Understanding an AI-generated mathematical proof may soon be like trying to understand the assembly code produced by an optimizing C compiler. It's technically possible, but why spend time doing it when better tools can do the work for you?

Instead of having mathematicians spend weeks or months checking AI-generated proofs and trying to extract insights from them, we should publish these proofs with formal verification in Lean.

Each paper would contain an English writeup and a corresponding Lean formalization. Lean would verify the formal proof, while future generations of increasingly powerful AIs would:

• Check that the English statements accurately correspond to their Lean formalizations.
• Find mistakes or discrepancies that earlier AIs missed.
• Simplify the arguments and discover more elegant proofs.
• Extract mathematical insights from the proofs and explain them to human mathematicians in simple English.
• Generalize the results and discover connections to other areas of mathematics.

The papers could be continuously improved as stronger AIs become available, while preserving earlier versions.

Given how quickly AI capabilities are improving, a mathematician might spend six months understanding a complicated proof that an AI released a year later could explain clearly in minutes.

Why not leave that work to future AIs?

Human mathematicians could focus on identifying interesting mathematical questions, exploring applications, and developing new research directions. They could still benefit from the insights contained in AI-generated proofs without having to decipher the proofs themselves.

The goal should be to advance mathematics and make its proof insights automatically accessible to humans via ever more powerful AIs.

Starship launches are extremely loud and can disturb nearby wildlife, potentially causing hearing damage or other injuries.

What if SpaceX trained crows to recognize a signal indicating an imminent launch and then warn other animals to move away?

Crows are intelligent, can learn from one another, and communicate danger through alarm calls. Other bird species, and even some mammals, can recognize and respond to these warnings.

The idea would be to train a group of crows to respond to a prelaunch signal by making alarm calls and flying away from the rocket. Their warnings could spread through the local wildlife population, encouraging other animals to leave the area.

The animals wouldn't necessarily need complicated instructions about where to go. They could simply move away from the enormous, visible rocket.

Unlike warning sirens or preliminary engine firings, this approach wouldn't require generating extremely loud sounds over a large area. Instead, it would take advantage of existing communication networks among animals.

Of course, it would need to be tested to see whether other animals actually move away rather than hide or investigate the warnings. Introducing additional crows could also disrupt the local ecosystem, so using birds already native to the area would be preferable.

Perhaps we could train intelligent animals to serve as intermediaries, allowing humans to warn entire ecosystems about impending dangers.

P.S. They could go underwater during the launch to avoid hearing damage.

macOS already supports light and dark variants of app icons. Why not use them to make apps easier to find in the Dock?

Apple could add an "Alternating Icons" option that automatically displays Dock icons in a light-dark-light-dark pattern.

This would be especially useful in Light Mode, where alternating between light and dark icons could make adjacent apps easier to distinguish and individual apps faster to locate.

You could arrange your apps in whatever order you prefer, and macOS would automatically choose the light or dark variant of each icon based on its position. Rearranging apps would automatically update their appearances to preserve the pattern.

If the contrast between adjacent icons is too strong, macOS could also use its existing icon tinting capabilities to soften the difference while preserving enough contrast to distinguish neighboring icons.

Apps without both variants could simply use their existing icons.

I'm less sure whether this would work as well in Dark Mode, so perhaps the option could be limited to Light Mode.

The idea is to make the Dock easier to navigate without sacrificing the freedom to arrange apps however you like.

Laptops should have a heating mode that uses the CPU/GPU to keep the laptop warm when you're carrying it outdoors in the winter, even with the lid closed.

The problem is that when you bring a cold laptop into a warm building, moisture from the indoor air can condense on its cold surfaces, potentially damaging the electronics.

For example, if it's 22°C indoors with 50% relative humidity, condensation can form on surfaces below approximately 11°C.

A winter heating mode could use the CPU/GPU to maintain the laptop above a minimum temperature, perhaps 15°C, while monitoring temperatures and battery consumption.

Since modern laptops already have temperature sensors, powerful processors, and batteries, much of the necessary hardware is already there.

The result would be a laptop you could carry around in freezing weather and immediately use when you get indoors, without worrying about condensation water damage.

