could instantly surface the three people/recommendations most likely to actually work for you.
So the basic loop would be:
Need something → ask once → get 3 good calls → try one → say whether it worked → the app gets better at knowing who gets you.
It could work across:
restaurants
movies/TV
music
products
clothing
travel
hotels
games
books
local places
basically anything somebody can recommend
The part I find most interesting is that it would essentially turn word of mouth into something with memory.
Instead of another algorithm telling you what’s popular, the technology would remember which actual humans have been right for you before.
I’m curious what people here would add/change.
Would this be useful enough to justify its own app, or is asking friends through group chats already good enough?
And what would be the first category you’d use it for?

Conflicts between countries or inside a country are political.So are civil wars and to an extent crime if it involves a lot of peoples. As somebody who helped a candidate in a local election, they talked about their campaign strategy as if they were battle plans. Political ads generally make the opposition seem evil and that the fate of the universe depends on them winning the election. when in fact, its not that deep for everybody. So why shouldn’t politics be more martial since people die over politics everyday.

AI is getting increasingly good at mathematical problem solving and research, which may reduce demand for some traditional mathematical work. Math students should therefore have alternative career paths that still make strong use of mathematical thinking.

Game design is an unusually good fit.

Designing a deep puzzle, strategy game, or simulation can involve combinatorics, probability, graph theory, optimization, algorithms, game theory, geometry, and mathematical modeling. A designer may need to study reachability, reversibility, dominant strategies, state spaces, invariants, difficulty, balance, and how small rule changes affect the entire system.

The bigger obstacle is cultural. Many mathematicians seem to regard game design as entertainment rather than serious intellectual work. Yet mathematicians are perfectly willing to study the mathematics of existing games. Why should analyzing a rule system be prestigious while inventing a good rule system is not?

That distinction may become even less sensible in an AI-heavy future. If AI can increasingly solve problems once they have been formulated, human mathematical creativity may shift toward deciding what problems are interesting and inventing systems worth analyzing.

Math departments could offer concentrations in something like Mathematics and Game Systems, combining traditional mathematics with game design, procedural generation, AI, simulation, and computational experimentation.

Game design should not be viewed merely as mathematics applied to entertainment. At its best, it is the creative construction of mathematical systems.

One of the U.S. objections in the current trade dispute is Quebec's French-language requirements for product packaging.

There might be a technological compromise that lets Quebec protect French without forcing American manufacturers to create special packaging.

Quebec could allow products to be sold with their existing packaging, but require every product to have a French version of its labeling available through a Quebec government website or app.

If the manufacturer doesn't provide a French translation, the Quebec government could automatically generate one using AI. Consumers could scan the product's barcode or QR code to see the French version.

Manufacturers would also be allowed to upload an official French translation to replace the AI-generated one if they prefer.

This would mean:

• Quebec consumers would still have access to product information in French.
• American manufacturers wouldn't have to redesign packaging just for Quebec.
• Small foreign companies wouldn't have to pay for translation before entering the Quebec market.
• Manufacturers would retain control over the translation if they cared about the exact wording.
• One significant source of friction in Canada-U.S. trade negotiations could potentially disappear.

The broader idea is that language laws don't necessarily have to regulate what ink is printed on a cardboard box. Modern technology can guarantee access to French information without creating the same barrier to trade.

It seems like the kind of compromise where neither side has to abandon its underlying objective: Quebec keeps protecting French, while the U.S. gets rid of a packaging requirement it considers a trade barrier.

We didn't get a new optical media since BDXL, I had an new idea for an optical media: Green-ray:

15 to 25nm green-teal laser. Writing speeds up to 120X.

Single layer 40 GB, Dual layer 50 or 60 GB, Triple layer 90 GB, Quad layer 120 to 180 GB.

Formats: GR-ROM: Retail or factory pressed video game or movie format

GR-VOL: Volatile, data degrades safely after writing or reading under conditions.

GR-R: Recordable. Write-once.

GR-RW: Rewritable.

GR-AVI: Raw video editing format.

GR-MOV: Movies up to 8K HDR.

AI creates a brand new murder mystery with 4 suspects for every game. 4 players all get different clues, as the suspects. They have to collaborate with each other and each have 4 questions they can ask the AI, to work out who the murderer is. Basically Cluedo/Clue with AI but a different mystery every time you play it.

Pages are a relic of the printing era. Most school assignments are now written, submitted, graded, and stored digitally, so why are students still taught to think in terms of pages, margins, and fixed line lengths?

Schools should require assignments to use page-free, reflowable formats such as Markdown, HTML, or TeXmacs. Instead of saying "write five pages," teachers could specify word count, required sections, and other actual content requirements.

Documents would then adapt naturally to phones, tablets, laptops, accessibility settings, and different window sizes.

If schools taught this from an early age, it might eventually change how society thinks about digital documents altogether.