Madeline McCurry-Schmidt talking about the new HIV vaccine’s success in preclinical study.

The new vaccine works by intervening in a process called B cell maturation. B cells make antibodies. Like many immune cells, B cells have an early “naive” stage before they are ready to make antibodies. B cells start to mature once they get the signal that a pathogen, such as a virus, is trying to attack. B cells see pieces of that pathogen’s molecular structure and start producing antibodies that can bind to that structure and halt infection.

It can take a little while for B cells to find the right “bullseye” on a pathogen. But B cells keep trying. As they mature, B cells tweak their antibody production, refining antibody structures to bind to a pathogen in just the right, vulnerable spots.

Scientists describe B cell development as a training process or bootcamp. In most cases, the body is left with a well-honed B cell army.

HIV is hard to beat because it doesn’t give B cells a chance to develop effective antibodies. The first problem is that HIV disguises itself from the immune system. The virus is wrapped in an ever-shifting cloak of sugar molecules, called glycans. This lets HIV sneak undetected past human cells, which are also covered in glycans.

The second big problem is that HIV mutates very quickly. “The worldwide diversity of HIV mutations is extraordinary. Even the diversity within one individual person living with HIV is dramatic,” says LJI Instructor Patrick Madden, Ph.D., who served as study co-first author with Jon Steichen, Ph.D., an institute investigator at Scripps Research.

The third problem is that HIV changes its shape when it infects human cells. Even if B cells get a glimpse of its viral structure—snap!—the structure changes.

Taken together, these problems rarely give B cells a chance to hone their antibody responses against HIV. Even if a B cell manages to make neutralizing antibodies, the virus can mutate or change its shape, rendering those antibodies useless.

I love it when someone explains science in a simple language that I can understand.

Rohan Rajiv talking about the difference between hard and valuable problems.

[…] there’s very little correlation between a problem being difficult and a problem being valuable.

It is easy to fall into the trap of assuming the two travel together. If something is taking all our effort and ingenuity, surely it must matter — and we take the struggle itself as proof we’re doing important work.

But difficulty and value sit on separate axes. Plenty of brutally hard problems are worth little once solved. And some of the most valuable ones are relatively simple — but executing them well is worth a lot.

Golden words by M. Pattabiraman:

“Can you suggest a good international fund—or include one in my portfolio?”

I hear some version of this question regularly in my work as a SEBI-registered investment adviser. Usually, I respond with another question:

“If I hid the last three years of returns from you, would you still want to invest internationally?”

Take a moment before answering. If the answer is still yes, there may be a genuine portfolio reason for investing abroad. If the answer suddenly becomes less certain, perhaps the attraction is not diversification but recent performance. That difference matters.

Ursula K Le Guin’s rant on what is technology.

Technology is the active human interface with the material world.

But the word is consistently misused to mean only the enormously complex and specialised technologies of the past few decades, supported by massive exploitation both of natural and human resources.

This is not an acceptable use of the word. “Technology” and “hi tech” are not synonymous, and a technology that isn’t “hi,” isn’t necessarily ‘“low” in any meaningful sense.

We have been so desensitized by a hundred and fifty years of ceaselessly expanding technical prowess that we think nothing less complex and showy than a computer or a jet bomber deserves to be called “technology” at all. As if linen were the same thing as flax — as if paper, ink, wheels, knives, clocks, chairs, aspirin pills, were natural objects, born with us like our teeth and fingers — as if steel saucepans with copper bottoms and fleece vests spun from recycled glass grew on trees, and we just picked them when they were ripe…

One way to illustrate that most technologies are, in fact, pretty “hi,” is to ask yourself of any manmade object, Do I know how to make one?

Nick Maggiulli’s thought provoking post on how focusing to maximise optionality is actually a trade-off with your future self. He shares his own personal example.

I got married at 35 and I just had my first child at 36. Because I will have children slightly later than some of my peers, I wasn’t as committed in my 20s and early 30s. While that was fun at the time, my non-commitment means that I’ll have less time with my grandchildren in the future. All else equal, I traded off a few years with my grandchildren for a few extra years of being single. I’m not here to judge this decision, but it’s the kind of tradeoff that is overlooked by the “maximize optionality” crowd. These influencers are happy to tell you about the freedom you get from not committing, but they don’t tell you what you give up for that freedom. Because what you give up only becomes apparent at the end of your life. Every year you spend with options is another year you don’t spend on the thing you eventually commit to.This is true of your future career, your future spouse, and in every other part of your life. Of course, options are essential when you have no idea what you like. But once you know, the benefits of optionality begin to decline. But the real reason why I think more people should commit to something is that studies show that the act of commitmentmakes you happier.

An anecdote about Joseph Heller.

Joseph Heller, an important and funny writer now dead, and I were at a party given by a billionaire on Shelter Island.

I said, “Joe, how does it make you feel to know that our host only yesterday may have made more money than your novel ‘Catch-22’ has earned in its entire history?”

And Joe said, “I’ve got something he can never have.”

And I said, “What on earth could that be, Joe?”

And Joe said, “The knowledge that I’ve got enough.”

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.