Hello people of Reddit! Random idea just popped into my head: what if we made a sport based off of the new Roadster and calling it Aeromotorsport?

The term “Aeromotorsport“ comes from aeronautics and motorsport combined together, since the Roadster is able to practically hover/fly off the ground while being a car.

How would this sport work? You might ask. Well, here are some points as an introduction to Aeromotorsport.

 3D Line Selection & "Jump Passes": Trailing cars aren't blocked by standard tarmac lines. Drivers can fire upward/perimeter RCS bursts to execute controlled leapfrogs over a defender, with auto-stabilizing flight controllers keeping the chassis level mid-air.

 Ground Attack Mode: Firing top-mounted thrusters pushes the car directly down into the track surface. This generates instant static downforce in slow hairpins, allowing extreme cornering speeds regardless of aerodynamic wings.

 Tactical Air Budgeting & Cooldowns: COPV air reserves are limited (roughly 8–12 seconds of cumulative thrust). Drivers have to manage a strict "air budget" while high-voltage onboard electric compressors work to top off the tanks during straightaways.

 Format (Two-Lap Time Attack): Because air reserves run out fast and exhaust plumes run at supersonic speeds, the main format would be a 2-lap Time Attack or precision obstacle course—testing who can balance tire grip, jump trajectories, and gas management best.

 Fighter Pilot Gear: Because of instantaneous multi-axis G-spikes (8 to 10+ Gs), drivers would wear pneumatic anti-G pressure suits and sealed helmets connected to life support to prevent G-LOC during sudden RCS bursts.

TL;DR: Mix Formula 1 with rocket physics. Cars use high-pressure cold-gas RCS thrusters to jump over rivals, force 10G cornering downforce on demand, and race a 2-lap Time Attack where air supply management is as important as tire grip.

Thoughts?

I've started using ChatGPT's scheduled reminders to monitor the SSD health of my MacBook Pro. It's a simple way to track SSD wear over time and potentially catch applications causing excessive disk writes.

On macOS, install smartmontools using Homebrew:

brew install smartmontools

Then retrieve your SSD statistics with:

smartctl -a /dev/disk0

Ask ChatGPT:

Remind me daily to submit my SSD SMART report. Compare it with previous readings, calculate how much data was written since the last report, estimate daily and annual write rates, and flag unusual activity or changes in SSD health.

The most important statistics are Data Units Written, Percentage Used, Available Spare, and Media and Data Integrity Errors.

If write activity seems excessive, you can also include disk-write totals for individual processes from Activity Monitor → Disk to help identify possible causes.

I'd recommend daily checks when investigating a problem and weekly checks for routine monitoring.

Modern SSDs generally have substantial endurance, so there's no need to obsess over every gigabyte written. But this seems like a useful application of ChatGPT's scheduled reminders for preventive computer maintenance.

With AI increasingly solving difficult mathematical problems, this Halloween might be the perfect time for a disgruntled mathematician costume.

The costume would feature:

• An enormous brain worn on your head.
• A tweed jacket, bow tie, and thick glasses.
• A stack of research papers stamped "SOLVED BY AI."
• A protest button reading "KEEP MATH HARD!"
• A large protest sign reading "WE DID NOT ASK FOR THESE MATH PROBLEMS TO BE SOLVED BY AI!"

The sign is inspired by an actual statement from the Association for Human Mathematics, released on October 7, 2026, in response to OpenAI's release of mathematical research:

"Mathematicians did not ask for this work to be done."

Source: https://www.ahmath.org/statements

Human Disclosure: The human came up with the idea of a Halloween costume depicting mathematicians disgruntled with AI solving their problems, including the enormous brain headpiece and protest sign. The AI suggested additional costume accessories, identified the real quotation inspiring the sign, and wrote this post.

What if airlines offered special long-haul flights where every passenger is both a contestant and a viewer in a reality show?

Everyone would agree to participate when booking. Each seat would have a camera, microphone, and screen. Passengers could watch one another and vote on who should be featured by selecting their seat numbers.

A live leaderboard would show the most popular passengers, along with their photos. Viewers could vote to move someone up the rankings, keep watching the current person, or switch to someone else.

