Dynomight—with calculations—proves that is not possible to reduce your indoor CO₂ by having plants.

People produce ~1 kilogram of carbon dioxide per day. That’s around 5.7 × 10²³ molecules or 0.948 moles per hour. (You may remember from high school that a mole is a gigantic number made up to avoid having factors of 10²³ everywhere.) Let’s keep it simple and call it one mole per hour.

Meanwhile, plants turn carbon dioxide into oxygen through photosynthesis, i.e. the chemical reaction of (6 water molecules) + (6 carbon dioxide molecules) + (energy) → (1 glucose molecule) + (6 oxygen molecules). The minimum energy physically needed to convert 1 mole of carbon dioxide into glucose and oxygen via this reaction is ~477 kilojoules.

So we’ve already got a lower bound. Say you have magical plants that somehow channel all incoming energy into photosynthesis with perfect efficiency. They’ll need ~477 kilojoules per hour, which converts to a continuous usage of 132.5 watts.

 That’s a bit more than what’s used by two incandescent light bulbs, which isn’t too bad.

But you don’t have magical plants.

Note: I cannot vouch for the calculations mentioned in the post, but hey, Gemini says its sound.

This comment by cgearhart on Hacker News emphasising on “proof of understanding” in the age of AI.

…everyone thinks that every problem is “a couple hours” with AI now, and they all want zero external dependencies because they can move faster alone. As a result, we’re now in an even worse “yet-another-…” age where everyone has built approximately the same (but somehow incompatible) versions of all the same beginner-level software, and (ironically) while they want no external dependencies they’re also pushing for org-level mandates to require everyone else to use their solution. Meanwhile, no one wants to do the slow/bottleneck part that cant easily be automated or scaled; they just throw an “agent” at it and call it done—but there’s nothing _there_. You can trust the agent on easy tasks and you can’t trust it on hard ones, but you can’t tell which ones are easy or hard. Improvements in foundation model tech move thresholds of the problem but can’t eliminate it.

Long story short, I think we’re in a phase where the organizational value function is lagging behind the tech. A “proof of concept” used to be correlated with “proof of work” and some amount of domain understanding, but I think now what we need is a focus on “proof of understanding” or else you’re probably just wasting tokens on a baby version of the problem. A decent proxy right now is that if you have zero external dependencies then your solution is probably a toy.

Wade Rupard talking about production of green ammonia.

“It seemed like an elegant concept when we were first looking at it that you could take a wind turbine, and that’s producing energy from wind above a cornfield, or small grain field, and then produce a nutrient that you can use right underneath the wind farms in rural Minnesota,” says Michael Reese, the green ammonia research lead at the University of Minnesota.

For Minnesota, the economic stakes are significant. Farmers in the state spend up to $1 billion annually on synthetic nitrogen fertilizer, yet Minnesota has virtually no commercial ammonia production capacity. As a result, fertilizer purchases send substantial amounts of money outside the state each year.

“All that money goes out of the state of Minnesota,” Reese says. “There are no ammonia or nitrogen fertilizer production facilities in the state other than our small systems here.”

Researchers believe green ammonia could eventually help reverse that trend by creating a Minnesota-based fertilizer industry powered by the state’s abundant wind resources.

The concept is simple in theory but difficult in practice.

Reminds me of pumped storage plant. There are some interesting ideas out there to go green.

Noah Smith talking about what more intelligence will do for us. It starts off with this.

Not a lot of people expected that AI would come for the mathematicians before it came for the truck drivers, but it did.

Ha!

I see a lot of parallels here with Yuval Noah Harari’s quote from his book 21 Lessons for the 21st Century.

Many doctors focus almost exclusively on processing information: they absorb medical data, analyze it, and produce a diagnosis. Nurses, in contrast, need good motor and emotional skills in order to give a painful injection, replace a bandage, or restrain a violent patient. Therefore we will probably have an AI family doctor on our smartphone decades before we have a reliable nurse robot.

Noah quotes multiple people in his post to support his thought. One of them is from François Chollet.

One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. “Future AI will have 10,000 IQ”, that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like “making the tower taller”, it’s more like “making the ball rounder”. At some point it’s already pretty damn spherical and any improvement is marginal.

Now of course smart humans aren’t quite at the optimal bound yet on an individual level, and machines will have many advantages besides intelligence — mostly the removal of biological bottlenecks: greater processing speed, unlimited working memory, unlimited memory with perfect recall… but these are mostly things humans can also access through externalized cognitive tools.

It’s a long and interesting read.

Dex Horthy:

The cost function of bad architecture is measured in months and years. If you have a coding episode and you only find out months later “somebody vibed this a little too hard” it’s really hard to propagate that reward signal back across the gap.

Sasha Putilin writes in the defense of em dash:

I fucking love em dashes. There is no greater joy in life than typing a sentence, realising it needs a clarification, a caveat, an addendum — but in the same sentence, not a new one — and, guess what, the em dash is right there for you. You want to package things neatly together — and the em dash lets you do exactly this. Em dashes make writing feel like you are assembling lego bricks.