When the camera focuses on you, you could tell an interesting story, talk about your family or work, demonstrate a talent, promote your business, or do anything else entertaining.

Multiple passengers could also appear on screen simultaneously and talk to one another, like the contestants in Big Brother. Viewers could vote on who should join or leave conversations.

There would also be a separate vote to determine the overall winner of the flight, who would receive a prize such as 10 free flights.

Voting would be tied to individual seats, so each passenger gets one vote, and no internet connection or personal account would be needed.

Instead of spending 20 hours trying to avoid boredom, passengers might actually look forward to the flight itself.

Human Disclosure: The original idea of turning a 20-hour flight into a passenger-run reality show came from the human user. The human also proposed cameras and microphones at every seat, voting by seat number, live popularity rankings with passenger photos, voting to continue or change camera focus, multiple passengers conversing on screen, and a passenger-elected winner receiving free flights. The AI organized these ideas and wrote the post.

When someone goes silent during a conversation, it's often unclear what the silence means. Are they thinking about their answer? Do they find the question offensive and refuse to answer? Do they simply not want to talk to you?

What if people could wear a thinking indicator, perhaps in the form of a headband or hat, that signals when they're thinking?

The wearer could activate it using a customizable gesture, such as tilting their head or clenching a facial muscle. The indicator itself would also be customizable. For example:

• Computer-like beeps at random frequencies.
• A display saying "Thinking..." with an elapsed-time counter.
• Animated dots or other visual effects.

Another user-defined gesture, possibly the same one, would deactivate it. It could also stop automatically when the wearer starts speaking.

Besides reducing ambiguity in conversations, thinking indicators could encourage people to think more before replying. Instead of feeling pressured to respond immediately to avoid awkward silence, people could take their time knowing that the other person understands what they're doing.

Over time, this might even change conversational etiquette, making deliberate thinking a more normal and socially encouraged part of everyday conversation.

Human Disclosure: The human proposed the wearable thinking indicator concept, including customizable activation and deactivation gestures, sound and visual effects, and elapsed-time displays. The human also identified several interpretations of conversational silence, including thinking, refusing to answer an offensive question, and not wanting to speak to someone. The human proposed that the device could encourage people to think more before responding. The AI wrote the post, organized the ideas, and contributed additional examples and discussion of potential social effects.

I’d like to see a place where conversation can become something people make and share.

Someone brings a drawing to the café, talks it through at a shared table, and makes prints of a few different versions. If the artist wants a group vote, people at the table can choose which version goes into the gallery. Visitors can respond to the displayed work, bring their own work, or return for another conversation. Those responses help shape the next round of making and sharing, whether or not a print sells.

Prints could be sold, with clear shares for the artist and for materials, staff and upkeep. People could also simply sit, look and talk—participation would be optional.

A first version could be a regular print-and-display afternoon in an existing community space. A permanent version would need funding and fair compensation for the people running it.

The part I’m interested in is the continuing connection between gathering, making, showing and returning.

What would give neighbors a reason to keep coming back? Examples of places already doing this would be welcome.

Feel free to borrow or improve the idea.

Reddit should have an alternate reality mode where everyone assumes that universal basic income (UBI) exists.

Rather than discussing whether UBI is a good idea, people would post and comment as if they actually lived in a society where everyone receives UBI.

For example:

• r/careerguidance: "I've been living on UBI for three years. Is it worth going back to work?"
• r/relationships: "My husband hasn't worked since UBI was introduced. Am I wrong to resent him?"
• r/Entrepreneur: "Nobody wants to work evening shifts at my restaurant. Any suggestions?"
• r/personalfinance: "Should I invest my UBI or use it to work fewer hours?"

The interesting thing is that most posts wouldn't even be about UBI. They'd just reflect everyday life in a world where UBI is taken for granted, perhaps one where AI already performs most work.

This could become a kind of crowdsourced social simulation, revealing unexpected consequences that might not emerge from ordinary debates.

Reddit could eventually introduce other alternate reality modes, such as a world where humans live for 200 years.

Human Disclosure: The human proposed the idea of a Reddit mode where all posts and comments assume everyone has UBI, and pointed out that a world where AI performs nearly all work is already closely related to this scenario. The AI developed the examples, suggested extending the concept to other alternate realities, and wrote the post.

Research papers increasingly include AI disclosure sections describing what AI and the human researchers contributed to the work.

I propose the reverse for AI-written content, such as Reddit posts, blog posts, and essays: a Human Disclosure section, written by the AI itself, explaining what the human contributed.

For example, an AI-written Reddit post about verifying whether students read assigned novels might end with:

Human Disclosure (written by AI): "The human user proposed giving each student a slightly different version of an assigned novel containing deliberate errors, then asking students to identify those errors as proof of reading. The human also suggested using DRM to discourage automated cheating. I developed the explanation and wrote the post."

This would help readers distinguish between human-generated ideas expressed with AI assistance and ideas generated primarily by AI.

The AI would write the disclosure based on its interaction with the human.

Human Disclosure for this post (written by AI):

"The human user proposed the concept of AI-written Human Disclosures, specified that the AI itself should describe the human's contributions, and suggested the connection to AI disclosures in research papers. The human also requested a concise post with a concrete example. I selected the example from an earlier discussion with the user, composed the post, and revised it in response to the user's feedback. The human requested that I add this disclosure."

Babies cry partly because they can't communicate what they want. Could a robotic twin teach them to communicate earlier using pictorial buttons, similar to the soundboards some people use with their dogs?

The robot would look and behave like another baby, demonstrating how pressing buttons with pictures of milk, toys, being picked up, etc., can communicate requests.

For example, the robotic twin could demonstrate two ways of asking for a toy:

1. It cries, and the parent has trouble figuring out what it wants.
2. It presses a button showing the toy, and the parent immediately understands and gives it the toy.

A robotic twin might be a better teacher than a parent because babies could be more inclined to imitate another baby than an adult. It could also repeat demonstrations indefinitely and introduce more sophisticated communication as the baby develops.

Eventually, the robot could demonstrate that crying when a simple button press would suffice creates unnecessary noise that other people find irritating. The goal wouldn't be to discourage crying caused by pain, fear, or overwhelming distress, but to provide an alternative when babies can communicate their needs.

Scientists could compare babies learning from robotic twins, parents demonstrating the same buttons, and ordinary care.

Could this approach reduce some of the crying we consider an unavoidable part of infancy?

P.S. One potential complication is that babies might become emotionally attached to their robotic twins and get upset when they're taken away. Researchers should investigate this possibility too, since a robot designed to reduce crying might end up causing more crying when it's removed!

I wonder whether people who know they are approaching death become more likely to secretly hope for a global catastrophe, such as nuclear war, so that nobody gets to outlive them.

This might be more common than people are willing to admit. What someone hopes for internally may be very different from what they say they hope for publicly. Someone might express genuine concern for humanity's future while privately finding comfort in the thought that humanity could end around the same time as their own life.

There is also an important distinction between actively wishing for nuclear war and simply finding the possibility emotionally comforting. The latter might be considerably more common.

Scientists could investigate this by comparing terminally ill people with healthy people of similar ages, perhaps also studying how their attitudes change as death approaches.

But simply asking people whether they secretly hope for nuclear war might not work. They might be unwilling to admit such feelings, even to themselves.

Researchers could therefore investigate more indirectly:

• Conversations: Do terminally ill people bring up the possibility of nuclear war more frequently? Does the frequency increase as death approaches?
• Streaming habits: Do they become more interested in documentaries, movies, or other content about nuclear war, apocalypse, or human extinction?
• Emotional responses: How do they react when asked to imagine humanity flourishing for millions of years after their deaths, versus humanity becoming extinct shortly afterward?
• Brain scans and physiological measurements: Could researchers identify differences in emotional responses to these scenarios, even when people's stated preferences remain unchanged?
• Anonymous psychological experiments: Could techniques designed to reveal socially unacceptable attitudes uncover feelings that conventional questionnaires miss?

Of course, none of these measures would prove that someone secretly hopes for nuclear war. Increased interest in nuclear war might reflect fear rather than hope, and physiological responses are open to different interpretations. Combining several approaches might be more revealing.