Em dashes can be used instead of brackets in a sentence — like this one for example — and you can just type your shit and keep going. Brackets are for people who raise their hand in meetings and say: “this might be a stupid question”. A parenthetical is whispering: “Sorry, don’t mind me, I’ll be quick”. Em dash kicks the door open and announces itself. Use brackets when you are embarrassed by your own thought.

And also occasionally — occasionally — one just needs a random dramatic pause. Punctuation marks originally evolved as pause or breathing markers, after all. It’s not like grammar and punctuation rules were sent to us by god in their final form. People were just writing shit, and at some point the most popular patterns got codified. Sure, you need commas to signify a small break. Then em dash is a natural way to express a larger, longer, break. A period is great to express a complete thought — and it shifts the register of the next letter.

Matthew Morgan explaining why Bill Watterson despised the idea of merchandising Calvin and Hobbes.

It was still early days in the ten-year run of Calvin and Hobbes when the syndicate approached Watterson with its big ideas of Calvin sweatshirts, Spaceman Spiff bumper stickers, an animated Calvin and Hobbes Saturday show, maybe a movie, and — worst of all — a Hobbes doll. Watterson really loathed the Hobbes doll. To make sense of how much it bothered him, we need to talk about the tiger in the room.

When Watterson created Hobbes, his focus was on the character more than the conceit of a teddy that comes to life. Watterson told Rich West for The Comics Journalthat “there’s something a little peculiar about [Hobbes] that’s, hopefully, not readily categorised”. But Watterson’s readers often wanted Hobbes categorised into either “real” or “imaginary”. So Watterson came up with a compelling non-answer to the question:

“Calvin sees Hobbes one way, and everyone else sees Hobbes another way. I show two versions of reality, and each makes complete sense to the participant who sees it. I think that’s how life works.”

You could say Hobbes is both imaginatively real and really imaginary, depending on your perspective. Hobbes can be either, which also means he’s both. Is Hobbes a tiger or a toy? Yes.

Watterson insisted that if he wasn’t going to settle the question of Hobbes, then he definitely wouldn’t let some toy manufacturer settle it by turning Hobbes “into a stuffed toy for real, and deprive the strip of an element of its magic”. He’d sound off wherever he could on how “licensing usually cheapens the original creation” by saturating a market with characters until readers are bored of seeing them; how a multi-paneled story with dynamic action cannot be respected by the vagaries of a coffee mug illustration; how subtlety is sacrificed for immediacy; how selling off “everything fun and magical” means “the strip’s world is diminished”.

He has a hundred lines like these, articulations of higher reasoning against merchandising, but just once, in the Tenth Anniversary Book, he drops the high-and-mighty in favour of I-the-mighty: “Calvin and Hobbes was designed to be a comic strip and that’s all I want it to be. It’s the one place where everything works the way I intend it to.”

Tom Bedor sharing a few examples of how, in the past, suppression of encryption by the U.S government didn’t work. In the same vein, it won’t work with suppression of AI models from China.

Open source software is very difficult to suppress

In reality, the argument about suppressing open source models is mostly beside the point. History tells us that suppression of open source software is extremely difficult, and attempting to do so only serves to weaken companies against international competitors. A brief history of encryption is illustrative:

Today, PGP is a commonplace tool anyone can use, and most devs are at least familiar with. But when Phil Zimmermann invented it in 1991, the U.S. government considered encryption to be military technology. A criminal investigation was opened against Zimmermann.

When Netscape created SSL, the U.S. government allowed it to only release a weakened version of it internationally. These controls backfired: it was much easier to acquire the weakened, “international” version, so even many Americans used it.

Export controls did not succeed in limiting encryption as the government wished. SSL, PGP, and similar tools were readily available throughout the world, and the controls disadvantaged Americans. Eventually, courts ruled that releasing encryption source code is protected speech, and the U.S. government relaxed encryption export controls.

But are the models from China really open source? This comment on Hacker News by petcat:

This is not “open source” AI.

Photoshop source code + OSI license = open source

Photoshop binary = open weight

Photoshop SAAS web app = closed model like GPT, Opus/Fable etc.

There is nothing “open source” about the Chinese models in question. All they’re doing is allowing you to run their binary yourself instead of through their API.

Linus Torvalds:

AI is a tool, just like other tools we use. And it’s clearly a useful one.

It may not have been that “clearly” even just a year ago, but it’s no longer in question today.

There are other questions around AI (like what the economy of it will actually look like in the end), but “is it useful” is no longer one of those questions. Anybody who doubts that clearly hasn’t actually used it.

Yes, it can also be a somewhat painful tool, both for maintainer workloads and just from a “it keeps finding embarrassing bugs” standpoint.

But the solution is not to put your head in the sand and sing “La La La, I can’t hear you” at the top of your voice like some people seem to do.

The solution is to make sure those LLM tools help maintainers instead of just causing them pain. There’s no question on that side.

We’re not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it.

And no, AI isn’t perfect. But Christ, anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time.

Because it’s not like natural intelligence is always all that great either.