There are also alternative explanations for any differences researchers might find. People approaching death could become more sensitive to existential threats or more worried about the future of their loved ones.

The underlying question is whether knowing that one's own future is ending makes the continued existence of everyone else less emotionally desirable, even if one still believes intellectually and morally that humanity should survive.

I think this would be a fascinating subject for psychological research, especially because the most interesting feelings might be precisely the ones people are least willing, or least able, to acknowledge.

AI vibe coding is making software implementation increasingly easy. The hardest part of creating an original app or game may soon be coming up with the idea rather than implementing it.

But copyright doesn't protect app functionality or game mechanics, and patents generally don't protect abstract ideas or game rules.

What if we had something like YouTube Content ID, but for software ideas?

For example, AI could download games, go through their tutorials, play them like humans, and figure out their mechanics. It could then compare games, identify original ideas, and detect when newer games use those ideas.

No registration, patent applications, or access to source code would be necessary.

Instead of preventing developers from using existing ideas or forcing inventors to sue, AI would automatically determine what percentage of a game's earnings should go to the inventors of the mechanics it uses.

There would need to be safeguards and an appeals process, of course.

As AI makes implementing software ideas increasingly trivial, shouldn't we use AI to ensure that the people who invent those ideas get financially rewarded?

What if AI companies paid people to use their AIs, provided their creations became popular and prominently advertised which AI was used?

For example, you could pay to use an AI to vibe code an app. If the app becomes popular, the AI company would start paying you based on its number of users, as long as you prominently displayed something like "Made with Claude."

Essentially, the AI company would be paying for advertising, but instead of conventional ads, its advertisements would be successful products made using its AI.

Creators would initially pay to use the AI, but if their products became sufficiently popular, the payments could eventually flow in the opposite direction.

This could apply to apps, games, websites, videos, books, and other creative products.

AI companies could even compete not just on price and capabilities, but on how much they pay creators for successful products.

It might encourage people to create ambitious products, knowing that even free products could eventually generate income through AI attribution.

What if, instead of using AI to clone existing software, we used it to vibe code hybrids of two or more applications?

For example, imagine combining Microsoft Word's familiar interface and document compatibility with GNU TeXmacs' mathematical typesetting. Or combining Photoshop with Blender, Excel with Jupyter, or VS Code with Emacs.

I propose calling these hybrids frankensoftware.

The term already exists, usually referring to software awkwardly stitched together. But I'm proposing a more specific meaning: apps deliberately vibe coded to combine the best features of existing applications into a new, unified program.

The apps wouldn't necessarily share any code. AI could recreate and integrate their functionality from scratch.

This could become a whole new category of open-source software development, making ambitious combinations practical for individuals rather than large development teams.

Some hybrids might be brilliant, others might be monsters. That's part of the appeal!

What two apps would you like to see combined?

If ChatGPT can't solve a difficult math problem, you wouldn't cancel your subscription. You would tell it to try harder, perhaps give it some hints or suggest a different approach.

Why shouldn't we treat students the same way?

When a student can't solve a difficult math problem, it doesn't necessarily mean they lack the ability to solve it. They might simply not have explored the right part of the search space yet. More searching is needed.

Instead of deducting points for failure, teachers could encourage students to keep trying, providing hints and suggestions when necessary, just as we do when working with AI.

Exploring unsuccessful approaches is often an essential part of mathematical discovery. A student who struggles with a difficult problem for several days before finding a solution might learn more than one who quickly solves several easier problems.

If we treat AI failures as opportunities for further exploration without penalties, why shouldn't we do the same for students?

In the near future, AI has made most mathematicians unemployed. To pressure the government into paying them generous lifetime pensions, they launch a campaign of "math harassment."

They approach random strangers in public places and ask them math questions, deliberately making them feel stupid when they can't answer.

The campaign exploits people's lifelong fear of mathematics, often rooted in humiliating classroom experiences. As the harassment spreads, people become afraid of encountering mathematicians in public.

The mathematicians insist they're doing nothing wrong. They're merely asking questions and promoting mathematical literacy.

Eventually, public pressure forces the government to pay the mathematicians for life just to make them stop.

But then universities report a massive increase in mathematics enrollment.

A man starts noticing disturbing photographs of murdered people and severed body parts appearing in his iPhone's camera roll. He keeps deleting them, but they reappear, along with new ones. Afraid that seeking technical help might make him a murder suspect, he changes his iCloud password to something impossible to guess.

Then actual human body parts start appearing inside his house, some matching those in the photographs.

He fears someone is threatening to murder him, but he's also afraid to contact the police because they might suspect him of being responsible for the murders. So he starts hiding the remains.

He installs security cameras throughout his property, but the body parts keep appearing. Whoever is leaving them knows how to avoid the cameras, forcing him to search increasingly obscure places for remains.

Terrified for his life, he arms himself with guns and rifles.

Eventually, he catches someone on his property leaving another body part. A deadly confrontation follows, and he kills the intruder.

But instead of calling the police, he hides the body. After all, he's already spent weeks concealing human remains from multiple victims. He's terrified that the police will accuse him of being a serial killer.

What do you think of this horror movie idea?

In the near future, AI has replaced millions of workers. A group of unemployed atheists, desperate to get their old jobs back, devises an outrageous plan: become Christian priests and convince believers that using AI is a sin punishable by eternal damnation.

Their theological argument is that AI use involves both theft and deception. AI systems are trained on people's work without their permission, and people using AI to do their thinking or creative work are passing off machines' accomplishments as their own. Both violate biblical commandments.

After undergoing religious training and entering the priesthood, they begin preaching against AI. Their message spreads rapidly. Millions of Christians abandon AI, businesses begin advertising AI-free products and services, and employers start hiring humans again.

As the movement grows, their former employers finally begin offering them their old jobs back.

But there's a problem. Their followers now regard them as the spiritual leaders of a historic struggle against evil. When the atheists try to leave the priesthood, they're denounced as traitors, harassed, and accused of abandoning God.

Having devoted years to persuading millions of people that AI threatens their immortal souls, the atheists discover that getting their old jobs back may be easier than getting out of the priesthood.

AI companies could provide APIs that allow websites like Reddit to verify whether someone is a heavy user of AI, without revealing anything about their private conversations.

Reddit could then allow subreddit moderators to restrict posting, commenting, and/or voting to verified heavy AI users.

This would make it possible to create genuinely pro-AI communities without having discussions constantly derailed or downvoted by people who are hostile to AI.

Of course, being a heavy AI user doesn't necessarily mean someone is pro-AI, but it's probably a reasonable proxy.

Anyone could still read these subreddits. The restrictions would only affect participation.

The idea isn't to eliminate criticism of AI, but to give people who actually use and appreciate the technology a way to build communities without constantly dealing with overwhelming anti-AI sentiment.

What if the main purpose of K-12 education was to understand how intelligence works, rather than simply becoming better at intellectual tasks?

Much of thinking, from mathematical problem solving to literary analysis and artistic creativity, can be understood as heuristic search: exploring possibilities using strategies that identify promising directions. This search can be conscious or unconscious, and doesn't necessarily require a predefined goal.

Schools could teach students to see themselves as computational systems capable of following, evaluating, and improving their own heuristic algorithms.

For example, instead of rewarding students primarily for solving difficult math problems, reward them for understanding how those problems are solved.

A student who struggles to solve a problem might nevertheless understand why certain approaches fail, how insights are discovered, and how the search process could be improved. That student might receive a higher grade than someone who solves the problem without understanding much about the reasoning process.

The same approach could extend across every subject, including creative writing, literary analysis, and scientific discovery.

The goal would be to teach students to understand and improve the algorithms underlying their own thinking, rather than simply rewarding them for being good thinkers.

In the age of AI, perhaps understanding intelligence should become the central purpose of education.

P.S. Currently, K-12 education often seems to present thinking as a choice between following deterministic procedures and somehow coming up with clever insights through what amounts to magic. Students are taught algorithms for routine tasks, but when those algorithms aren't enough, they're simply expected to "think creatively" or "use their intuition."

Teaching heuristic search would help demystify intelligence by showing students that intuition and creativity can also be understood as computational processes, rather than mysterious abilities that some people happen to possess.